Big data management method based on SaaS platform

Abstract

The invention discloses a big data management method based on an SaaS platform, and relates to the field of software services. Comprising the following steps: data storage and isolation; constructing a multi-tenant framework on the SaaS platform, and distributing an independent data instance for each cooperative merchant; through a Hive partition table technology, data storage isolation is carried out according to merchant IDs, and it is ensured that data of all merchants do not interfere with one another; a data security policy; aES 256 bit encryption and an SSL / TLS transmission protocol are implemented, and encryption processing is carried out on all transmitted and stored data. According to the method, analysis is performed according to the previous data operation behaviors of the user, so that the storage space is estimated, and the space size of the corresponding storage unit is adjusted in a targeted manner according to the storage space, so that resource waste caused by space redundancy can be prevented while reliable storage is ensured.

Description

Technical Field

[0001] The present invention relates to the field of software services, and in particular to a big data management method based on a SaaS platform. Background Art

[0002] Software as a Service (SaaS) allows users to use cloud-based applications through an Internet connection. Common examples are email, calendars, and office tools. It does not require users to install software products on their own computers or servers.

[0003] SaaS provides a complete software solution that users can purchase from a cloud service provider on a pay-as-you-go basis. Organizations rent applications and their users connect to the application over the Internet (usually using a web browser). All infrastructure, middleware, application software, and application data are located in the service provider's data center. The service provider is responsible for managing the hardware and software and ensuring the availability and security of applications and data under appropriate service agreements. SaaS enables organizations to quickly go into production with applications at minimal upfront cost.

[0004] However, in the prior art, although the SaaS platform adopts a separate block organizational structure for user data storage, it is unable to predict the user's storage capacity, and thus is unable to adjust the capacity of the user's storage unit, resulting in insufficient storage space or redundant storage resources.

[0005] To this end, the present invention proposes a big data management method based on the SaaS platform. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a big data management method based on the SaaS platform.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A big data management method based on a SaaS platform includes the following steps:

[0009] S1: Data storage and isolation;

[0010] S11: Build a multi-tenant architecture on the SaaS platform and allocate independent data instances to each partner;

[0011] S12: Hive partition table technology is used to isolate data storage based on merchant IDs to ensure that the data of each merchant does not interfere with each other;

[0012] S2: Data security policy;

[0013] S21: Implement AES256-bit encryption and SSL / TLS transmission protocols to encrypt all transmitted and stored data;

[0014] S22: Enable multi-factor authentication mechanism to strengthen user access control;

[0015] S3: data backup and recovery;

[0016] S31: Use the automatic backup function provided by the SaaS platform to set up daily backup;

[0017] S32: And configure long-term storage strategies and develop detailed disaster recovery plans to ensure that data can be quickly recovered in the event of system failure or catastrophic events.

[0018] Preferably: the step S11 includes the following steps:

[0019] S11: dividing the entire storage architecture into a plurality of different storage units;

[0020] S12: Assign each merchant to a storage unit, assign the merchant an identity, and use the identity as the name of the storage unit;

[0021] S13: Estimate the merchant's storage space requirements based on changes in the merchant's storage data;

[0022] S14: Based on the estimated storage space requirements of the merchant, the storage space size of the storage unit is increased or decreased accordingly.

[0023] Preferably, in step S13, the method for estimating storage space requirements includes the following steps:

[0024] S131: Taking the current node as the starting point, obtain the data occupied size of n-1 nodes, which are A1, A2, ... A n ;

[0025] S132: Set the discreteness threshold σ and calculate the discreteness σ of the data A .

[0026] In step S14, the logic of changing the storage space is:

[0027] S133: If σ A If it is less than σ, the estimated storage space requirement remains unchanged;

[0028] S134: If σ A is greater than σ, then Increase storage space;

[0029] S135: If σA is greater than σ, then This reduces storage space.

[0030] Preferably: in the step S132, in

[0031] Preferably: the size of the storage space increased in step S134 is (A i+1 -A i ) max , which represents the maximum value of the difference between two adjacent nodes.

