Data processing method for compliance evaluation of data element processing operation

Abstract

The invention discloses a data processing method for compliance evaluation of data element processing operation, and relates to the technical field of data processing and compliance management, and the method comprises the steps: collecting data elements and regulatory rules, carrying out the preprocessing, building a knowledge graph of the association of the data elements and the regulatory rules based on the preprocessed data elements and regulatory rules, and carrying out the compliance evaluation of the data elements and the regulatory rules. The method comprises the following steps: monitoring data element processing operation in real time, extracting operation meta-information, performing rule matching in combination with a knowledge graph, fusing a rule matching result, the knowledge graph and the operation meta-information to obtain data processing features, constructing a compliance evaluation model by using a machine learning algorithm, inputting the data processing features into the compliance evaluation model, and analyzing potential risks. According to the method, high-risk operation is pre-warned and limited, the compliance evaluation model is optimized according to user feedback, a dynamic compliance evaluation method is formed, and the method has important significance in the field of data processing and compliance management.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and compliance management, and in particular to a data processing method for compliance assessment of data element processing operations. Background Art

[0002] With the rapid development of information technology and the widespread popularization of big data applications, the processing and circulation of data elements have become an important part of modern society and corporate operations. Data processing operations often involve the use and sharing of sensitive data such as personal privacy and commercial secrets. Regulatory agencies in various countries have successively issued a series of laws and regulations related to data protection. The implementation of these laws and regulations provides clear guidance for the legal use of data elements, but also puts forward increasingly high compliance requirements for data processing activities. In existing technologies, the compliance of data processing operations is usually evaluated through manual review, rule matching tools and simple automation tools. With the increase in data scale and complexity, traditional methods have gradually exposed problems such as low efficiency, insufficient accuracy, and difficulty in dynamically adapting to changes in regulations, and cannot meet the complex needs of modern enterprises in data compliance management; There are many major deficiencies in the existing technologies in the field of data compliance assessment. Most of the existing compliance assessment models lack a dynamic feedback mechanism, and it is difficult to optimize the compliance assessment models based on user feedback and changes in the operating environment, resulting in a gradual decline in the predictive ability of the compliance assessment models. The real-time requirements for data processing operations are increasing, and the existing technologies often perform poorly in real-time monitoring and dynamic risk assessment. They are unable to efficiently capture operational meta-information and conduct rapid assessments. The impact of data processing operation factors on compliance assessments has not been fully reflected in the existing compliance assessment models, resulting in the evaluation results being difficult to fully reflect the actual risks of data processing operations. In this context, how to effectively combine existing technologies with regulatory knowledge graphs, real-time data monitoring, and dynamic risk assessment has become a hot topic of common concern to academia and industry. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a data processing method for compliance assessment of data element processing operations to solve the problem that the real-time requirements of data processing operations are increasingly increasing, while the existing technologies often perform poorly in real-time monitoring and dynamic risk assessment, and are unable to efficiently capture operation meta-information and conduct rapid assessment.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a data processing method for compliance assessment of data element processing operations, comprising: Collect data elements and regulatory rules and perform pre-processing; Based on the pre-processed data elements and regulations, a knowledge graph is constructed that associates data elements with regulations. Monitor data element processing operations in real time, extract operation meta-information, and perform rule matching based on knowledge graphs; The rule matching results, knowledge graph and operation meta-information are integrated to obtain data processing features; Use machine learning algorithms to build compliance assessment models, input data processing features into the compliance assessment models, analyze potential risks, and issue early warnings and restrictions on high-risk operations; Collect user feedback and optimize the compliance assessment model based on user feedback; the specific steps of constructing a knowledge graph that associates data elements with regulations and rules are as follows: Calculating data elements Regulations and rules specific conditions in The semantic similarity of , the expression is: ; Calculate the correlation between data elements and regulations and rules. The expression is: ; in, Represents data elements, Indicates regulations and rules. Indicates the degree of association, represents the cosine similarity, Indicates regulations and rules The specific conditions in Representing data elements Regulations and rules specific conditions in The semantic similarity of Indicates regulations and rules The weight of Representing data elements The complexity of Indicates regulations and rules complexity.

