CN113177235A

CN113177235A - Data processing method combining big data and cloud computing and big data server

Info

Publication number: CN113177235A
Application number: CN202110531196.7A
Authority: CN
Inventors: 李孔雀
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-12-01
Filing date: 2020-12-01
Publication date: 2021-07-27
Also published as: CN112487495B; CN112487495A; CN113312673A

Abstract

According to the data processing method and the big data server combining big data and cloud computing, the similarity fusion weight value can be determined based on the feature similarity between service data features, the similarity situation of service interaction data at a service level can be accurately reflected by service similarity information based on the similarity fusion weight value, and meanwhile, the similarity fusion weight value is simplified, so that the condition that the service similarity information occupies more storage space of the big data server can be avoided.

Description

Data processing method combining big data and cloud computing and big data server

The application is a divisional application with the application number of 202011388984.7, the application date of "12/01/2020", and the name of "data processing method and big data server based on big data and cloud computing".

Technical Field

The application relates to the technical field of big data and cloud computing, in particular to a data processing method combining big data and cloud computing and a big data server.

Background

With the development of big data, the current business processing can be almost performed at the cloud, and the business processing efficiency of various industries is greatly improved. The continuous improvement of the functions of the intelligent terminal enables a user to interact business data on the cloud. In some business scenarios, business data sharing is inevitably required, however, when business data sharing is performed, how to ensure the security of data sharing is a technical problem that needs to be noticed.

Disclosure of Invention

The first aspect of the application discloses a data processing method combining big data and cloud computing, which comprises the following steps:

acquiring first service interaction data and second service interaction data obtained after service data acquisition is carried out on a target user terminal, wherein the first service interaction data is non-shared service data with a service authentication signature, and the second service interaction data is shared service data with a modification authority identifier;

determining service similarity information of corresponding service data sets in the first service interaction data and the second service interaction data, and determining a target service data set which corresponds to the first service interaction data and the second service interaction data and meets a preset index condition based on the service similarity information of the corresponding service data sets;

performing permission updating processing on the target service data set in the second service interaction data based on the target service data set in the first service interaction data; and summarizing the current shared service data with the target authority identification in the second service interaction data after the authority updating processing to obtain the service data to be used.

In a preferred embodiment, determining the service similarity information of the corresponding service data set in the first service interaction data and the second service interaction data includes:

determining feature similarity of service data features of each service data set in the first service interaction data and feature similarity of service data features of each service data set in the second service interaction data;

determining a similarity fusion weight value of the corresponding service data sets in the first service interaction data and the second service interaction data based on the feature similarity of the service data features of each service data set in the first service interaction data and the feature similarity of the service data features of each service data set in the second service interaction data, wherein the service similarity information includes the similarity fusion weight value;

wherein determining that the similarity fusion weight value of the corresponding service data set in the first service interaction data and the second service interaction data comprises at least one of:

calculating the service data time sequence similarity of the corresponding service data sets in the first service interaction data and the second service interaction data based on the feature similarity of the service data features of the service data sets in the first service interaction data and the feature similarity of the service data features of the service data sets in the second service interaction data to determine a similarity fusion weight value;

calculating similarity fusion coefficients of service data features of the corresponding service data sets in the first service interaction data and the second service interaction data based on feature similarity of service data features of the service data sets in the first service interaction data and feature similarity of service data features of the service data sets in the second service interaction data to determine a similarity fusion weight value;

determining a service event label of the corresponding service data set in the first service interaction data and the second service interaction data, and determining the similarity fusion weight value based on the determined service event label and the feature similarity of the service data features of the corresponding service data set in the first service interaction data and the second service interaction data.

In a preferred embodiment, determining, based on the service similarity information of the corresponding service data set, a target service data set that corresponds to the first service interaction data and the second service interaction data and satisfies a preset index condition includes: sorting the corresponding service data sets in the first service interaction data and the second service interaction data according to the sequence of similarity evaluation values of the service similarity information from large to small;

determining the target service data set from the ordered corresponding service data sets by one of the following methods:

sequentially selecting a set number of the corresponding service data sets as the target service data sets;

sequentially selecting the corresponding service data sets with set proportion as the target service data sets;

determining the corresponding service data set with the similarity fusion weight value corresponding to the service similarity information smaller than a first preset weight value as the target service data set;

sequentially carrying out iterative updating on each corresponding service data set included in the corresponding service data set of which the similarity fusion weight value corresponding to the service similarity information is smaller than a second preset weight value according to preset iteration times, and determining the target service data set based on an iterative updating result;

and selecting the target service data set based on the variation curve of the accumulated service data time sequence similarity of the corresponding service data set.

In a preferred embodiment, each corresponding service data set included in the corresponding service data set whose similarity fusion weight value corresponding to the service similarity information is smaller than a second predetermined weight value is iteratively updated according to a preset iteration number, and determining the target service data set based on an iteration update result includes:

determining iterative update times corresponding to the service similarity information of each corresponding service data set included in the corresponding service data set, wherein the similarity fusion weight value corresponding to the service similarity information is smaller than a second preset weight value, and the corresponding iterative update times are larger when the similarity fusion weight value corresponding to the service similarity information is smaller;

according to the determined iteration updating times, performing iteration updating on each corresponding service data set included in the corresponding service data set, of which the similarity fusion weight value corresponding to the service similarity information is smaller than a second preset weight value;

sequencing the corresponding service data sets after iterative updating according to the sequence of the timeliness weights of the data sets from small to large so as to obtain the target service data set;

wherein selecting the target service data set based on the variation curve of the cumulative service data time sequence similarity of the corresponding service data set comprises:

sequentially selecting a set number of the corresponding service data sets, and calculating the time sequence similarity of the first service data of the set number of the corresponding service data sets, wherein the set number is a predetermined minimum matching number;

sequentially selecting the corresponding service data sets with the set number added by one, and calculating the time sequence similarity of the second service data of the corresponding service data sets with the set number added by one;

when the similarity difference between the first service data time sequence similarity and the second service data time sequence similarity is larger than or equal to a preset similarity difference, determining the corresponding service data sets of the set number as the target service data sets;

and when the similarity difference between the first service data time sequence similarity and the second service data time sequence similarity is determined to be smaller than the preset similarity difference, repeatedly selecting one more corresponding service data set than the previous selected number until the difference between the service data time sequence similarity of the corresponding service data set selected later and the service data time sequence similarity of the corresponding service data set selected earlier is greater than or equal to the preset similarity difference, and determining the corresponding service data set selected earlier as the target service data set.

