CN111698328A - Enterprise big data analysis and processing platform based on hybrid cloud - Google Patents
Enterprise big data analysis and processing platform based on hybrid cloud Download PDFInfo
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Abstract
The invention relates to an enterprise big data analysis and processing platform based on a hybrid cloud, which comprises: the system comprises an enterprise data receiving module, an enterprise data classifying module, a mixed cloud distribution module, a first data storage module, a second data storage module, a storage relationship establishing module, an identity verification module, a data calling instruction receiving module, a data calling module, a data output module, a data calling frequency acquiring module and a first storage migration module. And after the identity authentication is passed, enterprise data is called, and the enterprise data set of which the called times are greater than or equal to a preset high-time threshold value is migrated from the public cloud server to the private cloud server. The enterprise big data analysis and processing platform based on the hybrid cloud provided by the invention realizes the analysis and processing of enterprise data based on the hybrid cloud, and meets the intelligent requirements of the enterprise big data analysis and processing.
Description
Technical Field
The invention relates to an enterprise big data analysis and processing platform based on a hybrid cloud.
Background
Private clouds are built for individual use by one customer and thus provide the most effective control over data, security and quality of service. The private cloud can be deployed in a firewall of an enterprise data center, or can be deployed in a safe host hosting place, and the core attribute of the private cloud is a proprietary resource. The public cloud generally refers to a cloud which can be used and is provided by a third-party provider for a user, the public cloud can be generally used through the Internet and can be free or low in cost, and the core attribute of the public cloud is a shared resource service.
The hybrid cloud integrates public cloud and private cloud, and is a main mode and development direction of cloud computing in recent years. The private cloud is mainly oriented to enterprise users, for safety, enterprises prefer to store data in the private cloud, but meanwhile hope to obtain computing resources of the public cloud, in the situation, the hybrid cloud is adopted more and more, the public cloud and the private cloud are mixed and matched to obtain the best effect, and the personalized solution achieves the purposes of saving money and being safe.
The enterprise big data analysis and processing is crucial to the development of enterprises, and based on the gradual development of mixed cloud, an enterprise big data analysis and processing platform based on mixed cloud is urgently needed.
Disclosure of Invention
In order to solve the problems, the invention provides an enterprise big data analysis and processing platform based on a hybrid cloud.
The invention adopts the following technical scheme:
a hybrid cloud-based enterprise big data analysis processing platform comprises:
the enterprise data receiving module is used for receiving enterprise data;
the enterprise data classification module is used for classifying the received enterprise data according to a preset classification mechanism to obtain N enterprise data sets with different data categories, wherein each enterprise data set comprises at least one enterprise data; wherein N is more than or equal to 2;
the hybrid cloud allocation module is used for allocating the enterprise data sets according to a preset initial allocation mechanism and judging that the enterprise data sets are allocated to the private cloud server or the public cloud server, wherein the enterprise data sets allocated to the private cloud server are defined as a first enterprise data set, and the enterprise data sets allocated to the public cloud server are defined as a second enterprise data set;
the first data storage module is used for storing the first enterprise data set to the private cloud server;
the second data storage module is used for storing the second enterprise data set to the public cloud server;
the storage relation establishing module is used for establishing a storage relation, wherein the storage relation comprises a corresponding relation between the data category of each first enterprise data set and the private cloud server and a corresponding relation between the data category of each second enterprise data set and the public cloud server;
the identity authentication module is used for receiving the identity information of the data transfer personnel and authenticating the identity information of the data transfer personnel;
the data calling instruction receiving module is used for receiving a data calling instruction after the identity information of the data calling personnel passes the verification, wherein the data calling instruction comprises a target data category of a target enterprise data set required to be called;
the data calling module is used for determining that the target enterprise data set is stored in the private cloud server or the public cloud server according to the target data type in the data calling instruction and the storage relation, calling the target enterprise data set from the determined private cloud server or the public cloud server, and recording the data calling process of the target enterprise data set;
the data output module is used for outputting the called target enterprise data set;
the data calling times acquisition module is used for acquiring the times of calling each enterprise data set in a preset time period according to each recorded data calling process; and
the first storage and migration module is used for comparing the called times of the enterprise data sets with a preset high-order threshold, acquiring a second enterprise data set of the enterprise data sets, the called times of which are greater than or equal to the preset high-order threshold, for the enterprise data sets, and migrating the acquired second enterprise data set from the public cloud server to the private cloud server and updating the storage relationship.
