Accounting data processing method and system based on financial robot

Abstract

The invention discloses an accounting data processing method and system based on a financial robot, and relates to the field of financial robots, and the method comprises the steps: obtaining original financial data; preprocessing the original financial data, analyzing the preprocessed original financial data, and determining sensitive data and sensitivity corresponding to the sensitive data; packaging the sensitive data according to the sensitivity, and obtaining a storage address of the packaged sensitive data; determining accessible users according to the data sensitivity; performing analytic statistics on the access data of the accessible users, and constructing an attribute key moment according to an analytic statistics result; and encrypting the storage address by using the attribute key moment. Through sensitivity analysis and data encapsulation, the sensitive data in the original financial data is subjected to hierarchical encryption processing, the risk of data leakage is effectively prevented, the attribute key matrix is utilized to dynamically encrypt the storage address, the cracking difficulty is increased, and the security of the financial data is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of RPD financial robots, and in particular to an accounting data processing method and system based on a financial robot. Background Art

[0002] RPA Robotic Process Automation is built on the basis of AI+automation technology. It uses robots as virtual labor to interact with existing user systems according to pre-set programs and complete expected tasks. It automates routine rule-based operations, such as creating new bank accounts, obtaining data from tables and recording them in the system, extracting billing data from web pages, sending emails or letters to customers, etc.; RPA robots are needed when processing accounting data. By using RPA robots to replace manual labor, the time saved can allow corporate employees to spend more time focusing on higher-value work. The following technical problems may arise during specific use: When processing data, some of the corresponding key data will be displayed. If this part of the data is relatively confidential, it will lead to lower security during the entire data processing.

[0003] Patent CN202111189670.9 provides an accounting data processing method based on an RPA financial robot. Although this method can encrypt confidential data, it does not perform hierarchical encryption on confidential data, and often lacks effective data protection measures, and there is still a risk of data leakage. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides an accounting data processing method based on a financial robot. The method realizes comprehensive protection and secure processing of original financial data through preprocessing, sensitivity analysis, data encapsulation, address encryption and other steps.

[0005] In one aspect, the present invention provides an accounting data processing method based on a financial robot, the method comprising:

[0006] Obtaining raw financial data;

[0007] Preprocessing the original financial data, analyzing the preprocessed original financial data, and determining sensitive data and the sensitivity corresponding to the sensitive data;

[0008] Encapsulating the sensitive data according to the sensitivity, and obtaining the storage address of the encapsulated sensitive data;

[0009] Determine accessible users according to the data sensitivity; analyze and count the access data of accessible users, and construct an attribute key matrix according to the analysis and statistical results; and encrypt the storage address using the attribute key matrix.

[0010] Preferably, preprocessing the original financial data includes:

[0011] Formulate the validity judgment standard of financial data according to the internal financial data management regulations of the enterprise and industry regulations, filter the original financial data according to the standard, and delete invalid data;

[0012] Find missing data and determine the missing pattern based on the location of the missing data in the original financial data; delete or fill in the financial data based on the amount of missing data and the missing pattern;

[0013] Standardize financial data.

[0014] Preferably, the sensitive data is encapsulated according to the sensitivity, including:

[0015] Determine the encryption function and encapsulation function based on sensitivity;

[0016] encrypting the sensitivity using the encryption function;

[0017] The metadata of the sensitive data and the content summary of the metadata are extracted, and the encrypted sensitive data, metadata and content summary are encapsulated using the encapsulation function.

[0018] Preferably, the formula for encapsulating the encrypted sensitive data, metadata and content summary using the encapsulation function is:

[0019] E=g(∑w i x+a)

[0020] Among them, g(·) is the encapsulation function, x i is the encrypted sensitive number, a is the metadata of the sensitive data, and w i A summary of the metadata content.

[0021] Preferably, the access data includes analyzing and counting the access data of accessible users, and constructing an attribute key matrix according to the analysis and counting results, including:

[0022] extracting access features from the access data;

[0023] The user access features are sorted to obtain an access feature sequence, and an attribute key matrix is ​​generated according to the user key corresponding to the access feature sequence.

