CN114461630A - Intelligent attribution analysis method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses an intelligent attribution analysis method, which comprises the following steps: acquiring an initial data set and performing data replacement processing on the abnormal value to obtain a standard data set; dividing the standard dataset into an attributed phenomenon dataset and a corresponding attribution factor dataset; importing the attributed phenomenon data set and the attributed factor data set into a preset model library, and calculating the prediction success rate of each model in the model library; determining an optimal attribution phenomenon prediction model according to the prediction success rate; and selecting a model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and utilizing the contribution degree of each attribution factor data of the model interpretation algorithm. In addition, the invention also relates to a block chain technology, and the initial data set and the contribution degree of each attribution factor can be stored in the nodes of the block chain. The invention also provides an intelligent attribution analysis device, electronic equipment and a storage medium. The present invention can improve the accuracy of attribution analysis.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an intelligent attribution analysis method, an intelligent attribution analysis device, electronic equipment and a computer readable storage medium.
Background
The attribution analysis is an analysis method for explaining which factors constitute a certain phenomenon or effect, is widely applied to various industries such as the internet advertising industry, the insurance industry and the like, and is used for analyzing the user behavior of the industry data source and improving the viscosity of industry users.
The current main attribution algorithms comprise two types of attribution based on rules and attribution based on data driving, the mathematical relationship needs to be set manually in advance, but the service scenes of the actual industry are complicated and changeable, and the attribution analysis algorithm which is universal for the service scenes of each industry is lacked, so that the accuracy of the existing attribution analysis method is not high.
Disclosure of Invention
The invention provides an intelligent attribution analysis method, an intelligent attribution analysis device and a computer readable storage medium, and mainly aims to solve the problem of low accuracy in attribution analysis.
In order to achieve the above object, the present invention provides an intelligent attribution analysis method, which includes:
acquiring an initial data set, and performing data replacement processing on abnormal values in the initial data set to obtain a standard data set;
dividing the standard dataset into an attributed phenomenon dataset and an attribution factor dataset corresponding to the attributed phenomenon dataset by using a pre-constructed variable library;
importing the attributed phenomenon data set and the attribution factor data set into a preset attributed phenomenon prediction model library, and calculating the prediction success rate of each attributed phenomenon prediction model in the attributed phenomenon prediction model library;
determining an optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;
and selecting a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and calculating the contribution degree of each attribution factor data in the attribution factor data set to the attributed phenomenon data by using the model interpretation algorithm.
Optionally, the performing data replacement processing on the abnormal value in the initial data set to obtain a standard data set includes:
calculating a local reachable density ratio between each initial data in the initial data set and neighborhood data of the initial data;
when the local density ratio is smaller than or equal to a preset density ratio threshold, determining the initial data to be an abnormal value;
and performing data replacement processing on the abnormal value by using a preset correct data set to obtain a standard data set.
Optionally, the dividing the standard dataset into an attributed phenomenon dataset and an attribution factor dataset corresponding to the attributed phenomenon dataset by using a pre-constructed variable library, comprising:
comparing the variable data in the standard data set with a pre-constructed variable database, determining the variable data in the standard data set which is consistent with the variable database as attributed phenomenon data, and determining the variable data in the standard data set which is inconsistent with the variable database as attributed factor data;
calculating a degree of association between the attributed phenomenon data and the attribution factor data, and determining the attribution factor data with the degree of association larger than a preset threshold as target attribution factor data corresponding to the attributed phenomenon data;
and aggregating the attributed phenomenon data and the target attribution factor data to obtain an attributed phenomenon data set and an attribution factor data set corresponding to the attributed phenomenon data set.
Optionally, the calculating the degree of association between the attributed phenomenon data and the attribution factor data includes:
wherein r (X, Y) is the degree of correlation, X is the attributed phenomenon data, Y is the Yth attribution factor data, Cov (X, Y) is the covariance between the attributed phenomenon data and the attribution factor data, σxIs the standard deviation, σ, of the attributed phenomenon datayIs the standard deviation of the attribution factor data.