Preferably, the enterprise big data analysis processing platform further includes a second storage and migration module, configured to compare the number of times of being called of each enterprise data set with a preset low-number threshold, for an enterprise data set whose number of times of being called is less than or equal to the preset low-number threshold, obtain a first enterprise data set in the enterprise data set whose number of times of being called is less than or equal to the preset low-number threshold, migrate the obtained first enterprise data set from the private cloud server to the public cloud server, and update the storage relationship;
wherein the preset low-order threshold is smaller than the preset high-order threshold.
Preferably, the data output module is specifically configured to:
acquiring the target enterprise data set obtained by calling;
encrypting the called target enterprise data set according to a preset encryption mechanism to obtain an encrypted target enterprise data set and a key for decrypting the encrypted target enterprise data set;
outputting the encrypted target enterprise dataset;
and if the specific feedback character string is received within the preset validity period, outputting the key.
Preferably, the identity information of the data transfer personnel received by the identity verification module includes actual face image information of the data transfer personnel and voice information of the data transfer personnel;
correspondingly, the identity verification module verifies the identity information of the data transfer personnel, and comprises the following steps:
inputting the actual face image information into a preset face image database, judging whether the actual face image information is certain face image information in the face image database, and if the actual face image information is certain face image information in the face image database, acquiring first target identity information corresponding to the certain face image information; the face image database comprises at least two pieces of face image information and first identity information corresponding to the face image information, and the face image information in the face image database is face image information of people with data calling authority;
performing voiceprint calling on the voice information to obtain actual voiceprint information, inputting the actual voiceprint information into a preset voiceprint database, judging whether the actual voiceprint information is certain voiceprint information in the voiceprint database, and if the actual voiceprint information is certain voiceprint information in the voiceprint database, obtaining second target identity information corresponding to the certain voiceprint information; the voiceprint database comprises at least two voiceprint information and second identity information corresponding to the voiceprint information, and the voiceprint information in the voiceprint database is the voiceprint information of a person with data calling authority;
and comparing the first target identity information with the second target identity information, and if the first target identity information and the second target identity information are the same identity information, judging that the identity information of the data transfer personnel is verified.
The enterprise big data analysis and processing platform based on the hybrid cloud has the advantages that: classifying the enterprise data, distributing each enterprise data set according to a preset preliminary distribution mechanism, storing the enterprise data sets to a private cloud server or a public cloud server, and establishing a storage relation according to the storage position of each enterprise data set, wherein the storage relation is used for subsequent enterprise data retrieval; when enterprise data is required to be called, authentication is required to be carried out firstly to judge whether a data calling person qualifies for data calling, the enterprise data can be called only after the authentication is passed, the security of the enterprise data is improved, and when the data is called, a target enterprise data set is called from a private cloud server or a public cloud server according to the target data type and the storage relationship in a data calling instruction; and acquiring the number of times of calling each enterprise data set in a preset time period according to the recorded data calling process, selecting a second enterprise data set from the enterprise data sets for the enterprise data sets of which the number of times of calling is greater than or equal to a preset high-number threshold value, wherein the number of times of calling is large, and the obtained second enterprise data set is migrated from the public cloud server to the private cloud server to update the storage relationship because the second enterprise data set is stored in the public cloud server. The public cloud server is used through the Internet, the data reading speed is limited by the Internet speed, when the network is unstable or the Internet speed is poor, the calling of enterprise data sets can be influenced, the private cloud server does not have the defects, and the enterprise data sets which are called frequently are used more frequently and are important for corresponding enterprises. The enterprise big data analysis and processing platform based on the hybrid cloud provided by the invention realizes the analysis and processing of enterprise data based on the hybrid cloud, and meets the intelligent requirements of the enterprise big data analysis and processing.