[0006] As a preferred solution of the data processing method for compliance assessment of data element processing operations described in the present invention, the data elements and regulatory rules are collected and pre-processed, and the specific steps are: The data elements are user data, device data and transaction data; Preprocessing of data elements refers to data cleaning, data standardization, and data labeling; Preprocessing of regulations and rules refers to using natural language processing (NLP) to parse and extract text to obtain terms for specific conditions, structured storage, and rule conflict detection.

[0007] As a preferred solution of the data processing method for compliance assessment of data element processing operations described in the present invention, the data element processing operations are monitored in real time, operation meta-information is extracted, and rule matching is performed in combination with the knowledge graph. The specific steps are as follows: Use the event-driven monitoring tool Kafka to monitor the event stream of data processing operations and capture operation metadata ,in, Indicates the data elements involved in the operation, Indicates the operating frequency; Combined with the knowledge graph for rule matching, the expression is: ; in, Indicates the matching degree, Indicates the data elements involved in the operation Regulations and rules specific conditions in The semantic similarity of represents the natural number base, represents the adjustment coefficient, Represents the operating frequency attenuation factor.

[0008] As a preferred solution of the data processing method for compliance assessment of data element processing operations described in the present invention, wherein: the rule matching results, knowledge graph and operation meta-information are integrated to obtain data processing features, which are expressed as follows: ; in, Represents data processing characteristics, Indicates the complexity of the data elements involved in the operation, represents the adjustment parameter of the matching degree, represents the adjustment parameter of the operating frequency, Represents the natural logarithm function.

[0009] As a preferred solution of the data processing method for compliance assessment of data element processing operations described in the present invention, wherein: the use of a machine learning algorithm to construct a compliance assessment model comprises the following specific steps: Select the deep neural network DNN algorithm to build a compliance assessment model; Setting the architecture of the compliance assessment model based on the deep neural network DNN; The architecture of the compliance assessment model refers to an input layer, a hidden layer, and an output layer; Collecting large historical compliance assessment data sets; The historical compliance assessment data set is divided into training set, test set and validation set in proportion, and input into the compliance assessment model for training, testing and validation of the compliance assessment model respectively.

[0010] As a preferred solution of the data processing method for compliance assessment of data element processing operations described in the present invention, the specific steps of inputting data processing features into the compliance assessment model, analyzing potential risks, and issuing warnings and restrictions on high-risk operations are as follows: The data processing features obtained by integrating rule matching results, knowledge graphs and operation meta-information are input into the compliance assessment model to calculate the potential risk value. The expression is: ; in, represents the potential risk value, express Activation function, represents the weight matrix, represents the transpose operation, represents the bias term; Setting the Threshold ,when When , it indicates that the current operation is a low-risk operation and no processing is performed; when , it indicates that the current operation is a high-risk operation, the current operation is blocked, and an early warning is sent to the user.

[0011] As a preferred solution of the data processing method for compliance assessment of data element processing operations described in the present invention, wherein: the collecting of user feedback and optimizing the compliance assessment model according to the user feedback comprises the following specific steps: Calculate data processing feature offset based on user feedback , the expression is: ; in, Indicates the number of samples of user feedback, The index of the sample representing the user feedback. Indicates The user-annotated feedback value of the sample feedbacked by users, Indicates The potential risk value of the sample reported by users, Indicates Data processing characteristics of samples of user feedback; Add feedback feature offset to the compliance assessment model input layer The correction term redefines the output formula of risk value, and the expression is: ; represents the adjusted risk value, Indicates feature offset The regulating factor.

[0012] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the data processing method for compliance assessment of data element processing operations as described in the first aspect of the present invention.

[0013] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the data processing method for compliance assessment of data element processing operations as described in the first aspect of the present invention.

