CN113032403A - Data insight method, device, electronic equipment and storage medium - Google Patents
Data insight method, device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention relates to big data technology, and discloses a data insight method, which comprises the following steps: determining the data of the insight object from the business data according to the preset business index; carrying out single-dimensional insight on the insight object data to obtain a single-dimensional insight result; carrying out time sequence insights on the insights object data to obtain time sequence insights results; carrying out multi-dimensional insight on the insight object data to obtain a multi-dimensional insight result; and collecting the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result to generate an insight report, and generating and sending an early warning prompt according to the insight report. In addition, the invention also relates to a block chain technology, and the service data can be stored in the nodes of the block chain. The invention also provides a data insight device, electronic equipment and a computer readable storage medium. The invention can improve the accuracy of the data insight result and improve the efficiency.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a data insights method and device, electronic equipment and a computer readable storage medium.
Background
With the advent of the big data era, how to perform deep analysis from massive data, the value behind mining data becomes more and more important. For example, in the banking industry, mining valid information from sales performance may promote management strategies and discover potential opportunities and problems.
Most of the existing data insight analysis methods are data extracted from forms, and the data are simply processed. The method is easy to cause errors, omission or repetition of report data, so that the insight result has larger deviation, the analysis mode is single, the data is often only converted into readable effective information, the data correlation of each service index is poor, once a new service data analysis scene is met, the data correlation needs to be modified or re-created, and the efficiency is lower.
Disclosure of Invention
The invention provides a data insight method, a data insight device and a computer readable storage medium, and mainly aims to improve the accuracy and efficiency of data insight results.
In order to achieve the above object, the present invention provides a data insights method, including:
determining the data of the insight object from the business data according to the preset business index;
carrying out single-dimensional insight on the insight object data to obtain a single-dimensional insight result;
carrying out time sequence insights on the insights object data to obtain time sequence insights results;
carrying out multi-dimensional insight on the insight object data to obtain a multi-dimensional insight result;
and collecting the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result to generate an insight report, and generating and sending an early warning prompt according to the insight report.
Optionally, the performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result includes:
selecting data with a field type of a numerical type from the insight object data to obtain a numerical data set;
sorting the data in the numerical data set to obtain a sorting result;
generating extreme value insight information when the sequencing result meets a preset extreme value condition;
selecting data with field types being enumerated in the insight object data to obtain an enumerated data set;
generating contribution degree insight information when the data in the enumeration data set meet a preset trigger condition;
and combining the extreme value insight information and the contribution degree insight information to obtain a single-dimensional insight result.
Optionally, the generating the contribution degree insight information when the data in the enumeration data set satisfies a preset trigger condition includes:
acquiring all enumeration values of each field in the enumeration data set;
counting the occupation ratio of each enumerated value in the corresponding field;
and when the occupation ratio exceeds a preset amplitude, filling the occupation ratio and the corresponding enumeration value into a preset enumeration early warning template to obtain contribution degree insight information.
Optionally, the performing time-series insight on the insight object data to obtain a time-series insight result includes:
selecting data of a time dimension from the insight data, and sequencing the data according to time to obtain a time sequence data set;
carrying out mutation point detection on the time sequence data set to obtain mutation point information;
abnormal point detection is carried out on the time sequence data set to obtain abnormal point information;
performing trend detection on the time series data set to obtain trend information;
carrying out periodic detection on the time sequence data set to obtain periodic information;
and merging the mutation point information, the abnormal point information, the trend information and the period information to obtain a time sequence insight result.
Optionally, the performing mutation point detection on the time series data set to obtain mutation point information includes:
sequentially selecting one data in the time sequence data set through traversal operation to obtain current data;
acquiring previous data and next data of current data, and respectively calculating the deviation between the previous data and the current data and the deviation between the next data and the current data to obtain a front deviation value and a rear deviation value;
and when the front deviation amount or the rear deviation amount is larger than a preset deviation threshold value, filling the current data, the front deviation amount or the rear deviation amount into a preset mutation early warning template to obtain mutation point information.
Optionally, the performing anomaly detection on the time series data set to obtain anomaly information includes:
calculating the average value of all data under each field in the time sequence data set;
comparing each data in the time sequence data set with the average value corresponding to the field of the data to obtain an average deviation;
and when the average deviation is greater than a preset deviation threshold value, filling data corresponding to the average deviation and the average deviation into a preset abnormal early warning template to obtain abnormal point information.
