A business data management method and system based on data analysis

Abstract

The application relates to the field of business management, in particular to a business data management method and system based on data analysis; the method comprises the following steps: collecting business data in a business database, and dividing each field in a customer data table of each customer; calculating an information confusion coefficient, calculating a uniqueness ratio; obtaining a sensitivity original score; constructing a field sensitivity score based on the sensitivity original score, calculating a field correlation factor, and obtaining a field coupling coefficient; dividing the encryption strength level of each field of each user based on the field coupling coefficient, combining the field coupling coefficient to obtain the key length and the update round number of each field in the encryption process, encrypting the business data of each field based on the key length and the update round number, and managing the business data; the method improves the refinement, intelligence and security level of business data management.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of business management, and in particular to a business data management method and system based on data analysis. BACKGROUND

[0002] With the development of information technology and digital economy, the business data generated by the daily operation of enterprises is growing explosively, and the data types cover multiple dimensions such as customer identity information, transaction records, financial data, and consumption behavior. These data are not only the core resources for internal decision-making and business optimization of enterprises, but also carry the personal privacy of customers and the business secrets of enterprises. Their security and reliability directly affect the sustainable development and social trust of enterprises; however, business data is extremely vulnerable to various risks during storage, transmission, and sharing. On the one hand, external attack methods are becoming more and more complex, such as frequency analysis, feature correlation inference, dictionary attack, and side channel attack, which may lead to sensitive data leakage; on the other hand, internal risks cannot be ignored, such as misuse of authority, improper operation, or malicious disclosure by internal personnel, which may damage the data security line. Once the data is leaked, the enterprise will not only suffer direct economic losses, but also face serious consequences such as regulatory penalties and damage to brand reputation.

[0003] Existing business data encryption and management methods mostly rely on traditional symmetric or asymmetric encryption mechanisms, and in actual application, a unified key length and fixed encryption round number are generally used to encrypt all data fields, such as the AES algorithm. In this case, different sensitive fields cannot be differentiated for protection: for high-sensitivity or high-coupling fields, fixed parameter encryption is often insufficient to resist targeted reasoning and frequency attacks, and there is a security risk; while for low-sensitivity fields, the use of over-strong encryption strategies causes waste of computing resources, increase of system response delay, and thus affects the overall business efficiency. SUMMARY

[0004] In order to solve the problem that the traditional business data encryption management method cannot balance between security and efficiency, the present application provides a business data management method and system based on data analysis.

[0005] In a first aspect, the present application provides a business data management method based on data analysis, which adopts the following technical solution:

[0006] The service data in the service database is collected, and each field in the customer data table of each customer is divided; an information disorder coefficient is calculated based on the frequency of the service data of the field, and a uniqueness ratio is calculated by analyzing the proportion of each field; a difference absolute value between the uniqueness ratio and a preset value and a sum result of a label value of the field are taken as a sensitivity original score of each field, reflecting the original sensitivity of the service data in the field; a field sensitivity score is constructed based on the sensitivity original score, a correlation between any two fields of each customer is analyzed, and a field correlation factor is calculated; and a field coupling coefficient is obtained by combining the field sensitivity score;

[0007] Based on the field coupling coefficient, the encryption strength level of each field of each user is divided, the key length and the update round number of each field of each customer in the encryption process are obtained by combining the field coupling coefficient, the update round number is the sum of the product of the preset maximum increase round number and the field coupling coefficient and the down rounding value, and the preset basic round number, and the service data of each field is encrypted based on the key length and the update round number, so that the service data is managed.

[0008] Beneficial effects: through the analysis method gradually constructed by the frequency distribution, the uniqueness ratio, the sensitivity original score, the sensitivity score, the field correlation factor and the coupling coefficient of the service data, the key length and the round number in the encryption parameter can be adaptively adjusted according to the sensitivity and coupling of different fields, so that the problem of "one size fits all" resource waste and security deficiency in the traditional AES encryption is avoided. This scheme not only improves the data security and can better resist frequency analysis and field association inference attacks, but also reduces the operation overhead on low-sensitive data and improves the overall system running efficiency.

[0009] Further, the information disorder coefficient is obtained by:

[0010] For each field of each customer, the frequency of the service data in the corresponding field of all customers is counted as the service frequency of each field, and the Shannon entropy of each field of each customer is calculated as the information disorder coefficient of each field of each customer based on the service frequency of each field.