[0032] Preferably: the size of the storage space reduced in step S135 is (A i+1 -A i ) min , which represents the minimum value of the difference between two adjacent nodes.

[0033] Preferably: in the step S134, the size of the storage space is increased to k*D, D is the current storage space, k is the step coefficient, k∈(0.05, 0.15).

[0034] Preferably: in the step S135, the size of the storage space is reduced to k*D, D is the current storage space, k is the step coefficient, k∈(0.05, 0.15).

[0035] Preferably: the step S12 includes the following steps:

[0036] S121: Create a partition table. Use the Hive command line interface (CLI) to connect to the Hive service. Use HiveSQL (HQL) to create a partition table and set the merchant ID as the partition column.

[0037] S122: Load data: Make sure that the data file is prepared and contains all necessary fields. The merchant ID in the data file should correspond to the partition column of the Hive partition table. Use Hive's LOAD DATA statement or INSERT INTO TABLE statement to load the data into the corresponding partition.

[0038] Preferably: in the step S22, the authentication factors of the multi-factor authentication include any one or more combinations of account password, mobile phone verification code, and fingerprint recognition.

[0039] The beneficial effects of the present invention are:

[0040] 1. The present invention estimates the storage space by analyzing the user's previous data operation behavior, and then specifically adjusts the size of the corresponding storage unit space according to the storage space, thereby ensuring reliable storage while preventing resource waste caused by space redundancy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of a big data management method based on the SaaS platform proposed by the present invention. DETAILED DESCRIPTION

[0042] The technical solution of the present invention is further described in detail below in conjunction with specific implementation methods.

[0043] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", and "set" should be understood in a broad sense, for example, they can be fixedly connected or set, or detachably connected or set, or integrally connected or set. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] Embodiment 1:

[0045] A big data management method based on a SaaS platform comprises the following steps:

[0046] S1: Data storage and isolation;

[0047] S11: Build a multi-tenant architecture on the SaaS platform and allocate independent data instances to each partner;

[0048] S12: Hive partition table technology is used to isolate data storage based on merchant IDs to ensure that the data of each merchant does not interfere with each other;

[0049] S2: Data security policy;

[0050] S21: Implement AES256-bit encryption and SSL / TLS transmission protocols to encrypt all transmitted and stored data;

[0051] S22: Enable multi-factor authentication mechanism to strengthen user access control;

[0052] S3: data backup and recovery;

[0053] S31: Use the automatic backup function provided by the SaaS platform to set up daily backup;

[0054] S32: And configure long-term storage strategies and develop detailed disaster recovery plans to ensure that data can be quickly recovered in the event of system failure or catastrophic events.

[0055] The step S11 includes the following steps:

[0056] S11: dividing the entire storage architecture into a plurality of different storage units;

[0057] S12: Assign each merchant to a storage unit, assign the merchant an identity, and use the identity as the name of the storage unit;

[0058] S13: Estimate the merchant's storage space requirements based on changes in the merchant's storage data;

[0059] S14: Based on the estimated storage space requirements of the merchant, the storage space size of the storage unit is increased or decreased accordingly.

[0060] In step S13, the method for estimating storage space requirements includes the following steps:

[0061] S131: Taking the current node as the starting point, obtain the data occupied size of n-1 nodes, which are A1, A2, ... A n ;

[0062] S132: Set the discreteness threshold σ and calculate the discreteness σ of the data A .

[0063] In step S14, the logic of changing the storage space is:

[0064] S133: If σ A If it is less than σ, the estimated storage space requirement remains unchanged;

[0065] S134: If σ A is greater than σ, then Increase storage space;

[0066] S135: If σ A is greater than σ, then This reduces storage space.

[0067] In the step S132, in

[0068] The size of the storage space increased in step S134 is (A i+1 -A i ) max , which represents the maximum value of the difference between two adjacent nodes.

[0069] The size of the storage space reduced in step S135 is (A i+1 -A i ) min, which represents the minimum value of the difference between two adjacent nodes.