Optionally, the performing multidimensional insights on the insights object data to obtain multidimensional insights results includes:
dividing the data of the insight object according to whether the data has time attributes to obtain a time sequence data set and a non-time sequence data set;
detecting the correlation of the data in the time sequence data set by using a preset correlation formula to generate time sequence correlation information;
detecting the correlation of the data in the non-time sequence data set by using a preset correlation formula to generate non-time sequence correlation information;
detecting the similarity of the data in the non-time sequence data set to generate clustering abnormal information;
and combining the time sequence correlation information, the non-time sequence correlation information and the clustering abnormal information to obtain a multi-dimensional insight result.
In order to solve the above problems, the present invention also provides a data insight apparatus, comprising:
the data acquisition module is used for determining the data of the insight object from the business data according to the preset business index;
the single-dimensional insight module is used for carrying out single-dimensional insight on the insight object data to obtain a single-dimensional insight result;
the time sequence insights module is used for carrying out time sequence insights on the insights object data to obtain time sequence insights results;
the multi-dimensional insight module is used for carrying out multi-dimensional insight on the insight object data to obtain a multi-dimensional insight result;
and the prompt module is used for collecting the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result to generate an insight report, and generating and sending an early warning prompt according to the insight report.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the data insight methods described above.
To solve the above problem, the present invention also provides a computer-readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the data insight method described above.
The embodiment of the invention combs the data in advance through the preset service index, thereby ensuring the integrity of the insight data; and single-dimensional insight, time sequence insight and multi-dimensional insight are carried out on the data of the insight object, the scope of the insight is ensured, the accuracy of the insight result is improved, the multi-dimensional insight is used for analyzing the data of a plurality of fields, the relevance among the data can be deeply excavated, the modification or reconstruction of data relevance caused by new service data is avoided, and the working efficiency is improved. Therefore, the data insight method, the data insight device, the electronic equipment and the computer readable storage medium can improve the accuracy of the data insight result and improve the efficiency.
Drawings
FIG. 1 is a schematic flow chart of a data insight method according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a data insight apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the data insight method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a data insight method. The execution subject of the data insight method includes, but is not limited to, at least one of the electronic devices of the server, the terminal, and the like, which can be configured to execute the method provided by the embodiments of the present application. In other words, the data insight method may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Fig. 1 is a schematic flow chart of a data insight method according to an embodiment of the present invention.
In this embodiment, the data insight method includes:
and S1, determining the data of the insight object from the business data according to the preset business index.
The preset service index in the embodiment of the invention is a data index based on historical service data, for example, the index of bank sales service, including but not limited to five modules of operation performance index, financial condition analysis, public service, retail service, operation and personnel management, covers nearly hundreds of indexes such as interest income, non-interest income, total deposit amount, overdue amount and overdue rate, ROA, interest bearing debt and non-interest bearing debt, and covers the whole flow of bank sales management.
In detail, the determining the data of the insight object from the business data according to the preset business index includes:
determining a data field name corresponding to the service index according to a preset mapping relation table;
and acquiring the service data under the field with the same name as the data field from a preset database to obtain the data of the insight object.
Further, when the service data is stored in the database, each field name generally uses a general english name or abbreviation in the data application range, and the preset mapping relationship table refers to a table of a corresponding relationship between the field name stored in the database and the actual service index name. To further emphasize the privacy and security of the traffic data, the traffic data may also be stored in a node of a block chain.
Furthermore, the insight object data comprises a plurality of service data tables, wherein each service data table comprises a plurality of fields, and the plurality of service data tables comprise service data with a plurality of dimensions.
And S2, performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result.
In detail, the performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result includes:
selecting data with a field type of a numerical type from the insight object data to obtain a numerical data set;
sorting the data in the numerical data set to obtain a sorting result;
generating extreme value insight information when the sequencing result meets a preset extreme value condition;
selecting data with field types being enumerated in the insight object data to obtain an enumerated data set;
generating contribution degree insight information when the data in the enumeration data set meet a preset trigger condition;
and combining the extreme value insight information and the contribution degree insight information to obtain a single-dimensional insight result.
The preset extreme value condition comprises that the difference between the maximum data and the second maximum data in the sequencing result is larger than a preset amplitude and the difference between the minimum data and the second minimum data in the sequencing result is larger than the preset amplitude; the triggering condition is that the ratio of a single enumerated value or the sum of the ratios of two enumerated values in the enumerated data exceeds a preset amplitude.