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Fig. 1 is a schematic structural diagram of an enterprise big data analysis processing platform based on a hybrid cloud.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
As shown in fig. 1, the embodiment provides a hybrid cloud-based enterprise big data analysis processing platform, including: the system comprises an enterprise data receiving module, an enterprise data classifying module, a mixed cloud distribution module, a first data storage module, a second data storage module, a storage relationship establishing module, an identity verification module, a data calling instruction receiving module, a data calling module, a data output module, a data calling frequency acquiring module and a first storage migration module. Each module of the enterprise big data analysis processing platform provided in this embodiment may be in a software form, or in a hardware form, and if the module is in the software form, the enterprise big data analysis processing platform is a software system, and a hardware execution main body of the enterprise big data analysis processing platform may be a computer device or a server device. It should be understood that the hardware execution subject is specifically determined by the actual application scenario.
The enterprise data receiving module is used for receiving enterprise data. The enterprise data is determined by the actual enterprise type and size, and may include data of various aspects of the enterprise, such as: enterprise employee data, enterprise production data, enterprise sales data, and the like, wherein the enterprise employee data may include data such as personal basic information and contract information of each employee, the enterprise production data may include data such as production data and product spot check qualification rate of each quarter of the enterprise, and the enterprise sales data may include data such as sales data and sales growth rate of each quarter. It should be understood that the enterprise data is not obtained only by direct retrieval from the storage server, but also by a department in the enterprise that is specially responsible for data arrangement.
The enterprise data classification module is used for classifying the received enterprise data according to a preset classification mechanism to obtain N enterprise data sets of different data types, wherein each enterprise data set comprises at least one enterprise data, and N is more than or equal to 2. The enterprise data classification module is used for classifying the received enterprise data according to a preset classification mechanism to obtain enterprise data sets of at least two different data types, and each enterprise data set comprises at least one enterprise data. The preset classification mechanism is set by actual needs, such as: classifying the enterprise data according to different departments, wherein the enterprise data of the same department is one type of enterprise data to form an enterprise data set, and the enterprise data of different departments belong to different enterprise data sets; or classifying according to the attributes of the data, classifying the related data of the product into one class, classifying the related data of the enterprise employee into one class, classifying the enterprise management data into one class, classifying the enterprise logistics data into one class, and the like. It should be understood that the classification mechanism may be determined by the type and size of the enterprise, but regardless of the classification mechanism, the received enterprise data is classified according to the classification mechanism to obtain enterprise data sets of at least two different data categories, each enterprise data set including at least one enterprise data.
Each enterprise data set has a corresponding data category, such as: if the enterprise data is classified according to different departments, the data category of the enterprise data set is the data category corresponding to the department, and the data category of the enterprise data set may specifically be: a logistics department enterprise data set, a production department enterprise data set, a sales department enterprise data set and a management department enterprise data set; if the classification is performed according to the attributes of the data, the data type of the enterprise data set is the data type corresponding to the data attributes, and the data type of the enterprise data set may specifically be: enterprise data sets corresponding to products, enterprise data sets corresponding to employees, enterprise data sets corresponding to management and enterprise data sets corresponding to logistics. It should be understood that the data category of an enterprise data set may be understood as the name of the corresponding enterprise data set.
The hybrid cloud allocation module is used for allocating the enterprise data sets according to a preset initial allocation mechanism, and determining that the enterprise data sets are allocated to the private cloud server or the public cloud server. And the preset initial allocation mechanism is used for allocating the obtained at least two enterprise data sets to the private cloud server or the public cloud server. The preset initial allocation mechanism is set by actual conditions, such as: data considered by the enterprise to be relatively important is distributed to the private cloud servers, and data considered by the enterprise to be relatively less important is distributed to the public cloud servers. It should be understood that whether data is important may vary from enterprise to enterprise, as different enterprises may consider different data as important due to different trends. After the enterprise data sets are distributed according to the initial distribution mechanism, two types of enterprise data sets can be obtained, namely the enterprise data set distributed to the private cloud server and the enterprise data set distributed to the public cloud server. For ease of illustration, the enterprise data set assigned to the private cloud server is defined as a first enterprise data set and the enterprise data set assigned to the public cloud server is defined as a second enterprise data set.
It should be understood that the private cloud server is established in advance and is exclusive to the private cloud server of the enterprise; the public cloud server is provided for the third-party provider to use by the enterprise, and the enterprise big data analysis processing platform needs to be in communication interaction with the public cloud server through the Internet.
