A user behavior data security management method based on a CDP customer data platform

Abstract

The application relates to the technical field of data security management, and particularly discloses a user behavior data security management method based on a CDP (Customer Data Platform), which comprises the following steps: step S1: setting a plurality of business operations and setting a permission level; when an access person wants to access user behavior data, access information is acquired; step S2: acquiring historical access data, calculating a business frequency value and a business close value; step S3: after the access person completes one access, the access information and target data are recorded; a cache time period is set, when a new access person accesses in the cache time period, identity comparison and business comparison are carried out; if the identity and the business are consistent, a data permission index and an access permission index are calculated; step S4: according to the data permission index and the access permission index, low-risk data is obtained, and the low-risk data is directly displayed to the new access person; a loading time is predicted, and distributed verification is carried out on the verification information within the loading time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data security management, in particular to a user behavior data security management method based on a CDP customer data platform. BACKGROUND

[0002] User behavior data refers to various data generated by users in the process of interacting with products or services, which can reflect the behavior habits, preferences and needs of users, including browsing behavior data, clicking behavior data and search behavior data, etc.

[0003] In today's digital age, data is an important asset for enterprises and organizations, and its security management is crucial. Among them, the access and management of user behavior data is a key task. In actual business scenarios, when accessing user behavior data, there is a situation that for some specific operations, due to the complexity of business logic or the requirement of data sensitivity, it is often necessary to access related data multiple times. For example, in some business processes involving multiple steps, the system may need to query and process the user's behavior data multiple times at different stages to ensure the integrity and accuracy of the business process.

[0004] However, if complex permission verification mechanisms are used every time these user behavior data is accessed, although it can effectively protect the data from illegal access and abuse from a security perspective, it will bring a significant problem, that is, it will cause the efficiency of the entire access process to become slower. Specifically, complex permission verification may involve multiple steps of checking and verification, such as confirmation of the authenticity of the user's identity, matching verification of the user's role, and fine-grained checking of operation permissions. Each step requires the system to perform corresponding processing and judgment, which undoubtedly increases the system's processing time and resource overhead. When this multiple access and complex permission verification occurs frequently, the response time of the entire system is prolonged, which affects the efficiency of the business and reduces the user experience SUMMARY

[0005] The purpose of the present application is to provide a user behavior data security management method based on a CDP customer data platform to solve the above technical problems.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] A user behavior data security management method based on a CDP customer data platform, comprising the following steps:

[0008] Step S1: Set up several business operations, and set the access permission level of each access personnel and the permission level of each user behavior data; when an access personnel accesses user behavior data, obtain the access information of the access personnel;

[0009] Step S2: obtaining historical access data, obtaining a business frequency value of user behavior data according to the historical access data, and obtaining a business closeness value of an access person;

[0010] Step S3: after an access person completes an access, recording access information of the access person and recording target data; setting a cache time period, if there is a new access person to access in the cache time period, obtaining an identity comparison result and a business comparison result; if the identity comparison result and the business comparison result are consistent, obtaining a data permission index and an access permission index according to the access information;

[0011] Step S4: obtaining low-risk data according to the data permission index and the access permission index, directly displaying the low-risk data to a new access person, and predicting a loading time, and performing distributed verification on the verification information within the loading time.

[0012] As a further scheme of the application: the access information includes identity information, verification information and target business operation of the access person, and the target business operation is a business operation to be performed by the access person.

[0013] As a further scheme of the application: the historical access data includes identity information and target business operation of an access person when each user behavior data is accessed.

[0014] As a further scheme of the application: the obtaining process of the business frequency value and the business closeness value includes:

[0015] obtaining a business frequency value Bfv between the user behavior data and the i th business operation according to the historical access data i =Nob i / Ntv, wherein Ntv represents a total number of times of access of the user behavior data, Nob i represents a number of times of target business operation being the i th business operation when the user behavior data is accessed;

[0016] and obtaining a business closeness value Bcv between the access person and the i th business operation according to the historical access data i =nob i / ntv, wherein ntv represents a total number of times of access of all user behavior data by the access person, nob i represents a number of times of target business operation being the i th business operation when the access person accesses.

[0017] As a further aspect of the present invention: the process of setting the cache time period includes obtaining the time interval between each access based on the historical access data, obtaining the average value of all time intervals, and recording it as the cache time period.