Further, the generating of the extremum insight information when the sorting result meets a preset extremum condition includes:
acquiring maximum value data and second maximum value data in the sequencing result; calculating the difference between the maximum value data and the second maximum value data to obtain the highest difference; when the maximum difference is larger than a preset amplitude value, filling the maximum value data, the second maximum value data and the maximum difference into a preset maximum early warning template to obtain extreme value insight information; or acquiring the minimum value data and the second minimum value data in the sequencing result; calculating the difference between the minimum value data and the second minimum value data to obtain the lowest difference; and when the minimum difference is larger than a preset amplitude value, filling the minimum value data, the second minimum value data and the minimum difference into a preset minimum early warning template to obtain extreme value insight information.
The highest early warning template and the lowest early warning template are sentence texts with spaces which are edited in advance, the related specific data part is a space, and the complete sentence information can be obtained by filling the specific data. The extreme value insight data is early warning information containing specific data.
Further, the generating the contribution degree insight information when the data in the enumeration data set meets a preset trigger condition includes:
acquiring all enumeration values of each field in the enumeration data set;
counting the occupation ratio of each enumerated value in the corresponding field;
and when the occupation ratio exceeds a preset amplitude, filling the occupation ratio and the corresponding enumeration value into a preset enumeration early warning template to obtain contribution degree insight information.
The counting of the occupation ratio of each enumerated value in the corresponding field refers to calculating the occupation ratio of the number of data pieces corresponding to each enumerated value in each field in the enumerated data set, for example, the enumerated values in the gender field include "male" and "female", the enumerated data set is new customer information in one month, 78 male customers exist, 22 female customers exist, the occupation ratio corresponding to the enumerated value "male" is 78%, and the occupation ratio corresponding to the enumerated value "female" is 22%.
The enumeration early warning template is similar to the highest early warning template and is a sentence text with a blank space which is edited in advance.
For example, the current season asset in the insight object data includes data of a plurality of customer managers, the data are sorted from small to large, the maximum value is 10 ten thousand, the second largest value is 4 ten thousand, the difference is (10-4)/4 x 100% ═ 150%, which is larger than 50%, and the data are filled in the highest early warning template, so that an extreme value insight data "the current season asset of the customer manager week is remarkably represented, reaching 10 ten thousand, and is 150% higher than the second 4 ten thousand" is obtained.
And S3, performing time sequence insights on the insights object data to obtain time sequence insights results.
In detail, the performing time series insight on the insight object data to obtain a time series insight result includes:
selecting data of a time dimension from the insight data, and sequencing the data according to time to obtain a time sequence data set;
carrying out mutation point detection on the time sequence data set to obtain mutation point information;
abnormal point detection is carried out on the time sequence data set to obtain abnormal point information;
performing trend detection on the time series data set to obtain trend information;
carrying out periodic detection on the time sequence data set to obtain periodic information;
and merging the mutation point information, the abnormal point information, the trend information and the period information to obtain a time sequence insight result.
The time sequence data set comprises a plurality of fields, each field comprises a plurality of pieces of data, and if the time sequence data set comprises AUM (autonomous Underwater management) net inflow and AUM net outflow in one month of a bank, the AUM net inflow and the AUM net outflow are the fields, and a specific numerical value of each day is one piece of data.
Further, the performing mutation point detection on the time series data set to obtain mutation point information includes:
sequentially selecting one data in the time sequence data set through traversal operation to obtain current data;
acquiring previous data and next data of current data, and respectively calculating the deviation between the previous data and the current data and the deviation between the next data and the current data to obtain a front deviation value and a rear deviation value;
and when the front deviation amount or the rear deviation amount is larger than a preset deviation threshold value, filling the current data, the front deviation amount or the rear deviation amount into a preset mutation early warning template to obtain mutation point information.
For example, the time-series data set includes 24 ten thousand days of AUM net inflow data of northwest Shanghai branch in 12 2019, 18 ten thousand days of the former day, and the deviation is calculated as (24-18)/24 being 25%, the generated mutation point information is "20 days of 12 months 2019, and 24 ten thousand of AUM net inflow data of northwest Shanghai branch is suddenly increased, and the change range reaches 25%".
