A cross-domain heterogeneous data compliance auditing and repairing method

Abstract

The application discloses a cross-domain heterogeneous data compliance auditing and repairing method, comprising at least one data type integrated database, a data processor management terminal in communication with the data type integrated database, and a graph neural network auditing and repairing algorithm arranged on the data processor management terminal; the method integrates cross-domain heterogeneous data through the data type integrated database, inputs the obtained cross-domain heterogeneous data type into the calculated graph neural network auditing and repairing algorithm for auditing and repairing statistics, so as to obtain the number of structured and unstructured form auditing and repairing labels corresponding to the cross-domain heterogeneous data; when the number of labels exceeds a preset character length, a compliance processing signal is output, and the number of cross-domain heterogeneous data and the corresponding structured and unstructured form are automatically audited and repaired, thereby improving the accuracy and efficiency of auditing and repairing the cross-domain heterogeneous data.

Description

Technical Field

[0001] This invention relates to the field of cross-domain heterogeneous data auditing and remediation, and in particular to a method for cross-domain heterogeneous data compliance auditing and remediation. Background Technology

[0002] Existing telecom support systems (BSS) in the industry are typically divided into two main functional components: Customer Relationship Management (CRM) and Convergent Billing System (CBS). CRM is primarily responsible for customer interaction and handling various operator services, while CBS mainly handles billing and accounting functions. When pricing, billing, and collecting payments from end customers, CBS needs to rely on information about the various products ordered by the user. Therefore, CRM needs to synchronize user data and product information to the CBS system during service processing. Simultaneously, CBS may trigger changes to user information during business processes, and CBS needs to synchronize these changes to CRM.

[0003] With the increasing prevalence of data exchange and sharing applications, user terminals frequently need to perform bidirectional data synchronization and consistency audits on the synchronized data. Existing data consistency auditing methods can generally be categorized as follows:

[0004] 1. Perform batch comparisons between the database tables of the initiating and receiving systems using synchronized timestamps, and restore data consistency based on the differences in the comparisons.

[0005] 2. By using a database table trigger mechanism, when the data in the database table of the receiving system changes, a reverse query and comparison of the relevant data in the initiating system is triggered to achieve data consistency auditing.

[0006] 3. Develop a third-party auditing program to capture data changes in the database tables of both the initiating and receiving systems in real time, and restore data consistency by comparing incremental changes.

[0007] The first method is less efficient and has weaker real-time performance. When the initiator's system table and the receiver's system table are large, it will increase the system pressure on both the initiator and the receiver. In addition, this method cannot perform comparisons when the tables do not have a timestamp field.

[0008] The second method offers higher real-time performance and can perform incremental comparisons. However, this method is only feasible when both the initiating and receiving systems are readily accessible within the same domain. The trigger logic in the database table of the receiving system can still increase the load on the receiving system, especially when data changes frequently, which may cause problems. Furthermore, there are drawbacks in extreme cases, such as when the receiving system triggers a reverse query comparison, and the corresponding data on the initiating system changes again, which may lead to inaccurate comparisons.

[0009] The third method relies entirely on the logical implementation of a third-party auditing program. This program must ensure the reliability of real-time data capture from both the initiating and receiving systems, guaranteeing no data loss or duplication. It also performs real-time comparison of the corresponding data through the auditing program's logic. This method places high demands on the logical implementation of the third-party auditing program and is complex to implement.

[0010] Therefore, this invention proposes a method for compliance auditing and remediation of cross-domain heterogeneous data. Summary of the Invention

[0011] To overcome the shortcomings of the prior art, the present invention provides a method for cross-domain heterogeneous data auditing and compliance repair, in order to solve the above problems.

