CN113064719A - Data resource obtaining method based on big data and edge computing and edge cloud platform - Google Patents
Data resource obtaining method based on big data and edge computing and edge cloud platform Download PDFInfo
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Abstract
The specification discloses a data resource obtaining method based on big data and edge computing and an edge cloud platform, and relates to the technical field of big data and edge computing. When the method is applied, firstly, authorization authentication information used for extracting real-time resource distribution information corresponding to each edge terminal is sent to each edge terminal; the authorization authentication information carries a dynamic check code and a dynamic random number of the edge cloud platform; detecting whether an authorization confirmation instruction fed back by each edge terminal based on the authorization authentication information corresponding to the edge terminal is received; and when the authorization confirmation instruction is received, synchronously acquiring the real-time resource distribution information of the edge terminal and the data flow information of the corresponding data terminal equipment stored by the edge terminal in real time. Therefore, synchronous acquisition of the data traffic information and the real-time resource distribution information can be ensured, and the accuracy and the reliability of the acquired data traffic information are further ensured.
Description
Technical Field
The application relates to the technical field of big data and edge computing, in particular to a data resource obtaining method based on big data and edge computing and an edge cloud platform.
Background
Nowadays, with the rapid development of new infrastructure services and applications such as 5g communication, internet of things technology, virtual reality technology, artificial intelligence and the like, new requirements and challenges are provided for the transmission capacity of a data network and the distribution processing capacity of data traffic. Further, the development of network application services attached to 5g communication has caused data traffic in data networks to exhibit explosive growth. For the reason that traditional cloud computing cannot meet the requirement, a data processing technology capable of improving the distribution processing efficiency of data traffic is urgently needed to meet the current data processing requirement. For example, how to ensure the accuracy and reliability of the acquired data traffic information is a technical problem that needs to be improved currently.
Disclosure of Invention
The specification provides a data resource obtaining method based on big data and edge computing and an edge cloud platform, so as to solve or partially solve the technical problems in the prior art.
The first aspect of the present specification provides a data resource acquisition method based on big data and edge calculation, where the method includes:
acquiring data traffic information of corresponding data terminal equipment through each edge terminal while acquiring real-time resource distribution information of each edge terminal; generating resource distribution map data of a plurality of edge terminals communicated with the edge cloud platform according to the acquired real-time resource distribution information, and performing iterative updating on the resource distribution map data by adopting each group of data traffic information;
detecting whether a first target graph node with a set state identifier exists in the resource distribution map data in real time in the process of adopting each group of data flow information to carry out iterative updating on the resource distribution map data;
when the first target graph node is detected to exist in the resource distribution map data, generating a target instruction for indicating a second target graph node to share physical resources to the first target graph node, and issuing the target instruction to the second target graph node, so that the second target graph node shares the physical resources of the second target graph node to the first target graph node according to the target instruction.
Optionally, generating a target instruction for instructing a second target graph node to share a physical resource with the first target graph node includes:
listing connection parameters corresponding to the directed connection of the first target graph node;
performing first screening on the directed connecting lines according to the connecting line parameters to obtain a plurality of first target directed connecting lines;
performing secondary screening on the plurality of first target directed connecting lines according to the acquired running log of the first target edge terminal corresponding to the first target directed connecting line to obtain a plurality of second target directed connecting lines;
generating a target instruction based on target connecting line parameters corresponding to the second target directional connecting line and a state log text of a second target edge terminal corresponding to the second target directional connecting line; and the second target edge terminal corresponds to a second target graph node.
Optionally, the first screening of the directed links according to the link parameters to obtain a plurality of first target directed links includes:
determining multiple groups of target parameters for representing existence of the bidirectional compatible identification from the connection parameters, and determining the directed connection corresponding to each group of target parameters as a first target directed connection; and the bidirectional compatible identifier represents that two edge terminals connected with the first target directional connecting line meet local compatible conditions.
Optionally, the second screening of the multiple first target directional links is performed according to the obtained running log of the first target edge terminal corresponding to the first target directional link, so as to obtain multiple second target directional links, including:
extracting log interaction data of each group of running logs and log time sequence track characteristics of each group of running logs; a first data list used for representing log updating distribution of the running log is built based on the log interaction data, log time sequence track characteristics are integrated according to running script files reserved for building the first data list from small to large in time sequence weight in parallel to obtain a track characteristic queue, and queue distribution data of the track characteristic queue are extracted to build a second data list; the first data list and the second data list respectively comprise a plurality of service data packets with different interactive activity coefficients;
after determining a logic description value of service logic information of any one first service data packet in the first data list, determining a second service data packet corresponding to an interactive activity coefficient which is the same as a numerical value represented by a median of the correlation degree from the second data list and determining the second service data packet as a reference data packet based on the correlation degree between other first service data packets in the first data list;
mapping the logic description value to a coordinate plane where the graph data corresponding to the reference data packet is located according to a similarity description vector between the first data list and the second data list, so as to obtain a logic mapping value of the logic description value in the coordinate plane; calculating a difference value between the logic mapping value and the logic description value and normalizing the difference value according to a preset reference value to obtain a difference value ratio; judging whether the difference ratio is within a set interval or not; if the difference ratio is not located in the set interval, weighting the difference ratio according to the terminal state factors of the first target edge terminals corresponding to each group of first target directed connecting lines until the weighted ratio is located in the set interval;
acquiring a connection characteristic queue of each group of first target directed connections and a queue priority corresponding to each connection characteristic queue, and fusing the first target directed connections according to the weighting ratio and each queue priority to obtain a reference directed connection for representing that the edge terminal is in a normal working state; and calculating the global distance of the similarity between each group of first target directed connecting lines and the reference directed connecting line, and determining the first target directed connecting lines with the global distance smaller than a set distance as the second target directed connecting lines.