The first data storage module is used for storing the first enterprise data set to the private cloud server, it should be understood that after the first enterprise data set is stored to the private cloud server, the private cloud server may further output a first storage feedback signal for indicating that the first enterprise data set has been stored to the private cloud server, and the first data storage module receives the first storage feedback signal output by the private cloud server when the first enterprise data set is stored by the private cloud server, so as to determine that the storage is completed.
Similarly, the second data storage module is configured to store the second enterprise data set to the public cloud server, it should be understood that, after the second enterprise data set is stored to the public cloud server, the public cloud server may further output a second storage feedback signal for indicating that the second enterprise data set has been stored to the public cloud server, and the second data storage module receives the second storage feedback signal output by the public cloud server when the second enterprise data set is stored to determine that the storage is completed.
The storage relationship establishing module is used for establishing a storage relationship according to the storage process, wherein the storage relationship is used for representing the corresponding relationship between each enterprise data set and the server, and comprises the corresponding relationship between the data category of each first enterprise data set and the private cloud server and the corresponding relationship between the data category of each second enterprise data set and the public cloud server. As a specific embodiment, the storage relationship may be represented in a storage relationship table, as shown in table 1.
TABLE 1
| A1 | Private cloud server |
| A2 | Private cloud server |
| A3 | Private cloud server |
| B1 | Public cloud server |
| B2 | Public cloud server |
| B3 | Public cloud server |
In Table 1, A1, A2, and A3 are three different data categories for a first enterprise data set, and B1, B2, and B3 are three different data categories for a second enterprise data set.
When a data transfer person needs to transfer a certain enterprise data set, the data transfer person inputs the identity information of the data transfer person, and the identity verification module is used for receiving the identity information of the data transfer person and verifying the identity information of the data transfer person.
As a specific embodiment, the identity information input by the data caller includes actual face image information of the data caller and voice information of the data caller. The actual face image information is collected by related face image collecting equipment, and the voice information is collected by related audio collecting equipment. Since the voice information is used to extract the voiceprint, the voice information can be a specific voice segment, such as a voice segment "identification".
Correspondingly, the identity information of the data retrieval personnel received by the identity verification module comprises actual face image information of the data retrieval personnel and voice information of the data retrieval personnel.
The identity authentication module is preset with a face image database and a voiceprint database. The human face image database comprises at least two pieces of human face image information and first identity information corresponding to the human face image information, the specific number of the human face image information is set according to actual needs, and the human face image information in the human face image database is the human face image information of personnel with data calling permission. The first identity information is an identifier used for representing the uniqueness of the identity of the person corresponding to the face image information, and can be name information or an identity card number. The face image database is collected and recorded in advance, for example, the face image information of each person with the data retrieval authority is collected, the collected face image information is associated with the corresponding first identity information, and then the face image information is stored in the database to form the face image database.
The voiceprint database comprises at least two voiceprint information and second identity information corresponding to the voiceprint information, the number of the voiceprint information is set according to actual conditions, and the voiceprint information in the voiceprint database is the voiceprint information of personnel with data calling permission. The second identity information is an identifier used for representing the uniqueness of the identity of the person corresponding to the voiceprint information, and can be name information or an identity card number. For the convenience of subsequent comparison, the second identity information and the first identity information are the same kind of identity information, such as: all name information or all identity card numbers. The voiceprint database is also collected and recorded in advance, the voice section corresponding to each voiceprint information in the voiceprint database can also be a specific voice section in the text, such as the voice section 'identity verification', voiceprint comparison is facilitated, namely, each person with data calling authority reads the specific voice section, each obtained voice signal is subjected to voiceprint extraction to obtain the voiceprint information, each obtained voiceprint information is associated with corresponding second identity information, and then the voiceprint information and the corresponding second identity information are stored in the database to form the voiceprint database.
