CN112667646B - Data storage method based on big data and cloud computing platform - Google Patents

Data storage method based on big data and cloud computing platform Download PDF

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CN112667646B
CN112667646B CN202110098652.3A CN202110098652A CN112667646B CN 112667646 B CN112667646 B CN 112667646B CN 202110098652 A CN202110098652 A CN 202110098652A CN 112667646 B CN112667646 B CN 112667646B
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CN112667646A (en
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梁志彬
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Huang Zebin
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Abstract

The application provides a data storage method based on big data and cloud computing and a cloud computing platform, and relates to the technical field of cloud computing, wherein a service data sequence and a protocol data sequence are obtained by respectively carrying out service data extraction and protocol data extraction on a plurality of data blocks of a target data packet to be stored in a storage mode; then, respectively extracting key information of the service data sequence and the protocol data sequence through a first key information extraction strategy and a second key information extraction strategy to obtain a first key information data set containing service data and a second key information data set containing protocol data; then, target information matching is carried out on the basis of the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content, and therefore a target data packet is stored in a warehouse on the basis of the target extraction data set; compared with the prior art, the safety of data packet storage can be improved.

Description

Data storage method based on big data and cloud computing platform
Technical Field
The application relates to the technical field of cloud computing, in particular to a data storage method based on big data and cloud computing and a cloud computing platform.
Background
With the development of the internet and big data technology, some service providing platforms can collect the use information of the user through the service platform which is responsible for maintenance, so that the big data analysis is carried out on the data of a large number of users in a centralized manner, the information such as the behavior preference of the user is analyzed, the service provided by the information optimizing platform based on the behavior preference and the like obtained through analysis is improved, and the use experience of the user is improved.
For the collected user information, because the user information includes important contents such as user information, the information needs to be stored securely to avoid stealing the user data.
However, in the prior art, when storing such information, only a simple storage means is generally used, and data security is low.
Disclosure of Invention
The application aims to provide a data storage method based on big data and cloud computing and a cloud computing platform, so as to solve at least part of technical problems.
In order to achieve the purpose, the technical scheme adopted by the application is as follows:
in a first aspect, the present application provides a data saving method based on big data and cloud computing, where the method includes:
acquiring a target data packet to be stored in a storage;
respectively extracting service data and protocol data from a plurality of data blocks in the target data packet to obtain a service data sequence and a protocol data sequence;
performing first key information extraction on the service data sequence through a first key information extraction strategy to obtain a first key information data set comprising service data;
performing second key information extraction on the protocol data sequence through a second key information extraction strategy to obtain a second key information data set comprising protocol data;
performing target information matching based on the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content in the target data packet; the target extraction content comprises at least one of service data and protocol data, and the target extraction data set is used for storing the target data packet in a storage mode.
In a second aspect, the present application provides a cloud computing platform, the control device comprising a memory for storing one or more programs; a processor; when the one or more programs are executed by the processor, the big data and cloud computing-based data saving method is realized.
In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the above-mentioned big data and cloud computing-based data saving method.
According to the data storage method and the cloud computing platform based on big data and cloud computing, a plurality of data blocks of a target data packet to be stored in a storage are subjected to business data extraction and protocol data extraction respectively to obtain a business data sequence and a protocol data sequence, namely, the data blocks in the target data packet are classified into a business data class and a protocol data class; then, respectively extracting key information of the service data sequence and the protocol data sequence through a first key information extraction strategy and a second key information extraction strategy to obtain a first key information data set containing service data and a second key information data set containing protocol data; then, target information matching is carried out on the basis of the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content, and therefore a target data packet is stored in a warehouse on the basis of the target extraction data set; compared with the prior art, the safety of data packet storage can be improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly explain the technical solutions of the present application, the drawings needed for the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also derive other related drawings from these drawings without inventive effort.
Fig. 1 is a block diagram of a cloud computing platform according to the present disclosure.
Fig. 2 is a flowchart of a data saving method based on big data and cloud computing according to the present application.
Fig. 3 is a flowchart of a data saving device based on big data and cloud computing according to the present application.
Detailed Description
To make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in some embodiments of the present application, and it is obvious that the described embodiments are some, but not all embodiments of the present application. The components of the present application, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as presented in the figures, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments obtained by a person of ordinary skill in the art based on a part of the embodiments in the present application without any creative effort belong to the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
It can be understood that the data storage method based on big data and cloud computing provided by the application can be applied to various scenes, such as intelligent medical treatment, live webcasting, cloud games, intelligent shopping, smart city management, intelligent automobile management systems, financial data management platforms, cloud office, cloud conference and other systems.
Referring to fig. 1, fig. 1 is a block diagram of a cloud computing platform 100 provided in the present application, where the cloud computing platform 100 includes a memory 101, a processor 102, and a communication interface 103, and the memory 101, the processor 102, and the communication interface 103 are electrically connected to each other directly or indirectly to implement data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
The memory 101 may be used to store software programs and modules, and the processor 102 executes the software programs and modules stored in the memory 101 to execute various functional applications and data processing, so as to execute the steps of the data saving method based on big data and cloud computing provided by the present application. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Programmable Read-Only Memory (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
Referring to fig. 2, fig. 2 is a flowchart of a data saving method based on big data and cloud computing according to the present application, where the data saving method includes the following steps:
and S310, acquiring a target data packet to be stored in a storage.