In a preferred embodiment, performing permission update processing on the target service data set in the second service interaction data based on the target service data set in the first service interaction data includes:

using a data list to represent each service data set included in the target service data set in the first service interaction data, forming a first data list characteristic matrix by each service data set represented by the data list, and performing matrix element correction processing and matrix structure transformation processing on the first data list characteristic matrix to obtain a first target matrix;

using a data list to represent each service data set included in the target service data set in the second service interaction data, forming a second data list characteristic matrix by each service data set represented by the data list, and performing matrix element correction processing and matrix structure transformation processing on the second data list characteristic matrix to obtain a second target matrix;

updating the data use permission of the target service data set in the first service interaction data based on the first target matrix to obtain first updated service data; and updating the data usage permission of the target service data set in the second service interaction data based on the second target matrix and the first updated service data.

In a preferred embodiment, the updating the data usage right of the target service data set in the first service interaction data based on the first target matrix to obtain first updated service data includes:

judging whether a matrix characteristic description value of a first target matrix corresponding to each target service data set in the first service interaction data is within a first authority updating description value threshold range or not;

setting a matrix characteristic description value of a first target matrix of a target service data set of which the first target matrix is within the threshold range of the first permission updating description value as a selected numerical value, and maintaining the matrix characteristic description values of the first target matrices of other target service data sets unchanged to obtain the first updated service data;

wherein updating the data usage right of the target service data set in the second service interaction data based on the second target matrix and the first updated service data comprises:

judging whether the sum of a matrix characteristic description value of a second target matrix corresponding to each target service data set in the second service interaction data and a first set description value is within a second authority updating description value threshold range or not, wherein the first set description value is the product of a matrix characteristic description index and a set authority security index of a first target matrix corresponding to the target service data set corresponding to the first service interaction data; setting a second target matrix of a target service data set of which the sum of the second target matrix and the first set description value is within the threshold range of the second permission updating description value as a selected numerical value, and maintaining the matrix characteristic description values of the second target matrices of other target service data sets unchanged to obtain second updated service data;

or the like, or, alternatively,

and taking the product of a matrix characteristic description value of a second target matrix corresponding to each target service data set in the second service interaction data and a second set description value as a second target matrix of the target service data set to obtain second updated service data, wherein the second set description value is the ratio of the difference between the service data time sequence similarity corresponding to the target service data set corresponding to the first service interaction data and the historical time sequence similarity mean value to the service data time sequence similarity corresponding to the target service data set corresponding to the first service interaction data.

In a preferred embodiment, summarizing the current shared service data with the target permission identifier in the second service interaction data after permission update processing to obtain the service data to be used includes one of:

summarizing the current shared service data with the target authority identification by adopting a mode of determining the time sequence similarity of the service data to obtain the service data to be used;

summarizing the current shared service data with the target authority identification by adopting a mode of determining a similarity fusion coefficient to obtain the service data to be used;

summarizing the current shared service data with the target authority identification by adopting a shared data security verification model to obtain the service data to be used;

comparing the matrix characteristic description value of the service data set subjected to the authority updating processing and the matrix characteristic description value of the service data set not subjected to the authority updating processing in the current shared service data with the target authority identification, and averaging the matrix characteristic description value with the minimum similarity difference between the matrix characteristic description value of the service data set subjected to the authority updating processing and the matrix characteristic description value of the service data set not subjected to the authority updating processing to determine the matrix characteristic description value of the current shared service data with the target authority identification so as to obtain the service data to be used.

In a preferred embodiment, summarizing the current shared service data with the target permission identifier by using a shared data security verification model to obtain the service data to be used includes:

dividing the current shared service data into a plurality of service data segment sets through the shared data security check model; each service data segment set comprises a plurality of service data segments;

obtaining a second sharing check index of the service data section based on the first sharing check index of the current sharing service data; wherein the first shared check index is obtained by using a historical abnormal shared record;

for at least one of the sets of traffic data segments: respectively taking the second shared verification index of at least one service data segment as a reference index, and acquiring the optimal verification index of the at least one service data segment by using a preset index extraction algorithm; determining to obtain the optimal check index of other service data segments in the service data segment set by using the optimal check index of the at least one service data segment;

determining a target shared check index corresponding to the service data segment based on the second shared check index and the optimal check index of the service data segment;

splicing the service data segments according to a calibration index clustering result corresponding to the target shared calibration index to obtain the service data to be used; the service data to be used is shared by a plurality of user terminals;

wherein, the determining to obtain the optimal check index of other service data segments in the service data segment set by using the optimal check index of the at least one service data segment includes: selecting a corresponding check index determination mode based on the number of the service data segments of the at least one service data segment and the relative sequence position in the service data segment set; and determining the optimal check index of the at least one service data segment by using the check index determining mode to obtain the optimal check index of other service data segments in the service data segment set.