Optionally, generating a target instruction based on the target connection parameter corresponding to the second target directional connection and the state log text of the second target edge terminal corresponding to the second target directional connection includes:
determining an operation stability record of an instruction receiving thread of the second target edge terminal based on target connecting line parameters corresponding to the second target directional connecting line, and determining specified text data of which the state labels do not change along with the updating of the stability labels in the operation stability record from state log texts corresponding to the operation stability record;
extracting a plurality of data fields of the specified text data according to field separators in the specified text data, dividing the plurality of data fields into a first field set and a second field set based on the data centrality corresponding to the specified text data, and adjusting at least part of the first field set in the first field set to the second field set according to the field similarity between each first field in the first field set and at least part of second fields in the second field set and the field call accumulated value of each first field at the current moment until the current overlap rate is not higher than the preset overlap rate when determining that the current overlap rate between the first clustering feature information of the first field set and the second clustering feature information of the second field set is higher than the preset overlap rate;
determining an instruction transceiving protocol field of a corresponding second target edge terminal according to all second fields in the second field set, and generating a target instruction corresponding to the second target edge terminal according to the instruction transceiving protocol field; and the target instructions corresponding to different second target edge terminals are different.
Optionally, the sharing, by the second target graph node according to the target instruction, the physical resource of the second target graph node to the first target graph node specifically includes:
analyzing the target instruction to acquire physical resource allocation information included in the target instruction; the physical resource allocation information comprises a physical resource sharing occupation ratio and a physical resource sharing time period;
acquiring thread configuration distribution information from a time slice resource allocation thread and modifying the thread configuration distribution information according to the physical resource sharing ratio so as to butt joint an api interface of the first target graph node; wherein the first target graph node shares the time slice resource corresponding to the physical resource sharing proportion in the second target graph node through the api interface;
and starting a preset timer and acquiring accumulated time when the modification of the thread configuration distribution information is finished, and recovering the modified thread configuration distribution information to terminate the physical resource sharing with the first target graph node when the accumulated time reaches a target time corresponding to a physical resource sharing time period.
Optionally, acquiring the real-time resource distribution information of each edge terminal and acquiring data traffic information of the corresponding data end device through each edge terminal includes:
sending authorization authentication information for extracting real-time resource distribution information corresponding to each edge terminal; the authorization authentication information carries a dynamic check code and a dynamic random number of the edge cloud platform;
detecting whether an authorization confirmation instruction fed back by each edge terminal based on the authorization authentication information corresponding to the edge terminal is received, and synchronously acquiring real-time resource distribution information of the edge terminal and data flow information of corresponding data end equipment stored by the edge terminal in real time when the authorization confirmation instruction is received.
Optionally, the method further comprises:
when the authorization confirmation instruction is not received, continuously sending authorization authentication information for real-time resource distribution information to the corresponding edge terminal; and the dynamic verification code and the dynamic random number of the edge cloud platform carried in the authorization authentication information are different from the previous dynamic verification code and the previous dynamic random number.
A second aspect of the present description provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described method.
A third aspect of the present specification provides an edge cloud platform comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
Through one or more technical schemes of this description, this description has following beneficial effect or advantage:
the method comprises the steps of firstly generating resource distribution map data according to acquired real-time resource distribution information and adopting data flow information to conduct iterative updating, secondly detecting whether a first target graph node with a set state identifier exists in the resource distribution map data or not in real time, and finally generating and issuing a target instruction to a second target graph node when the first target graph node is detected so that the second target graph node can share physical resources of the second target graph node to the first target graph node according to the target instruction. Therefore, the physical resource sharing of the edge terminals can be realized when the physical resources of the edge terminals corresponding to the first target graph node are insufficient, so that the distribution processing efficiency of the data flow of the edge terminals is improved, and the existing data processing requirements are further met.
The above description is only an outline of the technical solution of the present specification, and the embodiments of the present specification are described below in order to make the technical means of the present specification more clearly understood, and the present specification and other objects, features, and advantages of the present specification can be more clearly understood.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the specification. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 illustrates a communication architecture diagram of a big data and edge computation based data resource acquisition system according to one embodiment of the present description;
FIG. 2 is a flow diagram illustrating a data resource acquisition method based on big data and edge computation according to one embodiment of the present description;
FIG. 3 is a block diagram of a data resource acquisition device based on big data and edge computation according to an embodiment of the present specification;
FIG. 4 illustrates a schematic diagram of an edge cloud platform, according to one embodiment of the present description.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to meet the current data processing requirements, the inventor innovatively provides a technical scheme of a data resource obtaining method based on big data and edge computing and an edge cloud platform, the scheme can be understood as edge computing compared with the traditional cloud computing, and is different from the existing edge computing, and on the premise that the existing data processing requirements are met based on physical resource sharing, the scheme realizes position sensing, localization and heterogeneous compatibility of an edge terminal, and further improves the distribution processing efficiency of data traffic.
