CN113515368A - Data integration method combining big data and edge calculation and storage medium - Google Patents
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Abstract
The disclosed data integration method and storage medium combining big data and edge calculation first implement service processing results uploaded by target edge calculation node equipment, then respectively generate corresponding service transmission information and transmission path information among the service transmission information aiming at each service processing result by searching pre-established service data label distribution, then screen the transmission path information based on equipment log text extracted from each target edge calculation node equipment to determine target path parameters, and finally integrate each service processing result according to the target path parameters to obtain a comprehensive processing result. Therefore, the business processing results can be cloud-ended, so that clear division of business data processing and business result integration is realized, the edge computing node equipment can efficiently process the business data, and the cloud big data center can effectively integrate the business processing results reported by the edge computing node equipment.
Description
Technical Field
The present disclosure relates to the field of big data and edge computing technologies, and in particular, to a data integration method and a storage medium combining big data and edge computing.
Background
With the development of science and technology, the data scale and the data category of various service data (such as industrial production data, smart city monitoring data, vehicle automatic driving control data, intelligent medical scheduling data and the like) are gradually increased, which brings great data processing challenges to the traditional cloud computing mode and often causes overload of the traditional cloud computing mode.
To improve this technical problem, the prior art gradually converts the cloud computing mode into the edge computing mode. Therefore, on one hand, a large amount of business data processing can be marginalized to reduce load pressure of a cloud computing mode on a cloud server, and on the other hand, a large amount of business data can be configured in a distributed mode, so that the efficiency and timeliness of data processing are improved.
However, the inventor finds that the existing service data processing method based on edge calculation has difficulty in realizing effective integration between service data.
Disclosure of Invention
In order to improve the technical problem that effective integration between service data is difficult to realize in the related art, the present disclosure provides a data integration method and a storage medium that combine big data and edge calculation.
In a first aspect, a data integration method combining big data and edge computing is provided, and is applied to a cloud big data center, and the method includes the following steps:
acquiring service processing results which are generated after processing issued to-be-processed service data and uploaded by any two target edge computing node devices in a plurality of edge computing node devices respectively;
respectively generating service transmission information corresponding to each service processing result and transmission path information among the service transmission information aiming at each service processing result by searching pre-established service data label distribution;
screening the transfer path information based on an equipment log text extracted from each target edge computing node equipment to determine target path parameters which do not change with the updating of each group of equipment log texts in the transfer path information;
and integrating the service processing results according to the target path parameters to obtain a comprehensive processing result.
Alternatively, the service data label distribution is established by:
acquiring service behavior track information respectively recorded by two target edge computing node devices and service interaction data of the corresponding target edge computing node devices in each service behavior track information;
detecting a behavior track pointing parameter of each service behavior track information; fitting the behavior track pointing parameters of the business behavior track information through the time sequence characteristic matching of the behavior track pointing parameters to obtain a queue label of a business behavior queue of the business behavior track information;
aiming at each service behavior track information, determining the service type of target edge computing node equipment for recording the service behavior track information based on a queue label of a service behavior queue of the service behavior track information; respectively performing service mapping on each service behavior track information according to the service type of each target edge computing node device so as to generate a service data mapping path corresponding to the service behavior track information in the cloud big data center;
clustering all the service data mapping paths to obtain path network distribution maps corresponding to all the service data mapping paths in the cloud big data center;
according to the service interaction data of each target edge computing node device, obtaining a path label list in the path network distribution diagram; and extracting the service data labels from the path network distribution map based on the path label list to obtain service data label distribution.
Alternatively, according to the target path parameter, integrating the service processing results to obtain a comprehensive processing result, including:
listing the target path parameter relative to the service data logic information of each service processing result; the service data logic information comprises a logic relation of a corresponding service processing result on a control thread corresponding to the cloud big data center;
and performing iterative integration on each service processing result according to the logic priority of the service data logic information and the correlation coefficient between the service data logic information to obtain a comprehensive processing result.
Alternatively, iteratively integrating the service processing results according to the logic priority of the service data logic information and the correlation coefficient between the service data logic information to obtain a comprehensive processing result, including:
determining a target sorting sequence of the logic priority, wherein the service data logic information corresponding to the target sorting sequence is service data logic information of which the correlation coefficient is greater than a set coefficient value and the difference between the logic priority and the median of all the logic priorities is not less than a preset difference;
switching the coding script of the logic coding data of the business data logic information corresponding to the target sequencing sequence into a target coding script corresponding to the cloud big data center;
judging whether the business data logic information corresponding to the target sequencing sequence has iteration weight or not according to the target coding script; if the business data logic information corresponding to the target sorting sequence has the iteration weight, calibrating the correlation coefficient among all the business processing results according to the magnitude sequence of the iteration weight to obtain a plurality of first business processing results corresponding to the calibrated correlation coefficient and a plurality of second business processing results which are not calibrated;
and iterating the first service processing result based on the iteration weight, adding the service processing result with the maximum service influence degree in the second service processing results to the iteration process in each iteration process, and adding the service processing result with the minimum service influence degree in the second service processing results to the next iteration process in the next iteration process until the cross iteration between the first service processing result and the second service processing result is completed to obtain the comprehensive processing result.
