Service optimization system and method for service provider

Abstract

The invention relates to the field of service optimization, and provides a service optimization system and method for a service provider, and the method comprises the steps: obtaining data storage node information, and building a virtual storage model according to the data storage node information; acquiring source data, transmitting the source data to a server to obtain target storage data, dividing the target storage data to obtain block data, and setting a database; disorderly arranging the target storage data to obtain disordered storage data, and storing the disordered storage data into a data storage node; calculating data chaos indexes of the data storage nodes, and taking the data storage nodes of which the data chaos indexes do not meet a preset index as unsafe nodes; and enhancing chaos arrangement of the block data of the unsafe nodes to obtain enhanced chaos storage data, and taking the unsafe nodes corresponding to the enhanced chaos storage data as safe nodes. When the method is used, the cloud storage service of a cloud computing service provider can be optimized, and the risk of data leakage is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the field of service optimization, and more specifically, to a service optimization system and method for a service provider. Background Art

[0002] Service providers refer to companies or individuals that provide some form of service to meet customer needs and expectations. In the modern economic system, service providers play a vital role. They support the operation and development of individuals, companies, institutions or the entire society by providing various types of services. At present, user data of web applications is sensitive to a certain extent, such as personal information, login credentials, user hobbies and other data. It is difficult to ensure the privacy and security of users when user data is stored locally, so user data needs to be stored on cloud servers. Cloud computing service providers usually use distributed storage in cloud storage services to ensure high availability and scalability of user data. However, due to the large number and dispersion of distributed storage nodes, data in storage nodes is prone to data leakage. These risks are generally caused by too many storage nodes, making it easy for attackers to hit a storage node, and the data of each storage node is generally stored in sequential encryption. Once the sequentially encrypted data is cracked, some sensitive data can be easily identified, which poses a great threat to data storage security. Summary of the invention

[0003] The purpose of the present invention is to provide a service optimization system and method for a service provider to solve the deficiencies in the background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a service optimization method for a service provider, comprising the following steps: Acquire data storage node information of the server, wherein the data storage node information includes a node network address and a node storage capacity, and establish a virtual storage model according to the data storage node information; Acquire source data from the user end, transfer the source data to the server end according to the virtual storage model to obtain target storage data, divide the target storage data into block data and set up a database on the server end; Based on the virtual storage model and the database, the target storage data is randomly arranged to obtain chaotic storage data, and the chaotic storage data is divided and stored in corresponding data storage nodes; Calculate the data chaos index of the data in the data storage node, and regard the data storage node whose data chaos index does not meet the preset index as an unsafe node; The block data in the unsafe node is arranged in a reinforced disorder to obtain the reinforced disorder storage data, and the unsafe node corresponding to the reinforced disorder storage data is used as a safe node.

[0005] In a preferred embodiment, the step of establishing a virtual storage model according to the data storage node information includes: Acquire multiple storage nodes corresponding to the server as data storage nodes, acquire the network address of the data storage node as the node network address, acquire the storage capacity of the data storage node as the node storage capacity, and use the node network address and the node storage capacity as the data storage node information; The network address of the user end is obtained as the user network address, multiple data storage nodes corresponding to the server end are connected to the server end, the user end is connected to the server end, the data storage node information is marked on the corresponding data storage node, and the user network address is marked on the corresponding user end to obtain a virtual storage model.

[0006] In a preferred embodiment, the step of dividing the target storage data into block data and setting a database on the server side includes: Obtain source data from the user end, and transfer the source data to the server end according to the virtual storage model to obtain target storage data; The server divides the target storage data according to a preset value to obtain a plurality of block data of equal data size, numbers the block data with different data contents according to the data contents of the block data to obtain data numbers of the block data, and numbers the block data according to the data sequence of the block data in the target storage data to obtain sequential numbers; Based on the block data, the sequence number is bound to the corresponding data number and stored in the database, and the data number is bound to the corresponding block data and stored in the database.

[0007] In a preferred embodiment, the steps of arranging the target storage data in a disordered manner based on the virtual storage model and the database to obtain disordered storage data, and dividing the disordered storage data and storing them in corresponding data storage nodes include: Arranging the block data of the target storage data in a disorderly manner to obtain disordered storage data; According to the number of data storage nodes corresponding to the virtual storage model, the chaotic storage data is divided into a plurality of storage blocks according to the storage capacity of the data storage nodes, wherein the storage blocks contain a plurality of block data; The multiple storage blocks are respectively transmitted to the corresponding data storage nodes and stored.