[0024] Preferably, sorting the user access features to obtain an access feature sequence, and generating an attribute key matrix according to the user key corresponding to the access feature sequence includes:

[0025] When there are abnormalities in the user access features, the abnormal user access features are eliminated; the remaining user access features are sorted to obtain an access feature sequence;

[0026] Determine the insertion feature according to the difference between the abnormal user access feature and the feature threshold, and determine the insertion parameter according to the proportion of the abnormal user access feature;

[0027] The insertion feature is inserted into the access feature sequence according to the insertion parameter to obtain a target access feature sequence.

[0028] Preferably, sorting the user access features to obtain an access feature sequence, and generating an attribute key matrix according to the user key corresponding to the access feature sequence further comprises: combining all target access feature sequences to obtain a target access feature matrix;

[0029] An attribute key matrix is ​​determined according to the target access feature matrix; wherein each user corresponds to multiple access features, each access feature corresponds to multiple access feature intervals, each access feature interval corresponds to an attribute key, and each insertion parameter corresponds to an attribute key.

[0030] On the other hand, an accounting data processing system based on a financial robot includes a financial robot, wherein the financial robot includes:

[0031] Data acquisition module, used to obtain original financial data;

[0032] A preprocessing module, used to preprocess the original financial data, analyze the preprocessed original financial data, and determine sensitive data and the sensitivity corresponding to the sensitive data;

[0033] An encapsulation storage module is used to encapsulate the sensitive data according to the sensitivity and obtain the storage address of the encapsulated sensitive data;

[0034] The address encryption module is used to determine the accessible users according to the data sensitivity; analyze and count the access data of the accessible users, and construct an attribute key matrix according to the analysis and statistical results; and encrypt the storage address using the attribute key matrix.

[0035] The beneficial effects of the present invention are as follows: the present invention proposes an accounting data processing method based on a financial robot, which realizes comprehensive protection and security processing of original financial data through preprocessing, sensitivity analysis, data encapsulation, address encryption and other steps. Specifically, it includes: obtaining original financial data, performing preprocessing and sensitivity analysis to determine sensitive data and its sensitivity; encrypting and encapsulating sensitive data according to sensitivity, and obtaining the storage address of the encapsulated sensitive data; determining accessible users according to data sensitivity, analyzing and counting the access data of accessible users, constructing an attribute key matrix, and using the matrix to encrypt the storage address.

[0036] In addition, the present invention also provides an accounting data processing system based on a financial robot, including a data acquisition module, a preprocessing module, a packaging storage module and an address encryption module to implement the above method. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0038] Figure 1 The present invention provides a flowchart of an accounting data processing method based on a financial robot;

[0039] Figure 2 A system block diagram of an accounting data processing system based on a financial robot is provided for the present invention. DETAILED DESCRIPTION

[0040] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0041] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0042] Example 1

[0043] like Figure 1 As shown, the present invention provides an accounting data processing method based on a financial robot, the method comprising:

[0044] Step 1: Obtain original financial data.

[0045] The original financial data may be obtained through a variety of channels, including an enterprise financial system or an external system, which is not limited in the embodiments of the present invention.

[0046] Step 2: preprocess the original financial data, analyze the preprocessed original financial data, and determine sensitive data and the sensitivity corresponding to the sensitive data.

[0047] In an embodiment of the present invention, preprocessing the original financial data includes: formulating a validity judgment standard for financial data according to the internal financial data management regulations of the enterprise and industry regulations, screening and filtering the original financial data according to the standard, and deleting invalid data; searching for missing data, and determining the missing pattern according to the position of the missing data in the original financial data; deleting or filling the financial data according to the amount of missing data and the missing pattern; and standardizing the financial data.

[0048] First, according to the internal financial data management regulations of the enterprise and industry regulations, a set of detailed and strict financial data validity judgment standards will be formulated. This set of standards covers multiple aspects such as data integrity, accuracy, and compliance to ensure the authenticity and reliability of the data.

[0049] Next, the original financial data will be comprehensively screened and filtered according to this set of validity judgment standards. In this process, any data that does not meet the standards will be considered invalid data and deleted. In this way, it can be ensured that the data processed subsequently is valid and reliable.