To achieve the above object, please first refer to fig. 1, which is a schematic view of a communication architecture of a data resource acquiring system 100 based on big data and edge computing according to an embodiment of the present invention, where the data resource acquiring system 100 includes an edge cloud platform 200 and a plurality of edge terminals 300. The edge cloud platform 200 and the edge terminals 300 are communicatively connected to each other, and one edge terminal 300 directly interfaces with one data receiving end device 400. In this embodiment, the data end device 400 may be a mobile phone, a tablet computer, a notebook computer, or the like, may also be an intelligent bracelet, an intelligent home, or the like, and may also be a factory automation device, and is not limited herein. The edge terminal 300 is used for performing distributed processing on the data generated by the data side device 400.
Further, on the basis of fig. 1, please refer to fig. 2 in combination, which provides a flowchart of a data resource obtaining method based on big data and edge computing, and the method may be applied to the edge cloud platform 200 in fig. 1, and specifically may include the contents described in the following steps S21 to S23.
Step S21, acquiring data traffic information of corresponding data terminal equipment through each edge terminal while acquiring real-time resource distribution information of each edge terminal; and generating resource distribution map data of a plurality of edge terminals communicated with the edge cloud platform according to the acquired real-time resource distribution information, and performing iterative update on the resource distribution map data by adopting each group of data traffic information.
In a specific scheme, the real-time resource distribution information is distribution information of time slice resources, and the data traffic information is used for representing a data generation rate of data end equipment. Furthermore, the resource distribution map data includes a plurality of map nodes, each map node corresponds to an edge terminal, different map nodes are connected by a directed connection line, and the directed connection line is a unidirectional connection line or a multidirectional connection line.
Step S22, detecting whether a first target graph node having a set status identifier exists in the resource distribution map data in real time in the process of iteratively updating the resource distribution map data by using each group of data traffic information.
In a specific scheme, the set state identifier is an identifier for characterizing that physical resources of the edge terminal corresponding to the graph node are insufficient.
Step S23, when it is detected that the first target graph node exists in the resource distribution map data, generating a target instruction for instructing a second target graph node to share physical resources to the first target graph node and issuing the target instruction to the second target graph node, so that the second target graph node shares the physical resources of the second target graph node to the first target graph node according to the target instruction.
Through the content described in the above steps S21 to S23, first, resource distribution map data is generated according to the obtained real-time resource distribution information and iterative updating is performed by using data flow information, then, whether a first target graph node having a set state identifier exists in the resource distribution map data is detected in real time, and finally, when the first target graph node is detected, a target instruction is generated and issued to a second target graph node, so that the second target graph node shares physical resources of the second target graph node to the first target graph node according to the target instruction. Therefore, the physical resource sharing of the edge terminals can be realized when the physical resources of the edge terminals corresponding to the first target graph node are insufficient, so that the distribution processing efficiency of the data flow of the edge terminals is improved, and the existing data processing requirements are further met.
In practical implementation, in order not to affect normal operations of other edge terminals and to take into account local incompatibility between edge terminals, the generation of the target instruction for instructing the second target graph node to share the physical resource with the first target graph node, which is described in step S23, may further include the following contents described in step S231-step S234.
Step S231, listing the link parameters corresponding to the directed links of the first target graph node.
Step S232, the directed connecting lines are screened for the first time according to the connecting line parameters to obtain a plurality of first target directed connecting lines.
Step S233, performing a second screening on the plurality of first target directional links according to the obtained running log of the first target edge terminal corresponding to the first target directional link, to obtain a plurality of second target directional links.
Step S234, generating a target instruction based on the target connecting line parameter corresponding to the second target directional connecting line and the state log text of the second target edge terminal corresponding to the second target directional connecting line; and the second target edge terminal corresponds to a second target graph node.
It is understood that, in the above steps S231 to S234, the first filtering can take into account the local incompatibility between the edge terminals, and the second filtering can ensure that the generated target instruction does not affect the normal operation of the second target edge terminal when being issued to the second target edge terminal.
In a possible implementation manner, the performing, in step S232, the first screening on the directional links according to the link parameters to obtain a plurality of first target directional links specifically includes: and determining multiple groups of target parameters for representing the existence of the bidirectional compatible identifier from the connecting line parameters, and determining the directed connecting line corresponding to each group of target parameters as a first target directed connecting line. The bidirectional compatible identifier represents that two edge terminals connected with the first target directed connecting line can meet local compatible conditions.
In practical applications, in order to ensure accuracy and reliability of screening the second target directional links, the second screening of the plurality of first target directional links according to the obtained running log of the first target edge terminal corresponding to the first target directional link in step S233 is performed to obtain a plurality of second target directional links, which may further include the contents described in the following steps S2331 to S2334.
Step S2331, extracting log interaction data of each group of running logs and log time sequence track characteristics of each group of running logs; a first data list used for representing log updating distribution of the running log is built based on the log interaction data, log time sequence track characteristics are integrated according to running script files reserved for building the first data list from small to large in time sequence weight in parallel to obtain a track characteristic queue, and queue distribution data of the track characteristic queue are extracted to build a second data list; the first data list and the second data list respectively comprise a plurality of service data packets with different interactive activity coefficients.