[0012] To achieve the above objectives, the preferred embodiment of the present invention provides the following technical solution: In terms of method, the preferred embodiment of the present invention provides a cross-domain heterogeneous data audit and repair compliance processing method, applied to a cross-domain heterogeneous data audit and repair method. The method includes at least one data type integration database, a data processing personnel management terminal communicating with the data type integration database, and a graph neural network audit and repair algorithm set on the data processing personnel management terminal. The method includes: the data type integration database integrating cross-domain heterogeneous data types as the cross-domain heterogeneous data types obtained by the cross-domain heterogeneous data audit and repair method; inputting the obtained cross-domain heterogeneous data types into the calculated graph neural network audit and repair algorithm for audit and repair statistics to obtain the quantity of cross-domain heterogeneous data and the corresponding structured and unstructured forms; judging the structured and unstructured forms, and issuing a compliance processing signal when the structured and unstructured forms exceed a preset character length.

[0013] In a preferred embodiment of the present invention, before the step of inputting the obtained cross-domain heterogeneous data type into the calculated graph neural network audit and repair algorithm for audit and repair statistics, the method includes: obtaining a set of calculated data types of cross-domain heterogeneous data, including different statistical sub-data types, each of the statistical sub-data types including a single data type and the corresponding compliance restriction form of the cross-domain heterogeneous data, the compliance restriction form including the quantity information, structured and unstructured form information of the cross-domain heterogeneous data.

[0014] Using the aforementioned set of computational data types, a deep learning model is employed to optimize the graph neural network audit and repair algorithm, resulting in an optimized graph neural network audit and repair algorithm.

[0015] In a preferred embodiment of the present invention, the step of inputting the obtained cross-domain heterogeneous data types into the calculated graph neural network audit and repair algorithm for audit and repair statistics includes: matching the data type characteristics of the cross-domain heterogeneous data types with the data type characteristics of the statistical sub-data types in the calculated data type set of the graph neural network audit and repair algorithm to obtain the compliance rate of the cross-domain heterogeneous data types and the statistical sub-data types; selecting the statistical sub-data type with the highest compliance rate in the calculated data type set, and using the compliance restriction form of the selected statistical sub-data type as the actual monitoring value of the cross-domain heterogeneous data types to obtain the quantity of cross-domain heterogeneous data and the structured and unstructured forms of the cross-domain heterogeneous data.

[0016] In a preferred embodiment of the present invention, the step of inputting the obtained cross-domain heterogeneous data types into the calculated graph neural network audit and repair algorithm for audit and repair statistics includes: the number of cross-domain heterogeneous data in structured and unstructured forms integrated into the statistical data type integration database within a preset time period.

[0017] In a preferred embodiment of the present invention, the step of determining the structured and unstructured forms and issuing a compliance processing signal when the structured and unstructured forms exceed a preset character length includes: dynamic changes in the number of tags and real-time anomaly information of the occurrence time, frequency, and size of the cross-domain heterogeneous data; calculating the cumulative number and incremental number of the cross-domain heterogeneous data in the region based on the user type, structured and unstructured forms; and issuing a compliance processing signal. The compliance processing range includes regional level 1, level 2, and level 3 alarms, with higher levels indicating lower compliance.

[0018] In a preferred embodiment of the present invention, the above-mentioned cross-domain heterogeneous data method further includes an edge computing gateway, which is communicatively connected to the data processing personnel management terminal. The edge computing gateway is equipped with the graph neural network algorithm. The data type integration database integrates various cross-domain heterogeneous data types as the cross-domain heterogeneous data types obtained by the cross-domain heterogeneous data audit and repair method. The step includes: the data type integration database uploading the integrated various cross-domain heterogeneous data types to the edge computing gateway.

[0019] In a preferred embodiment of the present invention, the step of inputting the acquired cross-domain heterogeneous data types into the calculated graph neural network audit and repair algorithm for audit and repair statistics includes: the edge computing gateway inputs the acquired various cross-domain heterogeneous data types into the graph neural network audit and repair algorithm of the edge computing gateway to perform audit and repair counting on various cross-domain heterogeneous data types.

