CN114461630A - Intelligent attribution analysis method, device, equipment and storage medium - Google Patents

Abstract

The invention relates to artificial intelligence technology, and discloses an intelligent attribution analysis method, comprising: acquiring an initial data set and performing data replacement processing on abnormal values to obtain a standard data set; dividing the standard data set into attributable phenomenon data import the attribution phenomenon data set and attribution factor data set into the preset model library, and calculate the prediction success rate of each model in the model library; determine the optimal rate according to the prediction success rate Attribution phenomenon prediction model; select the model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and use the model to explain the contribution of each attribution factor data of the algorithm. In addition, the present invention also relates to the blockchain technology, and the initial data set and the contribution of each attribution factor can be stored in the nodes of the blockchain. The present invention also provides an intelligent attribution analysis device, an electronic device and a storage medium. The present invention can improve the accuracy of attribution analysis.

Description

Intelligent attribution analysis method, device, equipment and storage medium

technical field

The present invention relates to the technical field of artificial intelligence, and in particular, to an intelligent attribution analysis method, device, electronic device and computer-readable storage medium.

Background technique

Attribution analysis is an analysis method to explain what factors constitute a phenomenon or effect. It is widely used in various industries such as the Internet advertising industry and the insurance industry. It is used to analyze the user behavior of industry data and improve the stickiness of industry users. .

At present, the main attribution algorithms include rule-based attribution and data-driven attribution algorithms, both of which need to manually set mathematical relationships in advance. Due to the analysis algorithm, the accuracy of the existing attribution analysis methods is not high.

SUMMARY OF THE INVENTION

The present invention provides an intelligent attribution analysis method, device and computer-readable storage medium, the main purpose of which is to solve the problem of low accuracy in attribution analysis.

In order to achieve the above purpose, an intelligent attribution analysis method provided by the present invention includes:

Obtaining an initial data set, performing data replacement processing on outliers in the initial data set, and obtaining a standard data set;

Using a pre-built variable library to divide the standard data set into an attribution phenomenon data set and an attribution factor data set corresponding to the attribution phenomenon data set;

Import the attribution phenomenon data set and the attribution factor data set into a preset attribution phenomenon prediction model library, and calculate the prediction success rate of each attribution phenomenon prediction model in the attribution phenomenon prediction model library;

Determine the optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;

Select a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and use the model interpretation algorithm to calculate the contribution of each attribution factor data in the attribution factor data set to the attributable phenomenon data .

Optionally, the described abnormal value in the initial data set is carried out data replacement processing to obtain a standard data set, including:

calculating the local reachable density ratio between each initial data in the initial data set and the neighborhood data of the initial data;

When the local density ratio is less than or equal to a preset density ratio threshold, determining that the initial data is an abnormal value;

The abnormal value is subjected to data replacement processing using a preset correct data set to obtain a standard data set.

Optionally, using a pre-built variable library to divide the standard data set into an attributable phenomenon data set and an attribution factor data set corresponding to the attributable phenomenon data set, including:

comparing the variable data in the standard data set with a pre-built variable library, determining the variable data in the standard data set that is consistent with the variable library as attributable phenomenon data, and comparing the standard data The variable data that is inconsistent with the variable library is determined as attribution factor data;

Calculate the correlation degree between the attribution phenomenon data and the attribution factor data, and determine the attribution factor data whose correlation degree is greater than a preset threshold as the target attribution factor corresponding to the attribution phenomenon data data;

Collecting the attribution phenomenon data and the target attribution factor data to obtain an attribution phenomenon data set and an attribution factor data set corresponding to the attribution phenomenon data set.

Optionally, the calculating the correlation between the attributable phenomenon data and the attribution factor data includes:

Among them, r(X, Y) is the degree of correlation, X is the attributable phenomenon data, Y is the Y-th attribution factor data, and Cov(X, Y) is the attributable phenomenon data and all is the covariance between the attribution factor data, σ _x is the standard deviation of the attributable phenomenon data, and σ _y is the standard deviation of the attribution factor data.

