A customer information management method and related device based on cloud environment

Abstract

The present invention discloses a customer information management method based on a cloud environment and a related device, which relates to the field of data processing technology. The method includes: performing information security analysis based on customer information composition parameters generated by information composition analysis of customer information to obtain information security parameters; obtaining category identification information of customer information based on category logic chains, and obtaining corresponding local density based on preset information fields and category identification information; classifying customer information based on local density and information security parameters; storing and encrypting customer information of each category; obtaining a mapping relationship between storage performance information and encrypted customer information of each category, and determining an information storage strategy based on the mapping relationship combined with a node partitioning strategy; and storing encrypted customer information of each category in a storage node corresponding to a cloud storage center. The present invention improves the reliability of classified storage of customer information, so that the classified storage of customer information can achieve a more ideal effect.

Description

Technical Field

[0001] The present invention mainly relates to the field of data processing technology, and in particular to a customer information management method and related devices based on a cloud environment. Background Art

[0002] With the development of vocational education companies, and with the continuous increase in the number of customers during the operation, the amount of customer information data has also increased significantly, and its management requirements have become increasingly higher. Therefore, all enterprises attach great importance to the storage management of customer information. In the current storage management of customer information, there is a lack of information security analysis of customer information, which leads to the problem of easy leakage of customer information, greatly affecting the security of customer information storage. At the same time, in the current customer information storage, the classification of customer information is usually carried out by the relevant personnel who manage customer information, but this is less efficient in the classification of customer information, and the stability of customer information classification cannot be fully guaranteed, resulting in errors in the storage category of customer information. When storing customer information, it is usually stored and encrypted. The current customer information storage encryption is mainly performed through storage lookup tables and traditional key generation algorithms. However, this method not only has low encryption security, but also seriously occupies computing resources and too much storage space. In addition, when determining the storage node of customer information, hot and cold partitions are rarely considered. The lack of consideration of hot and cold partitions will cause the load of the storage node to be unable to achieve a good balance, which is not conducive to the management of customer information and makes the management of customer information unable to achieve the expected effect. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. The present invention provides a customer information management method and related devices based on a cloud environment, which improves the reliability of customer information classification storage and enables customer information classification storage to achieve a more ideal effect.

[0004] In order to solve the above technical problems, the present invention provides a customer information management method based on a cloud environment, the method comprising:

[0005] Acquire customer information based on a preset interface, perform information composition analysis on the customer information to obtain customer information composition parameters, and perform information security analysis based on the customer information composition parameters to obtain information security parameters;

[0006] Acquire category identification information of the customer information based on the category logic chain, acquire preset information fields of the customer information, and acquire corresponding local density based on the preset information fields and the category identification information;

[0007] Classifying the customer information based on the local density and information security parameters to obtain customer information of each category;

[0008] Based on the cloud trusted center, customer information of various categories is stored and encrypted to obtain encrypted customer information of various categories;

[0009] Obtain storage performance information of each storage node in the cloud storage center, obtain a mapping relationship between the storage performance information and each category of encrypted customer information, and determine an information storage strategy based on the mapping relationship in combination with a node partitioning strategy;

[0010] Based on the information storage strategy, the encrypted customer information of each category is stored in the storage node corresponding to the cloud storage center.

[0011] Optionally, performing information composition analysis on the customer information to obtain customer information composition parameters, and performing information security analysis based on the customer information composition parameters to obtain information security parameters, includes:

[0012] Performing format type analysis on the customer information to obtain format type information;

[0013] Performing data type analysis on the customer information to obtain data type information;

[0014] Performing data volume analysis on the customer information to obtain data volume information, and generating customer information composition parameters based on the format type information, data type information and data volume information;

[0015] Performing parameter attribute analysis on the parameters constituting the customer information to obtain parameter attribute information;

[0016] Obtaining historical risk events and risk event evaluation indicators, constructing an evaluation hierarchy structure using the risk event evaluation indicators based on the analytic hierarchy process, and determining an initial judgment matrix form based on the evaluation hierarchy structure;

[0017] Constructing an initial judgment matrix based on the initial judgment matrix form, and generating corresponding risk weights based on the initial judgment matrix using the historical risk events;

[0018] The association relationship between the risk weight and the parameter attribute information is obtained, and information security analysis is performed based on the association relationship to obtain information security parameters.

[0019] Optionally, the acquiring the category identification information of the customer information based on the category logic chain includes:

[0020] Acquire a category logic diagram, extract a plurality of logic nodes and logic structures from the category logic diagram, and generate logic context information based on the logic nodes and logic structures;

[0021] Obtain several categories of entity information and establish a pointing logical chain for each category of entity information;

[0022] A category logic chain is constructed based on the pointing logic chain and the logic context information, a category determination logic of the customer information is determined based on the category logic chain, and category identification information of the customer information is determined based on the category determination logic.

[0023] Optionally, the acquiring the corresponding local density based on the preset information field and the category identification information includes:

[0024] Calculate a first distance between the customer information based on the preset information field, calculate a second distance between the customer information based on the category identification information, and calculate a target distance based on the first distance and the second distance;

[0025] Based on the target distance, the corresponding local density is calculated using a preset threshold combined with a preset local density calculation formula.