[0011] Beneficial effects: the uncertainty of the field value distribution is described by the Shannon entropy, which can quantitatively reflect the complexity of the field information. For the field with uniform frequency distribution, the information disorder coefficient is higher, and it is more difficult to crack after encryption by statistical means, thereby enhancing the ability to defend against attacks.

[0012] Further, the uniqueness ratio is obtained by: taking the total number of customers as the total number of service data records, and taking the set composed of the service data of all customers in each field as the field value set of each field;

[0013] For each field, the ratio between the total number of elements in the field value set of the field and the total number of business data records is calculated as the uniqueness ratio of the field.

[0014] Beneficial effects: The uniqueness ratio can measure the distinguishability of the field value. When the ratio is close to 1, it means that the field has strong identification, such as the identity card number field; when the ratio is too low, it means that the field has the risk of frequency attack. By including the uniqueness ratio in the sensitivity scoring system, the encryption strategy can take into account both types of risks of strong identification and weak distinguishability, and achieve more accurate protection.

[0015] Further, the label value of the field includes: when the field of the customer is a sensitive field, the label value of the field of the customer is assigned as a numerical value 1; when the field of the customer is a non-sensitive field, the field of the customer is assigned as a numerical value 0.

[0016] Further, the calculation formula of the field sensitivity score is: ; in the formula, represents the field sensitivity score of each field of each customer; is a normalization function, is a preset tuning factor; is the information confusion coefficient of each field, is the sensitivity original score of each field of each customer.

[0017] Further, the field correlation factor acquisition method includes: for each customer, selecting any two fields, respectively calculating the probabilities of the two fields appearing in all fields as the independent probabilities of the two fields; calculating the probability of the two fields appearing in all fields at the same time as the joint probability of the two fields; for the any two fields, calculating the product of the independent probabilities of the two fields as a first product, and calculating the ratio between the joint probability of the two fields and the first product as the field correlation factor between the two fields of each customer.

[0018] Beneficial effects: The field correlation factor reveals the potential dependency between two fields through the probability relationship, and can capture the combined sensitivity problem that is difficult to discover by a single field. For example, the association between the birthday field and the identity card number field can lead to potential leakage. This method effectively prevents reasoning attacks caused by insufficient protection of a single field.

[0019] Further, the field coupling coefficient acquisition method is: calculating the absolute value of the difference between the field correlation factor and the value 1 as a first difference absolute value, performing normalization processing on all first difference absolute values as the field correlation coefficient; for each field of each customer, calculating the product of the field correlation coefficient between the field and any other field and the field sensitive score of the other field as the second product between the field and the other field, calculating the ratio between the sum of the second products between the field and all other fields and the sum of the field sensitive scores of all other fields as the field coupling coefficient of each field of each customer.

[0020] Further, the field coupling coefficient is used to divide the encryption strength level of each field of each user, including: dividing the interval [0, 1] into three intervals from small to large, defined as the first interval, the second interval and the third interval, when the field coupling coefficient of the field of the customer is in the first interval, the encryption strength level of the field is defined as low strength; when the field coupling coefficient of the field of the customer is in the second interval, the encryption strength level of the field is defined as medium strength; when the field coupling coefficient of the field of the customer is in the third interval, the encryption strength level of the field is defined as high strength.

[0021] Beneficial effects: the field coupling coefficient comprehensively considers the sensitivity of the field itself and the interaction with other fields, and sets the encryption strength by grading, avoiding insufficient encryption of high-risk fields and excessive encryption of low-risk fields. While ensuring strong protection of high-sensitive data, the system performance consumption is reduced, and differentiated and fine data management is achieved.

[0022] Further, the key length of each field of each customer in the encryption process is obtained, including: when the encryption strength level of the field of the customer is low, medium and high, the corresponding key length is selected as 128, 192 and 256.

[0023] In the second aspect, the application provides a business data management system based on data analysis, which adopts the following technical scheme:

[0024] A business data management system based on data analysis, comprising a processor and a memory, the memory storing computer program instructions, when the computer program instructions are executed by the processor, the method for managing business data based on data analysis is realized.

[0025] The above-mentioned business data management method based on data analysis is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a system is made according to the memory and the processor, and the use is facilitated.