Optionally, the calculating a prediction success rate of each attribution phenomenon prediction model in the to-be-attributed phenomenon prediction model library includes:
dividing the attributed data set and the attribution factor data set into a training sample and a testing sample according to a preset proportion;
performing model training on each attribution phenomenon prediction model in the preset attribution phenomenon prediction model library according to the training samples to obtain a plurality of initial prediction models;
performing model prediction on the test sample by using each initial prediction model to obtain test data of each initial test model;
performing a difference calculation on the test data of each initial test model and the attributed phenomenon data of the test sample;
and determining the test data with the difference value smaller than a preset threshold value as correct prediction data, and calculating the proportion of the correct prediction data in the test data of each initial test model to obtain the prediction success rate of each attribution phenomenon prediction model.
Optionally, the calculating, by using the model interpretation algorithm, a contribution degree of each attribution factor data in the attribution factor data set to the attributed phenomenon data 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;
performing data disturbance on the attribution factor data according to the disturbance range to obtain new data of the attribution factors;
training by adopting the new data of each attribution factor based on the model interpretation algorithm to obtain a target linear regression model, and obtaining the weight corresponding to each attribution factor data;
and multiplying each attribution factor data by the corresponding weight to obtain the contribution degree of each attribution factor data.
Optionally, the training with the new data of each attribution factor based on the model interpretation algorithm to obtain a target linear regression model includes:
respectively calculating the distance between each attribution factor data and the new data of each attribution factor, and taking the distance value as the weight of the new data of each attribution factor;
the optimal attribution phenomenon model is utilized to carry out attribution phenomenon prediction on the new data of each attribution factor to obtain attribution phenomenon prediction data, and the attribution phenomenon prediction data are used as label data corresponding to the new data of each attribution factor;
and training by adopting the label data and the new data of each attribution factor with the weight based on a preset model interpretation algorithm to obtain a target linear regression model.
In order to solve the above problems, the present invention also provides an intelligent attribution analyzing apparatus, the apparatus comprising:
the standard data set acquisition module is used for acquiring an initial data set and performing data replacement processing on abnormal values in the initial data set to obtain a standard data set;
a standard data set dividing module for dividing the standard data set into an attributed phenomenon data set and an attribution factor data set corresponding to the attributed phenomenon data set by using a pre-constructed variable library;
the model prediction success rate calculation module is used for importing the attributed phenomenon data set and the attribution factor data set into a preset attributed phenomenon prediction model library and calculating the prediction success rate of each attributed phenomenon prediction model in the attributed phenomenon prediction model library;
the optimal attribution phenomenon prediction model determining module is used for determining an optimal attribution phenomenon prediction model from the attribution phenomenon prediction model base according to the model prediction success rate;
and the attribution factor data contribution calculation module is used for selecting a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model and calculating the contribution of each attribution factor data in the attribution factor data set to the attributed phenomenon data by using the model interpretation algorithm.
In order to solve the above problem, the present invention also provides an electronic device, including:
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 memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the intelligent attribution analysis method as 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 intelligent attribution analysis method as described above.
According to the embodiment of the invention, the standard data set is divided into the attributed phenomenon data set and the corresponding attributed factor data set through the pre-constructed variable library, and the data is divided according to different industry variables, so that the accuracy of the data is improved; importing the attributed phenomenon data set and the corresponding attribution factor data set into a preset attributed phenomenon prediction model library, and calculating the prediction success rate of each attributed phenomenon prediction model; determining an optimal attribution phenomenon prediction model from an attribution phenomenon prediction model library according to the prediction success rate, and selecting the optimal attribution phenomenon prediction model which is most consistent with the data according to different data, so that the accuracy of attribution analysis is further improved; and selecting a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and calculating the contribution degree of each attribution factor data in the attribution factor data set to the attribution phenomenon data by using the model interpretation algorithm to obtain a more accurate attribution analysis result. Therefore, the intelligent attribution analysis method, the intelligent attribution analysis device, the electronic equipment and the computer readable storage medium can solve the problem of low accuracy in attribution analysis.
Drawings
Fig. 1 is a schematic flow chart of an intelligent attribution analysis method according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating standard data set partitioning according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for calculating a model prediction success rate according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart illustrating a process of calculating attribution factor data contribution according to an embodiment of the present invention;
FIG. 5 is a functional block diagram of an intelligent attribute analysis device according to 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 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 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, such as a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the present application. In other words, the smart-attribution analysis method may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. 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 schematic flow chart of an intelligent attribution analysis method according to an embodiment of the present invention is shown. In this embodiment, the intelligent attribution analysis method includes:
s1, acquiring an initial data set, and performing data replacement processing on abnormal values in the initial data set to obtain a standard data set;
in the embodiment of the invention, the initial data set is different business data sets of different industries, such as related data of the internet advertising industry, related data of the insurance industry, related data in an intelligent customer service scene and the like.