[0008] In a preferred embodiment, the step of arranging the block data of the target storage data in a disordered manner to obtain the disordered storage data comprises: Traverse multiple blocks of target storage data, and use the currently traversed block as operation data; A sequential number of a block data other than the operation data is randomly selected as the selection number, the block data corresponding to the selection number is used as the exchange data, and the storage positions of the operation data and the exchange data are exchanged until the block data traversal of the target storage data is completed to obtain the chaotic storage data.

[0009] In a preferred embodiment, the step of calculating the data chaos index of the data in the data storage node and treating the data storage node corresponding to the data chaos index that does not meet the preset index as an unsafe node includes: Set the data chaos index, initialize the data chaos index to zero, traverse the block data in the data storage node, and use the currently traversed block data as the target data; Check whether the sequence number of the next block data of the target data is connected to the sequence number of the target data. If so, no operation is performed. If not, the data chaos index is increased by one until the block data in the data storage node is traversed and the data chaos index of the data storage node is obtained. The connected block data is marked as unsafe data. The corresponding data storage nodes whose data chaos index does not meet the preset index are regarded as unsafe nodes.

[0010] In a preferred embodiment, the step of performing enhanced chaotic arrangement on the block data in the unsafe node to obtain enhanced chaotic storage data, and using the unsafe node corresponding to the enhanced chaotic storage data as a safe node comprises: Evenly divide multiple unsafe data in the unsafe node to obtain multiple sub-block data of equal data size; Traverse the sub-block data and use the currently traversed sub-block data as the sub-operation data; Randomly select a sub-block data of unsafe data other than the sub-operation data as the sub-replacement data, and exchange the data position of the sub-replacement data with the sub-block data corresponding to the unsafe data, until the sub-block data of the unsafe data is traversed and the enhanced chaotic storage data is obtained; The unsafe nodes corresponding to the chaotically stored data will be strengthened as safe nodes.

[0011] The present invention also provides a service optimization system for a service provider, comprising: Establishing a module, used to obtain data storage node information of the server, wherein the data storage node information includes a node network address and a node storage capacity, and establishing a virtual storage model according to the data storage node information; The setting module is connected to the establishment module and is used to obtain source data from the user end, transmit the source data to the server end according to the virtual storage model to obtain target storage data, divide the target storage data into block data and set the database on the server end; A partitioning module, connected to the setting module, is used to randomly arrange the target storage data based on the virtual storage model and the database to obtain chaotic storage data, and to partition the chaotic storage data and store them into corresponding data storage nodes; A judgment module, connected to the division module, is used to calculate the data chaos index of the data in the data storage node, and regard the data storage node whose data chaos index does not meet the preset index as an unsafe node; The strengthening module is connected to the judging module and is used for performing enhanced chaotic arrangement on the block data in the unsafe node to obtain enhanced chaotic storage data, and taking the unsafe node corresponding to the enhanced chaotic storage data as a safe node.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention can store user data in a chaotic arrangement in a cloud server, thereby better protecting the user's privacy data, reducing the risk of user data leakage, better ensuring the security and reliability of user data cloud storage, and improving the user's data storage experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0014] Figure 1 The present invention is a flow chart of the method.

