CN112685469A - Business data analysis method and device based on Internet of things - Google Patents
Business data analysis method and device based on Internet of things Download PDFInfo
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- CN112685469A CN112685469A CN202011553880.7A CN202011553880A CN112685469A CN 112685469 A CN112685469 A CN 112685469A CN 202011553880 A CN202011553880 A CN 202011553880A CN 112685469 A CN112685469 A CN 112685469A
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Abstract
According to the service data analysis method and device based on the Internet of things, the service index record track set of different service index analysis time nodes of the service data to be analyzed is analyzed, and the service data analysis instruction corresponding to the service data to be analyzed is generated timely and accurately, so that the service analysis terminal can realize real-time monitoring and analysis of the service data to be analyzed based on the service data analysis instruction, the service data is prevented from being analyzed mechanically, further relevant important analysis angles or analysis contents of the service data to be analyzed are prevented from being omitted in the actual analysis process, and thus deep analysis and mining of the service data to be analyzed can be ensured, and the data value of the service data to be analyzed can be obtained as much as possible. By updating the service data analysis instruction in real time, the integrity and the orderliness of the service data to be analyzed can be ensured.
Description
Technical Field
The application relates to the technical field of business data analysis of the Internet of things, in particular to a business data analysis method and device based on the Internet of things.
Background
With the development of the internet of things technology and the computer technology, a business data processing technology is developed nowadays, the business data processing technology refers to the business management by processing and analyzing generated business data, however, in the process of performing real-time monitoring and analysis on business data to be analyzed by the existing business data analysis technology, mechanical analysis is often performed by a scheduled analysis method, which may cause the relevant important analysis angle or analysis content of the business data to be analyzed to be omitted in the analysis process.
Disclosure of Invention
The first aspect of the application discloses a business data analysis method based on the internet of things, which is applied to the internet of things equipment and comprises the following steps:
acquiring a service index recording track set corresponding to each service index analysis time node of service data to be analyzed in a first service analysis time period, wherein the first service analysis time period comprises at least two service index analysis time nodes, and the service index recording track set corresponding to each service index analysis time node comprises an index recording track of target service data marked or received in the corresponding service index analysis time node by a service attribute marking module in the service data to be analyzed;
determining index recording track similarity among service index recording track sets corresponding to all service index analysis time nodes in the first service analysis time period; determining service running state data of the service data to be analyzed in the first service analysis time period according to index record track similarity among service index record track sets corresponding to all service index analysis time nodes in the first service analysis time period;
determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the service operation state data; and sending the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
Optionally, the acquiring a set of service index recording tracks corresponding to each service index analysis time node of the service data to be analyzed in the first service analysis time period includes:
acquiring an index recording track of target service data marked in a set time interval step length after a first service index analysis time node starts by a service attribute marking module in the service data to be analyzed, and determining a service index recording track set corresponding to the first service index analysis time node according to the index recording track of the target service data marked in the set time interval step length after the first service index analysis time node starts by the service attribute marking module in the service data to be analyzed, wherein the first service index analysis time node is any service index analysis time node in the first service analysis time interval;
under the condition that a service attribute marking module in the service data to be analyzed does not mark target service data within a set time interval step length after a second service index analysis time node starts, determining a service index record track set corresponding to the second service index analysis time node according to an index record track of the target service data received by the service attribute marking module in the service data to be analyzed, wherein the second service index analysis time node is any service index analysis time node except the first service index analysis time node within the first service analysis time interval.
Optionally, the method further comprises:
the service attribute marking module in the service data to be analyzed does not mark target service data within a set time interval step length after the third service index analysis time node starts, and the service index record track set corresponding to the service index analysis time node of the first set accumulated quantity which is continuous before the third service index analysis time node is determined according to the index record track of the target service data received by the service attribute marking module, a target service data marking instruction is sent to the service attribute marking module, so that the service attribute marking module marks target service data in response to the target service data marking instruction, the third service index analysis time node is any service index analysis time node except the first service index analysis time node and the second service index analysis time node in the first service analysis time period;
and acquiring an index recording track of the target service data marked by the service attribute marking module in response to the target service data marking instruction, and determining a service index recording track set corresponding to the third service index analysis time node according to the index recording track of the target service data marked by the service attribute marking module in response to the target service data marking instruction.
Optionally, the determining the similarity of the index record tracks between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period includes:
determining a reference index recording track subset from a service index recording track set corresponding to each service index analysis time node in a first service analysis time period; respectively determining index recording track similarity between each service index recording track set except the reference index recording track subset in the service index recording track set corresponding to each service index analysis time node in the first service analysis time period and the reference index recording track subset;
or
And respectively determining the similarity of the index recording tracks between the service index recording track sets corresponding to every two adjacent service index analysis time nodes in the first service analysis time period.
