Independent station operation data classification and deep multi-dimensional processing method and system

Abstract

The present invention relates to the field of independent station operation, and specifically to an independent station operation data classification and deep multi-dimensional processing method and system, which obtains target access behavior from the user access log of the independent station, traces the target access behavior, and obtains the operation data corresponding to the user accessing the independent station; based on the use record of the operation data, the operation data is classified into a number of operation sub-data, and integrated into a number of operation data sets through data replication and backup; the operation data set is deeply multi-dimensionally analyzed to obtain the operation loopholes and business processing change trends of the independent station, so as to determine the first block to be repaired and the second block to change the business processing status; based on the operation thread of the first block and the business flow of the second block, the corresponding blocks are respectively repaired and the business processing status is adjusted, so as to effectively deal with the safety hazards and collapse risks of the independent station, and ensure the continuity and reliability of the operation of the independent station.

Description

Technical Field

[0001] The present invention relates to the technical field of independent station operation, and in particular to a method and system for classification and deep multi-dimensional processing of independent station operation data. Background Art

[0002] An independent website is a completely independent website that is built and operated by the enterprise itself, including an independent server, website program, and separate domain name. An independent website does not rely on third-party platforms at all, thus avoiding resource competition between independent websites and other competitors on third-party platforms, and improving the autonomy and stability of resource management of independent websites. In addition, when users visit an independent website, the independent website will form independent user data, that is, it has complete independent control over user data and does not need to share user data, which is more conducive to the independent website's continuous data accumulation and in-depth data mining.

[0003] However, the working performance of the independent station itself is limited. When a large number of users visit at the same time or when a Trojan virus invades, it is easy to cause problems such as system crashes or downtime. In order to ensure the safe and stable operation of the independent station, it is particularly important to analyze and predict the operating data of the independent station. Analysis based on the operating data of the independent station can provide accurate and reliable guidance and assistance for dealing with possible operational failures, security risks and system crash risks of the independent station. Summary of the invention

[0004] In view of the defects of the prior art, the present invention provides a method and system for classification and deep multi-dimensional processing of independent station operation data, which identifies the target access behavior from the user access log of the independent station, traces the target access behavior, and obtains the operation data corresponding to the user's access to the independent station. By extracting the operation data associated with the target access behavior, the workload of subsequent operation data analysis can be reduced and the operation status prediction for the specific working scene of the independent station can be accurately performed; based on the usage record of the operation data, the operation data is classified into a number of operation sub-data, and integrated into a number of operation data sets through data replication and backup, so as to ensure that there is sufficient data for operation status prediction for different working scenes of the independent station, and realize the diversification of operation data; the operation data set is deeply multi-dimensionally analyzed to obtain the operation vulnerabilities and business processing change trends of the independent station, so as to determine the first block to be repaired and the second block to change the business processing status, accurately limit the operation block for adjusting the operation status of the independent station, do not need to conduct a large-scale investigation of the independent station, and reduce the workload of adjusting the operation status; based on the operation thread of the first block and the business flow of the second block, the corresponding blocks are respectively repaired and the business processing status is adjusted, so as to effectively deal with the security risks and crash risks of the independent station, and ensure the continuity and reliability of the operation of the independent station.

[0005] The present invention provides a method for classification and deep multi-dimensional processing of independent station operation data, comprising the following steps:

[0006] Step S1, obtaining a user access log of an independent site, analyzing the access log to obtain a target access behavior of the user to the independent site; tracing the target access behavior to obtain the operation data of the user in the process of accessing the independent site, and identifying and locating the operation data;

[0007] Step S2, based on the usage record of the operation data at the independent station, classify the operation data to obtain a plurality of operation sub-data; based on the identification and positioning result of the operation data, perform backup processing on the plurality of operation sub-data to obtain a plurality of backup operation sub-data, and integrate the plurality of backup operation sub-data into a plurality of corresponding operation data sets;

[0008] Step S3, performing in-depth multi-dimensional analysis on each operational data set to obtain operational vulnerabilities and business processing change trends of the independent station; based on the operational vulnerabilities and business processing change trends, determining a first block of the independent station for which vulnerability repair is to be performed and a second block for which business processing status is to be changed;

[0009] Step S4: based on the operation thread information of the first block, perform vulnerability repair processing on the first block; based on the business flow information of the second block, adjust the business processing state of the second block.

[0010] In one embodiment disclosed in the present application, in step S1, a user access log of an independent station is obtained, the access log is analyzed to obtain a target access behavior of the user to the independent station; the target access behavior is traced to obtain the operation data of the user in the process of accessing the independent station, and the operation data is identified and located, including:

[0011] Perform login identity recognition on the user accessing the independent site to determine whether the user is a stranger; if so, obtain the access log corresponding to the user on the independent site based on the user's identity information; if not, do not obtain the access log corresponding to the user on the independent site;

[0012] Perform access attribute identification on the access log to obtain the access type and access block location initiated by the user to the independent station; based on the access type and the access block location, determine whether the access behavior initiated by the user will increase the thread processing capacity of the independent station; if so, determine that the access behavior belongs to the target access behavior; otherwise, determine that the access behavior does not belong to the target access behavior;

[0013] The cookies data of the target access behavior is traced and analyzed to determine the operation data associated with the independent station during the period of the target access behavior, and the operation data is identified and located to determine the storage interval of the operation data in the independent station.