Alternatively, the screening the transfer path information based on the device log text extracted from each target edge computing node device to determine a target path parameter that does not change with the update of each group of device log texts in the transfer path information, further includes:
sending a text extraction request carrying a request field and a first verification result to each target edge computing node device; the first verification result is obtained by the cloud big data center performing cyclic redundancy check calculation on the request field based on a pre-stored first dynamic random number and a first identity verification code; when an authorization instruction fed back by the target edge computing node device based on the request field is received, accessing a set storage area corresponding to the target edge computing node device and acquiring a device log text corresponding to the target edge computing node device from the set storage area; wherein the target edge computing node device feeds back the authorization instruction by: determining a second dynamic random number corresponding to the first dynamic random number and a second identity check code corresponding to the first identity check code according to an authentication relationship established with the cloud big data center in advance, performing cyclic redundancy check calculation on the request field by adopting the second dynamic random number and the second identity check code to obtain a second check result, and feeding back the authorization instruction to the cloud big data center when the first check result is consistent with the second check result;
determining text updating information corresponding to each group of device log texts, constructing a text updating list based on the determined text updating information, and mapping list structure information of the text updating list into a preset coordinate plane in a graph data form so as to draw a graph data set of the text updating list in the coordinate plane;
performing feature extraction on each graph data node in the graph data set to obtain a plane feature corresponding to each graph data node, performing model parameter adjustment on a preset identification model according to the feature dimension of the plane feature, and identifying the plane feature by adopting the identification model with the adjusted model parameter to obtain a plurality of feature clusters;
calculating the clustering index weight of each feature cluster, defining the feature cluster corresponding to the maximum clustering index weight as a dynamic feature cluster, and respectively determining text regions corresponding to the updatable texts in each group of equipment log texts according to the dynamic feature cluster; and judging whether each group of path parameters in the transmission path information are converged in each text region or not, and determining the path parameters converged in each text region in the transmission path information as target path parameters.
Alternatively, by searching for pre-established service data label distribution, service delivery information corresponding to each service processing result and delivery path information between each service delivery information are respectively generated for each service processing result, which specifically includes:
determining processing evaluation labels corresponding to the service processing results from the service data label distribution;
judging whether each processing evaluation label is matched with a stability label of the service interaction stability of the corresponding service processing result, if so, generating service transmission information corresponding to the service processing result according to mapping information of the processing evaluation label in the service processing result;
determining an information packet with periodically changed information capacity among service transmission information, and extracting an information set with at least two address identifiers from the information packet to integrate according to the time sequence priority of the service transmission information so as to obtain transmission path information among the service transmission information.
Alternatively, obtaining a service processing result, which is generated after processing issued to-be-processed service data and uploaded by any two target edge computing node devices in the plurality of edge computing node devices respectively, includes:
judging whether the protocol text similarity between a first data transmission protocol of one target edge computing node device and a second data transmission protocol of another target edge computing node device is greater than a set similarity or not;
when the protocol text similarity is greater than the set similarity, acquiring a service processing result of one target edge computing node device according to a first set transceiving frequency and acquiring a service processing result of the other target edge computing node device according to a second set transceiving frequency; wherein the first set transceiving frequency and the second set transceiving frequency are complementary.
In a second aspect, a cloud big data center is provided, where the cloud big data center includes a data integration device, and a module deployed in the data integration device implements the method when running.
In a third aspect, a cloud big data center is provided, where the cloud big data center includes a processor and a memory that communicate with each other; wherein:
the memory is used for storing a computer program;
the processor is used for reading the computer program from the memory and executing the computer program to realize the method.
In a fourth aspect, a storage medium for a computer is provided, on which a computer program is stored, which computer program realizes the above-mentioned method when running.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
Firstly, service processing results uploaded by target edge computing node equipment are searched, then corresponding service transmission information and transmission path information among the service transmission information are respectively generated aiming at the service processing results through searching pre-established service data label distribution, then the transmission path information is screened based on equipment log texts extracted from each target edge computing node equipment to determine target path parameters, and finally, the service processing results are integrated according to the target path parameters to obtain comprehensive processing results. Therefore, the business processing results can be cloud-ended, so that clear division of business data processing and business result integration is realized, the edge computing node equipment can efficiently process the business data, and the cloud big data center can effectively integrate the business processing results reported by the edge computing node equipment. Therefore, the comprehensive processing result corresponding to the service data to be processed can be analyzed globally.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic illustration of an implementation environment according to the present disclosure;
FIG. 2 is a flow diagram illustrating a method of data integration incorporating big data and edge computation in accordance with an exemplary embodiment;
FIG. 3 is a flow diagram illustrating a data consolidation apparatus that combines big data and edge computation in accordance with an exemplary embodiment;
fig. 4 is a schematic diagram illustrating a hardware structure of a cloud big data center according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Regarding the technical problems mentioned in the background art, the inventors have studied and analyzed the existing edge computing architecture, and found that there is usually heterogeneity between edge computing node devices in the existing edge computing architecture, where one edge computing node device is incompatible with the service data of other edge computing devices, and the service data processed by each edge computing node device is allocated according to the service scenario corresponding to the edge computing node device, which may result in that effective integration of the service data processing results cannot be achieved on the edge computing node device side.
To solve the technical problem, embodiments of the present invention provide a data integration method and a storage medium combining big data and edge computing, which can cloud a service data processing result to realize clear division of service data processing and service data integration, that is: the edge computing node equipment efficiently processes the service data, and the cloud big data center effectively integrates the service data processing results reported by the edge computing node equipment.
To achieve the above solution, please refer to fig. 1, which is a schematic diagram of an architecture of a data integration system 100 combining big data and edge computing according to an embodiment of the present invention, wherein the data integration system 100 may include a cloud big data center 200 and a plurality of edge computing node devices 400. The cloud big data center 200 and each edge computing node device 400 are connected in communication, so as to implement the method described in the following steps S21-S24 shown in fig. 2.
Step S21, obtaining service processing results generated after processing the issued to-be-processed service data uploaded by any two target edge computing node devices in the plurality of edge computing node devices, respectively.
For example, the to-be-processed business data may be industrial production data, smart city monitoring data, vehicle automatic driving control data, intelligent medical scheduling data, and the like, which is not limited herein.
Step S22, by searching for the pre-established service data label distribution, service delivery information corresponding to the service processing result and delivery path information between the service delivery information are respectively generated for each service processing result.
For example, the service data distribution tag is used for representing an association relationship between service data to be processed issued by the cloud big data center. The service delivery information is used for representing result-oriented description between service processing results. The transfer path information is used for characterizing the cascading priority among the service transfer information.
Step S23, screening the transfer path information based on the device log text extracted from each target edge computing node device, so as to determine a target path parameter that does not change with the update of each group of device log texts in the transfer path information.
For example, the device log text records the whole process of the target edge computing node device when processing the to-be-processed service data. The target path parameters are used for characterizing the path architecture of the path transfer information.