[0018] As a further aspect of the present invention: the process of obtaining the identity comparison result and the business comparison result includes:

[0019] Record the new visitor as the current visitor, obtain the identity information of the current visitor and record it as the current identity information, and obtain the target business operation of the current visitor and record it as the current business operation;

[0020] The texts of the identity information and the current identity information are obtained respectively and recorded as the identity information text and the current identity information text. Text recognition is performed on the identity information text and the current identity information text respectively to obtain the text features of the identity information text and the current identity information text respectively. The similarity between the two text features is obtained and recorded as the identity comparison result.

[0021] The texts of the target business operation and the current business operation are obtained respectively and denoted as the target business operation text and the current business operation text. Text recognition is performed on the target business operation text and the current business operation text respectively to obtain the text features of the target business operation text and the current business operation text, which are denoted as business text features. The similarity between the two business text features is obtained and denoted as business similarity. The business similarity is denoted as business comparison result.

[0022] As a further aspect of the present invention: the process of determining whether the identity comparison result or the business comparison result is consistent includes:

[0023] A first similarity threshold and a second similarity threshold are set. If the similarity exceeds the first similarity threshold, the identity comparison result is recorded as consistent; otherwise, the identity comparison result is recorded as inconsistent. If the business similarity exceeds the second similarity threshold, the business comparison result is recorded as consistent; otherwise, the business comparison result is recorded as inconsistent.

[0024] As a further aspect of the present invention: the process of obtaining the permission index and the access permission index includes:

[0025] Obtain all permission levels of the target data, sort them from low to high to obtain the permission level sequence {L1, L2, ..., L...} n}, where L n This represents the nth permission level, where n is the total number of permission levels; each permission level in the permission level sequence is numbered, and the number corresponding to the permission level of the target data is obtained, which is denoted as the permission level index;

[0026] According to the permission level of the target data, a permission level index Pi of the target data is obtained; according to the permission level index, a data permission index DPI of the target data is obtained = 1Pi+ 2Bfv, wherein 1Pi+ 2Bfv β β 1 is a first weight coefficient, 2 is a second weight coefficient, and Bfv is a business frequency value between the target data and a current business operation; β β 1 > 0, 2 > 0, and Bfv is a business frequency value between the target data and a current business operation; β β

[0027] All access permission levels of the access personnel are obtained, and after being sorted from low to high, an access permission level sequence {L1´, L2´,..., L m ´} is obtained, wherein L m ´ represents an mth access permission level, m is a total number of access permission levels; each access permission level in the access permission level sequence is numbered, and the number is recorded as a level number; a level number corresponding to the access permission level of the current access personnel is obtained, and the level number is recorded as an access level index;

[0028] According to the access permission level of the current access personnel, an access level index Ai of the current access personnel is obtained; according to the access level index, an access permission index API of the current access personnel is obtained = 1Ai+ 2Bcv, wherein 1Ai+ 2Bcv. β β Bcv is a business closeness value between the current access personnel and a current business operation.

[0029] As a further scheme of the application, the obtaining process of the low-risk data includes: selecting a plurality of target data with a data permission index less than or equal to an access permission index, and recording the target data as low-risk data.

[0030] As a further scheme of the application, the process of the distributed verification includes:

[0031] Obtaining a time required for consuming user behavior data of a query unit data volume, and recording the time as a query time t c ; obtaining a time required for consuming user behavior data of a display unit data volume, and recording the time as a display time t z ; obtaining a data volume V of each low-risk data, and then loading a time T = V (t c +t z );

[0032] ​​​​​A plurality of verification nodes are established for distributed verification of the verification information; access information of the current access personnel is obtained to obtain verification information of the current access personnel; the verification information is divided into a plurality of sub-information, and the sub-information is distributed to each verification node, and the distribution process meets a constraint where t k represents the time consumed by the verification node to verify the kth sub-information, k is the total number of sub-information, and num is the total number of verification nodes;

[0033] If the verification information passes the verification, the remaining data of the current access personnel is queried and displayed, and the remaining data is all user behavior data in the target data except the low-risk data.