[0026] The application has the following technical effects:

[0027] The application introduces multi-dimensional sensitivity and coupling quantization indicators in business data management process, improves the deficiency of traditional encryption algorithm using fixed key length and round number for global unified encryption, proposes a quantization mechanism based on information confusion coefficient and uniqueness ratio, which can accurately depict the complexity and distinguishability of data distribution of each field, avoids one-sidedness of judging sensitivity only by field label, thereby realizing fine-grained identification of potential high-risk fields in the early stage of data management, constructs field sensitive score and field correlation factor, which not only reflects the sensitivity of the field itself, but also considers the correlation between cross-fields, and can capture the combined reasoning risk that cannot be exposed by a single field. For example, cross-field inference problems such as identity card number and birth date can be effectively identified through this mechanism, thereby expanding the coverage of encryption protection. The field coupling coefficient is introduced and the encryption strength is graded controlled based on the value, realizing differentiated configuration of encryption resources: high-risk fields use longer key and more rounds for enhanced protection, and low-risk fields use lightweight encryption strategy to avoid unnecessary performance consumption. Compared with the traditional AES encryption method of one-size-fits-all, the application effectively balances security and computing efficiency.

[0028] In the encryption execution phase, the key length and update round number of AES are dynamically adjusted by combining the field coupling coefficient with the preset parameter, so that the encryption strength of each field can be adaptively adjusted with the change of data sensitivity and correlation. This dynamic adjustment mechanism significantly enhances the resistance of the system to frequency analysis attack, correlation attack and inference attack, while ensuring that the overall running efficiency is not dragged down by excessive encryption; solves the problem that the traditional business data encryption management method cannot balance security and efficiency, and improves the fine-grained, intelligent and security level of business data management. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a method flowchart of a business data management method based on data analysis according to the application. DETAILED DESCRIPTION

[0030] The embodiment of the application discloses a business data management method based on data analysis, collects business data in a business database, divides each field in a customer data table of each customer, calculates an information confusion coefficient, calculates a uniqueness ratio, obtains a sensitivity original score, constructs a field sensitive score based on the sensitivity original score, calculates a field correlation factor, obtains a field coupling coefficient, divides the encryption strength level of each field of each user based on the field coupling coefficient, obtains the key length and update round number of each field of each customer in the encryption process in combination with the field coupling coefficient, encrypts the business data of each field based on the key length and update round number, and manages the business data. The fine-grained, intelligent and security level of business data management is improved.

[0031] Reference Figure 1 A business data management method based on data analysis comprises steps S1-S4.

[0032] Step S1: Collect business data in a business database, and divide each field in a customer data table of each customer.

[0033] Collect data aggregated in the business database, and pre-process data in the business database; in an embodiment of the application, data in the business database is cleaned, missing values, duplicate data and data violating data definition and business logic in the data are processed, wherein the method of data cleaning is a known technology, and the application will not be described in detail; thus, the pre-processed business database is obtained.

[0034] For all business data, a customer data table of all customers is constructed, for example, for each customer, the customer number, name, gender, ID number, phone number, address and purchase category of the customer are taken as the customer data table of each customer, and other business data can be selected by the implementer as the customer data table of each customer based on actual conditions, and each of the above categories is taken as a field of business data of the customer. In detail, for example, the name of the customer is A, and the number is 001, then A is taken as a field and 001 is taken as a field.

[0035] Thus, each field of the customer data table of each customer is divided.

[0036] Step S2: Calculate an information disorder coefficient based on the frequency of business data in the field, analyze the proportion of each field to calculate a uniqueness ratio; take the absolute value of the difference between the uniqueness ratio and a preset value and the sum of the label values of the fields as the original sensitivity score of each field to reflect the original sensitivity of the business data in the field; construct a field sensitivity score based on the original sensitivity score, analyze the correlation between any two fields of each customer to calculate a field correlation factor, and obtain a field coupling coefficient in combination with the field sensitivity score.

[0037] The data quality in the business data is managed. Due to the confidentiality of the business database, an encryption algorithm needs to be used to encrypt the business data, so as to ensure the security of the business data. In the process of encrypting the business data, the traditional AES encryption algorithm can be used to encrypt and manage the business data to a certain extent to prevent the business data from being leaked, but due to the strong correlation between the business data and the different degrees of vulnerability of the business data itself, the encrypted data can still be inferred.