And S320, respectively performing service data extraction and protocol data extraction on the plurality of data blocks in the target data packet to obtain a service data sequence and a protocol data sequence.
In this embodiment, all data in the target database may be divided into a plurality of data blocks according to the received time sequence, and each data block may include service data and protocol data. For example, the service data may be a surveillance video code stream shot by a surveillance camera, or goods sales data generated by an intelligent shelf; the Protocol data may be a heartbeat message or an ARP (Address Resolution Protocol) message.
In this embodiment, in the service data sequence and the protocol data sequence obtained by performing step S320, the service data sequence includes all service data in the corresponding data block, and the protocol data sequence includes all protocol data in the corresponding data block.
S330, extracting first key information of the service data sequence to obtain a first key information data set comprising service data.
And S340, performing second key information extraction on the protocol data sequence through a second key information extraction strategy to obtain a second key information data set comprising the protocol data.
In this embodiment, for the extracted service data sequence and the extracted protocol data sequence, key information extraction is performed on the service data sequence and the protocol data sequence respectively through a first key information extraction policy configured for the service data in advance and a second key information extraction policy configured for the protocol data in advance, so as to obtain a first key information data set and a second key information data set respectively. It is understood that the first key information data set obtained based on the step S330 is a data set including service data, and the second key information data set obtained based on the step S340 is a data set including protocol data.
And S350, performing target information matching based on the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content in the target data packet.
In this embodiment, the target extraction content may be extraction content input by a user, that is: the target extraction content represents the content which needs to be extracted by the user; wherein the target extraction content includes at least one of service data and protocol data, that is: the user can extract a part of service data, a part of protocol data, a part of service data and a part of protocol data. In this way, after the first key information data set and the second key information data set are obtained, the target extraction data set corresponding to the target extraction content in the target data packet is obtained by performing target information matching on the first key information data set and the second key information data set based on the target extraction content, and then the target data packet may be put into storage based on the target extraction data set, for example, the target data packet may be compressed and encrypted by using a hash value corresponding to the target extraction data set as a compression key, so that the compressed and encrypted target data packet is put into storage.
Therefore, by adopting the technical scheme provided by the application, the service data sequence and the protocol data sequence are obtained by respectively extracting the service data and the protocol data of the plurality of data blocks of the target data packet to be stored in the storage, namely, the data blocks in the target data packet are classified into the service data class and the protocol data class; then, respectively extracting key information of the service data sequence and the protocol data sequence through a first key information extraction strategy and a second key information extraction strategy to obtain a first key information data set containing service data and a second key information data set containing protocol data; then, target information matching is carried out on the basis of the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content, and therefore a target data packet is stored in a warehouse on the basis of the target extraction data set; compared with the prior art, the safety of data packet storage can be improved.
As an embodiment, when the service data sequence and the protocol data sequence are extracted in step S320, in order to improve the accuracy of the service data and the protocol data, the following scheme may be adopted: firstly, respectively extracting service data from a plurality of data blocks in the target data packet to obtain service data extraction windows in the data blocks and initial service data corresponding to the service data extraction windows, for example, in this embodiment, each service data extraction window may be an extraction window corresponding to service data, such as video data, audio data, and the like; then, determining a service data sequence based on the service data extraction window and corresponding initial service data in each data block; then, respectively carrying out protocol data identification on a plurality of data blocks in the target data packet to obtain protocol data contents corresponding to the data blocks; then, respectively identifying protocol types of a plurality of data blocks in the target data packet to obtain target protocol types followed by the data blocks; then, associating the protocol data content with the target protocol type; and then, extracting protocol data based on a data packet of a target protocol type associated with preset target protocol data content in the target data packet to obtain a protocol data sequence.
In addition, as an embodiment, when step 330 is executed to extract the first key information data set, for each data block corresponding to the service data sequence, when the number of the initial service data of the data block is at least two, a service flag value of each initial service data may be obtained; the service marking value is used for indicating the counted times of the corresponding service data in a preset time interval; on one hand, when the initial service data with the highest service tag value is one, the initial service data with the highest service tag value is used as the target service data of the corresponding data block; on the other hand, when the number of the initial service data with the highest service mark value is at least two, acquiring the service type priority of the corresponding service data extraction window aiming at the initial service data with the highest service mark value; next, determining target service data corresponding to the corresponding data block according to the initial service data corresponding to the service type priority with the highest corresponding priority; then, for each data block, acquiring a target window ratio of a service data extraction window corresponding to corresponding target service data in each data block; the target window ratio is used for indicating the ratio of the length of the corresponding service data extraction window to the length of all the service data extraction windows; next, when the target window ratio is within a preset window ratio interval, retaining a corresponding service data extraction result; the reserved service data extraction result comprises a service data extraction window and target service data corresponding to the service data extraction window; then, when the target window ratio is not within the preset window ratio interval, setting the service data extraction result of the corresponding data block as an empty service data set; next, obtaining an updated service data sequence based on the service data extraction result corresponding to each data block; then, carrying out timestamp label identification on the updated service data sequence to obtain multiple groups of service initial data and service end data; next, determining the service data duration between each group of service initial data and service end data; then, when the service data duration is greater than or equal to a first set duration threshold, taking a key information data set formed by service start data and service end data of a corresponding group as a first alternative key information data set; next, for each first alternative key information data set, determining the feature service category with the largest occurrence count according to the updated target service data corresponding to each data block in the first alternative key information data set; then, the characteristic service category is used as a service category to which the service data included in the corresponding first candidate key information data set belongs; then, determining the service category to which each first alternative key information data set belongs; and then, when at least two first candidate key information data sets which are adjacent in time sequence all belong to the same service category, combining the at least two first candidate key information data sets to obtain a first key information data set corresponding to the same service category. Therefore, by adopting the scheme provided by the application, the first key information data set can be accurately extracted, and the pollution of dirty data is avoided.