Step S2332, after determining the logic description value of the service logic information of any one first service data packet in the first data list, based on the correlation between other first service data packets in the first data list, determining, from the second data list, a second service data packet corresponding to the interactive activity coefficient having the same value as the value represented by the median of the correlation, and determining the second service data packet as a reference data packet.
Step S2333, mapping the logic description value to a coordinate plane where the graph data corresponding to the reference data packet is located according to the similarity description vector between the first data list and the second data list, so as to obtain a logic mapping value of the logic description value in the coordinate plane; calculating a difference value between the logic mapping value and the logic description value and normalizing the difference value according to a preset reference value to obtain a difference value ratio; judging whether the difference ratio is within a set interval or not; and if the difference ratio is not located in the set interval, weighting the difference ratio according to the terminal state factors of the first target edge terminals corresponding to each group of the first target directed connecting lines until the weighted ratio is located in the set interval.
Step S2334, obtaining connection feature queues of each group of first target directed connection lines and queue priorities corresponding to the connection feature queues, and fusing the first target directed connection lines according to the weighting ratio and the queue priorities to obtain reference directed connection lines used for representing that the edge terminal is in a normal working state; and calculating the global distance of the similarity between each group of first target directed connecting lines and the reference directed connecting line, and determining the first target directed connecting lines with the global distance smaller than a set distance as the second target directed connecting lines.
In specific implementation, based on the contents described in the above steps S2331-S2334, the accuracy and reliability of screening the second target directional connecting line can be ensured.
In an implementation example, the generating of the target instruction based on the target connection parameter corresponding to the second target directional connection and the state log text of the second target edge terminal corresponding to the second target directional connection, which is described in step S234, may specifically include the following contents described in steps S2341 to S2343.
Step S2341, determining an operation stability record of the instruction receiving thread of the second target edge terminal based on the target connection parameter corresponding to the second target directional connection, and determining, from the status log text corresponding to the operation stability record, specified text data whose status label does not change with an update of the stability label in the operation stability record.
Step S2342, extracting a plurality of data fields of the designated text data according to the field separators in the designated text data, dividing the plurality of data fields into a first field set and a second field set based on the data centrality corresponding to the designated text data, and when it is determined that the current overlap rate between the first clustering feature information of the first field set and the second clustering feature information of the second field set is higher than a preset overlap rate, adjusting at least part of the first field set in the first field set to the second field set according to the field similarity between each first field in the first field set and at least part of the second fields in the second field set and the field call accumulated value of each first field at the current time until the current overlap rate is not higher than the preset overlap rate.
Step S2343, determining a corresponding instruction transceiving protocol field of the second target edge terminal according to all second fields in the second field set, and generating a target instruction corresponding to the second target edge terminal according to the instruction transceiving protocol field; and the target instructions corresponding to different second target edge terminals are different.
By applying the contents described in the steps S2341 to S2343, the target instruction can be generated specifically for different second target edge terminals, so that the target instruction is issued accurately and efficiently.
In a specific implementation process, in step S23, the second target graph node may specifically share the physical resource of the second target graph node to the first target graph node according to the target instruction in the following specific manner.
Step a, analyzing the target instruction to acquire physical resource allocation information included in the target instruction; the physical resource allocation information includes a physical resource sharing ratio and a physical resource sharing period.
Step b, acquiring thread configuration distribution information from a time slice resource allocation thread according to the physical resource sharing ratio, and modifying the thread configuration distribution information so as to butt joint an api interface of the first target graph node; and the first target graph node shares the time slice resource corresponding to the physical resource sharing proportion in the second target graph node through the api interface.
And c, starting a preset timer and acquiring accumulated time when the modification of the thread configuration distribution information is finished, and recovering the modified thread configuration distribution information to terminate the physical resource sharing with the first target graph node when the accumulated time reaches a target time corresponding to a physical resource sharing time period.
In specific implementation, through the steps a to c, the second target graph node can dynamically realize sharing of physical resources (time slice resources) based on the physical resource allocation information included in the target instruction, so that the processing efficiency of data distribution is improved, and a plurality of edge terminals can be ensured to operate efficiently and cooperatively.
In one possible implementation manner, in order to ensure the accuracy and reliability of the obtained data traffic information, the step S21 of acquiring the real-time resource distribution information of each edge terminal and simultaneously acquiring the data traffic information of the corresponding data end device by each edge terminal may specifically include the following contents described in step S211 and step S212.
Step S211, sending authorization authentication information for extracting real-time resource distribution information corresponding to each edge terminal; the authorization authentication information carries a dynamic check code and a dynamic random number of the edge cloud platform.
In this way, based on the above steps S211 and S212, when an authorization confirmation instruction fed back by each edge terminal based on the authorization authentication information corresponding to the edge terminal is received, the real-time resource distribution information of the edge terminal and the data traffic information of the corresponding data end device stored by the edge terminal in real time can be synchronously acquired, so that the synchronous acquisition of the data traffic information and the real-time resource distribution information is ensured, and the accuracy and reliability of the acquired data traffic information are further ensured.
