Methods, devices, electronic equipment and media for maintaining consistency of heterogeneous data in different locations

Abstract

This disclosure provides a method, apparatus, electronic device, and medium for maintaining consistency of heterogeneous data across different locations. One specific implementation of the method includes: collecting various heterogeneous data sets from different locations; performing data standardization processing on each heterogeneous data set; encoding, storing, and configuring permissions for each standardized heterogeneous data set; encrypting and sending the standardized heterogeneous data set to a coordinator; performing the following consistency maintenance processing on the metadata and master data stored by the business nodes corresponding to preset business node identifiers: obtaining the metadata and master data as the metadata and master data to be detected, respectively; identifying the heterogeneous data corresponding to the preset business node identifier among the heterogeneous data sets as the target heterogeneous data set; performing consistency detection processing on the metadata and master data to be detected; and updating the business nodes. This implementation improves the integrity and accuracy of the standardized data.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and more specifically to methods, apparatus, electronic devices, and media for maintaining consistency of heterogeneous data in different locations. Background Technology

[0002] With the popularization of technologies such as cloud computing and big data, distributed systems have become the mainstream of modern IT architecture. In distributed systems, data consistency is particularly important. Maintaining consistency of heterogeneous data across different locations is a technique for standardizing and maintaining the consistency of data stored on business nodes (heterogeneous data in different locations). Currently, the common approach to standardizing and maintaining the consistency of data stored on business nodes (heterogeneous data in different locations) is to directly perform standardization and consistency maintenance on the data stored on the business nodes.

[0003] However, when using the above methods to standardize and maintain the consistency of data stored on business nodes (heterogeneous data in different locations), the following technical problems often arise:

[0004] When directly standardizing and maintaining the consistency of data (heterogeneous data from different locations) stored in business nodes, the lack of permission settings for the standardized data increases the risk of data leakage. At the same time, data without permission settings is easily modified or deleted illegally, resulting in low integrity and accuracy of the standardized data.

[0005] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not form prior art known to those skilled in the art. Summary of the Invention

[0006] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0007] Some embodiments of this disclosure provide methods, apparatus, electronic devices, and computer-readable media for maintaining consistency of heterogeneous data in different locations, in order to solve one or more of the technical problems mentioned in the background section above.

[0008] In a first aspect, some embodiments of this disclosure provide a method for maintaining consistency of heterogeneous data across different locations. The method includes: collecting heterogeneous data across different locations corresponding to preset business node identifiers; performing data standardization processing on the heterogeneous data to obtain standardized heterogeneous data corresponding to the preset business node identifiers, wherein each standardized heterogeneous data includes standardized metadata, standardized master data, and standardized topic data; performing encoding, storage, and permission configuration operations on the standardized heterogeneous data; and, in response to receiving a request from a preset local client containing a preset business node identifier and data encoding, encrypting and sending the standardized heterogeneous data corresponding to the data encoding to the coordinator corresponding to the preset business node identifier for use. The aforementioned coordinator translates the standardized heterogeneous data from different locations to the aforementioned preset local client. For each preset business node identifier, the following consistency maintenance processing is performed on the metadata and master data stored in the business node corresponding to the preset business node identifier: In response to receiving the consistency maintenance request information sent by the aforementioned business node, the metadata and master data are obtained from the aforementioned business node as the metadata and master data to be detected, respectively; the heterogeneous data corresponding to the aforementioned preset business node identifier among the aforementioned heterogeneous data is determined as the target heterogeneous data; based on the aforementioned target heterogeneous data, the consistency detection processing is performed on the metadata and master data to be detected to obtain consistency detection information; based on the aforementioned consistency detection information, the aforementioned business node is updated.

[0009] Secondly, some embodiments of this disclosure provide a heterogeneous data consistency maintenance device, comprising: a collection unit configured to collect heterogeneous data corresponding to preset service node identifiers in different locations; a data standardization processing unit configured to perform data standardization processing on the heterogeneous data in different locations to obtain standardized heterogeneous data corresponding to the preset service node identifiers in different locations, wherein each standardized heterogeneous data in the standardized heterogeneous data includes standardized metadata, standardized master data, and standardized topic data; a storage unit configured to perform encoded storage and permission configuration operations on the standardized heterogeneous data in different locations; and a sending unit configured to, in response to receiving an acquisition request information containing preset service node identifiers and data encoding sent by a preset local client, encrypt and send the standardized heterogeneous data corresponding to the data encoding to the preset service node identifiers in different locations. The node identifier corresponds to a coordinator, which is used to translate the standardized heterogeneous data from different locations to the preset local client. The consistency maintenance processing unit is configured to perform the following consistency maintenance processing on the metadata and master data stored in the business node corresponding to each of the preset business node identifiers: in response to receiving a consistency maintenance request information sent by the business node, obtain the metadata and master data from the business node as the metadata and master data to be detected, respectively; determine the heterogeneous data corresponding to the preset business node identifier among the heterogeneous data from different locations as the target heterogeneous data; perform consistency detection processing on the metadata and master data to be detected based on the target heterogeneous data to obtain consistency detection information; and perform update processing on the business node based on the consistency detection information.