The identity verification module verifies that the data calls the identity information of the personnel, and specifically comprises the following steps:
inputting the actual face image information of the data transfer personnel into a preset face image database, and judging whether the actual face image information is a certain face image information in the face image database, wherein the embodiment provides a specific implementation process, which comprises the following steps:
(1) acquiring the matching degree of the actual face image information and each face image information in a face image database, wherein the matching degree is the similarity, and the higher the matching degree is, the more similar the corresponding two pieces of face image information are;
(2) comparing each matching degree with a preset face image matching degree threshold, wherein the preset face image matching degree threshold is set according to actual needs, such as 90%;
(3) if a certain matching degree is greater than or equal to a preset face image matching degree threshold value, the matching degree is higher, the similarity between the actual face image information and the face image information corresponding to the matching degree in the face image database is higher, the two pieces of face image information can be judged to be the same face image information, the actual face image information is judged to be a certain piece of face image information in the face image database, and the face image information in the face image database is determined to be obtained; and if all the matching degrees are smaller than the preset face image matching degree threshold value, the fact that the similarity between the actual face image information and each face image information in the face image database is not high is shown, and the fact that the actual face image information is not a certain face image information in the face image database is judged.
If the actual face image information of the data transfer personnel is one of the face image information in the face image database, first identity information corresponding to the determined face image information is acquired, and the first identity information is first target identity information.
The identity authentication module carries out voiceprint calling on the obtained voice information to obtain actual voiceprint information, and since the voiceprint calling on the voice information to obtain the actual voiceprint information belongs to conventional technical means, the details are not repeated. Inputting the actual voiceprint information into a preset voiceprint database, and judging whether the actual voiceprint information is a certain voiceprint information in the voiceprint database, wherein the embodiment provides a specific implementation process:
(1) acquiring the matching degree of the actual voiceprint information and each voiceprint information in the voiceprint database, wherein the matching degree is the similarity, and the higher the matching degree is, the more similar the corresponding two voiceprint information are;
(2) comparing each matching degree with a preset voiceprint matching degree threshold, wherein the preset voiceprint matching degree threshold is set according to actual needs, such as 90%;
(3) if a certain matching degree is greater than or equal to a preset voiceprint matching degree threshold value, the matching degree is higher, the similarity between the actual voiceprint information and the voiceprint information corresponding to the matching degree in the voiceprint database is higher, the two pieces of voiceprint information can be judged to be the same voiceprint information, the actual voiceprint information is judged to be certain voiceprint information in the voiceprint database, and the voiceprint information in the voiceprint database is determined to be obtained; and if all the matching degrees are smaller than the preset voiceprint matching degree threshold value, the similarity between the actual voiceprint information and each voiceprint information in the voiceprint database is not high, and the actual voiceprint information is judged not to be a certain voiceprint information in the voiceprint database.
And if the actual voiceprint information is certain voiceprint information in the voiceprint database, acquiring second identity information corresponding to the determined voiceprint information, wherein the second identity information is second target identity information.
Through the two authentication processes, the first target identity information and the second target identity information can be obtained, then the first target identity information and the second target identity information are compared, if the first target identity information and the second target identity information are the same identity information, the fact that the identity authentication is passed through the two authentication processes is shown, the finally obtained identity information corresponds to the same person, and then the identity information authentication of the data transfer person is judged to be passed.
The data calling instruction receiving module is used for receiving a data calling instruction sent by a data calling person after the identity information of the data calling person passes the verification. It should be understood that the meaning of "receiving" in the data call instruction sent by the receiving data call person may be: the data calling personnel sends a data calling instruction after passing the identity verification, and then the data calling instruction receiving module receives the data calling instruction; or, the data calling personnel inputs the data calling instruction at the same time of inputting the identity information, but the data calling instruction receiving module responds to the data calling instruction only after the identity information is verified. The data retrieval command may be input by a special input device, such as a keyboard, and the data retrieval command may be a character string of a specific format and number of bits. Moreover, the data call instruction includes a target data category of the target enterprise data set that is required to be called.
The data calling module is used for determining the storage position of the target enterprise data set according to the target data type and the storage relation in the data calling instruction, namely storing the target enterprise data set in the private cloud server or the public cloud server, and then calling the target enterprise data set according to the target data type from the determined private cloud server or the public cloud server. Such as: if the target data type is a2, and it can be known from table 1 that the server stored in the target data type a2 is a private cloud server, the target enterprise data set is retrieved from the private cloud server according to the target data type a 2.
The data calling module also records the data calling process of the target enterprise data set, including the data calling moment and the target enterprise data set, which is the object of the data calling.