Further, the performing anomaly point detection on the time series data set to obtain anomaly point information includes:
calculating the average value of all data under each field in the time sequence data set;
comparing each data in the time sequence data set with the average value of the corresponding field to obtain an average deviation;
and when the average deviation is greater than a preset deviation threshold value, filling data corresponding to the average deviation and the average deviation into a preset abnormal early warning template to obtain abnormal point information.
For example, the time-series data set includes the AUM net inflow data of the shanghai branches in 2 months in 2020, the average value in the current month is 10, the daily data is compared with the average value, the AUM net inflow in 1 day is 6 ten thousand, the average deviation is (6-10)/10 is-40%, the generated mutation point information is "2 months in 2020 and 1 day, and the AUM net inflow data of the shanghai branches is abnormal and 40% lower than the average level".
Further, the performing trend detection on the time series data set to obtain trend information includes:
generating a trend graph from the data of each field in the time sequence data set according to the time sequence to obtain a plurality of trend graphs;
calculating the slope of the trend line in each trend graph;
and when the slope is greater than a preset slope threshold value, calculating the rising rate or the falling rate of the data in the trend graph corresponding to the slope, and filling the rising rate or the falling rate and the corresponding field into a preset trend early warning template to obtain trend information.
For example, a trend graph is generated by collecting the time series data set with the one-year AUM net outflow data of the beijing changshan branch, the slope is calculated to be 3 and larger than a preset threshold value 1, and then the average monthly ascending rate is calculated to be 2.4%, so that the trend information is obtained, wherein the one-year AUM net outflow of the beijing changshan branch is in an ascending trend and is in an average ascending 2.4% "
Further, the periodically detecting the time series data set to obtain periodic information includes:
performing Fourier transform on the data in the time series data set to obtain a spectrogram;
and calculating the period duration according to the frequency of the spectrogram, and filling fields corresponding to the spectrogram and the period duration into a preset period early warning template to obtain period information.
Wherein the frequency calculation cycle duration according to the spectrogram can be calculated using a matrix factory (MATLAB) tool.
For example, the loan balance number of the Shanghai northwest tributary in the time series data set in 2019 each day is converted into a spectrogram, and then the perimeter is obtained by MATLAB calculation to be 90 days, and the obtained cycle information is that "the loan balance number of the Shanghai northwest tributary in 2019 has periodicity, and the cycle duration is 90 days".
In the embodiment of the invention, the mutation early warning template, the abnormality early warning template, the trend early warning template and the period early warning template are all sentence texts with spaces which are edited in advance, and complete data information can be expressed when specific data are filled in.
And S4, carrying out multi-dimensional insight on the insight object data to obtain a multi-dimensional insight result.
In detail, the performing multidimensional insights on the insights object data to obtain multidimensional insights results includes:
dividing the data of the insight object according to whether the data has time attributes to obtain a time sequence data set and a non-time sequence data set;
detecting the correlation of the data in the time sequence data set by using a preset correlation formula to generate time sequence correlation information;
detecting the correlation of the data in the non-time sequence data set by using a preset correlation formula to generate non-time sequence correlation information;
detecting the similarity of the data in the non-time sequence data set to generate clustering abnormal information;
and combining the time sequence correlation information, the non-time sequence correlation information and the clustering abnormal information to obtain a multi-dimensional insight result.
Further, the correlation formula includes:
wherein r (X, Y) is a phaseCorrelation coefficients, X and Y are data in the non-time-series data set, Cov (X, Y) is the covariance of data X and data Y, σXIs the standard deviation, σ, of the data XYIs the standard deviation of the data Y.
According to the embodiment of the invention, the correlation coefficient between different data in the time sequence data set is calculated according to the correlation formula, and when the correlation coefficient is larger than a preset threshold value, time sequence correlation information is generated.
Similarly, in the embodiment of the present invention, a correlation coefficient between different data in the non-time series data set is calculated according to the correlation formula, and when the correlation coefficient is greater than a preset threshold, non-time series correlation information is generated.
Further, the embodiment of the invention uses the Euclidean distance formula to detect the similarity of the data in the non-time sequence data set, the smaller the calculated distance is, the larger the similarity is, the larger the distance is, the smaller the similarity is, and when the distance is smaller than a preset threshold value, clustering abnormal information is generated.
And S5, collecting the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result to generate an insight report, and generating and sending an early warning prompt according to the insight report.
The embodiment of the invention collects the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result, generates an insight report corresponding to the insight object data, sends an early warning prompt message and sends the insight report to a user. The user can master the association between the data through the insight report, analyze the development trend of the data and quickly acquire the available information in the insight object data.