[0010] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0011] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0012] The above embodiments of this disclosure have the following beneficial effects: the method for maintaining consistency of heterogeneous data in different locations through some embodiments of this disclosure reduces the risk of data leakage and improves the integrity and accuracy of data after standardization. Specifically, the reason for the high risk of data leakage and the low integrity and accuracy of data after standardization is that when directly performing standardization and consistency maintenance on the data (heterogeneous data) stored by the collected business nodes, no permission settings are made for the standardized data, which increases the risk of data leakage. At the same time, data without permission settings is easily modified or deleted illegally, resulting in low integrity and accuracy of the standardized data. Based on this, the method for maintaining consistency of heterogeneous data in different locations through some embodiments of this disclosure first collects heterogeneous data corresponding to each preset business node identifier. Thus, each heterogeneous data of each business node can be collected. Then, the above heterogeneous data is subjected to data standardization processing to obtain each standardized heterogeneous data corresponding to each preset business node identifier, wherein each standardized heterogeneous data includes standardized metadata, standardized master data, and standardized subject data. Therefore, data standardization can be performed on various geographically dispersed and heterogeneous data to improve their uniqueness and consistency. Then, the standardized geographically dispersed and heterogeneous data is encoded, stored, and configured with permissions. This allows for permission configuration of the standardized data, reducing the risk of data leakage and minimizing the likelihood of unauthorized modification or deletion, thus improving the integrity and accuracy of the standardized data. Subsequently, in response to a request from a preset local client containing a preset business node identifier and data encoding, the standardized geographically dispersed and heterogeneous data corresponding to the aforementioned data encoding is encrypted and sent to the coordinator corresponding to the preset business node identifier. The coordinator then translates the standardized geographically dispersed and heterogeneous data to the preset local client. This allows the coordinator to enable public-private network interoperability, enabling the free flow of information between public and private networks and improving the security and stability of information transmission. Next, for each of the aforementioned preset business node identifiers, the following consistency maintenance processing is performed on the metadata and master data stored in the business node corresponding to the aforementioned preset business node identifier: First, in response to receiving the consistency maintenance request information sent by the aforementioned business node, the metadata and master data are obtained from the aforementioned business node as the metadata to be detected and the master data to be detected, respectively. Thus, the metadata to be detected and the master data to be detected stored in the business node can be obtained. Second, the heterogeneous data corresponding to the aforementioned preset business node identifier among the aforementioned heterogeneous data in different locations is determined as the target heterogeneous data.Therefore, the target heterogeneous data for consistency testing of the metadata and master data to be tested can be obtained. The third step involves performing consistency testing on the metadata and master data to be tested based on the target heterogeneous data, obtaining consistency testing information. This allows for consistency testing of the metadata and master data to be tested. The fourth step involves updating the business nodes based on the consistency testing information. This allows for updating the business nodes. Furthermore, because permission configuration is applied to the standardized heterogeneous data stored on the business nodes during standardization and consistency maintenance, the risk of data leakage is reduced. Simultaneously, permission configuration reduces the number of times the standardized heterogeneous data is illegally modified or deleted, improving the integrity and accuracy of the standardized data. Attached Figure Description

[0013] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0014] Figure 1 This is a flowchart of some embodiments of the method for maintaining consistency of heterogeneous data in different locations according to this disclosure;

[0015] Figure 2 This is a structural schematic diagram of some embodiments of the heterogeneous data consistency maintenance device according to the present disclosure;

[0016] Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0019] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0020] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0021] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0022] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] Figure 1 A flow 100 is shown illustrating some embodiments of a method for maintaining consistency of heterogeneous data in different locations according to this disclosure. This method for maintaining consistency of heterogeneous data in different locations includes the following steps:

[0024] Step 101: Collect heterogeneous data from different locations corresponding to each preset business node identifier.

[0025] In some embodiments, the executing entity (e.g., a server) of the method for maintaining consistency of heterogeneous data in different locations can collect heterogeneous data corresponding to various preset business node identifiers via wired or wireless connections. In practice, the executing entity can collect heterogeneous data from various business nodes corresponding to various preset business node identifiers. Each preset business node identifier can represent the name of a business node. The business node can be a distributed node in a distributed business system (e.g., a distributed node server). The business system can be a system developed to meet business needs. Each piece of heterogeneous data includes metadata, master data, and subject data. The subject data can be a data set corresponding to business requirement information. The business requirement information includes requirement information and data requirement information. The requirement information can represent the requirements proposed by the business node corresponding to the target business node identifier to achieve business objectives, optimize decision-making, etc. The data requirement information can include at least one data identifier. For example, the preset business requirement information can be "Requirement information: financial institution risk management; Data requirement information: user credit data, market data, macroeconomic data". At least one of the aforementioned data identifiers can be "user credit data, market data, or macroeconomic data." The aforementioned subject data can be a dataset related to "financial institution risk management." For example, the aforementioned dataset can include, but is not limited to, user credit data, market data, and macroeconomic data.

[0026] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future wireless connection methods.

[0027] Step 102: Perform data standardization processing on each heterogeneous data in different locations to obtain each standardized heterogeneous data in different locations corresponding to each preset business node identifier.

[0028] In some embodiments, the aforementioned execution entity may perform data standardization processing on the aforementioned heterogeneous data in different locations to obtain standardized heterogeneous data in different locations corresponding to the aforementioned preset business node identifiers. Each standardized heterogeneous data in the aforementioned standardized heterogeneous data includes standardized metadata, standardized master data, and standardized subject data.

[0029] In some optional implementations of certain embodiments, the aforementioned execution entity may perform data standardization processing on the aforementioned heterogeneous data in different locations through the following steps to obtain standardized heterogeneous data corresponding to the aforementioned preset business node identifiers:

[0030] The first step is to perform the following data standardization process on each of the aforementioned heterogeneous data sets from different locations:

[0031] The first sub-step is to identify the metadata included in the aforementioned heterogeneous data from different locations as the target metadata.

[0032] The second sub-step involves standardizing the target metadata based on a pre-defined metadata mapping table to obtain standardized metadata. The target metadata may include at least one field (a data item being mapped or transformed). The pre-defined metadata mapping table defines the correspondence between fields (i.e., fields of the target metadata) and target fields (i.e., fields of the standardized metadata). In practice, for each of the at least one field included in the target metadata, the executing entity can compare the field with each field in the pre-defined metadata mapping table, identifying the field in the pre-defined metadata mapping table that matches the field as the query field. Then, the executing entity can identify the target field in the pre-defined metadata mapping table corresponding to the query field as the field to be transformed. Next, the executing entity can update the target metadata with the field to be transformed. Finally, the executing entity can identify the updated target metadata as standardized metadata.

[0033] The third sub-step involves identifying the master data included in the aforementioned heterogeneous data from different locations as the target master data. This target master data includes various business master data sets, each of which corresponds to category attribute information. This category attribute information characterizes the content attributes of the business master data. The business master data can be shared data between systems (e.g., customer, supplier, and account-related data).

[0034] The fourth sub-step involves performing uniqueness cleaning on each business master data included in the aforementioned target master data to obtain cleaned business master data. In practice, the aforementioned executing entity can use data deduplication technology to perform uniqueness cleaning on each of the aforementioned business master data to obtain cleaned business master data.