[0038] Therefore, based on the above analysis, the total number of customers is taken as the total number of business data records, and the set of business data of all customers in each field is taken as the field value set of each field. It should be noted that all elements in the field value set are unique. For example, taking the gender field of all customers as an example, the field value set of the gender field only includes {male, female}, and only one copy of the repeated value is retained.

[0039] For each field of each customer, the frequency of the business data in each field appearing in the corresponding field of all customers is counted as the business frequency of each field, and the Shannon entropy of each field of each customer is calculated as the information disorder coefficient of each field of each customer. The calculation method of Shannon entropy is a known technology, and will not be described herein.

[0040] Further, for each field, the ratio between the total number of elements in the field value set of each field and the total number of business data records is calculated as the uniqueness ratio of each field. It should be noted that when the value of the uniqueness ratio is closer to the value 1, it means that the business data of all customers in the field is not the same, for example, the ID number, at this time, the field has higher identifiability and higher risk. On the contrary, when the value of the uniqueness ratio is smaller than the value 1, it means that the field has lower distinguishability and is easy to be frequency analyzed, and the risk is higher. Therefore, the closer the uniqueness ratio is to the intermediate value, the stronger the resistance to analysis and the higher the security.

[0041] At the same time, each field of each customer is labeled with a label value, and when the field of the customer is a sensitive field, the label value of the field of the customer is assigned as 1; otherwise, the field of the customer is assigned as 0. In this application, the customer number and ID number in the customer data table of the customer are taken as sensitive fields.

[0042] Based on the above analysis, the sensitivity original score of each field is constructed, and the calculation formula is: In the formula, is the sensitivity original score of each field of each customer; is the uniqueness ratio of each field, is the label value of each field.

[0043] It should be noted that the sensitivity original score of each field reflects the sensitive degree of the field being easily cracked. When the field of the customer is a sensitive field, the value of 1 is taken, and the closer to the value 1 or the smaller than the value 1, the greater the risk of being cracked, the greater the value of, the greater the value of the sensitivity original score obtained; on the contrary, the smaller the sensitivity of the field being easily cracked, the smaller the value of the sensitivity original score obtained.

[0044] Further, in order to analyze the business sensitivity of each field of each customer, the confusion degree of information contained in the field content, and the identifiable situation, a field sensitivity score of each field of each customer is constructed, and the calculation formula is: ; in the formula, represents the field sensitivity score of each field of each customer; is a normalization function, is a preset parameter adjustment factor to prevent the denominator from being 0, in an embodiment of the present application, the value of is 0.1, and the implementer can select other values based on actual conditions; is an information confusion coefficient of each field, is a sensitivity original score of each field of each customer.

[0045] It should be noted that the field sensitivity score is based on the information amount of the business data in the field and the sensitivity and uniqueness of the field, and comprehensively reflects the sensitivity of the field. When the information amount of the business data in the field is larger, the value of the information confusion coefficient obtained is larger, and the difficulty of cracking after encryption is higher. At the same time, when the value of the sensitivity original score is smaller, the possibility of the field of the customer being a sensitive field is smaller, and at this time, the value of the field sensitivity score obtained is smaller. Conversely, the value of the field sensitivity score obtained is larger.

[0046] Further, in the business data, each field of each customer does not exist in isolation, and there is often a strong or weak correlation between the fields. For example, the customer's ID number field and the customer's birth date field have a correlation relationship. The 6th to 14th bits of the ID number contain the birthday information. Based on this analysis, even if a certain field itself does not seem sensitive, once combined with other fields, sensitive information can be inferred. The sensitivity of a single field can only look at the risk of the single field itself, and cannot capture the risk of cross-field inference.

[0047] Therefore, based on the field sensitivity score, a field correlation factor between any two fields is constructed, specifically: for each customer, any two fields are selected, the probabilities of the two fields appearing in all fields are calculated respectively as the independent probabilities of the two fields; the probability of the two fields appearing in all fields at the same time is calculated as the joint probability of the two fields; for the two fields, the product of the independent probabilities of the two fields is calculated as a first product, and the ratio between the joint probability of the two fields and the first product is taken as the field correlation factor between the two fields of each customer.