[0026] Optionally, the classifying the customer information based on the local density and the information security parameter to obtain customer information of each category includes:

[0027] Preliminarily classifying the customer information based on the local density to obtain preliminarily classified customer information;

[0028] Calculating the information risk coefficient based on the information security parameter, and performing risk classification of the customer information based on the information risk coefficient to obtain corresponding risk classification information;

[0029] The customer information after the initial classification is reclassified based on the risk classification information to obtain customer information of each category.

[0030] Optionally, the cloud-based trusted center stores and encrypts customer information of each category to obtain encrypted customer information of each category, including:

[0031] Obtaining a cloud computing benchmark key based on a cloud trusted center, obtaining a dynamic feature matrix of each category of customer information, and generating key parameters of the cloud computing benchmark key based on the dynamic feature matrix;

[0032] Performing transcoding arrangement based on the cloud computing reference key to obtain a reference key matrix, and generating a quantum key based on the reference key matrix combined with the key parameters;

[0033] The encryption source code of each category of customer information is determined, and the cloud trusted center uses a virtual encryption program based on the quantum key and the encryption source code to store and encrypt the customer information of each category to obtain the encrypted customer information of each category.

[0034] Optionally, determining the information storage strategy based on the mapping relationship in combination with the node partition strategy includes:

[0035] Based on the mapping relationship, a matching optimization analysis is used to obtain a number of initial storage nodes corresponding to each category of encrypted customer information in the cloud storage center;

[0036] Divide a number of initial storage nodes into cold and hot areas to obtain a number of cold area storage nodes and a number of hot area storage nodes;

[0037] Conduct activity analysis on encrypted customer information of each category to obtain corresponding activity information;

[0038] Based on the activity level information, an information storage strategy is determined by utilizing a plurality of cold zone storage nodes and a plurality of hot zone storage nodes in combination with a priority matching strategy.

[0039] In addition, the present invention also provides a customer information management device based on a cloud environment, the device comprising:

[0040] Information security analysis module: used to obtain customer information based on a preset interface, perform information composition analysis on the customer information to obtain customer information composition parameters, and perform information security analysis based on the customer information composition parameters to obtain information security parameters;

[0041] A local density acquisition module: used for acquiring the category identification information of the customer information based on the category logic chain, acquiring the preset information field of the customer information, and acquiring the corresponding local density based on the preset information field and the category identification information;

[0042] A customer information classification module: used to classify the customer information based on the local density and information security parameters to obtain customer information of each category;

[0043] Information encryption module: used to store and encrypt various types of customer information based on the cloud trusted center to obtain encrypted customer information of various categories;

[0044] Information storage strategy determination module: used to obtain storage performance information of each storage node of the cloud storage center, obtain the mapping relationship between the storage performance information and each category of encrypted customer information, and determine the information storage strategy based on the mapping relationship combined with the node partition strategy;

[0045] Information storage module: used to store encrypted customer information of various categories in the storage nodes corresponding to the cloud storage center based on the information storage strategy.

[0046] In addition, the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory is used to store instructions, and the processor is used to call the instructions in the memory so that the electronic device executes the above-mentioned customer information management method based on the cloud environment.

[0047] In addition, the present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned customer information management method based on a cloud environment.

[0048] In an embodiment of the present invention, information security analysis is performed based on the customer information composition parameters generated by information composition analysis of customer information, which can take into account the parameter security impact of customer information, avoid the problem of customer information leakage, and improve the security of customer information storage. Based on the preset information field and the category identification information generated by the category logic chain, the corresponding local density is obtained, and the customer information is classified based on the local density and information security parameters. The categories of customer information can be quickly and accurately divided through the local density, and the risk coefficient is considered in the classification of customer information through the information security parameter, as a consideration for the storage nodes and encryption processing of customer information with different risks, so that the classification of customer information is more comprehensive and reliable. The cloud trusted center uses a virtual encryption program based on quantum keys and encryption source code to store and encrypt customer information of each category, improve the encryption security of customer information, and avoid the problem of serious computing resource occupation and excessive storage space occupation. Based on the mapping relationship between storage performance information and encrypted customer information of each category combined with the node partitioning strategy, the information storage strategy is determined, and considering the hot and cold partitions of storage nodes, the energy consumption of the cloud storage center during operation can be greatly reduced, and the load of storage nodes can be balanced to improve computing efficiency. It not only improves the reliability of customer information classification storage, but also makes customer information classification storage achieve more ideal results and realizes intelligent classification storage management. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 is a flow chart of a customer information management method based on a cloud environment in an embodiment of the present invention;

[0051] Figure 2 It is a schematic diagram of the structure of a customer information management device based on a cloud environment in an embodiment of the present invention;

[0052] Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Embodiment 1

[0055] See also Figure 1 , Figure 1 : is a flow chart of a customer information management method based on a cloud environment in an embodiment of the present invention, the method comprising:

[0056] S11: acquiring customer information based on a preset interface, performing information composition analysis on the customer information to obtain customer information composition parameters, and performing information security analysis based on the customer information composition parameters to obtain information security parameters;

[0057] In the specific implementation process of the present invention, the information composition analysis of the customer information is performed to obtain customer information composition parameters, and information security analysis is performed based on the customer information composition parameters to obtain information security parameters, including: performing format type analysis on the customer information to obtain format type information; performing data type analysis on the customer information to obtain data type information; performing data volume analysis on the customer information to obtain data volume information, and generating customer information composition parameters based on the format type information, data type information and data volume information; performing parameter attribute analysis on the customer information composition parameters to obtain parameter attribute information; obtaining historical risk events and risk event evaluation indicators, constructing an evaluation hierarchy structure based on the analytic hierarchy process using the risk event evaluation indicators, and determining the form of an initial judgment matrix based on the evaluation hierarchy structure; constructing an initial judgment matrix based on the initial judgment matrix form, and generating corresponding risk weights based on the historical risk events using the initial judgment matrix; obtaining the correlation between the risk weight and the parameter attribute information, and performing information security analysis based on the correlation to obtain information security parameters.

[0058] Specifically, customer information is obtained based on a preset interface, and the customer information includes information such as customer age, gender, education background and telephone number. The customer information is format type analyzed to determine the format type contained in the customer information to be stored, such as pictures, text and numbers. The format type analysis can be performed through a format recognition model, which is a convergence model obtained by inputting a sample data set into a deep neural network for training, to obtain format type information. The customer information is data type analyzed to obtain a preset type tree, wherein the type tree includes multiple nodes, each of which corresponds to a judgment rule, and the judgment rule is used to determine whether the data belongs to the data type represented by each node, and the multiple nodes include leaf nodes without child nodes. The data type of the customer information is determined according to the preset type tree, and the data type of the customer information is determined by the data type represented by the leaf node, that is, the type tree is traversed from the starting node until the data type of the customer information is determined to obtain the data type information. The customer information is analyzed for data volume to obtain data volume information, and the customer information composition parameters are formed based on the format type information, data type information and data volume information. Perform parameter attribute analysis on the parameters of the customer information composition, extract historical customer record information, that is, the information storage record data of customers in the past time, determine the parameter attributes of the historical customer storage information according to the historical customer record information, and the parameter attributes include common parameters and characteristic parameters. Common parameters refer to data with low data security requirements, such as customer name and quantity, etc. The characteristic parameters refer to data with special security requirements of customers, such as financial flow data and personal information data, etc. The common parameters and characteristic parameters constitute the parameter attribute information. Obtain historical risk events and risk event evaluation indicators, such as historical risk events such as personal information leakage or theft, extract corresponding risk parameters from historical risk events, construct risk event evaluation indicators according to corresponding risk parameters, and construct an evaluation hierarchy structure based on the analytic hierarchy process using the risk event evaluation indicators. The evaluation hierarchy structure includes information layer, factor layer, criterion layer and target layer. The information layer is constructed through historical risk events, and the factor layer and criterion layer are constructed through risk event evaluation indicators. The initial judgment matrix form is determined based on the evaluation hierarchy structure, and the order and elements of the judgment matrix are determined according to the evaluation hierarchy structure, that is, the initial judgment matrix form is determined.Based on the initial judgment matrix form, an initial judgment matrix is ​​constructed, and based on the initial judgment matrix, the corresponding risk weight is generated by using the historical risk events, the consistency of the initial judgment matrix is ​​judged by the preset check coefficient, the maximum eigenvalue of the initial judgment matrix is ​​calculated, and the maximum eigenvalue is checked for consistency according to the preset check coefficient, and the preset check coefficient is obtained from the database. If the consistency check fails, the initial judgment matrix is ​​corrected, and if the consistency check passes, there is no need to correct the initial judgment matrix. After completing the consistency check of the initial judgment matrix, the final target judgment matrix can be obtained, and a fuzzy consistency matrix is ​​generated according to the target judgment matrix combined with historical risk events and corresponding risk parameters, and the risk weight is calculated according to the fuzzy consistency matrix to obtain the corresponding risk weight. The association relationship between the risk weight and the parameter attribute information is obtained, and information security analysis is performed based on the association relationship, that is, parameter security impact analysis is performed on the parameter attribute information according to the corresponding association relationship, the security risk coefficient of the parameter attribute information is determined according to the risk weight combined with the association relationship, and the security impact is quantified according to the security risk coefficient to obtain information security parameters, and the information security parameters include the risk coefficient of each parameter attribute information and the security impact quantification coefficient.

[0059] S12: acquiring category identification information of the customer information based on the category logic chain, acquiring a preset information field of the customer information, and acquiring a corresponding local density based on the preset information field and the category identification information;

[0060] In the specific implementation process of the present invention, the method of obtaining the category identification information of the customer information based on the category logic chain includes: obtaining a category logic diagram, extracting a number of logical nodes and logical structures from the category logic diagram, and generating logical context information based on the logical nodes and logical structures; obtaining a number of category entity information, and establishing a pointing logic chain for each category entity information; constructing a category logic chain based on the pointing logic chain and the logical context information, determining the category determination logic of the customer information based on the category logic chain, and determining the category identification information of the customer information based on the category determination logic.

