CN117891614A - Big data analysis platform based on cloud service - Google Patents
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Abstract
The invention relates to the field of data analysis, in particular to a cloud service-based big data analysis platform. The invention provides a big data analysis platform based on cloud service, which aims to overcome the defects of strong data one-sided performance, unsafe data transmission and memory waste in the prior art. According to the invention, data analysis is carried out in a big data mode, an identity verification link is added in the data transmission process, and data type judgment is added in the data analysis process, so that the defect that the traditional data analysis is limited by the data volume and has one-sided property is overcome, a more universal analysis result is provided for a user, the safety in the data transmission process is ensured, and the flexibility of the data analysis is improved.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a cloud service-based big data analysis platform.
Background
Along with the improvement of the social informatization degree, the application of data analysis in various fields is more and more extensive, the traditional data analysis is influenced by the unilateral property of the data, meanwhile, the situation that the data source of the data analysis is damaged often occurs due to the fact that the traditional data analysis is possibly attacked in the data transmission process, and in addition, different data types cannot be analyzed at the same time during the data analysis, but other types of data are temporarily stored in a memory, so that the memory space is wasted easily.
Therefore, development of a cloud service-based big data analysis platform is urgently needed to overcome the disadvantages in the prior art.
Disclosure of Invention
(1) Technical problem to be solved
The invention aims to overcome the defects of strong data one-sided performance, unsafe data transmission and memory waste in the prior art, and aims to provide a big data analysis platform based on cloud service.
(2) Technical proposal
In order to solve the technical problems, the invention provides the cloud service-based big data analysis platform, which is realized by the following modes:
the first module realizes the mining of the data, classifies the acquired data at the same time, transmits the data to the second module after classification, and verifies the data transmission request by the transfer verification node in the transmission process;
the second module issues instructions to control the computing nodes to compute data, meanwhile, the computing power of each computing node is monitored, load balancing is conducted, the computing results are processed, including abnormal information judgment and analysis results reporting to a user, and the final results are sent to the user terminal.
Furthermore, the first module digs the data by means of big data, and simultaneously, the user can upload the data in the first module by using the user terminal.
Further, the first module classifies the data, and merges the data with the same data type into the same group according to different data types and different data sizes read by the big data, and classifies the data with the same data type and different sizes into the same group at the same time, so that the difference value of the total data amount in the data with the same data type in each group is controlled within a certain threshold value.
Further, the first module transmits data to the second module through the transfer verification node, and the data transmission is started after the security is verified.
Further, the data transmission specifically comprises the following steps:
s100, a first module sends a data transmission request to a transfer verification node, the transfer verification node verifies the data transmission request, and a data receiving request code is generated after verification is passed;
s200, the transfer verification node sends the data receiving request code to a second module, the second module verifies the data receiving request code, and authentication information is generated after verification is passed, wherein the authentication information comprises a current transmission address and passing information;
s300, the second module sends the authentication information to a transfer verification node, and the transfer verification node sends the received authentication information to the first module;
and S400, the first module sends the data to the second module according to the transmission address in the authentication information, and the data transmission is completed.
Further, the step of verifying in S100 is as follows:
s101, a first module converts a data transmission request added password into a hash code, and sends the encrypted data transmission request to a transfer verification node;
s102, the transfer verification node adds a password to convert the password into a hash code according to a request address sent by the first module;
s103, comparing the hash code generated by the transfer verification node with the hash code sent by the first module through transfer verification of the transfer verification node;
s104, if the hash codes are consistent, transmitting error information of the error password to the first module, simultaneously recording correct times and adding 1, and if the hash codes are inconsistent, not recording the correct times;
s105, the first module sends the same request to the transfer verification node again for two times after receiving the error information;
s106, the transfer verification node records the correct times, and when the correct times are larger than the preset times of the user, the transfer verification node sends the correct information to the first module, wherein the preset times of the user are larger than or equal to 2.
Further, the second module sends a calculation command to the calculation nodes, sends different calculation data to different calculation nodes, and gives different command information to different calculation nodes, wherein the command information comprises calculation node numbers and grouping data, the grouping data is data grouped in the first module, and each calculation node calculates different tasks according to the received command information to complete integral calculation of the data.