On the basis of the above steps S211 and S212, the method further includes: when the authorization confirmation instruction is not received, continuously sending authorization authentication information for real-time resource distribution information to the corresponding edge terminal; and the dynamic check code and the dynamic random number of the edge cloud platform carried in the authorization authentication information are different from those before. Therefore, the authorization of the edge terminal can be continuously requested when the edge terminal is ensured to have authorization misjudgment, and the flexibility and reliability of the authorization authentication of the edge cloud platform are ensured.
In an alternative embodiment, the generating resource distribution map data of a plurality of edge terminals communicating with the edge cloud platform according to the obtained real-time resource distribution information described in step S21 may further include the following steps (11) to (14).
(11) And projecting the resource distribution track curve of the acquired real-time resource distribution information to a preset graph plane to obtain a resource projection curve of each resource distribution track curve in the preset graph plane.
(12) Extracting a curve time sequence characteristic set and a curve description characteristic set of each resource projection curve; the curve time sequence characteristic set is used for representing time sequence characteristic change of the resource projection curve, and the curve description characteristic set is used for representing track characteristic change of the resource projection curve.
(13) And clustering the resource projection curves in the preset graph plane according to the curve time sequence characteristic set and the curve description characteristic set to obtain at least two groups of cluster sets.
(14) And determining a correlation coefficient between the resource projection curves in each group of clustering sets, and generating resource distribution map data of a plurality of edge terminals communicated with the edge cloud platform according to the correlation coefficient and the direction information of the clustering labels of the resource projection curves in each group of clustering sets.
It can be understood that, through the steps (11) to (14), the integrity of the resource distribution map data can be ensured.
In another alternative embodiment, the step S21 of iteratively updating the resource profile data with each set of data traffic information may further include the following steps (21) to (23).
(21) And calculating the real-time flow value corresponding to each group of data flow information.
(22) And sequencing the data traffic information according to the sequence of the real-time traffic values from large to small to obtain a data traffic sequencing sequence.
(23) According to the set time step, periodically and sequentially adopting the data traffic information in the data traffic sequencing sequence to iteratively update the resource distribution map data; the iterative updating of the resource distribution map data includes modification of node weights of graph nodes in the resource distribution map data and directional adjustment of directed connecting lines.
It can be understood that, based on the steps (21) to (23), the resource distribution map data can be iteratively updated by sequentially using the data traffic information based on the magnitude order of the real-time traffic values, so that the accuracy and reliability of the iterative update can be ensured.
Further, in step S22, detecting whether the first target graph node having the set status flag exists in the resource profile data in real time may specifically include the following contents described in step S221 and step S222.
Step S221, extracting a graph node list of the resource distribution map data in real time, and determining a state identification set of each graph node in the graph node list; the state identification set comprises a plurality of state identifications counted according to the time sequence of iterative update.
Step S222, for the latest state identifier in the state identifier set, if the current matching degree between the word vector of the latest state identifier and the preset word vector is greater than the set matching degree, determining that the latest state identifier is the set state identifier, and determining the first target graph node corresponding to the set state identifier.
When the contents described in the above steps S221 to S222 are applied, the detection accuracy of the first target graph node to which the state flag is set can be ensured.
Based on the same inventive concept as the previous embodiment, please refer to fig. 3 in combination, a block diagram of a data resource obtaining apparatus 210 based on big data and edge calculation is provided, which specifically includes the following contents.
A1. A data resource acquisition device 210 based on big data and edge computation, the device comprising:
the data updating module 211 is configured to acquire real-time resource distribution information of each edge terminal and acquire data traffic information of corresponding data end equipment through each edge terminal; generating resource distribution map data of a plurality of edge terminals communicated with the edge cloud platform according to the acquired real-time resource distribution information, and performing iterative updating on the resource distribution map data by adopting each group of data traffic information;
a node detection module 212, configured to detect whether a first target graph node having a set state identifier exists in the resource distribution map data in real time in a process of performing iterative update on the resource distribution map data by using each set of data traffic information;
the resource sharing module 213 is configured to, when it is detected that the first target graph node exists in the resource distribution map data, generate a target instruction for instructing a second target graph node to share a physical resource with the first target graph node and issue the target instruction to the second target graph node, so that the second target graph node shares the physical resource of the second target graph node with the first target graph node according to the target instruction;
the data updating module 211 is specifically configured to:
projecting the resource distribution track curve of the acquired real-time resource distribution information to a preset graph plane to obtain a resource projection curve of each resource distribution track curve in the preset graph plane;
extracting a curve time sequence characteristic set and a curve description characteristic set of each resource projection curve; the curve time sequence characteristic set is used for representing time sequence characteristic change of the resource projection curve, and the curve description characteristic set is used for representing track characteristic change of the resource projection curve;
clustering the resource projection curves in the preset graph plane according to the curve time sequence feature set and the curve description feature set to obtain at least two groups of cluster sets;
and determining a correlation coefficient between the resource projection curves in each group of clustering sets, and generating resource distribution map data of a plurality of edge terminals communicated with the edge cloud platform according to the correlation coefficient and the direction information of the clustering labels of the resource projection curves in each group of clustering sets.
