CN114187114A - Asset data configuration method, device, equipment and storage medium - Google Patents

Asset data configuration method, device, equipment and storage medium Download PDF

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CN114187114A
CN114187114A CN202111519193.8A CN202111519193A CN114187114A CN 114187114 A CN114187114 A CN 114187114A CN 202111519193 A CN202111519193 A CN 202111519193A CN 114187114 A CN114187114 A CN 114187114A
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黄静看
吴燕平
韩延福
黄梦如
汤慧
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Ping An Property and Casualty Insurance Company of China Ltd
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Abstract

The invention relates to an artificial intelligence technology, and discloses an asset data configuration method, which comprises the following steps: the method comprises the steps of constructing a data calculation model set according to a data configuration instruction, performing configuration calculation on real-time service data by using a model in the data calculation model set to obtain a first calculation result, performing data initialization processing on data in a historical service data set to obtain a standard historical data set, performing configuration calculation on data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result, and performing comparison calculation on the first calculation result and the second calculation result to obtain a data configuration result. In addition, the invention also relates to a block chain technology, and the data configuration result can be stored in a node of the block chain. The invention also provides an asset data configuration method and device, electronic equipment and a computer readable storage medium. The invention can solve the problem of low asset data configuration efficiency.

Description

Asset data configuration method, device, equipment and storage medium
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an asset data configuration method, an asset data configuration device, electronic equipment and a computer readable storage medium.
Background
Asset allocation refers to allocating investment funds between different asset classes, typically between low-risk, low-income securities and high-risk, high-income securities, according to investment requirements. In the prior art, when large-class asset configuration is carried out, asset data is generally required to be manually input, then a large number of formulas are configured to carry out complex operation, and finally the investment ratio, the expected profitability, the fluctuation rate and the like of various assets are obtained. This configuration is not only error prone and inefficient, but also consumes a lot of manpower. In addition, the result obtained through the EXCEL calculation is relatively poor in visualization, and comparison of different configuration schemes is inconvenient to perform, so that the asset configuration efficiency and accuracy are low.
Disclosure of Invention
The invention provides an asset data configuration method, an asset data configuration device, equipment and a storage medium, and mainly aims to solve the problem of low asset data configuration efficiency.
In order to achieve the above object, the asset data configuration method provided by the present invention includes:
receiving a data configuration instruction, and constructing a data calculation model set according to the data configuration instruction;
acquiring real-time service data, and performing configuration calculation on the real-time service data by using a model in the data calculation model set to obtain a first calculation result;
acquiring a historical service data set, and performing data initialization processing on data in the historical service data set to obtain a standard historical data set;
performing configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result;
and comparing and calculating the first calculation result and the second calculation result to obtain a data configuration result.
Optionally, the constructing a data computation model set according to the data configuration instruction includes:
receiving a configuration parameter file input by a user, and extracting a basic configuration field in the configuration parameter file and a configuration parameter corresponding to the basic configuration field by using the data configuration instruction;
constructing different parameter configuration templates according to the basic configuration field and the configuration parameters corresponding to the basic configuration field;
and filling the configuration parameters in the parameter configuration template into a pre-constructed Python template to obtain the data calculation model set.
Optionally, the acquiring real-time service data includes:
acquiring a real-time data stream from a pre-constructed message middleware;
segmenting the real-time data stream by using a preset batch processing interval to obtain a discrete data stream comprising a plurality of segmented data sets;
extracting a segmentation data set in the discrete data stream by using a preset sliding window in a sliding manner to obtain a batch data set;
and taking the data in the batch data set as the real-time service data.
Optionally, the performing configuration calculation on the real-time service data by using the model in the data calculation model set to obtain a first calculation result includes:
receiving a model selection instruction, and selecting a target model from the data calculation model set by using the model selection instruction;
and performing configuration calculation on the real-time service data by using the target model to obtain the first calculation result.
Optionally, the performing data initialization processing on the data in the historical service data set to obtain a standard historical data set includes:
performing data duplication removal, data exception removal and data missing value filling on data in the historical service data set to obtain an original historical data set;
and classifying the original historical data set by using a preset classification label to obtain the standard historical data set.