[0014] In one embodiment disclosed in the present application, in the step S2, based on the usage record of the operation data at the independent station, the operation data is classified to obtain a plurality of operation sub-data; based on the identification and positioning result of the operation data, the plurality of operation sub-data is backed up to obtain a plurality of backup operation sub-data, and the plurality of backup operation sub-data is integrated into a corresponding plurality of operation data sets, including:

[0015] Analyze the usage records of the operation data at the independent station to obtain the usage scenario types of the operation data during the operation of the independent station; classify the operation data based on the usage scenario types to obtain a number of operation sub-data; wherein each operation sub-data corresponds to only one usage scenario type;

[0016] Based on the storage interval of the operation data in the independent station and the start and end characteristic codes of the corresponding operation sub-data, the operation sub-data are copied and backed up to obtain a number of backup operation sub-data; and based on the content attributes of each backup operation sub-data, each backup operation sub-data is integrated into the corresponding operation data set.

[0017] In one embodiment disclosed in the present application, in step S3, a deep multi-dimensional analysis is performed on each operational data set to obtain operational vulnerabilities and business processing change trends of the independent station; based on the operational vulnerabilities and the business processing change trends, a first block of the independent station to be repaired and a second block to change the business processing status are determined, including:

[0018] Performing neural network deep clustering analysis on each operational data set to obtain operational vulnerabilities and business processing change trends of the independent station work scenario corresponding to each operational data set; wherein the operational vulnerabilities include data security vulnerabilities and operational dead loop vulnerabilities of the independent station work scenario; the business processing change trend includes the change trend of the amount of business data that the independent station work scenario needs to process in the future time period;

[0019] Based on the operational vulnerability, the block of the independent station where illegal data tampering and operational dead loop occurred is determined, and this is used as the first block to be repaired; based on the business processing change trend, the block of the independent station where a sharp increase in business data occurred is determined, and this is used as the second block to change the business processing status.

[0020] In one embodiment disclosed in the present application, in the step S4, based on the operation thread information of the first block, the vulnerability repair process is performed on the first block; based on the service flow information of the second block, the service processing state of the second block is adjusted, including:

[0021] Monitor the operation thread of the first block to obtain the thread operation obstacle point of the first block; and repair the illegal data tampering or operation dead loop of the first block based on the obstacle type of the thread operation obstacle point;

[0022] The business data flow of the second block is monitored to obtain the business data receiving flow information of the second block; based on the business data receiving flow information, the business data of the second block is diverted or intercepted.

[0023] The present invention also provides an independent station operation data classification and deep multi-dimensional processing system, including:

[0024] An access behavior identification module is used to obtain user access logs of the independent site, analyze the access logs, and obtain the user's target access behavior to the independent site;

[0025] An operation data acquisition and positioning module is used to trace the target access behavior, obtain the operation data of the user in the process of accessing the independent station, and identify and locate the operation data;

[0026] An operation data classification module, used for classifying the operation data based on the usage record of the operation data at the independent station to obtain a plurality of operation sub-data;

[0027] An operation data set generation module, used for performing backup processing on a number of operation sub-data based on the identification and positioning result of the operation data to obtain a number of backup operation sub-data, and integrating the number of backup operation sub-data into a number of corresponding operation data sets;

[0028] A deep multi-dimensional analysis module is used to perform a deep multi-dimensional analysis on each operational data set to obtain operational loopholes and business processing change trends of the independent station;

[0029] A block determination module, configured to determine, based on the operation vulnerability and the business processing change trend, a first block of the independent station to be repaired for a vulnerability and a second block to be changed in business processing status;

[0030] A vulnerability repair module, used for performing vulnerability repair processing on the first block based on the operation thread information of the first block;

[0031] A business processing adjustment module is used to adjust the business processing state of the second block based on the business flow information of the second block.

[0032] In one embodiment disclosed in the present application, the access behavior identification module is used to obtain the user access log of the independent site, analyze the access log, and obtain the user's target access behavior to the independent site, including:

[0033] Perform login identity recognition on the user accessing the independent site to determine whether the user is a stranger; if so, obtain the access log corresponding to the user on the independent site based on the user's identity information; if not, do not obtain the access log corresponding to the user on the independent site;

[0034] Perform access attribute identification on the access log to obtain the access type and access block location initiated by the user to the independent station; based on the access type and the access block location, determine whether the access behavior initiated by the user will increase the thread processing capacity of the independent station; if so, determine that the access behavior belongs to the target access behavior; otherwise, determine that the access behavior does not belong to the target access behavior;

[0035] The operation data acquisition and positioning module is used to trace the target access behavior, obtain the operation data of the user in the process of accessing the independent station, and identify and locate the operation data, including:

[0036] The cookies data of the target access behavior is traced and analyzed to determine the operation data associated with the independent station during the period of the target access behavior, and the operation data is identified and located to determine the storage interval of the operation data in the independent station.

[0037] In one embodiment disclosed in the present application, the operation data classification module is used to classify the operation data based on the usage record of the operation data at the independent station to obtain a number of operation sub-data, including:

[0038] Analyze the usage records of the operation data at the independent station to obtain the usage scenario types of the operation data during the operation of the independent station; classify the operation data based on the usage scenario types to obtain a number of operation sub-data; wherein each operation sub-data corresponds to only one usage scenario type;

[0039] The operation data set generation module is used to perform backup processing on a plurality of operation sub-data based on the identification and positioning result of the operation data to obtain a plurality of backup operation sub-data, and integrate the plurality of backup operation sub-data into a corresponding plurality of operation data sets, including:

[0040] Based on the storage interval of the operation data in the independent station and the start and end characteristic codes of the corresponding operation sub-data, the operation sub-data are copied and backed up to obtain a number of backup operation sub-data; and based on the content attributes of each backup operation sub-data, each backup operation sub-data is integrated into the corresponding operation data set.