[0015] Figure 2 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0017] Example 1, please refer to Figure 1 As shown, the service optimization method for a service provider described in this embodiment includes the following steps: S1. Acquire data storage node information of the server, wherein the data storage node information includes a node network address and a node storage capacity, and establish a virtual storage model according to the data storage node information; S2, obtaining source data from the user end, transmitting the source data to the server end according to the virtual storage model to obtain target storage data, dividing the target storage data into block data and setting a database on the server end; S3, based on the virtual storage model and the database, the target storage data is randomly arranged to obtain chaotic storage data, and the chaotic storage data is divided and stored in corresponding data storage nodes; S4, calculating the data chaos index of the data in the data storage node, and treating the data storage node whose data chaos index does not meet the preset index as an unsafe node; S5, performing enhanced chaotic arrangement on the block data in the unsafe node to obtain enhanced chaotic storage data, and using the unsafe node corresponding to the enhanced chaotic storage data as a safe node; As described in the above steps S1-S5, the user data of the current web application is sensitive to a certain extent, such as personal information, login credentials, user hobbies and other data. It is difficult to ensure the privacy and security of the user by storing the user data locally, so the user data needs to be stored on the cloud server, and cloud computing service providers usually use distributed storage in cloud storage services to ensure the high availability and scalability of user data. However, due to the characteristics of the large number and dispersion of distributed storage nodes, the data of the storage nodes are prone to data leakage. These risks are generally caused by too many storage nodes, which makes it easy for attackers to hit a certain storage node, and the data of each storage node is generally stored in sequential encryption. Once the sequentially encrypted data is cracked, some sensitive data can be easily identified, which poses a great threat to the security of data storage. The present application can store the user data in a chaotic arrangement in the cloud server, which better protects the user's privacy data, reduces the risk of user data leakage, and can better ensure the security and reliability of the user data cloud storage, thereby improving the user's data storage experience. In one embodiment, the step S1 of establishing a virtual storage model according to the data storage node information includes: S11, obtaining multiple storage nodes corresponding to the server as data storage nodes, obtaining the network address of the data storage node as the node network address, obtaining the storage capacity of the data storage node as the node storage capacity, and using the node network address and the node storage capacity as the data storage node information; S12, obtaining the network address of the user end as the user network address, connecting multiple data storage nodes corresponding to the server end to the server end, connecting the user end to the server end, marking the data storage node information on the corresponding data storage node, marking the user network address on the corresponding user end to obtain a virtual storage model; As described in the above steps S11-S12, firstly, multiple storage nodes corresponding to the server are obtained as data storage nodes. The server here can be a cloud computer that can be connected to the Internet, such as a cloud server. The storage node here can be a cloud computer that can store data, such as a cloud server and a cloud disk. Then, the network address of the data storage node is obtained as the node network address, the storage capacity of the data storage node is obtained as the node storage capacity, and the node network address and the node storage capacity are used as the data storage node information. Then, the network address of the user end is obtained as the user network address. The network address here refers to the network address that can be interconnected between the server, the user end and the data storage node. The multiple data storage nodes corresponding to the server are connected to the server. Here, before connecting, the ping command is executed on the data storage node to ensure that all data storage nodes and the server can transfer data to each other. The data storage nodes that cannot be pinged are not connected to the server, and the user end is connected to the server. Here, the ping command must also be executed on the user end before connecting to ensure that the user data can be transmitted to the server. Finally, the data storage node information is marked on the corresponding data storage node, and the user network address is marked on the corresponding user end to obtain a virtual storage model, which is used to provide connection relationship and data storage node information for subsequent setting of database and partitioning source data, which can better display the topological connection relationship between the user end, server end, and data storage node, and has a good auxiliary effect on subsequent operations; In one embodiment, the step S2 of dividing the target storage data into block data and setting a database on the server side includes: S21, obtaining source data from the user end, and transmitting the source data to the server end according to the virtual storage model to obtain target storage data; S22, the server divides the target storage data according to a preset value to obtain a plurality of block data of equal data size, numbers the block data with different data contents according to the data contents of the block data to obtain data numbers of the block data, and numbers the block data according to the data sequence of the block data in the target storage data to obtain sequence numbers; S23, based on the block data, the sequence number is bound to the corresponding data number and stored in the database, and the data number is bound to the corresponding block data and stored in the database; As described in the above steps S21-S23, firstly, the source data of the user side is obtained, where the source data refers to the user data used for the web application, such as user information, login credentials, user hobbies and other privacy-sensitive information, and the source data is transmitted to the server side according to the virtual storage model to obtain the target storage data, and the server side divides the target storage data according to the preset value to obtain a plurality of block data of equal data size. For example, if the target storage data is 100MB, the server side divides the target storage data into 1M 100B block data, and then numbers the block data with different data contents according to the data content of the block data to obtain the data number of the block data. For example, if the data content of 20 block data is the same, the numbers of these 20 block data are all set to B. 0001, if there are two blocks of data with different contents, the two blocks of data are numbered B0002 and B0003 respectively, and all the blocks of data are numbered in this way to get the data number, and then the block data is numbered according to the data sequence of the block data in the target storage data to get the sequence number. For example, a target storage data is divided into three blocks of data, and they are numbered S0001, S0002, and S0003 according to their arrangement sequence to get the sequence number of the block data. Finally, the sequence number is bound to the corresponding data number based on the block data and stored in the database, and the data number is bound to the corresponding block data and stored in the database to store the sequence relationship between the block data and provide sequence comparison for the subsequent chaotic arrangement data; In one embodiment, the step S3 of arranging the target storage data in a disordered manner based on the virtual storage model and the database to obtain disordered storage data, and dividing the disordered storage data and storing them in corresponding data storage nodes includes: S31, arranging the block data of the target storage data in a disordered manner to obtain disordered storage data; S32, dividing the chaotic storage data into a plurality of storage blocks according to the storage capacity of the data storage nodes according to the number of data storage nodes corresponding to the virtual storage model, wherein the storage blocks contain a plurality of block data; S33, transmitting the multiple storage blocks to corresponding data storage nodes respectively and storing them; In one embodiment, the step S31 of arranging the block data of the target storage data in a disordered manner to obtain the disordered storage data includes: S311, traversing multiple blocks of target storage data, and using the currently traversed block as operation data; S312, randomly selecting a sequence number of a block data other than the operation data as the selection number, using the block data corresponding to the selection number as the exchange data, and exchanging the storage positions of the operation data and the exchange data until the block data traversal of the