The embodiment of the invention combs the data in advance through the preset service index, thereby ensuring the integrity of the insight data; and single-dimensional insight, time sequence insight and multi-dimensional insight are carried out on the data of the insight object, the scope of the insight is ensured, the accuracy of the insight result is improved, the multi-dimensional insight is used for analyzing the data of a plurality of fields, the relevance among the data can be deeply excavated, the modification or reconstruction of data relevance caused by new service data is avoided, and the working efficiency is improved. Therefore, the data insight method, the data insight device, the electronic equipment and the computer readable storage medium can improve the accuracy of the data insight result and improve the efficiency.
Fig. 2 is a functional block diagram of a data insight apparatus according to an embodiment of the present invention.
The data insight apparatus 100 according to the present invention may be installed in an electronic device. According to the realized functions, the data insight apparatus 100 may include a data acquisition module 101, a single-dimensional insight module 102, a time sequence insight module 103, a multi-dimensional insight module 104, and a prompt module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the data acquisition module 101 is configured to determine the data of the insight object from the business data according to a preset business index.
The preset service index in the embodiment of the invention is a data index based on historical service data, for example, the index of bank sales service, including but not limited to five modules of operation performance index, financial condition analysis, public service, retail service, operation and personnel management, covers nearly hundreds of indexes such as interest income, non-interest income, total deposit amount, overdue amount and overdue rate, ROA, interest bearing debt and non-interest bearing debt, and covers the whole flow of bank sales management.
In detail, the data obtaining module 101 is specifically configured to:
determining a data field name corresponding to the service index according to a preset mapping relation table;
and acquiring the service data under the field with the same name as the data field from a preset database to obtain the data of the insight object.
Further, when the service data is stored in the database, each field name generally uses a general english name or abbreviation in the data application range, and the preset mapping relationship table refers to a table of a corresponding relationship between the field name stored in the database and the actual service index name. To further emphasize the privacy and security of the traffic data, the traffic data may also be stored in a node of a block chain.
Furthermore, the insight object data comprises a plurality of service data tables, wherein each service data table comprises a plurality of fields, and the plurality of service data tables comprise service data with a plurality of dimensions.
The single-dimensional insight module 102 is configured to perform single-dimensional insight on the insight object data to obtain a single-dimensional insight result.
In detail, the single-dimensional insight module 102 is specifically configured to:
selecting data with a field type of a numerical type from the insight object data to obtain a numerical data set;
sorting the data in the numerical data set to obtain a sorting result;
generating extreme value insight information when the sequencing result meets a preset extreme value condition;
selecting data with field types being enumerated in the insight object data to obtain an enumerated data set;
generating contribution degree insight information when the data in the enumeration data set meet a preset trigger condition;
and combining the extreme value insight information and the contribution degree insight information to obtain a single-dimensional insight result.
The preset extreme value condition comprises that the difference between the maximum data and the second maximum data in the sequencing result is larger than a preset amplitude and the difference between the minimum data and the second minimum data in the sequencing result is larger than the preset amplitude; the triggering condition is that the ratio of a single enumerated value or the sum of the ratios of two enumerated values in the enumerated data exceeds a preset amplitude.
Further, the generating of the extremum insight information when the sorting result meets a preset extremum condition includes:
acquiring maximum value data and second maximum value data in the sequencing result; calculating the difference between the maximum value data and the second maximum value data to obtain the highest difference; when the maximum difference is larger than a preset amplitude value, filling the maximum value data, the second maximum value data and the maximum difference into a preset maximum early warning template to obtain extreme value insight information; or acquiring the minimum value data and the second minimum value data in the sequencing result; calculating the difference between the minimum value data and the second minimum value data to obtain the lowest difference; and when the minimum difference is larger than a preset amplitude value, filling the minimum value data, the second minimum value data and the minimum difference into a preset minimum early warning template to obtain extreme value insight information.
The highest early warning template and the lowest early warning template are sentence texts with spaces which are edited in advance, the related specific data part is a space, and the complete sentence information can be obtained by filling the specific data. The extreme value insight data is early warning information containing specific data.
Further, the generating the contribution degree insight information when the data in the enumeration data set meets a preset trigger condition includes:
acquiring all enumeration values of each field in the enumeration data set;
counting the occupation ratio of each enumerated value in the corresponding field;
and when the occupation ratio exceeds a preset amplitude, filling the occupation ratio and the corresponding enumeration value into a preset enumeration early warning template to obtain contribution degree insight information.