In a preferred embodiment, before obtaining first service interaction data and second service interaction data obtained after acquiring service data of a target user terminal, the method further includes:

acquiring third service interaction data obtained after service data acquisition is carried out on a target user terminal, wherein the third service interaction data is shared service data comprising at least two types of data sharing duration;

converting the third service interaction data into at least two second service interaction data, wherein different second service interaction data comprise different data sharing duration;

determining service similarity information of corresponding service data sets in the first service interaction data and the second service interaction data, and determining a target service data set which corresponds to the first service interaction data and the second service interaction data and meets a preset index condition based on the service similarity information of the corresponding service data sets comprises: performing the following operations for the first service interaction data and any one of the second service interaction data: determining service similarity information of the corresponding service data sets in the first service interaction data and the second service interaction data, and determining a target service data set which corresponds to the first service interaction data and the second service interaction data and meets a preset index condition based on the service similarity information of the corresponding service data sets;

performing permission update processing on the target service data set in the second service interaction data based on the target service data set in the first service interaction data comprises: performing the following operations for the first service interaction data and any one of the second service interaction data: performing permission updating processing on the target service data set in the second service interaction data based on the target service data set in the first service interaction data;

the step of summarizing the current shared service data with the target authority identifier in the second service interaction data after the authority updating processing to obtain the service data to be used includes: performing the following operations for the first service interaction data and any one of the second service interaction data: summarizing the current shared service data with the target authority identification in the second service interaction data after the authority updating processing to obtain service data to be used;

after obtaining the service data to be used, the method further includes: and summarizing the obtained at least two service data to be used to obtain the service data to be used corresponding to the third service interaction data.

A second aspect of the present application discloses a big data server, comprising a processing engine, a network module and a memory; the processing engine and the memory communicate via the network module, and the processing engine reads the computer program from the memory and runs it to perform the method of the first aspect.

Compared with the prior art, the data processing method combining big data and cloud computing and the big data server provided by the embodiment of the application have the following technical effects: before determining the service data to be used, the non-shared service data with the service authentication signature and the shared service data with the modification permission identifier are considered, so that a target service data set which corresponds to the first service interaction data and the second service interaction data and meets a preset index condition can be determined, and permission updating processing of the target service data set in the second service interaction data is further realized. Therefore, when the current shared service data is summarized, the adjustment of the data modification authority can be considered, so that the real-time update of the target authority identification of the current shared service data is realized. Therefore, a sharing protection mechanism aiming at the current sharing type service data can be established by updating the target authority identifier, and the service data to be used can be ensured not to be randomly tampered when the service data to be used is shared, so that a normal service sharing processing process is ensured, data information leakage of the user terminal in a data sharing state is avoided, and the safety of data sharing is ensured.

In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

The methods, systems, and/or processes of the figures are further described in accordance with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments in which reference numerals represent similar mechanisms throughout the various views of the drawings.

FIG. 1 is a block diagram illustrating an exemplary data processing system that combines big data and cloud computing, according to some embodiments of the present application.

FIG. 2 is a diagram illustrating the hardware and software components of an exemplary big data server, according to some embodiments of the present application.

FIG. 3 is a flow diagram illustrating an exemplary data processing method and/or process that combines big data and cloud computing according to some embodiments of the present application.

FIG. 4 is a block diagram illustrating an exemplary data processing apparatus that combines big data and cloud computing, according to some embodiments of the present application.

Detailed Description

On the basis of the background technology, the inventor researches and analyzes a common service data sharing technology, and finds that the common service data sharing technology does not consider the modification permission of the shared service data when data sharing is performed, which may cause the shared service data to be maliciously tampered, thereby affecting the shared service processing process, and possibly causing data information leakage of the user terminal in the data sharing state.

In order to improve or solve the above problems, embodiments of the present application provide a data processing method and a big data server that combine big data and cloud computing, and analyze and mine collected different service interaction data, thereby sorting out to-be-used service data carrying a target permission identifier, so that when the to-be-used service data is shared, it is ensured that the to-be-used service data is not tampered randomly, thereby ensuring a normal shared service processing process, avoiding data information leakage of a user terminal in a data sharing state, and further ensuring security of data sharing.

In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.

In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant guidance. It will be apparent, however, to one skilled in the art that the present application may be practiced without these specific details. In other instances, well-known methods, procedures, systems, compositions, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.

These and other features, functions, methods of execution, and combination of functions and elements of related elements in the structure and economies of manufacture disclosed in the present application may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form a part of this application. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale.

Flowcharts are used herein to illustrate the implementations performed by systems according to embodiments of the present application. It should be expressly understood that the processes performed by the flowcharts may be performed out of order. Rather, these implementations may be performed in the reverse order or simultaneously. In addition, at least one other implementation may be added to the flowchart. One or more implementations may be deleted from the flowchart.

Fig. 1 is a block diagram illustrating an exemplary big data and cloud computing combined data processing system 300 according to some embodiments of the present application, where the big data and cloud computing combined data processing system 300 may include a big data server 100 and a plurality of user terminals 200.

In some embodiments, as shown in FIG. 2, big data server 100 may include a processing engine 110, a network module 120, and a memory 130, processing engine 110 and memory 130 communicating through network module 120.

Processing engine 110 may process the relevant information and/or data to perform one or more of the functions described herein. For example, in some embodiments, processing engine 110 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, the Processing engine 110 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network module 120 may facilitate the exchange of information and/or data. In some embodiments, the network module 120 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 120 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 120 may include at least one network access point. For example, the network module 120 may include wired or wireless network access points, such as base stations and/or network access points.

The Memory 130 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 130 is used for storing a program, and the processing engine 110 executes the program after receiving the execution instruction.

It will be appreciated that the configuration shown in FIG. 2 is merely illustrative, and that the big data server 100 may also include more or fewer components than shown in FIG. 2, or have a different configuration than shown in FIG. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

Fig. 3 is a flowchart of an exemplary data processing method and/or process combining big data and cloud computing according to some embodiments of the present application, where the data processing method combining big data and cloud computing is applied to the big data server 100 in fig. 1 and/or fig. 2, and may specifically include the contents described in the following steps S11-S13.