Optionally, the calculating the prediction success rate of each attribution phenomenon prediction model in the to-be-attributed phenomenon prediction model library includes:

dividing the attribution data set and the attribution factor data set into training samples and test samples according to a preset ratio;

Perform model training on each attribution phenomenon prediction model in the preset attribution phenomenon prediction model library according to the training sample to obtain a plurality of initial prediction models;

Use each of the initial prediction models to perform model prediction on the test samples to obtain test data of each of the initial test models;

performing difference calculation between the test data of each of the initial test models and the attributable phenomenon data of the test sample;

Determine the test data whose difference is less than the preset threshold as correct prediction data, and calculate the proportion of the correct prediction data in the test data of each of the initial test models to obtain the predictions of the respective attribution phenomenon prediction models. Success rate.

Optionally, calculating the contribution of each attribution factor data in the attribution factor data set to the attributable phenomenon data by using the model interpretation algorithm includes:

calculating the standard deviation of each attribution factor data in the attribution factor data set, and determining the disturbance range of each attribution factor data according to the standard deviation;

Perform data perturbation on each attribution factor data according to the perturbation range to obtain new data of each attribution factor;

Based on the model interpretation algorithm, the target linear regression model is obtained by training the new data of the attribution factors, and the weights corresponding to the attribution factor data are obtained;

The respective attribution factor data is multiplied by the corresponding weight to obtain the contribution of the respective attribution factor data.

Optionally, the target linear regression model is obtained by training the new data of each attribution factor based on the model interpretation algorithm, including:

Calculate the distances between the respective attribution factor data and the new data of the respective attribution factors, and use the distance value as the weight of the new data of the respective attribution factors;

Use the optimal attribution phenomenon model to perform attribution phenomenon prediction on the new data of each attribution factor, obtain attributable phenomenon prediction data, and use the attributable phenomenon prediction data as the attributions The label data corresponding to the new data of the factor;

The target linear regression model is obtained by training the label data and the weighted new data of each attribution factor based on a preset model interpretation algorithm.

In order to solve the above problems, the present invention also provides an intelligent attribution analysis device, the device includes:

a standard data set obtaining module, used for obtaining an initial data set, and performing data replacement processing on outliers in the initial data set to obtain a standard data set;

a standard data set dividing module, configured to use a pre-built variable library to divide the standard data set into an attributable phenomenon data set and an attribution factor data set corresponding to the attributable phenomenon data set;

The model prediction success rate calculation module is used to import the attribution phenomenon data set and the attribution factor data set into a preset attribution phenomenon prediction model library, and calculate each attribute in the attribution phenomenon prediction model library. The prediction success rate of the phenomenon-based prediction model;

an optimal attribution phenomenon prediction model determination module, configured to determine an optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;

The attribution factor data contribution degree calculation module is used to select a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and use the model interpretation algorithm to calculate each attribution factor data in the attribution factor data set. Contribution to the attributed phenomenon data.

In order to solve the above problems, the present invention also provides an electronic device, the electronic device includes:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the intelligent attribution analysis method described above .

In order to solve the above problems, the present invention also provides a computer-readable storage medium, where at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is executed by a processor in an electronic device to realize the above-mentioned The intelligent attribution analysis method described above.

In the embodiment of the present invention, the standard data set is divided into the attributable phenomenon data set and the corresponding attribution factor data set through a pre-built variable library, and the data is divided according to different industry variables, which is beneficial to improve the accuracy of the data; The attribution phenomenon data set and the corresponding attribution factor data set are imported into the preset attribution phenomenon prediction model library, and the prediction success rate of each attribution phenomenon prediction model is calculated; Determine the optimal attribution phenomenon prediction model, select the optimal attribution phenomenon prediction model that is most consistent with the data according to different data, and further improve the accuracy of attribution analysis; The model interpretation algorithm uses the model interpretation algorithm to calculate and calculate the contribution of each attribution factor data in the attribution factor data set to the attribution phenomenon data, so as to obtain a more accurate attribution analysis result. Therefore, the intelligent attribution analysis method, device, electronic device and computer-readable storage medium proposed by the present invention can solve the problem of low accuracy when performing attribution analysis.