[0041] In one embodiment disclosed in the present application, the deep multi-dimensional analysis module is used to perform deep multi-dimensional analysis on each operational data set to obtain operational vulnerabilities and business processing change trends of the independent station, including:

[0042] Performing neural network deep clustering analysis on each operational data set to obtain operational vulnerabilities and business processing change trends of the independent station work scenario corresponding to each operational data set; wherein the operational vulnerabilities include data security vulnerabilities and operational dead loop vulnerabilities of the independent station work scenario; the business processing change trend includes the change trend of the amount of business data that the independent station work scenario needs to process in the future time period;

[0043] The block determination module is used to determine the first block of the independent station to be repaired for vulnerability and the second block to change the business processing status based on the operation vulnerability and the business processing change trend, including:

[0044] Based on the operational vulnerability, the block of the independent station where illegal data tampering and operational dead loop occurred is determined, and this is used as the first block to be repaired; based on the business processing change trend, the block of the independent station where a sharp increase in business data occurred is determined, and this is used as the second block to change the business processing status.

[0045] In one embodiment disclosed in the present application, the vulnerability repair module is used to perform vulnerability repair processing on the first block based on the operation thread information of the first block, including:

[0046] Monitor the operation thread of the first block to obtain the thread operation obstacle point of the first block; and repair the illegal data tampering or operation dead loop of the first block based on the obstacle type of the thread operation obstacle point;

[0047] The service processing adjustment module is used to adjust the service processing state of the second block based on the service flow information of the second block, including:

[0048] The business data flow of the second block is monitored to obtain the business data receiving flow information of the second block; based on the business data receiving flow information, the business data of the second block is diverted or intercepted.

[0049] Compared with the prior art, the independent station operation data classification and deep multi-dimensional processing method and system obtain the target access behavior from the user access log of the independent station, trace the target access behavior, and obtain the operation data corresponding to the user's access to the independent station. By extracting the operation data associated with the target access behavior, the workload of subsequent operation data analysis can be reduced and the operation status prediction for the specific working scene of the independent station can be accurately performed; based on the usage record of the operation data, the operation data is classified into a number of operation sub-data, and integrated into a number of operation data sets through data replication and backup, so as to ensure that there is sufficient data for operation status prediction for different working scenes of the independent station, and realize the diversification of operation data; the operation data set is deeply multi-dimensionally analyzed to obtain the operation vulnerabilities and business processing change trends of the independent station, so as to determine the first block to be repaired and the second block to change the business processing status, accurately limit the operation block for adjusting the operation status of the independent station, do not need to conduct a large-scale investigation of the independent station, and reduce the workload of adjusting the operation status; based on the operation thread of the first block and the business flow of the second block, the corresponding blocks are respectively repaired and the business processing status is adjusted, effectively responding to the security risks and crash risks of the independent station, and ensuring the continuity and reliability of the operation of the independent station.

[0050] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A flow chart of the independent station operation data classification and deep multi-dimensional processing method provided by the present invention;

[0054] Figure 2 A schematic diagram of the framework of the independent station operation data classification and deep multi-dimensional processing system provided by the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 , is a flow chart of a method for classifying and deeply processing independent station operation data provided by an embodiment of the present invention. The method for classifying and deeply processing independent station operation data includes:

[0057] Step S1, obtaining the user access log of the independent site, analyzing the access log, obtaining the target access behavior of the user to the independent site; tracing the target access behavior, obtaining the operation data of the user in the process of accessing the independent site, and identifying and locating the operation data;

[0058] Step S2: based on the usage record of the operation data in the independent station, the operation data is classified to obtain a number of operation sub-data; based on the identification and positioning result of the operation data, the number of operation sub-data is backed up to obtain a number of backup operation sub-data, and the number of backup operation sub-data is integrated into a number of corresponding operation data sets;

[0059] Step S3, performing in-depth multi-dimensional analysis on each operational data set to obtain operational vulnerabilities and business processing change trends of the independent station; based on the operational vulnerabilities and business processing change trends, determining a first block of the independent station for which vulnerability repair is to be performed and a second block for which the business processing status is to be changed;

[0060] Step S4: Based on the operation thread information of the first block, the vulnerability repair process is performed on the first block; based on the business flow information of the second block, the business processing state of the second block is adjusted.

[0061] The independent station operation data classification and deep multi-dimensional processing method obtains the target access behavior from the user access log of the independent station, traces the target access behavior, obtains the operation data corresponding to the user accessing the independent station, and can reduce the workload of subsequent operation data analysis and accurately predict the operation status for the specific working scene of the independent station by extracting the operation data associated with the target access behavior; based on the use record of the operation data, the operation data is classified into a number of operation sub-data, and integrated into a number of operation data sets through data replication and backup, so as to ensure that there is sufficient data for operation status prediction for different independent station working scenes, and realize the diversification of operation data; the operation data set is deeply multi-dimensionally analyzed to obtain the operation loopholes and business processing change trends of the independent station, so as to determine the first block to be repaired and the second block to be changed in the business processing status, accurately limit the operation block for adjusting the operation status of the independent station, do not need to conduct a large-scale investigation of the independent station, and reduce the workload of adjusting the operation status; based on the operation thread of the first block and the business flow of the second block, respectively repair the loopholes and adjust the business processing status of the corresponding blocks, effectively deal with the security risks and collapse risks of the independent station, and ensure the continuity and reliability of the operation of the independent station.

[0062] Preferably, in step S1, the user access log of the independent site is obtained, the access log is analyzed to obtain the target access behavior of the user to the independent site; the target access behavior is traced to obtain the operation data of the user in the process of accessing the independent site, and the operation data is identified and located, including:

[0063] Perform login identification on users accessing the independent site to determine whether the user is a stranger; if so, obtain the user's corresponding access log on the independent site based on the user's identity information; if not, do not obtain the user's corresponding access log on the independent site;

[0064] Identify access attributes of access logs to obtain the access type and access block location initiated by the user to the independent station; based on the access type and access block location, determine whether the access behavior initiated by the user will increase the thread processing capacity of the independent station; if so, determine that the access behavior belongs to the target access behavior; otherwise, determine that the access behavior does not belong to the target access behavior;

[0065] Trace and analyze cookies data for target access behavior, determine the operational data generated by the independent site during the period of target access behavior, identify and locate the operational data, and determine the storage interval of the operational data on the independent site.