[0061] Furthermore, the obtaining of the corresponding local density based on the preset information field and the category identification information includes: calculating a first distance between customer information based on the preset information field, calculating a second distance between customer information based on the category identification information, and calculating a target distance based on the first distance and the second distance; and calculating the corresponding local density based on the target distance using a preset threshold combined with a preset local density calculation formula.

[0062] Specifically, a category logic diagram is obtained. The category logic diagram is the logic determined by the category, including execution category judgment, branch judgment and category classification, etc., which can be directly obtained from the database. Several logical nodes and logical structures are extracted from the category logic diagram. The category logic diagram contains several logical nodes and logical structures. Several logical nodes and logical structures can be extracted from the category logic diagram through the extraction function, and logical context information is generated based on the logical nodes and logical structures. Compilation processing is performed according to the logical nodes and logical structures. The logical context information mainly includes the running status data of the logical nodes, that is, each logical node is run according to the logical structure according to the execution engine, the execution parameter configuration information of the category logic diagram is obtained, and the compiler returns the running result of each logical node using the execution parameter configuration information to obtain the logical context information. Acquire several category entity information, the category entity information is the entity existence information of the information category of the customer information, such as male and female category entities for gender, and childhood, adolescence and youth category entities for age, establish a pointing logic chain for each category entity information, determine a logic training pair based on several category entity information, establish a corresponding pointing logic chain based on the information category in the logic training pair, and the pointing logic chain is the information category pointing entity category information in the logic training pair. Construct a category logic chain based on the pointing logic chain and the logic context information, that is, modify the pointing logic chain through the logic context information to improve the reliability of category pointing. The combination of the two can effectively ensure the accuracy when classifying information. Determine the category determination logic of the customer information based on the category logic chain, and determine the category identification information of the customer information based on the category determination logic. The category identification information can identify the category entity of the customer information, so that the customer information of the same category entity can be classified into one category. Obtain the preset information field of the customer information, which may be the region where the customer is located or the IT course information determined by the customer, etc. Calculate the first distance between the customer information based on the preset information field, and calculate the second distance between the customer information based on the category identification information. The first distance and the second distance may be calculated using the Euclidean distance calculation formula, and calculate the target distance based on the first distance and the second distance. The average value of the first distance and the second distance is the target distance. Calculate the corresponding local density based on the target distance using a preset threshold combined with a preset local density calculation formula.

[0063] S13: classifying the customer information based on the local density and the information security parameter to obtain customer information of each category;

[0064] In the specific implementation process of the present invention, the customer information is classified based on the local density and the information security parameter to obtain customer information of each category, including: preliminarily classifying the customer information based on the local density to obtain customer information after preliminarily classification; calculating the information risk coefficient based on the information security parameter, and dividing the risk of customer information based on the information risk coefficient to obtain corresponding risk division information; reclassifying the preliminarily classified customer information based on the risk division information to obtain customer information of each category.

[0065] Specifically, the customer information is preliminarily classified based on the local density, a reference point is determined based on the local density, and similar customer information is classified into one category based on the reference point to obtain the preliminarily classified customer information. The information risk coefficient is calculated based on the information security parameter, and a smaller weight coefficient is assigned to information with a smaller security impact according to the information security parameter, such as the number of customers, and a larger weight coefficient is assigned to information with a larger security impact according to the information security parameter, such as customer photos, and the information risk coefficient is obtained by multiplying the assigned weight coefficient with the risk coefficient, and the risk classification of the customer information is performed based on the information risk coefficient to obtain the corresponding risk classification information, and the information risk coefficient is compared with the preset risk segmentation threshold to classify it into low, medium and high risk levels. The preliminarily classified customer information is reclassified based on the risk classification information, that is, the information of different risk levels in the preliminarily classified customer information is reclassified to obtain customer information of each category, and the reclassification through the risk classification information can pave the way for subsequent storage encryption and storage node arrangement.

[0066] S14: storing and encrypting customer information of each category based on the cloud trusted center to obtain encrypted customer information of each category;

[0067] In the specific implementation process of the present invention, the cloud trusted center stores and encrypts customer information of each category to obtain encrypted customer information of each category, including: obtaining a cloud computing benchmark key based on the cloud trusted center, obtaining a dynamic feature matrix of customer information of each category, and generating key parameters of the cloud computing benchmark key based on the dynamic feature matrix; performing transcoding and arrangement based on the cloud computing benchmark key to obtain a benchmark key matrix, and generating a quantum key based on the benchmark key matrix and the key parameters; determining the encryption source code of customer information of each category, and the cloud trusted center uses a virtual encryption program to store and encrypt customer information of each category based on the quantum key and the encryption source code to obtain encrypted customer information of each category.