Optionally, the performing data deduplication, data anomaly removal and data missing value filling on the data in the historical service data set to obtain an original historical data set includes:
calculating a distance value of data in the historical service data set, and removing duplication of the data in the historical service data set according to the distance value to obtain a duplication-removed data set;
removing abnormal values of the duplicate data removing set by using a unilateral test formula to obtain an abnormal data removing set;
and carrying out missing value detection on the data in the abnormal data removing set by using a preset missing value detection function, and filling missing values based on a preset filling algorithm to obtain the standard data set.
Optionally, the comparing and calculating the first calculation result and the second calculation result to obtain a data configuration result includes:
generating a display page of the first calculation result and the second calculation result by using a preset visual plug-in;
and comparing the sizes of the first calculation result and the second calculation result in the display page, and taking the maximum calculation result as the data configuration result.
In order to solve the above problems, the present invention also provides an asset data configuring apparatus, comprising:
the model building module is used for receiving a data configuration instruction and building a data calculation model set according to the data configuration instruction;
the first configuration calculation module is used for acquiring real-time service data, and performing configuration calculation on the real-time service data by using the models in the data calculation model set to obtain a first calculation result;
the data initialization processing module is used for acquiring a historical service data set and performing data initialization processing on data in the historical service data set to obtain a standard historical data set;
and the second configuration calculation module is used for performing configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result, and performing comparison calculation on the first calculation result and the second calculation result to obtain a data configuration result.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one computer program; and
and a processor executing the computer program stored in the memory to implement the asset data configuration method described above.
In order to solve the above problem, the present invention also provides a computer-readable storage medium having at least one computer program stored therein, the at least one computer program being executed by a processor in an electronic device to implement the asset data configuration method described above.
According to the invention, the data calculation model set is constructed through the data configuration instruction, and the historical service data or the real-time service data are configured and calculated through the models in the data calculation model set, so that the asset configuration efficiency is greatly improved. And moreover, the data calculation model can be flexibly adjusted according to the data configuration instruction, the flexibility of asset configuration is improved, and the efficiency of asset configuration is further improved. Meanwhile, the accuracy of the data configuration result can be improved by comparing the calculation results of the real-time service data and the historical service data. Therefore, the asset data configuration method, the asset data configuration device, the electronic equipment and the computer readable storage medium provided by the invention can solve the problem of low asset data configuration efficiency.
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FIG. 1 is a schematic flow chart diagram of an asset data configuration method according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of an asset data configuration apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device implementing the asset data configuration 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 an asset data configuration method. The executing body of the asset data configuration method includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the present application. In other words, the asset data configuration method may be performed by software or hardware installed in a terminal device or a 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. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a flowchart of an asset data configuration method according to an embodiment of the present invention is shown. In this embodiment, the asset data configuration method includes:
and S1, receiving a data configuration instruction, and constructing a data calculation model set according to the data configuration instruction.
In the embodiment of the present invention, the data configuration instruction refers to an instruction for configuring parameters such as a data proportion and a data amount, which are input by a user. The data calculation model set can be a model set compiled in a computer language and used for performing configuration calculation on a certain index, such as the financial field, and the data calculation model can be a calculation model compiled by utilizing Python and used for calculating the asset profitability and the asset fluctuation rate.
Specifically, the constructing a data calculation model set according to the data configuration instruction includes:
receiving a configuration parameter file input by a user, and extracting a basic configuration field in the configuration parameter file and a configuration parameter corresponding to the basic configuration field by using the data configuration instruction;
constructing different parameter configuration templates according to the basic configuration field and the configuration parameters corresponding to the basic configuration field;
and filling the configuration parameters in the parameter configuration template into a pre-constructed Python template to obtain the data calculation model set.
In an embodiment of the present invention, the parameter configuration template includes different basic configuration fields and parameters corresponding to the basic configuration fields, for example, in the financial field, the basic configuration fields include: the system comprises the following parameters of 'asset income', 'configuration planning', 'asset relevance' and 'limitation condition', wherein the parameters corresponding to the asset income comprise: macroscopic expectations and macroscopic proportion of asset profitability and volatility; configuring parameters corresponding to the plan includes: investment allocation amounts for various assets; the parameters corresponding to asset relevance include: a correlation coefficient between investment targets; the parameters corresponding to the limiting conditions include: the upper and lower limits of the liquidity of each asset required by supervision, the upper and lower limits of the proportion of each asset, the investment limiting conditions of overseas assets, pledge rate and the like.