[0035] The fifth sub-step involves standardizing the various cleaning business master data included in the aforementioned cleaning master data to obtain standardized cleaning business master data. In practice, the executing entity can use data conversion and data mapping techniques to standardize the various cleaning business master data to obtain standardized cleaning business master data. Specifically, firstly, the executing entity can use data conversion techniques (such as using data conversion tools or scripts written in programming languages ​​(such as Python)) to convert specific data (such as dates and characters) contained in the various cleaning business master data into a unified format, obtaining standardized cleaning business master data with a unified data format. Then, the executing entity can use a pre-defined mapping table representing the mapping relationship between fields in the source data and fields in the target data, and through data integration tools (such as Apache Nifi, Talend, etc.), perform data conversion on the standardized cleaning business master data after the data format is unified, obtaining transformed business data. Finally, the executing entity can identify the transformed business data as the standardized cleaning business master data. As an example, the cleaning business master data could be "2024 / 11 / 15, Customer Name: A". After standardizing the data format, the master data for each standardized cleaning business can be "2024-11-15, Customer Name: A". The mapping table can be "Customer Name - Customer Name", and the master data for each standardized cleaning business can be "2024-11-15, Customer Name: A". Here, "A" can represent the customer's name.

[0036] The sixth sub-step is to identify the aforementioned standardized cleaning business master data as standardized master data.

[0037] The seventh sub-step involves determining the preset business node identifier corresponding to the aforementioned heterogeneous data from different locations as the target business node identifier.

[0038] The eighth sub-step involves obtaining the business requirement information corresponding to the aforementioned target business node identifier. For example, this business requirement information could be "Requirement Information: Financial Institution Risk Management; Data Requirement Information: User Credit Data, Market Data, Macroeconomic Data." In practice, the implementing entity can obtain the business requirement information from the business node corresponding to the aforementioned target business node identifier.

[0039] The ninth sub-step involves creating a thematic data structure corresponding to the aforementioned business requirement information. In practice, firstly, the executing entity can determine the number of at least one data identifier included in the aforementioned business requirement information as the target number. Then, the executing entity can execute a creation task corresponding to the preset data structure creation information to create the target number of data structures (e.g., tables or columns) as the thematic data structure. The preset data structure creation information can be pre-written instructions to guide the creation of the data structures.

[0040] The tenth sub-step involves storing the subject data included in the aforementioned heterogeneous data in the aforementioned subject data structure, thereby updating the aforementioned subject data structure.

[0041] The eleventh sub-step is to define the updated topic data structure as standardized topic data.

[0042] The twelfth sub-step involves identifying the aforementioned standardized metadata, standardized master data, and standardized topic data as standardized heterogeneous data corresponding to the aforementioned target business node identifier.

[0043] Step 103: Encode, store, and configure permissions for each standardized, heterogeneous data set located in different locations.

[0044] In some embodiments, the aforementioned execution entity may perform encoding, storage, and permission configuration operations on the aforementioned standardized heterogeneous data in different locations.

[0045] In the process of adopting technical solutions to address the problems mentioned in the background section, the following issues often arise:

[0046] Before configuring permissions for standardized heterogeneous data across different locations, it is necessary to store the standardized metadata, standardized master data, and standardized subject data included in the standardized heterogeneous data to facilitate client access and retrieval. However, directly storing the standardized metadata, standardized master data, and standardized subject data included in the standardized heterogeneous data without generating index identifiers based on the characteristics of the data for data storage may require clients to traverse all stored standardized heterogeneous data when retrieving data (standardized metadata, standardized master data, or standardized subject data), resulting in increased client waiting time for data retrieval.

[0047] Faced with the above problems, the inventors decided to adopt the following solution:

[0048] In some optional implementations of certain embodiments, the aforementioned execution entity may perform encoding, storage, and permission configuration operations on the aforementioned standardized heterogeneous data in different locations through the following steps:

[0049] The first step, for each standardized heterogeneous data point in the above-mentioned standardized heterogeneous data, is to perform the following steps:

[0050] The first sub-step involves generating metadata codes corresponding to the standardized metadata included in the aforementioned standardized heterogeneous data from different locations, and storing the aforementioned standardized metadata. The metadata codes serve as index identifiers for the standardized metadata. In practice, the executing entity can use the URN (Uniform Resource Name) protocol to encode (identify) the metadata, obtaining the metadata codes. For example, the metadata could be "Book Title: Programming Fundamentals, ISBN: 978-7-115-50000-1". Using "isbn" as the namespace identifier, the URN protocol identifier "urn", the namespace identifier "isbn", and the specific ISBN number are combined to form a complete URN code. The metadata code would then be "urn:isbn:978-7-115-50000-1".

[0051] The second sub-step involves obtaining the preset metadata permission setting role information corresponding to the aforementioned standardized metadata. In practice, the executing entity can obtain the preset metadata permission setting role information from a preset database. This preset metadata permission setting role information can be a list containing various user roles (e.g., administrator, regular user) and various permission settings. Each user role corresponds one-to-one with each permission setting. Each permission setting includes permission type information (e.g., operation permission) and permission content information (e.g., add, delete, modify).

[0052] The third sub-step involves configuring permissions for the standardized metadata based on the aforementioned role and permission settings. In practice, the executing entity can use Role-Based Access Control (RBAC) technology to configure permissions for the standardized metadata based on the aforementioned role and permission settings.

[0053] The fourth sub-step involves identifying the standardized master data included in the aforementioned standardized heterogeneous data from different locations as the standardized master data to be encoded.

[0054] The fifth sub-step involves determining the category attribute information corresponding to each business master data included in the aforementioned standardized master data to be encoded. Each business master data corresponds one-to-one with each category attribute information. The category attribute information includes sub-category attribute information. Each sub-category attribute information can represent an attribute of the business master data. For example, the category attribute information could be "Object: Customer; Type: Individual Customer; Attribute: Paid". The sub-category attribute information could be "Type: Individual Customer".

[0055] The sixth sub-step involves classifying and encoding the aforementioned business master data based on the category attribute information, obtaining corresponding business master data codes, and storing the aforementioned business master data. Each business master data code serves as an index identifier for each business master data item. In practice, for each business master data item, the executing entity can perform the following classification and encoding process: First, determine the category attribute information of the business master data as the target category attribute information. Second, for each category attribute sub-information in the target category attribute information, the executing entity can determine the corresponding code in a preset encoding table as the target code. Third, the executing entity can combine the obtained target codes according to a preset arrangement order to obtain the corresponding business master data code for the aforementioned business master data. The preset encoding table can be a mapping table from category attribute sub-information to codes. As an example, the target category attribute information can be "Object: Customer; Type: Individual Customer; Attribute: Paid". The above category attribute sub-information can be "Type: Individual Customer", "Object: Customer", or "Attribute: Paid". The above preset coding table can be represented as "Customer corresponds to K, Individual Customer corresponds to P, Paid corresponds to A", and the above business master data coding can be KPA.