target storage data is completed to obtain the chaotic storage data; As described in the above steps S31-S33, the block data of the target storage data are arranged in a disordered manner. First, multiple block data of the target storage data need to be traversed, and the currently traversed block data is used as the operation data. Then, a sequence number of a block data other than the operation data is randomly selected as the selection number, and the block data corresponding to the selection number is used as the exchange data. The storage positions of the operation data and the exchange data are exchanged until the block data traversal of the target storage data is completed to obtain the disordered storage data. For example, the target storage data has 1M block data. First, the first block data is selected as the operation data. The sequence number of the operation data is S0001. Then, a sequence number excluding the sequence number S0001 is randomly extracted from the sequence numbers of the 1M block data. Assuming that the sequence number S1902 is extracted, the block data corresponding to S1902 and S0001 are found from the database, and the block data corresponding to the S0001 number is swapped with the block data corresponding to S1902. Then, the next sequence number is traversed. The block data numbered S0002 is obtained until 1M block data are traversed to obtain the randomly arranged target storage data, and the randomly arranged target storage data is used as the chaotic storage data, and then the chaotic storage data is divided into multiple storage blocks according to the storage capacity of the data storage node according to the number of data storage nodes corresponding to the virtual storage model, and the storage block contains multiple block data. For example, the number of data storage nodes connected to the server in the virtual storage model is 200, and the storage capacity of each data storage node is not guaranteed to be the same, then 1M block data is divided according to the storage capacity of the data storage node until the 1M block data is divided to obtain several storage blocks, each storage block has a corresponding data storage node, and the storage block contains multiple block data, and finally the multiple storage blocks are respectively transmitted to the corresponding data storage nodes and stored, so as to disrupt and store the data order of the source data, better protect the user's privacy data, reduce the risk of user data leakage, and improve the security and reliability of the data storage service; In one embodiment, the step S4 of calculating the data chaos index of the data in the data storage node and treating the data storage node corresponding to the data chaos index not satisfying the preset index as an unsafe node includes: S41, setting a data chaos index, initializing the data chaos index to zero, traversing block data in the data storage node, and taking the currently traversed block data as target data; S42, check whether the sequence number of the next block data of the target data is connected to the sequence number of the target data, if so, do not perform any operation, if not, increase the data chaos index by one, until the block data in the data storage node is traversed and the data chaos index of the data storage node is obtained, and mark the connected block data as unsafe data; S43, treating the corresponding data storage nodes whose data chaos index does not meet the preset index as unsafe nodes; As described in the above steps S41-S43, a data chaos index is set and initialized to zero to measure the degree of data chaos. The larger the index, the more chaotic the data and the safer the storage service. Then, the block data in the data storage node is traversed, and the currently traversed block data is used as the target data. Check whether the sequence number of the next block data of the target data is connected to the sequence number of the target data. If they are connected, no operation is performed. If they are not connected, the data chaos index is increased by one until the block data traversal in the data storage node is completed to obtain the data chaos index of the data storage node. For example, if there are 50 block data in the data storage node, the first block data is first selected as the target data. The sequence number of the target data is S9283, and the sequence number of the block data connected to the target data is obtained. If the sequence number of the block data connected to the target data is S9284, the sequence number of the target data is obtained. If the data is S9284, then the data chaos index is increased by one. If the sequence number of the block data connected to the target data is any other sequence number, no processing is performed. Then the connected block data is marked as unsafe data. When all the block data in the data storage node are traversed, the data chaos index is obtained. The corresponding data storage node whose data chaos index does not meet the preset index is regarded as an unsafe node. For example, there are 50 block data in the data storage node. If the data chaos index exceeds 10% of the number of block data, that is, the data chaos index exceeds 5, the data storage node is regarded as an unsafe node to search for unsafe nodes in the storage service and unsafe data in the unsafe node. It can better find the security risks in the storage service and provide actionable data for subsequent strengthening of chaos arrangement. In one embodiment, the step S52 of performing enhanced chaotic arrangement on the block data in the unsafe node to obtain enhanced chaotic storage data, and using the unsafe node corresponding to the enhanced chaotic storage data as a safe node includes: S51, evenly dividing the multiple unsafe data in the unsafe node into multiple sub-block data of equal data size; S52, traversing the sub-block data, and using the sub-block data currently traversed as sub-operation data; S53, randomly selecting a sub-block data of unsafe data other than the sub-operation data as sub-replacement data, and exchanging the data positions of the sub-replacement data and the sub-block data corresponding to the unsafe data, until the sub-block data of the unsafe data are traversed and the enhanced chaotic storage data is obtained; S54, strengthening the unsafe node corresponding to the chaotic storage data as a safe node; As described in the above steps S51-S54, multiple unsafe data in the unsafe node are evenly divided to obtain multiple sub-block data of equal data size. For example, there are 4 unsafe data in the unsafe node, and the 4 unsafe data are evenly divided to obtain 8 sub-block data of equal data size. Then, the sub-block data are traversed, and the currently traversed sub-block data is used as the sub-operation data. Then, a sub-block data of unsafe data other than the sub-operation data is randomly selected as the sub-replacement data, and the data position of the sub-replacement data is exchanged with the sub-block data corresponding to the unsafe data. For example, the first sub-block data is used as the sub-operation data, and a sub-block data of the currently traversed sub-block data is randomly selected. In addition to the sub-block data of the sub-operation data, the randomly selected sub-block data is swapped with the sub-operation data until the sub-block data traversal is completed to obtain the enhanced chaotic storage data. Finally, the unsafe node corresponding to the enhanced chaotic storage data is used as a safe node to ensure that every data stored in the data storage node is safe. The chaotic arrangement operation of the data can be strengthened when the data is not chaotic enough, which better protects the user's privacy data and reduces the risk of data leakage. It can better ensure the security and reliability of the user's data cloud storage, improve the user's data storage experience, and optimize the cloud storage service of the cloud computing service provider; Example 2, please refer to Figure 2 As shown, the service optimization system for a service provider described in this embodiment includes: Establishing a module, used to obtain data storage node information of the server, wherein the data storage node information includes a node network address and a node storage capacity, and establishing a virtual storage model according to the data storage node information; The setting module is connected to the establishment module and is used to obtain source data from the user end, transmit the source data to the server end according to the virtual storage model to obtain target storage data, divide the target storage data into block data and set the database on the server end; A partitioning module, connected to the setting module, is used to randomly arrange the target storage data based on the virtual storage model and the database to obtain chaotic storage data, and to partition the chaotic storage data and store them into corresponding data storage nodes; A judgment module, connected to the division module, is used to calculate the data chaos index of the data in the data storage node, and regard the data storage node whose data chaos index does not meet the preset index as an unsafe node; The strengthening module is connected to the judging module and is used for performing enhanced chaotic arrangement on the block data in the unsafe node to obtain enhanced chaotic storage data, and taking the unsafe node corresponding to the enhanced chaotic storage data as a safe node.

