CN113032403B - Data insight method, device, electronic equipment and storage medium - Google Patents
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Abstract
The invention relates to big data technology, and discloses a data insight method, which comprises the following steps: determining insight object data from service data according to preset service indexes; performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result; performing time sequence insight on the insight object data to obtain a time sequence insight result; performing multidimensional insight on the insight object data to obtain a multidimensional 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 blockchain technology, and the service data can be stored in nodes of the blockchain. The invention further provides a data insight device, electronic equipment and a computer readable storage medium. The invention can improve the accuracy and efficiency of the data insight result.
Description
Technical Field
The present invention relates to the field of big data technologies, and in particular, to a data insight method, a device, an electronic apparatus, and a computer readable storage medium.
Background
With the advent of the big data age, how to perform deep analysis from massive data, the value after mining the data becomes more and more important. For example, in banking industry, mining valid information from sales performance may promote management strategies and discovery of potential opportunities and problems.
The existing data insight analysis method mainly extracts data from the form and processes the data simply. 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 relevance of the data of each business index is poor, the data relevance needs to be modified or re-created once a new business data analysis scene is encountered, and the efficiency is low.
Disclosure of Invention
The invention provides a data insight method, a data insight device and a computer readable storage medium, and aims to improve accuracy and efficiency of data insight results.
In order to achieve the above object, the present invention provides a data insight method, including:
determining insight object data from service data according to preset service indexes;
performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result;
Performing time sequence insight on the insight object data to obtain a time sequence insight result;
performing multidimensional insight on the insight object data to obtain a multidimensional 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 the single-dimensional insight on the insight object data to obtain a single-dimensional insight result includes:
selecting data with a field type of numerical value from the insight object data to obtain a numerical value data set;
sorting the data in the numerical data set to obtain a sorting result;
generating extremum insight information when the sorting result meets a preset extremum condition;
selecting data with the field type of enumeration from the insight object data to obtain an enumeration 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 contribution 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 duty ratio of each enumeration value in the corresponding field;
and when the duty ratio exceeds a preset amplitude, filling the duty ratio and the corresponding enumeration value into a preset enumeration early warning template to obtain contribution degree insight information.
Optionally, the performing the time sequence insight on the insight object data to obtain a time sequence insight result includes:
selecting time dimension data from the insight data, and sorting according to time to obtain a time sequence data set;
performing mutation point detection on the time sequence data set to obtain mutation point information;
performing outlier detection on the time sequence data set to obtain outlier information;
trend detection is carried out on the time sequence data set, so that trend information is obtained;
periodically detecting the time sequence data set to obtain period information;
and combining 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 sequence data set to obtain mutation point information includes:
sequentially selecting one data in the time sequence data set through traversing 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 next data and the current data to obtain a previous deviation amount and a next deviation amount;
and when the front deviation amount or the rear deviation amount is larger than a preset deviation threshold value, filling 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 detecting the abnormal point on the time sequence data set to obtain abnormal 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 corresponding to the field of each data to obtain average deviation;
and when the average deviation is larger than a preset deviation threshold, filling the 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 insight on the insight object data to obtain multidimensional insight results includes:
dividing the insight object data according to whether the data has time attributes or not 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, and generating time sequence correlation information;
detecting the correlation of the data in the non-time sequence data set by using a preset correlation formula, and generating 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 abnormality information to obtain a multidimensional insight result.
In order to solve the above-mentioned problems, the present invention also provides a data insight apparatus, the apparatus comprising:
the data acquisition module is used for determining insight object data from service data according to preset service indexes;
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 insight module is used for performing time sequence insight on the insight object data to obtain a time sequence insight result;
the multi-dimensional insight module is used for carrying out multi-dimensional insight on the insight object data to obtain multi-dimensional insight results;
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-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one instruction; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the instructions stored in the memory to realize the data insight method.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one instruction that is executed by a processor in an electronic device to implement the data insight method described above.