[0056] The seventh sub-step involves obtaining the preset master data permission setting role information corresponding to the aforementioned standardized master data. In practice, the executing entity can obtain the preset master data permission setting role information from a preset database. This preset master data permission setting role information can be a list containing various user roles (e.g., administrator, ordinary user) and various permission settings. Each user role corresponds one-to-one with each permission setting. Each permission setting includes permission type information (e.g., operation permission) and permission content information (e.g., add, delete, modify).

[0057] The eighth sub-step involves configuring permissions for each stored business master data based on the preset master data permission settings and role information. In practice, the aforementioned execution entity can use Role-Based Access Control (RBAC) technology to configure permissions for each business master data based on the preset master data permission settings and role information.

[0058] The ninth sub-step involves identifying the standardized topic data included in the aforementioned standardized heterogeneous data from different locations as the standardized topic data to be encoded.

[0059] The tenth sub-step involves determining the business requirements information corresponding to the standardized topic data to be encoded mentioned above.

[0060] The eleventh sub-step involves generating a subject data code corresponding to the standardized subject data to be coded, based on the aforementioned business requirement information, and storing the standardized subject data to be coded. The subject data code serves as the index identifier for the stored standardized subject data to be coded. In practice, the executing entity can determine the requirement information included in the aforementioned business requirement information as the target requirement information. Then, the executing entity can query a preset requirement code table to find the code corresponding to the target requirement information as the subject data code for the standardized subject data to be coded. The preset requirement code table represents the correspondence between requirement information and codes. For example, the preset requirement code table could be represented as "Financial institution risk management corresponds to J, customer relationship maintenance corresponds to W". Therefore, when the business requirement information for the standardized subject data to be coded is "Requirement information: Financial institution risk management, Data requirement information: User credit data, Market data, Macroeconomic data", the target requirement information can be "Financial institution risk management". The subject data code corresponding to the standardized subject data to be coded can be "J".

[0061] The twelfth sub-step involves configuring permissions for the stored standardized topic data to be encoded. In practice, the executing entity can obtain a list of preset role permission settings corresponding to the standardized topic data from a preset database. Then, the executing entity can use Role-Based Access Control (RBAC) technology to configure permissions for the standardized topic data based on the preset role permission settings list. This preset role permission settings list includes various user roles (e.g., administrator, regular user) and their respective permission settings. Each user role corresponds one-to-one with each permission setting. Each permission setting includes permission type information (e.g., operation permission) and permission content information (e.g., add, delete, modify).

[0062] The above technical solution and its related content, as an inventive point of this disclosure, solve the technical problem of "increased waiting time for client to obtain data". Factors leading to increased waiting time for data acquisition are often as follows: When directly storing standardized metadata, standardized master data, and standardized topic data included in standardized heterogeneous data from different locations, since no index identifier for data storage is generated based on the characteristics of the data, when the client obtains data (standardized metadata, standardized master data, or standardized topic data), it may be necessary to traverse all stored standardized heterogeneous data, resulting in increased waiting time for the client to obtain data. Solving these factors can reduce the waiting time for the client to obtain data. To achieve this effect, firstly, for each of the aforementioned standardized heterogeneous data, the following steps are performed: Step 1: Generate metadata encoding corresponding to the standardized metadata included in the aforementioned standardized heterogeneous data, and store the aforementioned standardized metadata, wherein the aforementioned metadata encoding is the index identifier of the aforementioned standardized metadata. Thus, the index identifier for storing standardized metadata, i.e., the metadata encoding, can be generated. Step 2: Obtain the preset metadata permission setting role information corresponding to the aforementioned standardized metadata. Therefore, the preset metadata permission setting role information for configuring permissions on standardized metadata can be obtained. The third step is to configure permissions on the standardized metadata based on the above role permission setting information. The fourth step is to determine the standardized master data included in the above standardized heterogeneous data as the standardized master data to be encoded. The fifth step is to determine the category attribute information corresponding to each business master data included in the above standardized master data to be encoded, wherein each business master data corresponds one-to-one with each category attribute information. This yields the category attribute information used to generate each index identifier for each business master data. The sixth step is to perform classification and encoding processing on the above business master data based on the above category attribute information, obtaining the corresponding business master data codes, and storing the above business master data, wherein each business master data code is used as an index identifier for each business master data. This allows storing the business master data, with each business master data having a corresponding index identifier. Step 7: Obtain the preset master data permission setting role information corresponding to the aforementioned standardized master data. This allows for the acquisition of preset master data permission setting role information. Step 8: Based on the aforementioned preset master data permission setting role information, configure permissions for each stored business master data. This allows for the configuration of permissions for each stored business master data. Step 9: Identify the standardized topic data included in the aforementioned standardized heterogeneous data from different locations as the standardized topic data to be encoded.Step 10: Determine the business requirement information corresponding to the standardized topic data to be encoded. This yields the business requirement information used to generate the index identifier for the standardized topic data to be encoded. Step 11: Based on the above business requirement information, generate the topic data code corresponding to the standardized topic data to be encoded, and store the standardized topic data to be encoded, wherein the topic data code serves as the index identifier for the stored standardized topic data to be encoded. Thus, the standardized topic data to be encoded can be stored using the topic data code as the index identifier. Step 12: Perform permission configuration processing on the stored standardized topic data to be encoded. This allows for permission configuration processing of the standardized topic data to be encoded. Because metadata codes, various business master data codes, and topic data codes are generated before storing the standardized heterogeneous data (including standardized metadata, standardized master data, and standardized topic data) as index identifiers for storing standardized metadata, standardized master data, and standardized topic data respectively, the client can obtain the required standardized heterogeneous data based on the index identifiers, reducing traversal time and client waiting time for data retrieval.

[0063] Step 104: In response to receiving the acquisition request information containing the preset business node identifier and data encoding sent by the preset local client, the standardized heterogeneous data corresponding to the data encoding is encrypted and sent to the coordinator corresponding to the preset business node identifier, so that the coordinator can heterogeneously translate the standardized heterogeneous data to the preset local client.