[0050] At the same time, during the preprocessing process, special attention will be paid to the problem of missing data. Advanced algorithms and techniques will be used to find missing data in the original financial data, and the missing patterns will be determined based on the location of these data in the original financial data. This in-depth analysis of missing patterns will help to better understand the completeness and accuracy of the data.

[0051] Once the missing pattern is determined, a decision is made based on the amount of missing data and the missing pattern, choosing whether to remove the data or fill it in. If the amount of missing data is small and has little impact on the overall data analysis, you may choose to remove the data. However, if the amount of missing data is large or has a significant impact on the data analysis, appropriate methods will be used to fill it in to ensure the integrity of the data.

[0052] Finally, in the preprocessing stage, the financial data will be standardized. This includes unifying the data format, unit, etc. to ensure the consistency and comparability of the data. Through standardization, it is easier to compare, analyze and mine the data, thereby discovering more valuable information.

[0053] The pre-processing steps of the present invention include formulating validity judgment criteria, screening and filtering data, processing missing data, and standardization processing, etc. These steps jointly ensure the accuracy and consistency of the original financial data, laying a solid foundation for subsequent data processing and analysis.

[0054] Step 3: encapsulate the sensitive data according to the sensitivity, and obtain the storage address of the encapsulated sensitive data.

[0055] In an embodiment of the present invention, the sensitive data is encapsulated according to the sensitivity, including: determining an encryption function and an encapsulation function according to the sensitivity; encrypting the sensitivity using the encryption function; extracting the metadata of the sensitive data and the content summary of the metadata, and encapsulating the encrypted sensitive data, metadata and content summary using the encapsulation function.

[0056] In the embodiment of the present invention, the formula for encapsulating the encrypted sensitive data, metadata and content summary using the encapsulation function is:

[0057] E=g(∑w i x+a)

[0058] Among them, g(·) is the encapsulation function, x i is the encrypted sensitive number, a is the metadata of the sensitive data, and w i A summary of the metadata content.

[0059] In the embodiment of the present invention, preprocessing the raw financial data is a basic and critical step. After preprocessing, the data will be deeply analyzed to determine which data is sensitive data and to assess the sensitivity of the sensitive data. This process is crucial because it helps identify information that requires special protection and ensures that the information will not be leaked or improperly used in subsequent processing and storage.

[0060] Once the sensitive data and its sensitivity are identified, appropriate encryption and encapsulation functions are selected to protect the data according to the different levels of sensitivity. The encryption function is used to encrypt the sensitivity to ensure the confidentiality of this critical information. At the same time, the metadata of the sensitive data is extracted, which contains important information about the data itself, such as the source of the data, the creation time, etc. In addition, a content summary is generated to provide a quick overview of the sensitive data.

[0061] Next, the encrypted sensitive data, metadata, and content summary are encapsulated using the encapsulation function to form a secure encapsulation package. This encapsulation package not only protects the sensitive data itself, but also contains important contextual information about the data, ensuring the integrity and traceability of the data.

[0062] The sensitive data processing method in the embodiment of the present invention combines multiple technical means such as sensitivity analysis, encryption and encapsulation to ensure the security and confidentiality of sensitive data during processing and storage. This method not only improves the security of data processing, but also provides a solid foundation for subsequent data management and access control.

[0063] Step 4, determining accessible users according to the data sensitivity; analyzing and counting the access data of accessible users, and constructing an attribute key matrix according to the analysis and statistical results; and encrypting the storage address using the attribute key matrix.

[0064] In an embodiment of the present invention, the access data includes analyzing and counting the access data of accessible users, and constructing an attribute key matrix based on the analysis and statistical results, including: extracting access features from the access data; sorting the user access features to obtain an access feature sequence, and generating an attribute key matrix based on the user key corresponding to the access feature sequence.