And step S24, integrating the service processing results according to the target path parameters to obtain a comprehensive processing result.
For example, the comprehensive processing result is used to represent a global processing result corresponding to the to-be-processed service data before being distributed by the cloud big data.
Further, based on the content described in the above steps S21 to S24, the service processing results uploaded by the target edge computing node device are first calculated, then corresponding service delivery information and delivery path information between the service delivery information are respectively generated for each service processing result by searching for pre-established service data label distribution, then the delivery path information is screened based on the device log text extracted from each target edge computing node device to determine target path parameters, and finally, each service processing result is integrated according to the target path parameters to obtain a comprehensive processing result. Therefore, the business processing results can be cloud-ended, so that clear division of business data processing and business result integration is realized, the edge computing node equipment can efficiently process the business data, and the cloud big data center can effectively integrate the business processing results reported by the edge computing node equipment. Therefore, the comprehensive processing result corresponding to the service data to be processed can be analyzed globally.
The cloud big data center 200 can be applied to not only a smart city, but also a smart medical service, a smart industrial park, and a smart industrial internet, and the data integration system 100 can be applied to scenes such as big data, cloud computing, and edge computing, including but not limited to new energy automobile system management, intelligent online office, intelligent online education, cloud game data processing, e-commerce live delivery processing, cloud internet processing, block chain digital financial currency service, block chain supply chain financial service, and the like, without limitation. It is understood that when applied to the above-mentioned corresponding fields, the types of the service data are adjusted and further refined, and are not listed here.
In the implementation process, the inventor finds that in order to ensure the accuracy of the service delivery information and the delivery path information between the service delivery information, the integrity and the real-time performance of the service data label distribution need to be ensured. To achieve this, the service data label distribution in step S22 can be specifically realized by the following description of step S221 to step S225.
Step S221, acquiring service behavior trace information recorded by the two target edge computing node devices, respectively, and service interaction data of the corresponding target edge computing node device in each service behavior trace information.
Step S222, detecting behavior track pointing parameters of the behavior track information of each service; and fitting the behavior track pointing parameters of the business behavior track information through the time sequence characteristic pairing of the behavior track pointing parameters to obtain a queue label of a business behavior queue of the business behavior track information.
Step S223, aiming at each service behavior track information, determining the service type of the target edge computing node equipment for recording the service behavior track information based on the queue label of the service behavior queue of the service behavior track information; and respectively performing service mapping on each service behavior track information according to the service type of each target edge computing node device so as to generate a service data mapping path corresponding to the service behavior track information in the cloud big data center.
Step S224, clustering the service data mapping paths to obtain a path network distribution map corresponding to all service data mapping paths in the cloud big data center.
Step S225, a path label list in the path network distribution diagram is obtained according to the service interaction data of each target edge computing node device; and extracting the service data labels from the path network distribution map based on the path label list to obtain service data label distribution.
In this way, through the steps S221 to S225, the service data label distribution can be completely determined in real time based on the service behavior trace information recorded by the target edge computing node device, so as to ensure the accuracy of the service delivery information and the delivery path information between the service delivery information.
In a specific implementation, in order to ensure continuity of the comprehensive processing result in the data logic, the integration of the business processing results according to the target path parameter described in step S24 to obtain the comprehensive processing result may specifically include the contents described in step S241 and step S242 below.
Step S241, listing the target path parameter relative to the service data logic information of each service processing result; the service data logic information comprises a logic relation of a corresponding service processing result on a control thread corresponding to the cloud big data center.
Step S242, iteratively integrating the service processing results according to the logic priority of the service data logic information and the correlation coefficient between the service data logic information to obtain a comprehensive processing result.
By implementing the contents described in the above steps S241 and S242, the logic priority and the correlation coefficient of the service data logic information of the target path parameter can be analyzed, so as to implement iterative integration of the service processing results, and thus, the continuity of the comprehensive processing results on the data logic can be ensured.
In an implementation manner, the iteratively integrating the service processing results according to the logic priority of the service data logic information and the correlation coefficient between the service data logic information to obtain the comprehensive processing result, which is described in step S242, may further include what is described in the following step S2421 to step S2424.
Step S2421, determining a target sorting sequence of the logic priority, wherein the service data logic information corresponding to the target sorting sequence is service data logic information of which the correlation coefficient is greater than a set coefficient value and the difference value between the logic priority and the median of all the logic priorities is not less than a preset difference value.
Step S2422, switching the coding script of the logic coding data of the service data logic information corresponding to the target sorting sequence into a target coding script corresponding to the cloud big data center.
Step S2423, judging whether the business data logic information corresponding to the target sequencing sequence has iteration weight according to the target coding script; and if the business data logic information corresponding to the target sorting sequence has the iteration weight, calibrating the correlation coefficient among all the business processing results according to the magnitude sequence of the iteration weight to obtain a plurality of first business processing results corresponding to the calibrated correlation coefficient and a plurality of second business processing results which are not calibrated.
Step S2424, iterating the first service processing result based on the iteration weight, adding the service processing result with the maximum service influence degree in the plurality of second service processing results to the iteration process in each iteration process, and adding the service processing result with the minimum service influence degree in the plurality of second service processing results to the next iteration process in the next iteration process until the cross iteration between the first service processing result and the second service processing result is completed to obtain the comprehensive processing result.
It can be understood that through the descriptions of the above steps S2421 to S2424, precise iteration on each business processing result can be realized to ensure the accuracy and reliability of the comprehensive processing result.
In an implementation manner, in order to screen out target path parameters that do not carry a logical cluster field from the transfer path information, so as to ensure the parameter stability of the target path parameters, in step S23, the transfer path information is screened based on device log texts extracted from each target edge computing node device, so as to determine the target path parameters that do not change with the update of each set of device log texts in the transfer path information, which further includes the details described in the following steps S231-S234.