[0070] Embodiment 2:

[0071] A big data management method based on a SaaS platform comprises the following steps:

[0072] S1: Data storage and isolation;

[0073] S11: Build a multi-tenant architecture on the SaaS platform and allocate independent data instances to each partner;

[0074] S12: Hive partition table technology is used to isolate data storage based on merchant IDs to ensure that the data of each merchant does not interfere with each other;

[0075] S2: Data security policy;

[0076] S21: Implement AES256-bit encryption and SSL / TLS transmission protocols to encrypt all transmitted and stored data;

[0077] S22: Enable multi-factor authentication mechanism to strengthen user access control;

[0078] S3: data backup and recovery;

[0079] S31: Use the automatic backup function provided by the SaaS platform to set up daily backup;

[0080] S32: And configure long-term storage strategies and develop detailed disaster recovery plans to ensure that data can be quickly recovered in the event of system failure or catastrophic events.

[0081] The step S11 includes the following steps:

[0082] S11: dividing the entire storage architecture into a plurality of different storage units;

[0083] S12: Assign each merchant to a storage unit, assign the merchant an identity, and use the identity as the name of the storage unit;

[0084] S13: Estimate the merchant's storage space requirements based on changes in the merchant's storage data;

[0085] S14: Based on the estimated storage space requirements of the merchant, the storage space size of the storage unit is increased or decreased accordingly.

[0086] In step S13, the method for estimating storage space requirements includes the following steps:

[0087] S131: Taking the current node as the starting point, obtain the data occupied size of n-1 nodes, which are A1, A2, ... A n ;

[0088] S132: Set the discreteness threshold σ and calculate the discreteness σ of the data A .

[0089] In step S14, the logic of changing the storage space is:

[0090] S133: If σ A If it is less than σ, the estimated storage space requirement remains unchanged;

[0091] S134: If σ A is greater than σ, then Increase storage space;

[0092] S135: If σ A is greater than σ, then This reduces storage space.

[0093] In the step S132, in

[0094] In the step S134, the size of the storage space is increased to k*D, where D is the current storage space, k is the step coefficient, and k=0.05.

[0095] In the step S135, the size of the storage space is reduced to k*D, where D is the current storage space, k is the step coefficient, and k=0.05.

[0096] Embodiment 3:

[0097] A big data management method based on a SaaS platform comprises the following steps:

[0098] S1: Data storage and isolation;

[0099] S11: Build a multi-tenant architecture on the SaaS platform and allocate independent data instances to each partner;

[0100] S12: Hive partition table technology is used to isolate data storage based on merchant IDs to ensure that the data of each merchant does not interfere with each other;

[0101] S2: Data security policy;

[0102] S21: Implement AES256-bit encryption and SSL / TLS transmission protocols to encrypt all transmitted and stored data;

[0103] S22: Enable multi-factor authentication mechanism to strengthen user access control;

[0104] S3: data backup and recovery;

[0105] S31: Use the automatic backup function provided by the SaaS platform to set up daily backup;

[0106] S32: And configure long-term storage strategies and develop detailed disaster recovery plans to ensure that data can be quickly recovered in the event of system failure or catastrophic events.

[0107] The step S11 includes the following steps:

[0108] S11: dividing the entire storage architecture into a plurality of different storage units;

[0109] S12: Assign each merchant to a storage unit, assign the merchant an identity, and use the identity as the name of the storage unit;

[0110] S13: Estimate the merchant's storage space requirements based on changes in the merchant's storage data;

[0111] S14: Based on the estimated storage space requirements of the merchant, the storage space size of the storage unit is increased or decreased accordingly.

[0112] In step S13, the method for estimating storage space requirements includes the following steps:

[0113] S131: Taking the current node as the starting point, obtain the data occupied size of n-1 nodes, which are A1, A2, ... A n ;

[0114] S132: Set the discreteness threshold σ and calculate the discreteness σ of the data A .

[0115] In step S14, the logic of changing the storage space is:

[0116] S133: If σ A If it is less than σ, the estimated storage space requirement remains unchanged;

[0117] S134: If σ A is greater than σ, then Increase storage space;

[0118] S135: If σ A is greater than σ, then This reduces storage space.