In an optional embodiment of the invention, a Python calculation model is constructed through the parameter configuration template, so that data calculation can be automatically carried out, and the data processing efficiency is improved.
S2, acquiring real-time service data, and performing configuration calculation on the real-time service data by using the model in the data calculation model set to obtain a first calculation result.
In the embodiment of the invention, in the financial field, the real-time business data can be different types of large-scale assets, such as stock tickets, bonds, commodities, real estate, gold and the like.
In detail, the acquiring real-time service data includes:
acquiring a real-time data stream from a pre-constructed message middleware;
segmenting the real-time data stream by using a preset batch processing interval to obtain a discrete data stream comprising a plurality of segmented data sets;
extracting a segmentation data set in the discrete data stream by using a preset sliding window in a sliding manner to obtain a batch data set;
and taking the data in the batch data set as the real-time service data.
In this embodiment of the present invention, the message middleware may be Kafka message middleware. And simultaneously, the real-time data stream is subjected to Streaming processing by using an Apache Spark Streaming technology, the Apache Spark Streaming technology carries out high-throughput Streaming calculation by converting the data stream into an elastic Distributed data set (RDD), and the method has the characteristics of expandability, high throughput, fault tolerance and the like.
For example, the batch processing interval (batch interval) may be 2S, the real-time data stream is segmented every 2S to obtain a segmented data set (RDD) including 2S data, the sliding window (window length) may be 10S, and the batch data set includes 5 segmented data sets. The acquisition capability of real-time service data can be improved by the Apache Spark Streaming technology.
Specifically, the performing configuration calculation on the real-time service data by using the model in the data calculation model set to obtain a first calculation result includes:
receiving a model selection instruction, and selecting a target model from the data calculation model set by using the model selection instruction;
and performing configuration calculation on the real-time service data by using the target model to obtain the first calculation result.
For example, the allocation parameter in the target model is that the share ratio of the stock class assets and the credit class assets is 1:2, and the asset profitability is calculated to obtain the first calculation result.
In another optional embodiment of the invention, the method further comprises: and selecting a new data calculation model input by a user as the target model according to the model selection instruction.
In the embodiment of the invention, the target model can be selected from the data calculation model set or the data calculation model newly added by the user can be selected as the target model according to the model selection instruction, so that the accuracy rate of data calculation and the flexibility of data calculation are improved.
S3, acquiring a historical service data set, and performing data initialization processing on data in the historical service data set to obtain a standard historical data set.
In the embodiment of the present invention, the historical business data set may be the business data of a large class of assets in the past year. The data initialization processing comprises data cleaning and data classification.
In detail, the performing data initialization processing on the data in the historical service data set to obtain a standard historical data set includes:
performing data duplication removal, data exception removal and data missing value filling on data in the historical service data set to obtain an original historical data set;
and classifying the original historical data set by using a preset classification label to obtain the standard historical data set.
In an alternative embodiment of the present invention, for example, the category labels of the large categories of assets in the financial field may include: stock tickets: white spirit, medical treatment, new energy … …; claims of debt: white spirit, medical treatment, new energy … ….
In the embodiment of the present invention, the performing data duplication removal, data exception removal and data missing value filling on data in the historical service data set to obtain an original historical data set includes:
calculating a distance value of data in the historical service data set, and removing duplication of the data in the historical service data set according to the distance value to obtain a duplication-removed data set;
removing abnormal values of the duplicate data removing set by using a unilateral test formula to obtain an abnormal data removing set;
and carrying out missing value detection on the data in the abnormal data removing set by using a preset missing value detection function, and filling missing values based on a preset filling algorithm to obtain the standard data set.
In an alternative embodiment of the present invention, the distance value is calculated by the following distance formula:
Figure BDA0003408122780000071
wherein d represents the distance value between any two data in the historical service data set, and w1jAnd w2jRepresenting any two data in the historical traffic data set. And deleting any one of the data when the distance value is smaller than a preset distance value, and simultaneously keeping the two data if the distance value is not smaller than the preset distance value. Preferably, the preset distance value may be 0.1.