Step S231, a text extraction request carrying a request field and a first verification result is sent to each target edge computing node device; the first verification result is obtained by the cloud big data center performing cyclic redundancy check calculation on the request field based on a pre-stored first dynamic random number and a first identity verification code; when an authorization instruction fed back by the target edge computing node device based on the request field is received, accessing a set storage area corresponding to the target edge computing node device and acquiring a device log text corresponding to the target edge computing node device from the set storage area; wherein the target edge computing node device feeds back the authorization instruction by: and determining a second dynamic random number corresponding to the first dynamic random number and a second identity check code corresponding to the first identity check code according to an authentication relation established with the cloud big data center in advance, performing cyclic redundancy check calculation on the request field by adopting the second dynamic random number and the second identity check code to obtain a second check result, and feeding back the authorization instruction to the cloud big data center when the first check result is consistent with the second check result.
Step S232, determining text update information corresponding to each group of device log texts, constructing a text update list based on the determined text update information, mapping list structure information of the text update list to a preset coordinate plane in a graph data form, and drawing a graph data set of the text update list in the coordinate plane.
Step S233, performing feature extraction on each graph data node in the graph data set to obtain a plane feature corresponding to each graph data node, performing model parameter adjustment on a preset identification model according to a feature dimension of the plane feature, and identifying the plane feature by using the identification model with the adjusted model parameter to obtain a plurality of feature clusters.
Step S234, calculating the clustering index weight of each feature cluster, defining the feature cluster corresponding to the maximum clustering index weight as a dynamic feature cluster, and respectively determining text regions corresponding to the updatable texts in each group of equipment log texts according to the dynamic feature cluster; and judging whether each group of path parameters in the transmission path information are converged in each text region or not, and determining the path parameters converged in each text region in the transmission path information as target path parameters.
It can be understood that, by performing the steps S231 to S234, the target path parameters that do not carry the logical clustering field can be screened from the delivery path information, so as to ensure the parameter stability of the target path parameters.
In a specific embodiment, the step S22 is to search a pre-established service data label distribution, and generate service delivery information corresponding to the service processing result and delivery path information between the service delivery information for each service processing result, further including the following steps S221 to S223.
Step S221, determining a processing evaluation tag corresponding to each service processing result from the service data tag distribution.
Step S222, determining whether each processing evaluation tag matches with a stability tag of the service interaction stability of the corresponding service processing result, and if so, generating service delivery information corresponding to the service processing result according to mapping information of the processing evaluation tag in the service processing result.
Step S223, determining an information packet with periodically changing information capacity between service delivery information, and extracting an information set with at least two address identifiers in the information packet to integrate according to the time sequence priority of the service delivery information to obtain the delivery path information between service delivery information.
In this way, based on the above steps S221 to S223, the confidence and the real-time performance of the traffic delivery information and the delivery path information can be ensured.
In practical application, in order to ensure the accuracy of the obtained service processing result and avoid a defect of the service processing result, in step S21, the service processing result generated after processing the issued to-be-processed service data uploaded by any two target edge computing node devices in the plurality of edge computing node devices is obtained, which may specifically include the contents described in the following steps S211 and S212.
Step S211, determining whether the protocol text similarity between the first data transmission protocol of one of the target edge computing node devices and the second data transmission protocol of another one of the target edge computing node devices is greater than a set similarity.
Step S212, when the similarity of the protocol text is greater than the set similarity, acquiring a service processing result of one target edge computing node device according to a first set transceiving frequency and acquiring a service processing result of another target edge computing node device according to a second set transceiving frequency; wherein the first set transceiving frequency and the second set transceiving frequency are complementary.
It can be understood that, through the above steps S211 and S212, the service processing result corresponding to the higher protocol text similarity can be obtained by using the complementary transceiving frequencies, so that the accuracy of the obtained service processing result can be ensured on the premise of ensuring the timeliness of obtaining the service processing result, and the defect of the service processing result is avoided.
In an alternative embodiment, in order to implement the service correction on the integrated processing result, on the basis of the steps S21-S24, the method may further include the following steps described in step S25: and carrying out service correction on the comprehensive processing result according to the result separation identifier in the comprehensive processing result. Therefore, the service correction can be carried out on the comprehensive processing result based on different result separation marks, and the reliability and the accuracy of the comprehensive processing result are ensured.
In a specific embodiment, the performing of the service correction on the integrated processing result according to the result separation flag in the integrated processing result described in step S25 may specifically include what is described in the following step S251 to step S253.
Step S251, sorting the result separation identifiers according to a time sequence priority order and a permission priority order, respectively, to obtain a first sorting sequence corresponding to the time sequence priority and a second sorting sequence corresponding to the permission priority.
Step S252, performing traversal comparison on the first sorted sequence and the second sorted sequence, that is, comparing each result separation identifier in the first sorted sequence with the result separation identifier at the same position in the second sorted sequence to obtain a comparison result.
Step S253, when the comparison result indicates that the sequence correlation coefficient between the first sorted sequence and the second sorted sequence is greater than the target coefficient, performing service correction on the comprehensive processing result by using at least two result separation identifiers in the first sorted sequence as references; when the comparison result represents that the sequence correlation coefficient between the first sorting sequence and the second sorting sequence is smaller than or equal to the target coefficient, performing service correction on the comprehensive processing result by taking at least two result separation marks in the second sorting sequence as references; and the service correction comprises the correction of the data credibility and the data valid time period of the comprehensive processing result.
It can be understood that, through the above steps S251 to S253, flexible service correction can be performed on the integrated processing result according to the result separation flag.
Based on the similar inventive concept, please refer to fig. 3, which provides a data integration apparatus 300 combining big data and edge calculation, applied to a cloud big data center, the apparatus includes the following functional modules:
a processing result obtaining module 310, configured to obtain service processing results, which are generated after processing the issued to-be-processed service data and are respectively uploaded by any two target edge computing node devices in the multiple edge computing node devices;
a path information generating module 320, configured to search pre-established service data label distribution, and respectively generate service delivery information corresponding to each service processing result and delivery path information between each service delivery information for each service processing result;
a path parameter obtaining module 330, configured to screen the transfer path information based on an equipment log text extracted from each target edge computing node equipment, so as to determine a target path parameter that does not change with update of each group of equipment log texts in the transfer path information;
a processing result integration module 340, configured to integrate the processing results of the services according to the target path parameter to obtain a comprehensive processing result;
and a processing result correction module 350, configured to perform service correction on the comprehensive processing result according to the result separation identifier in the comprehensive processing result.