[0068] Specifically, a cloud computing benchmark key is obtained based on a cloud trusted center, and a cloud computing benchmark key is issued through a cloud computing center of the cloud trusted center. A dynamic feature matrix of each category of customer information is obtained, and a corresponding dynamic feature matrix is ​​constructed according to the update time, data type information, and data storage capacity of each category of customer information. A key parameter of the cloud computing benchmark key is generated based on the dynamic feature matrix, and a topological structure of a cloud computing network in the cloud trusted center is obtained. A topological feature matrix is ​​determined according to the topological structure. A product operation is performed on the topological feature matrix and the dynamic feature matrix to obtain the key parameter. Based on the cloud computing reference key, transcoding arrangement is performed, that is, a binary transcoding operation is performed on the cloud computing reference key to obtain a reference key matrix, and a quantum key is generated based on the reference key matrix in combination with the key parameter, the reference key matrix is ​​divided to obtain a plurality of block matrices, the number of block matrices in each row is used as the number of row matrices, the number of block matrices in each column is used as the number of column matrices, the number of row matrices is modulo according to the data type information of the customer information, the horizontal coordinate of the initial block matrix is ​​obtained, the number of column matrices is modulo according to the product of the update time of the customer information and the data storage amount, the vertical coordinate of the initial block matrix is ​​obtained, the initial block matrix is ​​determined according to the horizontal coordinate and the column coordinate, starting from the initial block matrix, all block matrices are scanned to obtain a scanning path sequence of all block matrices, a quantum walk is performed at the first position in the scanning path sequence, and a probability distribution matrix of the reference key matrix is ​​obtained; a quantum matrix is ​​obtained according to the probability distribution matrix and the key parameter; and the quantum matrix is ​​expanded and reorganized to obtain a quantum key. Determine the encryption source code of each category of customer information, select the corresponding encryption algorithm according to the category of customer information, divide the category of customer information according to information security parameters and risk factors, select the corresponding encryption algorithm according to its information security parameters and risk factors, obtain the encryption source code according to the encryption algorithm, at the specific underlying implementation level, the algorithm steps of the encryption algorithm are integrated into a piece of code that can be executed by the terminal, called source code, and the source code of the target encryption algorithm used in the encryption process is called encryption source code. The cloud trusted center uses a virtual encryption program based on the quantum key and encryption source code to store and encrypt the customer information of each category, generates a virtual instruction set from the quantum key and encryption source code, and integrates the virtual instruction set into the virtual encryption program, that is, the quantum key and the encryption source code are merged and obfuscated, and then the customer information of each category is encrypted. By merging and obfuscating the quantum key and the encryption source code, the key information is hidden in the virtual encryption program and cannot be cracked, thereby ensuring the security of the key, and then obtaining encrypted customer information of each category.

[0069] S15: Obtain storage performance information of each storage node of the cloud storage center, obtain a mapping relationship between the storage performance information and each category of encrypted customer information, and determine an information storage strategy based on the mapping relationship in combination with a node partition strategy;

[0070] In the specific implementation process of the present invention, the information storage strategy is determined based on the mapping relationship in combination with the node partitioning strategy, including: obtaining a number of initial storage nodes corresponding to each category of encrypted customer information in the cloud storage center using matching optimization analysis based on the mapping relationship; dividing the several initial storage nodes into hot and cold areas to obtain a number of cold area storage nodes and a number of hot area storage nodes; performing activity level analysis on the encrypted customer information of each category to obtain corresponding activity level information; and determining the information storage strategy based on the activity level information using a number of cold area storage nodes and a number of hot area storage nodes in combination with a priority matching strategy.

[0071] Specifically, the storage performance information of each storage node of the cloud storage center is obtained, and the storage performance information includes the storage space, space expansion attributes and space computing power of each storage node; the mapping relationship between the storage performance information and each category of encrypted customer information is obtained, that is, the data size and computing power requirements of each category of encrypted customer information are analyzed, and the storage space requirements and computing power requirements of each category of encrypted customer data are calculated according to the data volume and the encryption degree of the data, and the mapping relationship between the storage space requirements and computing power requirements of each category of encrypted customer data and the storage performance information is constructed. Based on the mapping relationship, a matching optimization analysis is used to obtain a number of initial storage nodes corresponding to each category of encrypted customer information in the cloud storage center, and a matching optimization coefficient of the storage node is set according to the mapping relationship. An optimization space is constructed through the matching optimization coefficient, and a number of corresponding initial storage nodes are determined through the optimization space according to the storage space requirements and computing power requirements of each category of encrypted customer information and the storage performance information of each storage node of the cloud storage center. A number of initial storage nodes are divided into hot and cold areas, and the load of the initial storage nodes in a preset node cycle is predicted based on a long short-term memory network. The initial hot and cold areas are divided according to a preset load threshold combined with the load of each initial storage node in the preset node cycle to obtain a number of initial cold zone storage nodes and a number of initial hot zone storage nodes. The average network distance between each initial cold zone storage node and all initial hot zone storage nodes is calculated. The network distance can be the data transmission time between the storage nodes. The evaluation value of each initial cold zone storage node is calculated based on the average network distance combined with the balance constant. The data block access frequency in each initial cold zone storage node is calculated. The target value is calculated based on the evaluation value and the data block access frequency. All initial cold zone storage nodes are matched according to the target value, and a preset number of initial cold zone storage nodes with high positions are taken and divided into hot zones to obtain the final number of cold zone storage nodes and several hot zone storage nodes. Perform activity analysis on encrypted customer information of each category, perform activity analysis on encrypted customer information of each category based on preset timing rules and Apache Doris technology, create a Tablestore data source table based on real-time streaming delivery technology; perform TP query processing based on the Tablestore data source table to obtain the life cycle of the Tablestore data source table; perform activity analysis based on the life cycle of the Tablestore data source table, and perform activity analysis through the column storage and dynamic stratification functions of Apache Doris technology itself and support for life cycle management of data source tables combined with data access frequency judgment rules and creation time judgment rules in preset timing rules to obtain corresponding activity information.Based on the activity information, the information storage strategy is determined by using several cold zone storage nodes and several hot zone storage nodes in combination with a priority matching strategy, that is, the encrypted customer information of each category is matched with the corresponding cold zone storage node or hot zone storage node according to the activity information, and the node with larger remaining space in the storage node is matched preferentially according to the data amount of the encrypted customer information.