In an optional embodiment of the present invention, the single-side test rejection includes a minimum single-side test rejection and a maximum single-side test rejection.
The calculation method for the minimum unilateral test rejection comprises the following steps:
Figure BDA0003408122780000072
wherein the content of the first and second substances,
Figure BDA0003408122780000073
representing the mean value of the data in the de-duplicated data set, YminRepresents the smallest data in the deduplication data set, G represents the test value, and S represents the standard deviation of the data in the deduplication data set. And when G is larger than a preset test threshold, determining the minimum data as abnormal data.
The calculation method for the maximum unilateral test rejection comprises the following steps:
Figure BDA0003408122780000074
wherein the content of the first and second substances,
Figure BDA0003408122780000081
representing the mean value of the data in the de-duplicated data set, YmaxRepresents the largest data in the de-duplicated data set, G represents the test value, and S represents the standard deviation of the data in the de-duplicated data set. And when G is larger than a preset test threshold value, determining the maximum data as abnormal data.
In an optional embodiment of the present invention, the missing value detection function may be a mismap function missing function, if no data missing value is detected, no processing is performed, and if a data missing value is detected, the missing value is filled by a preset filling algorithm in the embodiment of the present invention, where the preset filling algorithm includes:
Figure BDA0003408122780000082
wherein L (θ) represents a filled data missing value, xiRepresenting the ith data missing value, theta representing the probability parameter corresponding to the filled data missing value, n representing the number of data in the de-abnormal data set, p (x)i| θ) represents the probability of the data missing value of the padding.
In the embodiment of the invention, the data in the historical service data set can be subdivided by carrying out data initialization processing, so that the data calculation efficiency is improved.
And S4, performing configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result.
In detail, the performing configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result includes:
and performing configuration calculation on the data in the standard historical data set by using the target model selected by the model selection instruction to obtain a second calculation result.
In the embodiment of the invention, the historical data is configured and calculated through the target model selected by the user, the asset historical data does not need to be manually filled, and the accuracy and efficiency of the configuration and calculation of the historical data are improved.
And S5, comparing and calculating the first calculation result and the second calculation result to obtain a data configuration result.
In the embodiment of the invention, the first calculation result and the second calculation result are calculated by different target models aiming at a certain calculation index, so that the size comparison can be directly carried out, for example, when the asset profitability is calculated, the first calculation result is greater than the second calculation result, and the proportion, the amount and the like of asset data configuration in the first calculation result are taken as data configuration results.
In this embodiment of the present invention, the comparing and calculating the first calculation result and the second calculation result to obtain a data configuration result includes:
generating a display page of the first calculation result and the second calculation result by using a preset visual plug-in;
and comparing the sizes of the first calculation result and the second calculation result in the display page, and taking the maximum calculation result as the data configuration result.
In the embodiment of the present invention, a preset visualization plug-in may be used to visually display the calculation result, for example, the preset visualization plug-in may be amcharts, bonsaijs, and the like, and by comparing the first calculation result and the second calculation result in a display page, the optimal calculation result may be directly displayed, so that the visualization effect is improved.
In an optional embodiment of the invention, the asset allocation process is online, so that the calculation accuracy and efficiency can be improved, the labor cost of investment allocation is reduced, more diversified graphical display is provided through a visual plug-in, and the observability of a data result is improved. In addition, different calculation models can be used for optimizing asset allocation, and business personnel can optimize asset allocation by adopting different calculation models through the newly added python model, so that the accuracy and efficiency of asset allocation are further improved.
According to the invention, the data calculation model set is constructed through the data configuration instruction, and the historical service data or the real-time service data are configured and calculated through the models in the data calculation model set, so that the asset configuration efficiency is greatly improved. And moreover, the data calculation model can be flexibly adjusted according to the data configuration instruction, the flexibility of asset configuration is improved, and the efficiency of asset configuration is further improved. Meanwhile, the accuracy of the data configuration result can be improved by comparing the calculation results of the real-time service data and the historical service data. Therefore, the asset data configuration method provided by the invention can solve the problem of low asset data configuration efficiency.