[0066] In the above technical solution, the independent station is an independently operated website, and any user is allowed to access the independent station. Different users have different requirements for the information provided by the independent station, and different users have different information browsing habits in the independent station, which makes the access behavior attributes of different users (such as access target areas) different. In this way, the independent station also needs to perform different thread processing when dealing with the access behaviors of different users. When a large number of different users initiate access to the independent station in a short period of time, the independent station needs to consume a large amount of memory and computing power resources to deal with it, which may easily lead to unstable events such as illegal tampering of data or system crashes on the independent station. In order to ensure the stable operation of the independent station, it is necessary to collect the corresponding operational data of different users visiting the independent station, so as to provide sufficient and comprehensive data support for the subsequent independent station to deal with different situations. First, the login identity of the user visiting the independent site is identified, and the user's login identity on the independent site is compared with the user login history of the independent site. If the user's login identity does not exist in the user login history, it means that the user is logging into the independent site for the first time. The user is a stranger to the independent site. The data associated with the visit behavior of the stranger on the independent site is new and non-repetitive data for the independent site, which can improve the accuracy of subsequent independent site operation predictions. At this time, it is necessary to obtain the access log of the stranger on the independent site, which can effectively narrow the scope of subsequent operation data acquisition and reduce unnecessary repeated data verification operations.

[0067] The user's access log comprehensively records all the access behaviors initiated by the user after logging into the independent station. The types of these access behaviors are different. Some access behaviors are just data viewing, which will not substantially change the operating status of the independent station. Another part of the access behavior is to initiate corresponding task processing requirements, which will substantially change the operating status of the independent station (that is, increase the thread processing capacity of the independent station). It is the associated data of these access behaviors that have a substantial change on the operating status of the independent station that will play a key role in the subsequent prediction of the independent station's operating conditions. For this purpose, the access attributes of the access log are identified to obtain the access type and access block location initiated by the user to the independent station; based on the access type and access block location, it is determined whether the access behavior initiated by the user will increase the thread processing capacity of the independent station, and then it is determined whether the access behavior belongs to the target access behavior, and the scope of subsequent operational data acquisition is narrowed to the data part associated with the target access behavior, reducing the workload of operational data acquisition. In addition, the cookies data of the target access behavior is traced and analyzed to determine the operational data generated by the independent site during the period of the target access behavior, and the operational data is identified and located to determine the storage interval of the operational data in the independent site. In this way, the operational data is searched and obtained using the cookies data corresponding to the target access behavior as an index to ensure the efficiency of searching and obtaining the operational data.

[0068] Preferably, in step S2, based on the usage record of the operation data in the independent station, the operation data is classified to obtain a number of operation sub-data; based on the identification and positioning result of the operation data, the number of operation sub-data is backed up to obtain a number of backup operation sub-data, and the number of backup operation sub-data is integrated into a number of corresponding operation data sets, including:

[0069] Analyze the usage records of the operation data in the independent station to obtain the usage scenario types of the operation data in the operation process of the independent station; classify the operation data based on the usage scenario types to obtain a number of operation sub-data; wherein each operation sub-data corresponds to only one usage scenario type;

[0070] Based on the storage interval of the operation data in the independent station and the start and end characteristic codes of the corresponding operation sub-data, the operation sub-data are copied and backed up to obtain a number of backup operation sub-data; and based on the content attributes of each backup operation sub-data, each backup operation sub-data is integrated into the corresponding operation data set.

[0071] In the above technical solution, the operation data generated by the independent station is not only for simple data recording purposes, but it may also be used as raw data for the task processing process of other applications in the independent station, so that different operation data correspond to different usage scenarios within the independent station (such as task processing scenarios of different applications). The working status of different applications within the independent station also has a significant impact on the overall operation of the independent station. Therefore, it is necessary to accurately classify the operation data according to the usage records of the operation data within the independent station to obtain a number of operation sub-data, so that each operation sub-data corresponds to only one type of usage scenario, which is convenient for subsequent targeted and accurate operation status prediction of different operation scenarios of the independent station. In addition, based on the storage interval of the operation data in the independent station and the start and end feature codes of the corresponding operation sub-data, a number of operation sub-data are copied and backed up to obtain a number of backup operation sub-data; and based on the content attributes of each backup operation sub-data, each backup operation sub-data is integrated into the corresponding operation data set, which can ensure the diversity and richness of the data content of each operation data set itself, and provide a reliable data foundation for subsequent in-depth multi-dimensional analysis.

[0072] Preferably, in step S3, each operational data set is subjected to in-depth multi-dimensional analysis to obtain operational vulnerabilities and business processing change trends of the independent station; based on the operational vulnerabilities and business processing change trends, a first block of the independent station to be repaired for vulnerabilities and a second block to be changed in business processing status are determined, including:

[0073] Perform neural network deep clustering analysis on each operational data set to obtain operational vulnerabilities and business processing change trends of the independent station work scenario corresponding to each operational data set; operational vulnerabilities include data security vulnerabilities and operational dead loop vulnerabilities of the independent station work scenario; business processing change trends include the change trends of the amount of business data that the independent station work scenario needs to process in the future time period;

[0074] Based on operational vulnerabilities, the blocks where illegal data tampering and operational dead loops occur in the independent station are determined, and used as the first blocks to be repaired. Based on the trend of business processing changes, the blocks where business data surges occur in the independent station are determined, and used as the second blocks where the business processing status is to be changed.