Further, the second module can implement load balancing according to the load of each computing node, firstly, calculate the current load of all the nodes, obtain the calculated amount and the data calculation efficiency to be completed by the current node, calculate the load score of the current node, obtain the load score by the task amount/the calculation efficiency, that is, p=x/m, where P is the scoring result, x is the data amount to be processed of the computing node, and m is the data processing efficiency of the computing node, transfer the data to be processed in the computing node with high score to the computing node with low score, implement load balancing in the whole computing node, and implement by the following formula:
x:y=m:n
wherein x is the data volume to be processed of the computing node A, y is the data volume to be processed of the computing node B, m is the data processing efficiency of the computing node A, n is the data processing efficiency of the computing node B, and load balancing is completed by distributing the data processing volumes of the two nodes A, B.
Further, the second module completes specific analysis of the data, firstly judges the specific type of the data, classifies different data including time format data, character data and numerical data, and selects different analysis modes according to different data types.
Further, the second module can process the abnormal data, when the data analysis obtains the abnormal information, the data type is judged again corresponding to the abnormal data, whether the data types before and after the judgment are consistent or not is judged, if the data types are consistent, the abnormal information is directly reported, if the data types are inconsistent, the analysis is carried out by using a corresponding method according to the data types judged secondarily, and if the obtained result is still abnormal, the abnormal analysis result is reported to a user.
(3) Advantageous effects
The invention develops data analysis in a big data mode, solves the defect that the traditional data analysis is limited by the volume of data and has one-sided property, and provides more universal analysis results for users.
The invention adds the authentication link in the data transmission process, thereby guaranteeing the safety in the data transmission process.
The invention adds the judgment of the data type during the data analysis, can analyze the data of different types, improves the flexibility of data analysis and reduces the condition of memory waste.
Drawings
FIG. 1 is a schematic diagram of the operation flow of the present invention.
Fig. 2 is a schematic diagram of a data transmission flow according to the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples.
Example 1
The invention provides a cloud service-based big data analysis platform, which is realized in the following manner as shown in fig. 1 and 2:
the first module realizes the mining of the data, classifies the acquired data at the same time, transmits the data to the second module after classification, and verifies the data transmission request by the transfer verification node in the transmission process;
the second module issues instructions to control the computing nodes to compute data, meanwhile, the computing power of each computing node is monitored, load balancing is conducted, the computing results are processed, including abnormal information judgment and analysis results reporting to a user, and the final results are sent to the user terminal.
The method can be specifically summarized as the following steps:
s1, the first module is used for mining data by means of big data, and a user can upload the data on the first module by himself;
s2, classifying the data according to the data types and the data sizes by the first module, and classifying the data with the same data types and different data sizes into the same group;
s3, the first module sends data to the second module through the transfer verification node;
s4, the second module receives the data sent by the first module and then issues a command to the computing nodes, so that a plurality of computing nodes analyze different data and load balance the computing nodes;
and S5, after the data calculation is completed, sending the data calculation result to the user terminal.
The first module digs the data by depending on the big data, and simultaneously, the user can upload the data in the first module by himself by utilizing the user terminal, so that the comprehensiveness of the data is ensured, and the defect that the data can only be mined by depending on the big data and human cannot intervene is avoided.
The first module classifies data, firstly, each received data is identified and marked by using a data identification algorithm, the data type and the data size are included, so that each data is accurately classified, an index is established for each data type according to different data types read by big data, the index records the type, the size and the storage position of each data, subsequent data processing and analysis are facilitated, the data with the same data type are grouped into the same group by using a grouping strategy, and the grouping strategy can use a hash algorithm to ensure that the data with the same type is uniformly distributed into each group; meanwhile, setting a threshold value to classify data of the same data type and different sizes into the same group, namely classifying the data with the size X into the group with the threshold value of [ a, b ] when the data with the size X is in the same group, and in order to avoid the situation that the data in a single group is all the gross data, increasing the burden of a single computing node and simultaneously, the gross data cannot be distributed by load balancing, so when the data in the group exceeds a certain numerical value, resetting the threshold value, searching an average value c in the threshold value of [ a, b ], and dividing the data with the size X into the data with the size of [ a, c ] and the data with the size of [ c, b ], so that the total data difference value in the data of the same data type in each group is controlled within a certain threshold value.
The first module transmits data to the second module, the data transmission is started after the data is verified by the transfer verification node, and the specific steps of the data transmission are as follows:
s100, a first module sends a data transmission request to a transfer verification node, the transfer node verifies the data transmission request, and a data receiving request code is generated after verification is passed;
s200, the transfer verification node sends the data receiving request code to a second module, the second module verifies the data receiving request code, and authentication information is generated after verification is passed, wherein the authentication information comprises a current transmission address and passing information;
s300, the second module sends the authentication information to a transfer verification node, and the transfer verification node sends the received authentication information to the first module;
and S400, the first module sends the data to the second module according to the transmission address in the authentication information, and the data transmission is completed.