In some scenarios, the service data extraction result in the service data sequence includes an empty service data set and a non-empty service data set; namely: some of the traffic data sequences are extracted as empty sets without traffic data.
Based on this, when performing timestamp label identification on the updated service data sequence to obtain multiple sets of service start data and service end data, a data block corresponding to a first non-empty service data set in a current identification cycle in the updated service data sequence may be used as the service start data of a current set; then, traversing the data block behind the service initial data of the current group; when the service data extraction result corresponding to the traversed current data block is an empty service data set and the service data extraction results corresponding to the data blocks within a second set time length threshold from the current data block are all empty service data sets, taking the current data block as the service end data of the current group; and then, taking a data block corresponding to a first non-empty service data set after the service end data of the current group as service start data of the current group of next identification cycle, and returning the step of traversing the data block after the service start data of the current group to continue execution until obtaining multiple groups of service start data and service end data.
In addition, in order to improve the integrity of data processing and avoid data omission, when the service data extraction result corresponding to the traversed current data block is an empty service data set and the service data extraction results corresponding to the data blocks within the second set duration threshold from the current data block are both empty service data sets, before the current data block is taken as the service end data of the current group, when the key information data set duration determined by the traversed current data block and the service start data of the current group is less than a third set duration threshold, whether the service data extraction result corresponding to the current data block is an empty service data set may be determined first; when the service data extraction result corresponding to the current data block is a non-empty service data set, taking the current data block as one of the key information data sets corresponding to the current group; then, when the current data block corresponds to an empty service data set and a service data extraction result within a second set duration threshold from the current data block includes a non-empty service data set, taking a data block corresponding to a first non-empty service data set within the second set duration threshold from the current data block as a traversed next current data block, and returning to the step of determining whether the service data extraction result corresponding to the current data block is an empty service data set when the duration of a key information data set determined by the traversed current data block and the service start data of the current group is less than a third set duration threshold.
Optionally, in this embodiment, when a data block corresponding to a first non-empty service data set in a current identification cycle in the updated service data sequence is used as service start data of a current group, a target data packet corresponding to the first non-empty service data set in the current identification cycle in the updated service data sequence may be obtained first; then, when the service data extraction result corresponding to the next data packet of the target data packet is an empty service data set, setting the service data extraction result corresponding to the target data packet as an empty service data set; or, when the service data extraction result corresponding to the next data packet of the target data packet is a non-empty service data set, using the target data packet as the service start data of the current group.
In addition, in this embodiment, as an implementation manner, when performing second key information extraction on the protocol data sequence through a second key information extraction policy to obtain a second key information data set including protocol data, timestamp tag identification may be performed on each protocol data in the protocol data sequence to obtain a plurality of second candidate key information data sets including protocol data; and then merging the second alternative key information data sets belonging to the same protocol type according to the protocol type corresponding to each second alternative key information data set to obtain a second key information data set comprising protocol data. It can be understood that, unlike a wide variety of service data, the protocol data generally follows a strict protocol standard, and therefore, a simpler extraction method can be adopted.
In addition, in this embodiment, as an implementation manner, when performing target information matching based on the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content in the target data packet, core key information in the target extraction content sent by a maintenance device may be obtained first, and it is understood that the maintenance device is a device used by a manager; then, performing keyword screening on the core key information to obtain screened core key information, and adding the screened core key information to a corresponding information processing node; next, performing key information matching on the first key information data set and the second key information data set based on the screened core key information and the information processing node to obtain at least one piece of initial matching key information; and then, determining at least one target matching key information which accords with a preset information screening strategy in all the initial matching key information, and constructing all the target matching key information into a target extraction data set.
Optionally, as an embodiment, the core key information includes a plurality of core keywords; based on this, when the key words of the core key information are screened to obtain the screened core key information, the historical statistical times and the threshold statistical times of each core key word in the core key information can be obtained firstly; and then, deleting the core key words of which the difference between the historical statistical times and the threshold statistical times exceeds a set time threshold, and taking the rest core key words as the core key information after screening.
In addition, in this embodiment, as an implementation manner, when performing key information matching on the first key information data set and the second key information data set based on the screened core key information and the information processing node to obtain at least one initial matching key information, the information processing node may first be used to respectively calculate a first key information association degree of each screened core key information with the first key information data set and a second key information association degree of the information processing node; then, for each piece of screened core key information, when any one of the corresponding first key information relevance degree and the corresponding second key information relevance degree is greater than a preset relevance degree threshold value, determining the corresponding core key information as initial matching key information; next, for each piece of screened core key information, when the corresponding first key information relevance degree and the corresponding second key information relevance degree are both smaller than or equal to a preset relevance degree threshold value, determining the corresponding core key information as initial matching key information; then, for each of the screened core key information, when one of the corresponding first key information relevance degree and the corresponding second key information relevance degree is greater than a preset relevance degree threshold value, and the other one is less than or equal to the preset relevance degree threshold value, discarding the corresponding core key information. Therefore, according to the scheme provided by the application, the accuracy of acquiring the key information can be improved.