Fig. 2 is a functional block diagram of an asset data configuration apparatus according to an embodiment of the present invention.
The asset data configuration device 100 of the present invention may be installed in an electronic device. According to the implemented functions, the asset data configuration device 100 may include a model building module 101, a first configuration calculation module 102, a data initialization processing module 103, and a second configuration calculation module 104. 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 model building module 101 is configured to receive a data configuration instruction, and build a data calculation model set according to the data configuration instruction;
the first configuration calculation module 102 is configured to obtain real-time service data, and perform configuration calculation on the real-time service data by using a model in the data calculation model set to obtain a first calculation result;
the data initialization processing module 103 is configured to acquire a historical service data set, and perform data initialization processing on data in the historical service data set to obtain a standard historical data set;
the second configuration calculation module 104 is configured to perform configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result, and perform comparison calculation on the first calculation result and the second calculation result to obtain a data configuration result.
In detail, the specific implementation of each module of the asset data configuration device 100 is as follows:
step one, receiving a data configuration instruction, and constructing a data calculation model set according to the data configuration instruction.
In the embodiment of the present invention, the data configuration instruction refers to an instruction for configuring parameters such as a data proportion and a data amount, which are input by a user. The data calculation model set can be a model set compiled in a computer language and used for performing configuration calculation on a certain index, such as the financial field, and the data calculation model can be a calculation model compiled by utilizing Python and used for calculating the asset profitability and the asset fluctuation rate.
Specifically, the constructing a data calculation model set according to the data configuration instruction includes:
receiving a configuration parameter file input by a user, and extracting a basic configuration field in the configuration parameter file and a configuration parameter corresponding to the basic configuration field by using the data configuration instruction;
constructing different parameter configuration templates according to the basic configuration field and the configuration parameters corresponding to the basic configuration field;
and filling the configuration parameters in the parameter configuration template into a pre-constructed Python template to obtain the data calculation model set.
In an embodiment of the present invention, the parameter configuration template includes different basic configuration fields and parameters corresponding to the basic configuration fields, for example, in the financial field, the basic configuration fields include: the system comprises the following parameters of 'asset income', 'configuration planning', 'asset relevance' and 'limitation condition', wherein the parameters corresponding to the asset income comprise: macroscopic expectations and macroscopic proportion of asset profitability and volatility; configuring parameters corresponding to the plan includes: investment allocation amounts for various assets; the parameters corresponding to asset relevance include: a correlation coefficient between investment targets; the parameters corresponding to the limiting conditions include: the upper and lower limits of the liquidity of each asset required by supervision, the upper and lower limits of the proportion of each asset, the investment limiting conditions of overseas assets, pledge rate and the like.
In an optional embodiment of the invention, a Python calculation model is constructed through the parameter configuration template, so that data calculation can be automatically carried out, and the data processing efficiency is improved.
And step two, acquiring real-time service data, and performing configuration calculation on the real-time service data by using the model in the data calculation model set to obtain a first calculation result.
In the embodiment of the invention, in the financial field, the real-time business data can be different types of large-scale assets, such as stock tickets, bonds, commodities, real estate, gold and the like.
In detail, the acquiring real-time service data includes:
acquiring a real-time data stream from a pre-constructed message middleware;
segmenting the real-time data stream by using a preset batch processing interval to obtain a discrete data stream comprising a plurality of segmented data sets;
extracting a segmentation data set in the discrete data stream by using a preset sliding window in a sliding manner to obtain a batch data set;
and taking the data in the batch data set as the real-time service data.
In this embodiment of the present invention, the message middleware may be Kafka message middleware. And simultaneously, the real-time data stream is subjected to Streaming processing by using an Apache Spark Streaming technology, the Apache Spark Streaming technology carries out high-throughput Streaming calculation by converting the data stream into an elastic Distributed data set (RDD), and the method has the characteristics of expandability, high throughput, fault tolerance and the like.
For example, the batch processing interval (batch interval) may be 2S, the real-time data stream is segmented every 2S to obtain a segmented data set (RDD) including 2S data, the sliding window (window length) may be 10S, and the batch data set includes 5 segmented data sets. The acquisition capability of real-time service data can be improved by the Apache Spark Streaming technology.