[0072] S16: Based on the information storage strategy, the encrypted customer information of each category is stored in the storage node corresponding to the cloud storage center.

[0073] In the specific implementation process of the present invention, the encrypted customer information of each category is matched with the corresponding cold zone storage node or the hot zone storage node according to its activity level information, and the node with larger remaining space in the storage node is matched preferentially according to the data volume of the encrypted customer information. According to the above information storage strategy, the encrypted customer information of each category is stored in the storage node corresponding to the cloud storage center.

[0074] In an embodiment of the present invention, information security analysis is performed based on the customer information composition parameters generated by information composition analysis of customer information, which can take into account the parameter security impact of customer information, avoid the problem of customer information leakage, and improve the security of customer information storage. Based on the preset information field and the category identification information generated by the category logic chain, the corresponding local density is obtained, and the customer information is classified based on the local density and information security parameters. The categories of customer information can be quickly and accurately divided through the local density, and the risk coefficient is considered in the classification of customer information through the information security parameter, as a consideration for the storage nodes and encryption processing of customer information with different risks, so that the classification of customer information is more comprehensive and reliable. The cloud trusted center uses a virtual encryption program based on quantum keys and encryption source code to store and encrypt customer information of each category, improve the encryption security of customer information, and avoid the problem of serious computing resource occupation and excessive storage space occupation. Based on the mapping relationship between storage performance information and encrypted customer information of each category combined with the node partitioning strategy, the information storage strategy is determined, and considering the hot and cold partitions of storage nodes, the energy consumption of the cloud storage center during operation can be greatly reduced, and the load of storage nodes can be balanced to improve computing efficiency. It not only improves the reliability of customer information classification storage, but also makes customer information classification storage achieve more ideal results and realizes intelligent classification storage management.

[0075] Embodiment 2

[0076] See also Figure 2 , Figure 2 : is a schematic diagram of the structure of a cloud-based customer information management device in an embodiment of the present invention, the device comprising:

[0077] Information security analysis module 21: used to obtain customer information based on a preset interface, perform information composition analysis on the customer information to obtain customer information composition parameters, and perform information security analysis based on the customer information composition parameters to obtain information security parameters;

[0078] The local density acquisition module 22 is used to acquire the category identification information of the customer information based on the category logic chain, acquire the preset information field of the customer information, and acquire the corresponding local density based on the preset information field and the category identification information;

[0079] The customer information classification module 23 is used to classify the customer information based on the local density and information security parameters to obtain customer information of each category;

[0080] Information encryption module 24: used to store and encrypt customer information of various categories based on the cloud trusted center to obtain encrypted customer information of various categories;

[0081] The information storage strategy determination module 25 is used to obtain the storage performance information of each storage node of the cloud storage center, obtain the mapping relationship between the storage performance information and each category of encrypted customer information, and determine the information storage strategy based on the mapping relationship combined with the node partition strategy;

[0082] The information storage module 26 is used to store encrypted customer information of various categories in the storage nodes corresponding to the cloud storage center based on the information storage strategy.

[0083] In the specific implementation process of the present invention, the specific implementation method of the device item can refer to the implementation method of the above-mentioned method item, which will not be repeated here.

[0084] In an embodiment of the present invention, information security analysis is performed based on the customer information composition parameters generated by information composition analysis of customer information, which can take into account the parameter security impact of customer information, avoid the problem of customer information leakage, and improve the security of customer information storage. Based on the preset information field and the category identification information generated by the category logic chain, the corresponding local density is obtained, and the customer information is classified based on the local density and information security parameters. The categories of customer information can be quickly and accurately divided through the local density, and the risk coefficient is considered in the classification of customer information through the information security parameter, as a consideration for the storage nodes and encryption processing of customer information with different risks, so that the classification of customer information is more comprehensive and reliable. The cloud trusted center uses a virtual encryption program based on quantum keys and encryption source code to store and encrypt customer information of each category, improve the encryption security of customer information, and avoid the problem of serious computing resource occupation and excessive storage space occupation. Based on the mapping relationship between storage performance information and encrypted customer information of each category combined with the node partitioning strategy, the information storage strategy is determined, and considering the hot and cold partitions of storage nodes, the energy consumption of the cloud storage center during operation can be greatly reduced, and the load of storage nodes can be balanced to improve computing efficiency. It not only improves the reliability of customer information classification storage, but also makes customer information classification storage achieve more ideal results and realizes intelligent classification storage management.