[0014] The beneficial effects of the present invention are as follows: data query and preprocessing solve the problems of inconsistent sources and low quality of data and regulations, and provide high-quality input data for the entire process. Knowledge graph construction enhances the accuracy and intelligence of rule matching through quantified correlation and semantic matching. Real-time monitoring and rule matching are combined with real-time monitoring and dynamic frequency attenuation to improve the accuracy and timeliness of assessment. Data processing feature extraction generates data features that fully reflect risks through multi-dimensional fusion, providing reliable and efficient input for compliance assessment models. Through the key steps of data query preprocessing, knowledge graph construction, real-time monitoring and rule matching, feature extraction and user feedback optimization, the problems of inaccurate rule matching, lack of dynamic adaptability and insufficient real-time performance in the prior art are effectively solved, forming a dynamic, accurate and efficient compliance assessment method, which is of great significance to the field of data processing and compliance management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. Figure 1 is a flow chart of the data processing method for compliance assessment of data element processing operations in Example 1; Figure 2 This is a schematic diagram of calculating the potential risk value in Example 1. DETAILED DESCRIPTION

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0019] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a data processing method for compliance assessment of data element processing operations, comprising the following steps: S1. Collect data elements and regulations and pre-process them; Data query tools refer to SQL query tools and API interfaces; Database refers to the enterprise's internal database and regulatory database; Data elements refer to user data, device data, and transaction data; Regulations and rules refer to the text of laws and regulations related to data elements; Preprocessing of data elements refers to data cleaning, data standardization, and data labeling; Use pandas tools to complete missing value processing, outlier detection, duplicate value removal and format unification; Use the normalization, standardization, and encoding tools provided by sklearn.preprocessing to complete the standardization of numerical and categorical data; Use Label Studio to label data; Preprocessing of regulations and rules refers to using natural language processing (NLP) to parse and extract text to obtain specific conditions, structured storage, and rule conflict detection; Structured storage is achieved through the relational database MySQL; Use the logic engine Prolog for rule conflict detection; The data query tool collects data elements and regulatory rules from the company's internal database and regulatory database, and combines data cleaning, standardization and annotation to significantly improve data quality and solve the problems of inconsistent data sources and non-standardized formats in existing technologies; Collect data elements and regulatory rules from the company's internal database and regulatory database through SQL query tools and API interfaces, and pre-process the data, including data cleaning, standardization and annotation; perform text parsing and extraction, structured storage and rule conflict detection on regulatory rules; This step solves the problem of inconsistency in format and storage between user data, device data, transaction data and legal texts of regulations and rules from different data sources through unified query tools and interfaces. The preprocessing process further cleans and standardizes the data and regulations and rules to ensure data consistency and structured storage, thereby providing high-quality input data for subsequent knowledge graph construction. In addition, rule conflict detection helps to discover and resolve contradictions between different regulations and rules, avoiding mismatches caused by inconsistent rules; Targeted preprocessing of text parsing and extraction, structured storage, and rule conflict detection ensures that regulations and rules can be efficiently stored and called in a structured form, making subsequent knowledge graph construction more accurate; This step can significantly improve the quality and standardization of data and regulations, providing a solid foundation for the subsequent construction of knowledge graphs. Compared with the inefficient model of relying on manual data collection and processing in existing technologies, this step greatly improves processing efficiency and accuracy, ensuring the reliability of the compliance assessment process; This step provides high-quality basic data for knowledge graph construction and rule matching through standardized data and regulatory input, thereby improving the accuracy and reliability of the entire compliance assessment process.