As an implementation manner, when determining at least one target matching key information meeting a preset information screening policy in all the initial matching key information, first calculating each feature matching value of each initial matching key information based on at least one pre-configured feature value calculation policy, and calculating an initial evaluation score of each initial matching key information based on each feature matching value; then, determining an evaluation score threshold value corresponding to each initial matching key information based on the key information type to which each initial matching key information belongs; next, whether the initial evaluation score of each initial matching key information is larger than the corresponding evaluation score threshold value may be judged; then, at least one piece of initial matching key information, of which the corresponding initial evaluation score is greater than the respective corresponding evaluation score threshold value, is determined as target matching key information.
Referring to fig. 3, fig. 3 is a structural diagram of a data storage device based on big data and cloud computing according to the present application, where the data storage device 400 includes an obtaining module 410 and a processing module 420.
An obtaining module 410, configured to obtain a target data packet to be stored in a storage;
a processing module 420, configured to perform service data extraction and protocol data extraction on the multiple data blocks in the target data packet, respectively, to obtain a service data sequence and a protocol data sequence;
the processing module 420 is further configured to perform first key information extraction on the service data sequence through a first key information extraction policy to obtain a first key information data set including service data;
the processing module 420 is further configured to perform second key information extraction on the protocol data sequence through a second key information extraction strategy to obtain a second key information data set including protocol data;
the processing module 420 is further configured to perform target information matching based on the first key information data set and the second key information data set, so as to obtain a target extraction data set corresponding to target extraction content in the target data packet; the target extraction content comprises at least one of service data and protocol data, and the target extraction data set is used for storing the target data packet in a storage mode.
Optionally, the processing module 420 may be configured to extract service data and protocol data from a plurality of data blocks in the target data packet respectively to obtain a service data sequence and a protocol data sequence
Respectively extracting service data from a plurality of data blocks in the target data packet to obtain service data extraction windows in the data blocks and initial service data corresponding to the service data extraction windows;
determining a service data sequence based on the service data extraction window and corresponding initial service data in each data block;
respectively identifying protocol data of a plurality of data blocks in the target data packet to obtain protocol data contents corresponding to the data blocks;
respectively identifying protocol types of a plurality of data blocks in the target data packet to obtain target protocol types followed by the data blocks;
associating the protocol data content with the target protocol type;
and extracting protocol data based on a data packet of a target protocol type associated with preset target protocol data content in the target data packet to obtain a protocol data sequence.
Optionally, the processing module 420 may be configured to extract first key information from the service data sequence through a first key information extraction policy to obtain a first key information data set including service data
For each data block corresponding to the service data sequence, when the number of the initial service data of the data block is at least two, acquiring a service marking value of each initial service data; the service marking value is used for indicating the counted times of the corresponding service data in a preset time interval;
when the initial service data with the highest service tag value is one, taking the initial service data with the highest service tag value as the target service data of the corresponding data block;
when the number of the initial service data with the highest service marking value is at least two, acquiring the service type priority of the corresponding service data extraction window aiming at the initial service data with the highest service marking value;
determining target service data corresponding to the corresponding data block according to the initial service data corresponding to the service type priority with the highest corresponding priority;
for each data block, acquiring a target window ratio of a service data extraction window corresponding to corresponding target service data in each data block; the target window ratio is used for indicating the ratio of the length of the corresponding service data extraction window to the length of all the service data extraction windows;
when the target window ratio is within a preset window ratio interval, retaining a corresponding service data extraction result; the reserved service data extraction result comprises a service data extraction window and target service data corresponding to the service data extraction window;
when the target window ratio is not in the preset window ratio interval, setting the service data extraction result of the corresponding data block as a null service data set;
obtaining an updated service data sequence based on the service data extraction result corresponding to each data block;
performing timestamp label identification on the updated service data sequence to obtain multiple groups of service initial data and service end data;
determining the service data duration between each group of service initial data and service end data;
when the service data duration is greater than or equal to a first set duration threshold, taking a key information data set formed by service starting data and service ending data of a corresponding group as a first alternative key information data set;
for each first alternative key information data set, determining the feature service category with the largest occurrence count according to the updated target service data corresponding to each data block in the first alternative key information data set;
taking the characteristic service category as a service category to which service data included in the corresponding first alternative key information data set belongs;
determining the service category to which each first alternative key information data set belongs;
when at least two first alternative key information data sets which are adjacent in time sequence all belong to the same service category, merging the at least two first alternative key information data sets to obtain a first key information data set corresponding to the same service category.
Optionally, the service data extraction result in the service data sequence includes an empty service data set and a non-empty service data set;
when the processing module 420 performs timestamp label identification on the updated service data sequence to obtain multiple sets of service start data and service end data, it may be configured to:
taking a data block corresponding to a first non-empty service data set in a current identification cycle in the updated service data sequence as service initial data of a current group;
traversing the data block behind the service starting data of the current group;
when the service data extraction result corresponding to the traversed current data block is an empty service data set and the service data extraction results corresponding to the data blocks within a second set time length threshold from the current data block are all empty service data sets, taking the current data block as the service end data of the current group;
and taking the data block corresponding to the first non-empty service data set after the service end data of the current group as the service start data of the current group of the next identification cycle, and returning the step of traversing the data block after the service start data of the current group to continue execution until obtaining multiple groups of service start data and service end data.