Description of drawings

1 is a schematic flowchart of an intelligent attribution analysis method provided by an embodiment of the present invention;

FIG. 2 is a schematic flowchart of standard data set division provided by an embodiment of the present invention;

3 is a schematic flowchart of a calculation model predicting a success rate according to an embodiment of the present invention;

FIG. 4 is a schematic flowchart of calculating attribution factor data contribution degree according to an embodiment of the present invention;

5 is a functional block diagram of an intelligent attribution analysis device provided by an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of an electronic device implementing the intelligent attribution analysis method according to an embodiment of the present invention.

The realization, functional characteristics and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

The embodiment of the present application provides an intelligent attribution analysis method. The execution subject of the intelligent attribution analysis method includes, but is not limited to, at least one of electronic devices that can be configured to execute the method provided by the embodiments of the present application, such as a server and a terminal. In other words, the intelligent attribution analysis method can be executed by software or hardware installed in a terminal device or a server device, and the software can 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 can be an independent server, or can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery network (Content Delivery Network) , CDN), and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms.

Referring to FIG. 1 , it is a schematic flowchart of an intelligent attribution analysis method provided by an embodiment of the present invention. In this embodiment, the intelligent attribution analysis method includes:

S1, obtain an initial data set, perform data replacement processing on the abnormal values in the initial data set, and obtain a standard data set;

In the embodiment of the present invention, the initial data set is different business data sets of different industries, for example, relevant data of the Internet advertising industry, relevant data of the insurance industry, and relevant data in the scenario of intelligent customer service.

In detail, the described abnormal values in the initial data set are carried out data replacement processing to obtain a standard data set, including:

calculating the local reachable density ratio between each initial data in the initial data set and the neighborhood data of the initial data;

When the local density ratio is less than or equal to a preset density ratio threshold, determining that the initial data is an abnormal value;

The abnormal value is subjected to data replacement processing using a preset correct data set to obtain a standard data set.

In this embodiment of the present invention, the local reachable density is the reciprocal of the average distance from the neighborhood data of each initial data to the initial data, and if the local reachable density ratio is greater than the preset density ratio threshold, it can be It is considered that the adjacent data is the same cluster as the initial data and is not an outlier.

In detail, in this embodiment of the present invention, the following formula is used to calculate the local reachable density ratio of each initial data in the initial data set to the adjacent data:

Among them, LOF _k (P) is the local reachable density ratio, N _k (P) is the P-th initial data of the initial data set, ρ _k (P) is the local reachable density of the P-th data, ρ _k (O) is the average local reachability density of the neighborhood data O, and d _k (P, O) is the distance between the P-th initial data and the neighborhood data O.

In the embodiment of the present invention, by performing data replacement processing on the abnormal values in the initial data set, the abnormal data in the initial data set is eliminated, the rationality of the data in the initial data set is ensured, and the accuracy of subsequent model selection is improved.

S2, using a pre-built variable library to divide the standard data set into an attributable phenomenon data set and an attribution factor data set corresponding to the attributable phenomenon data set;

In this embodiment of the present invention, the attributable phenomenon data set includes unquantifiable elements, the attribution factor data set is a plurality of possible factors that affect the attributable phenomenon data, and the pre-built variable library contains Contains data for multiple pre-determined attributed phenomena.

In detail, referring to Fig. 2, the standard data set is divided into an attributable phenomenon data set and an attribution factor data set corresponding to the attributable phenomenon data set by using a pre-built variable library, include:

S21. Compare the variable data in the standard data set with a pre-built variable database, determine the variable data in the standard data set that is consistent with the variable database as the attributable phenomenon data, and compare the variable data in the standard data set with the variable database. The variable data in the standard data set that is inconsistent with the variable database is determined as attribution factor data;

S22. Calculate the degree of association between the attributable phenomenon data and the attribution factor data, and determine the attribution factor data whose association degree is greater than a preset threshold as the target attribute corresponding to the attributable phenomenon data factor data;

S23. Collect the attribution phenomenon data and the target attribution factor data to obtain an attribution phenomenon data set and an attribution factor data set corresponding to the attribution phenomenon data set.