The data output module is used for outputting the called target enterprise data set, and can directly output the data set or adopt a specific output process given by the following steps:
the data output module acquires the called target enterprise data set, and then encrypts the called target enterprise data set according to a preset encryption mechanism, wherein the preset encryption mechanism is set according to actual needs. And encrypting the target enterprise data set according to a preset encryption mechanism to obtain an encrypted target enterprise data set and a key for decrypting the encrypted target enterprise data set.
And after the data output module obtains the encrypted target enterprise data set and the key, the data output module outputs the encrypted target enterprise data set. After receiving the encrypted target enterprise data set, the data caller needs to output a specific feedback character string to the data output module within a preset validity period, wherein the length of the preset validity period is set by actual conditions, such as 60 s; the specific feedback string is specifically set by the actual need. And if the data output module receives the specific feedback character string within the preset validity period, outputting the key. And the data retrieval personnel can decrypt the encrypted target enterprise data set after receiving the key so as to obtain the target enterprise data set. It should be understood that the process of decrypting the encrypted target enterprise data set based on the key may be performed by the data export module or may be performed by an external device. Moreover, if the specific feedback string is not received within the preset validity period, for example, the feedback string received within the preset validity period is incorrect, or the specific feedback string is received beyond the preset validity period, the key is not output.
Therefore, the target enterprise data set is encrypted to obtain the encrypted target enterprise data set and the key, the encrypted target enterprise data set is output first, and the key is output if a specific feedback character string is received within a preset validity period. Because the specific feedback character string is only known by related personnel and cannot be known by other unrelated personnel, if the specific feedback character string is not received within the preset validity period, the key is not output, and the encrypted target enterprise data set cannot be decrypted. Therefore, the data security can be improved through the output process, and other irrelevant personnel are prevented from illegally acquiring the enterprise data set.
And in each data calling process, one enterprise data set is called, and then the data calling times acquisition module is used for acquiring the times of calling each enterprise data set in a preset time period according to each time data calling process obtained through recording. Wherein the preset time period is set by actual needs, such as one month. Moreover, the preset time period may be a time period set at will after the enterprise data set is stored in the private cloud server or the public cloud server, such as: and starting timing from the storage of the enterprise data set to the private cloud server or the public cloud server and continuing for a period of time. When the preset time period is a time period from the storage of the enterprise data set to the private cloud server or the public cloud server, the following records are required: when the first enterprise data set is stored in the private cloud server, the first enterprise data set is stored at a first storage moment when the first enterprise data set is stored in the private cloud server; and when the second enterprise data set is stored in the public cloud server, the second enterprise data set is stored at a second storage moment when the second enterprise data set is stored in the public cloud server. It should be understood that the first storage time and the second storage time are the same time.
The first storage and transfer module is preset with a preset high-order threshold value, and the preset high-order threshold value is set according to actual needs. The first storage and migration module is used for comparing the called times of the enterprise data sets with a preset high-order threshold value, so that two enterprise data sets can be obtained, wherein the called times are larger than or equal to the preset high-order threshold value, and the called times are smaller than the preset high-order threshold value. The number of times of being called is greater than or equal to the preset high-degree threshold value, which indicates that the number of times of being called is large, and correspondingly, the enterprise data set of which the number of times of being called is greater than or equal to the preset high-degree threshold value is relatively important; the number of times that the enterprise data set is called is less than the preset high-order threshold value, which means that the number of times that the enterprise data set is called is relatively less important.
It should be understood that the enterprise data sets that are called more than or equal to the predetermined high-order threshold may all be the first enterprise data set, may all be the second enterprise data set, or may both include the first enterprise data set and the second enterprise data set. Similarly, the enterprise data sets that are called less than the preset high-order threshold may all be the first enterprise data set, may all be the second enterprise data set, and may both include the first enterprise data set and the second enterprise data set.