Specifically, the performing configuration calculation on the real-time service data by using the model in the data calculation model set to obtain a first calculation result includes:
receiving a model selection instruction, and selecting a target model from the data calculation model set by using the model selection instruction;
and performing configuration calculation on the real-time service data by using the target model to obtain the first calculation result.
For example, the allocation parameter in the target model is that the share ratio of the stock class assets and the credit class assets is 1:2, and the asset profitability is calculated to obtain the first calculation result.
In another optional embodiment of the invention, the method further comprises: and selecting a new data calculation model input by a user as the target model according to the model selection instruction.
In the embodiment of the invention, the target model can be selected from the data calculation model set or the data calculation model newly added by the user can be selected as the target model according to the model selection instruction, so that the accuracy rate of data calculation and the flexibility of data calculation are improved.
And step three, acquiring a historical service data set, and performing data initialization processing on data in the historical service data set to obtain a standard historical data set.
In the embodiment of the present invention, the historical business data set may be the business data of a large class of assets in the past year. The data initialization processing comprises data cleaning and data classification.
In detail, the performing data initialization processing on the data in the historical service data set to obtain a standard historical data set includes:
performing data duplication removal, data exception removal and data missing value filling on data in the historical service data set to obtain an original historical data set;
and classifying the original historical data set by using a preset classification label to obtain the standard historical data set.
In an alternative embodiment of the present invention, for example, the category labels of the large categories of assets in the financial field may include: stock tickets: white spirit, medical treatment, new energy … …; claims of debt: white spirit, medical treatment, new energy … ….
In the embodiment of the present invention, the performing data duplication removal, data exception removal and data missing value filling on data in the historical service data set to obtain an original historical data set includes:
calculating a distance value of data in the historical service data set, and removing duplication of the data in the historical service data set according to the distance value to obtain a duplication-removed data set;
removing abnormal values of the duplicate data removing set by using a unilateral test formula to obtain an abnormal data removing set;
and carrying out missing value detection on the data in the abnormal data removing set by using a preset missing value detection function, and filling missing values based on a preset filling algorithm to obtain the standard data set.
In an alternative embodiment of the present invention, the distance value is calculated by the following distance formula:
Figure BDA0003408122780000121
wherein d represents the distance value between any two data in the historical service data set, and w1jAnd w2jRepresenting any two data in the historical traffic data set. And deleting any one of the data when the distance value is smaller than a preset distance value, and simultaneously keeping the two data if the distance value is not smaller than the preset distance value. Preferably, the preset distance value may be 0.1.
In an optional embodiment of the present invention, the single-side test rejection includes a minimum single-side test rejection and a maximum single-side test rejection.
The calculation method for the minimum unilateral test rejection comprises the following steps:
Figure BDA0003408122780000131
wherein the content of the first and second substances,
Figure BDA0003408122780000132
representing the mean value of the data in the de-duplicated data set, YminRepresents the smallest data in the deduplication data set, G represents the test value, and S represents the standard deviation of the data in the deduplication data set. And when G is larger than a preset test threshold, determining the minimum data as abnormal data.
The calculation method for the maximum unilateral test rejection comprises the following steps:
Figure BDA0003408122780000133
wherein the content of the first and second substances,
Figure BDA0003408122780000134
representing the mean value of the data in the de-duplicated data set, YmaxRepresenting the largest data in the de-duplicated data set, G representing the test value, S representing the de-duplicated data setStandard deviation of the pooled data. And when G is larger than a preset test threshold value, determining the maximum data as abnormal data.
In an optional embodiment of the present invention, the missing value detection function may be a mismap function missing function, if no data missing value is detected, no processing is performed, and if a data missing value is detected, the missing value is filled by a preset filling algorithm in the embodiment of the present invention, where the preset filling algorithm includes:
Figure BDA0003408122780000135
wherein L (θ) represents a filled data missing value, xiRepresenting the ith data missing value, theta representing the probability parameter corresponding to the filled data missing value, n representing the number of data in the de-abnormal data set, p (x)i| θ) represents the probability of the data missing value of the padding.