Step S11, acquiring first service interaction data and second service interaction data obtained after acquiring service data of the target user terminal.

For example, the first service interaction data is non-shared service data with a service authentication signature, and the second service interaction data is shared service data with a modification permission identifier. Further, the target user terminal may be a service terminal related to multiple fields, and the target user terminal may be a mobile phone, a tablet computer, a notebook computer, a wearable smart device, and the like, which is not limited herein. The service authentication signature can be a signature added by different user terminals during service interaction or processing for performing later data tracing or identity information tracing, and the signature can be a digital signature. The first service interaction data is not used for sharing. The second interactive service data can be used for sharing, and further, the modification authority identifies data which characterize the shared service data can be allowed to be modified during sharing. For example, the modification authority identification XX may be used to characterize that the shared service Data is shared, and the service Data1 in the shared service Data may be modified.

Step S12, determining service similarity information of corresponding service data sets in the first service interaction data and the second service interaction data, and determining a target service data set that corresponds to the first service interaction data and the second service interaction data and satisfies a preset index condition based on the service similarity information of the corresponding service data sets.

For example, the service data sets in the first service interaction data and the second service interaction data may be in a one-to-one correspondence. The service similarity information is used for representing similar information of different service data sets on the aspects of service types, service requirements, service processing forms and the like, and the service similarity information can realize comprehensive comparison among different service data sets from multiple dimensions. The preset index condition can be flexibly selected according to the actual service condition, and is explained in the following, so that the detailed description is omitted here.

Step S13, performing permission update processing on the target service data set in the second service interaction data based on the target service data set in the first service interaction data; and summarizing the current shared service data with the target authority identification in the second service interaction data after the authority updating processing to obtain the service data to be used.

It is to be understood that the service data sets in the first service interaction data and the second service interaction data may be in a one-to-one correspondence relationship, and then the target service data sets in the first service interaction data and the second service interaction data may also be in a one-to-one correspondence relationship. The authority updating process may be to adjust the data modification authority of the target service data set in the second service interaction data. For example, the shared service data corresponds to the modification permission identifier before the data modification permission is adjusted, the shared service data corresponds to the target permission identifier after the data modification permission is adjusted, and the target permission identifier and the modification permission identifier are different. The current shared service data can be data for subsequent sharing, the service data to be used is summarized data of the current shared service data, and the service data to be used can be published on a data sharing platform corresponding to the big data server, so that the service data to be used can be shared and used or modified by a plurality of user terminals.

It can be understood that through the above steps S11-S13, before determining the service data to be used, the non-shared service data having the service authentication signature and the shared service data having the modification permission identifier are considered first, so that a target service data set which corresponds to the first service interaction data and the second service interaction data and meets the preset index condition can be determined, and further permission update processing on the target service data set in the second service interaction data is implemented. Therefore, when the current shared service data is summarized, the adjustment of the data modification authority can be considered, so that the real-time update of the target authority identification of the current shared service data is realized. Therefore, a sharing protection mechanism aiming at the current sharing type service data can be established by updating the target authority identifier, and the service data to be used can be ensured not to be randomly tampered when the service data to be used is shared, so that a normal service sharing processing process is ensured, data information leakage of the user terminal in a data sharing state is avoided, and the safety of data sharing is ensured.

In the following, some alternative embodiments will be described, which should be understood as examples and not as technical features essential for implementing the present solution.

In a possible embodiment, in order to ensure that the service similarity information can accurately reflect the similarity between different service interaction data at the service level, and at the same time avoid the service similarity information occupying more storage space of a large data server, the step S12 of determining the service similarity information of corresponding service data sets in the first service interaction data and the second service interaction data may be step S121 and step S122.

Step S121, determining feature similarity of service data features of each service data set in the first service interaction data and feature similarity of service data features of each service data set in the second service interaction data.

For example, the service data features may be extracted through a preset feature extraction model, and the preset feature extraction model may be a convolutional neural network (a training process corresponding to the convolutional neural network is the prior art). The traffic data characteristics may be represented in the form of vectors.

Step S122, determining a similarity fusion weight value of the service data sets in the first service interaction data and the second service interaction data based on the feature similarity of the service data features of each service data set in the first service interaction data and the feature similarity of the service data features of each service data set in the second service interaction data.

For example, the similarity fusion weight value may be a fusion weight value corresponding to different feature similarities, and the service similarity information includes the similarity fusion weight value.

By adopting the design, the similarity fusion weight value can be determined based on the feature similarity between the service data features by applying the steps S121 and S122, so that the similarity information can accurately reflect the similarity between different service interaction data on the service level based on the similarity fusion weight value, and meanwhile, the similarity fusion weight value is simplified, so that the service similarity information can be prevented from occupying more storage space of a large data server.

Further, the determining the similarity fusion weight value of the corresponding service data set in the first service interaction data and the second service interaction data described in step S122 may include at least one of the following embodiments.

In the first embodiment, based on the feature similarity of the service data features of each service data set in the first service interaction data and the feature similarity of the service data features of each service data set in the second service interaction data, the service data timing sequence similarity of the corresponding service data set in the first service interaction data and the corresponding service data set in the second service interaction data is calculated to determine the similarity fusion weight value. For example, the traffic data time sequence similarity is used for representing the similarity of the traffic data set on a time sequence level.

In a second embodiment, based on the feature similarity of the service data features of each service data set in the first service interaction data and the feature similarity of the service data features of each service data set in the second service interaction data, a similarity fusion coefficient of the service data features of the corresponding service data sets in the first service interaction data and the second service interaction data is calculated to determine the similarity fusion weight value. For example, the similarity fusion coefficient may be determined according to a model parameter in a preset feature extraction model.