Optionally, when the service data extraction result corresponding to the traversed current data block is an empty service data set and the service data extraction results corresponding to the data blocks within the second set duration threshold from the current data block are all empty service data sets, before the current data block is used as the service end data of the current group, the processing module 420 is further configured to:
when the time length of a key information data set determined by the traversed current data block and the service initial data of the current group is less than a third set time length threshold, determining whether a service data extraction result corresponding to the current data block is an empty service data set;
when the service data extraction result corresponding to the current data block is a non-empty service data set, taking the current data block as one of the key information data sets corresponding to the current group;
and when the current data block corresponds to an empty service data set and a service data extraction result within a second set time length threshold from the current data block comprises a non-empty service data set, taking a data block corresponding to a first non-empty service data set within the second set time length threshold from the current data block as a traversed next current data block, and returning to the step of determining whether the service data extraction result corresponding to the current data block is an empty service data set when the key information data set time length determined by the traversed current data block and the service starting data of the current group is less than a third set time length threshold.
Optionally, the processing module 420 may be configured to use a data block corresponding to a first non-empty service data set in a current identification cycle in the updated service data sequence as service start data of a current group
Acquiring a target data packet corresponding to a first non-empty service data set in the current identification cycle in the updated service data sequence;
when the service data extraction result corresponding to the next data packet of the target data packet is an empty service data set, setting the service data extraction result corresponding to the target data packet as the empty service data set;
and when the service data extraction result corresponding to the next data packet of the target data packet is a non-empty service data set, taking the target data packet as the service initial data of the current group.
Optionally, when the processing module 420 performs second key information extraction on the protocol data sequence through a second key information extraction policy to obtain a second key information data set including protocol data, the processing module may be configured to:
performing timestamp label identification on each protocol data in the protocol data sequence to obtain a plurality of second alternative key information data sets comprising the protocol data;
and merging the second alternative key information data sets belonging to the same protocol type according to the protocol type corresponding to each second alternative key information data set to obtain a second key information data set comprising protocol data.
Optionally, when the target information matching is performed based on the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content in the target data packet, the processing module 420 may be configured to:
obtaining core key information in target extraction content sent by maintenance equipment;
performing keyword screening on the core key information to obtain screened core key information, and adding the screened core key information to a corresponding information processing node;
performing key information matching on the first key information data set and the second key information data set based on the screened core key information and the information processing node to obtain at least one piece of initial matching key information;
and determining at least one target matching key information which accords with a preset information screening strategy in all the initial matching key information, and constructing all the target matching key information into a target extraction data set.
Optionally, the core key information includes a plurality of core keywords;
the processing module 420 may be configured to, when performing keyword screening on the core key information to obtain the screened core key information:
acquiring historical statistics times and threshold statistics times of each core keyword in the core key information;
and deleting the core key words of which the difference value between the historical statistical times and the threshold statistical times exceeds a set time threshold, and taking the rest core key words as the core key information after screening.
Optionally, when the processing module 420 performs key information matching on the first key information data set and the second key information data set based on the filtered core key information and the information processing node to obtain at least one initial matching key information, it may be configured to:
respectively calculating a first key information association degree of each screened core key information with the first key information data set and a second key information association degree of the information processing node by using the information processing node;
for each piece of screened core key information, when any one of the corresponding first key information relevancy and the corresponding second key information relevancy is greater than a preset relevancy threshold, determining the corresponding core key information as initial matching key information;
for each piece of screened core key information, when the corresponding first key information relevance degree and the corresponding second key information relevance degree are both smaller than or equal to a preset relevance degree threshold value, determining the corresponding core key information as initial matching key information;
and for each piece of screened core key information, when one of the corresponding first key information relevance degree and the corresponding second key information relevance degree is greater than a preset relevance degree threshold value, and the other one is less than or equal to the preset relevance degree threshold value, discarding the corresponding core key information.
Optionally, when determining that at least one target matching key information in all the initial matching key information meets the preset information screening policy, the processing module 420 may be configured to:
calculating each feature matching value of each piece of initial matching key information based on at least one pre-configured feature value calculation strategy, and calculating an initial evaluation score of each piece of initial matching key information based on each feature matching value;
determining an evaluation score threshold value corresponding to each initial matching key information based on the key information type to which each initial matching key information belongs;
judging whether the initial evaluation score of each piece of initial matching key information is larger than the corresponding evaluation score threshold value;
and determining at least one piece of initial matching key information with the corresponding initial evaluation score larger than the corresponding evaluation score threshold value as target matching key information.
In addition, the application also provides a data storage system based on big data and cloud computing, and the data storage system comprises a data acquisition node and a data processing node.
The data acquisition node is used for acquiring a target data packet to be stored in a storage;
the data processing node is used for respectively extracting service data and protocol data from the plurality of data blocks in the target data packet to obtain a service data sequence and a protocol data sequence;
the data processing node is further used for extracting first key information from the service data sequence through a first key information extraction strategy to obtain a first key information data set containing service data;
the data processing node is further used for extracting second key information from the protocol data sequence through a second key information extraction strategy to obtain a second key information data set comprising protocol data;
the data processing node is further configured to perform target information matching based on the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content in the target data packet; the target extraction content comprises at least one of service data and protocol data, and the target extraction data set is used for storing the target data packet in a storage mode.