In detail, the performing data replacement processing on the abnormal value in the initial data set to obtain a standard data set includes:
calculating a local reachable density ratio between each initial data in the initial data set and neighborhood data of the initial data;
when the local density ratio is smaller than or equal to a preset density ratio threshold, determining that the initial data is an abnormal value;
and performing data replacement processing on the abnormal value by using a preset correct data set to obtain a standard data set.
In an embodiment of the present invention, the local reachable density is an inverse number of an average distance from neighborhood data of each initial data to the initial data, and if the local reachable density ratio is greater than a preset density ratio threshold, it may be determined that the neighboring data and the initial data are in the same cluster and are not an abnormal value.
In detail, in the embodiment of the present invention, the local reachable density ratio of each initial data to neighboring data in the initial data set is calculated by using the following formula:
wherein, LOFk(P) is the local achievable density ratio, Nk(P) is the P-th initial data, P, of the initial data setk(P) is the local achievable density, ρ, of the P-th datak(O) is the ortho-positionAverage local reachable density of the domain data O, dk(P, O) is the distance from the P-th initial data to the neighborhood data O.
In the embodiment of the invention, the abnormal values in the initial data set are subjected to data replacement processing, so that abnormal data in the initial data set are removed, the rationality of the data in the initial data set is ensured, and the accuracy of subsequent model selection is improved.
S2, dividing the standard data set into an attributed phenomenon data set and an attribution factor data set corresponding to the attributed phenomenon data set by utilizing a pre-constructed variable library;
in the embodiment of the invention, the attributed phenomenon data set is an unquantifiable element, the attribution factor data set is a plurality of possible factors influencing the attributed phenomenon data, and the pre-constructed variable library comprises a plurality of attributed phenomenon data determined in advance.
In detail, referring to fig. 2, the dividing the standard dataset into an attributed phenomenon dataset and an attribution factor dataset corresponding to the attributed phenomenon dataset using a pre-constructed variable library includes:
s21, comparing the variable data in the standard data set with a pre-constructed variable library, determining the variable data in the standard data set which are consistent with the variable library as attributed phenomenon data, and determining the variable data in the standard data set which are inconsistent with the variable library as attributed factor data;
s22, calculating the association degree of the attributed phenomenon data and the attribution factor data, and determining the attribution factor data with the association degree larger than a preset threshold value as target attribution factor data corresponding to the attributed phenomenon data;
s23, collecting the attributed phenomenon data and the target attribution factor data to obtain an attributed phenomenon data set and an attribution factor data set corresponding to the attributed phenomenon data set.
Further, in this embodiment of the present invention, the calculating the association degree between the attributed phenomenon data and the attribution factor data includes:
wherein r (X, Y) is the degree of correlation, X is the attributed phenomenon data, Y is the Yth attribution factor data, Cov (X, Y) is the covariance between the attributed phenomenon data and the attribution factor data, σxIs the standard deviation, σ, of the attributed phenomenon datayIs the standard deviation of the attribution factor data.
Specifically, the attributed variables are different in different industry data, so a corresponding pre-constructed variable library is selected according to the specific industry data of the embodiment, and the standard data set is divided into an attributed phenomenon data set and an attribution factor data set according to the pre-constructed variable library.
For example, in the embodiment of the present invention, the attributive variable in the advertisement industry may be advertisement income, the corresponding target attribution factor may be advertisement click rate, user browsing time, and the like, and the attributive variable in the insurance industry may be continuous guarantee rate, and the corresponding target attribution factor may be average guarantee rate of a customer, age composition of the customer, continuous guarantee rate of each application channel, and the like.
S3, importing the attributed phenomenon data set and the attribution factor data set into a preset attributed phenomenon prediction model library, and calculating the prediction success rate of each attributed phenomenon prediction model in the attributed phenomenon prediction model library;
in the 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 attenuation attribution models, bayesian models, XGBoost, linear regression models, fully-connected neural networks, and other models with prediction functions.