After authentication information is obtained through the transfer authentication node authentication, the first module and the second module can transmit information through the authentication information, so that data transmission is performed by bypassing the transfer authentication node, the limitation of data transmission speed during data transmission among multiple nodes is avoided, meanwhile, the resources occupied by the transfer authentication node are small, consumption of a user at the transfer authentication node is also saved, and the cost of the user is reduced.
The verification step in S100 is as follows:
s101, a first module converts a data transmission request added password into a hash code, and sends the encrypted data transmission request to a transfer verification node;
s102, the transfer verification node adds a password to convert the password into a hash code according to a request address sent by the first module;
s103, the transfer verification node compares the hash code generated by the transfer verification node with the hash code sent by the first module, and the plaintext of the transmission address does not need to appear at the transfer node, so that the data security is enhanced, and the data security of the user is better ensured;
s104, if the hash codes are consistent, error information of an error password is sent to the first module, meanwhile, the correct times are recorded and added with 1, if the hash codes are inconsistent, the correct times are not recorded, the problem of violent cracking is avoided, the error information is sent when the password is correct, the correct information is sent after the password is correct for many times, the data security is more reasonably protected, and the condition of violent cracking is prevented;
s105, the first module sends the same request to the transfer verification node again for two times after receiving the error information;
s106, the transfer verification node records the correct times, and when the correct times are larger than the preset times of the user, the correct information is sent to the first module, and the preset times set by the user are larger than or equal to 2, otherwise, the step is to lose the function of preventing violent cracking.
The second module sends calculation commands to the calculation nodes, and sends different calculation data to different calculation nodes to give different command information, wherein the command information comprises calculation node numbers and grouping data, the grouping data are grouped data in the first module, and each calculation node calculates different tasks according to the received command information and through a machine learning algorithm deployed on the calculation node, so that each calculation node can process different calculation tasks, and the efficiency of calculating the data is greatly improved.
The second module can realize load balancing according to the load of each computing node, firstly, the current load of all the nodes is calculated, the computing amount and the data computing efficiency of the current node to be completed are obtained, the load score of the current node is calculated, the load score is obtained through the task amount/the computing efficiency, namely, P is a scoring result, x is the data amount to be processed of the computing node, m is the data processing efficiency of the computing node, the data to be processed in the computing node with high score is transferred to the computing node with low score, the load balancing in the whole computing node is realized through the following formula:
x:y=m:n
wherein x is the data volume to be processed of the A computing node, y is the data volume to be processed of the B computing node, m is the data processing efficiency of the A computing node, n is the data processing efficiency of the B computing node, load balancing is completed by distributing the data processing volumes of the A, B two nodes, the overall efficiency of data computing is improved, the problem that a single computing node does not complete tasks for a long time of computing the data to cause the overall data computing to be behind is avoided, and the operation effect of the whole large data analysis platform is better improved.
The second module is used for completing specific analysis of the data, firstly judging the specific type of the data, judging the data types of different data by using a type () function in python, wherein the data types comprise time format data, character data and numerical data, and different analysis modes are selected according to the data types and the data analysis results required by users, so that various data types can be timely dealt with, the waste of memory and the time required by data analysis are reduced, and the resource utilization efficiency of the computing node is improved.
The second module can process the abnormal data, when the abnormal information is obtained through data analysis, the data type is judged again for the abnormal data, whether the data types are consistent or not is judged, if yes, the data are not processed, the abnormal information is directly reported, and the judgment is that in the two abnormal information judgment analyses, the data are of the same type, the data generally have fundamental defects, such as data damage, data loss or serious errors during data collection, and then the analysis of the data confirmed to be damaged is a resource waste; if the two data types are inconsistent, the corresponding method is used for analyzing the data types according to the secondary judgment, namely, a higher-level NLP model is used for analyzing the text data, a machine learning model is used for identifying the numerical data, whether the analysis result accords with expectations or not is checked, whether the abnormality exists or not is judged, and if the obtained result is still abnormal, the abnormal analysis result is reported to a user.
After the data analysis is completed, the second module transmits the calculation result to the user terminal, the user can check the analysis result of the data in real time, and meanwhile, the user can check the abnormal information, judge the abnormal information, and the data are timely and accurate.