[0064] In some embodiments, in response to receiving a request message containing a preset business node identifier and data encoding from a preset local client, the execution entity encrypts and sends standardized heterogeneous data corresponding to the data encoding to a coordinator corresponding to the preset business node identifier. The coordinator then heterogeneously translates the standardized heterogeneous data to the preset local client. Each coordinator corresponds one-to-one with a preset business node identifier. The coordinator can be a component that coordinates data synchronization and exchange between different business nodes and the preset local client. In practice, the coordinator can send the encrypted standardized heterogeneous data to the preset local client. The preset local client can decrypt the encrypted standardized heterogeneous data to obtain the standardized heterogeneous data. Then, the preset local client can obtain a preset mapping table corresponding to the preset business node identifier from a preset database. Then, the preset local client can translate the standardized heterogeneous data into translated heterogeneous data according to the preset mapping table, and then store the translated heterogeneous data in a local preset file. As an example, the standardized heterogeneous data from different locations mentioned above can be "2024-11-15, Customer Name: A", and the preset mapping table can translate the customer name into the customer name. The translated heterogeneous data from different locations mentioned above can be "2024-11-15, Customer Name: A".

[0065] In some optional implementations of certain embodiments, the aforementioned execution entity may encrypt and send standardized heterogeneous data corresponding to the aforementioned data encoding to the coordinator corresponding to the aforementioned preset service node identifier through the following steps:

[0066] The first step involves receiving a handshake request from the aforementioned preset local client. This handshake request includes encrypted random number information and a list of cipher suites. The cipher suite list includes information about each cipher suite. The encrypted random number can be an encrypted random number. This random number can be a random number generated by the preset local client. The preset local client can be a local device (such as a personal computer or smartphone) used to acquire standardized, heterogeneous data from different locations. Each cipher suite information represents a cipher suite.

[0067] The second step is to retrieve pre-stored decryption algorithm information from a pre-defined database. This decryption algorithm information can characterize the decryption algorithm (e.g., the AES decryption algorithm).

[0068] The third step involves decrypting the encrypted random number information based on the aforementioned decryption algorithm information to obtain the decrypted random number information. This decrypted random number information can be the decrypted random number information itself.

[0069] The fourth step involves generating random number information and obtaining a preset cipher suite information sequence. The preset cipher suite information in this sequence is arranged according to its corresponding preset priority information. In practice, the executing entity can generate random numbers using a random number generator. The preset priority information represents the preset security level of the cipher suite corresponding to the preset cipher suite information. The executing entity can obtain the preset cipher suite information sequence from a preset database.

[0070] Fifth, based on the above cipher suite information and the preset cipher suite information sequence, perform the following filtering steps:

[0071] The first sub-step involves performing the following steps for the first preset cipher suite information in the aforementioned preset cipher suite information sequence:

[0072] Sub-step one: In response to determining that each cipher suite information contains cipher suite information that is the same as the preset cipher suite information, the cipher suite information is determined as the filtered cipher suite information.

[0073] Sub-step two: In response to determining that no cipher suite information exists that is identical to the preset cipher suite information, the following update steps are performed:

[0074] Sub-step one involves deleting the preset cipher suite information from the preset cipher suite information sequence in order to update the preset cipher suite information sequence.

[0075] Sub-step two involves repeating the above filtering steps based on the updated preset password suite information sequence.

[0076] Step 6: Send the above-mentioned random number information and the above-mentioned filtering password suite information to the above-mentioned preset local client.

[0077] Step 7: Based on the aforementioned filtered cipher suite information, decryption random number information, and random number information, generate session key information. In practice, the executing entity can determine the cipher suite represented by the filtered cipher suite information as the filtered cipher suite. Then, the executing entity can execute the key exchange algorithm in the filtered cipher suite, and based on the aforementioned decryption random number information and random number information, generate a session key as the session key information.

[0078] Step 8: Based on the aforementioned session key information, encrypt the standardized heterogeneous data from different locations to obtain encrypted standardized heterogeneous data from different locations. This encrypted standardized heterogeneous data from different locations can be the encrypted version of the standardized heterogeneous data. In practice, the executing entity can execute a preset encryption algorithm (e.g., a symmetric encryption algorithm) and use the session key from the aforementioned session key information to encrypt the standardized heterogeneous data from different locations to obtain encrypted standardized heterogeneous data from different locations.

[0079] The ninth step is to send the aforementioned encrypted standardized heterogeneous data to the coordinator corresponding to the aforementioned preset business node identifier, and to delete the session key information.

[0080] The above-described technical solution and its related content, as an inventive point of this disclosure, solve the technical problem of "low security of standardized heterogeneous data in different locations during transmission." Factors leading to low security of standardized heterogeneous data in different locations during transmission often include: when directly encrypting standardized heterogeneous data in different locations using a fixed key, the risk of the long-term stored fixed key being stolen or leaked is high; attackers may obtain the fixed key through guessing, resulting in low security for standardized heterogeneous data encrypted with a fixed key during transmission. Solving these factors can improve the security of standardized heterogeneous data in different locations during transmission. To achieve this, firstly, a handshake request message sent by the aforementioned preset local client is received, wherein the handshake request message includes encrypted random number information and a cipher suite list, the cipher suite list including information on each cipher suite. Then, pre-stored decryption algorithm information is retrieved from a preset database. Thus, decryption algorithm information for decrypting the encrypted random number information can be obtained. Afterwards, according to the decryption algorithm information, the encrypted random number information is decrypted to obtain decrypted random number information. Thus, decrypted random number information for the session key information can be obtained. Then, random number information is generated, and a sequence of preset cipher suite information is obtained, wherein the preset cipher suite information in the above preset cipher suite information sequence is arranged according to the preset priority information corresponding to the preset cipher suite information. Thus, random number information for generating session key information can be obtained. Next, based on the above cipher suite information and the preset cipher suite information sequence, the following filtering steps are performed: First, for the first preset cipher suite information in the above preset cipher suite information sequence, the following steps are performed: First sub-step: In response to determining that there is a cipher suite information identical to the preset cipher suite information, the cipher suite information is determined as the filtered cipher suite information. Thus, filtered cipher suite information representing cipher suites supported by both the preset local client and the execution entity can be obtained. Second sub-step: In response to determining that there is no cipher suite information identical to the preset cipher suite information, the following update steps are performed: First, the preset cipher suite information is deleted from the preset cipher suite information sequence to update the preset cipher suite information sequence. Second, based on the updated preset cipher suite information sequence, the above filtering steps are performed again. Afterwards, the above random number information and the above filtered cipher suite information are sent to the above preset local client. Therefore, the random number information and the aforementioned filtering cipher suite information can be sent to the aforementioned preset local client. Then, based on the aforementioned filtering cipher suite information, the aforementioned decrypted random number information, and the aforementioned random number information, session key information is generated. Thus, session key information with randomness and unpredictability can be generated based on the decrypted random number information and the random number information.Since the session key information is dynamically generated based on two random number sets (decryption random number set and random number set) and the filtering cipher suite information, even if the previously generated session key information is leaked, it will not affect the security of the standardized heterogeneous data encryption in this case, thus improving the security of the standardized heterogeneous data during transmission. Subsequently, based on the aforementioned session key information, the standardized heterogeneous data in this case is encrypted to obtain encrypted standardized heterogeneous data. Therefore, by using session key information with randomness and unpredictability to encrypt standardized heterogeneous data, the possibility of attackers obtaining the key through guessing is reduced, improving the security of the standardized heterogeneous data during transmission. Finally, the encrypted standardized heterogeneous data is sent to the coordinator corresponding to the preset service node identifier, and the session key information is deleted. This deletion of the session key ensures that the key is not misused or abused, thereby maintaining communication security.