[0020] S2. Based on the pre-processed data elements and regulations, a knowledge graph is constructed to associate data elements with regulations; Calculate data elements using cosine similarity function Regulations and rules specific conditions in The semantic similarity of , the expression is: ; Calculate the correlation between data elements and regulations and rules. The expression is: ; in, Represents data elements, Indicates regulations and rules. Indicates the degree of association, represents the cosine similarity, Indicates regulations and rules The specific conditions in Representing data elements Regulations and rules specific conditions in The semantic similarity of Indicates regulations and rules The weight of Representing data elements The complexity of Indicates regulations and rules complexity; Based on the pre-processed data elements and regulatory rules, a knowledge graph is constructed and the correlation is used , which solves the problem that the matching of data and regulatory rules in traditional technologies only relies on keywords and ignores semantic relevance; By data element Regulations and rules specific conditions in The semantic similarity of The introduction of regulatory weight , implemented the strategy of giving priority to matching important regulations, and enhanced the accuracy of rule matching; The knowledge graph expresses the complex relationship between data elements and regulations in a graph structure, making it easy to quickly call related information in real-time monitoring for efficient rule matching; Through the construction of knowledge graphs, the complex relationship between data elements and regulations can be expressed in a graph structure. This structured expression method greatly enhances the flexibility and accuracy of subsequent rule matching. The semantic similarity calculation in the formula solves the problem that traditional keyword matching cannot capture deep semantic relationships. By introducing regulatory weights The normalization of the complexity further optimizes the priority of important rules and the fairness of matching; This step can not only accurately reflect the semantic association between data elements and regulatory rules, but also improve the efficiency of rule matching through structured storage of knowledge graphs. Compared with the simple string matching method in the existing technology, this step makes rule matching more intelligent and semantic, and provides a basis for rapid calling for real-time monitoring.

[0021] S3, real-time monitoring of data element processing operations, extraction of operation meta-information, and rule matching based on knowledge graphs; Use the event-driven monitoring tool Kafka to monitor the event stream of data processing operations and capture operation metadata ,in, Indicates the data elements involved in the operation, Indicates the operating frequency; Combined with the knowledge graph for rule matching, the expression is: ; in, Indicates the matching degree, Indicates the data elements involved in the operation Regulations and rules specific conditions in The semantic similarity of represents the natural number base, represents the adjustment coefficient, represents the operating frequency attenuation factor; Use Kafka event-driven tools to monitor data processing operations in real time and capture operation metadata , which solves the problem of lack of real-time performance in existing technologies; The rule matching formula dynamically combines operation frequency and semantic similarity to avoid misjudgment of high-frequency and low-risk operations, thus improving the robustness of matching. The introduction of real-time monitoring tools enables the evaluation of data processing operations to capture metadata of data elements and operation frequencies in real time, solving the problem of insufficient real-time requirements in existing technologies. By combining the knowledge graph with the frequency attenuation factor in the formula , dynamically balances the impact of high-frequency operations on rule matching, effectively avoiding the situation where high-frequency, low-risk operations are misjudged as high-risk; By combining real-time monitoring and dynamic rule matching, the characteristics of data operations can be quickly captured and their compliance can be accurately evaluated. This approach not only improves real-time performance, but also improves the robustness of rule matching through the design of frequency attenuation factors, avoiding misjudgments caused by high-frequency operations in existing technologies; The combination of real-time monitoring and rule matching not only supports dynamic risk assessment, but also provides key input data for subsequent feature extraction.

[0022] S4, integrating the rule matching results, knowledge graph and operation meta-information to obtain data processing features; The data processing characteristic expression is: ; in, Represents data processing characteristics, Indicates the complexity of the data elements involved in the operation, represents the adjustment parameter of the matching degree, represents the adjustment parameter of the operating frequency, represents the natural logarithm function; The data processing feature formula integrates the multi-dimensional factors of matching degree, correlation degree, semantic similarity, and frequency attenuation, solving the limitation of the existing technology that the features are single and cannot fully reflect the risks. This step generates a feature value with high expressive power by integrating the multi-dimensional features of the data processing operation. , providing reliable support for the subsequent compliance assessment model input. The natural logarithm function in the formula Highlights the influence of matching degree on features, while the frequency attenuation factor and the complexity normalization term This effectively balances the contribution of different features to the final eigenvalue; The introduction of natural logarithmic function and frequency attenuation factor can more accurately balance the nonlinear relationship between multiple factors and ensure the academic and practical nature of feature extraction; Data processing features as core features of compliance assessment model input The precise extraction of directly determines the accuracy of risk assessment; By integrating the rule matching results with the operation meta-information, this step generates data features that can fully reflect the operational risks of data processing. This feature extraction method is more comprehensive than the existing technology, solving the problem that a single feature cannot accurately describe the operational risk, and providing a guarantee for the efficient learning of the subsequent compliance assessment model.