Further, the path information generating module 320 is specifically configured to:
acquiring service behavior track information respectively recorded by two target edge computing node devices and service interaction data of the corresponding target edge computing node devices in each service behavior track information;
detecting a behavior track pointing parameter of each service behavior track information; fitting the behavior track pointing parameters of the business behavior track information through the time sequence characteristic matching of the behavior track pointing parameters to obtain a queue label of a business behavior queue of the business behavior track information;
aiming at each service behavior track information, determining the service type of target edge computing node equipment for recording the service behavior track information based on a queue label of a service behavior queue of the service behavior track information; respectively performing service mapping on each service behavior track information according to the service type of each target edge computing node device so as to generate a service data mapping path corresponding to the service behavior track information in the cloud big data center;
clustering all the service data mapping paths to obtain path network distribution maps corresponding to all the service data mapping paths in the cloud big data center;
according to the service interaction data of each target edge computing node device, obtaining a path label list in the path network distribution diagram; and extracting the service data labels from the path network distribution map based on the path label list to obtain service data label distribution.
Further, the processing result integration module 340 is specifically configured to:
listing the target path parameter relative to the service data logic information of each service processing result; the service data logic information comprises a logic relation of a corresponding service processing result on a control thread corresponding to the cloud big data center;
and performing iterative integration on each service processing result according to the logic priority of the service data logic information and the correlation coefficient between the service data logic information to obtain a comprehensive processing result.
Further, the processing result integration module 340 is specifically configured to:
determining a target sorting sequence of the logic priority, wherein the service data logic information corresponding to the target sorting sequence is service data logic information of which the correlation coefficient is greater than a set coefficient value and the difference between the logic priority and the median of all the logic priorities is not less than a preset difference;
switching the coding script of the logic coding data of the business data logic information corresponding to the target sequencing sequence into a target coding script corresponding to the cloud big data center;
judging whether the business data logic information corresponding to the target sequencing sequence has iteration weight or not according to the target coding script; if the business data logic information corresponding to the target sorting sequence has the iteration weight, calibrating the correlation coefficient among all the business processing results according to the magnitude sequence of the iteration weight to obtain a plurality of first business processing results corresponding to the calibrated correlation coefficient and a plurality of second business processing results which are not calibrated;
and iterating the first service processing result based on the iteration weight, adding the service processing result with the maximum service influence degree in the second service processing results to the iteration process in each iteration process, and adding the service processing result with the minimum service influence degree in the second service processing results to the next iteration process in the next iteration process until the cross iteration between the first service processing result and the second service processing result is completed to obtain the comprehensive processing result.
Further, the path parameter obtaining module 330 is configured to:
sending a text extraction request carrying a request field and a first verification result to each target edge computing node device; the first verification result is obtained by the cloud big data center performing cyclic redundancy check calculation on the request field based on a pre-stored first dynamic random number and a first identity verification code; when an authorization instruction fed back by the target edge computing node device based on the request field is received, accessing a set storage area corresponding to the target edge computing node device and acquiring a device log text corresponding to the target edge computing node device from the set storage area; wherein the target edge computing node device feeds back the authorization instruction by: determining a second dynamic random number corresponding to the first dynamic random number and a second identity check code corresponding to the first identity check code according to an authentication relationship established with the cloud big data center in advance, performing cyclic redundancy check calculation on the request field by adopting the second dynamic random number and the second identity check code to obtain a second check result, and feeding back the authorization instruction to the cloud big data center when the first check result is consistent with the second check result;
determining text updating information corresponding to each group of device log texts, constructing a text updating list based on the determined text updating information, and mapping list structure information of the text updating list into a preset coordinate plane in a graph data form so as to draw a graph data set of the text updating list in the coordinate plane;
performing feature extraction on each graph data node in the graph data set to obtain a plane feature corresponding to each graph data node, performing model parameter adjustment on a preset identification model according to the feature dimension of the plane feature, and identifying the plane feature by adopting the identification model with the adjusted model parameter to obtain a plurality of feature clusters;
calculating the clustering index weight of each feature cluster, defining the feature cluster corresponding to the maximum clustering index weight as a dynamic feature cluster, and respectively determining text regions corresponding to the updatable texts in each group of equipment log texts according to the dynamic feature cluster; and judging whether each group of path parameters in the transmission path information are converged in each text region or not, and determining the path parameters converged in each text region in the transmission path information as target path parameters.
Further, the path information generating module 320 is configured to:
determining processing evaluation labels corresponding to the service processing results from the service data label distribution;
judging whether each processing evaluation label is matched with a stability label of the service interaction stability of the corresponding service processing result, if so, generating service transmission information corresponding to the service processing result according to mapping information of the processing evaluation label in the service processing result;
determining an information packet with periodically changed information capacity among service transmission information, and extracting an information set with at least two address identifiers from the information packet to integrate according to the time sequence priority of the service transmission information so as to obtain transmission path information among the service transmission information.
Further, the processing result obtaining module 310 is specifically configured to:
judging whether the protocol text similarity between a first data transmission protocol of one target edge computing node device and a second data transmission protocol of another target edge computing node device is greater than a set similarity or not;
when the protocol text similarity is greater than the set similarity, acquiring a service processing result of one target edge computing node device according to a first set transceiving frequency and acquiring a service processing result of the other target edge computing node device according to a second set transceiving frequency; wherein the first set transceiving frequency and the second set transceiving frequency are complementary.