Optionally, the service index record track set corresponding to each service index analysis time node in the first service analysis time period includes a dynamic service index record track set and a static service index record track set, and the service running state data includes first service running state data determined according to index record track similarity corresponding to the dynamic service index record track set of each service index analysis time node specified in the first service analysis time period, and second service running state data determined according to index record track similarity corresponding to the static service index record track set of each service index analysis time node specified in the first service analysis time period; the determining, according to the service operation state data, a service data analysis instruction of the service data to be analyzed in the first service analysis time period includes: determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the first service operation state data and the second service operation state data;
the determining the service running state data of the service data to be analyzed in the first service analysis time period according to the index record track similarity between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period includes: determining at least one target dynamic business index record track set of which the identification weight of a mark corresponding to target business data is lower than a first preset identification weight and at least one target static business index record track set of which the identification weight of a mark corresponding to the target business data is lower than a second preset identification weight from the business index record track sets corresponding to all business index analysis time nodes in the first business analysis time period; determining the first service running state data according to the index recording track similarity corresponding to the at least one target dynamic service index recording track set, and determining the second service running state data according to the index recording track similarity corresponding to the at least one target static service index recording track set;
the determining, according to the first service operation state data and the second service operation state data, a service data analysis instruction of the service data to be analyzed in the first service analysis time period includes: determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period as a continuity analysis instruction under the condition that a service state abnormity coefficient corresponding to the first service operation state data is not larger than a preset first target abnormity coefficient and a service state abnormity coefficient corresponding to the second service operation state data is not larger than a preset second target abnormity coefficient; determining that a service data analysis instruction of the service data to be analyzed in the first service analysis time period is an intermittent analysis instruction under the condition that a service state abnormity coefficient corresponding to the first service operation state data is not larger than the first target abnormity coefficient and a service state abnormity coefficient corresponding to the second service operation state data is smaller than the second target abnormity coefficient; and under the condition that the business state abnormity coefficient corresponding to the first business operation state data is smaller than the first target abnormity coefficient and the business state abnormity coefficient corresponding to the second business operation state data is smaller than the second target abnormity coefficient, determining a business data analysis instruction of the business data to be analyzed in the first business analysis time period as a time delay analysis instruction.
Optionally, the determining, according to the index record track similarity between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period, the service running state data of the service data to be analyzed in the first service analysis time period includes:
determining an integrity coefficient of similarity of each index recording track according to track characteristic distribution of the service index recording tracks contained in a service index recording track set corresponding to each service index analysis time node in the first service analysis time period;
and determining the service operation state data of the service data to be analyzed in the first service analysis time period according to the index record track similarity among the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period and the integrity coefficient of the index record track similarity.
Optionally, the issuing the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed and updating the service data analysis instruction in real time includes:
determining an instruction format sequence in the service data analysis instruction, and generating a first format feature set corresponding to the instruction format sequence, wherein the instruction format sequence is obtained by analyzing the service data analysis instruction by using a preset format analysis model based on the internet of things equipment, and a format text corresponding to the sequence is unchanged; acquiring a terminal configuration parameter list of the service analysis terminal, and calculating a matching description weight between the instruction format sequence and the terminal configuration parameter list according to the first format feature set;
if the matching description weight between the instruction format sequence and the terminal configuration parameter list is smaller than a preset matching threshold, matching an instruction receiving log corresponding to the service analysis terminal with the first format feature set to obtain a second format feature set; converting the instruction receiving log into an instruction list, taking the instruction list as a first object to be processed, taking the service data analysis instruction as a second object to be processed, and performing instruction matching to obtain a first matching result; screening the first matching result according to the second format feature set to obtain a second matching result with a confidence weight higher than that of the first matching result; determining the second matching result and first instruction operation information of the instruction list, and matching operation logic information corresponding to the first instruction operation information with the instruction receiving log to obtain instruction operation indication information;
if the matching description weight between the instruction format sequence and the terminal configuration parameter list is greater than or equal to the matching threshold, determining the first matching result and second instruction operation information of the instruction list, and matching operation logic information in the second instruction operation information with the instruction receiving log to obtain instruction operation indication information;
operating the service data analysis instruction based on the instruction operation instruction information to obtain a target instruction, and sending the target instruction to the service analysis terminal; after the target instruction is sent to the service analysis terminal, extracting multi-dimensional information of target service data in a target area based on generation time information corresponding to the service data analysis instruction to obtain a first multi-dimensional information set and a second multi-dimensional information set corresponding to the target service data; the first multi-dimensional information set is used for representing a feature set corresponding to service value information corresponding to the target service data, and the second multi-dimensional information set is used for representing a feature set corresponding to a newly added service data index corresponding to the target service data;
after the first multi-dimensional information set and the second multi-dimensional information set are obtained, a first business change data set of the first multi-dimensional information set and a second business change data set of the second multi-dimensional information set are obtained, wherein the first multi-dimensional information set comprises first business category information, and the second multi-dimensional information set comprises second business category information; acquiring each group of data nodes in the first service change data set and each group of data nodes in the second service change data set to obtain service change node distribution; determining a relevance index between any two groups of data nodes in the service change node distribution to obtain an initial relevance index queue; adjusting the relevance indexes smaller than the set relevance indexes in the initial relevance index queue to be set relevance indexes to obtain a current relevance index queue; performing update frequency identification on the current relevance index queue to obtain a real-time service demand identification result, wherein the real-time service demand identification result is used for indicating that the first service category information and the second service category information are the same service category information or different service category information; and updating the service data analysis command in real time based on the real-time service demand identification result, and returning to execute the step of issuing the service data analysis command to the service analysis terminal corresponding to the service data to be analyzed.