[0023] S5. Use machine learning algorithms to build a compliance assessment model, input data processing features into the compliance assessment model, analyze potential risks, and issue warnings and restrictions on high-risk operations; Select the deep neural network DNN algorithm to build a compliance assessment model; Setting the architecture of the compliance assessment model based on the deep neural network DNN; The architecture of the compliance assessment model refers to the input layer, hidden layer, and output layer; Collecting large historical compliance assessment data sets; The historical compliance assessment data set is divided into a training set, a test set, and a validation set in proportion, and the sets are respectively input into the compliance assessment model for training, testing, and validation of the compliance assessment model; The compliance assessment model is trained using a binary cross entropy loss function, and the loss function expression is: ; in, represents the value of the loss function, represents the number of samples, Indicates The true labels of samples, represents the index of the sample, Indicates The predicted value of samples, represents the logarithmic function; Use the test set to test the model, use the validation set to tune the model parameters, select the optimal parameters based on the performance of the validation set, and use the early stopping method to prevent overfitting; Through the input layer, hidden layer and output layer architecture of the deep neural network DNN, the compliance assessment model can extract complex nonlinear features from a large number of historical compliance assessment data sets and conduct deep learning of rule matching results, knowledge graphs and operation meta-information; Deep learning methods can simulate complex nonlinear relationships, solving the limitation of traditional rule matching methods that cannot fully reflect the potential risks of data operations; Compared with traditional linear models and rule-driven methods, DNN can more effectively capture the complex interactions between data features, significantly improving the accuracy and reliability of potential risk assessment, thereby reducing the possibility of misjudgment and missed judgment; Through the Softmax activation function, the compliance assessment model can convert the potential risk value Mapping to a range of 0 to 1 provides intuitive risk probability values, facilitating risk classification and management of subsequent operations; By collecting a large number of historical compliance assessment data sets and dividing the data sets into training sets, test sets, and validation sets in proportion, the compliance assessment model can be trained and validated on diverse data to ensure its adaptability to data processing operations in different scenarios; The division and training process of the data set can ensure that the compliance assessment model can not only learn the rules in historical data, but also effectively respond to emerging operating modes and regulatory changes. When a new high-risk operating mode emerges, the compliance assessment model can predict risks based on similar historical operating characteristics to avoid missed risks caused by changes. Use the test set to evaluate the performance of the compliance assessment model to further ensure the reliability of the compliance assessment model in practical applications; The data-driven training process significantly improves the generalization ability of the compliance assessment model, enabling it to adapt to different data processing scenarios and changes in regulations and rules, ensuring that the compliance assessment maintains a stable risk prediction capability in a complex and changing environment; The data processing features obtained by integrating rule matching results, knowledge graphs and operation meta-information are input into the compliance assessment model to calculate the potential risk value. The expression is: ; in, represents the potential risk value, express Activation function, represents the weight matrix, represents the transpose operation, represents the bias term; Setting the Threshold ,when When , it indicates that the current operation is a low-risk operation and no processing is performed; when When it is displayed, it indicates that the current operation is a high-risk operation, the current operation is blocked, and an early warning is sent to the user; First, collect a large number of historical compliance assessment data sets and calculate the potential risk value of each historical compliance assessment data set. , set preliminary thresholds by analyzing the distribution of potential risk values ; According to specific user needs, the threshold Adjustments can be made. If the risk cost is high, the threshold can be appropriately lowered so that the compliance assessment model can issue early warnings, which ensures a balance between the sensitivity of the warning and the false alarm rate. The dynamic risk assessment mechanism can classify operations based on the data characteristics of real-time input, avoiding the lag in operational risk assessment in traditional static rules. In high-frequency data processing scenarios, the compliance assessment model can quickly predict risks and take corresponding measures to reduce the threat of high-risk operations to data security and regulatory compliance. The early warning function can promptly notify users of potential risks, helping them quickly adjust their operating behaviors to avoid violations and triggering supervision; The dynamic risk assessment mechanism significantly improves the real-time nature and emergency response capabilities of compliance assessments. It can not only effectively prevent high-risk operations, but also help users reduce the incidence of illegal operations through accurate early warnings.