The embodiment of the invention pre-combs the data through the preset business index, thereby ensuring the integrity of the insight data; and through carrying out single-dimensional insight, time sequence insight and multidimensional insight on the insight object data, the scope of the insight is ensured, the accuracy of the insight result is improved, the multidimensional insight is that the data of a plurality of fields are analyzed, the relevance among the data can be deeply mined, the modification or reconstruction of the data relevance caused by new service data is avoided, and the working efficiency is improved. Therefore, the data insight method, the device, the electronic equipment and the computer readable storage medium can improve the accuracy and the efficiency of the data insight result.
Drawings
FIG. 1 is a flowchart of a data insight method according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a data insight device 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 achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of 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 a server, a 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 can be performed by software or hardware installed at the terminal device or the server device, the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a flow chart of a data insight method according to an embodiment of the present invention is shown.
In this embodiment, the data insight method includes:
s1, determining insight object data from service data according to preset service indexes.
The preset business index in the embodiment of the invention is a data index based on historical business data, such as an index of a sales business of a bank, including but not limited to five modules of management performance index, financial condition analysis, public business, retail business, operation and personnel management, and covers nearly hundred indexes of interest income, non-interest income, total deposit amount, overdue amount and overdue rate, ROA, liability, non-liability and the like, and covers the whole flow of sales management of the bank.
In detail, the determining the insight object data from the service data according to the preset service index includes:
determining a data field name corresponding to the service index according to a preset mapping relation table;
and acquiring service data under the field with the same name as the data field from a preset database to obtain insight object data.
Further, when the service data is stored in the database, the field names generally use common english names or abbreviations within the application range of the data, and the preset mapping relation table is a table of the correspondence relation between the field names stored in the database and the actual service index names. To further emphasize the privacy and security of the traffic data, the traffic data may also be stored in nodes of a blockchain.
Further, 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 are composed of service data with a plurality of dimensions.
S2, performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result.
In detail, the step of performing the single-dimensional insight on the insight object data to obtain a single-dimensional insight result includes:
selecting data with a field type of numerical value from the insight object data to obtain a numerical value data set;
sorting the data in the numerical data set to obtain a sorting result;
generating extremum insight information when the sorting result meets a preset extremum condition;
selecting data with the field type of enumeration from the insight object data to obtain an enumeration 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 extremum condition comprises that the difference between the largest data and the second largest data in the sequencing result is larger than a preset amplitude, and the difference between the smallest data and the second smallest data in the sequencing result is larger than the preset amplitude; the triggering condition is that the ratio of a single enumeration value or the sum of the ratio of two enumeration values in enumeration data exceeds a preset amplitude.
Further, the generating extremum insight information when the sorting result meets a preset extremum condition includes:
obtaining 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 highest difference is larger than a preset amplitude value, filling the maximum value data, the second maximum value data and the highest difference into a preset highest early warning template to obtain extremum insight information; or obtaining the minimum value data and the second small value data in the sequencing result; calculating the difference between the minimum value data and the second small 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 small value data and the minimum difference into a preset minimum early warning template to obtain extremum insight information.
The highest early warning template and the lowest early warning template are sentence texts which are edited in advance and have spaces, specific data are related to the space, and specific data are filled in the sentence texts to obtain complete sentence information. The extremum insight data is early warning information containing specific data.
Further, the generating contribution 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 duty ratio of each enumeration value in the corresponding field;
and when the duty ratio exceeds a preset amplitude, filling the duty ratio and the corresponding enumeration value into a preset enumeration early warning template to obtain contribution degree insight information.
The counting the duty ratio of each enumeration value in the corresponding field refers to calculating the duty ratio of the number of data pieces corresponding to each enumeration value in each field in the enumeration data set, for example, the enumeration value under the gender field comprises 'men' and 'women', the enumeration data set is new client information within one month, 78 male clients and 22 female clients are provided, the duty ratio corresponding to the enumeration value 'men' is 78%, and the duty ratio corresponding to the enumeration value 'women' is 22%.