[0075] In the above technical scheme, each operation data set is for a specific operation scenario of the independent station. The neural network model is used to perform a neural network deep clustering analysis on each operation data set to obtain the operation vulnerabilities and business processing change trends of the independent station work scenario corresponding to each operation data set. In this way, the data security vulnerabilities and operation dead loop vulnerabilities of the independent station work scenario, as well as the change trend of the amount of business data that the independent station work scenario needs to process in the future time period can be accurately determined, and the operation problems and operation status of the independent station in different work scenarios can be comprehensively predicted. In addition, based on the operation vulnerabilities, the blocks where illegal data tampering and operation dead loops occur in the independent station are determined, and used as the first block to be repaired; based on the business processing change trend, the blocks where the business data surge event occurs in the independent station are determined, and used as the second block to change the business processing status. In order to accurately limit the operation blocks for adjusting the operation status of the independent station, there is no need to conduct a large-scale investigation of the independent station, reducing the workload of adjusting the operation status.

[0076] Preferably, in step S4, based on the operation thread information of the first block, performing vulnerability repair processing on the first block; based on the service flow information of the second block, adjusting the service processing state of the second block, including:

[0077] Monitor the operation thread of the first block to obtain the thread operation obstacle point of the first block; based on the obstacle type of the thread operation obstacle point, repair the illegal data tampering or the operation dead loop of the first block;

[0078] The business data flow of the second block is monitored to obtain the business data receiving flow information of the second block; based on the business data receiving flow information, the business data of the second block is diverted or intercepted.

[0079] In the above technical scheme, the operation thread of the first block is monitored to obtain the thread operation obstacle point of the first block. The thread operation obstacle point may be, but is not limited to, a data location point where a data editing error occurs during the operation of the thread of the first block itself or an operation location point where the operation timeouts and freezes; then, based on the obstacle type of the thread operation obstacle point, the first block is repaired for illegal data tampering or operation dead loop repair, so as to quickly troubleshoot and repair the security risks of the independent station. In addition, the business data traffic of the second block is monitored to obtain the business data receiving traffic information of the second block, so as to accurately predict the super-large business data traffic that the independent station may encounter during operation; based on the business data receiving traffic information, the business data of the second block is diverted or intercepted, that is, the business data traffic sent to the second block is diverted to other blocks for processing or the business data traffic sent to the second block is intermittently intercepted, so as to avoid the second block receiving a large amount of business data at one time and exceeding its own data processing load, reduce the risk of system crash of the independent station, and ensure the continuity and reliability of the operation of the independent station.

[0080] See also Figure 2 , is a schematic diagram of the framework of the independent station operation data classification and deep multi-dimensional processing system provided by an embodiment of the present invention. The independent station operation data classification and deep multi-dimensional processing system includes:

[0081] The access behavior identification module is used to obtain the user access logs of the independent site, analyze the access logs, and obtain the user's target access behavior to the independent site;

[0082] The operation data acquisition and positioning module is used to trace the target access behavior, obtain the operation data of the user's access to the independent station, and identify and locate the operation data;

[0083] An operation data classification module is used to classify the operation data based on the usage records of the operation data on the independent station to obtain a number of operation sub-data;

[0084] An operation data set generation module is used to perform backup processing on a number of operation sub-data based on the identification and positioning results of the operation data, obtain a number of backup operation sub-data, and integrate the number of backup operation sub-data into a number of corresponding operation data sets;

[0085] The deep multi-dimensional analysis module is used to conduct deep multi-dimensional analysis on each operational data set to obtain the operational loopholes and business processing change trends of the independent station;

[0086] A block determination module, used to determine a first block of the independent station for which vulnerability repair is to be performed and a second block for which the business processing status is to be changed based on the operational vulnerability and the business processing change trend;

[0087] A vulnerability repair module, used for performing vulnerability repair processing on the first block based on the operation thread information of the first block;

[0088] The business processing adjustment module is used to adjust the business processing state of the second block based on the business flow information of the second block.

[0089] The independent station operation data classification and deep multi-dimensional processing system obtains the target access behavior from the user access log of the independent station, traces the target access behavior, and obtains the operation data corresponding to the user's access to the independent station. By extracting the operation data associated with the target access behavior, the subsequent operation data analysis workload can be reduced and the operation status prediction for the specific working scene of the independent station can be accurately performed; based on the use record of the operation data, the operation data is classified into a number of operation sub-data, and integrated into a number of operation data sets through data replication and backup, so as to ensure that there is sufficient data for operation status prediction for different independent station working scenes, and realize the diversification of operation data; the operation data set is deeply multi-dimensionally analyzed to obtain the operation vulnerabilities and business processing change trends of the independent station, so as to determine the first block to be repaired and the second block to change the business processing status, accurately limit the operation block for adjusting the operation status of the independent station, do not need to conduct a large-scale investigation of the independent station, and reduce the workload of adjusting the operation status; based on the operation thread of the first block and the business flow of the second block, the corresponding blocks are respectively repaired and the business processing status is adjusted, so as to effectively deal with the security risks and collapse risks of the independent station, and ensure the continuity and reliability of the operation of the independent station.

[0090] Preferably, the access behavior identification module is used to obtain the user access log of the independent site, analyze the access log, and obtain the user's target access behavior to the independent site, including:

[0091] Perform login identification on users accessing the independent site to determine whether the user is a stranger; if so, obtain the user's corresponding access log on the independent site based on the user's identity information; if not, do not obtain the user's corresponding access log on the independent site;

[0092] Identify access attributes of access logs to obtain the access type and access block location initiated by the user to the independent station; based on the access type and access block location, determine whether the access behavior initiated by the user will increase the thread processing capacity of the independent station; if so, determine that the access behavior belongs to the target access behavior; otherwise, determine that the access behavior does not belong to the target access behavior;

[0093] The operation data acquisition and positioning module is used to trace the target access behavior, obtain the operation data of the user's access to the independent station, and identify and locate the operation data, including:

[0094] Trace and analyze cookies data for target access behavior, determine the operational data generated by the independent site during the period of target access behavior, identify and locate the operational data, and determine the storage interval of the operational data on the independent site.