[0018] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A service optimization system and method for a service provider, characterized in that: The following steps are involved: Acquire data storage node information of the server, wherein the data storage node information includes a node network address and a node storage capacity, and establish a virtual storage model according to the data storage node information; Acquire source data from the user end, transfer the source data to the server end according to the virtual storage model to obtain target storage data, divide the target storage data into block data and set up a database on the server end; Based on the virtual storage model and the database, the target storage data is randomly arranged to obtain chaotic storage data, and the chaotic storage data is divided and stored in corresponding data storage nodes; Calculate the data chaos index of the data in the data storage node, and regard the data storage node whose data chaos index does not meet the preset index as an unsafe node; The block data in the unsafe node is arranged in a reinforced disorder to obtain the reinforced disorder storage data, and the unsafe node corresponding to the reinforced disorder storage data is used as a safe node.

2. A service optimization method for a service provider as claimed in claim 1, characterized in that: The step of establishing a virtual storage model according to the data storage node information includes: Acquire multiple storage nodes corresponding to the server as data storage nodes, acquire the network address of the data storage node as the node network address, acquire the storage capacity of the data storage node as the node storage capacity, and use the node network address and the node storage capacity as the data storage node information; The network address of the user end is obtained as the user network address, multiple data storage nodes corresponding to the server end are connected to the server end, the user end is connected to the server end, the data storage node information is marked on the corresponding data storage node, and the user network address is marked on the corresponding user end to obtain a virtual storage model.