For the enterprise data sets with the called times larger than or equal to the preset high-time threshold value, the first storage and migration module obtains a second enterprise data set in the enterprise data sets with the called times larger than or equal to the preset high-time threshold value. And because the second enterprise data set is stored in the public cloud server, the obtained second enterprise data set is migrated from the public cloud server to the private cloud server, and the storage relationship is updated. The enterprise data sets with the calling times larger than or equal to the preset high-frequency threshold value are relatively important, the enterprise data sets are stored in the public cloud server and are not stored in the private cloud server safely, the data calling efficiency and the data calling reliability are influenced by the network when the enterprise data sets are stored in the public cloud server, when the network is unstable, the data calling efficiency and the data calling reliability are greatly reduced, and even the data calling is failed, therefore, the second enterprise data sets in the important enterprise data sets are migrated from the public cloud server to the private cloud server, the private cloud server is an exclusive resource of an enterprise, the data storage is safer and is not influenced by the network, the second enterprise data sets in the important enterprise data sets are stored by the private cloud server, and the data calling efficiency can be improved, Safety and reliability.
Updating the deposit relationship can ensure that the target enterprise data set is called from the correct server upon subsequent data calls, for example: if the data type corresponding to the second enterprise data set in the enterprise data sets with the called times larger than or equal to the preset high-order threshold value is B2, the second enterprise data set corresponding to the data type B2 is migrated from the public cloud server to the private cloud server, and the updated storage relationship is shown in table 2.
TABLE 2
| A1 | Private cloud server |
| A2 | Private cloud server |
| A3 | Private cloud server |
| B1 | Public cloud server |
| B2 | Private cloud server |
| B3 | Public cloud server |
Then it is subsequently called from the private cloud server when the enterprise data set corresponding to data category B2 is called.
Further, the enterprise big data analysis processing platform further comprises a second storage and migration module, wherein a preset low-frequency threshold value is preset in the second storage and migration module, and the preset low-frequency threshold value is set according to actual needs. It should be understood that the predetermined low threshold is smaller than the predetermined high threshold, and further, the difference between the predetermined high threshold and the predetermined low threshold is greater than or equal to the predetermined difference, and the logical relationship indicates that there is a certain difference between the high threshold and the predetermined low threshold, so that the predetermined high threshold is a larger value and the predetermined low threshold is a smaller value.
The second storage and migration module is used for comparing the called times of the enterprise data sets with a preset low-time threshold value, so that two enterprise data sets can be obtained, namely the enterprise data set with the called times smaller than or equal to the preset low-time threshold value and the enterprise data set with the called times larger than the preset low-time threshold value. The enterprise data sets that are called less than or equal to the preset low-frequency threshold indicate that the number of calls is low, and accordingly, the enterprise data sets that are called less than or equal to the preset low-frequency threshold are relatively unimportant. The enterprise data sets that are called less than or equal to the preset low-frequency threshold may all be the first enterprise data set, may all be the second enterprise data set, and may both include the first enterprise data set and the second enterprise data set.
For the enterprise data sets with the called times smaller than or equal to the preset low-time threshold, the second storage and migration module acquires the first enterprise data set in the enterprise data sets with the called times smaller than or equal to the preset low-time threshold. And because the first enterprise data set is stored in the private cloud server, the acquired first enterprise data set is migrated from the private cloud server to the public cloud server, and the storage relationship is updated. Because the enterprise data sets with the called times smaller than or equal to the preset low-frequency threshold value are relatively unimportant, the unimportant enterprise data sets are migrated from the private cloud server to the public cloud server, the data storage capacity of the private cloud server can be reduced, the processing speed of the private cloud server is increased, the data processing burden of the private cloud server is reduced, and the operation reliability and the safety of the private cloud server are further improved. Moreover, the storage space which is reserved in the private cloud server can store the more important enterprise data sets.
As a specific embodiment, the preset low-frequency threshold may be 0, and then, the enterprise data set which is called for 0 within the preset time period is migrated from the private cloud server to the public cloud server, so that the storage space of the private cloud server can be released, and the situation that the processing speed of the private cloud server is slowed down due to the fact that the "zombie data" occupies the resources of the private cloud server for a long time is avoided.
The above-mentioned embodiments are merely illustrative of the technical solutions of the present invention in a specific embodiment, and any equivalent substitutions and modifications or partial substitutions of the present invention without departing from the spirit and scope of the present invention should be covered by the claims of the present invention.