Further, in this embodiment of the present invention, the calculating the correlation degree between the attributable phenomenon data and the attribution factor data includes:

Among them, r(X, Y) is the degree of correlation, X is the attributable phenomenon data, Y is the Y-th attribution factor data, and Cov(X, Y) is the attributable phenomenon data and all is the covariance between the attribution factor data, σ _x is the standard deviation of the attributable phenomenon data, and σ _y is the standard deviation of the attribution factor data.

Specifically, the attributable variables are also different in different industry data. Therefore, a corresponding pre-built variable library should be selected according to the specific industry data of the embodiment, and the standard data set should be divided into two groups according to the pre-built variable library. Attributed phenomenon dataset and attribution factor dataset.

For example, in this embodiment of the present invention, in the advertising industry, the attribution variable may be advertising revenue, and the corresponding target attribution factor may be advertising click-through rate, user browsing time, etc., and the attribution variable in the insurance industry The dependent variable can be the renewal rate, and the corresponding target attribution factor can be the average insured age of the customer, the age composition of the customer, and the renewal rate of each insurance channel.

S3. Import the attribution phenomenon data set and the attribution factor data set into a preset attribution phenomenon prediction model library, and calculate the success of the prediction of each attribution phenomenon prediction model in the attribution phenomenon prediction model library Rate;

In this embodiment of the present invention, the preset attribution phenomenon prediction model library may include hundreds of prediction models with prediction functions, including but not limited to time decay attribution models, Bayesian models, XGBoost, linear regression models, Models with predictive capabilities such as fully connected neural networks.

In detail, referring to Fig. 3, the calculation of the prediction success rate of each attribution phenomenon prediction model in the to-be-attributed phenomenon prediction model library includes:

S31. Divide the attribution data set and the attribution factor data set into training samples and test samples according to a preset ratio;

S32, performing model training on each attribution phenomenon prediction model in the preset attribution phenomenon prediction model library according to the training sample, to obtain a plurality of initial prediction models;

S33, utilize each described initial prediction model to carry out model prediction to described test sample, obtain the test data of each described initial test model;

S34, performing difference calculation between the test data of each of the initial test models and the attributable phenomenon data of the test sample;

S35. Determine the test data whose difference is less than a preset threshold as correct prediction data, and calculate the proportion of the correct prediction data in the test data of each initial test model to obtain the each attribution phenomenon prediction model prediction success rate.

Specifically, in this embodiment of the present invention, the preset threshold may be set to different thresholds according to different attributable phenomenon data sets. For example, if the attributable phenomenon data is the renewal rate, the threshold may be is 0.01.

S4, determining the optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;

In the embodiment of the present invention, an attribution phenomenon prediction model that best matches the standard data set is selected from the preset attribution phenomenon prediction model library according to the model success rate, that is, an optimal attribution phenomenon prediction model.

For example, in an actual application scenario of the present invention, the preset attribution phenomenon prediction model library includes a time decay attribution model, a Bayesian model, XGBoost, a linear model, a fully connected neural network, etc., wherein the time decay attribution model The prediction success rate of the model is 89%, the prediction success rate of the Bayesian model is 85%, the prediction success rate of XGBoost is 89%, the prediction success rate of the linear model is 78%, and the prediction success rate of the fully connected neural network is 94%. %, the fully connected neural network is determined as the optimal attribution phenomenon prediction model.

In the embodiment of the present invention, the optimal attribution phenomenon prediction model is determined by the model prediction success rate, different attribution phenomenon prediction models can be selected according to different industry data, the applicable scope of different industry data is improved, and the subsequent attribution phenomenon is guaranteed at the same time. Due to the accuracy of factor data contribution calculation.