The enumeration early warning template is similar to the highest early warning template and is a sentence text with blank space edited in advance.
For example, the data of the plurality of client managers are included under the current season asset in the insight object data, the data are sorted from small to large, the maximum value is 10 ten thousand, the second maximum value is 4 ten thousand, the difference is (10-4)/4 x 100% = 150%, and the maximum value is more than 50%, and the maximum value is filled in the highest early warning template, so that the current season asset expression of the extreme value insight data ' client manager week ' is prominent, and the maximum value is up to 10 ten thousand and higher than the second value by 150% '.
And S3, performing time sequence insight on the insight object data to obtain a time sequence insight result.
In detail, the performing the time sequence insight on the insight object data to obtain a time sequence insight result includes:
selecting time dimension data from the insight data, and sorting according to time to obtain a time sequence data set;
performing mutation point detection on the time sequence data set to obtain mutation point information;
performing outlier detection on the time sequence data set to obtain outlier information;
trend detection is carried out on the time sequence data set, so that trend information is obtained;
periodically detecting the time sequence data set to obtain period information;
and combining 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 data, for example, the time sequence data set comprises AUM net inflow and AUM net outflow in one month of a bank, wherein the AUM net inflow and AUM net outflow are fields, and a specific numerical value of each day is one data.
Further, the performing mutation point detection on the time sequence data set to obtain mutation point information includes:
Sequentially selecting one data in the time sequence data set through traversing 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 next data and the current data to obtain a previous deviation amount and a next deviation amount;
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 the AUM net inflow data of 2019, 24 ten thousand on 20 days and 18 ten thousand on the previous day, the deviation is calculated as (24-18)/24=25%, the generated mutation point information is "the AUM net inflow data of 2019, 12 months and 20 days on the northwest, shanghai, the mutation occurs in 24 ten thousand, and the variation range reaches 25%".
Further, the detecting the abnormal point of the time sequence data set to obtain abnormal 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 average deviation;
And when the average deviation is larger than a preset deviation threshold, filling the 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 AUM net inflow data of the line of the Shanghai permanent road in 2020, the average value of the AUM net inflow data in 2020 is 10, the average value of the AUM net inflow data in 2020 is 6 ten thousand, the average deviation is (6-10)/10= -40%, the generated mutation point information is 'the line of the Shanghai permanent road in 2020 is 1 month 2, and the AUM net inflow data in the line of the Shanghai permanent road is 6 ten thousand, and the average level is 40%'.
Further, the trend detection on the time sequence data set to obtain trend information includes:
generating a trend graph according to the time sequence of the data of each field in the time sequence data set to obtain a plurality of trend graphs;
calculating the slope of a trend line in each trend graph;
when the slope is larger than a preset slope cutting threshold value, calculating the rising rate or the falling rate of data in a trend graph corresponding to the slope, and filling the rising rate or the falling rate and a corresponding field into a preset trend early warning template to obtain trend information.
For example, generating a trend graph from the AUM net outflow data of the Beijing mountain branch for one year in the time series data set, calculating a slope, wherein the slope is 3 and is greater than a preset threshold value 1, and calculating an average monthly rising rate of 2.4% to obtain trend information of 'the AUM net outflow of the Beijing mountain branch is in an rising trend within one year, and the average rising rate is 2.4%'.
Further, the periodically detecting the time sequence data set to obtain period information includes:
performing Fourier transform on the data in the time sequence data set to obtain a spectrogram;
calculating the period duration according to the frequency of the spectrogram, and filling the field 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 by using a matrix factory (MATLAB) tool.
For example, the residual loan amount of the northwest branches in the Shanghai in the time sequence data set in 2019 is converted into a spectrogram, and then the MATLAB calculation is performed to obtain the period information of "the residual loan amount of the northwest branches in the Shanghai in 2019 has periodicity, and the period 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 sentence texts with spaces edited in advance, and complete data information can be expressed after specific data are filled in.
And S4, performing multidimensional insight on the insight object data to obtain multidimensional insight results.