Further, the processing result correction module 350 is specifically configured to:
sorting the result separation identifiers according to the magnitude sequence of the time sequence priority and the magnitude sequence of the authority priority respectively to obtain a first sorting sequence corresponding to the time sequence priority and a second sorting sequence corresponding to the authority priority;
traversing and comparing the first sequencing sequence and the second sequencing sequence, namely comparing each result separation identifier in the first sequencing sequence with the result separation identifier at the same position in the second sequencing sequence to obtain a comparison result;
when the comparison result represents that the sequence correlation coefficient between the first sequencing sequence and the second sequencing sequence is greater than a target coefficient, performing service correction on the comprehensive processing result by taking at least two result separation identifications in the first sequencing sequence as references; when the comparison result represents that the sequence correlation coefficient between the first sorting sequence and the second sorting sequence is smaller than or equal to the target coefficient, performing service correction on the comprehensive processing result by taking at least two result separation marks in the second sorting sequence as references; and the service correction comprises the correction of the data credibility and the data valid time period of the comprehensive processing result.
For the description of the processing result obtaining module 310, the path information generating module 320, the path parameter obtaining module 330, the processing result integrating module 340, and the processing result correcting module 350, please refer to the description of the steps of the method shown in fig. 2, which is not described herein again.
Based on the same inventive concept, the data integration system combining big data and edge computing is further provided, and comprises a cloud big data center and a plurality of edge computing node devices, wherein the cloud big data center is communicated with the edge computing node devices;
any two target edge computing node devices of the plurality of edge computing node devices are respectively configured to: uploading a service processing result generated after processing the issued to-be-processed service data to the cloud big data center;
the cloud big data center is used for:
acquiring service processing results which are generated after processing issued to-be-processed service data and uploaded by any two target edge computing node devices in a plurality of edge computing node devices respectively;
respectively generating service transmission information corresponding to each service processing result and transmission path information among the service transmission information aiming at each service processing result by searching pre-established service data label distribution;
screening the transfer path information based on an equipment log text extracted from each target edge computing node equipment to determine target path parameters which do not change with the updating of each group of equipment log texts in the transfer path information;
integrating the service processing results according to the target path parameters to obtain comprehensive processing results;
and carrying out service correction on the comprehensive processing result according to the result separation identifier in the comprehensive processing result.
For the description of the data integration system combining big data and edge calculation, reference may be made to the description of the method shown in fig. 2, which is not described herein again.
On the basis of the above, please refer to fig. 4 in combination, a schematic diagram of a hardware structure of a cloud big data center 200 is provided, where the cloud big data center 200 includes a processor 210 and a memory 220 that are in communication with each other; wherein:
the memory 220 is used for storing computer programs;
the processor 210 is configured to read the computer program from the memory 220 and execute the computer program to implement the method shown in fig. 2.
On the basis of the above, a storage medium for a computer, on which a computer program is stored, which computer program, when running, implements the method as shown in fig. 2.
To sum up, the data integration method and the storage medium combining big data and edge computing disclosed by the present disclosure first perform service processing results uploaded by target edge computing node devices, then generate corresponding service delivery information and delivery path information between the service delivery information for each service processing result by searching pre-established service data label distribution, then screen the delivery path information based on device log text extracted from each target edge computing node device to determine target path parameters, and finally integrate each service processing result according to the target path parameters to obtain a comprehensive processing result. Therefore, the business processing results can be cloud-ended, so that clear division of business data processing and business result integration is realized, the edge computing node equipment can efficiently process the business data, and the cloud big data center can effectively integrate the business processing results reported by the edge computing node equipment.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A data integration method combining big data and edge calculation is applied to a cloud big data center and comprises the following steps:
acquiring service processing results which are generated after processing issued to-be-processed service data and uploaded by any two target edge computing node devices in a plurality of edge computing node devices respectively;
respectively generating service transmission information corresponding to each service processing result and transmission path information among the service transmission information aiming at each service processing result by searching pre-established service data label distribution; the service data distribution tag is used for representing the incidence relation between service data to be processed issued by the cloud big data center, the service transmission information is used for representing the result guide description between service processing results, and the transmission path information is used for representing the cascade priority between the service transmission information;
screening the transfer path information based on an equipment log text extracted from each target edge computing node equipment to determine target path parameters which do not change with the updating of each group of equipment log texts in the transfer path information; the device log text records the whole process of the target edge computing node device when processing the to-be-processed service data, and the target path parameter is used for representing the path architecture of the path transmission information;
integrating the service processing results according to the target path parameters to obtain comprehensive processing results; the comprehensive processing result is used for representing a corresponding global processing result of the to-be-processed business data before being distributed by the cloud big data center.
2. The method of claim 1, wherein the method further comprises:
and carrying out service correction on the comprehensive processing result according to the result separation identifier in the comprehensive processing result.
3. The method of claim 2, wherein performing traffic correction on the integrated processing result according to the result separation flag in the integrated processing result comprises:
sorting the result separation identifiers according to the magnitude sequence of the time sequence priority and the magnitude sequence of the authority priority respectively to obtain a first sorting sequence corresponding to the time sequence priority and a second sorting sequence corresponding to the authority priority;
traversing and comparing the first sequencing sequence and the second sequencing sequence, namely comparing each result separation identifier in the first sequencing sequence with the result separation identifier at the same position in the second sequencing sequence to obtain a comparison result;
when the comparison result represents that the sequence correlation coefficient between the first sequencing sequence and the second sequencing sequence is greater than a target coefficient, performing service correction on the comprehensive processing result by taking at least two result separation identifications in the first sequencing sequence as references; when the comparison result represents that the sequence correlation coefficient between the first sorting sequence and the second sorting sequence is smaller than or equal to the target coefficient, performing service correction on the comprehensive processing result by taking at least two result separation marks in the second sorting sequence as references; and the service correction comprises the correction of the data credibility and the data valid time period of the comprehensive processing result.
4. The method of claim 1, wherein integrating the business process results to obtain a composite process result according to the target path parameters comprises:
listing the target path parameter relative to the service data logic information of each service processing result; the service data logic information comprises a logic relation of a corresponding service processing result on a control thread corresponding to the cloud big data center;
and performing iterative integration on each service processing result according to the logic priority of the service data logic information and the correlation coefficient between the service data logic information to obtain a comprehensive processing result.