A2. As the apparatus in a1, the resource sharing module 213 is specifically configured to:
listing connection parameters corresponding to the directed connection of the first target graph node;
performing first screening on the directed connecting lines according to the connecting line parameters to obtain a plurality of first target directed connecting lines;
performing secondary screening on the plurality of first target directed connecting lines according to the acquired running log of the first target edge terminal corresponding to the first target directed connecting line to obtain a plurality of second target directed connecting lines;
generating a target instruction based on target connecting line parameters corresponding to the second target directional connecting line and a state log text of a second target edge terminal corresponding to the second target directional connecting line; and the second target edge terminal corresponds to a second target graph node.
A3. The apparatus of a2, the resource sharing module 213, further configured to:
determining multiple groups of target parameters for representing existence of the bidirectional compatible identification from the connection parameters, and determining the directed connection corresponding to each group of target parameters as a first target directed connection; and the bidirectional compatible identifier represents that two edge terminals connected with the first target directional connecting line meet local compatible conditions.
A4. The apparatus of a2, the resource sharing module 213, further configured to:
extracting log interaction data of each group of running logs and log time sequence track characteristics of each group of running logs; a first data list used for representing log updating distribution of the running log is built based on the log interaction data, log time sequence track characteristics are integrated according to running script files reserved for building the first data list from small to large in time sequence weight in parallel to obtain a track characteristic queue, and queue distribution data of the track characteristic queue are extracted to build a second data list; the first data list and the second data list respectively comprise a plurality of service data packets with different interactive activity coefficients;
after determining a logic description value of service logic information of any one first service data packet in the first data list, determining a second service data packet corresponding to an interactive activity coefficient which is the same as a numerical value represented by a median of the correlation degree from the second data list and determining the second service data packet as a reference data packet based on the correlation degree between other first service data packets in the first data list;
mapping the logic description value to a coordinate plane where the graph data corresponding to the reference data packet is located according to a similarity description vector between the first data list and the second data list, so as to obtain a logic mapping value of the logic description value in the coordinate plane; calculating a difference value between the logic mapping value and the logic description value and normalizing the difference value according to a preset reference value to obtain a difference value ratio; judging whether the difference ratio is within a set interval or not; if the difference ratio is not located in the set interval, weighting the difference ratio according to the terminal state factors of the first target edge terminals corresponding to each group of first target directed connecting lines until the weighted ratio is located in the set interval;
acquiring a connection characteristic queue of each group of first target directed connections and a queue priority corresponding to each connection characteristic queue, and fusing the first target directed connections according to the weighting ratio and each queue priority to obtain a reference directed connection for representing that the edge terminal is in a normal working state; and calculating the global distance of the similarity between each group of first target directed connecting lines and the reference directed connecting line, and determining the first target directed connecting lines with the global distance smaller than a set distance as the second target directed connecting lines.
A5. The apparatus of a2, the resource sharing module 213, further configured to:
determining an operation stability record of an instruction receiving thread of the second target edge terminal based on target connecting line parameters corresponding to the second target directional connecting line, and determining specified text data of which the state labels do not change along with the updating of the stability labels in the operation stability record from state log texts corresponding to the operation stability record;
extracting a plurality of data fields of the specified text data according to field separators in the specified text data, dividing the plurality of data fields into a first field set and a second field set based on the data centrality corresponding to the specified text data, and adjusting at least part of the first field set in the first field set to the second field set according to the field similarity between each first field in the first field set and at least part of second fields in the second field set and the field call accumulated value of each first field at the current moment until the current overlap rate is not higher than the preset overlap rate when determining that the current overlap rate between the first clustering feature information of the first field set and the second clustering feature information of the second field set is higher than the preset overlap rate;
determining an instruction transceiving protocol field of a corresponding second target edge terminal according to all second fields in the second field set, and generating a target instruction corresponding to the second target edge terminal according to the instruction transceiving protocol field; and the target instructions corresponding to different second target edge terminals are different.
A6. The apparatus of any of a1-a5, the sharing, by the second target graph node, the physical resources of the second target graph node to the first target graph node according to the target instructions comprising in particular:
analyzing the target instruction to acquire physical resource allocation information included in the target instruction; the physical resource allocation information comprises a physical resource sharing occupation ratio and a physical resource sharing time period;
acquiring thread configuration distribution information from a time slice resource allocation thread and modifying the thread configuration distribution information according to the physical resource sharing ratio so as to butt joint an api interface of the first target graph node; wherein the first target graph node shares the time slice resource corresponding to the physical resource sharing proportion in the second target graph node through the api interface;
and starting a preset timer and acquiring accumulated time when the modification of the thread configuration distribution information is finished, and recovering the modified thread configuration distribution information to terminate the physical resource sharing with the first target graph node when the accumulated time reaches a target time corresponding to a physical resource sharing time period.
A7. The apparatus of a1, the data update module 211, configured to:
sending authorization authentication information for extracting real-time resource distribution information corresponding to each edge terminal; the authorization authentication information carries a dynamic check code and a dynamic random number of the edge cloud platform;
detecting whether an authorization confirmation instruction fed back by each edge terminal based on the authorization authentication information corresponding to the edge terminal is received, and synchronously acquiring real-time resource distribution information of the edge terminal and data flow information of corresponding data end equipment stored by the edge terminal in real time when the authorization confirmation instruction is received.