In detail, the performing multidimensional insight on the insight object data to obtain multidimensional insight results includes:
dividing the insight object data according to whether the data has time attributes or not 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, and generating time sequence correlation information;
detecting the correlation of the data in the non-time sequence data set by using a preset correlation formula, and generating 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 abnormality information to obtain a multidimensional insight result.
Further, the correlation formula includes:
wherein r (X, Y) is a correlation coefficient, X and Y are data in the non-time series data set, cov (X, Y) is covariance of data X and data Y, sigma X Is the standard deviation of data X, sigma Y Is the standard deviation of 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, the embodiment of the invention calculates the correlation coefficient between different data in the non-time sequence data set according to the correlation formula, and generates non-time sequence correlation information when the correlation coefficient is larger than a preset threshold value.
Further, the embodiment of the invention detects the similarity of the data in the non-time sequence data set by using the Euclidean distance formula, 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, the 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.
According to the embodiment of the invention, the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result are collected, an insight report corresponding to the insight object data is generated, an early warning prompt message is sent, and the insight report is sent to a user. And the user can grasp 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 pre-combs the data through the preset business index, thereby ensuring the integrity of the insight data; and through carrying out single-dimensional insight, time sequence insight and multidimensional insight on the insight object data, the scope of the insight is ensured, the accuracy of the insight result is improved, the multidimensional insight is that the data of a plurality of fields are analyzed, the relevance among the data can be deeply mined, the modification or reconstruction of the data relevance caused by new service data is avoided, and the working efficiency is improved. Therefore, the data insight method, the device, the electronic equipment and the computer readable storage medium can improve the accuracy and the efficiency of the data insight result.
Fig. 2 is a functional block diagram of a data insight device according to an embodiment of the present invention.
The data insight apparatus 100 of the present invention can be installed in an electronic device. Depending on the implemented functionality, the data insight device 100 may include a data acquisition module 101, a single-dimensional insight module 102, a timing insight module 103, a multi-dimensional insight module 104, and a hinting module 105. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the data acquisition module 101 is configured to determine insight object data from service data according to a preset service index.
The preset business index in the embodiment of the invention is a data index based on historical business data, such as an index of a sales business of a bank, including but not limited to five modules of management performance index, financial condition analysis, public business, retail business, operation and personnel management, and covers nearly hundred indexes of interest income, non-interest income, total deposit amount, overdue amount and overdue rate, ROA, liability, non-liability and the like, and covers the whole flow of sales management of the bank.
In detail, the data acquisition 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 service data under the field with the same name as the data field from a preset database to obtain insight object data.
Further, when the service data is stored in the database, the field names generally use common english names or abbreviations within the application range of the data, and the preset mapping relation table is a table of the correspondence relation between the field names stored in the database and the actual service index names. To further emphasize the privacy and security of the traffic data, the traffic data may also be stored in nodes of a blockchain.
Further, 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 are composed of 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, and 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 numerical value from the insight object data to obtain a numerical value data set;
sorting the data in the numerical data set to obtain a sorting result;
generating extremum insight information when the sorting result meets a preset extremum condition;
selecting data with the field type of enumeration from the insight object data to obtain an enumeration 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 extremum condition comprises that the difference between the largest data and the second largest data in the sequencing result is larger than a preset amplitude, and the difference between the smallest data and the second smallest data in the sequencing result is larger than the preset amplitude; the triggering condition is that the ratio of a single enumeration value or the sum of the ratio of two enumeration values in enumeration data exceeds a preset amplitude.
Further, the generating extremum insight information when the sorting result meets a preset extremum condition includes:
obtaining 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 highest difference is larger than a preset amplitude value, filling the maximum value data, the second maximum value data and the highest difference into a preset highest early warning template to obtain extremum insight information; or obtaining the minimum value data and the second small value data in the sequencing result; calculating the difference between the minimum value data and the second small 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 small value data and the minimum difference into a preset minimum early warning template to obtain extremum insight information.