S5. Select a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and use the model interpretation algorithm to calculate the difference between the attribution factor data in the attribution factor data set and the attribution phenomenon data. contribution.

In the embodiment of the present invention, the corresponding model interpretation algorithm is to estimate the contribution degree of the variables in the optimal attribution phenomenon prediction model, namely attribution factors, that is, each attribution factor data contributes to the optimal attribution phenomenon. The size of the role played by the prediction results of the optimal attribution phenomenon prediction model.

Specifically, in this embodiment of the present invention, a model interpretation algorithm corresponding to the optimal attribution phenomenon prediction model may be called from a pre-stored model interpretation algorithm library according to the type of the optimal attribution phenomenon prediction model.

In detail, referring to FIG. 4 , the calculation of the contribution of each attribution factor data in the attribution factor data set to the attributable phenomenon data by using the model interpretation algorithm includes:

S51. Calculate the standard deviation of each attribution factor data in the attribution factor data set, and determine the disturbance range of each attribution factor data according to the standard deviation;

S52. Perform data perturbation on each attribution factor data according to the perturbation range to obtain new data of each attribution factor;

S53, adopting the new data training of each attribution factor based on the model interpretation algorithm to obtain a target linear regression model, and obtaining the weight corresponding to the each attribution factor data;

S54. Multiply the respective attribution factor data by the corresponding weight to obtain the contribution degree of the respective attribution factor data.

Further, in the embodiment of the present invention, the target linear regression model is obtained by training the new data of each attribution factor based on the model interpretation algorithm, including:

Calculate the distances between the respective attribution factor data and the new data of the respective attribution factors, and use the distance value as the weight of the new data of the respective attribution factors;

Use the optimal attribution phenomenon model to perform attribution phenomenon prediction on the new data of each attribution factor, obtain attributable phenomenon prediction data, and use the attributable phenomenon prediction data as the attributions The label data corresponding to the new data of the factor;

The target linear regression model is obtained by training the label data and the weighted new data of each attribution factor based on a preset model interpretation algorithm.

For example, in an actual application scenario of the present invention, when the optimal attribution phenomenon prediction model is a fully connected neural network, the Deeplift algorithm can be invoked to explain the optimal attribution phenomenon prediction model. When the phenomenon prediction model is the XGBoost model, the Shapley Value can be called to interpret the XGBoost model, and the contribution of each attribution factor in the optimal attribution phenomenon prediction model can be obtained.

In the embodiment of the present invention, the contribution degree of each attribution factor data to the attributable phenomenon data is obtained through the model interpretation algorithm, and the user behavior that the industry data is mainly derived from is found, and the contribution degree of each attribution factor is made according to the contribution degree of each attribution factor. Corresponding coping strategies, for example, in the insurance industry data, it is found that the rate of renewal of insurance has decreased, and it is calculated by using the intelligent attribution analysis method that it is mainly caused by the increase in the complaint rate of old customers of an insurance channel.

In the embodiment of the present invention, the standard data set is divided into the attributable phenomenon data set and the corresponding attribution factor data set through a pre-built variable library, and the data is divided according to different industry variables, which is beneficial to improve the accuracy of the data; The attribution phenomenon data set and the corresponding attribution factor data set are imported into the preset attribution phenomenon prediction model library, and the prediction success rate of each attribution phenomenon prediction model is calculated; Determine the optimal attribution phenomenon prediction model, select the optimal attribution phenomenon prediction model that is most consistent with the data according to different data, further improve the accuracy of attribution analysis; select the corresponding attribution phenomenon prediction model according to the type of the optimal attribution phenomenon The model interpretation algorithm uses the model interpretation algorithm to calculate and calculate the contribution of each attribution factor data in the attribution factor data set to the attribution phenomenon data, so as to obtain a more accurate attribution analysis result. Therefore, the intelligent attribution analysis method proposed by the present invention can solve the problem of low accuracy in attribution analysis.