[0048] It should be noted that the greater the field correlation factor is greater than the value 1, the more the two fields together appear than the two fields appear independently, and the more the two fields present a positive correlation; on the contrary, the smaller the field correlation factor is less than the value 1, the less the two fields together appear than the two fields appear independently, and the more the two fields present a negative correlation; that is, the more the value of the field correlation factor deviates from the value 1, the stronger the correlation between the two fields; on the contrary, the weaker the correlation between the two fields.

[0049] The field correlation factor can identify those fields with low self-entropy but high coupling with other sensitive fields, avoid focusing on surface sensitive fields while ignoring combined sensitive fields, and improve the coverage of security protection.

[0050] Further, the absolute value of the difference between the field correlation factor and the value 1 is taken as the first difference absolute value, and the first difference absolute value is normalized as the field correlation coefficient; for each field of each customer, the product of the field correlation coefficient between the field and any other field and the field sensitive score of the other field is taken as the second product between the field and the other field, and the ratio between the sum of the second products between the field and all other fields and the sum of the field sensitive scores of all other fields is taken as the field coupling coefficient of each field of each customer.

[0051] It should be noted that the field coupling coefficient integrates the sensitivity of the field itself and the high coupling between the fields of the customer. When the field coupling coefficient has a higher value, it means that the sensitivity of the field is high, and the coupling degree between the field and other fields is also high, and the probability of being cracked after encryption is higher; on the contrary, when the field coupling coefficient has a lower value, the probability of being cracked after encryption is smaller.

[0052] Step S3: dividing the encryption strength level of each field of each user based on the field coupling coefficient.

[0053] At this point, the field coupling coefficient of each field of each customer is obtained, and the encryption strength level of each field of each customer is constructed based on the field coupling coefficient. Since the value range of the field coupling coefficient is [0, 1], [0, 1] is evenly divided into three intervals, which are defined as the first interval, the second interval and the third interval from small to large. When the field coupling coefficient of the field of the customer is in the first interval, the encryption strength level of the field is defined as low strength; when the field coupling coefficient of the field of the customer is in the second interval, the encryption strength level of the field is defined as medium strength; when the field coupling coefficient of the field of the customer is in the third interval, the encryption strength level of the field is defined as high strength.

[0054] Step S4: the coupling coefficient of the fields is used to obtain the key length and the update round number of each field of each customer in the encryption process, the update round number is the sum of the down-round value of the product of the preset maximum increase round number and the field coupling coefficient and the preset basic round number, and the service data of each field is encrypted based on the key length and the update round number, and the service data is managed.

[0055] In the encryption of the fields of the customer using the AES, the parameters involved are the key length and the round number, and the traditional application usually uses the same AES parameter, such as AES-128 and fixed round number, for all the fields, which is insufficient in security cost when facing high-sensitivity or high-coupling fields, and causes resource waste for low-sensitivity fields; therefore, the present application drives the hierarchical adjustment of the AES core parameters based on the encryption strength grade, and performs encryption on demand and hierarchical reinforcement.

[0056] Specifically, in the present application, when the encryption strength grade of the fields of the customer is low, medium and high respectively, the corresponding key length is selected as 128, 192 and 256 in the process of encryption using the AES, and the round number is calculated based on the field coupling coefficient, and the calculation formula is: ; in the formula, is the update round number of each field of each customer when using the AES for encryption; is the preset basic round number corresponding to the AES key length, in an embodiment of the present application, when the key length of the field of the customer is selected as 128, the preset basic round number is selected as 10, when the key length of the field of the customer is selected as 192, the preset basic round number is selected as 12, and when the key length of the field of the customer is selected as 256, the preset basic round number is selected as 14, is the preset maximum increase round number, in an embodiment of the present application, is selected as 8, is the field coupling coefficient of each field of each customer; represents the down-round function; for the preset value in the formula, the implementer can select other values based on the actual situation.

[0057] It should be noted that the update round number can be automatically obtained based on the difficulty of the business data to be cracked, and different update round numbers are obtained for the business data of different fields, more update round numbers are obtained for the business data that is easy to crack, and vice versa, the business data that is difficult to crack is encrypted with fewer round numbers.

[0058] So far, in the process of using AES algorithm to encrypt each field of each customer, different key lengths and round numbers are adopted, when the field of the customer is a field that is easy to be cracked, the length and the round number of the key need to be increased to strengthen the encryption effect of the business data in the field, prevent the business data from being leaked, and enhance the management effect of the business data, when the field of the customer is a field that is difficult to be cracked, a shorter key length is selected, and the round number is reduced, so that the performance waste caused by blindly reinforcing the business data equally is avoided.