[0119] In the step S132, in

[0120] In the step S134, the size of the storage space is increased to k*D, where D is the current storage space, k is the step coefficient, and k=0.1.

[0121] In the step S135, the size of the storage space is reduced to k*D, where D is the current storage space, k is the step coefficient, and k=0.1.

[0122] Embodiment 4:

[0123] A big data management method based on a SaaS platform comprises the following steps:

[0124] S1: Data storage and isolation;

[0125] S11: Build a multi-tenant architecture on the SaaS platform and allocate independent data instances to each partner;

[0126] S12: Hive partition table technology is used to isolate data storage based on merchant IDs to ensure that the data of each merchant does not interfere with each other;

[0127] S2: Data security policy;

[0128] S21: Implement AES256-bit encryption and SSL / TLS transmission protocols to encrypt all transmitted and stored data;

[0129] S22: Enable multi-factor authentication mechanism to strengthen user access control;

[0130] S3: data backup and recovery;

[0131] S31: Use the automatic backup function provided by the SaaS platform to set up daily backup;

[0132] S32: And configure long-term storage strategies and develop detailed disaster recovery plans to ensure that data can be quickly recovered in the event of system failure or catastrophic events.

[0133] The step S11 includes the following steps:

[0134] S11: dividing the entire storage architecture into a plurality of different storage units;

[0135] S12: Assign each merchant to a storage unit, assign the merchant an identity, and use the identity as the name of the storage unit;

[0136] S13: Estimate the merchant's storage space requirements based on changes in the merchant's storage data;

[0137] S14: Based on the estimated storage space requirements of the merchant, the storage space size of the storage unit is increased or decreased accordingly.

[0138] In step S13, the method for estimating storage space requirements includes the following steps:

[0139] S131: Taking the current node as the starting point, obtain the data occupied size of n-1 nodes, which are A1, A2, ... A n ;

[0140] S132: Set the discreteness threshold σ and calculate the discreteness σ of the data A .

[0141] In step S14, the logic of changing the storage space is:

[0142] S133: If σ A If it is less than σ, the estimated storage space requirement remains unchanged;

[0143] S134: If σ A is greater than σ, then Increase storage space;

[0144] S135: If σ A is greater than σ, then This reduces storage space.

[0145] In the step S132, in

[0146] In the step S134, the size of the storage space is increased to k*D, where D is the current storage space, k is the step coefficient, and k=0.15.

[0147] In the step S135, the size of the storage space is reduced to k*D, where D is the current storage space, k is the step coefficient, and k=0.15.

[0148] Embodiment 5:

[0149] A big data management method based on a SaaS platform comprises the following steps:

[0150] S1: Data storage and isolation;

[0151] S11: Build a multi-tenant architecture on the SaaS platform and allocate independent data instances to each partner;

[0152] S12: Hive partition table technology is used to isolate data storage based on merchant IDs to ensure that the data of each merchant does not interfere with each other;

[0153] S2: Data security policy;

[0154] S21: Implement AES256-bit encryption and SSL / TLS transmission protocols to encrypt all transmitted and stored data;

[0155] S22: Enable multi-factor authentication mechanism to strengthen user access control;

[0156] S3: data backup and recovery;

[0157] S31: Use the automatic backup function provided by the SaaS platform to set up daily backup;

[0158] S32: And configure long-term storage strategies and develop detailed disaster recovery plans to ensure that data can be quickly recovered in the event of system failure or catastrophic events.

[0159] The step S11 includes the following steps:

[0160] S11: dividing the entire storage architecture into a plurality of different storage units;

[0161] S12: Assign each merchant to a storage unit, assign the merchant an identity, and use the identity as the name of the storage unit;

[0162] S13: Estimate the merchant's storage space requirements based on changes in the merchant's storage data;

[0163] S14: Based on the estimated storage space requirements of the merchant, the storage space size of the storage unit is increased or decreased accordingly.