In the embodiment of the invention, the data in the historical service data set can be subdivided by carrying out data initialization processing, so that the data calculation efficiency is improved.
And fourthly, performing configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result.
In detail, the performing configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result includes:
and performing configuration calculation on the data in the standard historical data set by using the target model selected by the model selection instruction to obtain a second calculation result.
In the embodiment of the invention, the historical data is configured and calculated through the target model selected by the user, the asset historical data does not need to be manually filled, and the accuracy and efficiency of the configuration and calculation of the historical data are improved.
And fifthly, comparing and calculating the first calculation result and the second calculation result to obtain a data configuration result.
In the embodiment of the invention, the first calculation result and the second calculation result are calculated by different target models aiming at a certain calculation index, so that the size comparison can be directly carried out, for example, when the asset profitability is calculated, the first calculation result is greater than the second calculation result, and the proportion, the amount and the like of asset data configuration in the first calculation result are taken as data configuration results.
In this embodiment of the present invention, the comparing and calculating the first calculation result and the second calculation result to obtain a data configuration result includes:
generating a display page of the first calculation result and the second calculation result by using a preset visual plug-in;
and comparing the sizes of the first calculation result and the second calculation result in the display page, and taking the maximum calculation result as the data configuration result.
In the embodiment of the present invention, a preset visualization plug-in may be used to visually display the calculation result, for example, the preset visualization plug-in may be amcharts, bonsaijs, and the like, and by comparing the first calculation result and the second calculation result in a display page, the optimal calculation result may be directly displayed, so that the visualization effect is improved.
In an optional embodiment of the invention, the asset allocation process is online, so that the calculation accuracy and efficiency can be improved, the labor cost of investment allocation is reduced, more diversified graphical display is provided through a visual plug-in, and the observability of a data result is improved. In addition, different calculation models can be used for optimizing asset allocation, and business personnel can optimize asset allocation by adopting different calculation models through the newly added python model, so that the accuracy and efficiency of asset allocation are further improved.
According to the invention, the data calculation model set is constructed through the data configuration instruction, and the historical service data or the real-time service data are configured and calculated through the models in the data calculation model set, so that the asset configuration efficiency is greatly improved. And moreover, the data calculation model can be flexibly adjusted according to the data configuration instruction, the flexibility of asset configuration is improved, and the efficiency of asset configuration is further improved. Meanwhile, the accuracy of the data configuration result can be improved by comparing the calculation results of the real-time service data and the historical service data. Therefore, the asset data configuration device provided by the invention can solve the problem of low asset data configuration efficiency.
Fig. 3 is a schematic structural diagram of an electronic device implementing an asset data configuration method according to an embodiment of the present invention.
The electronic device may include a processor 10, a memory 11, a communication interface 12, and a bus 13, and may further include a computer program, such as an asset data configuration program, stored in the memory 11 and operable 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, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device 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. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of an asset data configuration program, etc., 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 whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., asset data configuration programs, etc.) stored in the memory 11 and calling data stored in the memory 11.
The communication interface 12 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (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, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
The bus 13 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 13 may be divided into an address bus, a data bus, a control bus, etc. The bus 13 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 having components, and those skilled in the art will appreciate that the structure shown in fig. 3 does not constitute a limitation of the electronic device, and may include 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 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 that functions of charge management, discharge management, power consumption management and the like are realized 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 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 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 to establish a communication connection between the electronic device and other electronic devices.
Optionally, the electronic device 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, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
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 asset data configuration program stored in the memory 11 of the electronic device is a combination of instructions that, when executed in the processor 10, may implement:
receiving a data configuration instruction, and constructing a data calculation model set according to the data configuration instruction;
acquiring real-time service data, and performing configuration calculation on the real-time service data by using a model in the data calculation model set to obtain a first calculation result;
acquiring a historical service data set, and performing data initialization processing on data in the historical service data set to obtain a standard historical data set;
performing configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result;
and comparing and calculating the first calculation result and the second calculation result to obtain a data configuration result.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, 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:
receiving a data configuration instruction, and constructing a data calculation model set according to the data configuration instruction;
acquiring real-time service data, and performing configuration calculation on the real-time service data by using a model in the data calculation model set to obtain a first calculation result;
acquiring a historical service data set, and performing data initialization processing on data in the historical service data set to obtain a standard historical data set;
performing configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result;
and comparing and calculating the first calculation result and the second calculation result to obtain a data configuration result.