[0095] In the above technical solution, the independent station is an independently operated website, and any user is allowed to access the independent station. Different users have different requirements for the information provided by the independent station, and different users have different information browsing habits in the independent station, which makes the access behavior attributes of different users (such as access target areas) different. In this way, the independent station also needs to perform different thread processing when dealing with the access behaviors of different users. When a large number of different users initiate access to the independent station in a short period of time, the independent station needs to consume a large amount of memory and computing power resources to deal with it, which may easily lead to unstable events such as illegal tampering of data or system crashes on the independent station. In order to ensure the stable operation of the independent station, it is necessary to collect the corresponding operational data of different users visiting the independent station, so as to provide sufficient and comprehensive data support for the subsequent independent station to deal with different situations. First, the login identity of the user visiting the independent site is identified, and the user's login identity on the independent site is compared with the user login history of the independent site. If the user's login identity does not exist in the user login history, it means that the user is logging into the independent site for the first time. The user is a stranger to the independent site. The data associated with the visit behavior of the stranger on the independent site is new and non-repetitive data for the independent site, which can improve the accuracy of subsequent independent site operation predictions. At this time, it is necessary to obtain the access log of the stranger on the independent site, which can effectively narrow the scope of subsequent operation data acquisition and reduce unnecessary repeated data verification operations.

[0096] The user's access log comprehensively records all the access behaviors initiated by the user after logging into the independent station. The types of these access behaviors are different. Some access behaviors are just data viewing, which will not substantially change the operating status of the independent station. Another part of the access behavior is to initiate corresponding task processing requirements, which will substantially change the operating status of the independent station (that is, increase the thread processing capacity of the independent station). It is the associated data of these access behaviors that have a substantial change on the operating status of the independent station that will play a key role in the subsequent prediction of the independent station's operating conditions. For this purpose, the access attributes of the access log are identified to obtain the access type and access block location initiated by the user to the independent station; based on the access type and access block location, it is determined whether the access behavior initiated by the user will increase the thread processing capacity of the independent station, and then it is determined whether the access behavior belongs to the target access behavior, and the scope of subsequent operational data acquisition is narrowed to the data part associated with the target access behavior, reducing the workload of operational data acquisition. In addition, the cookies data of the target access behavior is traced and analyzed to determine the operational data generated by the independent site during the period of the target access behavior, and the operational data is identified and located to determine the storage interval of the operational data in the independent site. In this way, the operational data is searched and obtained using the cookies data corresponding to the target access behavior as an index to ensure the efficiency of searching and obtaining the operational data.

[0097] Preferably, the operation data classification module is used to classify the operation data based on the usage records of the operation data in the independent station to obtain a number of operation sub-data, including:

[0098] Analyze the usage records of the operation data in the independent station to obtain the usage scenario types of the operation data in the operation process of the independent station; classify the operation data based on the usage scenario types to obtain a number of operation sub-data; wherein each operation sub-data corresponds to only one usage scenario type;

[0099] The operation data set generation module is used to perform backup processing on a number of operation sub-data based on the identification and positioning results of the operation data, obtain a number of backup operation sub-data, and integrate the number of backup operation sub-data into a number of corresponding operation data sets, including:

[0100] Based on the storage interval of the operation data in the independent station and the start and end characteristic codes of the corresponding operation sub-data, the operation sub-data are copied and backed up to obtain a number of backup operation sub-data; and based on the content attributes of each backup operation sub-data, each backup operation sub-data is integrated into the corresponding operation data set.

[0101] In the above technical solution, the operation data generated by the independent station is not only for simple data recording purposes, but it may also be used as raw data in the task processing process of other applications in the independent station, so that different operation data correspond to different usage scenarios within the independent station (such as task processing scenarios of different applications). The working status of different applications within the independent station also has a significant impact on the overall operation of the independent station. Therefore, it is necessary to accurately classify the operation data according to the usage records of the operation data within the independent station to obtain a number of operation sub-data, so that each operation sub-data corresponds to only one type of usage scenario, which is convenient for subsequent targeted and accurate operation status prediction of different operation scenarios of the independent station. In addition, based on the storage interval of the operation data in the independent station and the start and end feature codes of the corresponding operation sub-data, a number of operation sub-data are copied and backed up to obtain a number of backup operation sub-data; and based on the content attributes of each backup operation sub-data, each backup operation sub-data is integrated into the corresponding operation data set, which can ensure the diversity and richness of the data content of each operation data set itself, and provide a reliable data foundation for subsequent in-depth multi-dimensional analysis.

[0102] Preferably, the deep multi-dimensional analysis module is used to perform deep multi-dimensional analysis on each operational data set to obtain operational vulnerabilities and business processing change trends of the independent station, including:

[0103] Perform neural network deep clustering analysis on each operational data set to obtain operational vulnerabilities and business processing change trends of the independent station work scenario corresponding to each operational data set; operational vulnerabilities include data security vulnerabilities and operational dead loop vulnerabilities of the independent station work scenario; business processing change trends include the change trends of the amount of business data that the independent station work scenario needs to process in the future time period;

[0104] The block determination module is used to determine the first block of the independent station for vulnerability repair and the second block for business processing status change based on the operation vulnerability and business processing change trend, including:

[0105] Based on operational vulnerabilities, the blocks where illegal data tampering and operational dead loops occur in the independent station are determined, and used as the first blocks to be repaired. Based on the trend of business processing changes, the blocks where business data surges occur in the independent station are determined, and used as the second blocks where the business processing status is to be changed.