[0164] In step S13, the method for estimating storage space requirements includes the following steps:

[0165] S131: Taking the current node as the starting point, obtain the data occupied size of n-1 nodes, which are A1, A2, ... A n ;

[0166] S132: Set the discreteness threshold σ and calculate the discreteness σ of the data A .

[0167] In step S14, the logic of changing the storage space is:

[0168] S133: If σ A If it is less than σ, the estimated storage space requirement remains unchanged;

[0169] S134: If σ A is greater than σ, then Increase storage space;

[0170] S135: If σA is greater than σ, then This reduces storage space.

[0171] In the step S132, in

[0172] The step S12 includes the following steps:

[0173] S121: Create a partition table. Use the Hive command line interface (CLI) to connect to the Hive service. Use HiveSQL (HQL) to create a partition table and set the merchant ID as the partition column.

[0174] S122: Load data: Make sure that the data file is prepared and contains all necessary fields. The merchant ID in the data file should correspond to the partition column of the Hive partition table. Use Hive's LOAD DATA statement or INSERT INTO TABLE statement to load the data into the corresponding partition.

[0175] Embodiment 6:

[0176] A big data management method based on a SaaS platform comprises the following steps:

[0177] S1: Data storage and isolation;

[0178] S11: Build a multi-tenant architecture on the SaaS platform and allocate independent data instances to each partner;

[0179] S12: Hive partition table technology is used to isolate data storage based on merchant IDs to ensure that the data of each merchant does not interfere with each other;

[0180] S2: Data security policy;

[0181] S21: Implement AES256-bit encryption and SSL / TLS transmission protocols to encrypt all transmitted and stored data;

[0182] S22: Enable multi-factor authentication mechanism to strengthen user access control;

[0183] S3: data backup and recovery;

[0184] S31: Use the automatic backup function provided by the SaaS platform to set up daily backup;

[0185] S32: And configure long-term storage strategies and develop detailed disaster recovery plans to ensure that data can be quickly recovered in the event of system failure or catastrophic events.

[0186] The step S11 includes the following steps:

[0187] S11: dividing the entire storage architecture into a plurality of different storage units;

[0188] S12: Assign each merchant to a storage unit, assign the merchant an identity, and use the identity as the name of the storage unit;

[0189] S13: Estimate the merchant's storage space requirements based on changes in the merchant's storage data;

[0190] S14: Based on the estimated storage space requirements of the merchant, the storage space size of the storage unit is increased or decreased accordingly.

[0191] In step S13, the method for estimating storage space requirements includes the following steps:

[0192] S131: Taking the current node as the starting point, obtain the data occupied size of n-1 nodes, which are A1, A2, ... A n ;

[0193] S132: Set the discreteness threshold σ and calculate the discreteness σ of the data A .

[0194] In step S14, the logic of changing the storage space is:

[0195] S133: If σ A If it is less than σ, the estimated storage space requirement remains unchanged;

[0196] S134: If σ A is greater than σ, then Increase storage space;

[0197] S135: If σ A is greater than σ, then This reduces storage space.

[0198] In the step S132, in

[0199] The step S12 includes the following steps:

[0200] S121: Create a partition table. Use the Hive command line interface (CLI) to connect to the Hive service. Use HiveSQL (HQL) to create a partition table and set the merchant ID as the partition column.

[0201] S122: Load data: Make sure that the data file is prepared and contains all necessary fields. The merchant ID in the data file should correspond to the partition column of the Hive partition table. Use Hive's LOAD DATA statement or INSERT INTO TABLE statement to load the data into the corresponding partition.

[0202] In the step S22, the authentication factors of the multi-factor authentication include the account number and password.

[0203] Embodiment 7:

[0204] A big data management method based on a SaaS platform comprises the following steps:

[0205] S1: Data storage and isolation;

[0206] S11: Build a multi-tenant architecture on the SaaS platform and allocate independent data instances to each partner;

[0207] S12: Hive partition table technology is used to isolate data storage based on merchant IDs to ensure that the data of each merchant does not interfere with each other;

[0208] S2: Data security policy;

[0209] S21: Implement AES256-bit encryption and SSL / TLS transmission protocols to encrypt all transmitted and stored data;

[0210] S22: Enable multi-factor authentication mechanism to strengthen user access control;

[0211] S3: data backup and recovery;

[0212] S31: Use the automatic backup function provided by the SaaS platform to set up daily backup;

[0213] S32: And configure long-term storage strategies and develop detailed disaster recovery plans to ensure that data can be quickly recovered in the event of system failure or catastrophic events.