[0085] A computer-readable storage medium provided in an embodiment of the present invention stores a computer program on the computer-readable storage medium, and when the program is executed by a processor, a customer information management method based on a cloud environment in any one of the above embodiments is implemented. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic card or optical card. That is, the storage device includes any medium that can store or transmit information in a readable form by a device (e.g., a computer, a mobile phone), which can be a read-only memory, a disk or an optical disk, etc.

[0086] Embodiment 3

[0087] See also Figure 3 , Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention.

[0088] The embodiment of the present invention further provides an electronic device, such as Figure 3 As shown, the electronic device includes a memory 31, a processor 33, and a computer program 32 stored in the memory 31 and executable on the processor 33. Those skilled in the art will appreciate that Figure 3 The electronic device shown does not constitute a limitation on all devices, and may include more or fewer components than shown, or combine certain components. The memory 31 can be used to store the computer program 32 and various functional modules, and the processor 33 runs the computer program 32 stored in the memory 31, thereby executing various functional applications and data processing of the device. The memory can be an internal memory or an external memory, or include both internal and external memories. The internal memory can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, or a random access memory. The external memory can include a hard disk, a floppy disk, a ZIP disk, a U disk, a magnetic tape, etc. The processor 33 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general processor may be a microprocessor, a single chip microcomputer or the processor 33 may also be any conventional processor, etc. The processor and memory disclosed in the present invention include but are not limited to these types of processors and memories. The processor and memory disclosed in the present invention are only examples and not limitations.

[0089] As an embodiment, the electronic device includes: one or more processors 33, a memory 31, and one or more computer programs 32, wherein the one or more computer programs 32 are stored in the memory 31 and are configured to be executed by the one or more processors 33, and the one or more computer programs 32 are configured to execute the cloud environment-based customer information management method in any of the above-mentioned embodiments. Please refer to the above-mentioned embodiments for the specific implementation process, which will not be repeated here.

[0090] In an embodiment of the present invention, information security analysis is performed based on the customer information composition parameters generated by information composition analysis of customer information, which can take into account the parameter security impact of customer information, avoid the problem of customer information leakage, and improve the security of customer information storage. Based on the preset information field and the category identification information generated by the category logic chain, the corresponding local density is obtained, and the customer information is classified based on the local density and information security parameters. The categories of customer information can be quickly and accurately divided through the local density, and the risk coefficient is considered in the classification of customer information through the information security parameter, as a consideration for the storage nodes and encryption processing of customer information with different risks, so that the classification of customer information is more comprehensive and reliable. The cloud trusted center uses a virtual encryption program based on quantum keys and encryption source code to store and encrypt customer information of each category, improve the encryption security of customer information, and avoid the problem of serious computing resource occupation and excessive storage space occupation. Based on the mapping relationship between storage performance information and encrypted customer information of each category combined with the node partitioning strategy, the information storage strategy is determined, and considering the hot and cold partitions of storage nodes, the energy consumption of the cloud storage center during operation can be greatly reduced, and the load of storage nodes can be balanced to improve computing efficiency. It not only improves the reliability of customer information classification storage, but also makes customer information classification storage achieve more ideal results and realizes intelligent classification storage management.

[0091] In addition, the above is a detailed introduction to a customer information management method based on a cloud environment and related devices provided by an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in the field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A customer information management method based on a cloud environment, characterized in that: The method comprises: Acquire customer information based on a preset interface, perform information composition analysis on the customer information to obtain customer information composition parameters, and perform information security analysis based on the customer information composition parameters to obtain information security parameters; Acquire category identification information of the customer information based on the category logic chain, acquire preset information fields of the customer information, and acquire corresponding local density based on the preset information fields and the category identification information; Classifying the customer information based on the local density and information security parameters to obtain customer information of each category; Based on the cloud trusted center, customer information of various categories is stored and encrypted to obtain encrypted customer information of various categories; Obtain storage performance information of each storage node in the cloud storage center, obtain a mapping relationship between the storage performance information and each category of encrypted customer information, and determine an information storage strategy based on the mapping relationship in combination with a node partitioning strategy; Based on the information storage strategy, the encrypted customer information of each category is stored in the storage node corresponding to the cloud storage center; Among them, the classifying the customer information based on the local density and the information security parameter to obtain customer information of each category includes: preliminarily classifying the customer information based on the local density to obtain customer information after preliminarily classification; calculating the information risk coefficient based on the information security parameter, and classifying the risk of customer information based on the information risk coefficient to obtain corresponding risk classification information; and reclassifying the preliminarily classified customer information based on the risk classification information to obtain customer information of each category.