[0065] In an embodiment of the present invention, user access features are sorted to obtain an access feature sequence, and an attribute key matrix is ​​generated according to the user key corresponding to the access feature sequence, including: when there is an abnormality in the user access feature, the abnormal user access feature is eliminated; the remaining user access features are sorted to obtain an access feature sequence; an insertion feature is determined according to the difference between the abnormal user access feature and the feature threshold, and an insertion parameter is determined according to the proportion of the abnormal user access feature; the insertion feature is inserted into the access feature sequence according to the insertion parameter to obtain a target access feature sequence; all target access feature sequences are combined to obtain a target access feature matrix; and an attribute key matrix is ​​determined according to the target access feature matrix; wherein each user corresponds to multiple access features, each access feature corresponds to multiple access feature intervals, each access feature interval corresponds to an attribute key, and each insertion parameter corresponds to an attribute key.

[0066] In the embodiment of the present invention, determining the accessible users according to the sensitivity of the data is a core step, which is crucial for ensuring the security of the data. Through this step, access to sensitive data can be strictly restricted to ensure that only authorized users can view and use the data.

[0067] Subsequently, it is also necessary to analyze and count the access data of accessible users. This step aims to gain a deep understanding of the patterns and characteristics of user access data, thereby providing strong support for subsequent data management and access control. By analyzing the statistical results, an attribute key matrix can be constructed, which will be used to encrypt the storage address and further enhance the security of the data.

[0068] There are several key points to note when constructing the attribute key matrix. First, it is necessary to accurately extract access features from the access data, and these features should be able to fully reflect the user's access behavior. Second, when sorting user access features, it is necessary to ensure the accuracy and rationality of the sorting in order to obtain a meaningful access feature sequence. At the same time, if abnormal user access features are found, they should be removed in time to avoid interference with subsequent steps.

[0069] In addition, when generating the attribute key matrix based on the access feature sequence, the influence of abnormal user access features needs to be considered. The access feature sequence can be adjusted by determining the insertion features and insertion parameters to obtain a more accurate and comprehensive target access feature sequence. Finally, all target access feature sequences are combined to obtain a target access feature matrix, and the attribute key matrix is ​​determined based on the matrix.

[0070] It should be noted that in this process, each user corresponds to multiple access features, each access feature corresponds to multiple access feature intervals, and each access feature interval and insertion parameter corresponds to an attribute key. Therefore, when constructing the attribute key matrix, it is necessary to ensure the accuracy and consistency of these relationships.

[0071] The embodiment of the present invention constructs an attribute key matrix based on user access characteristics and patterns through a series of steps to encrypt storage addresses. Traditional static encryption usually uses a fixed key to encrypt data, while the attribute key matrix dynamically generates keys based on user access characteristics and patterns. This means that the encryption process is more flexible and can adapt to different access scenarios and security requirements.

[0072] In summary, an embodiment of the present invention provides an accounting data processing method based on a financial robot, the method comprising obtaining original financial data; preprocessing the original financial data, analyzing the preprocessed original financial data, determining sensitive data and the sensitivity corresponding to the sensitive data; encapsulating the sensitive data according to the sensitivity, and obtaining the storage address of the encapsulated sensitive data; determining accessible users according to the sensitivity of the data; analyzing and counting the access data of the accessible users, and constructing an attribute key matrix according to the analysis and statistical results; and encrypting the storage address using the attribute key matrix. The present invention performs hierarchical encryption processing on the sensitive data in the original financial data through sensitivity analysis and data encapsulation, effectively preventing the risk of data leakage, and dynamically encrypts the storage address using the attribute key matrix, increasing the difficulty of cracking, and further improving the security of financial data.

[0073] Example 2

[0074] like Figure 2As shown, an embodiment of the present invention provides an accounting data processing system based on a financial robot, the system includes a financial robot, and the financial robot includes:

[0075] Data acquisition module, used to obtain original financial data;

[0076] A preprocessing module, used to preprocess the original financial data, analyze the preprocessed original financial data, and determine sensitive data and the sensitivity corresponding to the sensitive data;

[0077] An encapsulation storage module is used to encapsulate the sensitive data according to the sensitivity and obtain the storage address of the encapsulated sensitive data;

[0078] The address encryption module is used to determine the accessible users according to the data sensitivity; analyze and count the access data of the accessible users, and construct an attribute key matrix according to the analysis and statistical results; and encrypt the storage address using the attribute key matrix.