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 embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
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 for asset data configuration, the method comprising:
receiving a data configuration instruction, and constructing a data calculation model set according to the data configuration instruction;
acquiring real-time service data, and performing configuration calculation on the real-time service data by using a model in the data calculation model set to obtain a first calculation result;
acquiring a historical service data set, and performing data initialization processing on data in the historical service data set to obtain a standard historical data set;
performing configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result;
and comparing and calculating the first calculation result and the second calculation result to obtain a data configuration result.
2. The asset data deployment method of claim 1, wherein said building a set of data calculation models from said data deployment instructions comprises:
receiving a configuration parameter file input by a user, and extracting a basic configuration field in the configuration parameter file and a configuration parameter corresponding to the basic configuration field by using the data configuration instruction;
constructing different parameter configuration templates according to the basic configuration field and the configuration parameters corresponding to the basic configuration field;
and filling the configuration parameters in the parameter configuration template into a pre-constructed Python template to obtain the data calculation model set.
3. The asset data deployment method of claim 1, wherein said obtaining real-time service data comprises:
acquiring a real-time data stream from a pre-constructed message middleware;
segmenting the real-time data stream by using a preset batch processing interval to obtain a discrete data stream comprising a plurality of segmented data sets;
extracting a segmentation data set in the discrete data stream by using a preset sliding window in a sliding manner to obtain a batch data set;
and taking the data in the batch data set as the real-time service data.
4. The asset data configuration method of claim 1, wherein said performing configuration calculations on said real-time business data using said models in said set of data calculation models to obtain a first calculation result comprises:
receiving a model selection instruction, and selecting a target model from the data calculation model set by using the model selection instruction;
and performing configuration calculation on the real-time service data by using the target model to obtain the first calculation result.
5. The asset data configuration method according to claim 1, wherein the performing data initialization processing on the data in the historical service data set to obtain a standard historical data set comprises:
performing data duplication removal, data exception removal and data missing value filling on data in the historical service data set to obtain an original historical data set;
and classifying the original historical data set by using a preset classification label to obtain the standard historical data set.
6. The asset data configuration method according to claim 5, wherein said performing data deduplication, data anomaly removal and data missing value filling on the data in the historical business data set to obtain an original historical data set comprises:
calculating a distance value of data in the historical service data set, and removing duplication of the data in the historical service data set according to the distance value to obtain a duplication-removed data set;
removing abnormal values of the duplicate data removing set by using a unilateral test formula to obtain an abnormal data removing set;
and carrying out missing value detection on the data in the abnormal data removing set by using a preset missing value detection function, and filling missing values based on a preset filling algorithm to obtain the standard data set.
7. The asset data allocation method according to claim 1, wherein said comparing the first calculation result and the second calculation result to obtain a data allocation result comprises:
generating a display page of the first calculation result and the second calculation result by using a preset visual plug-in;
and comparing the sizes of the first calculation result and the second calculation result in the display page, and taking the maximum calculation result as the data configuration result.
8. An asset data configuration apparatus, characterized in that the apparatus comprises:
the model building module is used for receiving a data configuration instruction and building a data calculation model set according to the data configuration instruction;
the first configuration calculation module is used for acquiring real-time service data, and performing configuration calculation on the real-time service data by using the models in the data calculation model set to obtain a first calculation result;
the data initialization processing module is used for acquiring a historical service data set and performing data initialization processing on data in the historical service data set to obtain a standard historical data set;
and the second configuration calculation module is used for performing configuration calculation on the data in the standard historical data set by using the model in the data calculation model set to obtain a second calculation result, and performing comparison calculation on the first calculation result and the second calculation result to obtain a data configuration result.
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 a computer program executable by the at least one processor to enable the at least one processor to perform the asset data configuration method of any of claims 1 to 7.
10. A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the asset data configuration method of any of claims 1 to 7.
CN202111519193.8A 2021-12-13 2021-12-13 Asset data configuration method, device, equipment and storage medium Pending CN114187114A (en)

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