[0081] Step 105: For each preset business node identifier in the preset business node identifier, perform the following consistency maintenance process on the metadata stored in the business node corresponding to the preset business node identifier and the master data:

[0082] Step 1051: In response to receiving the consistency maintenance request information sent by the business node, obtain the metadata and master data from the business node as the metadata to be detected and the master data to be detected, respectively.

[0083] In some embodiments, the execution entity may, in response to receiving a consistency maintenance request message sent by the service node, obtain metadata and master data from the service node as the metadata to be detected and the master data to be detected, respectively. The consistency maintenance request message may be a request message for performing consistency detection processing on the metadata and master data. The request message may be a message or an instruction.

[0084] Step 1052: Identify the heterogeneous data in each heterogeneous data set that corresponds to the preset business node identifier as the target heterogeneous data set.

[0085] In some embodiments, the execution entity may determine the heterogeneous data corresponding to the preset business node identifier among the heterogeneous data in each of the heterogeneous data in different locations as the target heterogeneous data.

[0086] Step 1053: Based on the target heterogeneous data, perform consistency detection processing on the metadata to be detected and the master data to be detected to obtain consistency detection information.

[0087] In some embodiments, the execution entity may perform consistency detection processing on the metadata to be detected and the master data to be detected based on the target heterogeneous data in different locations to obtain consistency detection information.

[0088] In some optional implementations of certain embodiments, the execution entity may perform consistency detection processing on the metadata to be detected and the master data to be detected based on the target heterogeneous data in different locations through the following steps to obtain consistency detection information:

[0089] The first step is to identify the metadata included in the aforementioned heterogeneous data from different locations as reference metadata.

[0090] The second step involves performing metadata consistency checks on the aforementioned reference metadata to obtain metadata consistency check information. In practice, the executing entity can compare each byte in the reference metadata with the corresponding byte in the metadata to be checked, byte by byte. Upon determining that each byte in the reference metadata is identical to its corresponding byte in the metadata to be checked, the executing entity can identify the information indicating metadata consistency as metadata consistency check information. Upon determining that at least one byte in the reference metadata is different from a byte in the metadata to be checked, the executing entity can identify the information indicating metadata inconsistency as metadata consistency check information. The metadata consistency check information can be text information. Optionally, the executing entity can perform a hash operation on the reference metadata to obtain a first hash value. Then, the executing entity can perform a hash operation on the metadata to be checked to obtain a second hash value. Afterward, the executing entity can compare the first hash value with the second hash value. Upon determining that the first hash value and the second hash value are identical, the executing entity can identify the information indicating metadata consistency as metadata consistency check information. In response to determining that the first hash value and the second hash value are the same, the aforementioned executing entity can identify the information of inconsistent metadata as metadata consistency detection information.

[0091] The third step is to identify the master data included in the aforementioned target heterogeneous data as reference master data.

[0092] The fourth step involves performing master data consistency detection processing on the master data to be detected based on the aforementioned reference master data, thereby obtaining master data consistency detection information. In practice, the executing entity can use word embedding technology to convert the aforementioned reference master data and the master data to be detected into a first vector corresponding to the aforementioned reference master data and a second vector corresponding to the aforementioned master data to be detected, respectively. Then, the executing entity can determine the similarity between the first vector and the second vector as the target similarity. In response to determining that the target similarity is a preset value (e.g., 1), the executing entity can determine the information representing master data consistency as master data consistency detection information. In response to determining that the target similarity is less than a preset value, the executing entity can determine the information representing master data inconsistency as master data consistency detection information. The aforementioned similarity can be cosine similarity.

[0093] The fifth step is to determine the above metadata consistency detection information and the above master data consistency detection information as consistency detection information.

[0094] Step 1054: Update the business nodes based on the consistency detection information.

[0095] In some embodiments, the aforementioned execution entity may update the aforementioned business nodes based on the aforementioned consistency detection information.

[0096] In some optional implementations of certain embodiments, the aforementioned execution entity may update the aforementioned business nodes based on the aforementioned consistency detection information through the following steps:

[0097] The first step is to send the reference metadata to the business node in response to the determination that the metadata consistency detection information included in the above consistency detection information represents metadata inconsistency, so that the business node can update the stored metadata with the reference metadata.

[0098] The second step is to send the reference master data to the business node in response to the determination that the master data consistency detection information included in the above consistency detection information indicates that the master data is inconsistent, so that the business node can update the stored master data with the reference master data.

[0099] Optionally, the aforementioned implementing entity may also perform the following steps:

[0100] The first step is to respond to the new metadata request information sent by the coordinator with the corresponding preset business node identifier, and determine the preset metadata mapping table as the preset metadata mapping table to be compared. The aforementioned new metadata request information includes the new metadata and the target field corresponding to the new metadata.

[0101] The second step is to add the new metadata and the target field to the preset metadata mapping table in response to the determination that the new metadata request information is different from each metadata in the preset metadata mapping table to be compared. This update is done by adding the new metadata and the target field to the preset metadata mapping table.