In a third implementation manner, the service event labels of the corresponding service data sets in the first service interaction data and the second service interaction data are determined, and the similarity fusion weight value is determined based on the determined service event labels and the feature similarity of the service data features of the corresponding service data sets in the first service interaction data and the second service interaction data. For example, the service event tags are used to characterize different service event information for different service data sets.

In practical application, when the three methods for determining the similarity fusion weight values are applied, one of the methods can be selected for application, a plurality of methods can be simultaneously selected for application, and when a plurality of methods are simultaneously selected for application, the determined similarity fusion weight values can be subjected to mean value calculation to determine the final similarity fusion weight values. In this way, the similarity fusion weight value can be flexibly determined, so that the embodiment can be used in different service scenes.

In some examples, the determining, based on the service similarity information of the corresponding service data set, a target service data set corresponding to the first service interaction data and the second service interaction data and satisfying a preset criterion condition in step S12 may include step S120: and sequencing the corresponding service data sets in the first service interaction data and the second service interaction data according to the sequence of similarity evaluation values of the service similarity information from large to small. For example, the similarity evaluation value may be determined according to related data and information referred to for determining the traffic similarity information, and the similarity evaluation value may be used to represent the reliability or confidence of the traffic similarity information.

Further, the target service data set may be determined from the sorted corresponding service data sets in one of the following manners.

(1) And selecting the corresponding service data sets with set quantity as the target service data sets in sequence. For example, the set number may be preset, and is not limited herein.

(2) And selecting the corresponding service data sets with set proportion as the target service data sets in sequence. For example, the set ratio may be preset, and is not limited herein.

(3) And determining the corresponding service data set with the similarity fusion weight value corresponding to the service similarity information smaller than a first preset weight value as the target service data set.

For example, the first predetermined weight value may be preset, and is not limited herein. The first predetermined weight value may be a dynamic weight value.

(4) And carrying out iterative updating on each corresponding service data set included in the corresponding service data set with the similarity fusion weight value corresponding to the service similarity information smaller than a second preset weight value according to preset iteration times in sequence, and determining the target service data set based on the iterative updating result.

For example, the second predetermined weight value may be preset, and is not limited herein. The second predetermined weight value may be a static weight value. The iterative updating may be a data augmentation of the business data set. The preset iteration number is selected according to the actual service requirement, and is not limited herein.

(5) And selecting the target service data set based on the variation curve of the accumulated service data time sequence similarity of the corresponding service data set.

For example, the variation curve may be obtained by fitting the accumulated time sequence similarity of the service data according to the time sequence order.

It can be understood that, based on the five target service data sets determined, flexible determination of the target service data sets can be ensured, so that it is ensured that the target service data sets can be determined in a suitable manner in different service scenarios, and further subsequent service data sharing is realized.

The above five ways can be further improved or optimized according to practical application, and only the above fourth way and the above fifth way are further described below, and for other ways, those skilled in the art can make an unambiguous derivation based on the above.

Further, in the fourth manner, the iteratively updating, according to a preset iteration number, each corresponding service data set included in the corresponding service data set whose similarity fusion weight value corresponding to the service similarity information is smaller than a second predetermined weight value in order, and determining the target service data set based on an iteration update result may include the following steps (41) to (43).

(41) Determining the number of iterative update times corresponding to the service similarity information of each corresponding service data set included in the corresponding service data set, wherein the similarity fusion weight value corresponding to the service similarity information is smaller than a second preset weight value, and the smaller the similarity fusion weight value corresponding to the service similarity information is, the larger the corresponding iterative update times are.

(42) And according to the determined iteration updating times, performing iteration updating on each corresponding service data set included in the corresponding service data set of which the similarity fusion weight value corresponding to the service similarity information is smaller than a second preset weight value.

(43) And sequencing the corresponding service data sets after iterative updating according to the sequence of the timeliness weights of the data sets from small to large so as to obtain the target service data set.

For example, the timeliness weight of the data set is used for representing the accuracy of the service data set in time sequence, and the longer the timeliness weight of the data set is, the longer the duration of the data accuracy of the service data set is, and the data accuracy can be suitable for more service scenes.

Further, in the fifth mode, the selecting the target service data set based on the variation curve of the cumulative service data time sequence similarity of the corresponding service data set may include steps (51) to (54).

(51) And selecting a set number of the corresponding service data sets in sequence, and calculating the time sequence similarity of the first service data of the set number of the corresponding service data sets, wherein the set number is a predetermined minimum matching number.

(52) And sequentially selecting the corresponding service data sets with the set number added by one, and calculating the time sequence similarity of the second service data of the corresponding service data sets with the set number added by one.

(53) And when the similarity difference between the first service data time sequence similarity and the second service data time sequence similarity is determined to be greater than or equal to a preset similarity difference, determining the corresponding service data sets of the set number as the target service data sets.

(54) And when the similarity difference between the first service data time sequence similarity and the second service data time sequence similarity is determined to be smaller than the preset similarity difference, repeatedly selecting one more corresponding service data set than the previous selected number until the difference between the service data time sequence similarity of the corresponding service data set selected later and the service data time sequence similarity of the corresponding service data set selected earlier is greater than or equal to the preset similarity difference, and determining the corresponding service data set selected earlier as the target service data set.

In practical implementation, the inventor finds that, in order to ensure that the permission update can adapt to multiple service requirements and multiple service scenarios that may occur subsequently, deep analysis needs to be performed on service interaction data, and for this purpose, the step of performing permission update processing on the target service data set in the second service interaction data based on the target service data set in the first service interaction data, which is described in step S13, may include step S131 to step S133.