[0020] In terms of method, a preferred embodiment of the present invention provides a method for cross-domain heterogeneous data compliance audit and repair. The method includes at least one data type integration database, a data processing personnel management terminal communicating with the data type integration database, and a cross-domain heterogeneous data audit and repair compliance processing database connected to the data processing personnel management terminal. The data type integration database integrates various cross-domain heterogeneous data types as the cross-domain heterogeneous data types obtained by the cross-domain heterogeneous data audit and repair compliance processing method. The data processing personnel management terminal includes a graph neural network audit and repair algorithm. The cross-domain heterogeneous data audit and repair compliance processing database includes: a data type module, used to input the obtained cross-domain heterogeneous data types into the calculated graph neural network audit and repair algorithm for audit and repair statistics to obtain the quantity of cross-domain heterogeneous data and the corresponding structured and unstructured forms; and a compliance processing judgment module, used to judge the structured and unstructured forms, and when the structured and unstructured forms exceed a preset character length, a compliance processing signal is issued.

[0021] In a preferred embodiment of the present invention, the aforementioned cross-domain heterogeneous data audit and remediation compliance processing database further includes: a data type acquisition module, used to acquire a set of computational data types for cross-domain heterogeneous data, including different statistical sub-data types. Each statistical sub-data type includes the data type of a single cross-domain heterogeneous data and the corresponding compliance restriction form of the cross-domain heterogeneous data. The compliance restriction form includes the quantity information, structured and unstructured form information of the cross-domain heterogeneous data.

[0022] The deep learning algorithm optimization module is used to optimize the graph neural network audit and repair algorithm using the computational data set and a deep learning model, so as to obtain the optimized graph neural network audit and repair algorithm.

[0023] In a preferred embodiment of the present invention, the compliance processing judgment module is further configured to: obtain dynamic change information on the number of tags with time, frequency, and size in the workshop area and real-time anomaly information; calculate the cumulative number and incremental number of cross-domain heterogeneous data in the area based on the user type, structured and unstructured forms; and issue a compliance processing signal. The compliance processing scope includes level one, level two, and level three alarms in the area. The higher the level, the lower the compliance.

[0024] Compared with existing technologies, the method provided by this invention has at least the following beneficial effects: the method can automatically audit and repair the quantity of cross-domain heterogeneous data and its corresponding structured and unstructured forms. When the structured and unstructured forms exceed a preset character length, a compliance processing signal is issued, improving the accuracy and efficiency of auditing and repairing cross-domain heterogeneous data. In addition, the compliance processing signal helps relevant personnel to identify the appropriate processing period for cross-domain heterogeneous data as early as possible. Specifically, the method integrates various cross-domain heterogeneous data types into a data type integration database as the cross-domain heterogeneous data types obtained by the cross-domain heterogeneous data audit and repair method; then, the obtained cross-domain heterogeneous data types are input into a calculated graph neural network audit and repair algorithm for audit and repair statistics to obtain the quantity of cross-domain heterogeneous data and its corresponding structured and unstructured forms; by using the data processing personnel management terminal to audit and repair the quantity of cross-domain heterogeneous data and its corresponding structured and unstructured forms, the operation of cross-domain heterogeneous data audit and repair statistics is simplified, and the audit and repair time is shortened. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a flowchart of the steps of the method of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0028] It should be noted that in this invention, the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", and "outer" are all based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the database or elements of this invention must have a specific orientation, and therefore should not be construed as a limitation of this invention.

[0029] Example

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0032] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0033] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0034] Please refer to Figure 1 This is one of the flowcharts illustrating a preferred embodiment of the cross-domain heterogeneous data audit and repair compliance processing method provided by the present invention. In this embodiment, the cross-domain heterogeneous data audit and repair compliance processing method can be applied to the aforementioned cross-domain heterogeneous data audit and repair method. The cross-domain heterogeneous data audit and repair method may include at least one data type integration database and a data processing personnel management terminal communicating with the data type integration database. The data processing personnel management terminal includes a graph neural network audit and repair algorithm. This algorithm performs statistical audit and repair on cross-domain heterogeneous data types, obtaining the number of cross-domain heterogeneous data types and their corresponding structured and unstructured forms. When the structured and unstructured forms exceed a preset character length, a compliance processing signal is issued.