[0102] The above embodiments of this disclosure have the following beneficial effects: the method for maintaining consistency of heterogeneous data in different locations through some embodiments of this disclosure reduces the risk of data leakage and improves the integrity and accuracy of data after standardization. Specifically, the reason for the high risk of data leakage and the low integrity and accuracy of data after standardization is that when directly performing standardization and consistency maintenance on the data (heterogeneous data) stored by the collected business nodes, no permission settings are made for the standardized data, which increases the risk of data leakage. At the same time, data without permission settings is easily modified or deleted illegally, resulting in low integrity and accuracy of the standardized data. Based on this, the method for maintaining consistency of heterogeneous data in different locations through some embodiments of this disclosure first collects heterogeneous data corresponding to each preset business node identifier. Thus, each heterogeneous data of each business node can be collected. Then, the above heterogeneous data is subjected to data standardization processing to obtain each standardized heterogeneous data corresponding to each preset business node identifier, wherein each standardized heterogeneous data includes standardized metadata, standardized master data, and standardized subject data. Therefore, data standardization can be performed on various geographically dispersed and heterogeneous data to improve their uniqueness and consistency. Then, the standardized geographically dispersed and heterogeneous data is encoded, stored, and configured with permissions. This allows for permission configuration of the standardized data, reducing the risk of data leakage and minimizing the likelihood of unauthorized modification or deletion, thus improving the integrity and accuracy of the standardized data. Subsequently, in response to a request from a preset local client containing a preset business node identifier and data encoding, the standardized geographically dispersed and heterogeneous data corresponding to the aforementioned data encoding is encrypted and sent to the coordinator corresponding to the preset business node identifier. The coordinator then translates the standardized geographically dispersed and heterogeneous data to the preset local client. This allows the coordinator to enable public-private network interoperability, enabling the free flow of information between public and private networks and improving the security and stability of information transmission. Next, for each of the aforementioned preset business node identifiers, the following consistency maintenance processing is performed on the metadata and master data stored in the business node corresponding to the aforementioned preset business node identifier: First, in response to receiving the consistency maintenance request information sent by the aforementioned business node, the metadata and master data are obtained from the aforementioned business node as the metadata to be detected and the master data to be detected, respectively. Thus, the metadata to be detected and the master data to be detected stored in the business node can be obtained. Second, the heterogeneous data corresponding to the aforementioned preset business node identifier among the aforementioned heterogeneous data in different locations is determined as the target heterogeneous data.Therefore, the target heterogeneous data for consistency testing of the metadata and master data to be tested can be obtained. The third step is to perform consistency testing on the metadata and master data to be tested based on the target heterogeneous data, obtaining consistency testing information. This allows for consistency testing of the metadata and master data to be tested. The fourth step is to update the business nodes based on the consistency testing information. This allows for updating the business nodes. Furthermore, because permission configuration is applied to the standardized heterogeneous data stored on the business nodes during the standardization and consistency maintenance process, the risk of data leakage is reduced. Simultaneously, permission configuration reduces the number of times the standardized heterogeneous data is illegally modified or deleted, improving the integrity and accuracy of the standardized data.

[0103] Further reference Figure 2 As an implementation of the methods shown in the figures, this disclosure provides some embodiments of a heterogeneous data consistency maintenance device located in different locations. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0104] like Figure 2As shown, a heterogeneous data consistency maintenance device 200 in some embodiments includes: an acquisition unit 201, a generation unit 202, a processing unit 203, an input unit 204, and an input unit 205. The acquisition unit 201 is configured to acquire heterogeneous data corresponding to each preset business node identifier; the data standardization processing unit 202 is configured to perform data standardization processing on the heterogeneous data to obtain standardized heterogeneous data corresponding to each preset business node identifier, wherein each standardized heterogeneous data includes standardized metadata, standardized master data, and standardized topic data; the storage unit 203 is configured to perform encoded storage and permission configuration operations on the standardized heterogeneous data; the sending unit 204 is configured to, in response to receiving an acquisition request information containing a preset business node identifier and data encoding sent by a preset local client, encrypt and send the standardized heterogeneous data corresponding to the data encoding to the coordinator corresponding to the preset business node identifier, for use by the aforementioned data. The coordinator translates the standardized heterogeneous data from different locations to the preset local client. The consistency maintenance processing unit 205 is configured to perform the following consistency maintenance processing on the metadata and master data stored in the business node corresponding to each preset business node identifier: in response to receiving the consistency maintenance request information sent by the business node, the metadata and master data are obtained from the business node as the metadata and master data to be detected, respectively; the heterogeneous data corresponding to the preset business node identifier in the heterogeneous data is determined as the target heterogeneous data; based on the target heterogeneous data, the consistency detection processing is performed on the metadata and master data to be detected to obtain consistency detection information; based on the consistency detection information, the business node is updated.

[0105] It is understandable that the units described in the device 200 are related to the reference. Figure 1 The steps in the method described above correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, and will not be repeated here.

[0106] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0107] like Figure 3As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0108] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0109] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0110] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0111] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0112] The computer-readable medium may be contained within an electronic device or may exist independently, not assembled into the electronic device. The computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: collect heterogeneous data from various locations corresponding to preset business node identifiers; perform data standardization processing on the heterogeneous data to obtain standardized heterogeneous data corresponding to the preset business node identifiers, wherein each standardized heterogeneous data includes standardized metadata, standardized master data, and standardized subject data; perform encoding, storage, and permission configuration operations on the standardized heterogeneous data; and, in response to receiving a request from a preset local client containing a preset business node identifier and data encoding, encrypt and send the standardized heterogeneous data corresponding to the data encoding to the coordinator corresponding to the preset business node identifier. This allows the aforementioned coordinator to heterogeneously translate the standardized, geographically dispersed, heterogeneous data to the aforementioned preset local client. For each preset business node identifier, the following consistency maintenance processing is performed on the metadata and master data stored in the business node corresponding to the preset business node identifier: In response to receiving the consistency maintenance request information sent by the aforementioned business node, the metadata and master data are obtained from the aforementioned business node as the metadata and master data to be detected, respectively; the geographically dispersed, heterogeneous data corresponding to the aforementioned preset business node identifier among the aforementioned geographically dispersed, heterogeneous data is determined as the target geographically dispersed, heterogeneous data; based on the aforementioned target geographically dispersed, heterogeneous data, consistency detection processing is performed on the metadata and master data to be detected to obtain consistency detection information; based on the aforementioned consistency detection information, the aforementioned business node is updated.

[0113] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0115] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit, a generation unit, a processing unit, an input unit, and an input module. The names of these units do not necessarily limit the specific unit; for example, an acquisition unit may also be described as "a unit that acquires heterogeneous data from various locations corresponding to preset service node identifiers."