As shown in FIG. 5 , it is a functional block diagram of an intelligent attribution analysis device provided by an embodiment of the present invention.

The intelligent attribution analysis apparatus 100 of the present invention can be installed in an electronic device. According to the realized functions, the intelligent attribution analysis device 100 may include a standard data set acquisition module 101, a standard data set division module 102, a model prediction success rate calculation module 103, an optimal attribution phenomenon prediction model determination module 104, and an attribution model The factor data contribution degree calculation module 105 . The modules in the present invention can also be called units, which refer to a series of computer program segments that can be executed by the electronic device processor and can perform fixed functions, and are stored in the memory of the electronic device.

In this embodiment, the functions of each module/unit are as follows:

The standard data set obtaining module 101 is configured to obtain an initial data set, and perform data replacement processing on outliers in the initial data set to obtain a standard data set;

The standard data set dividing module 102 is configured to use a pre-built variable library to divide the standard data set into an attributable phenomenon data set and an attribution factor data set corresponding to the attributable phenomenon data set;

The model prediction success rate calculation module 103 is used to import the attribution phenomenon data set and the attribution factor data set into a preset attribution phenomenon prediction model library, and calculate the attribution phenomenon prediction model library The prediction success rate of each attribution phenomenon prediction model;

The optimal attribution phenomenon prediction model determining module 104 is configured to determine the optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;

The attribution factor data contribution degree calculation module 105 is configured to select a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and use the model interpretation algorithm to calculate each attribution factor in the attribution factor data set. The contribution of the factor data to the attributable phenomenon data.

In detail, each module described in the intelligent attribution analysis device 100 in the embodiment of the present invention adopts the same technical means as the intelligent attribution analysis method described in FIG. 1 to FIG. 4, and can generate The same technical effect will not be repeated here.

As shown in FIG. 6 , it is a schematic structural diagram of an electronic device for implementing an intelligent attribution analysis method provided by an embodiment of the present invention.

The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as smart attribution Analysis program.

The processor 10 may be composed of integrated circuits in some embodiments, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits packaged with the same function or different functions, including one or A combination of multiple central processing units (Central Processing Units, CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and by running or executing the program or module stored in the memory 11 (for example, executing intelligent Attribution analysis program, etc.), and call data stored in the memory 11 to perform various functions of the electronic device and process data.

The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. . In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of the electronic device, such as a pluggable mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) equipped on the electronic device. ) card, flash memory card (Flash Card) and so on. Further, the memory 11 may also include both an internal storage unit of an electronic device and an external storage device. The memory 11 can not only be used to store application software installed in the electronic device and various types of data, such as codes of intelligent attribution analysis programs, etc., but also can be used to temporarily store data that has been output or will be output.

The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus or the like. The bus can be divided into address bus, data bus, control bus and so on. The bus is configured to enable connection communication between the memory 11 and at least one processor 10 and the like.

The communication interface 13 is used for communication between the above electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (eg, a WI-FI interface, a Bluetooth interface, etc.), which is generally 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, the user interface may also be a standard wired interface or a wireless interface. Optionally, 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, organic light-emitting diode) touch device, and the like. The display may also be appropriately called a display screen or a display unit, which is used for displaying information processed in the electronic device and for displaying a visual user interface.

The figure only shows an electronic device with components, and those skilled in the art can understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than those shown in the figure. , or a combination of certain components, or a different arrangement of components.

For example, although not shown, the electronic device may also include a power source (such as a battery) for powering the various components, preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device Implement functions such as charge management, discharge management, and power management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and any other components. The electronic device may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

It should be understood that the embodiments are only used for illustration, and are not limited by this structure in the scope of the patent application.