[0035] In this embodiment, the method for cross-domain heterogeneous data auditing and remediation compliance processing may include the following steps:

[0036] Step T100: Obtain the set of computational data types for cross-domain heterogeneous data, including different statistical sub-data types. Each statistical sub-data type includes the data type of a single cross-domain heterogeneous data and the corresponding compliance restriction form of the cross-domain heterogeneous data. The compliance restriction form includes the quantity information, structured and unstructured form information of the cross-domain heterogeneous data, wherein the structured and unstructured form information includes the growth cycle information of the cross-domain heterogeneous data.

[0037] In this embodiment, the computational data type set of the cross-domain heterogeneous data may include data types (i.e., the statistical sub-data types) from different periods of the various types of cross-domain heterogeneous data mentioned above. The compliance limitation form may include the quantity, structured and unstructured forms of the cross-domain heterogeneous data mentioned above. The number of statistical sub-data types in the computational data type set can be set according to specific circumstances and is not specifically limited here.

[0038] Step T200: Using the computational data type set, the graph neural network audit and repair algorithm is optimized using a deep learning model to obtain the optimized graph neural network audit and repair algorithm.

[0039] In this embodiment, the computational data type set can be used to calculate the graph neural network audit and repair algorithm using a graph neural network algorithm to obtain an optimized graph neural network audit and repair algorithm. Understandably, the computational data type set calculates the data type characteristics corresponding to cross-domain heterogeneous data in various structured and unstructured forms. For example, it can be calculated using algorithms such as AlexNet, Vggl6, and Inception. The data type characteristics may include one or more of RGB values, grayscale values, and texture.

[0040] Optionally, the graph neural network algorithm may be, but is not limited to, convolutional neural network algorithms, recurrent neural network algorithms, deep neural network algorithms, etc., and no specific limitation is made here.

[0041] The data processing personnel management terminal or edge computing gateway can be configured with graph neural network auditing and repair algorithms. Alternatively, both the data processing personnel management terminal and the edge computing gateway can be configured with graph neural network auditing and repair algorithms to audit and repair cross-domain heterogeneous data types. Using the data processing personnel management terminal to audit and repair cross-domain heterogeneous data types can reduce the computational load on the edge computing gateway. Furthermore, directly auditing and repairing cross-domain heterogeneous data types from the data processing personnel management terminal reduces the uploading of such data, thus improving the speed of auditing and repair statistics.

[0042] Step T300: The data type integration database integrates various cross-domain heterogeneous data types as the cross-domain heterogeneous data types obtained by the cross-domain heterogeneous data audit and repair method.

[0043] In this embodiment, cross-domain heterogeneous data types can be acquired manually or using equipment or tools. Specifically, for example, relevant personnel can go to the production workshop to photograph the cross-domain heterogeneous data types using a data type integration database. The data type integration database then uploads the acquired cross-domain heterogeneous data types to the edge computing gateway via the first communication module. The method of acquiring the data types is not specifically limited here. Generally, the acquired cross-domain heterogeneous data types contain only one head of cross-domain heterogeneous data for auditing and compliance processing. If the cross-domain heterogeneous data type contains multiple heads of cross-domain heterogeneous data, the data type can be segmented so that each cross-domain heterogeneous data type contains one head of cross-domain heterogeneous data.

[0044] In other implementations, instead of uploading the integrated cross-domain heterogeneous data types to the edge computing gateway, the integrated data types can be directly input into the graph neural network audit and repair algorithm in the data processing personnel management terminal for statistical audit and repair. Then, the occurrence period of cross-domain heterogeneous data can be predicted, and a compliance processing signal can be issued based on the occurrence period.

[0045] Step T400 involves inputting the obtained cross-domain heterogeneous data types into the calculated graph neural network audit and repair algorithm for audit and repair statistics, in order to obtain the quantity of cross-domain heterogeneous data and its corresponding structured and unstructured forms.

[0046] In step T400, the graph neural network auditing and repair algorithm can be performed in parallel, simultaneously conducting statistical audits and repairs on different cross-domain heterogeneous data types to improve the efficiency of auditing and repair. The graph neural network auditing and repair algorithm can be used to analyze the data type characteristics of cross-domain heterogeneous data types, matching the extracted data type characteristics with the calculated data type characteristics to achieve statistical auditing and repair of cross-domain heterogeneous data.