The highest early warning template and the lowest early warning template are sentence texts which are edited in advance and have spaces, specific data are related to the space, and specific data are filled in the sentence texts to obtain complete sentence information. The extremum insight data is early warning information containing specific data.
Further, the generating contribution 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 duty ratio of each enumeration value in the corresponding field;
and when the duty ratio exceeds a preset amplitude, filling the duty ratio and the corresponding enumeration value into a preset enumeration early warning template to obtain contribution degree insight information.
The counting the duty ratio of each enumeration value in the corresponding field refers to calculating the duty ratio of the number of data pieces corresponding to each enumeration value in each field in the enumeration data set, for example, the enumeration value under the gender field comprises 'men' and 'women', the enumeration data set is new client information within one month, 78 male clients and 22 female clients are provided, the duty ratio corresponding to the enumeration value 'men' is 78%, and the duty ratio corresponding to the enumeration value 'women' is 22%.
The enumeration early warning template is similar to the highest early warning template and is a sentence text with blank space edited in advance.
For example, the data of the plurality of client managers are included under the current season asset in the insight object data, the data are sorted from small to large, the maximum value is 10 ten thousand, the second maximum value is 4 ten thousand, the difference is (10-4)/4 x 100% = 150%, and the maximum value is more than 50%, and the maximum value is filled in the highest early warning template, so that the current season asset expression of the extreme value insight data ' client manager week ' is prominent, and the maximum value is up to 10 ten thousand and higher than the second value by 150% '.
The time sequence insight module 103 is used for performing time sequence insight on the insight object data to obtain a time sequence insight result.
In detail, the timing insight module 103 is specifically configured to:
selecting time dimension data from the insight data, and sorting according to time to obtain a time sequence data set;
performing mutation point detection on the time sequence data set to obtain mutation point information;
performing outlier detection on the time sequence data set to obtain outlier information;
trend detection is carried out on the time sequence data set, so that trend information is obtained;
periodically detecting the time sequence data set to obtain period information;
and combining 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 data, for example, the time sequence data set comprises AUM net inflow and AUM net outflow in one month of a bank, wherein the AUM net inflow and AUM net outflow are fields, and a specific numerical value of each day is one data.
Further, the performing mutation point detection on the time sequence data set to obtain mutation point information includes:
Sequentially selecting one data in the time sequence data set through traversing 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 next data and the current data to obtain a previous deviation amount and a next deviation amount;
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 the AUM net inflow data of 2019, 24 ten thousand on 20 days and 18 ten thousand on the previous day, the deviation is calculated as (24-18)/24=25%, the generated mutation point information is "the AUM net inflow data of 2019, 12 months and 20 days on the northwest, shanghai, the mutation occurs in 24 ten thousand, and the variation range reaches 25%".
Further, the detecting the abnormal point of the time sequence data set to obtain abnormal 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 average deviation;
And when the average deviation is larger than a preset deviation threshold, filling the 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 AUM net inflow data of the line of the Shanghai permanent road in 2020, the average value of the AUM net inflow data in 2020 is 10, the average value of the AUM net inflow data in 2020 is 6 ten thousand, the average deviation is (6-10)/10= -40%, the generated mutation point information is 'the line of the Shanghai permanent road in 2020 is 1 month 2, and the AUM net inflow data in the line of the Shanghai permanent road is 6 ten thousand, and the average level is 40%'.
Further, the trend detection on the time sequence data set to obtain trend information includes:
generating a trend graph according to the time sequence of the data of each field in the time sequence data set to obtain a plurality of trend graphs;
calculating the slope of a trend line in each trend graph;
when the slope is larger than a preset slope cutting threshold value, calculating the rising rate or the falling rate of data in a trend graph corresponding to the slope, and filling the rising rate or the falling rate and a corresponding field into a preset trend early warning template to obtain trend information.