A8. The apparatus of a7, the data update module 211, further configured to:
when the authorization confirmation instruction is not received, continuously sending authorization authentication information for real-time resource distribution information to the corresponding edge terminal; and the dynamic verification code and the dynamic random number of the edge cloud platform carried in the authorization authentication information are different from the previous dynamic verification code and the previous dynamic random number.
Based on the same inventive concept as the foregoing embodiment, a data resource acquisition system based on big data and edge calculation is also provided, which is described in detail as follows.
B1. A data resource acquisition system based on big data and edge computing comprises an edge cloud platform and a plurality of edge terminals, wherein the edge cloud platform is in communication connection with the edge terminals, and one edge terminal is directly butted with one data terminal receiving device;
the edge cloud platform is used for:
acquiring data traffic information of corresponding data terminal equipment through each edge terminal while acquiring real-time resource distribution information of each edge terminal; generating resource distribution map data of a plurality of edge terminals communicated with the edge cloud platform according to the acquired real-time resource distribution information, and performing iterative updating on the resource distribution map data by adopting each group of data traffic information;
detecting whether a first target graph node with a set state identifier exists in the resource distribution map data in real time in the process of adopting each group of data flow information to carry out iterative updating on the resource distribution map data;
when the first target graph node exists in the resource distribution map data, generating a target instruction for indicating a second target graph node to share physical resources to the first target graph node, and issuing the target instruction to the second target graph node;
the second target graph node is to:
and sharing the physical resources of the second target graph node to the first target graph node according to the target instruction.
B2. As in the system described in B1, the edge cloud platform is specifically configured to:
listing connection parameters corresponding to the directed connection of the first target graph node;
performing first screening on the directed connecting lines according to the connecting line parameters to obtain a plurality of first target directed connecting lines;
performing secondary screening on the plurality of first target directed connecting lines according to the acquired running log of the first target edge terminal corresponding to the first target directed connecting line to obtain a plurality of second target directed connecting lines;
generating a target instruction based on target connecting line parameters corresponding to the second target directional connecting line and a state log text of a second target edge terminal corresponding to the second target directional connecting line; and the second target edge terminal corresponds to a second target graph node.
B3. The system of B2, the edge cloud platform further to:
determining multiple groups of target parameters for representing existence of the bidirectional compatible identification from the connection parameters, and determining the directed connection corresponding to each group of target parameters as a first target directed connection; and the bidirectional compatible identifier represents that two edge terminals connected with the first target directional connecting line meet local compatible conditions.
B4. The system of B2, the edge cloud platform further to:
extracting log interaction data of each group of running logs and log time sequence track characteristics of each group of running logs; a first data list used for representing log updating distribution of the running log is built based on the log interaction data, log time sequence track characteristics are integrated according to running script files reserved for building the first data list from small to large in time sequence weight in parallel to obtain a track characteristic queue, and queue distribution data of the track characteristic queue are extracted to build a second data list; the first data list and the second data list respectively comprise a plurality of service data packets with different interactive activity coefficients;
after determining a logic description value of service logic information of any one first service data packet in the first data list, determining a second service data packet corresponding to an interactive activity coefficient which is the same as a numerical value represented by a median of the correlation degree from the second data list and determining the second service data packet as a reference data packet based on the correlation degree between other first service data packets in the first data list;
mapping the logic description value to a coordinate plane where the graph data corresponding to the reference data packet is located according to a similarity description vector between the first data list and the second data list, so as to obtain a logic mapping value of the logic description value in the coordinate plane; calculating a difference value between the logic mapping value and the logic description value and normalizing the difference value according to a preset reference value to obtain a difference value ratio; judging whether the difference ratio is within a set interval or not; if the difference ratio is not located in the set interval, weighting the difference ratio according to the terminal state factors of the first target edge terminals corresponding to each group of first target directed connecting lines until the weighted ratio is located in the set interval;
acquiring a connection characteristic queue of each group of first target directed connections and a queue priority corresponding to each connection characteristic queue, and fusing the first target directed connections according to the weighting ratio and each queue priority to obtain a reference directed connection for representing that the edge terminal is in a normal working state; and calculating the global distance of the similarity between each group of first target directed connecting lines and the reference directed connecting line, and determining the first target directed connecting lines with the global distance smaller than a set distance as the second target directed connecting lines.
B5. The system of B2, the edge cloud platform further to:
determining an operation stability record of an instruction receiving thread of the second target edge terminal based on target connecting line parameters corresponding to the second target directional connecting line, and determining specified text data of which the state labels do not change along with the updating of the stability labels in the operation stability record from state log texts corresponding to the operation stability record;
extracting a plurality of data fields of the specified text data according to field separators in the specified text data, dividing the plurality of data fields into a first field set and a second field set based on the data centrality corresponding to the specified text data, and adjusting at least part of the first field set in the first field set to the second field set according to the field similarity between each first field in the first field set and at least part of second fields in the second field set and the field call accumulated value of each first field at the current moment until the current overlap rate is not higher than the preset overlap rate when determining that the current overlap rate between the first clustering feature information of the first field set and the second clustering feature information of the second field set is higher than the preset overlap rate;
determining an instruction transceiving protocol field of a corresponding second target edge terminal according to all second fields in the second field set, and generating a target instruction corresponding to the second target edge terminal according to the instruction transceiving protocol field; and the target instructions corresponding to different second target edge terminals are different.