A second aspect of the present application discloses a business data analysis device based on the internet of things, which is applied to internet of things equipment, the device includes:
the system comprises an index record track acquisition module, a service index analysis time period acquisition module and a service index analysis time period analysis module, wherein the index record track acquisition module is used for acquiring a service index record track set corresponding to each service index analysis time node of service data to be analyzed in a first service analysis time period, the first service analysis time period comprises at least two service index analysis time nodes, and the service index record track set corresponding to each service index analysis time node comprises an index record track of target service data marked or received by a service attribute marking module in the service data to be analyzed in the corresponding service index analysis time node;
the operation state data determining module is used for determining index record track similarity among service index record track sets corresponding to each service index analysis time node in the first service analysis time period; determining service running state data of the service data to be analyzed in the first service analysis time period according to index record track similarity among service index record track sets corresponding to all service index analysis time nodes in the first service analysis time period;
the data analysis instruction updating module is used for determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the service operation state data; and sending the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
Compared with the prior art, the service data analysis method and device based on the internet of things provided by the embodiment of the invention have the following technical effects:
firstly, acquiring a service index recording track set corresponding to each service index analysis time node of service data to be analyzed in a first service analysis time period, secondly, determining index recording track similarity between the service index recording track sets corresponding to the service index analysis time nodes in the first service analysis time period, determining service running state data of the service data to be analyzed in the first service analysis time period, and finally, determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the service running state data, issuing the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
Therefore, by analyzing the service index record track set of different service index analysis time nodes corresponding to the service data to be analyzed, the service data analysis instruction corresponding to the service data to be analyzed can be timely and accurately generated, so that the service analysis terminal corresponding to the service data to be analyzed can realize real-time monitoring and analysis of the service data to be analyzed based on the service data analysis instruction, the service data is prevented from being mechanically analyzed, further relevant important analysis angles or analysis contents of the service data to be analyzed are prevented from being omitted in the actual analysis process, in this way, deep analysis and mining of the service data to be analyzed can be ensured, and the data value of the back of the service data to be analyzed can be obtained as much as possible.
In addition, by updating the service data analysis instruction in real time, the real-time update data condition of the service analysis terminal can be taken into account, so that the response adjustment of the newly added service data index or the emergency of other services is realized based on the Internet of things equipment, and the integrity and the orderliness of the service data to be analyzed can be ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed 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 invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
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 flow diagram illustrating an exemplary internet of things based business data analysis method and/or process in accordance with some embodiments of the invention.
Fig. 2 is a block diagram of an example internet of things-based traffic data analysis apparatus, according to some embodiments of the invention.
Detailed Description
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention 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 invention 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 invention.
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 invention. 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 flowchart illustrating an exemplary method and/or process for analyzing service data based on the internet of things according to some embodiments of the present invention, where the method for analyzing service data based on the internet of things is applied to an internet of things device, and may specifically include the contents described in the following steps 31 to 33.
Step 31, acquiring a service index record track set corresponding to each service index analysis time node of the service data to be analyzed in the first service analysis time period.
In this embodiment, the first service analysis time period includes at least two service index analysis time nodes, and the service index record track set corresponding to each service index analysis time node includes an index record track of target service data marked or received in the corresponding service index analysis time node by the service attribute marking module in the service data to be analyzed.
Step 32, determining index recording track similarity among service index recording track sets corresponding to each service index analysis time node in the first service analysis time period; and determining the service running state data of the service data to be analyzed in the first service analysis time period according to the index record track similarity between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period.