For example, generating a trend graph from the AUM net outflow data of the Beijing mountain branch for one year in the time series data set, calculating a slope, wherein the slope is 3 and is greater than a preset threshold value 1, and calculating an average monthly rising rate of 2.4% to obtain trend information of 'the AUM net outflow of the Beijing mountain branch is in an rising trend within one year, and the average rising rate is 2.4%'.
Further, the periodically detecting the time sequence data set to obtain period information includes:
performing Fourier transform on the data in the time sequence data set to obtain a spectrogram;
calculating the period duration according to the frequency of the spectrogram, and filling the field 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 by using a matrix factory (MATLAB) tool.
For example, the residual loan amount of the northwest branches in the Shanghai in the time sequence data set in 2019 is converted into a spectrogram, and then the MATLAB calculation is performed to obtain the period information of "the residual loan amount of the northwest branches in the Shanghai in 2019 has periodicity, and the period 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 sentence texts with spaces edited in advance, and complete data information can be expressed after specific data are filled in.
The multidimensional insight module 104 is configured to perform multidimensional insight on the insight object data, and obtain a multidimensional insight result.
In detail, the multidimensional insight module 104 is specifically configured to:
dividing the insight object data according to whether the data has time attributes or not 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, and generating time sequence correlation information;
detecting the correlation of the data in the non-time sequence data set by using a preset correlation formula, and generating 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 abnormality information to obtain a multidimensional insight result.
Further, the correlation formula includes:
wherein r (X, Y) is a correlation coefficient, X and Y are data in the non-time series data set, cov (X, Y) is covariance of data X and data Y, sigma X Is the standard deviation of data X, sigma Y Is the standard deviation of 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, the embodiment of the invention calculates the correlation coefficient between different data in the non-time sequence data set according to the correlation formula, and generates non-time sequence correlation information when the correlation coefficient is larger than a preset threshold value.
Further, the embodiment of the invention detects the similarity of the data in the non-time sequence data set by using the Euclidean distance formula, 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, the clustering abnormal information is generated.
The prompt module 105 is configured to aggregate 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.
According to the embodiment of the invention, the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result are collected, an insight report corresponding to the insight object data is generated, an early warning prompt message is sent, and the insight report is sent to a user. And the user can grasp 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 program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an 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 in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or 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 for storing application software installed in the electronic device 1 and various types of data, such as codes of the data insight program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules (e.g., a data insight program, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person 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 shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or 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, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The data insight program 12 stored by the memory 11 in the electronic device 1 is a combination of instructions which, when run in the processor 10, can implement:
determining insight object data from service data according to preset service indexes;
performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result;
performing time sequence insight on the insight object data to obtain a time sequence insight result;
Performing multidimensional insight on the insight object data to obtain a multidimensional 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 above instructions by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 3, which are not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a 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, can implement:
Determining insight object data from service data according to preset service indexes;
performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result;
performing time sequence insight on the insight object data to obtain a time sequence insight result;
performing multidimensional insight on the insight object data to obtain a multidimensional 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 several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
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 characteristics 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 blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (6)
1. A method of data insight, the method comprising:
determining insight object data from service data according to preset service indexes;
performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result;
performing time sequence insight on the insight object data to obtain a time sequence insight result;
performing multidimensional insight on the insight object data to obtain a multidimensional insight result;
the single-dimensional insight result, the time sequence insight result and the multi-dimensional insight result are collected to generate an insight report, and an early warning prompt is generated and sent according to the insight report;
The step of performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result comprises the following steps: selecting data with a field type of numerical value from the insight object data to obtain a numerical value data set; sorting the data in the numerical data set to obtain a sorting result; generating extremum insight information when the sorting result meets a preset extremum condition; selecting data with the field type of enumeration from the insight object data to obtain an enumeration data set; generating contribution degree insight information when the data in the enumeration data set meet a preset trigger condition; combining the extreme value insight information and the contribution degree insight information to obtain a single-dimensional insight result;
generating contribution degree insight information when the data in the enumeration data set meets a preset trigger condition comprises: acquiring all enumeration values of each field in the enumeration data set; counting the duty ratio of each enumeration value in the corresponding field; when the duty ratio exceeds a preset amplitude, filling the duty ratio and the corresponding enumeration value into a preset enumeration early warning template to obtain contribution degree insight information;
performing time sequence insight on the insight object data to obtain a time sequence insight result, wherein the time sequence insight result comprises: selecting time dimension data from the insight data, and sorting according to time to obtain a time sequence data set; performing mutation point detection on the time sequence data set to obtain mutation point information; performing outlier detection on the time sequence data set to obtain outlier information; trend detection is carried out on the time sequence data set, so that trend information is obtained; periodically detecting the time sequence data set to obtain period information; combining the mutation point information, the abnormal point information, the trend information and the period information to obtain a time sequence insight result;
The step of performing multidimensional insight on the insight object data to obtain multidimensional insight results comprises the following steps: dividing the insight object data according to whether the data has time attributes or not 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, and generating time sequence correlation information; detecting the correlation of the data in the non-time sequence data set by using a preset correlation formula, and generating 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 abnormality information to obtain a multidimensional insight result.