[0047] Further, step T400 may include other sub-steps. For example, the data type characteristics of the cross-domain heterogeneous data type are matched with the data type characteristics of the statistical sub-data type in the computational data type set of the graph neural network audit and repair algorithm to obtain the compliance rate of the cross-domain heterogeneous data type and the statistical sub-data type; the statistical sub-data type with the highest compliance rate is selected from the computational data type set, and the compliance restriction form of the selected statistical sub-data type is used as the actual monitoring value of the cross-domain heterogeneous data type to obtain the quantity of cross-domain heterogeneous data and its structured and unstructured forms.

[0048] Furthermore, step T400 may also include the number of various cross-domain heterogeneous data in structured and unstructured forms integrated into the statistical data type integration database within a preset time period. The preset time period can be set according to specific circumstances. For example, all cross-domain heterogeneous data types integrated in a day can be used as the test sample, and the test sample can be statistically audited and repaired to obtain the number of cross-domain heterogeneous data, the quantity of each type of cross-domain heterogeneous data, and its structured and unstructured forms. The optimal processing period for the cross-domain heterogeneous data can then be estimated, allowing data processing personnel to query the optimal processing period through the data processing personnel management terminal. Understandably, the aforementioned optimal processing period refers to the most effective time for preventing or controlling cross-domain heterogeneous data.

[0049] Step T500: Determine the structured and unstructured forms. When the structured and unstructured forms exceed a preset character length, issue a compliance processing signal.

[0050] In this embodiment, the preset character length can be, for example, by acquiring dynamic changes in the number of tags related to the occurrence time, frequency, and size of the cross-domain heterogeneous data, as well as real-time anomaly information, and calculating the occurrence period of the cross-domain heterogeneous data based on the user type, structured and unstructured forms, as the optimal processing period, and generating a corresponding compliance processing signal. This allows data processing personnel to view the estimated compliance status and compliance processing signal.

[0051] Data processing personnel can query historical statistical audit and remediation records, as well as compliance processing signal records, through the data processing personnel management terminal. Additionally, data processing personnel can use the data processing personnel management terminal to query in real time the proportion and corresponding quantity of structured and unstructured forms of cross-domain heterogeneous data in each workshop area, as well as other compliance processing information. Based on the above design, the cross-domain heterogeneous data audit and remediation compliance processing method provided by this invention can replace manual auditing and remediation of the quantity, structured and unstructured forms of cross-domain heterogeneous data by an edge computing gateway or data processing personnel management terminal. This simplifies the steps of auditing and remediating cross-domain heterogeneous data, improves the accuracy of auditing and remediation, and facilitates the widespread adoption of cross-domain heterogeneous data auditing and remediation, as well as cross-domain heterogeneous data prevention and compliance processing.

[0052] In this embodiment, the cross-domain heterogeneous data audit and remediation compliance processing method may include at least one data type integration database, a data processing personnel management terminal, and a cross-domain heterogeneous data audit and remediation compliance processing database. The data type integration database is communicatively connected to the data processing personnel management terminal and is used to upload various integrated cross-domain heterogeneous data types to the data processing personnel management terminal for auditing and remediation statistics of cross-domain heterogeneous data types. A compliance processing signal is issued when the audit and remediation results exceed a preset character length. The data type integration database and the data processing personnel management terminal may be the same database as described above.

[0053] The cross-domain heterogeneous data audit and remediation compliance processing database may include an audit and remediation statistics module and a compliance processing judgment module. In other embodiments, the cross-domain heterogeneous data audit and remediation compliance processing database may also include a data type acquisition module and a deep learning algorithm optimization module.

[0054] The audit and repair statistics module is used to input the cross-domain heterogeneous data types into the calculated graph neural network audit and repair algorithm for audit and repair statistics, so as to obtain the quantity of cross-domain heterogeneous data and the corresponding structured and unstructured forms. Specifically, the audit and repair statistics module can be used to execute step T400. The specific operation content can be referred to the detailed description of step T400, which will not be repeated here.