[0106] In the above technical scheme, each operation data set is for a specific operation scenario of the independent station. The neural network model is used to perform a neural network deep clustering analysis on each operation data set to obtain the operation vulnerabilities and business processing change trends of the independent station work scenario corresponding to each operation data set. In this way, the data security vulnerabilities and operation dead loop vulnerabilities of the independent station work scenario, as well as the change trend of the amount of business data that the independent station work scenario needs to process in the future time period can be accurately determined, and the operation problems and operation status of the independent station in different work scenarios can be comprehensively predicted. In addition, based on the operation vulnerabilities, the blocks where illegal data tampering and operation dead loops occur in the independent station are determined, and used as the first block to be repaired; based on the business processing change trend, the blocks where the business data surge event occurs in the independent station are determined, and used as the second block to change the business processing status. In order to accurately limit the operation blocks for adjusting the operation status of the independent station, there is no need to conduct a large-scale investigation of the independent station, reducing the workload of adjusting the operation status.

[0107] Preferably, the vulnerability repair module is used to perform vulnerability repair processing on the first block based on the operation thread information of the first block, including:

[0108] Monitor the operation thread of the first block to obtain the thread operation obstacle point of the first block; based on the obstacle type of the thread operation obstacle point, repair the illegal data tampering or the operation dead loop of the first block;

[0109] The service processing adjustment module is used to adjust the service processing state of the second block based on the service flow information of the second block, including:

[0110] The business data flow of the second block is monitored to obtain the business data receiving flow information of the second block; based on the business data receiving flow information, the business data of the second block is diverted or intercepted.

[0111] In the above technical scheme, the operation thread of the first block is monitored to obtain the thread operation obstacle point of the first block. The thread operation obstacle point may be, but is not limited to, a data location point where a data editing error occurs during the operation of the thread of the first block itself or an operation location point where the operation timeouts and freezes; then, based on the obstacle type of the thread operation obstacle point, the first block is repaired for illegal data tampering or operation dead loop repair, so as to quickly troubleshoot and repair the security risks of the independent station. In addition, the business data traffic of the second block is monitored to obtain the business data receiving traffic information of the second block, so as to accurately predict the super-large business data traffic that the independent station may encounter during operation; based on the business data receiving traffic information, the business data of the second block is diverted or intercepted, that is, the business data traffic sent to the second block is diverted to other blocks for processing or the business data traffic sent to the second block is intermittently intercepted, so as to avoid the second block receiving a large amount of business data at one time and exceeding its own data processing load, reduce the risk of system crash of the independent station, and ensure the continuity and reliability of the operation of the independent station.

[0112] It can be seen from the contents of the above embodiments that the independent station operation data classification and deep multi-dimensional processing method and system obtain the target access behavior from the user access log of the independent station, trace the target access behavior, and obtain the operation data corresponding to the user accessing the independent station. By extracting the operation data associated with the target access behavior, it is possible to reduce the workload of subsequent operation data analysis and accurately predict the operation status for specific working scenarios of the independent station; based on the usage record of the operation data, the operation data is classified into several operation sub-data, and integrated into several operation data sets through data replication and backup, to ensure that there is sufficient data for operation status prediction for different independent station working scenarios, and to achieve diversification of operation data; deep multi-dimensional analysis of the operation data set to obtain the operation vulnerabilities and business processing change trends of the independent station, thereby determining the first block to be repaired and the second block to change the business processing status, accurately defining the operation block for adjusting the operation status of the independent station, without the need to conduct a large-scale investigation of the independent station, and reducing the workload of adjusting the operation status; based on the operation thread of the first block and the business flow of the second block, the corresponding blocks are respectively repaired and the business processing status is adjusted, effectively responding to the security risks and crash risks of the independent station, and ensuring the continuity and reliability of the operation of the independent station.

[0113] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. The classification and deep multi-dimensional processing method of independent station operation data is characterized by: It includes the following steps: Step S1, obtaining a user access log of an independent site, analyzing the access log to obtain a target access behavior of the user to the independent site; tracing the target access behavior to obtain the operation data of the user in the process of accessing the independent site, and identifying and locating the operation data; Step S2, based on the usage record of the operation data at the independent station, classify the operation data to obtain a number of operation sub-data; based on the identification and positioning result of the operation data, back up the several operation sub-data to obtain a number of backup operation sub-data, and integrate the several backup operation sub-data into the corresponding several operation data sets, including analyzing the usage record of the operation data at the independent station to obtain the usage scenario type of the operation data during the operation of the independent station; based on the usage scenario type, classify the operation data to obtain a number of operation sub-data; wherein each operation sub-data corresponds to only one usage scenario type; based on the storage interval of the operation data at the independent station and the start and end feature codes of the several operation sub-data corresponding to it, copy and backup the several operation sub-data to obtain a number of backup operation sub-data; and based on the content attribute of each backup operation sub-data, integrate each backup operation sub-data into the corresponding operation data set; Step S3, performing a deep multi-dimensional analysis on each operation data set to obtain the operation vulnerabilities and business processing change trends of the independent station; based on the operation vulnerabilities and the business processing change trends, determining the first block of the independent station to be repaired and the second block to change the business processing status, including performing a neural network deep clustering analysis on each operation data set to obtain the operation vulnerabilities and business processing change trends of the independent station working scenario corresponding to each operation data set; wherein the operation vulnerabilities include data security vulnerabilities and operation dead loop vulnerabilities of the independent station working scenario; the business processing change trend includes the change trend of the amount of business data that needs to be processed by the independent station working scenario in the future time period; based on the operation vulnerabilities, determining the blocks of the independent station where illegal data tampering and operation dead loop occur, and using them as the first blocks to be repaired; based on the business processing change trend, determining the blocks of the independent station where a business data surge event occurs, and using them as the second blocks to change the business processing status; Step S4: based on the operation thread information of the first block, perform vulnerability repair processing on the first block; based on the business flow information of the second block, adjust the business processing state of the second block.