The counting of the occupation ratio of each enumerated value in the corresponding field refers to calculating the occupation ratio of the number of data pieces corresponding to each enumerated value in each field in the enumerated data set, for example, the enumerated values in the gender field include "male" and "female", the enumerated data set is new customer information in one month, 78 male customers exist, 22 female customers exist, the occupation ratio corresponding to the enumerated value "male" is 78%, and the occupation ratio corresponding to the enumerated value "female" is 22%.
The enumeration early warning template is similar to the highest early warning template and is a sentence text with a blank space which is edited in advance.
For example, the current season asset in the insight object data includes data of a plurality of customer managers, the data are sorted from small to large, the maximum value is 10 ten thousand, the second largest value is 4 ten thousand, the difference is (10-4)/4 x 100% ═ 150%, which is larger than 50%, and the data are filled in the highest early warning template, so that an extreme value insight data "the current season asset of the customer manager week is remarkably represented, reaching 10 ten thousand, and is 150% higher than the second 4 ten thousand" is obtained.
The time sequence insights module 103 is configured to perform time sequence insights on the insights object data to obtain time sequence insights results.
In detail, the timing insights module 103 is specifically configured to:
selecting data of a time dimension from the insight data, and sequencing the data according to time to obtain a time sequence data set;
carrying out mutation point detection on the time sequence data set to obtain mutation point information;
abnormal point detection is carried out on the time sequence data set to obtain abnormal point information;
performing trend detection on the time series data set to obtain trend information;
carrying out periodic detection on the time sequence data set to obtain periodic information;
and merging the mutation point information, the abnormal point information, the trend information and the period information to obtain a time sequence insight result.
The time sequence data set comprises a plurality of fields, each field comprises a plurality of pieces of data, and if the time sequence data set comprises AUM (autonomous Underwater management) net inflow and AUM net outflow in one month of a bank, the AUM net inflow and the AUM net outflow are the fields, and a specific numerical value of each day is one piece of data.
Further, the performing mutation point detection on the time series data set to obtain mutation point information includes:
sequentially selecting one data in the time sequence data set through traversal operation to obtain current data;
acquiring previous data and next data of current data, and respectively calculating the deviation between the previous data and the current data and the deviation between the next data and the current data to obtain a front deviation value and a rear deviation value;
and when the front deviation amount or the rear deviation amount is larger than a preset deviation threshold value, filling the current data, the front deviation amount or the rear deviation amount into a preset mutation early warning template to obtain mutation point information.
For example, the time-series data set includes 24 ten thousand days of AUM net inflow data of northwest Shanghai branch in 12 2019, 18 ten thousand days of the former day, and the deviation is calculated as (24-18)/24 being 25%, the generated mutation point information is "20 days of 12 months 2019, and 24 ten thousand of AUM net inflow data of northwest Shanghai branch is suddenly increased, and the change range reaches 25%".
Further, the performing anomaly point detection on the time series data set to obtain anomaly point information includes:
calculating the average value of all data under each field in the time sequence data set;
comparing each data in the time sequence data set with the average value of the corresponding field to obtain an average deviation;
and when the average deviation is greater than a preset deviation threshold value, filling data corresponding to the average deviation and the average deviation into a preset abnormal early warning template to obtain abnormal point information.
For example, the time-series data set includes the AUM net inflow data of the shanghai branches in 2 months in 2020, the average value in the current month is 10, the daily data is compared with the average value, the AUM net inflow in 1 day is 6 ten thousand, the average deviation is (6-10)/10 is-40%, the generated mutation point information is "2 months in 2020 and 1 day, and the AUM net inflow data of the shanghai branches is abnormal and 40% lower than the average level".
Further, the performing trend detection on the time series data set to obtain trend information includes:
generating a trend graph from the data of each field in the time sequence data set according to the time sequence to obtain a plurality of trend graphs;
calculating the slope of the trend line in each trend graph;
and when the slope is greater than a preset slope threshold value, calculating the rising rate or the falling rate of the data in the trend graph corresponding to the slope, and filling the rising rate or the falling rate and the corresponding field into a preset trend early warning template to obtain trend information.