[0024] S6. Collect user feedback and optimize the compliance assessment model based on user feedback; Calculate data processing feature offset based on user feedback , the expression is: ; in, Indicates the number of samples of user feedback, The index of the sample representing the user feedback. Indicates The user-annotated feedback value of the sample feedbacked by users, Indicates The potential risk value of the sample reported by users, Indicates Data processing characteristics of samples of user feedback; Calculate feature offset based on user feedback Dynamically correct the input features of the compliance assessment model, solving the problem in the existing technology that the compliance assessment model cannot adapt to changes in the distribution of feedback data; Add feedback feature offset to the compliance assessment model input layer The correction term redefines the output formula of risk value, and the expression is: ; represents the adjusted risk value, Indicates feature offset The regulatory factors; The scalable compliance assessment model architecture supports the introduction of new data processing features and regulatory rules, and enhances the compliance assessment model's ability to adapt to new scenarios. When regulations are updated and data processing requirements change, the compliance assessment model can quickly adapt to new requirements by adjusting input features. The introduction of user feedback further enhances the self-learning ability of the compliance assessment model, ensuring that it maintains efficient risk prediction capabilities in long-term operation; The scalability and continuous optimization of the compliance assessment model make the compliance assessment long-term applicable and can be adjusted as regulations and business environments change, avoiding aging and performance degradation. The revised risk value formula improves the adaptability and assessment accuracy of the compliance assessment model; The user feedback optimization mechanism enables the compliance assessment model to continuously learn and improve, ensuring its applicability in the face of changes in regulations and data processing environments.

[0025] This embodiment also provides a computer device, which is suitable for the case of a data processing method for compliance assessment of data element processing operations, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the data processing method for compliance assessment of data element processing operations proposed in the above embodiment.

[0026] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0027] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the data processing method for implementing compliance assessment for data element processing operations as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination of them, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0028] In summary, the present invention solves the problems of inconsistent sources and low quality of data and regulations and rules through: data query and preprocessing, providing high-quality input data for the entire process; knowledge graph construction enhances the accuracy and intelligence of rule matching through quantified correlation and semantic matching; real-time monitoring and rule matching are combined with real-time monitoring and dynamic frequency attenuation to improve the accuracy and timeliness of assessment; data processing feature extraction generates data features that comprehensively reflect risks through multi-dimensional fusion, providing reliable and efficient input for compliance assessment models; through the key steps of data query preprocessing, knowledge graph construction, real-time monitoring and rule matching, feature extraction and user feedback optimization, the problems of inaccurate rule matching, lack of dynamic adaptability and insufficient real-time performance in the prior art are effectively solved, forming a dynamic, accurate and efficient compliance assessment method, which is of great significance to the field of data processing and compliance management.

[0029] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A data processing method for compliance assessment of data element processing operations, characterized in that: include, Collect data elements and regulatory rules and perform pre-processing; Based on the pre-processed data elements and regulations, a knowledge graph is constructed that associates data elements with regulations. Monitor data element processing operations in real time, extract operation meta-information, and perform rule matching based on knowledge graphs; The rule matching results, knowledge graph and operation meta-information are integrated to obtain data processing features; Use machine learning algorithms to build compliance assessment models, input data processing features into the compliance assessment models, analyze potential risks, and issue early warnings and restrictions on high-risk operations; Collect user feedback and optimize the compliance assessment model based on user feedback; The specific steps of constructing the knowledge graph that associates data elements with regulations and rules are as follows: Calculating data elements Regulations and rules specific conditions in The semantic similarity of , the expression is: ； Calculate the correlation between data elements and regulations and rules. The expression is: ； in, Represents data elements, Indicates regulations and rules. Indicates the degree of association, represents the cosine similarity, Indicates regulations and rules The specific conditions in Representing data elements Regulations and rules specific conditions in The semantic similarity of Indicates regulations and rules The weight of Representing data elements The complexity of Indicates regulations and rules complexity.