Claims (4)
1. The utility model provides an enterprise big data analysis processing platform based on mix cloud which characterized in that includes:
the enterprise data receiving module is used for receiving enterprise data;
the enterprise data classification module is used for classifying the received enterprise data according to a preset classification mechanism to obtain N enterprise data sets with different data categories, wherein each enterprise data set comprises at least one enterprise data; wherein N is more than or equal to 2;
the hybrid cloud allocation module is used for allocating the enterprise data sets according to a preset initial allocation mechanism and judging that the enterprise data sets are allocated to the private cloud server or the public cloud server, wherein the enterprise data sets allocated to the private cloud server are defined as a first enterprise data set, and the enterprise data sets allocated to the public cloud server are defined as a second enterprise data set;
the first data storage module is used for storing the first enterprise data set to the private cloud server;
the second data storage module is used for storing the second enterprise data set to the public cloud server;
the storage relation establishing module is used for establishing a storage relation, wherein the storage relation comprises a corresponding relation between the data category of each first enterprise data set and the private cloud server and a corresponding relation between the data category of each second enterprise data set and the public cloud server;
the identity authentication module is used for receiving the identity information of the data transfer personnel and authenticating the identity information of the data transfer personnel;
the data calling instruction receiving module is used for receiving a data calling instruction after the identity information of the data calling personnel passes the verification, wherein the data calling instruction comprises a target data category of a target enterprise data set required to be called;
the data calling module is used for determining that the target enterprise data set is stored in the private cloud server or the public cloud server according to the target data type in the data calling instruction and the storage relation, calling the target enterprise data set from the determined private cloud server or the public cloud server, and recording the data calling process of the target enterprise data set;
the data output module is used for outputting the called target enterprise data set;
the data calling times acquisition module is used for acquiring the times of calling each enterprise data set in a preset time period according to each recorded data calling process; and
the first storage and migration module is used for comparing the called times of the enterprise data sets with a preset high-order threshold, acquiring a second enterprise data set of the enterprise data sets, the called times of which are greater than or equal to the preset high-order threshold, for the enterprise data sets, and migrating the acquired second enterprise data set from the public cloud server to the private cloud server and updating the storage relationship.
2. The hybrid cloud-based enterprise big data analysis processing platform of claim 1,
the enterprise big data analysis processing platform further comprises a second storage and migration module, wherein the second storage and migration module is used for comparing the called times of the enterprise data sets with a preset low-time threshold, acquiring a first enterprise data set in the enterprise data sets, the called times of which are less than or equal to the preset low-time threshold, migrating the acquired first enterprise data set from the private cloud server to the public cloud server, and updating the storage relationship;
wherein the preset low-order threshold is smaller than the preset high-order threshold.
3. The hybrid cloud-based enterprise big data analysis processing platform of claim 1,
the data output module is specifically configured to:
acquiring the target enterprise data set obtained by calling;
encrypting the called target enterprise data set according to a preset encryption mechanism to obtain an encrypted target enterprise data set and a key for decrypting the encrypted target enterprise data set;
outputting the encrypted target enterprise dataset;
and if the specific feedback character string is received within the preset validity period, outputting the key.
4. The hybrid cloud-based enterprise big data analysis processing platform of claim 1,
the identity information of the data transfer personnel received by the identity verification module comprises actual face image information of the data transfer personnel and voice information of the data transfer personnel;
correspondingly, the identity verification module verifies the identity information of the data transfer personnel, and comprises the following steps:
inputting the actual face image information into a preset face image database, judging whether the actual face image information is certain face image information in the face image database, and if the actual face image information is certain face image information in the face image database, acquiring first target identity information corresponding to the certain face image information; the face image database comprises at least two pieces of face image information and first identity information corresponding to the face image information, and the face image information in the face image database is face image information of people with data calling authority;
performing voiceprint calling on the voice information to obtain actual voiceprint information, inputting the actual voiceprint information into a preset voiceprint database, judging whether the actual voiceprint information is certain voiceprint information in the voiceprint database, and if the actual voiceprint information is certain voiceprint information in the voiceprint database, obtaining second target identity information corresponding to the certain voiceprint information; the voiceprint database comprises at least two voiceprint information and second identity information corresponding to the voiceprint information, and the voiceprint information in the voiceprint database is the voiceprint information of a person with data calling authority;
and comparing the first target identity information with the second target identity information, and if the first target identity information and the second target identity information are the same identity information, judging that the identity information of the data transfer personnel is verified.
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