Step S131, using a data list to represent each service data set included in the target service data set in the first service interaction data, forming a first data list feature matrix from each service data set represented by the data list, and performing matrix element modification processing and matrix structure transformation processing on the first data list feature matrix to obtain a first target matrix.

For example, the data list may be a list configured in advance for performing representation of the service data set, and the data list may enable simplified representation of the service data set, so as to reduce occupation of a storage space of the large data server as much as possible on the premise of ensuring data fidelity of the service data set. The data list feature matrix can be obtained by extracting features of the data list. The matrix element correction processing may be to eliminate redundant data from matrix elements in the data list feature matrix to ensure the simplicity of the data list feature matrix. The matrix structure transformation process may ensure that the target matrix can be used directly by the big data server.

Step S132, using a data list to represent each service data set included in the target service data set in the second service interaction data, forming a second data list feature matrix from each service data set represented by the data list, and performing matrix element modification processing and matrix structure transformation processing on the second data list feature matrix to obtain a second target matrix.

Step S133, performing data usage right update on the target service data set in the first service interaction data based on the first target matrix to obtain first updated service data; and updating the data usage permission of the target service data set in the second service interaction data based on the second target matrix and the first updated service data.

For example, the data usage rights update may be performed according to the usage record of the target service data set.

It can be understood that, by executing the above steps S131 to S133, deep analysis can be performed on the service interaction data, so as to implement simplified representation of the service data set through the data list, and implement processing of the data list feature matrix, so that data usage permission update of the target service data set can be implemented based on the target matrix, and thus, permission update can be ensured to adapt to multiple service requirements and multiple service scenarios that may occur subsequently.

Further, the step S133 of performing data usage right update on the target service data set in the first service interaction data based on the first target matrix to obtain first updated service data further includes: judging whether a matrix characteristic description value of a first target matrix corresponding to each target service data set in the first service interaction data is within a first authority updating description value threshold range or not; setting the matrix characteristic description value of the first target matrix of the target service data set of which the first target matrix is within the threshold range of the first permission updating description value as a selected numerical value, and maintaining the matrix characteristic description values of the first target matrices of other target service data sets unchanged to obtain the first updated service data. For example, the matrix characterization values are used to distinguish between different target matrices and different matrix characterizations. The first permission update description value threshold range and the selected value can be configured according to actual situations, and are not further described here.

Further, the data usage right updating of the target service data set in the second service interaction data based on the second target matrix and the first updated service data may be implemented by the following embodiment a or embodiment b.

In embodiment a, it is determined whether a sum of a matrix feature description value of a second target matrix corresponding to each target service data set in the second service interaction data and a first set description value is within a second permission update description value threshold range, where the first set description value is a product of a matrix feature description index and a set permission security index of a first target matrix corresponding to a target service data set corresponding to the first service interaction data; and setting a second target matrix of the target service data set of which the sum of the second target matrix and the first set description value is within the threshold range of the second permission updating description value as a selected numerical value, and maintaining the matrix characteristic description values of the second target matrices of other target service data sets unchanged to obtain second updated service data.

In embodiment b, a product of a matrix feature description value of a second target matrix corresponding to each target service data set in the second service interaction data and a second set description value is used as the second target matrix of the target service data set to obtain second updated service data, where the second set description value is a ratio of a difference between a service data time sequence similarity corresponding to the target service data set in the first service interaction data and a historical time sequence similarity mean value to a service data time sequence similarity corresponding to the target service data set in the first service interaction data.

In this way, the data usage right of the target service data set can be updated through different implementation modes, so that the update of the data usage right can be highly matched with the subsequent sharing service.

In an actual implementation process, the step S13 may be implemented by summarizing the current shared service data with the target permission identifier in the second service interaction data after permission update processing to obtain the service data to be used, where the summarizing is implemented by a summarizing manner including one of the following manners. Of course, the present invention is not limited to the following traffic data aggregation method.

And in the first service data summarizing mode, the current shared service data with the target authority identifier is summarized in a mode of determining the time sequence similarity of the service data so as to obtain the service data to be used.

And in the second service data summarizing mode, the current shared service data with the target authority identifier is summarized in a mode of determining a similarity fusion coefficient so as to obtain the service data to be used.

And in a third service data summarizing mode, the current shared service data with the target authority identification is summarized by adopting a shared data security verification model to obtain the service data to be used.

For example, the shared data security check model may be a classifier or other machine learning model, and is not limited herein.

And a fourth service data summarizing mode, comparing the matrix characteristic description value of the service data set subjected to the authority updating processing and included in the current shared service data with the target authority identification with the matrix characteristic description value of the service data set not subjected to the authority updating processing, and determining the matrix characteristic description value of the current shared service data with the target authority identification by averaging the matrix characteristic description value of the service data set subjected to the authority updating processing and the matrix characteristic description value of the service data set not subjected to the authority updating processing, which have the smallest similarity difference with the matrix characteristic description value of the service data set not subjected to the authority updating processing, so as to obtain the service data to be used.

Therefore, in practical application, any one of the service data summarization manners can be adopted to realize summarization of the current shared service data, so that the usability of the scheme is increased, and the dependency of the summarization of the service data on a certain service processing scene or a certain service processing manner is weakened.

On the basis, in the third business data summarizing manner, summarizing the current shared business data with the target authority identifier by using a shared data security check model to obtain the to-be-used business data may include the following steps S21 to S25.

Step S21, dividing the current sharing type service data into a plurality of service data segment sets through the sharing data security check model; wherein each service data segment set comprises a plurality of service data segments.

Step S22, obtaining a second shared check index of the service data segment based on the first shared check index of the current shared service data; wherein the first shared check index is obtained by using a historical abnormal shared record.

Step S23, for at least one of the sets of traffic data segments: respectively taking the second shared verification index of at least one service data segment as a reference index, and acquiring the optimal verification index of the at least one service data segment by using a preset index extraction algorithm; and determining to obtain the optimal check indexes of other service data segments in the service data segment set by using the optimal check index of the at least one service data segment.