[0034] The beneficial effects of the present application are:

[0035] The present application can verify the identity and business operation of the access personnel in a short time through the cache mechanism and identity and business comparison, reduce the time consumption of repeated verification, preferentially display low-risk data to ensure that the access personnel can quickly obtain the required information and improve user experience, effectively prevent single point failure and malicious attacks through parallel verification of the verification information of the access personnel by multiple verification nodes, and enhance overall security, dynamically evaluate the risk level of data access by calculating the data permission index (DPI) and access permission index (API), ensure that only access personnel meeting the permission requirements can access specific data, fully utilize computing resources by dividing the verification information into multiple sub-information and distributing it to multiple verification nodes for parallel verification, improve verification efficiency, dynamically adjust the distribution mode of sub-information according to the performance and current load of the verification node to ensure the efficiency of the verification process, set time constraints to ensure that the verification process is completed within a reasonable time and prevent the verification process from affecting user experience for too long, perform risk classification management on data by calculating DPI and API, preferentially process low-risk data, and reduce system risk, dynamically adjust the access permission of the access personnel according to the historical behavior data to ensure that they can efficiently complete their work while preventing abuse of permissions. BRIEF DESCRIPTION OF DRAWINGS

[0036] The present application will be further described below with reference to the accompanying drawings.

[0037] Figure 1 is a flowchart of a user behavior data security management method based on a CDP customer data platform. DETAILED DESCRIPTION

[0038] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0039] Please refer to Figure 1 As shown in the figure, the present application is a user behavior data security management method based on CDP customer data platform, comprising the following steps:

[0040] Step S1: set a plurality of business operations, and set the access permission level of each access personnel and the permission level of each user behavior data; when the access personnel accesses the user behavior data, the identity information and verification information of the access personnel are obtained, and the target business operation of the access personnel is obtained, the target business operation is the business operation to be executed by the access personnel; when the access personnel accesses the user behavior data, the verification information is verified, if the verification is passed, the access personnel is allowed to access the user behavior data;

[0041] It can be understood that a plurality of business operations are set first, and each access personnel and user behavior data is allocated with a corresponding permission level; when the access personnel attempts to access the user behavior data, the identity information and verification information thereof are obtained, and the target business operation to be executed is determined; the verification information of the access personnel is verified;

[0042] As a preferred embodiment of the present application, the setting of the business operation includes commodity recommendation, user grouping, abnormal login monitoring and sales prediction;

[0043] It should be noted that the business operation is the business operation name which needs to be completed by querying the user behavior data in the enterprise, the business operation can be diversified according to different industries, different business fields and specific business processes, and the business operation covers the core business link of the daily operation of the enterprise;

[0044] As a preferred embodiment of the present application, the identity information includes account number, mobile phone number or name;

[0045] As a preferred embodiment of the present application, if the verification is not passed, the access personnel is not allowed to access the user behavior data, and the access personnel is prompted that the verification is not passed;

[0046] It should be noted that the verification information of the access personnel is different according to different verification manners, when the verification manner is password verification, the verification information is the password input by the access personnel; when the verification manner is biological feature verification, the verification information is the biological feature of the access personnel, and the biological feature includes a fingerprint, a face contour;

[0047] Step S2: obtaining historical access data, the historical access data including identity information of an access personnel and target business operation when each user behavior data is accessed; obtaining a business frequency value Bfv between the user behavior data and the i th business operation according to the historical access data i =Nob i / Ntv, wherein Ntv represents the total number of times that the user behavior data is accessed, Nob i represents the number of times that the target business operation is the i th business operation when the user behavior data is accessed;

[0048] And obtaining a business closeness value Bcv between the access personnel and the i th business operation according to the historical access data i =nob i / ntv, wherein ntv represents the total number of times that the access personnel accesses all user behavior data, nob i represents the number of times that the target business operation is the i th business operation when the access personnel accesses;

[0049] It can be understood that the business frequency value is calculated by the access frequency between a certain user behavior data and a specific business operation, so as to quantify the dependence degree or the correlation strength of the business operation on the user behavior data, and a high Bfv value indicates that the user behavior data is highly related to the specific business operation; the business closeness value is calculated by the access frequency between a certain access personnel and a specific business operation, so as to quantify the dependence degree or the correlation strength of the access personnel on the business operation, and a high Bcv value indicates that the access personnel has a higher operation frequency on the specific business operation;

[0050] Step S3: when the access personnel completes an access, recording the target business operation, the identity information and the verification information of the access personnel, and recording all user behavior data accessed by the access personnel, as target data;

[0051] Setting a cache time period, if there is a new access personnel to access in the cache time period, recording the new access personnel as a current access personnel; obtaining the identity information of the current access personnel, as current identity information, and obtaining the target business operation of the current access personnel, as current business operation; comparing the identity information and the current identity information to obtain an identity comparison result, and comparing the target business operation and the current business operation to obtain a business comparison result;