Optionally, the data processing node may be configured to extract service data and protocol data from the multiple data blocks in the target data packet to obtain a service data sequence and a protocol data sequence
Respectively extracting service data from a plurality of data blocks in the target data packet to obtain service data extraction windows in the data blocks and initial service data corresponding to the service data extraction windows;
determining a service data sequence based on the service data extraction window and corresponding initial service data in each data block;
respectively identifying protocol data of a plurality of data blocks in the target data packet to obtain protocol data contents corresponding to the data blocks;
respectively identifying protocol types of a plurality of data blocks in the target data packet to obtain target protocol types followed by the data blocks;
associating the protocol data content with the target protocol type;
and extracting protocol data based on a data packet of a target protocol type associated with preset target protocol data content in the target data packet to obtain a protocol data sequence.
Optionally, the data processing node may be configured to extract the first key information of the service data sequence through a first key information extraction policy to obtain a first key information data set including the service data
For each data block corresponding to the service data sequence, when the number of the initial service data of the data block is at least two, acquiring a service marking value of each initial service data; the service marking value is used for indicating the counted times of the corresponding service data in a preset time interval;
when the initial service data with the highest service tag value is one, taking the initial service data with the highest service tag value as the target service data of the corresponding data block;
when the number of the initial service data with the highest service marking value is at least two, acquiring the service type priority of the corresponding service data extraction window aiming at the initial service data with the highest service marking value;
determining target service data corresponding to the corresponding data block according to the initial service data corresponding to the service type priority with the highest corresponding priority;
for each data block, acquiring a target window ratio of a service data extraction window corresponding to corresponding target service data in each data block; the target window ratio is used for indicating the ratio of the length of the corresponding service data extraction window to the length of all the service data extraction windows;
when the target window ratio is within a preset window ratio interval, retaining a corresponding service data extraction result; the reserved service data extraction result comprises a service data extraction window and target service data corresponding to the service data extraction window;
when the target window ratio is not in the preset window ratio interval, setting the service data extraction result of the corresponding data block as a null service data set;
obtaining an updated service data sequence based on the service data extraction result corresponding to each data block;
performing timestamp label identification on the updated service data sequence to obtain multiple groups of service initial data and service end data;
determining the service data duration between each group of service initial data and service end data;
when the service data duration is greater than or equal to a first set duration threshold, taking a key information data set formed by service starting data and service ending data of a corresponding group as a first alternative key information data set;
for each first alternative key information data set, determining the feature service category with the largest occurrence count according to the updated target service data corresponding to each data block in the first alternative key information data set;
taking the characteristic service category as a service category to which service data included in the corresponding first alternative key information data set belongs;
determining the service category to which each first alternative key information data set belongs;
when at least two first alternative key information data sets which are adjacent in time sequence all belong to the same service category, merging the at least two first alternative key information data sets to obtain a first key information data set corresponding to the same service category.
Optionally, the service data extraction result in the service data sequence includes an empty service data set and a non-empty service data set;
when the data processing node performs timestamp label identification on the updated service data sequence to obtain multiple sets of service start data and service end data, the data processing node may be configured to:
taking a data block corresponding to a first non-empty service data set in a current identification cycle in the updated service data sequence as service initial data of a current group;
traversing the data block behind the service starting data of the current group;
when the service data extraction result corresponding to the traversed current data block is an empty service data set and the service data extraction results corresponding to the data blocks within a second set time length threshold from the current data block are all empty service data sets, taking the current data block as the service end data of the current group;
and taking the data block corresponding to the first non-empty service data set after the service end data of the current group as the service start data of the current group of the next identification cycle, and returning the step of traversing the data block after the service start data of the current group to continue execution until obtaining multiple groups of service start data and service end data.
Optionally, when the service data extraction result corresponding to the traversed current data block is an empty service data set and the service data extraction results corresponding to the data blocks within the second set duration threshold from the current data block are all empty service data sets, before the current data block is used as the service end data of the current group, the data processing node is further configured to:
when the time length of a key information data set determined by the traversed current data block and the service initial data of the current group is less than a third set time length threshold, determining whether a service data extraction result corresponding to the current data block is an empty service data set;
when the service data extraction result corresponding to the current data block is a non-empty service data set, taking the current data block as one of the key information data sets corresponding to the current group;
and when the current data block corresponds to an empty service data set and a service data extraction result within a second set time length threshold from the current data block comprises a non-empty service data set, taking a data block corresponding to a first non-empty service data set within the second set time length threshold from the current data block as a traversed next current data block, and returning to the step of determining whether the service data extraction result corresponding to the current data block is an empty service data set when the key information data set time length determined by the traversed current data block and the service starting data of the current group is less than a third set time length threshold.
Optionally, when the data processing node uses, in the updated service data sequence, a data block corresponding to a first non-empty service data set in a current identification cycle as service start data of a current group, the data processing node may be configured to use the data block as service start data of the current group
Acquiring a target data packet corresponding to a first non-empty service data set in the current identification cycle in the updated service data sequence;
when the service data extraction result corresponding to the next data packet of the target data packet is an empty service data set, setting the service data extraction result corresponding to the target data packet as the empty service data set;
and when the service data extraction result corresponding to the next data packet of the target data packet is a non-empty service data set, taking the target data packet as the service initial data of the current group.
Optionally, when the data processing node performs second key information extraction on the protocol data sequence through a second key information extraction policy to obtain a second key information data set including protocol data, the data processing node may be configured to:
performing timestamp label identification on each protocol data in the protocol data sequence to obtain a plurality of second alternative key information data sets comprising the protocol data;
and merging the second alternative key information data sets belonging to the same protocol type according to the protocol type corresponding to each second alternative key information data set to obtain a second key information data set comprising protocol data.