In detail, referring to fig. 3, the calculating the prediction success rate of each attribution phenomenon prediction model in the to-be-attributed phenomenon prediction model library includes:
s31, dividing the attributed data set and the attribution factor data set into training samples and testing samples according to a preset proportion;
s32, performing model training on each attribution phenomenon prediction model in the preset attribution phenomenon prediction model library according to the training samples to obtain a plurality of initial prediction models;
s33, performing model prediction on the test sample by using each initial prediction model to obtain test data of each initial test model;
s34, calculating the difference value of the test data of each initial test model and the attributed phenomenon data of the test sample;
and S35, determining the test data with the difference value smaller than a preset threshold value as correct prediction data, and calculating the proportion of the correct prediction data in the test data of each initial test model to obtain the prediction success rate of each attribution phenomenon prediction model.
Specifically, in the embodiment of the present invention, the preset threshold may be set to different thresholds according to different sets of attributed phenomenon data, for example, if the attributed phenomenon data is a persistence rate, the threshold may be 0.01.
S4, determining an optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;
in the embodiment of the invention, an attribution phenomenon prediction model which is most matched with the standard data set, namely an optimal attribution phenomenon prediction model, is selected from the preset attribution phenomenon prediction model base according to the model success rate.
For example, in an actual application scenario of the present invention, the preset attribution phenomenon prediction model library includes a time attenuation attribution model, a bayesian model, an XGBoost, a linear model, a fully-connected neural network, and the like, where a prediction success rate of the time attenuation attribution model is 89%, a prediction success rate of the bayesian model is 85%, a prediction success rate of the XGBoost is 89%, a prediction success rate of the linear model is 78%, and a prediction success rate of the fully-connected neural network is 94%, and the fully-connected neural network is determined to be the optimal attribution phenomenon prediction model.
In the embodiment of the invention, the optimal attribution phenomenon prediction model is determined according to the model prediction success rate, different attribution phenomenon prediction models can be selected according to different industry data, the application range of the different industry data is improved, and the accuracy of the subsequent attribution factor data contribution degree calculation is ensured.
S5, selecting a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and calculating the contribution degree of each attribution factor data in the attribution factor data set to the attributed phenomenon data by using the model interpretation algorithm.
In the embodiment of the present invention, the corresponding model interpretation algorithm is to predict the contribution degree of a variable, that is, an attribution factor, in the optimal attribution phenomenon prediction model, that is, the contribution of each attribution factor data to the prediction result of the optimal attribution phenomenon prediction model.
Specifically, in the embodiment of the present invention, a model interpretation algorithm corresponding to the optimal attributed phenomenon prediction model may be called from a pre-stored model interpretation algorithm library according to the type of the optimal attributed phenomenon prediction model.
In detail, referring to fig. 4, the calculating, by using the model interpretation algorithm, the contribution degree of each attribution factor data in the attribution factor data set to the attributed phenomenon data includes:
s51, 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;
s52, performing data disturbance on the attribution factor data according to the disturbance range to obtain new data of the attribution factors;
s53, training by adopting the new data of each attribution factor based on the model interpretation algorithm to obtain a target linear regression model, and obtaining the weight corresponding to each attribution factor data;
and S54, multiplying the attribution factor data by the corresponding weight to obtain the contribution degree of the attribution factor data.
Further, in the embodiment of the present invention, the training to obtain the target linear regression model by using the new data of each attribution factor based on the model interpretation algorithm includes:
respectively calculating the distance between each attribution factor data and the new data of each attribution factor, and taking the distance value as the weight of the new data of each attribution factor;
the optimal attribution phenomenon model is utilized to carry out attribution phenomenon prediction on the new data of each attribution factor to obtain attribution phenomenon prediction data, and the attribution phenomenon prediction data are used as label data corresponding to the new data of each attribution factor;
and training by adopting the label data and the new data of each attribution factor with the weight based on a preset model interpretation algorithm to obtain a target linear regression model.
For example, in an actual application scenario of the present invention, when the optimal attribution phenomenon prediction model is a fully connected neural network, a deepft algorithm may be invoked to explain the optimal attribution phenomenon prediction model, and when the optimal attribution phenomenon prediction model is an XGBoost model, a sharey Value may be invoked to explain the XGBoost model, so as to obtain the contribution of each attribution factor in the optimal attribution phenomenon prediction model.
In the embodiment of the invention, the contribution degree of each attribution factor data to the attributed phenomenon data is obtained through a model interpretation algorithm, so that the user behavior from which the industry data mainly comes is found, a corresponding coping strategy is made according to the contribution degree of each attribution factor, for example, in insurance industry data, the continuous guarantee rate is found to be reduced, and the contribution degree is calculated by using the intelligent attribution analysis method and mainly caused by the increase of the complaint rate of old customers of a certain insurance channel.