[0052] When and only when the identity comparison result and the business comparison result are consistent, according to the permission level of the target data, a permission level index Pi of the target data is obtained, and according to the access permission level of the current access personnel, an access level index Ai of the current access personnel is obtained; according to the permission level index, a data permission index DPI of the target data is obtained β 1Pi+ β 2Bfv, wherein β 1 is a first weight coefficient, β 2 is a second weight coefficient, and β 1> β 2>0, Bfv is a business frequency value between the target data and the current business operation; and according to the access level index, an access permission index API of the current access personnel is obtained β 1Ai+ β 2Bcv, wherein Bcv is a business closeness value between the current access personnel and the current business operation;

[0053] It can be understood that, by comparing the identity information of the current access personnel and the target business operation with the previously recorded information, the identity comparison result and the business comparison result are obtained; only when the identity comparison result and the business comparison result are consistent, the subsequent permission evaluation is performed; by calculating the data permission index (DPI) and the access permission index (API), the risk level of data access is quantified;

[0054] It should be noted that, in the data permission index and the access permission index, the permission level of the target data or the access personnel is one of the factors affecting the risk level of access; the business frequency value of the target business operation represents the correlation strength between the target business operation and the target data, and the business closeness value represents the correlation strength between the access personnel and the target business operation before, both of which are used to reflect the risk level of the behavior of the access personnel;

[0055] As a preferred embodiment of the present application, the setting process of the cache time period comprises:

[0056] According to the historical access data, the time interval between each access is obtained, and the average value of all time intervals is obtained, which is recorded as the cache time period;

[0057] As a preferred embodiment of the present application, the obtaining process of the identity comparison result and the business comparison result comprises:

[0058] The texts of the identity information and the current identity information are obtained respectively and recorded as the identity information text and the current identity information text. Text recognition is performed on the identity information text and the current identity information text respectively to obtain the text features of the identity information text and the current identity information text respectively. The similarity between the two text features is obtained and recorded as the identity comparison result.

[0059] The texts of the target business operation and the current business operation are obtained respectively and denoted as the target business operation text and the current business operation text. Text recognition is performed on the target business operation text and the current business operation text respectively to obtain the text features of the target business operation text and the current business operation text, which are denoted as business text features. The similarity between the two business text features is obtained and denoted as business similarity. The business similarity is denoted as business comparison result.

[0060] In a preferred embodiment of the present invention, the process of determining whether the identity comparison result or the business comparison result is consistent includes:

[0061] A first similarity threshold and a second similarity threshold are set. If the similarity exceeds the first similarity threshold, the identity comparison result is recorded as consistent; otherwise, the identity comparison result is recorded as inconsistent. If the business similarity exceeds the second similarity threshold, the business comparison result is recorded as consistent; otherwise, the business comparison result is recorded as inconsistent.

[0062] In a preferred embodiment of the present invention, the process of obtaining the access level index and the data permission index includes:

[0063] Obtain all permission levels of the target data, sort them from low to high to obtain the permission level sequence {L1, L2, ..., L...} n}, where L n This represents the nth permission level, where n is the total number of permission levels; each permission level in the permission level sequence is numbered, and the number corresponding to the permission level of the target data is obtained, which is denoted as the permission level index;

[0064] Obtain all access permission levels of the accessed personnel, and sort them from low to high to obtain the access permission level sequence {L1', L2', ..., L...} m ´}, where L m ´ represents the m-th access permission level, where m is the total number of access permission levels; each access permission level in the access permission level sequence is numbered and denoted as the level number; the level number corresponding to the access permission level of the current visitor is obtained and denoted as the access level index;

[0065] As a preferred embodiment of the present application, if the identity comparison result and the business comparison result are inconsistent, verification information of the current access personnel is obtained, denoted as current verification information, the current verification information is verified, and after verification, the current access personnel is allowed to access;

[0066] It should be noted that, only when the identity comparison result and the business comparison result are consistent, it is indicated that the current access personnel and the last access personnel are the same person, and the business operations they perform are also the same. The identity comparison result being consistent means that the identity information of the current access personnel is completely matched with the identity information in the last access record, indicating that the current access personnel and the last access personnel are the same person. The business comparison result being consistent means that the target business operation of the current access personnel is completely matched with the target business operation in the last access record, indicating that the current access personnel and the last access personnel perform the same business operation.

[0067] Step S4: a plurality of verification nodes are established, the verification nodes are used for distributed verification of the verification information; a plurality of target data with a data permission index less than or equal to the access permission index are selected, denoted as low-risk data; the low-risk data is directly displayed to the current access personnel, a loading time of the low-risk data is predicted, the loading time is a time consumed for querying and displaying the low-risk data, verification information of the current access personnel is obtained, and the verification nodes perform distributed verification on the verification information within the loading time according to the loading time; if the verification information passes the verification, the remaining data of the current access personnel is queried and displayed, the remaining data is all user behavior data of the target data except the low-risk data.