Optionally, when the data processing node performs target information matching based on the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content in the target data packet, the data processing node may be configured to:
obtaining core key information in target extraction content sent by maintenance equipment;
performing keyword screening on the core key information to obtain screened core key information, and adding the screened core key information to a corresponding information processing node;
performing key information matching on the first key information data set and the second key information data set based on the screened core key information and the information processing node to obtain at least one piece of initial matching key information;
and determining at least one target matching key information which accords with a preset information screening strategy in all the initial matching key information, and constructing all the target matching key information into a target extraction data set.
Optionally, the core key information includes a plurality of core keywords;
the data processing node may be configured to, when performing keyword screening on the core key information to obtain screened core key information:
acquiring historical statistics times and threshold statistics times of each core keyword in the core key information;
and deleting the core key words of which the difference value between the historical statistical times and the threshold statistical times exceeds a set time threshold, and taking the rest core key words as the core key information after screening.
Optionally, when the data processing node performs key information matching on the first key information data set and the second key information data set based on the screened core key information and the information processing node to obtain at least one initial matching key information, the data processing node may be configured to:
respectively calculating a first key information association degree of each screened core key information with the first key information data set and a second key information association degree of the information processing node by using the information processing node;
for each piece of screened core key information, when any one of the corresponding first key information relevancy and the corresponding second key information relevancy is greater than a preset relevancy threshold, determining the corresponding core key information as initial matching key information;
for each piece of screened core key information, when the corresponding first key information relevance degree and the corresponding second key information relevance degree are both smaller than or equal to a preset relevance degree threshold value, determining the corresponding core key information as initial matching key information;
and for each piece of screened core key information, when one of the corresponding first key information relevance degree and the corresponding second key information relevance degree is greater than a preset relevance degree threshold value, and the other one is less than or equal to the preset relevance degree threshold value, discarding the corresponding core key information.
Optionally, when determining that at least one target matching key information in all the initial matching key information meets the preset information screening policy, the data processing node may be configured to:
calculating each feature matching value of each piece of initial matching key information based on at least one pre-configured feature value calculation strategy, and calculating an initial evaluation score of each piece of initial matching key information based on each feature matching value;
determining an evaluation score threshold value corresponding to each initial matching key information based on the key information type to which each initial matching key information belongs;
judging whether the initial evaluation score of each piece of initial matching key information is larger than the corresponding evaluation score threshold value;
and determining at least one piece of initial matching key information with the corresponding initial evaluation score larger than the corresponding evaluation score threshold value as target matching key information.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to some embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in some embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to some embodiments of the present application. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic or optical disk, etc. for storing program codes.
The above description is only a few examples of the present application and is not intended to limit the present application, and those skilled in the art will appreciate that various modifications and variations can be made in the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (9)

1. A data saving method based on big data and cloud computing is characterized by comprising the following steps:
acquiring a target data packet to be stored in a storage;
respectively extracting service data and protocol data from a plurality of data blocks in the target data packet to obtain a service data sequence and a protocol data sequence;
performing first key information extraction on the service data sequence through a first key information extraction strategy to obtain a first key information data set comprising service data;
performing second key information extraction on the protocol data sequence through a second key information extraction strategy to obtain a second key information data set comprising protocol data;
performing target information matching based on the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content in the target data packet; the target extraction content comprises at least one of service data and protocol data, and the target extraction data set is used for storing the target data packet in a storage mode;
the extracting the first key information of the service data sequence through the first key information extracting strategy to obtain a first key information data set including service data comprises:
for each data block corresponding to the service data sequence, when the number of the initial service data of the data block is at least two, acquiring a service marking value of each initial service data; the service marking value is used for indicating the counted times of the corresponding service data in a preset time interval;
when the initial service data with the highest service tag value is one, taking the initial service data with the highest service tag value as the target service data of the corresponding data block;
when the number of the initial service data with the highest service marking value is at least two, acquiring the service type priority of the corresponding service data extraction window aiming at the initial service data with the highest service marking value;
determining target service data corresponding to the corresponding data block according to the initial service data corresponding to the service type priority with the highest corresponding priority;
for each data block, acquiring a target window ratio of a service data extraction window corresponding to corresponding target service data in each data block; the target window ratio is used for indicating the ratio of the length of the corresponding service data extraction window to the length of all the service data extraction windows;
when the target window ratio is within a preset window ratio interval, retaining a corresponding service data extraction result; the reserved service data extraction result comprises a service data extraction window and target service data corresponding to the service data extraction window;
when the target window ratio is not in the preset window ratio interval, setting the service data extraction result of the corresponding data block as a null service data set;
obtaining an updated service data sequence based on the service data extraction result corresponding to each data block;
performing timestamp label identification on the updated service data sequence to obtain multiple groups of service initial data and service end data;
determining the service data duration between each group of service initial data and service end data;
when the service data duration is greater than or equal to a first set duration threshold, taking a key information data set formed by service starting data and service ending data of a corresponding group as a first alternative key information data set;
for each first alternative key information data set, determining the feature service category with the largest occurrence count according to the updated target service data corresponding to each data block in the first alternative key information data set;
taking the characteristic service category as a service category to which service data included in the corresponding first alternative key information data set belongs;
determining the service category to which each first alternative key information data set belongs;
when at least two first alternative key information data sets which are adjacent in time sequence all belong to the same service category, merging the at least two first alternative key information data sets to obtain a first key information data set corresponding to the same service category.