5. The method of claim 4, wherein iteratively integrating the service processing results according to the logic priority of the service data logic information and the correlation coefficient between the service data logic information to obtain a comprehensive processing result comprises:
determining a target sorting sequence of the logic priority, wherein the service data logic information corresponding to the target sorting sequence is service data logic information of which the correlation coefficient is greater than a set coefficient value and the difference between the logic priority and the median of all the logic priorities is not less than a preset difference;
switching the coding script of the logic coding data of the business data logic information corresponding to the target sequencing sequence into a target coding script corresponding to the cloud big data center;
judging whether the business data logic information corresponding to the target sequencing sequence has iteration weight or not according to the target coding script; if the business data logic information corresponding to the target sorting sequence has the iteration weight, calibrating the correlation coefficient among all the business processing results according to the magnitude sequence of the iteration weight to obtain a plurality of first business processing results corresponding to the calibrated correlation coefficient and a plurality of second business processing results which are not calibrated;
and iterating the first service processing result based on the iteration weight, adding the service processing result with the maximum service influence degree in the second service processing results to the iteration process in each iteration process, and adding the service processing result with the minimum service influence degree in the second service processing results to the next iteration process in the next iteration process until the cross iteration between the first service processing result and the second service processing result is completed to obtain the comprehensive processing result.
6. The method of claim 1, wherein the delivery path information is screened based on device log text extracted from each target edge computing node device to determine target path parameters in the delivery path information that do not change with updates to each set of device log text, further comprising:
sending a text extraction request carrying a request field and a first verification result to each target edge computing node device; the first verification result is obtained by the cloud big data center performing cyclic redundancy check calculation on the request field based on a pre-stored first dynamic random number and a first identity verification code; when an authorization instruction fed back by the target edge computing node device based on the request field is received, accessing a set storage area corresponding to the target edge computing node device and acquiring a device log text corresponding to the target edge computing node device from the set storage area; wherein the target edge computing node device feeds back the authorization instruction by: determining a second dynamic random number corresponding to the first dynamic random number and a second identity check code corresponding to the first identity check code according to an authentication relationship established with the cloud big data center in advance, performing cyclic redundancy check calculation on the request field by adopting the second dynamic random number and the second identity check code to obtain a second check result, and feeding back the authorization instruction to the cloud big data center when the first check result is consistent with the second check result;
determining text updating information corresponding to each group of device log texts, constructing a text updating list based on the determined text updating information, and mapping list structure information of the text updating list into a preset coordinate plane in a graph data form so as to draw a graph data set of the text updating list in the coordinate plane;
performing feature extraction on each graph data node in the graph data set to obtain a plane feature corresponding to each graph data node, performing model parameter adjustment on a preset identification model according to the feature dimension of the plane feature, and identifying the plane feature by adopting the identification model with the adjusted model parameter to obtain a plurality of feature clusters;
calculating the clustering index weight of each feature cluster, defining the feature cluster corresponding to the maximum clustering index weight as a dynamic feature cluster, and respectively determining text regions corresponding to the updatable texts in each group of equipment log texts according to the dynamic feature cluster; and judging whether each group of path parameters in the transmission path information are converged in each text region or not, and determining the path parameters converged in each text region in the transmission path information as target path parameters.
7. The method according to claim 1, wherein by searching for a pre-established service data label distribution, service delivery information corresponding to each service processing result and delivery path information between each service delivery information are respectively generated for each service processing result, specifically comprising:
determining processing evaluation labels corresponding to the service processing results from the service data label distribution;
judging whether each processing evaluation label is matched with a stability label of the service interaction stability of the corresponding service processing result, if so, generating service transmission information corresponding to the service processing result according to mapping information of the processing evaluation label in the service processing result;
determining an information packet with periodically changed information capacity among service transmission information, and extracting an information set with at least two address identifiers from the information packet to integrate according to the time sequence priority of the service transmission information so as to obtain transmission path information among the service transmission information.
8. The method of claim 1, wherein obtaining service processing results generated after processing the issued to-be-processed service data uploaded by any two target edge computing node devices in the plurality of edge computing node devices respectively comprises:
judging whether the protocol text similarity between a first data transmission protocol of one target edge computing node device and a second data transmission protocol of another target edge computing node device is greater than a set similarity or not;
when the protocol text similarity is greater than the set similarity, acquiring a service processing result of one target edge computing node device according to a first set transceiving frequency and acquiring a service processing result of the other target edge computing node device according to a second set transceiving frequency; wherein the first set transceiving frequency and the second set transceiving frequency are complementary.
9. The method of claim 1, wherein the service data label distribution is established by:
acquiring service behavior track information respectively recorded by two target edge computing node devices and service interaction data of the corresponding target edge computing node devices in each service behavior track information;
detecting a behavior track pointing parameter of each service behavior track information; fitting the behavior track pointing parameters of the business behavior track information through the time sequence characteristic matching of the behavior track pointing parameters to obtain a queue label of a business behavior queue of the business behavior track information;
aiming at each service behavior track information, determining the service type of target edge computing node equipment for recording the service behavior track information based on a queue label of a service behavior queue of the service behavior track information; respectively performing service mapping on each service behavior track information according to the service type of each target edge computing node device so as to generate a service data mapping path corresponding to the service behavior track information in the cloud big data center;
clustering all the service data mapping paths to obtain path network distribution maps corresponding to all the service data mapping paths in the cloud big data center;
according to the service interaction data of each target edge computing node device, obtaining a path label list in the path network distribution diagram; and extracting the service data labels from the path network distribution map based on the path label list to obtain service data label distribution.