[0116] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0117] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of technical features, but should also cover other technical solutions formed by arbitrary combinations of technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for maintaining consistency of heterogeneous data across different locations, comprising: Collect heterogeneous data from different locations corresponding to each preset business node identifier; The process involves standardizing the heterogeneous data from different locations to obtain standardized heterogeneous data corresponding to each preset business node identifier. Each standardized heterogeneous data set includes standardized metadata, standardized master data, and standardized topic data. The standardization process itself includes: For each piece of heterogeneous data in the aforementioned heterogeneous datasets, the following data standardization process is performed: The metadata included in the heterogeneous data from different locations is identified as the target metadata; Based on a preset metadata mapping table, the target metadata is subjected to data standardization processing to obtain standardized metadata; The master data included in the heterogeneous data in different locations is determined as the target master data, wherein the target master data includes various business master data, and each business master data in each business master data corresponds to category attribute information; The uniqueness cleaning process is performed on each business master data included in the target master data to obtain each cleaned business master data as the cleaned master data. The cleaning master data includes various cleaning business master data, which are then standardized to obtain standardized cleaning business master data. The standardized cleaning business master data is identified as standardized master data; The preset business node identifier corresponding to the heterogeneous data in different locations is determined as the target business node identifier; Obtain the business requirement information corresponding to the target business node identifier; Create a topic data structure corresponding to the aforementioned business requirement information; The topic data included in the heterogeneous data from different locations is stored in the topic data structure in order to update the topic data structure; The updated topic data structure is defined as standardized topic data. The standardized metadata, the standardized master data, and the standardized topic data are identified as standardized heterogeneous data in different locations corresponding to the target business node identifier; The standardized heterogeneous data from different locations are encoded, stored, and have their permissions configured. In response to receiving a request message containing a preset business node identifier and data encoding sent by a preset local client, the standardized heterogeneous data corresponding to the data encoding is encrypted and sent to the coordinator corresponding to the preset business node identifier, so that the coordinator can heterogeneously translate the standardized heterogeneous data to the preset local client. For each preset business node identifier, the metadata stored in the business node corresponding to the preset business node identifier is maintained in accordance with the following consistency process with the master data: In response to receiving the consistency maintenance request information sent by the service node, metadata and master data are obtained from the service node as metadata to be detected and master data to be detected, respectively; The heterogeneous data corresponding to the preset business node identifier in each of the heterogeneous data in different locations is identified as the target heterogeneous data. Based on the target heterogeneous data, consistency detection processing is performed on the metadata to be detected and the master data to be detected to obtain consistency detection information. Based on the consistency detection information, the service node is updated.

2. The method according to claim 1, wherein, The method further includes: In response to receiving a new metadata request message with a corresponding preset service node identifier sent by the coordinator, the preset metadata mapping table is determined as the preset metadata mapping table to be compared, wherein the new metadata request message includes new metadata and a target field corresponding to the new metadata; In response to the determination that the new metadata included in the new metadata request information is different from each metadata in the preset metadata mapping table to be compared, the new metadata and the target field are added to the preset metadata mapping table to update the preset metadata mapping table.

3. The method according to claim 1, wherein, The process of performing consistency detection on the metadata and master data to be detected based on the target heterogeneous data in different locations to obtain consistency detection information includes: The metadata included in the target heterogeneous data is determined as reference metadata; Based on the reference metadata, metadata consistency detection processing is performed on the metadata to be detected to obtain metadata consistency detection information; The master data included in the target heterogeneous data is determined as the reference master data; Based on the reference master data, the master data to be detected is subjected to master data consistency detection processing to obtain master data consistency detection information; The metadata consistency detection information and the master data consistency detection information are identified as consistency detection information.

4. The method according to claim 3, wherein, The update process for the service node based on the consistency detection information includes: In response to determining that the metadata consistency detection information included in the consistency detection information indicates that the metadata is inconsistent, the reference metadata is sent to the service node so that the service node can update the stored metadata with the reference metadata; In response to determining that the master data consistency detection information included in the consistency detection information indicates master data inconsistency, the reference master data is sent to the service node so that the service node can update the stored master data with the reference master data.

5. A device for maintaining heterogeneous data consistency across different locations, comprising: The data collection unit is configured to collect heterogeneous data from different locations corresponding to each preset business node identifier. A data standardization processing unit is configured to perform data standardization processing on the various heterogeneous data from different locations to obtain standardized heterogeneous data corresponding to the various preset business node identifiers. Each standardized heterogeneous data includes standardized metadata, standardized master data, and standardized topic data. The data standardization processing for each heterogeneous data to obtain standardized heterogeneous data corresponding to the various preset business node identifiers includes: for each heterogeneous data, performing the following data standardization processing: determining the metadata included in the heterogeneous data as target metadata; performing data standardization processing on the target metadata based on a preset metadata mapping table to obtain standardized metadata; and determining the master data included in the heterogeneous data as target master data, wherein the target master data includes various business node identifiers. The data includes, where each business master data in each business master data corresponds to category attribute information; the uniqueness cleaning process is performed on each business master data included in the target master data to obtain each cleaned business master data as cleaned master data; the cleaned business master data included in the cleaned master data is standardized to obtain each standardized cleaned business master data; the standardized cleaned business master data is determined as standardized master data; the preset business node identifier corresponding to the heterogeneous data in different locations is determined as the target business node identifier; business requirement information corresponding to the target business node identifier is obtained; a theme data structure corresponding to the business requirement information is created; the theme data included in the heterogeneous data in different locations is stored in the theme data structure to update the theme data structure; the updated theme data structure is determined as standardized theme data; the standardized metadata, the standardized master data, and the standardized theme data are determined as standardized heterogeneous data corresponding to the target business node identifier. The storage unit is configured to encode, store, and configure permissions for the various standardized, heterogeneous data in different locations. The sending unit is configured to, in response to receiving an acquisition request information containing a preset service node identifier and a data encoding sent by a preset local client, encrypt and send standardized heterogeneous data corresponding to the data encoding to the coordinator corresponding to the preset service node identifier, so that the coordinator can heterogeneously translate the standardized heterogeneous data to the preset local client. The consistency maintenance processing unit is configured to perform the following consistency maintenance processing on the metadata and master data stored in the business node corresponding to each of the preset business node identifiers: in response to receiving a consistency maintenance request information sent by the business node, obtain the metadata and master data from the business node as the metadata and master data to be detected, respectively; determine the heterogeneous data corresponding to the preset business node identifier in the various heterogeneous data as the target heterogeneous data; perform consistency detection processing on the metadata and master data to be detected based on the target heterogeneous data to obtain consistency detection information; and perform update processing on the business node based on the consistency detection information.

6. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 4.

7. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

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