For example, a trend graph is generated by collecting the time series data set with the one-year AUM net outflow data of the beijing changshan branch, the slope is calculated to be 3 and larger than a preset threshold value 1, and then the average monthly ascending rate is calculated to be 2.4%, so that the trend information is obtained, wherein the one-year AUM net outflow of the beijing changshan branch is in an ascending trend and is in an average ascending 2.4% "
Further, the periodically detecting the time series data set to obtain periodic information includes:
performing Fourier transform on the data in the time series data set to obtain a spectrogram;
and calculating the period duration according to the frequency of the spectrogram, and filling fields corresponding to the spectrogram and the period duration into a preset period early warning template to obtain period information.
Wherein the frequency calculation cycle duration according to the spectrogram can be calculated using a matrix factory (MATLAB) tool.
For example, the loan balance number of the Shanghai northwest tributary in the time series data set in 2019 each day is converted into a spectrogram, and then the perimeter is obtained by MATLAB calculation to be 90 days, and the obtained cycle information is that "the loan balance number of the Shanghai northwest tributary in 2019 has periodicity, and the cycle duration is 90 days".
In the embodiment of the invention, the mutation early warning template, the abnormality early warning template, the trend early warning template and the period early warning template are all sentence texts with spaces which are edited in advance, and complete data information can be expressed when specific data are filled in.
The multi-dimensional insight module 104 is configured to perform multi-dimensional insight on the insight object data to obtain a multi-dimensional insight result.
In detail, the multi-dimensional insight module 104 is specifically configured to:
dividing the data of the insight object according to whether the data has time attributes to obtain a time sequence data set and a non-time sequence data set;
detecting the correlation of the data in the time sequence data set by using a preset correlation formula to generate time sequence correlation information;
detecting the correlation of the data in the non-time sequence data set by using a preset correlation formula to generate non-time sequence correlation information;
detecting the similarity of the data in the non-time sequence data set to generate clustering abnormal information;
and combining the time sequence correlation information, the non-time sequence correlation information and the clustering abnormal information to obtain a multi-dimensional insight result.
Further, the correlation formula includes:
where r (X, Y) is a correlation coefficient, X and Y are data in the non-time-series data set, Cov (X, Y) is a covariance of data X and data Y, σXIs the standard deviation, σ, of the data XYIs the standard deviation of the data Y.
According to the embodiment of the invention, the correlation coefficient between different data in the time sequence data set is calculated according to the correlation formula, and when the correlation coefficient is larger than a preset threshold value, time sequence correlation information is generated.
Similarly, in the embodiment of the present invention, a correlation coefficient between different data in the non-time series data set is calculated according to the correlation formula, and when the correlation coefficient is greater than a preset threshold, non-time series correlation information is generated.
Further, the embodiment of the invention uses the Euclidean distance formula to detect the similarity of the data in the non-time sequence data set, the smaller the calculated distance is, the larger the similarity is, the larger the distance is, the smaller the similarity is, and when the distance is smaller than a preset threshold value, clustering abnormal information is generated.
The prompt module 105 is configured to collect the single-dimensional insight result, the time sequence insight result, and the multi-dimensional insight result to generate an insight report, and generate and send an early warning prompt according to the insight report.
The embodiment of the invention collects the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result, generates an insight report corresponding to the insight object data, sends an early warning prompt message and sends the insight report to a user. The user can master the association between the data through the insight report, analyze the development trend of the data and quickly acquire the available information in the insight object data.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a data insight method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a data insight 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of the data insight 12, but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (e.g., data insight programs, etc.) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The data insight program 12 stored by the memory 11 in the electronic device 1 is a combination of instructions which, when executed in the processor 10, may enable:
determining the data of the insight object from the business data according to the preset business index;
carrying out single-dimensional insight on the insight object data to obtain a single-dimensional insight result;
carrying out time sequence insights on the insights object data to obtain time sequence insights results;
carrying out multi-dimensional insight on the insight object data to obtain a multi-dimensional insight result;
and collecting the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result to generate an insight report, and generating and sending an early warning prompt according to the insight report.
Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiments corresponding to fig. 1 to fig. 3, which is not repeated herein.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
determining the data of the insight object from the business data according to the preset business index;
carrying out single-dimensional insight on the insight object data to obtain a single-dimensional insight result;
carrying out time sequence insights on the insights object data to obtain time sequence insights results;
carrying out multi-dimensional insight on the insight object data to obtain a multi-dimensional insight result;
and collecting the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result to generate an insight report, and generating and sending an early warning prompt according to the insight report.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method of data insights, the method comprising:
determining the data of the insight object from the business data according to the preset business index;
carrying out single-dimensional insight on the insight object data to obtain a single-dimensional insight result;
carrying out time sequence insights on the insights object data to obtain time sequence insights results;
carrying out multi-dimensional insight on the insight object data to obtain a multi-dimensional insight result;
and collecting the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result to generate an insight report, and generating and sending an early warning prompt according to the insight report.
2. The data insight method of claim 1, wherein the performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result comprises:
selecting data with a field type of a numerical type from the insight object data to obtain a numerical data set;
sorting the data in the numerical data set to obtain a sorting result;
generating extreme value insight information when the sequencing result meets a preset extreme value condition;
selecting data with field types being enumerated in the insight object data to obtain an enumerated data set;
generating contribution degree insight information when the data in the enumeration data set meet a preset trigger condition;
and combining the extreme value insight information and the contribution degree insight information to obtain a single-dimensional insight result.
3. The data insight method of claim 2, wherein the generating of the contribution insight information when the data in the enumerated dataset satisfies a preset trigger condition comprises:
acquiring all enumeration values of each field in the enumeration data set;
counting the occupation ratio of each enumerated value in the corresponding field;
and when the occupation ratio exceeds a preset amplitude, filling the occupation ratio and the corresponding enumeration value into a preset enumeration early warning template to obtain contribution degree insight information.
4. The data insight method of claim 1, wherein the performing temporal insight on the insight object data to obtain a temporal insight result comprises:
selecting data of a time dimension from the insight data, and sequencing the data according to time to obtain a time sequence data set;
carrying out mutation point detection on the time sequence data set to obtain mutation point information;
abnormal point detection is carried out on the time sequence data set to obtain abnormal point information;
performing trend detection on the time series data set to obtain trend information;
carrying out periodic detection on the time sequence data set to obtain periodic information;
and merging the mutation point information, the abnormal point information, the trend information and the period information to obtain a time sequence insight result.
5. The data insight method of claim 4, wherein the performing mutation point detection on the time series data set to obtain mutation point information comprises:
sequentially selecting one data in the time sequence data set through traversal operation to obtain current data;
acquiring previous data and next data of current data, and respectively calculating the deviation between the previous data and the current data and the deviation between the next data and the current data to obtain a front deviation value and a rear deviation value;
and when the front deviation amount or the rear deviation amount is larger than a preset deviation threshold value, filling the current data, the front deviation amount or the rear deviation amount into a preset mutation early warning template to obtain mutation point information.
6. The data insight method of claim 4, wherein the performing outlier detection on the time series data set to obtain outlier information comprises:
calculating the average value of all data under each field in the time sequence data set;
comparing each data in the time sequence data set with the average value corresponding to the field of the data to obtain an average deviation;
and when the average deviation is greater than a preset deviation threshold value, filling data corresponding to the average deviation and the average deviation into a preset abnormal early warning template to obtain abnormal point information.
7. The data insight method of claim 1, wherein the performing multi-dimensional insight on the insight object data to obtain a multi-dimensional insight result comprises:
dividing the data of the insight object according to whether the data has time attributes to obtain a time sequence data set and a non-time sequence data set;
detecting the correlation of the data in the time sequence data set by using a preset correlation formula to generate time sequence correlation information;
detecting the correlation of the data in the non-time sequence data set by using a preset correlation formula to generate non-time sequence correlation information;
detecting the similarity of the data in the non-time sequence data set to generate clustering abnormal information;
and combining the time sequence correlation information, the non-time sequence correlation information and the clustering abnormal information to obtain a multi-dimensional insight result.
8. A data insights apparatus, characterized in that the apparatus comprises:
the data acquisition module is used for determining the data of the insight object from the business data according to the preset business index;
the single-dimensional insight module is used for carrying out single-dimensional insight on the insight object data to obtain a single-dimensional insight result;
the time sequence insights module is used for carrying out time sequence insights on the insights object data to obtain time sequence insights results;
the multi-dimensional insight module is used for carrying out multi-dimensional insight on the insight object data to obtain a multi-dimensional insight result;
and the prompt module is used for collecting the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result to generate an insight report, and generating and sending an early warning prompt according to the insight report.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the data insight method of any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a method of data insight as claimed in any one of claims 1 to 7.
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