10. A storage medium for a computer, having stored thereon a computer program which, when executed, implements the method of any of claims 1-9.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115118465A (en) * | 2022-06-13 | 2022-09-27 | 北京寰宇天穹信息技术有限公司 | Cloud edge-side cooperative zero trust access control method and system based on trusted label |
| CN116896483A (en) * | 2023-09-08 | 2023-10-17 | 成都拓林思软件有限公司 | Data protection system |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113111625B (en) * | 2021-04-30 | 2024-07-23 | 善诊(上海)信息技术有限公司 | Medical text label generation system, method and computer readable storage medium |
| CN113382073B (en) * | 2021-06-08 | 2022-06-21 | 重庆邮电大学 | Monitoring system and method for edge nodes in cloud edge-side industrial control system |
| CN113419856B (en) * | 2021-06-23 | 2023-06-23 | 平安银行股份有限公司 | Intelligent current limiting method, device, electronic equipment and storage medium |
| CN113873042B (en) * | 2021-10-11 | 2022-06-07 | 北京国信未来城市数字科技研究院有限公司 | Edge intelligent controller and data processing method |
| CN115878802B (en) * | 2022-12-16 | 2024-09-17 | 天讯瑞达通信技术有限公司 | Telecom customer label generation system based on big data |
| CN115859159B (en) * | 2023-02-16 | 2023-05-05 | 北京爱企邦科技服务有限公司 | Data evaluation processing method based on data integration |
| CN116774946B (en) * | 2023-07-17 | 2024-01-05 | 广州华企联信息科技有限公司 | Geometric data storage optimization method and system based on cloud edge fusion |
| CN117749800B (en) * | 2024-02-20 | 2024-05-03 | 四川雷克斯智慧科技股份有限公司 | Method and related device for realizing edge data storage and transmission on new energy power generation side |
| CN118509434B (en) * | 2024-07-17 | 2024-10-29 | 浙江大学 | Track data storage method and system oriented to edge environment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170060574A1 (en) * | 2015-08-27 | 2017-03-02 | FogHorn Systems, Inc. | Edge Intelligence Platform, and Internet of Things Sensor Streams System |
| CN110197128A (en) * | 2019-05-08 | 2019-09-03 | 华南理工大学 | The recognition of face architecture design method planned as a whole based on edge calculations and cloud |
| CN110209716A (en) * | 2018-02-11 | 2019-09-06 | 北京华航能信科技有限公司 | Intelligent internet of things water utilities big data processing method and system |
| CN111339183A (en) * | 2020-02-11 | 2020-06-26 | 腾讯云计算（北京）有限责任公司 | Data processing method, edge node, data center and storage medium |
Family Cites Families (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9912638B2 (en) * | 2012-04-30 | 2018-03-06 | Zscaler, Inc. | Systems and methods for integrating cloud services with information management systems |
| CN105357041A (en) * | 2015-10-30 | 2016-02-24 | 上海帝联信息科技股份有限公司 | Edge node server, and log file uploading method and system |
| US11983639B2 (en) * | 2016-10-24 | 2024-05-14 | Oracle International Corporation | Systems and methods for identifying process flows from log files and visualizing the flow |
| US10574547B2 (en) * | 2018-04-12 | 2020-02-25 | Cisco Technology, Inc. | Anomaly detection and correction in wireless networks |
| CN108737569B (en) * | 2018-06-22 | 2020-04-28 | 浙江大学 | Service selection method facing mobile edge computing environment |
| US11157478B2 (en) * | 2018-12-28 | 2021-10-26 | Oracle International Corporation | Technique of comprehensively support autonomous JSON document object (AJD) cloud service |
| US11210126B2 (en) * | 2019-02-15 | 2021-12-28 | Cisco Technology, Inc. | Virtual infrastructure manager enhancements for remote edge cloud deployments |
| CN111131379B (en) * | 2019-11-08 | 2021-06-01 | 西安电子科技大学 | Distributed flow acquisition system and edge calculation method |
| CN110968478B (en) * | 2019-11-21 | 2023-04-25 | 掌阅科技股份有限公司 | Log acquisition method, server and computer storage medium |
| CN111145843A (en) * | 2019-11-27 | 2020-05-12 | 陕西医链区块链集团有限公司 | Multi-center integration platform and method based on medical big data |
| CN111131421B (en) * | 2019-12-13 | 2022-07-29 | 中国科学院计算机网络信息中心 | Method for interconnection and intercommunication of industrial internet field big data and cloud information |
| CN111881164B (en) * | 2020-07-14 | 2021-03-23 | 江苏永鼎通信有限公司 | Data processing method based on edge computing and path analysis and big data cloud platform |
-
2020
- 2020-08-23 CN CN202110396750.5A patent/CN113515368B/en active Active
- 2020-08-23 CN CN202010853210.0A patent/CN111949410B/en active Active
- 2020-08-23 CN CN202110396749.2A patent/CN113515367B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170060574A1 (en) * | 2015-08-27 | 2017-03-02 | FogHorn Systems, Inc. | Edge Intelligence Platform, and Internet of Things Sensor Streams System |
| CN110209716A (en) * | 2018-02-11 | 2019-09-06 | 北京华航能信科技有限公司 | Intelligent internet of things water utilities big data processing method and system |
| CN110197128A (en) * | 2019-05-08 | 2019-09-03 | 华南理工大学 | The recognition of face architecture design method planned as a whole based on edge calculations and cloud |
| CN111339183A (en) * | 2020-02-11 | 2020-06-26 | 腾讯云计算（北京）有限责任公司 | Data processing method, edge node, data center and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| TIAN WANG: "《Big Data Cleaning Based on Mobile Edge》", 《IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS》 * |
| 刘洋: "《基于边缘计算的数据获取与处理系统设计与实现》", 《中国优秀硕士学位论文全文数据库（电子期刊）信息科技辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115118465A (en) * | 2022-06-13 | 2022-09-27 | 北京寰宇天穹信息技术有限公司 | Cloud edge-side cooperative zero trust access control method and system based on trusted label |
| CN115118465B (en) * | 2022-06-13 | 2023-11-28 | 北京寰宇天穹信息技术有限公司 | Cloud edge end cooperative zero trust access control method and system based on trusted label |
| CN116896483A (en) * | 2023-09-08 | 2023-10-17 | 成都拓林思软件有限公司 | Data protection system |
| CN116896483B (en) * | 2023-09-08 | 2023-12-05 | 成都拓林思软件有限公司 | Data protection system |
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