2. The data processing method for compliance assessment of data element processing operations according to claim 1, characterized in that: The data elements and regulations are collected and preprocessed, and the specific steps are as follows: The data elements are user data, device data and transaction data; The regulations and rules refer to the text of laws and regulations related to data elements; Preprocessing of data elements refers to data cleaning, data standardization, and data labeling; Preprocessing of regulations and rules refers to using natural language processing (NLP) to parse and extract text to obtain specific conditions, structured storage, and rule conflict detection.

3. The data processing method for compliance assessment of data element processing operations according to claim 2, characterized in that: The data element processing operation is monitored in real time, the operation meta-information is extracted, and rule matching is performed in combination with the knowledge graph. The specific steps are as follows: Use the event-driven monitoring tool Kafka to monitor the event stream of data processing operations and capture operation metadata ,in, Indicates the data elements involved in the operation, Indicates the operating frequency; Combined with the knowledge graph for rule matching, the expression is: ； in, Indicates the matching degree, Indicates the data elements involved in the operation Regulations and rules specific conditions in The semantic similarity of represents the natural number base, represents the adjustment coefficient, Represents the operating frequency attenuation factor.

4. The data processing method for compliance assessment of data element processing operations according to claim 3, characterized in that: The rule matching results, knowledge graph and operation meta-information are integrated to obtain data processing features, which are expressed as follows: ； in, Represents data processing characteristics, Indicates the complexity of the data elements involved in the operation, represents the adjustment parameter of the matching degree, represents the adjustment parameter of the operating frequency, Represents the natural logarithm function.

5. The data processing method for compliance assessment of data element processing operations according to claim 4, characterized in that: The specific steps of using machine learning algorithms to build a compliance assessment model are as follows: Select the deep neural network DNN algorithm to build a compliance assessment model; Setting the architecture of the compliance assessment model based on the deep neural network DNN; The architecture of the compliance assessment model refers to an input layer, a hidden layer, and an output layer; Collecting large historical compliance assessment data sets; The historical compliance assessment data set is divided into training set, test set and validation set in proportion, and input into the compliance assessment model for training, testing and validation of the compliance assessment model respectively.

6. The data processing method for compliance assessment of data element processing operations according to claim 5, characterized in that: The specific steps of inputting data processing characteristics into the compliance assessment model, analyzing potential risks, and issuing early warnings and restrictions on high-risk operations are as follows: The data processing features obtained by integrating rule matching results, knowledge graphs and operation meta-information are input into the compliance assessment model to calculate the potential risk value. The expression is: ； in, represents the potential risk value, express Activation function, represents the weight matrix, represents the transpose operation, represents the bias term; Setting the Threshold ,when When , it indicates that the current operation is a low-risk operation and no processing is performed; when , it indicates that the current operation is a high-risk operation, the current operation is blocked, and an early warning is sent to the user.

7. The data processing method for compliance assessment of data element processing operations according to claim 6, characterized in that: The specific steps of collecting user feedback and optimizing the compliance assessment model based on the user feedback are as follows: Calculate data processing feature offset based on user feedback , the expression is: ； in, Indicates the number of samples of user feedback, The index of the sample representing the user feedback. Indicates The user-annotated feedback value of the sample feedbacked by users, Indicates The potential risk value of the sample reported by users, Indicates Data processing characteristics of samples of user feedback; Add feedback feature offset to the compliance assessment model input layer The correction term redefines the output formula of risk value, and the expression is: ； represents the adjusted risk value, Indicates feature offset The regulating factor.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, it implements the steps of the data processing method for compliance assessment of data element processing operations described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data processing method for compliance assessment of data element processing operations described in any one of claims 1 to 7 are implemented.

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