Step S24, determining a target shared check index corresponding to the service data segment based on the second shared check index and the optimal check index of the service data segment.

Step S25, splicing the service data segments according to the check index clustering result corresponding to the target shared check index to obtain the service data to be used; wherein the service data to be used is shared by a plurality of user terminals.

It can be understood that through the steps S21-S25, the shared check index can be analyzed when summarizing the current shared service data, so as to ensure that no data security problem occurs when the service data to be used is used subsequently.

Further, the step S23 of determining the best check index of the other service data segments in the service data segment set by using the best check index of the at least one service data segment may include: selecting a corresponding check index determination mode based on the number of the service data segments of the at least one service data segment and the relative sequence position in the service data segment set; and determining the optimal check index of the at least one service data segment by using the check index determining mode to obtain the optimal check index of other service data segments in the service data segment set.

In the actual application process, before implementing the step of acquiring the first service interaction data and the second service interaction data obtained after the service data acquisition is performed on the target user terminal described in step S11, the method may further include the following steps.

And acquiring third service interaction data obtained after service data acquisition is carried out on the target user terminal, wherein the third service interaction data is shared service data comprising at least two types of data sharing duration.

And converting the third service interaction data into at least two second service interaction data, wherein different second service interaction data comprise different data sharing duration.

Determining service similarity information of corresponding service data sets in the first service interaction data and the second service interaction data, and determining a target service data set which corresponds to the first service interaction data and the second service interaction data and meets a preset index condition based on the service similarity information of the corresponding service data sets comprises: performing the following operations for the first service interaction data and any one of the second service interaction data: determining the service similarity information of the corresponding service data sets in the first service interaction data and the second service interaction data, and determining a target service data set which corresponds to the first service interaction data and the second service interaction data and meets a preset index condition based on the service similarity information of the corresponding service data sets.

Performing permission update processing on the target service data set in the second service interaction data based on the target service data set in the first service interaction data comprises: performing the following operations for the first service interaction data and any one of the second service interaction data: and performing permission updating processing on the target service data set in the second service interaction data based on the target service data set in the first service interaction data.

The step of summarizing the current shared service data with the target authority identifier in the second service interaction data after the authority updating processing to obtain the service data to be used includes: performing the following operations for the first service interaction data and any one of the second service interaction data: and summarizing the current shared service data with the target authority identification in the second service interaction data after the authority updating processing to obtain the service data to be used.

By the design, based on the content described in the above steps, the shared service data including at least two types of data sharing duration can be taken into consideration, so that diversified processing of the service data to be used is realized, and different subsequent service requirements are met.

In an alternative embodiment, on the basis of the steps S11-S13, the following steps S14 may be further included: and backing up the service data to be used. By the design, when the service data to be used is in a shared state, if part of the service data to be used is lost or tampered due to misoperation of some user terminals, the service data to be used can be continuously shared based on the backup service data to be used, so that stagnation of subsequent shared services is avoided.

In an alternative embodiment, in step S25, the service data segments are spliced according to the check index clustering result corresponding to the target shared check index to obtain the service data to be used, which further includes the contents described in the following steps a to d.

Step a, determining a data segment splicing list of the service data segments and splicing indication labels of all the data segments based on the verification index clustering result corresponding to the target shared verification index.

And b, under the condition that the service data segment contains the dynamic splicing label directory based on the data segment splicing list, determining the data segment splicing weight between each data segment splicing indication label under the static splicing label directory corresponding to the dynamic splicing label directory of the service data segment and each data segment splicing indication label under the dynamic splicing label directory of the service data segment according to the data segment splicing indication label under the dynamic splicing label directory of the service data segment and the label correlation coefficient of the data segment splicing indication label under the dynamic splicing label directory of the service data segment.

Step c, adjusting the data segment splicing indication labels meeting the splicing condition between the data segment splicing indication labels under the static splicing label catalog and the dynamic splicing label catalog corresponding to the business data segment and the dynamic splicing label catalog to the corresponding dynamic splicing label catalog based on the data segment splicing weight; under the condition that a static splicing label directory corresponding to the dynamic splicing label directory of the service data segment currently contains a plurality of data segment splicing indication labels, determining data segment splicing weights among the data segment splicing indication labels under the static splicing label directory corresponding to the dynamic splicing label directory of the service data segment currently according to the data segment splicing indication labels under the dynamic splicing label directory of the service data segment and label correlation coefficients of the data segment splicing indication labels under the dynamic splicing label directory of the service data segment, and clustering the data segment splicing indication labels under the static splicing label directory corresponding to the dynamic splicing label directory currently according to the data segment splicing weights among the data segment splicing indication labels; and setting a label adjustment level for each type of data segment splicing indication label obtained by the clustering according to the data segment splicing indication label under the dynamic splicing label directory of the service data segment and the label correlation coefficient of the data segment splicing indication label under the dynamic splicing label directory of the service data segment, and adjusting each type of data segment splicing indication label to be under the dynamic splicing label directory corresponding to the label adjustment level.

And d, screening out the service data segments to be spliced based on the corresponding data segment splicing indication labels in the dynamic splicing label directory, and splicing the service data segments to be spliced according to the time sequence to obtain the service data to be used.

By means of the design, through the steps a to d, the data segment splicing list of the service data segments and the splicing indication labels of the data segments can be considered, so that secondary distribution and adjustment of the splicing indication labels of the data segments are achieved, and the splicing indication labels of the data segments in the dynamic splicing label directory can be matched with an actual shared service scene, so that the service data segments to be spliced, which are screened out based on the corresponding splicing indication labels of the data segments in the dynamic splicing label directory, can be matched with a subsequent shared service scene, and the service data to be used, which are obtained by splicing, can be used by all user terminals in the shared service scene.