[0214] The step S11 includes the following steps:

[0215] S11: dividing the entire storage architecture into a plurality of different storage units;

[0216] S12: Assign each merchant to a storage unit, assign the merchant an identity, and use the identity as the name of the storage unit;

[0217] S13: Estimate the merchant's storage space requirements based on changes in the merchant's storage data;

[0218] S14: Based on the estimated storage space requirements of the merchant, the storage space size of the storage unit is increased or decreased accordingly.

[0219] In step S13, the method for estimating storage space requirements includes the following steps:

[0220] S131: Taking the current node as the starting point, obtain the data occupied size of n-1 nodes, which are A1, A2, ... A n ;

[0221] S132: Set the discreteness threshold σ and calculate the discreteness σ of the data A .

[0222] In step S14, the logic of changing the storage space is:

[0223] S133: If σ A If it is less than σ, the estimated storage space requirement remains unchanged;

[0224] S134: If σ A is greater than σ, then Increase storage space;

[0225] S135: If σ A is greater than σ, then This reduces storage space.

[0226] In the step S132, in

[0227] The step S12 includes the following steps:

[0228] S121: Create a partition table. Use the Hive command line interface (CLI) to connect to the Hive service. Use HiveSQL (HQL) to create a partition table and set the merchant ID as the partition column.

[0229] S122: Load data: Make sure that the data file is prepared and contains all necessary fields. The merchant ID in the data file should correspond to the partition column of the Hive partition table. Use Hive's LOAD DATA statement or INSERT INTO TABLE statement to load the data into the corresponding partition.

[0230] In the step S22, the authentication factors of the multi-factor authentication include:

[0231] Embodiment 8:

[0232] A big data management method based on a SaaS platform comprises the following steps:

[0233] S1: Data storage and isolation;

[0234] S11: Build a multi-tenant architecture on the SaaS platform and allocate independent data instances to each partner;

[0235] S12: Hive partition table technology is used to isolate data storage based on merchant IDs to ensure that the data of each merchant does not interfere with each other;

[0236] S2: Data security policy;

[0237] S21: Implement AES256-bit encryption and SSL / TLS transmission protocols to encrypt all transmitted and stored data;

[0238] S22: Enable multi-factor authentication mechanism to strengthen user access control;

[0239] S3: data backup and recovery;

[0240] S31: Use the automatic backup function provided by the SaaS platform to set up daily backup;

[0241] S32: And configure long-term storage strategies and develop detailed disaster recovery plans to ensure that data can be quickly recovered in the event of system failure or catastrophic events.

[0242] The step S11 includes the following steps:

[0243] S11: dividing the entire storage architecture into a plurality of different storage units;

[0244] S12: Assign each merchant to a storage unit, assign the merchant an identity, and use the identity as the name of the storage unit;

[0245] S13: Estimate the merchant's storage space requirements based on changes in the merchant's storage data;

[0246] S14: Based on the estimated storage space requirements of the merchant, the storage space size of the storage unit is increased or decreased accordingly.

[0247] In step S13, the method for estimating storage space requirements includes the following steps:

[0248] S131: Taking the current node as the starting point, obtain the data occupied size of n-1 nodes, which are A1, A2, ... A n ;

[0249] S132: Set the discreteness threshold σ and calculate the discreteness σ of the data A .

[0250] In step S14, the logic of changing the storage space is:

[0251] S133: If σ A If it is less than σ, the estimated storage space requirement remains unchanged;

[0252] S134: If σ A is greater than σ, then Increase storage space;

[0253] S135: If σ A is greater than σ, then This reduces storage space.