[0068] As a preferred embodiment of the present application, if the permission indexes of all target data are greater than the access permission index, verification information of the current access personnel is obtained, denoted as current verification information, the current verification information is verified, and after verification, the current access personnel is allowed to access.

[0069] As a preferred embodiment of the present application, the prediction process of the loading time comprises:

[0070] a time consumed for querying unit data volume of user behavior data is obtained, denoted as query time t c , and a time consumed for displaying unit data volume of user behavior data is obtained, denoted as display time t z ; data volumes V of the low-risk data are obtained, and the loading time T = V (t c +t z );

[0071] As a preferred embodiment of the present application, the process of the distributed verification comprises:

[0072] The verification information is divided into several sub-information, and the sub-information is distributed to each verification node, and the distribution process meets the constraint , wherein t k represents the time consumed by the verification node to verify the kth sub-information, k is the total number of sub-information, and num is the total number of verification nodes;

[0073] As a preferred embodiment of the present application, the process of the distributed verification further comprises:

[0074] Obtaining the verification duration T t =t1+t2+...+t k , if T t >T is always true, then the minimum value of the verification duration is obtained, the distribution mode corresponding to the minimum value is obtained, and the verification information is verified in a distributed manner according to the distribution mode;

[0075] It can be understood that the verification information is divided into several sub-information, each sub-information can be independently verified, and the sub-information is distributed to different verification nodes to realize parallel processing; the loading time T is the maximum verification time allowed, and the total number of sub-information cannot exceed the total number of verification nodes; if all verification nodes are used and the verification duration exceeds the loading time, the system finds the minimum value of the verification duration and performs distributed verification according to the distribution mode;

[0076] It should be noted that by dividing the verification information into multiple sub-information and distributing it to multiple verification nodes for parallel verification, the efficiency of the verification process can be significantly improved, and parallel processing reduces the total verification time and improves the response speed; by setting the constraint condition, it is ensured that the verification process is completed within a reasonable time, and this real-time performance ensures user experience and prevents the verification process from affecting system performance for too long; by optimizing the distribution mode of the sub-information, the system can fully utilize the computing resources of all verification nodes;

[0077] As a preferred embodiment of the present application, if the verification information fails to pass the verification, the remaining data is no longer queried and displayed to the current access personnel, and the current access personnel is prompted that the verification has failed;

[0078] The above describes one embodiment of the present application in detail, but the content is only a preferred embodiment of the present application and cannot be considered as limiting the scope of the present application. Any equivalent changes and improvements made within the scope of the present application shall still belong to the patent coverage of the present application.

Claims

1. A method for managing user behavior data security based on a CDP customer data platform, characterized in that, The method comprises the following steps: Step S1: setting a plurality of business operations, and setting access permission levels of each access person and permission levels of each user behavior data; When an access person accesses user behavior data, access information of the access person is acquired; Step S2: acquiring historical access data, obtaining a business frequency value of the user behavior data according to the historical access data, and obtaining a business closeness value of the access person; Step S3: after an access person completes an access, access information of the access person is recorded, and target data is recorded; a cache time period is set, if there is a new access person to access in the cache time period, an identity comparison result and a business comparison result are obtained; if the identity comparison result and the business comparison result are consistent, a data permission index and an access permission index are obtained according to the access information; Step S4: according to the data permission index and the access permission index, low-risk data is obtained, the low-risk data is directly displayed to a new access person, and a loading time is predicted, and distributed verification is performed on verification information within the loading time; In step S3, the process of obtaining the permission index and the access permission index comprises: Obtain all permission levels of the target data, sort them from low to high to obtain the permission level sequence {L1, L2, ..., L...} n }, where L n This represents the nth permission level, where n is the total number of permission levels; each permission level in the permission level sequence is numbered, and the number corresponding to the permission level of the target data is obtained, which is denoted as the permission level index; According to the permission level of the target data, a permission level index Pi of the target data is obtained; according to the permission level index, a data permission index DPI of the target data is obtained β 1Pi+ β 2Bfv, wherein β 1 is a first weight coefficient, β 2 is a second weight coefficient, and β 1>0, β 2>0, and Bfv is a service frequency value between the target data and a current service operation. obtaining all access permission levels of the access personnel, and obtaining an access permission level sequence {L1´, L2´,..., L m ´} by sorting the access permission levels from low to high, wherein L m ´ represents the mth access permission level, and m is the total number of access permission levels; numbering each access permission level in the access permission level sequence, denoted as level number; obtaining the level number corresponding to the access permission level of the current access personnel, denoted as access level index; According to the access permission level of the current access personnel, an access level index Ai of the current access personnel is obtained; according to the access level index, an access permission index API of the current access personnel is obtained β 1 Ai + β 2 Bcv, wherein Bcv is a business closeness value between the current access personnel and the current business operation;​ In step S4, the process of distributed verification comprises: The time consumed for acquiring the user behavior data of the query unit data volume is denoted as query time t c , and the time consumed for acquiring the user behavior data of the display unit data volume is denoted as display time t z ; the data volume V of each low-risk data is acquired, and the loading time T = V (t c +t z ) is loaded. A plurality of verification nodes are established, and the verification nodes are used for distributed verification of the verification information; access information of the current access personnel is acquired to obtain verification information of the current access personnel; the verification information is divided into a plurality of sub-information, and the sub-information is distributed to each verification node, and a distribution process satisfies a constraint wherein t k represents time consumed by the verification node for verifying the kth sub-information, k is a total number of the sub-information, and num is a total number of the verification nodes If the verification information is verified, the remaining data is inquired and displayed to the current access person, and the remaining data is all user behavior data in the target data except the low-risk data.