[0079] It should be understood that an accounting data processing system based on a financial robot provided in an embodiment of the present invention and an accounting data processing method based on a financial robot provided in the above embodiment are based on the same inventive concept. For more specific working principles of each module in the embodiment of the present invention, reference can be made to the above embodiment and will not be repeated in the embodiment of the present invention.

[0080] Finally, 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An accounting data processing method based on a financial robot, characterized in that: include Obtaining raw financial data; Preprocessing the original financial data, analyzing the preprocessed original financial data, and determining sensitive data and the sensitivity corresponding to the sensitive data; Encapsulating the sensitive data according to the sensitivity, and obtaining the storage address of the encapsulated sensitive data; Determining users who can access the data according to the sensitivity of the data; Analyze and count the access data of accessible users, and construct attribute key matrix based on the analysis and statistical results; The storage address is encrypted using the attribute key matrix.

2. The accounting data processing method based on a financial robot according to claim 1 is characterized in that: Preprocessing the original financial data includes: Formulate the validity judgment standard of financial data according to the internal financial data management regulations of the enterprise and industry regulations, filter the original financial data according to the standard, and delete invalid data; Find missing data and determine the missing pattern based on the location of the missing data in the original financial data; delete or fill in the financial data based on the amount of missing data and the missing pattern; Standardize financial data.

3. The accounting data processing method based on a financial robot according to claim 1 is characterized in that: The sensitive data is encapsulated according to the sensitivity, including: Determine the encryption function and encapsulation function based on sensitivity; encrypting the sensitivity using the encryption function; The metadata of the sensitive data and the content summary of the metadata are extracted, and the encrypted sensitive data, metadata and content summary are encapsulated using the encapsulation function.

4. The accounting data processing method based on a financial robot according to claim 3 is characterized in that: The formula for encapsulating the encrypted sensitive data, metadata and content summary using the encapsulation function is: E=g(∑w i x+a) Among them, g(·) is the encapsulation function, x i is the encrypted sensitive number, a is the metadata of the sensitive data, and w i A summary of the metadata content.

5. The accounting data processing method based on a financial robot according to claim 1 is characterized in that: The access data includes analyzing and counting the access data of accessible users, and constructing an attribute key matrix according to the analysis and counting results, including: extracting access features from the access data; The user access features are sorted to obtain an access feature sequence, and an attribute key matrix is ​​generated according to the user key corresponding to the access feature sequence.

6. The accounting data processing method based on a financial robot according to claim 5 is characterized in that: The user access features are sorted to obtain an access feature sequence, and the attribute key matrix is ​​generated according to the user key corresponding to the access feature sequence, including: When there are abnormalities in the user access features, the abnormal user access features are eliminated; the remaining user access features are sorted to obtain an access feature sequence; Determine the insertion feature according to the difference between the abnormal user access feature and the feature threshold, and determine the insertion parameter according to the proportion of the abnormal user access feature; The insertion feature is inserted into the access feature sequence according to the insertion parameter to obtain a target access feature sequence.

7. The accounting data processing method based on a financial robot according to claim 6 is characterized in that: Sorting the user access features to obtain an access feature sequence, and generating an attribute key matrix according to the user key corresponding to the access feature sequence also includes: Combine all target access feature sequences to obtain target access feature moments; An attribute key matrix is ​​determined according to the target access feature matrix; wherein each user corresponds to multiple access features, each access feature corresponds to multiple access feature intervals, each access feature interval corresponds to an attribute key, and each insertion parameter corresponds to an attribute key.

8. An accounting data processing system based on a financial robot, characterized in that: Including a financial robot, the financial robot includes: Data acquisition module, used to obtain original financial data; A preprocessing module, used to preprocess the original financial data, analyze the preprocessed original financial data, and determine sensitive data and the sensitivity corresponding to the sensitive data; An encapsulation storage module is used to encapsulate the sensitive data according to the sensitivity and obtain the storage address of the encapsulated sensitive data; The address encryption module is used to determine the accessible users according to the data sensitivity; analyze and count the access data of the accessible users, and construct an attribute key matrix according to the analysis and statistical results; and encrypt the storage address using the attribute key matrix.

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