Step 33, determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the service operation state data; and sending the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
It can be understood that, by executing the above steps 31 to 33, firstly, a service index record track set corresponding to each service index analysis time node of the service data to be analyzed in the first service analysis time period is obtained, secondly, the index record track similarity between the service index record track sets corresponding to each service index analysis time node in the first service analysis time period is determined, the service running state data of the service data to be analyzed in the first service analysis time period is determined, and finally, a service data analysis instruction of the service data to be analyzed in the first service analysis time period is determined according to the service running state data and is issued to the service analysis terminal corresponding to the service data to be analyzed, and the service data analysis instruction is updated in real time.
Therefore, by analyzing the service index record track set of different service index analysis time nodes corresponding to the service data to be analyzed, the service data analysis instruction corresponding to the service data to be analyzed can be timely and accurately generated, so that the service analysis terminal corresponding to the service data to be analyzed can realize real-time monitoring and analysis of the service data to be analyzed based on the service data analysis instruction, the service data is prevented from being mechanically analyzed, further relevant important analysis angles or analysis contents of the service data to be analyzed are prevented from being omitted in the actual analysis process, in this way, deep analysis and mining of the service data to be analyzed can be ensured, and the data value of the back of the service data to be analyzed can be obtained as much as possible. In addition, by updating the service data analysis instruction in real time, the real-time update data condition of the service analysis terminal can be taken into account, so that the response adjustment of the newly added service data index or the emergency of other services is realized based on the Internet of things equipment, and the integrity and the orderliness of the service data to be analyzed can be ensured.
In a specific implementation, the acquiring of the service index record track set corresponding to each service index analysis time node of the service data to be analyzed in the first service analysis time period in step 31 may include the following contents described in step 311 and step 312.
Step 311, obtaining an index recording track of the target service data marked by the service attribute marking module in the service data to be analyzed within a set time interval step after the first service index analysis time node starts, and determining a set of service index recording tracks corresponding to the first service index analysis time node according to the index recording track of the target service data marked by the service attribute marking module in the service data to be analyzed within the set time interval step after the first service index analysis time node starts, where the first service index analysis time node is any service index analysis time node in the first service analysis time interval.
Step 312, in a case that the service attribute marking module in the service data to be analyzed does not mark the target service data within a set time interval step after a second service index analysis time node starts, determining a service index recording track set corresponding to the second service index analysis time node according to the index recording track of the target service data received by the service attribute marking module in the service data to be analyzed, where the second service index analysis time node is any service index analysis time node other than the first service index analysis time node within the first service analysis time interval.
In addition, on the basis of steps 311 and 312, the following descriptions of steps 313 and 314 may be included.
Step 313, the service attribute marking module in the service data to be analyzed does not mark the target service data within the set time interval step length after the third service index analysis time node starts, and the service index record track set corresponding to the service index analysis time node of the first set accumulated quantity which is continuous before the third service index analysis time node is determined according to the index record track of the target service data received by the service attribute marking module, a target service data marking instruction is sent to the service attribute marking module, so that the service attribute marking module marks target service data in response to the target service data marking instruction, the third service index analysis time node is any service index analysis time node except the first service index analysis time node and the second service index analysis time node in the first service analysis time period.
Step 314, acquiring an index recording track of the target service data marked by the service attribute marking module in response to the target service data marking instruction, and determining a service index recording track set corresponding to the third service index analysis time node according to the index recording track of the target service data marked by the service attribute marking module in response to the target service data marking instruction.
By the design, the target service data can be marked in the set time interval step length corresponding to different service index analysis time nodes based on the service attribute marking module by executing the steps 311 to 314, so that the integrity of the service index record track set is ensured, and the target service data index record track is prevented from being missed in certain time intervals.
Optionally, the determining of the index record track similarity between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period in step 32 may be implemented by any one of the following two implementation manners.
In the first embodiment, a reference index recording track subset is determined from a service index recording track set corresponding to each service index analysis time node in a first service analysis time period; and respectively determining index recording track similarity between each service index recording track set except the reference index recording track subset in the service index recording track set corresponding to each service index analysis time node in the first service analysis time period and the reference index recording track subset.
In a second implementation manner, index record track similarities between service index record track sets corresponding to every two adjacent service index analysis time nodes in the first service analysis time period are respectively determined.
Therefore, the similarity of the index recording tracks can be accurately calculated in the service analysis time period.
In an actual application process, the service index record track set corresponding to each service index analysis time node in the first service analysis time period includes a dynamic service index record track set and a static service index record track set, and the service operation state data includes first service operation state data determined according to index record track similarity corresponding to the dynamic service index record track set of each service index analysis time node specified in the first service analysis time period, and second service operation state data determined according to index record track similarity corresponding to the static service index record track set of each service index analysis time node specified in the first service analysis time period.