2. The independent station operation data classification and deep multi-dimensional processing method according to claim 1, characterized in that: In the step S1, a user access log of the independent site is obtained, and the access log is analyzed to obtain a target access behavior of the user to the independent site; Tracing the target access behavior, obtaining the operation data of the user in the process of accessing the independent website, and identifying and locating the operation data, including: Perform login identity recognition on the user accessing the independent site to determine whether the user is a stranger; if so, obtain the access log corresponding to the user on the independent site based on the user's identity information; if not, do not obtain the access log corresponding to the user on the independent site; Perform access attribute identification on the access log to obtain the access type and access block location initiated by the user to the independent station; based on the access type and the access block location, determine whether the access behavior initiated by the user will increase the thread processing capacity of the independent station; if so, determine that the access behavior belongs to the target access behavior; otherwise, determine that the access behavior does not belong to the target access behavior; The cookies data of the target access behavior is traced and analyzed to determine the operation data associated with the independent station during the period of the target access behavior, and the operation data is identified and located to determine the storage interval of the operation data in the independent station.

3. The independent station operation data classification and deep multi-dimensional processing method according to claim 1, characterized in that: In the step S4, based on the operation thread information of the first block, a vulnerability repair process is performed on the first block; Based on the service flow information of the second block, adjusting the service processing state of the second block includes: Monitor the operation thread of the first block to obtain the thread operation obstacle point of the first block; and repair the illegal data tampering or operation dead loop of the first block based on the obstacle type of the thread operation obstacle point; The business data flow of the second block is monitored to obtain the business data reception flow information of the second block; based on the business data reception flow information, the business data of the second block is diverted or intercepted.

4. Independent station operation data classification and deep multi-dimensional processing system, characterized by: include: An access behavior identification module is used to obtain user access logs of the independent site, analyze the access logs, and obtain the user's target access behavior to the independent site; An operation data acquisition and positioning module is used to trace the target access behavior, obtain the operation data of the user in the process of accessing the independent station, and identify and locate the operation data; An operation data classification module is used to classify the operation data based on the usage records of the operation data at the independent station to obtain a number of operation sub-data, including analyzing the usage records of the operation data at the independent station to obtain the usage scenario type of the operation data during the operation of the independent station; based on the usage scenario type, classify the operation data to obtain a number of operation sub-data; wherein each operation sub-data corresponds to only one usage scenario type; An operation data set generation module is used to perform backup processing on a number of operation sub-data based on the identification and positioning results of the operation data to obtain a number of backup operation sub-data, and integrate the number of backup operation sub-data into a number of corresponding operation data sets, including performing copy and backup processing on a number of operation sub-data based on the storage interval of the operation data in the independent station and the start and end characteristic codes of the corresponding number of operation sub-data to obtain a number of backup operation sub-data; and integrating each backup operation sub-data into a corresponding operation data set based on the content attributes of each backup operation sub-data; A deep multi-dimensional analysis module is used to perform a deep multi-dimensional analysis on each operational data set to obtain the operational vulnerabilities and business processing change trends of the independent station, including performing a neural network deep clustering analysis on each operational data set to obtain the operational vulnerabilities and business processing change trends of the independent station work scenario corresponding to each operational data set; wherein the operational vulnerabilities include data security vulnerabilities and operational dead loop vulnerabilities of the independent station work scenario; the business processing change trend includes the change trend of the amount of business data that the independent station work scenario needs to process in the future time period; A block determination module is used to determine, based on the operational vulnerability and the business processing change trend, a first block of the independent station to be repaired for the vulnerability and a second block of the business processing status to be changed, including, based on the operational vulnerability, determining a block of the independent station where illegal data tampering and operational dead loop occur, and using this as the first block to be repaired for the vulnerability; based on the business processing change trend, determining a block of the independent station where a business data surge event occurs, and using this as the second block to be changed for the business processing status; A vulnerability repair module, used for performing vulnerability repair processing on the first block based on the operation thread information of the first block; A business processing adjustment module is used to adjust the business processing state of the second block based on the business flow information of the second block.

5. The independent station operation data classification and deep multi-dimensional processing system according to claim 4, characterized in that: The access behavior identification module is used to obtain the user access log of the independent site, analyze the access log, and obtain the user's target access behavior to the independent site, including: Perform login identity recognition on the user accessing the independent site to determine whether the user is a stranger; if so, obtain the access log corresponding to the user on the independent site based on the user's identity information; if not, do not obtain the access log corresponding to the user on the independent site; Perform access attribute identification on the access log to obtain the access type and access block location initiated by the user to the independent station; based on the access type and the access block location, determine whether the access behavior initiated by the user will increase the thread processing capacity of the independent station; if so, determine that the access behavior belongs to the target access behavior; otherwise, determine that the access behavior does not belong to the target access behavior; The operation data acquisition and positioning module is used to trace the target access behavior, obtain the operation data of the user in the process of accessing the independent station, and identify and locate the operation data, including: The cookies data of the target access behavior is traced and analyzed to determine the operation data associated with the independent station during the period of the target access behavior, and the operation data is identified and located to determine the storage interval of the operation data in the independent station.

6. The independent station operation data classification and deep multi-dimensional processing system according to claim 4, characterized in that: The vulnerability repair module is used to perform vulnerability repair processing on the first block based on the operation thread information of the first block, including: Monitor the operation thread of the first block to obtain the thread operation obstacle point of the first block; and repair the illegal data tampering or operation dead loop of the first block based on the obstacle type of the thread operation obstacle point; The service processing adjustment module is used to adjust the service processing state of the second block based on the service flow information of the second block, including: The business data flow of the second block is monitored to obtain the business data reception flow information of the second block; based on the business data reception flow information, the business data of the second block is diverted or intercepted.

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