2. The method for managing customer information based on a cloud environment according to claim 1, characterized in that: The performing information composition analysis on the customer information to obtain customer information composition parameters, and performing information security analysis based on the customer information composition parameters to obtain information security parameters, includes: Performing format type analysis on the customer information to obtain format type information; Performing data type analysis on the customer information to obtain data type information; Performing data volume analysis on the customer information to obtain data volume information, and generating customer information composition parameters based on the format type information, data type information and data volume information; Performing parameter attribute analysis on the parameters constituting the customer information to obtain parameter attribute information; Obtaining historical risk events and risk event evaluation indicators, constructing an evaluation hierarchy structure using the risk event evaluation indicators based on the analytic hierarchy process, and determining an initial judgment matrix form based on the evaluation hierarchy structure; Constructing an initial judgment matrix based on the initial judgment matrix form, and generating corresponding risk weights based on the initial judgment matrix using the historical risk events; The association relationship between the risk weight and the parameter attribute information is obtained, and information security analysis is performed based on the association relationship to obtain information security parameters.

3. The method for managing customer information based on a cloud environment according to claim 1, characterized in that: The step of obtaining the category identification information of the customer information based on the category logic chain includes: Acquire a category logic diagram, extract a plurality of logic nodes and logic structures from the category logic diagram, and generate logic context information based on the logic nodes and logic structures; Obtaining several categories of entity information and establishing a pointing logical chain for each category of entity information; A category logic chain is constructed based on the pointing logic chain and the logic context information, a category determination logic of the customer information is determined based on the category logic chain, and category identification information of the customer information is determined based on the category determination logic.

4. The method for managing customer information based on a cloud environment according to claim 1, characterized in that: The acquiring the corresponding local density based on the preset information field and the category identification information includes: Calculate a first distance between the customer information based on the preset information field, calculate a second distance between the customer information based on the category identification information, and calculate a target distance based on the first distance and the second distance; Based on the target distance, the corresponding local density is calculated using a preset threshold combined with a preset local density calculation formula.

5. The method for managing customer information based on a cloud environment according to claim 1, characterized in that: The cloud-based trusted center stores and encrypts customer information of various categories to obtain encrypted customer information of various categories, including: Obtaining a cloud computing benchmark key based on a cloud trusted center, obtaining a dynamic feature matrix of each category of customer information, and generating key parameters of the cloud computing benchmark key based on the dynamic feature matrix; Performing transcoding arrangement based on the cloud computing reference key to obtain a reference key matrix, and generating a quantum key based on the reference key matrix combined with the key parameters; The encryption source code of each category of customer information is determined, and the cloud trusted center uses a virtual encryption program based on the quantum key and the encryption source code to store and encrypt the customer information of each category to obtain the encrypted customer information of each category.

6. The method for managing customer information based on a cloud environment according to claim 1, characterized in that: The determining of the information storage strategy based on the mapping relationship in combination with the node partition strategy includes: Based on the mapping relationship, a matching optimization analysis is used to obtain a number of initial storage nodes corresponding to each category of encrypted customer information in the cloud storage center; Divide a number of initial storage nodes into cold and hot areas to obtain a number of cold area storage nodes and a number of hot area storage nodes; Conduct activity analysis on encrypted customer information of each category to obtain corresponding activity information; Based on the activity level information, an information storage strategy is determined by utilizing a plurality of cold zone storage nodes and a plurality of hot zone storage nodes in combination with a priority matching strategy.

7. A customer information management device based on a cloud environment, characterized in that: The device comprises: Information security analysis module: used to obtain customer information based on a preset interface, perform information composition analysis on the customer information to obtain customer information composition parameters, and perform information security analysis based on the customer information composition parameters to obtain information security parameters; A local density acquisition module: used for acquiring the category identification information of the customer information based on the category logic chain, acquiring the preset information field of the customer information, and acquiring the corresponding local density based on the preset information field and the category identification information; A customer information classification module: used to classify the customer information based on the local density and information security parameters to obtain customer information of each category; Information encryption module: used to store and encrypt various types of customer information based on the cloud trusted center to obtain encrypted customer information of various categories; Information storage strategy determination module: used to obtain storage performance information of each storage node of the cloud storage center, obtain the mapping relationship between the storage performance information and each category of encrypted customer information, and determine the information storage strategy based on the mapping relationship combined with the node partition strategy; Information storage module: used to store encrypted customer information of various categories in the storage nodes corresponding to the cloud storage center based on the information storage strategy; Among them, the classifying the customer information based on the local density and the information security parameter to obtain customer information of each category includes: preliminarily classifying the customer information based on the local density to obtain customer information after preliminarily classification; calculating the information risk coefficient based on the information security parameter, and classifying the risk of customer information based on the information risk coefficient to obtain corresponding risk classification information; and reclassifying the preliminarily classified customer information based on the risk classification information to obtain customer information of each category.

8. An electronic device, comprising a processor and a memory, characterized in that: The memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the customer information management method based on a cloud environment as described in any one of claims 1 to claim 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the customer information management method based on a cloud environment according to any one of claims 1 to 6.

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