3. A service optimization method for a service provider as claimed in claim 1, characterized in that: The step of dividing the target storage data into block data and setting a database on the server side includes: Obtain source data from the user end, and transfer the source data to the server end according to the virtual storage model to obtain target storage data; The server divides the target storage data according to a preset value to obtain a plurality of block data of equal data size, numbers the block data with different data contents according to the data contents of the block data to obtain data numbers of the block data, and numbers the block data according to the data sequence of the block data in the target storage data to obtain sequential numbers; Based on the block data, the sequence number is bound to the corresponding data number and stored in the database, and the data number is bound to the corresponding block data and stored in the database.

4. A service optimization method for a service provider as claimed in claim 1, characterized in that: The steps of arranging the target storage data in a disordered manner based on the virtual storage model and the database to obtain disordered storage data, and dividing the disordered storage data and storing them in corresponding data storage nodes include: Arranging the block data of the target storage data in a disorderly manner to obtain disordered storage data; According to the number of data storage nodes corresponding to the virtual storage model, the chaotic storage data is divided into a plurality of storage blocks according to the storage capacity of the data storage nodes, wherein the storage blocks contain a plurality of block data; The multiple storage blocks are respectively transmitted to the corresponding data storage nodes and stored.

5. A service optimization method for a service provider as claimed in claim 4, characterized in that: The step of arranging the block data of the target storage data in a disordered manner to obtain the disordered storage data comprises: Traverse multiple blocks of target storage data, and use the currently traversed block as operation data; A sequential number of a block data other than the operation data is randomly selected as the selection number, the block data corresponding to the selection number is used as the exchange data, and the storage positions of the operation data and the exchange data are exchanged until the block data traversal of the target storage data is completed to obtain the chaotic storage data.

6. A service optimization method for a service provider as claimed in claim 1, characterized in that: The step of calculating the data chaos index of the data in the data storage node and treating the data storage node corresponding to the data chaos index not satisfying the preset index as an unsafe node comprises: Set the data chaos index, initialize the data chaos index to zero, traverse the block data in the data storage node, and use the currently traversed block data as the target data; Check whether the sequence number of the next block data of the target data is connected to the sequence number of the target data. If so, no operation is performed. If not, the data chaos index is increased by one until the block data in the data storage node is traversed and the data chaos index of the data storage node is obtained. The connected block data is marked as unsafe data. The corresponding data storage nodes whose data chaos index does not meet the preset index are regarded as unsafe nodes.

7. A service optimization method for a service provider as claimed in claim 1, characterized in that: The step of performing enhanced chaotic arrangement on the block data in the unsafe node to obtain enhanced chaotic storage data, and using the unsafe node corresponding to the enhanced chaotic storage data as a safe node comprises: Evenly divide multiple unsafe data in the unsafe node to obtain multiple sub-block data of equal data size; Traverse the sub-block data and use the currently traversed sub-block data as the sub-operation data; Randomly select a sub-block data of unsafe data other than the sub-operation data as the sub-replacement data, and exchange the data position of the sub-replacement data with the sub-block data corresponding to the unsafe data, until the sub-block data of the unsafe data is traversed and the enhanced chaotic storage data is obtained; The unsafe nodes corresponding to the chaotically stored data will be strengthened as safe nodes.

8. A service optimization system for a service provider, used to implement a service optimization method for a service provider as claimed in any one of claims 1 to 7, characterized in that: include: Establishing a module, used to obtain data storage node information of the server, wherein the data storage node information includes a node network address and a node storage capacity, and establishing a virtual storage model according to the data storage node information; The setting module is connected to the establishment module and is used to obtain source data from the user end, transmit the source data to the server end according to the virtual storage model to obtain target storage data, divide the target storage data into block data and set the database on the server end; A partitioning module, connected to the setting module, is used to randomly arrange the target storage data based on the virtual storage model and the database to obtain chaotic storage data, and to partition the chaotic storage data and store them into corresponding data storage nodes; A judgment module, connected to the division module, is used to calculate the data chaos index of the data in the data storage node, and regard the data storage node whose data chaos index does not meet the preset index as an unsafe node; The strengthening module is connected to the judging module and is used for performing enhanced chaotic arrangement on the block data in the unsafe node to obtain enhanced chaotic storage data, and taking the unsafe node corresponding to the enhanced chaotic storage data as a safe node.

Images