On this basis, the determining, according to the service operation state data, the service data analysis instruction of the service data to be analyzed in the first service analysis time period in step 33 includes: and determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the first service operation state data and the second service operation state data. By the design, different service operation state data can be taken into account by the service data analysis instruction, so that the service data to be analyzed in the service analysis terminal can be comprehensively analyzed.
Further, determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the first service operation state data and the second service operation state data, and further including the contents described in the following steps (1) to (3).
And (1) determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period as a continuity analysis instruction under the condition that a service state abnormity coefficient corresponding to the first service running state data is not larger than a preset first target abnormity coefficient and a service state abnormity coefficient corresponding to the second service running state data is not larger than a preset second target abnormity coefficient.
And (2) determining that a service data analysis instruction of the service data to be analyzed in the first service analysis time period is an intermittent analysis instruction under the condition that a service state abnormity coefficient corresponding to the first service running state data is not larger than the first target abnormity coefficient and a service state abnormity coefficient corresponding to the second service running state data is smaller than the second target abnormity coefficient.
And (3) determining that a service data analysis instruction of the service data to be analyzed in the first service analysis time period is a delayed analysis instruction under the condition that a service state abnormity coefficient corresponding to the first service running state data is smaller than the first target abnormity coefficient and a service state abnormity coefficient corresponding to the second service running state data is smaller than the second target abnormity coefficient.
Thus, the service data analysis instructions corresponding to the service state abnormal coefficients of the different service operation state data under different conditions can be determined by executing the contents described in the steps (1) to (3), and when the service data to be analyzed is analyzed based on the different service data analysis instructions, the relevant important analysis angles of the service data to be analyzed or the analysis contents can be prevented from being omitted in the actual analysis process
In a possible example, the determining of the service operation state data of the service data to be analyzed in the first service analysis time period according to the index record track similarity between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period, which is described in step 32, may be implemented by any one of the following two implementation manners.
In the first embodiment, from the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period, at least one target dynamic service index record track set of which the identification weight of the mark corresponding to the target service data is lower than a first preset identification weight and at least one target static service index record track set of which the identification weight of the mark corresponding to the target service data is lower than a second preset identification weight are determined; and determining the first service running state data according to the index recording track similarity corresponding to the at least one target dynamic service index recording track set, and determining the second service running state data according to the index recording track similarity corresponding to the at least one target static service index recording track set.
In a second implementation manner, an integrity coefficient of similarity of each index recording track is determined according to track characteristic distribution of the service index recording track included in a service index recording track set corresponding to each service index analysis time node in the first service analysis time period; and determining the service operation state data of the service data to be analyzed in the first service analysis time period according to the index record track similarity among the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period and the integrity coefficient of the index record track similarity.
In this way, the service operation state data is determined by any one of the above two implementation manners, and the identification weight level or the integrity coefficient level of the mark corresponding to the target service data can be considered, so that the service operation state data can be determined flexibly and accurately.
In specific implementation, when the service data analysis instruction is issued and updated in real time, the matching with the instruction format of each service data in the service analysis terminal needs to be considered, so that the service analysis terminal is prevented from being unable to receive or being unable to execute the corresponding service data analysis instruction. Further, the service class information at the time of real-time update is also considered. In order to achieve the above object, the step 33 of issuing the service data analysis instruction to the service analysis terminal corresponding to the service data to be analyzed and updating the service data analysis instruction in real time may be further implemented by the following contents described in the steps 331 to 335.
Step 331, determining an instruction format sequence in the service data analysis instruction, and generating a first format feature set corresponding to the instruction format sequence, where the instruction format sequence is a sequence obtained by analyzing, by an internet of things device, based on the service data analysis instruction by using a preset format analysis model, and a format text corresponding to the sequence is unchanged; and acquiring a terminal configuration parameter list of the service analysis terminal, and calculating the matching description weight between the instruction format sequence and the terminal configuration parameter list according to the first format feature set.
Step 332, if the matching description weight between the instruction format sequence and the terminal configuration parameter list is smaller than a preset matching threshold, matching an instruction receiving log corresponding to the service analysis terminal with the first format feature set to obtain a second format feature set; converting the instruction receiving log into an instruction list, taking the instruction list as a first object to be processed, taking the service data analysis instruction as a second object to be processed, and performing instruction matching to obtain a first matching result; screening the first matching result according to the second format feature set to obtain a second matching result with a confidence weight higher than that of the first matching result; and determining the second matching result and first instruction operation information of the instruction list, and matching operation logic information corresponding to the first instruction operation information with the instruction receiving log to obtain instruction operation indication information.