2. The user behavior data security management method based on the CDP customer data platform according to claim 1, characterized in that, In step S1, the access information comprises identity information, verification information and target business operations of the access person, and the target business operations are business operations to be performed by the access person.

3. The user behavior data security management method based on the CDP customer data platform according to claim 2, characterized in that, In step S2, the historical access data comprises identity information and target business operations of an access person when each user behavior data is accessed.

4. The user behavior data security management method based on the CDP customer data platform according to claim 3, characterized in that, In step S2, the process of obtaining the business frequency value and the business closeness value comprises: According to the historical access data, a service frequency value Bfv between the user behavior data and the i-th service operation is obtained i =Nob i / Ntv, wherein Ntv represents the total number of times that the user behavior data is accessed, Nob i represents the number of times that the target service operation is the i-th service operation when the user behavior data is accessed. According to the historical access data, a business closeness value Bcv between the access personnel and the i-th business operation is obtained i =nob i / ntv, wherein ntv represents the total number of times that the access personnel accesses all user behavior data, and nob i represents the number of times that the target business operation is the i-th business operation when the access personnel accesses.

5. The user behavior data security management method based on the CDP customer data platform according to claim 3, characterized in that, In step S3, the process of setting the cache time period comprises: according to the historical access data, time intervals between each access are obtained, an average value of all time intervals is obtained, and is recorded as the cache time period.

6. The user behavior data security management method based on the CDP customer data platform according to claim 2, characterized in that, In step S3, the process of obtaining the identity comparison result and the business comparison result comprises: A new access person is recorded as a current access person, identity information of the current access person is acquired and is recorded as current identity information, and target business operations of the current access person are acquired and are recorded as current business operations; Texts of the identity information and the current identity information are acquired respectively and are recorded as identity information text and current identity information text respectively, text recognition is performed on the identity information text and the current identity information text respectively, text features of the identity information text and the current identity information text are obtained respectively, a similarity of the two text features is acquired and is recorded as an identity comparison result; Texts of the target business operation and the current business operation are respectively acquired and are respectively denoted as a target business operation text and a current business operation text. Text recognition is respectively performed on the target business operation text and the current business operation text, and text features of the target business operation text and the current business operation text are respectively obtained and are both denoted as a business text feature. A similarity of the two business text features is acquired and is denoted as a business similarity. The business similarity is denoted as a business comparison result.

7. The user behavior data security management method based on the CDP customer data platform according to claim 6, characterized in that, In step S3, the process of judging whether the identity comparison result or the business comparison result is consistent includes: First and second similarity threshold values are set. If the similarity exceeds the first similarity threshold value, the identity comparison result is recorded as consistent. Otherwise, the identity comparison result is recorded as inconsistent. If the business similarity exceeds the second similarity threshold value, the business comparison result is recorded as consistent. Otherwise, the business comparison result is recorded as inconsistent.

8. The user behavior data security management method based on the CDP customer data platform according to claim 1, characterized in that, In step S4, the process of obtaining the low-risk data includes selecting a plurality of target data whose data authority indexes are less than or equal to the access authority indexes, and recording the target data as low-risk data.

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