The foregoing examples have shown only the preferred embodiments of the invention, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that modifications, improvements and substitutions can be made by those skilled in the art without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. The big data analysis platform based on the cloud service is characterized by being realized by the following modes:
the first module realizes the mining of the data, classifies the acquired data at the same time, transmits the data to the second module after classification, and verifies the data transmission request by the transfer verification node in the transmission process;
the second module issues instructions to control the computing nodes to compute data, meanwhile, the computing power of each computing node is monitored, load balancing is conducted, the computing results are processed, including abnormal information judgment and analysis results reporting to a user, and the final results are sent to the user terminal.
2. The cloud service-based big data analysis platform according to claim 1, wherein the first module digs the data by the big data, and the user can upload the data in the first module by using the user terminal.
3. The cloud service-based big data analysis platform according to claim 1, wherein the first module classifies the data, merges the data with the same data type into the same group according to different data types and different data sizes read by the big data, and classifies the data with the same data type and different sizes into the same group at the same time, so that the difference value of the total data amount in the data with the same data type in each group is controlled within a certain threshold.
4. The cloud service-based big data analysis platform according to claim 1, wherein the first module transmits the data to the second module through the transit verification node, and starts data transmission after security verification.
5. The cloud service-based big data analysis platform according to claim 4, wherein the data transmission comprises the following specific steps:
s100, a first module sends a data transmission request to a transfer verification node, the transfer verification node verifies the data transmission request, and a data receiving request code is generated after verification is passed;
s200, the transfer verification node sends the data receiving request code to a second module, the second module verifies the data receiving request code, and authentication information is generated after verification is passed, wherein the authentication information comprises a current transmission address and passing information;
s300, the second module sends the authentication information to a transfer verification node, and the transfer verification node sends the received authentication information to the first module;
and S400, the first module sends the data to the second module according to the transmission address in the authentication information, and the data transmission is completed.
6. The cloud service-based big data analysis platform of claim 5, wherein the step of verifying in S100 is as follows:
s101, a first module converts a data transmission request added password into a hash code, and sends the encrypted data transmission request to a transfer verification node;
s102, the transfer verification node adds a password to convert the password into a hash code according to a request address sent by the first module;
s103, comparing the hash code generated by the transfer verification node with the hash code sent by the first module through transfer verification of the transfer verification node;
s104, if the hash codes are consistent, transmitting error information of the error password to the first module, simultaneously recording correct times and adding 1, and if the hash codes are inconsistent, not recording the correct times;
s105, the first module sends the same request to the transfer verification node again for two times after receiving the error information;
s106, the transfer verification node records the correct times, and when the correct times are larger than the preset times of the user, the transfer verification node sends the correct information to the first module, wherein the preset times of the user are larger than or equal to 2.
7. The cloud service-based big data analysis platform according to claim 1, wherein the second module sends a calculation command to the calculation nodes, sends different calculation data to different calculation nodes, and sends different command information to different calculation nodes, wherein the command information comprises a calculation node number and packet data, the packet data is data grouped in the first module, and each calculation node calculates different tasks according to the received command information to complete overall calculation of the data.
8. The cloud service-based big data analysis platform according to claim 1, wherein the second module is capable of realizing load balancing according to the load of each computing node, firstly calculating the current load of all nodes, obtaining the calculation amount and the data calculation efficiency to be completed by the current node, calculating the load score of the current node, and obtaining the load score through the task amount/the calculation efficiency, wherein P is a scoring result, x is the data amount to be processed of the computing node, m is the data processing efficiency of the computing node, transferring the data to be processed in the computing node with high score to the computing node with low score, and realizing the load balancing in the whole computing node by the following formula:
x:y=m:n
wherein x is the data volume to be processed of the computing node A, y is the data volume to be processed of the computing node B, m is the data processing efficiency of the computing node A, n is the data processing efficiency of the computing node B, and load balancing is completed by distributing the data processing volumes of the two nodes A, B.
9. The cloud service-based big data analysis platform according to claim 1, wherein the second module performs specific analysis on the data, first determines a specific type of the data, classifies different data, including time format data, character data and numerical data, and selects different analysis modes according to different data types.
10. The cloud service-based big data analysis platform according to claim 1, wherein the second module is capable of processing the abnormal data, judging the data type again corresponding to the abnormal data when the abnormal information is obtained by analyzing the data, judging whether the two data types are consistent or not, if so, directly reporting the abnormal information, if not, analyzing by using a corresponding method according to the secondarily judged data type, and if the obtained result is still abnormal, reporting the abnormal analysis result to the user.
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