Step 333, if the matching description weight between the instruction format sequence and the terminal configuration parameter list is greater than or equal to the matching threshold, determining the first matching result and the second instruction operation information of the instruction list, and matching the operation logic information in the second instruction operation information with the instruction receiving log to obtain instruction operation indication information.
Step 334, operating the service data analysis instruction based on the instruction operation instruction information to obtain a target instruction, and sending the target instruction to the service analysis terminal; after the target instruction is sent to the service analysis terminal, extracting multi-dimensional information of target service data in a target area based on generation time information corresponding to the service data analysis instruction to obtain a first multi-dimensional information set and a second multi-dimensional information set corresponding to the target service data; the first multidimensional information set is used for representing a feature set corresponding to service value information corresponding to the target service data, and the second multidimensional information set is used for representing a feature set corresponding to a newly added service data index corresponding to the target service data.
Step 335, after obtaining the first multi-dimensional information set and the second multi-dimensional information set, obtaining a first business change data set of the first multi-dimensional information set and a second business change data set of the second multi-dimensional information set, where the first multi-dimensional information set includes first business category information, and the second multi-dimensional information set includes second business category information; acquiring each group of data nodes in the first service change data set and each group of data nodes in the second service change data set to obtain service change node distribution; determining a relevance index between any two groups of data nodes in the service change node distribution to obtain an initial relevance index queue; adjusting the relevance indexes smaller than the set relevance indexes in the initial relevance index queue to be set relevance indexes to obtain a current relevance index queue; performing update frequency identification on the current relevance index queue to obtain a real-time service demand identification result, wherein the real-time service demand identification result is used for indicating that the first service category information and the second service category information are the same service category information or different service category information; and updating the service data analysis command in real time based on the real-time service demand identification result, and returning to execute the step of issuing the service data analysis command to the service analysis terminal corresponding to the service data to be analyzed.
It should be noted that after the real-time update, when the service data analysis command is returned to the service analysis terminal corresponding to the service data to be analyzed, the service data analysis command is different.
It can be understood that, by executing the contents described in the above steps 331 to 335, when issuing and updating the service data analysis command in real time, the matching between the service data analysis command and the command format of each service data in the service analysis terminal of the service analysis terminal can be considered, so as to avoid that the service analysis terminal cannot receive or cannot execute the corresponding service data analysis command. In addition, the service type information during real-time updating is also considered, so that accurate and real-time updating of the service data analysis instruction is ensured.
Fig. 2 is a block diagram of an exemplary service data analysis device 20 based on the internet of things according to some embodiments of the present invention, where the service data analysis device 20 based on the internet of things is applied to an apparatus of the internet of things, and the service data analysis device 20 based on the internet of things includes the following functional modules.
An index record track obtaining module 21, configured to obtain a service index record track set corresponding to each service index analysis time node of service data to be analyzed in a first service analysis time period, where the first service analysis time period includes at least two service index analysis time nodes, and the service index record track set corresponding to each service index analysis time node includes an index record track of target service data that is marked or received in the corresponding service index analysis time node by a service attribute marking module in the service data to be analyzed;
the operation state data determining module 22 is configured to determine index record track similarity between service index record track sets corresponding to service index analysis time nodes in the first service analysis time period; determining service running state data of the service data to be analyzed in the first service analysis time period according to index record track similarity among service index record track sets corresponding to all service index analysis time nodes in the first service analysis time period;
the data analysis instruction updating module 23 is configured to determine, according to the service operation state data, a service data analysis instruction of the service data to be analyzed in the first service analysis time period; and sending the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
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.
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 (8)
1. A business data analysis method based on the Internet of things is characterized by being applied to equipment of the Internet of things and comprising the following steps:
acquiring a service index recording track set corresponding to each service index analysis time node of service data to be analyzed in a first service analysis time period, wherein the first service analysis time period comprises at least two service index analysis time nodes, and the service index recording track set corresponding to each service index analysis time node comprises an index recording track of target service data marked or received in the corresponding service index analysis time node by a service attribute marking module in the service data to be analyzed;
determining index recording track similarity among service index recording track sets corresponding to all service index analysis time nodes in the first service analysis time period; determining service running state data of the service data to be analyzed in the first service analysis time period according to index record track similarity among service index record track sets corresponding to all service index analysis time nodes in the first service analysis time period;
determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the service operation state data; and sending the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
2. The method of claim 1, wherein the obtaining of the service index record track set corresponding to each service index analysis time node of the service data to be analyzed in the first service analysis time period comprises:
acquiring an index recording track of target service data marked in a set time interval step length after a first service index analysis time node starts by a service attribute marking module in the service data to be analyzed, and determining a service index recording track set corresponding to the first service index analysis time node according to the index recording track of the target service data marked in the set time interval step length after the first service index analysis time node starts by the service attribute marking module in the service data to be analyzed, wherein the first service index analysis time node is any service index analysis time node in the first service analysis time interval;
under the condition that a service attribute marking module in the service data to be analyzed does not mark target service data within a set time interval step length after a second service index analysis time node starts, determining a service index record track set corresponding to the second service index analysis time node according to an index record track of the target service data received by the service attribute marking module in the service data to be analyzed, wherein the second service index analysis time node is any service index analysis time node except the first service index analysis time node within the first service analysis time interval.