2. The method of claim 1, wherein the service data extraction result in the service data sequence comprises an empty service data set and a non-empty service data set;
the time stamp tag identification is performed on the updated service data sequence to obtain multiple sets of service start data and service end data, and the method comprises the following steps:
taking a data block corresponding to a first non-empty service data set in a current identification cycle in the updated service data sequence as service initial data of a current group;
traversing the data block behind the service starting data of the current group;
when the service data extraction result corresponding to the traversed current data block is an empty service data set and the service data extraction results corresponding to the data blocks within a second set time length threshold from the current data block are all empty service data sets, taking the current data block as the service end data of the current group;
and taking the data block corresponding to the first non-empty service data set after the service end data of the current group as the service start data of the current group of the next identification cycle, and returning the step of traversing the data block after the service start data of the current group to continue execution until obtaining multiple groups of service start data and service end data.
3. The method of claim 2, wherein when the service data extraction result corresponding to the traversed current data block is an empty service data set and the service data extraction results corresponding to the data blocks within the second set duration threshold from the current data block are all empty service data sets, before the current data block is used as the service end data of the current group, the method further comprises:
when the time length of a key information data set determined by the traversed current data block and the service initial data of the current group is less than a third set time length threshold, determining whether a service data extraction result corresponding to the current data block is an empty service data set;
when the service data extraction result corresponding to the current data block is a non-empty service data set, taking the current data block as one of the key information data sets corresponding to the current group;
and when the current data block corresponds to an empty service data set and a service data extraction result within a second set time length threshold from the current data block comprises a non-empty service data set, taking a data block corresponding to a first non-empty service data set within the second set time length threshold from the current data block as a traversed next current data block, and returning to the step of determining whether the service data extraction result corresponding to the current data block is an empty service data set when the time length of a key information data set determined by the traversed current data block and the service starting data of the current group is less than a third set time length threshold.
4. The method according to claim 3, wherein the using, as the service start data of the current group, the data block corresponding to the first non-empty service data set in the current identification cycle in the updated service data sequence comprises:
acquiring a target data packet corresponding to a first non-empty service data set in the current identification cycle in the updated service data sequence;
when the service data extraction result corresponding to the next data packet of the target data packet is an empty service data set, setting the service data extraction result corresponding to the target data packet as the empty service data set;
and when the service data extraction result corresponding to the next data packet of the target data packet is a non-empty service data set, taking the target data packet as the service initial data of the current group.
5. The method of claim 1, wherein the performing target information matching based on the first key information data set and the second key information data set to obtain a target extraction data set corresponding to target extraction content in the target data packet comprises:
obtaining core key information in target extraction content sent by maintenance equipment;
performing keyword screening on the core key information to obtain screened core key information, and adding the screened core key information to a corresponding information processing node;
performing key information matching on the first key information data set and the second key information data set based on the screened core key information and the information processing node to obtain at least one piece of initial matching key information;
and determining at least one target matching key information which accords with a preset information screening strategy in all the initial matching key information, and constructing all the target matching key information into a target extraction data set.
6. The method of claim 5, wherein the core key information comprises a plurality of core keywords;
the key word screening is carried out on the core key information to obtain the screened core key information, and the method comprises the following steps:
acquiring historical statistics times and threshold statistics times of each core keyword in the core key information;
and deleting the core key words of which the difference value between the historical statistical times and the threshold statistical times exceeds a set time threshold, and taking the rest core key words as the core key information after screening.
7. The method according to claim 5, wherein said performing key information matching on the first key information data set and the second key information data set based on the screened core key information and the information processing node to obtain at least one initial matching key information comprises:
respectively calculating a first key information association degree of each screened core key information with the first key information data set and a second key information association degree of the information processing node by using the information processing node;
for each piece of screened core key information, when any one of the corresponding first key information relevancy and the corresponding second key information relevancy is greater than a preset relevancy threshold, determining the corresponding core key information as initial matching key information;
for each piece of screened core key information, when the corresponding first key information relevance degree and the corresponding second key information relevance degree are both smaller than or equal to a preset relevance degree threshold value, determining the corresponding core key information as initial matching key information;
and for each piece of screened core key information, when one of the corresponding first key information relevance degree and the corresponding second key information relevance degree is greater than a preset relevance degree threshold value, and the other one is less than or equal to the preset relevance degree threshold value, discarding the corresponding core key information.
8. The method according to claim 5, wherein the determining at least one target matching key information that meets a preset information screening policy from among all initial matching key information comprises:
calculating each feature matching value of each piece of initial matching key information based on at least one pre-configured feature value calculation strategy, and calculating an initial evaluation score of each piece of initial matching key information based on each feature matching value;
determining an evaluation score threshold value corresponding to each initial matching key information based on the key information type to which each initial matching key information belongs;
judging whether the initial evaluation score of each piece of initial matching key information is larger than the corresponding evaluation score threshold value;
and determining at least one piece of initial matching key information with the corresponding initial evaluation score larger than the corresponding evaluation score threshold value as target matching key information.
9. A cloud computing platform, comprising:
a memory for storing one or more programs;
a processor;
the one or more programs, when executed by the processor, implement the big data and cloud computing-based data saving method of any of claims 1-8.
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