B6. The system of any one of B1-B5, the second target graph node specifically configured to:
analyzing the target instruction to acquire physical resource allocation information included in the target instruction; the physical resource allocation information comprises a physical resource sharing occupation ratio and a physical resource sharing time period;
acquiring thread configuration distribution information from a time slice resource allocation thread and modifying the thread configuration distribution information according to the physical resource sharing ratio so as to butt joint an api interface of the first target graph node; wherein the first target graph node shares the time slice resource corresponding to the physical resource sharing proportion in the second target graph node through the api interface;
and starting a preset timer and acquiring accumulated time when the modification of the thread configuration distribution information is finished, and recovering the modified thread configuration distribution information to terminate the physical resource sharing with the first target graph node when the accumulated time reaches a target time corresponding to a physical resource sharing time period.
B7. The system of B1, the edge cloud platform further to:
sending authorization authentication information for extracting real-time resource distribution information corresponding to each edge terminal; the authorization authentication information carries a dynamic check code and a dynamic random number of the edge cloud platform;
detecting whether an authorization confirmation instruction fed back by each edge terminal based on the authorization authentication information corresponding to the edge terminal is received, and synchronously acquiring real-time resource distribution information of the edge terminal and data flow information of corresponding data end equipment stored by the edge terminal in real time when the authorization confirmation instruction is received.
B8. The system of B7, the edge cloud platform further to:
when the authorization confirmation instruction is not received, continuously sending authorization authentication information for real-time resource distribution information to the corresponding edge terminal; and the dynamic verification code and the dynamic random number of the edge cloud platform carried in the authorization authentication information are different from the previous dynamic verification code and the previous dynamic random number.
Based on the same inventive concept as in the previous embodiments, the present specification further provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of any of the methods described above.
Based on the same inventive concept as in the previous embodiment, an embodiment of the present specification further provides an edge cloud platform, as shown in fig. 4, including a memory 204, a processor 202, and a computer program stored on the memory 204 and executable on the processor 202, where the processor 202 implements the steps of any one of the foregoing methods when executing the program.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, this description is not intended for any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present specification and that specific languages are described above to disclose the best modes of the specification.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present description may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the specification, various features of the specification are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the present specification as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this specification.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the description and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of this description may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components of a gateway, proxy server, system in accordance with embodiments of the present description. The present description may also be embodied as an apparatus or device program (e.g., computer program and computer program product) for performing a portion or all of the methods described herein. Such programs implementing the description may be stored on a computer-readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the specification, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The description may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (8)
1. A data resource acquisition method based on big data and edge calculation is characterized by comprising the following steps:
sending authorization authentication information for extracting real-time resource distribution information corresponding to each edge terminal; the authorization authentication information carries a dynamic check code and a dynamic random number of the edge cloud platform;
detecting whether an authorization confirmation instruction fed back by each edge terminal based on the authorization authentication information corresponding to the edge terminal is received;
and when the authorization confirmation instruction is received, synchronously acquiring the real-time resource distribution information of the edge terminal and the data flow information of the corresponding data terminal equipment stored by the edge terminal in real time.
2. The method of claim 1, further comprising:
when the authorization confirmation instruction is not received, continuously sending authorization authentication information for real-time resource distribution information to the corresponding edge terminal; and the dynamic verification code and the dynamic random number of the edge cloud platform carried in the authorization authentication information are different from the previous dynamic verification code and the previous dynamic random number.
3. The method according to claim 1 or 2, wherein the real-time resource distribution information is distribution information of time slice resources.
4. The method according to claim 1 or 2, wherein the data traffic information is used to characterize a data generation rate of a data end device.
5. The method of claim 1, further comprising:
and generating resource distribution map data of a plurality of edge terminals communicated with the edge cloud platform according to the acquired real-time resource distribution information.
6. The method according to claim 5, wherein generating resource distribution map data of a plurality of edge terminals communicating with the edge cloud platform according to the obtained real-time resource distribution information comprises:
projecting the resource distribution track curve of the acquired real-time resource distribution information to a preset graph plane to obtain a resource projection curve of each resource distribution track curve in the preset graph plane;
extracting a curve time sequence characteristic set and a curve description characteristic set of each resource projection curve; the curve time sequence characteristic set is used for representing time sequence characteristic change of the resource projection curve, and the curve description characteristic set is used for representing track characteristic change of the resource projection curve;
clustering the resource projection curves in the preset graph plane according to the curve time sequence feature set and the curve description feature set to obtain at least two groups of cluster sets;
and determining a correlation coefficient between the resource projection curves in each group of clustering sets, and generating resource distribution map data of a plurality of edge terminals communicated with the edge cloud platform according to the correlation coefficient and the direction information of the clustering labels of the resource projection curves in each group of clustering sets.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
8. An edge cloud platform comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any one of claims 1 to 6 are implemented when the computer program is executed by the processor.
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| US10382558B2 (en) * | 2016-11-17 | 2019-08-13 | Nokia Of America Corporation | Edge resource sharing |
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