3. The method of claim 2, wherein the method further comprises:
the service attribute marking module in the service data to be analyzed does not mark target service data within a set time interval step length after the third service index analysis time node starts, and the service index record track set corresponding to the service index analysis time node of the first set accumulated quantity which is continuous before the third service index analysis time node is determined according to the index record track of the target service data received by the service attribute marking module, a target service data marking instruction is sent to the service attribute marking module, so that the service attribute marking module marks target service data in response to the target service data marking instruction, the third service index analysis time node is any service index analysis time node except the first service index analysis time node and the second service index analysis time node in the first service analysis time period;
and acquiring an index recording track of the target service data marked by the service attribute marking module in response to the target service data marking instruction, and determining a service index recording track set corresponding to the third service index analysis time node according to the index recording track of the target service data marked by the service attribute marking module in response to the target service data marking instruction.
4. The method of claim 1, wherein the determining of index record track similarity between service index record track sets corresponding to service index analysis time nodes in the first service analysis time period comprises:
determining a reference index recording track subset from a service index recording track set corresponding to each service index analysis time node in a first service analysis time period; respectively determining index recording track similarity between each service index recording track set except the reference index recording track subset in the service index recording track set corresponding to each service index analysis time node in the first service analysis time period and the reference index recording track subset;
or
And respectively determining the similarity of the index recording tracks between the service index recording track sets corresponding to every two adjacent service index analysis time nodes in the first service analysis time period.
5. The method according to claim 1, wherein the service index record track set corresponding to each service index analysis time node in the first service analysis time period includes a dynamic service index record track set and a static service index record track set, and the service operation state data includes first service operation state data determined according to index record track similarity corresponding to the dynamic service index record track set of each service index analysis time node specified in the first service analysis time period, and second service operation state data determined according to index record track similarity corresponding to the static service index record track set of each service index analysis time node specified in the first service analysis time period; the determining, according to the service operation state data, a service data analysis instruction of the service data to be analyzed in the first service analysis time period includes: determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the first service operation state data and the second service operation state data;
the determining the service running state data of the service data to be analyzed in the first service analysis time period according to the index record track similarity between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period includes: determining at least one target dynamic business index record track set of which the identification weight of a mark corresponding to target business data is lower than a first preset identification weight and at least one target static business index record track set of which the identification weight of a mark corresponding to the target business data is lower than a second preset identification weight from the business index record track sets corresponding to all business index analysis time nodes in the first business analysis time period; determining the first service running state data according to the index recording track similarity corresponding to the at least one target dynamic service index recording track set, and determining the second service running state data according to the index recording track similarity corresponding to the at least one target static service index recording track set;
the determining, according to the first service operation state data and the second service operation state data, a service data analysis instruction of the service data to be analyzed in the first service analysis time period includes: determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period as a continuity analysis instruction under the condition that a service state abnormity coefficient corresponding to the first service operation state data is not larger than a preset first target abnormity coefficient and a service state abnormity coefficient corresponding to the second service operation state data is not larger than a preset second target abnormity coefficient; determining that a service data analysis instruction of the service data to be analyzed in the first service analysis time period is an intermittent analysis instruction under the condition that a service state abnormity coefficient corresponding to the first service operation state data is not larger than the first target abnormity coefficient and a service state abnormity coefficient corresponding to the second service operation state data is smaller than the second target abnormity coefficient; and under the condition that the business state abnormity coefficient corresponding to the first business operation state data is smaller than the first target abnormity coefficient and the business state abnormity coefficient corresponding to the second business operation state data is smaller than the second target abnormity coefficient, determining a business data analysis instruction of the business data to be analyzed in the first business analysis time period as a time delay analysis instruction.