[0055] The compliance processing judgment module is used to determine the structured and unstructured formats. When the structured and unstructured formats exceed a preset character length, a compliance processing signal is issued. Specifically, the compliance processing judgment module can be used to execute step T500, and the specific operation content can be found in the detailed description of step T500.

[0056] Furthermore, the compliance processing judgment module is also used to obtain the current local meteorological information, calculate the occurrence period of the cross-domain heterogeneous data based on the meteorological information and the structured and unstructured forms, and issue a compliance processing signal.

[0057] The data type acquisition module is used to acquire a set of computational data types for cross-domain heterogeneous data, including different statistical sub-data types. Each statistical sub-data type includes the data type of a single cross-domain heterogeneous data and the corresponding compliance constraint form of the cross-domain heterogeneous data. The compliance constraint form includes the quantity information and structured and unstructured form information of the cross-domain heterogeneous data, wherein the structured and unstructured form information includes the growth cycle information of the cross-domain heterogeneous data. Specifically, the data type acquisition module can be used to execute step T100. The specific operation content can be referred to the detailed description of step T100, which will not be repeated here.

[0058] The deep learning algorithm optimization module is used to optimize the graph neural network audit and repair algorithm using the aforementioned computational data set and a deep learning model, resulting in an optimized graph neural network audit and repair algorithm. Specifically, the deep learning algorithm optimization module can be used to execute step T200; the specific operation details can be found in the detailed description of step T200, which will not be repeated here.

[0059] In this embodiment of the invention, the cross-domain heterogeneous data method may include an edge computing gateway, a data type integration database, and a data processing personnel management terminal. The edge computing gateway is connected to at least one data type integration database via network communication, and is used to perform statistical auditing and repair of the data types sent by the data type integration database, and to make judgments based on the auditing and repair results. For example, when the structured and unstructured forms of cross-domain heterogeneous data exceed a preset character length, a compliance processing signal is issued. The data processing personnel management terminal can communicate with the edge computing gateway via the network, and can also perform auditing and repair statistics on cross-domain heterogeneous data types. Data processing personnel can understand the structured and unstructured forms of various cross-domain heterogeneous data types integrated in the data type integration database, as well as the compliance processing signals, through this management terminal. This cross-domain heterogeneous data auditing and repair method realizes automatic auditing and repair of cross-domain heterogeneous data and compliance processing, improving the efficiency of cross-domain heterogeneous data auditing and repair. In addition, the compliance processing signal helps the production workshop to prepare for the prevention and control of cross-domain heterogeneous data in advance.

[0060] In this embodiment, the data processing personnel management terminal can be, but is not limited to, a smartphone, a personal computer (PC), a tablet computer, a mobile internet device (MID), etc. The network can be, but is not limited to, a wired network or a wireless network.

[0061] In this embodiment, the data type integration database may include a first processor, a first communication module, a first memory, and a data type integration module. The first processor, the first communication module, the first memory, and the data type integration module are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0062] In this embodiment, the data type integration module can be, but is not limited to, a general-purpose camera or an infrared camera, used to acquire color data type or black-and-white data type of cross-domain heterogeneous data. Preferably, the data type integration module is a high-definition camera capable of clearly capturing color data type of cross-domain heterogeneous data.

[0063] The first processor can be a central processing unit (CPU) > network processor (NP) > graphics processing unit (GPU), etc.; it can also be a digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention.

[0064] The first communication module is used to establish a communication connection between the data type integration database and the edge computing gateway through the network, and to send and receive data through the network.

[0065] The first memory may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electric Erasable Programmable Read-Only Memory (EEPROM). In this embodiment, the first memory can be used to store cross-domain heterogeneous data types integrated by the data type integration module. Of course, the memory can also be used to store programs, which the first processor executes upon receiving an execution signal.