[0254] In the step S132, in

[0255] The step S12 includes the following steps:

[0256] S121: Create a partition table. Use the Hive command line interface (CLI) to connect to the Hive service. Use HiveSQL (HQL) to create a partition table and set the merchant ID as the partition column.

[0257] S122: Load data: Make sure that the data file is prepared and contains all necessary fields. The merchant ID in the data file should correspond to the partition column of the Hive partition table. Use Hive's LOAD DATA statement or INSERT INTO TABLE statement to load the data into the corresponding partition.

[0258] In the step S22, the authentication factor of the multi-factor authentication includes fingerprint recognition.

[0259] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A big data management method based on a SaaS platform, characterized in that: The following steps are involved: S1: Data storage and isolation; S11: Build a multi-tenant architecture on the SaaS platform and allocate independent data instances to each partner; S12: Using Hive partition table technology, data storage is isolated based on merchant ID to ensure that each merchant's data does not interfere with each other; S2: Data security policy; S21: Implement AES256-bit encryption and SSL / TLS transmission protocols to encrypt all transmitted and stored data; S22: Enable multi-factor authentication mechanism to strengthen user access control; S3: data backup and recovery; S31: Use the automatic backup function provided by the SaaS platform to set up daily backups; S32: And configure long-term storage strategies and develop detailed disaster recovery plans to ensure that data can be quickly restored in the event of system failure or catastrophic events.

2. A big data management method based on a SaaS platform according to claim 1, characterized in that: The step S11 includes the following steps: S11: Divide the entire storage architecture into multiple different storage units; S12: Assign each merchant to a storage unit, assign the merchant an identity, and use the identity as the name of the storage unit; S13: Estimate the merchant's storage space requirements based on changes in the merchant's storage data; S14: Based on the estimated storage space requirements of the merchant, the storage space size of the storage unit is increased or decreased accordingly.

3. The big data management method based on the SaaS platform according to claim 1, characterized in that: In step S13, the method for estimating storage space requirements includes the following steps: S131: Starting from the current node, obtain the data occupied by n-1 nodes, which are A1, A2, ..., A n ; S132: Set the discreteness threshold σ and calculate the discreteness σ of the data A . In step S14, the logic of changing the storage space is: S133: If σ A If it is less than σ, the estimated storage space requirement remains unchanged; S134: If σ A is greater than σ, then Increase storage space; S135: If σ A is greater than σ, then This reduces storage space.

4. A big data management method based on a SaaS platform according to claim 3, characterized in that: In the step S132, in 5. The big data management method based on the SaaS platform according to claim 3, characterized in that: The size of the storage space increased in step S134 is (A i+1 -A i ) max , which represents the maximum value of the difference between two adjacent nodes.

6. A big data management method based on a SaaS platform according to claim 3, characterized in that: The size of the storage space reduced in step S135 is (A i+1 -A i ) min , which represents the minimum value of the difference between two adjacent nodes.

7. The big data management method based on the SaaS platform according to claim 3, characterized in that: In the step S134, the size of the storage space is increased to k*D, where D is the current storage space, k is the step coefficient, and k∈(0.05, 0.15).

8. The big data management method based on the SaaS platform according to claim 3, characterized in that: In the step S135, the size of the storage space is reduced to k*D, where D is the current storage space, k is the step coefficient, and k∈(0.05, 0.15).

9. The big data management method based on the SaaS platform according to claim 1, characterized in that: In the step S12, it includes the following steps: S121: Create a partition table. Use the Hive command-line interface (CLI) to connect to the Hive service. Use HiveSQL (HQL) to create a partition table and set the merchant ID as the partition column. S122: Loading data: Ensure that the data file is prepared and contains all necessary fields. The merchant ID in the data file should correspond to the partition column of the Hive partition table. Use Hive's LOAD DATA statement or INSERT INTO TABLE statement to load the data into the corresponding partition.

10. The big data management method based on SaaS platform according to claim 1, characterized in that: In step S22, the authentication factors of the multi-factor authentication include any one or more combinations of account password, mobile phone verification code, and fingerprint recognition.

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