2. The data insight method of claim 1, wherein 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 traversing 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 next data and the current data to obtain a previous deviation amount and a next deviation amount;
And when the front deviation amount or the rear deviation amount is larger than a preset deviation threshold value, filling current data, the front deviation amount or the rear deviation amount into a preset mutation early warning template to obtain mutation point information.
3. The data insight method of claim 1, wherein the performing outlier detection on the time series data set to obtain outlier 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 each data to obtain average deviation;
and when the average deviation is larger than a preset deviation threshold, filling the data corresponding to the average deviation and the average deviation into a preset abnormal early warning template to obtain abnormal point information.
4. A data insight device, the device comprising:
the data acquisition module is used for determining insight object data from service data according to preset service indexes;
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 insight module is used for performing time sequence insight on the insight object data to obtain a time sequence insight result;
The multi-dimensional insight module is used for carrying out multi-dimensional insight on the insight object data to obtain multi-dimensional insight results;
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, generating and sending an early warning prompt according to the insight report;
the step of performing single-dimensional insight on the insight object data to obtain a single-dimensional insight result comprises the following steps: selecting data with a field type of numerical value from the insight object data to obtain a numerical value data set; sorting the data in the numerical data set to obtain a sorting result; generating extremum insight information when the sorting result meets a preset extremum condition; selecting data with the field type of enumeration from the insight object data to obtain an enumeration data set; generating contribution degree insight information when the data in the enumeration data set meet a preset trigger condition; combining the extreme value insight information and the contribution degree insight information to obtain a single-dimensional insight result;
generating contribution degree insight information when the data in the enumeration data set meets a preset trigger condition comprises: acquiring all enumeration values of each field in the enumeration data set; counting the duty ratio of each enumeration value in the corresponding field; when the duty ratio exceeds a preset amplitude, filling the duty ratio and the corresponding enumeration value into a preset enumeration early warning template to obtain contribution degree insight information;
Performing time sequence insight on the insight object data to obtain a time sequence insight result, wherein the time sequence insight result comprises: selecting time dimension data from the insight data, and sorting according to time to obtain a time sequence data set; performing mutation point detection on the time sequence data set to obtain mutation point information; performing outlier detection on the time sequence data set to obtain outlier information; trend detection is carried out on the time sequence data set, so that trend information is obtained; periodically detecting the time sequence data set to obtain period information; combining the mutation point information, the abnormal point information, the trend information and the period information to obtain a time sequence insight result;
the step of performing multidimensional insight on the insight object data to obtain multidimensional insight results comprises the following steps: dividing the insight object data according to whether the data has time attributes or not 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, and generating time sequence correlation information; detecting the correlation of the data in the non-time sequence data set by using a preset correlation formula, and generating 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 abnormality information to obtain a multidimensional insight result.
5. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
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 of claims 1-3.
6. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the data insight method of any of claims 1 to 3.
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