The intelligent attribution analysis program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions, and when running in the processor 10, can realize:

Obtaining an initial data set, performing data replacement processing on outliers in the initial data set, and obtaining a standard data set;

Using a pre-built variable library to divide the standard data set into an attribution phenomenon data set and an attribution factor data set corresponding to the attribution phenomenon data set;

Import the attribution phenomenon data set and the attribution factor data set into a preset attribution phenomenon prediction model library, and calculate the prediction success rate of each attribution phenomenon prediction model in the attribution phenomenon prediction model library;

Determine the optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;

Select a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and use the model interpretation algorithm to calculate the contribution of each attribution factor data in the attribution factor data set to the attributable phenomenon data .

Specifically, for the specific implementation method of the above-mentioned instruction by the processor 10, reference may be made to the description of the relevant steps in the embodiment corresponding to the accompanying drawings, which will not be repeated here.

Further, if the modules/units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they 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 the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only). Memory).

The present invention also provides a computer-readable storage medium, where the readable storage medium stores a computer program, and when executed by a processor of an electronic device, the computer program can realize:

Obtaining an initial data set, performing data replacement processing on outliers in the initial data set, and obtaining a standard data set;

Using a pre-built variable library to divide the standard data set into an attribution phenomenon data set and an attribution factor data set corresponding to the attribution phenomenon data set;

Import the attribution phenomenon data set and the attribution factor data set into a preset attribution phenomenon prediction model library, and calculate the prediction success rate of each attribution phenomenon prediction model in the attribution phenomenon prediction model library;

Determine the optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;

Select a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and use the model interpretation algorithm to calculate the contribution of each attribution factor data in the attribution factor data set to the attributable phenomenon data .

In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division manners in actual implementation.

The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.

It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, but that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics of the invention.

Therefore, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the invention is to be defined by the appended claims rather than the foregoing description, which are therefore intended to fall within the scope of the claims. All changes within the meaning and range of the equivalents of , are included in the present invention. Any reference signs in the claims shall not be construed as limiting the involved claim.

The blockchain referred to in the present invention is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

The embodiments of the present application may acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .

Furthermore, it is clear that the word "comprising" does not exclude other units or steps and the singular does not exclude the plural. Several units or means recited in the system claims can also be realized by one unit or means by means of software or hardware. The words first, second, etc. are used to denote names and do not denote any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent substitutions can be made without departing from the spirit and scope of the technical solutions of the present invention.

Claims (10)

1. an intelligent attribution analysis method, characterized in that, the method comprises: Obtaining an initial data set, performing data replacement processing on outliers in the initial data set, and obtaining a standard data set; Using a pre-built variable library to divide the standard data set into an attribution phenomenon data set and an attribution factor data set corresponding to the attribution phenomenon data set; Import the attribution phenomenon data set and the attribution factor data set into a preset attribution phenomenon prediction model library, and calculate the prediction success rate of each attribution phenomenon prediction model in the attribution phenomenon prediction model library; Determine the optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate; Select a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and use the model interpretation algorithm to calculate the contribution of each attribution factor data in the attribution factor data set to the attributable phenomenon data . 2. intelligent attribution analysis method as claimed in claim 1, is characterized in that, described to the abnormal value in described initial data set is carried out data replacement processing, obtains standard data set, comprises: calculating the local reachable density ratio between each initial data in the initial data set and the neighborhood data of the initial data; When the local density ratio is less than or equal to a preset density ratio threshold, determining that the initial data is an abnormal value; The abnormal value is subjected to data replacement processing using a preset correct data set to obtain a standard data set. 3 . The intelligent attribution analysis method according to claim 1 , wherein the standard data set is divided into attribution phenomenon data sets and attribution phenomenon data sets by using a pre-built variable library. 4 . The attribution factor dataset corresponding to the set, including: comparing the variable data in the standard data set with a pre-built variable library, determining the variable data in the standard data set that is consistent with the variable library as attributable phenomenon data, and comparing the standard data The variable data that is inconsistent with the variable library is determined as attribution factor data; Calculate the correlation degree between the attribution phenomenon data and the attribution factor data, and determine the attribution factor data whose correlation degree is greater than a preset threshold as the target attribution factor corresponding to the attribution phenomenon data data; Collecting the attribution phenomenon data and the target attribution factor data to obtain an attribution phenomenon data set and an attribution factor data set corresponding to the attribution phenomenon data set. 4. The intelligent attribution analysis method according to claim 3, wherein the calculating the correlation between the attribution phenomenon data and the attribution factor data comprises:

Among them, r(X, Y) is the degree of correlation, X is the attributable phenomenon data, Y is the Y-th attribution factor data, and Cov(X, Y) is the attributable phenomenon data and all is the covariance between the attribution factor data, σ _x is the standard deviation of the attributable phenomenon data, and σ _y is the standard deviation of the attribution factor data. 5. The intelligent attribution analysis method according to claim 1, wherein the calculation of the prediction success rate of each attribution phenomenon prediction model in the to-be-attributed phenomenon prediction model library comprises: dividing the attribution data set and the attribution factor data set into training samples and test samples according to a preset ratio; Perform model training on each attribution phenomenon prediction model in the preset attribution phenomenon prediction model library according to the training sample to obtain a plurality of initial prediction models; Use each of the initial prediction models to perform model prediction on the test samples to obtain test data of each of the initial test models; performing difference calculation between the test data of each of the initial test models and the attributable phenomenon data of the test sample; Determine the test data whose difference is less than the preset threshold as correct prediction data, and calculate the proportion of the correct prediction data in the test data of each of the initial test models to obtain the predictions of the respective attribution phenomenon prediction models. Success rate. 6 . The intelligent attribution analysis method according to claim 1 , wherein the model interpretation algorithm is used to calculate the contribution of each attribution factor data in the attribution factor data set to the attributable phenomenon data. 7 . degrees, including: calculating the standard deviation of each attribution factor data in the attribution factor data set, and determining the disturbance range of each attribution factor data according to the standard deviation; Perform data perturbation on each attribution factor data according to the perturbation range to obtain new data of each attribution factor; Based on the model interpretation algorithm, the target linear regression model is obtained by training the new data of the attribution factors, and the weights corresponding to the attribution factor data are obtained; The respective attribution factor data is multiplied by the corresponding weight to obtain the contribution of the respective attribution factor data. 7. The intelligent attribution analysis method according to any one of claims 1 to 6, wherein the model interpretation algorithm adopts the new data training of the respective attribution factors to obtain a target linear regression model, include: Calculate the distances between the respective attribution factor data and the new data of the respective attribution factors, and use the distance value as the weight of the new data of the respective attribution factors; Use the optimal attribution phenomenon model to perform attribution phenomenon prediction on the new data of each attribution factor, obtain attributable phenomenon prediction data, and use the attributable phenomenon prediction data as the attributions The label data corresponding to the new data of the factor; The target linear regression model is obtained by training the label data and the weighted new data of each attribution factor based on a preset model interpretation algorithm. 8. An intelligent attribution analysis device, wherein the device comprises: a standard data set obtaining module, used for obtaining an initial data set, and performing data replacement processing on outliers in the initial data set to obtain a standard data set; a standard data set dividing module, configured to use a pre-built variable library to divide the standard data set into an attributable phenomenon data set and an attribution factor data set corresponding to the attributable phenomenon data set; The model prediction success rate calculation module is used to import the attribution phenomenon data set and the attribution factor data set into a preset attribution phenomenon prediction model library, and calculate each attribute in the attribution phenomenon prediction model library. The prediction success rate of the phenomenon-based prediction model; an optimal attribution phenomenon prediction model determination module, configured to determine an optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate; The attribution factor data contribution degree calculation module is used to select a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and use the model interpretation algorithm to calculate each attribution factor data in the attribution factor data set. Contribution to the attributed phenomenon data. 9. An electronic device, characterized in that the electronic device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform any one of claims 1 to 7 The intelligent attribution analysis method described in item. 10 . A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the intelligent attribution analysis method according to any one of claims 1 to 7 is implemented. 11 .

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