Fig. 4 is a block diagram illustrating an exemplary big data and cloud computing combined data processing apparatus 140 according to some embodiments of the present application, where the big data and cloud computing combined data processing apparatus 140 may include the following functional modules.

The data obtaining module 141 is configured to obtain first service interaction data and second service interaction data obtained after service data acquisition is performed on a target user terminal, where the first service interaction data is non-shared service data with a service authentication signature, and the second service interaction data is shared service data with a modification permission identifier.

A data determining module 142, configured to determine service similarity information of corresponding service data sets in the first service interaction data and the second service interaction data, and determine, based on the service similarity information of the corresponding service data sets, a target service data set that corresponds to the first service interaction data and the second service interaction data and meets a preset index condition.

A data summarization module 143, configured to perform permission update processing on the target service data set in the second service interaction data based on the target service data set in the first service interaction data; and summarizing the current shared service data with the target authority identification in the second service interaction data after the authority updating processing to obtain the service data to be used.

It will be appreciated that the above description of the embodiment of the apparatus can be referred to the description of the embodiment of the method shown in figure 3.

Based on the same inventive concept, a system embodiment is also provided, which is further described as follows.

A1. A data processing system combining big data and cloud computing comprises a big data server and a user terminal which are communicated with each other; wherein the big data server is configured to:

It will be appreciated that the above description of the system embodiment may be referred to as the description of the method embodiment shown in figure 3.

It should be understood that, for technical terms that are not noun explanations to the above-mentioned contents, a person skilled in the art can deduce and unambiguously determine the meaning of the present invention according to the above-mentioned disclosure, for example, for some values, coefficients, weights and other terms, a person skilled in the art can deduce and determine according to the logical relationship before and after, the value range of these values can be selected according to the actual situation, for example, 0 to 1, for example, 1 to 10, for example, 50 to 100, but not limited thereto, and a person skilled in the art can determine some preset, reference, predetermined, set and target technical features/technical terms according to the above-mentioned disclosure without meaning. For some technical characteristic terms which are not explained and terms defined before and after (for example, previous, next), it is fully possible for those skilled in the art to reasonably and unambiguously derive the technical solutions described above based on the logical relations before and after. The foregoing will therefore be clear and complete to those skilled in the art. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present application, and thus the above contents are not an inventive judgment of the overall scheme.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific terminology to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of at least one embodiment of the present application may be combined as appropriate.

In addition, those skilled in the art will recognize that the various aspects of the application may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of procedures, machines, articles, or materials, or any new and useful modifications thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component", or "system". Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in at least one computer readable medium.

A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the execution of aspects of the present application may be written in any combination of one or more programming languages, including object oriented programming, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, or similar conventional programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages, such as Python, Ruby, and Groovy, or other programming languages. The programming code may execute entirely on the user's computer, as a stand-alone software package, partly on the user's computer, partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order of the process elements and sequences described herein, the use of numerical letters, or other designations are not intended to limit the order of the processes and methods unless otherwise indicated in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware means, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

It should also be appreciated that in the foregoing description of embodiments of the present application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the invention. However, this method of disclosure is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Claims

1. A data processing method combining big data and cloud computing is characterized by comprising the following steps:

determining feature similarity of service data features of all service data sets in the first service interaction data and feature similarity of service data features of all service data sets in the second service interaction data;

determining a similarity fusion weight value of corresponding service data sets in the first service interaction data and the second service interaction data based on the feature similarity of the service data features of each service data set in the first service interaction data and the feature similarity of the service data features of each service data set in the second service interaction data, wherein the service similarity information comprises the similarity fusion weight value;

2. The method of claim 1,

before the step of determining the feature similarity of the service data features of the service data sets in the first service interaction data and the feature similarity of the service data features of the service data sets in the second service interaction data, the method further includes:

after the step of determining the similarity fusion weight values of the service data sets corresponding to the first service interaction data and the second service interaction data based on the feature similarity of the service data features of the service data sets in the first service interaction data and the feature similarity of the service data features of the service data sets in the second service interaction data, the method further includes:

determining a target service data set which corresponds to the first service interaction data and the second service interaction data and meets a preset index condition based on the service similarity information of the corresponding service data set;

performing permission updating processing on the target service data set in the second service interaction data based on the target service data set in the first service interaction data;

and summarizing the current shared service data with the target authority identification in the second service interaction data after the authority updating processing to obtain the service data to be used.

3. The method according to claim 2, wherein after the step of summarizing the current shared service data with the target permission identifier in the second service interaction data after permission update processing to obtain the service data to be used, the method further comprises:

and backing up the service data to be used.

4. The method of claim 2, wherein determining a target service data set that corresponds to the first service interaction data and the second service interaction data and satisfies a preset index condition based on the service similarity information of the corresponding service data set comprises:

sorting the corresponding service data sets in the first service interaction data and the second service interaction data according to the sequence of similarity evaluation values of the service similarity information from large to small;

5. The method according to claim 4, wherein the iteratively updating, according to a preset iteration number, each corresponding service data set included in the corresponding service data set in which a similarity fusion weight value corresponding to the service similarity information is smaller than a second predetermined weight value in order, and the determining the target service data set based on the iteratively updating result includes:

and sequencing the corresponding service data sets after iterative updating according to the sequence of the timeliness weights of the data sets from small to large so as to obtain the target service data set.

6. The method of claim 5, wherein selecting the target service data set based on the variation curve of the cumulative service data time sequence similarity of the corresponding service data sets comprises:

7. A big data server is characterized by comprising a processing engine, a network module and a memory; the processing engine and the memory communicate through the network module, the processing engine reading a computer program from the memory and operating to perform the method of any of claims 1-6.