6. The method of claim 1, wherein the determining, according to the similarity of the index record tracks between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period, the service operation state data of the service data to be analyzed in the first service analysis time period comprises:
determining an integrity coefficient of similarity of each index recording track according to track characteristic distribution of the service index recording tracks contained in a service index recording track set corresponding to each service index analysis time node in the first service analysis time period;
and determining the service operation state data of the service data to be analyzed in the first service analysis time period according to the index record track similarity among the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period and the integrity coefficient of the index record track similarity.
7. The method according to any one of claims 1 to 6, wherein issuing the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed and updating the service data analysis instruction in real time comprises:
determining an instruction format sequence in the service data analysis instruction, and generating a first format feature set corresponding to the instruction format sequence, wherein the instruction format sequence is obtained by analyzing the service data analysis instruction by using a preset format analysis model based on the internet of things equipment, and a format text corresponding to the sequence is unchanged; acquiring a terminal configuration parameter list of the service analysis terminal, and calculating a matching description weight between the instruction format sequence and the terminal configuration parameter list according to the first format feature set;
if the matching description weight between the instruction format sequence and the terminal configuration parameter list is smaller than a preset matching threshold, matching an instruction receiving log corresponding to the service analysis terminal with the first format feature set to obtain a second format feature set; converting the instruction receiving log into an instruction list, taking the instruction list as a first object to be processed, taking the service data analysis instruction as a second object to be processed, and performing instruction matching to obtain a first matching result; screening the first matching result according to the second format feature set to obtain a second matching result with a confidence weight higher than that of the first matching result; determining the second matching result and first instruction operation information of the instruction list, and matching operation logic information corresponding to the first instruction operation information with the instruction receiving log to obtain instruction operation indication information;
if the matching description weight between the instruction format sequence and the terminal configuration parameter list is greater than or equal to the matching threshold, determining the first matching result and second instruction operation information of the instruction list, and matching operation logic information in the second instruction operation information with the instruction receiving log to obtain instruction operation indication information;
operating the service data analysis instruction based on the instruction operation instruction information to obtain a target instruction, and sending the target instruction to the service analysis terminal; after the target instruction is sent to the service analysis terminal, extracting multi-dimensional information of target service data in a target area based on generation time information corresponding to the service data analysis instruction to obtain a first multi-dimensional information set and a second multi-dimensional information set corresponding to the target service data; the first multi-dimensional information set is used for representing a feature set corresponding to service value information corresponding to the target service data, and the second multi-dimensional information set is used for representing a feature set corresponding to a newly added service data index corresponding to the target service data;
after the first multi-dimensional information set and the second multi-dimensional information set are obtained, a first business change data set of the first multi-dimensional information set and a second business change data set of the second multi-dimensional information set are obtained, wherein the first multi-dimensional information set comprises first business category information, and the second multi-dimensional information set comprises second business category information; acquiring each group of data nodes in the first service change data set and each group of data nodes in the second service change data set to obtain service change node distribution; determining a relevance index between any two groups of data nodes in the service change node distribution to obtain an initial relevance index queue; adjusting the relevance indexes smaller than the set relevance indexes in the initial relevance index queue to be set relevance indexes to obtain a current relevance index queue; performing update frequency identification on the current relevance index queue to obtain a real-time service demand identification result, wherein the real-time service demand identification result is used for indicating that the first service category information and the second service category information are the same service category information or different service category information; and updating the service data analysis command in real time based on the real-time service demand identification result, and returning to execute the step of issuing the service data analysis command to the service analysis terminal corresponding to the service data to be analyzed.
8. The utility model provides a business data analysis device based on thing networking which characterized in that is applied to thing networking equipment, the device includes:
the system comprises an index record track acquisition module, a service index analysis time period acquisition module and a service index analysis time period analysis module, wherein the index record track acquisition module is used for acquiring a service index record track set corresponding to each service index analysis time node of service data to be analyzed in a first service analysis time period, the first service analysis time period comprises at least two service index analysis time nodes, and the service index record track set corresponding to each service index analysis time node comprises an index record track of target service data marked or received by a service attribute marking module in the service data to be analyzed in the corresponding service index analysis time node;
the operation state data determining module is used for determining index record track similarity among service index record track sets corresponding to each service index analysis time node in the first service analysis time period; determining service running state data of the service data to be analyzed in the first service analysis time period according to index record track similarity among service index record track sets corresponding to all service index analysis time nodes in the first service analysis time period;
the data analysis instruction updating module is used for determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the service operation state data; and sending the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
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| CN113139182A (en) * | 2021-05-17 | 2021-07-20 | 毕晓柏 | Data intrusion detection method for online e-commerce platform |
| CN113139182B (en) * | 2021-05-17 | 2022-06-21 | 深圳市蜜蜂互联网络科技有限公司 | Data intrusion detection method for online e-commerce platform |
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