[0066] It is worth noting that the data processing personnel management terminal and the data type integration database provided by this invention can be the same device or different devices. For example, the data processing personnel management terminal and the data type integration database can be the same device, such as a smartphone. This smartphone can serve as the data processing personnel management terminal, allowing data processing personnel to view the structured and unstructured forms of cross-domain heterogeneous data and compliance processing signals. It can also directly audit and perform repair statistics on cross-domain heterogeneous data types and process compliance processing signals for the occurrence of cross-domain heterogeneous data. This smartphone can also serve as the data type integration database, used to integrate and upload cross-domain heterogeneous data types.

[0067] In this embodiment, the data processing personnel management terminal may include a second processor, a second communication module, a second memory, and a cross-domain heterogeneous data audit and remediation compliance processing database. The second processor, second communication module, second memory, and cross-domain heterogeneous data audit and remediation compliance processing database are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0068] Furthermore, the cross-domain heterogeneous data audit and remediation compliance processing database includes at least one software functional module that can be stored in the second memory or embedded in the operating system (OS) of the edge computing gateway in the form of software or firmware. The second memory can store data such as the quantity, type, and other data related to the cross-domain heterogeneous data. The second processor is used to execute executable modules stored in the second memory, such as the software functional modules and computer programs included in the cross-domain heterogeneous data audit and remediation compliance processing database. The second processor, second communication module, and second memory may have the same or similar structure and function as the first processor, first communication module, and first memory described above, which will not be elaborated further here.

[0069] In this embodiment, the edge computing gateway is communicatively connected to the data processing personnel management terminal and may have the same or similar structure. It can be used to perform statistical auditing and repair of cross-domain heterogeneous data types, predict the occurrence period of cross-domain heterogeneous data, and send compliance processing signals to the data processing personnel management terminal based on the occurrence period of the corresponding structured and unstructured forms of the cross-domain heterogeneous data. Of course, in other embodiments, the cross-domain heterogeneous data auditing and repair method may not include the edge computing gateway; the functions performed by the data processing personnel management terminal can be substituted for those of the edge computing gateway, which will not be elaborated further here.

[0070] In summary, this invention provides a method for compliance auditing and remediation of cross-domain heterogeneous data. The method uploads various cross-domain heterogeneous data types integrated into a data type integration database to an edge computing gateway. The edge computing gateway inputs the acquired cross-domain heterogeneous data types into a computed graph neural network auditing and remediation algorithm for auditing and remediation statistics, thereby obtaining the quantity of cross-domain heterogeneous data and its corresponding structured and unstructured forms. The structured and unstructured forms are then determined; when the structured and unstructured forms exceed a preset character length, a compliance processing signal is issued. This method can automatically audit and remediate the quantity of cross-domain heterogeneous data and its corresponding structured and unstructured forms, improving the accuracy and efficiency of cross-domain heterogeneous data auditing and remediation. Furthermore, the compliance processing signal helps relevant personnel to identify the appropriate processing period for cross-domain heterogeneous data early.