[0059] The embodiment of the application further discloses a business data management system based on data analysis, comprising a processor and a memory, and the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a business data management method based on data analysis according to the application is realized.

[0060] The above system further comprises a communication bus and a communication interface and other components well known to those skilled in the art, the settings and functions of which are known in the art, and thus will not be repeated here.

[0061] The above are preferred embodiments of the application, and do not limit the protection scope of the application, therefore: any equivalent changes made on the structure, shape, principle of the application should be covered within the protection scope of the application.

Claims

1. A data analysis-based business data management method, characterized by, The method comprises the following steps: collecting service data in a service database, and dividing each field in a customer data table of each customer; Based on the field-based service data frequency calculation information confusion coefficient, analyze the proportion of each field to calculate the uniqueness ratio; the absolute value of the difference between the uniqueness ratio and the preset value and the sum of the label value of the field are taken as the sensitivity original score of each field; based on the sensitivity original score, the field sensitivity score is constructed, the correlation between any two fields of each customer is analyzed to calculate the field correlation factor, and the field coupling coefficient is obtained by combining the field sensitivity score; Based on the field coupling coefficient, the encryption strength level of each field of each user is divided, the key length and update round number of each field of each customer in the encryption process are obtained by combining the field coupling coefficient, the update round number is the sum of the down rounding value of the product of the preset maximum increase round number and the field coupling coefficient and the preset basic round number, the business data of each field is encrypted based on the key length and the update round number, and the business data is managed; Wherein: For each field of each customer, the frequency of the business data in each field appearing in the corresponding field of all customers is counted as the business data frequency of each field, and the Shannon entropy of each field of each customer is calculated based on the business data frequency of each field as the information confusion coefficient of each field of each customer; The total number of customers is taken as the total number of business data records, and the set composed of the business data of all customers in each field is taken as the field value set of each field; for each field, the ratio between the total number of elements in the field value set of each field and the total number of business data records is calculated as the uniqueness ratio of each field; When the field of the customer is a sensitive field, the label value of the field of the customer is assigned as 1; otherwise, it is assigned as 0; The calculation formula of the field sensitivity score is: ; in the formula, represents the field sensitivity score of each field of each customer; is a normalization function, is a preset parameter adjustment factor; is an information confusion coefficient of each field, is a sensitivity original score of each field of each customer; For each customer, select any two fields, calculate the probability of the occurrence of the two fields in all fields respectively as the independent probability of the two fields; calculate the probability of the simultaneous occurrence of the two fields in all fields as the joint probability of the two fields; for any two fields, the product of the independent probability of the two fields is calculated as the first product, and the ratio between the joint probability of the two fields and the first product is taken as the field correlation factor between the two fields of each customer; The absolute value of the difference between the field correlation factor and the value 1 is calculated as the first difference absolute value, and all first difference absolute values are normalized as the field correlation coefficient; for each field of each customer, the product of the field correlation coefficient between the field and any other field and the field sensitivity score of any other field is calculated as the second product between the field and any other field, and the ratio between the sum of the second products between the field and all other arbitrary fields and the sum of the field sensitivity scores of all other arbitrary fields is taken as the field coupling coefficient of each field of each customer.

2. The business data management method based on data analysis according to claim 1, characterized in that, The encryption strength level of each field of each user is divided based on the field coupling coefficient, including: dividing the interval [0, 1] into three intervals from small to large, defined as a first interval, a second interval and a third interval, when the field coupling coefficient of the field of the customer is in the first interval, the encryption strength level of the field is defined as low strength; when the field coupling coefficient of the field of the customer is in the second interval, the encryption strength level of the field is defined as medium strength; when the field coupling coefficient of the field of the customer is in the third interval, the encryption strength level of the field is defined as high strength.

3. The business data management method based on data analysis according to claim 1, characterized in that, The key length of each field of each customer in the encryption process is obtained, including: when the encryption strength level of the field of the customer is low, medium and high, the corresponding key length is selected as 128, 192 and 256.

4. A data analysis based business data management system, characterized by, Including: A processor and a memory, the memory stores computer program instructions, when the computer program instructions are executed by the processor, a business data management method based on data analysis according to any one of claims 1-3 is realized.

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