[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for compliance auditing and remediation of cross-domain heterogeneous data, characterized in that, The method comprises the following steps: The data type integration database integrates various types of cross-domain heterogeneous data types as cross-domain heterogeneous data types obtained by the cross-domain heterogeneous data auditing and repairing method; The obtained cross-domain heterogeneous data types are input into the calculated graph neural network auditing and repairing algorithm for auditing and repairing statistics to obtain cross-domain heterogeneous data and corresponding structured and unstructured forms; When the structured and unstructured forms exceed the preset character length, a compliance processing signal is sent out; Before the step of inputting the obtained cross-domain heterogeneous data types into the calculated graph neural network auditing and repairing algorithm for auditing and repairing statistics, a calculation data type set of cross-domain heterogeneous data is obtained, which includes different statistical sub-data types, each statistical sub-data type including the data type of a single cross-domain heterogeneous data and the corresponding compliance limit form of the cross-domain heterogeneous data, and the compliance limit form including the quantity information, structured and unstructured form information of the cross-domain heterogeneous data; Using the calculation data type set, the graph neural network auditing and repairing algorithm is optimized using a deep learning model to obtain an optimized graph neural network auditing and repairing algorithm; The step of inputting the obtained cross-domain heterogeneous data types into the calculated graph neural network auditing and repairing algorithm for auditing and repairing statistics includes: matching the data type features of the cross-domain heterogeneous data types with the data type features of the statistical sub-data types in the calculation data type set in the graph neural network auditing and repairing algorithm to obtain the compliance rate of the cross-domain heterogeneous data types and the statistical sub-data types; selecting the statistical sub-data type with the highest compliance rate in the calculation data type set, and taking the compliance limit form of the selected statistical sub-data type as the actual monitoring value of the cross-domain heterogeneous data type to obtain the quantity and structured and unstructured form of the cross-domain heterogeneous data; The step of inputting the obtained cross-domain heterogeneous data types into the calculated graph neural network auditing and repairing algorithm for auditing and repairing statistics includes: counting the quantities of the structured and unstructured forms of various types of cross-domain heterogeneous data integrated by the data type integration database within a predetermined time period; The step of judging the structured and unstructured forms and sending out a compliance processing signal when the count of the structured and unstructured forms exceeds the preset character length includes: obtaining the label quantity dynamic change information and real-time change information of the appearance time, frequency and size of the cross-domain heterogeneous data, calculating the alarm range of the cross-domain heterogeneous data according to the user type, structured and unstructured form, and sending out a compliance processing signal; The edge computing gateway is communicatively connected with the data processing personnel management end, and the edge computing gateway is provided with a graph neural network algorithm, and the step of integrating various types of cross-domain heterogeneous data types by the data type integration database as the cross-domain heterogeneous data types obtained by the cross-domain heterogeneous data auditing and repairing method includes: uploading the integrated various types of cross-domain heterogeneous data types by the data type integration database to the edge computing gateway. The step of auditing and repairing the acquired cross-domain heterogeneous data type input calculated graph neural network auditing and repairing algorithm includes: the edge computing gateway inputs the acquired various types of cross-domain heterogeneous data into the graph neural network auditing and repairing algorithm of the edge computing gateway to audit and repair statistics of the various types of cross-domain heterogeneous data.

2. The method of claim 1, wherein, The method is implemented through different devices and modules, including a data type integration database, a data processor management end in communication with the data type integration database, and a cross-domain heterogeneous data auditing and repairing compliance processing database connected with the data processor management end, the data type integration database integrates various types of cross-domain heterogeneous data as cross-domain heterogeneous data types acquired by the cross-domain heterogeneous data auditing and repairing compliance processing method; The data processor management end includes a graph neural network auditing and repairing algorithm, and the cross-domain heterogeneous data auditing and repairing compliance processing database includes: a data type processing module configured to input the acquired cross-domain heterogeneous data type into the calculated graph neural network auditing and repairing algorithm to perform auditing and repair statistics, so as to obtain the number of cross-domain heterogeneous data and corresponding structured and unstructured forms; a data processing module configured to integrate real-time data of the corresponding cross-domain heterogeneous data acquired by the data type processing module, convert each data format into a universal format, audit and repair attribute information of each data service through the universal format, and distribute and store each data according to the service attribute information; pre-process and sort each data, and send the sorted data to a distributed data type and data system; a compliance processing judgment module configured to judge the structured and unstructured forms, and send a compliance processing signal when the structured and unstructured forms exceed a preset character length. A data type acquisition module is configured to acquire a calculation data type set of cross-domain heterogeneous data, including different statistical sub-data types, each statistical sub-data type including a data type of a single cross-domain heterogeneous data and a corresponding compliance limitation form of the cross-domain heterogeneous data, the compliance limitation form including number information, structured and unstructured form information of the cross-domain heterogeneous data; and a deep learning algorithm optimization module is configured to use the calculation data type set to optimize the graph neural network auditing and repairing algorithm using a deep learning model to obtain an optimized graph neural network auditing and repairing algorithm. The compliance processing judgment module is further configured to: dynamically change the label number information of the time, frequency, and size of the cross-domain heterogeneous data, and have real-time change information, calculate the cumulative number and the incremental number of the cross-domain heterogeneous data in the calculation area according to the user category and the structured and unstructured form, and send a compliance processing signal.

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