According to the embodiment of the invention, the standard data set is divided into the attributed phenomenon data set and the corresponding attributed factor data set through the pre-constructed variable library, and the data is divided according to different industry variables, so that the accuracy of the data is improved; importing the attributed phenomenon data set and the corresponding attribution factor data set into a preset attributed phenomenon prediction model library, and calculating the prediction success rate of each attributed phenomenon prediction model; determining an optimal attribution phenomenon prediction model from an attribution phenomenon prediction model library according to the prediction success rate, and selecting the optimal attribution phenomenon prediction model which is most consistent with the data according to different data, so that the accuracy of attribution analysis is further improved; and selecting a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and calculating the contribution degree of each attribution factor data in the attribution factor data set to the attribution phenomenon data by using the model interpretation algorithm to obtain a more accurate attribution analysis result. Therefore, the intelligent attribution analysis method provided by the invention can solve the problem of low accuracy in attribution analysis.
Fig. 5 is a functional block diagram of an intelligent attribution analysis device according to an embodiment of the present invention.
The smart attribution analyzing apparatus 100 of the present invention may be installed in an electronic device. According to the realized functions, the intelligent attribution analyzing device 100 may include a standard data set obtaining module 101, a standard data set partitioning module 102, a model prediction success rate calculating module 103, an optimal attribution phenomenon prediction model determining module 104 and an attribution factor data contribution degree calculating module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the standard data set acquisition module 101 is configured to acquire an initial data set, and perform data replacement processing on an abnormal value in the initial data set to obtain a standard data set;
the standard data set dividing module 102 is configured to divide the standard data set into an attributed phenomenon data set and an attribution factor data set corresponding to the attributed phenomenon data set by using a pre-constructed variable library;
the model prediction success rate calculation module 103 is configured to import the attributed phenomenon data set and the attribution factor data set into a preset attributed phenomenon prediction model library, and calculate a prediction success rate of each attributed phenomenon prediction model in the attributed phenomenon prediction model library;
the optimal attribution phenomenon prediction model determining module 104 is 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 calculating module 105 is configured to select a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and calculate the contribution degree of each attribution factor data in the attribution factor data set to the attributed phenomenon data by using the model interpretation algorithm.
In detail, when the modules in the intelligent attribution analysis device 100 according to the embodiment of the present invention are used, the same technical means as the intelligent attribution analysis method described in fig. 1 to 4 are adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 6 is a schematic structural diagram of an electronic device implementing an intelligent attribution analysis method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as an intelligent attribution analysis program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. 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., executing an intelligent attribution analysis program, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, 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 the smart attribute analysis program, etc., but also 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. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 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.
Only electronic devices having components are shown, and those skilled in the art will appreciate that the structures shown in the figures do not constitute limitations on the electronic devices, and may include fewer or more components than shown, or some components in combination, 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.
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 intelligent attribution analysis program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, which when executed in the processor 10, can realize:
acquiring an initial data set, and performing data replacement processing on abnormal values in the initial data set to obtain a standard data set;
dividing the standard dataset into an attributed phenomenon dataset and an attribution factor dataset corresponding to the attributed phenomenon dataset by using a pre-constructed variable library;
importing the attributed phenomenon data set and the attribution factor data set into a preset attributed phenomenon prediction model library, and calculating the prediction success rate of each attributed phenomenon prediction model in the attributed phenomenon prediction model library;
determining an optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;
and selecting a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and calculating the contribution degree of each attribution factor data in the attribution factor data set to the attributed phenomenon data by using the model interpretation algorithm.
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 integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring an initial data set, and performing data replacement processing on abnormal values in the initial data set to obtain a standard data set;
dividing the standard dataset into an attributed phenomenon dataset and an attribution factor dataset corresponding to the attributed phenomenon dataset by using a pre-constructed variable library;
importing the attributed phenomenon data set and the attribution factor data set into a preset attributed phenomenon prediction model library, and calculating the prediction success rate of each attributed phenomenon prediction model in the attributed phenomenon prediction model library;
determining an optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;
and selecting a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and calculating the contribution degree of each attribution factor data in the attribution factor data set to the attributed phenomenon data by using the model interpretation algorithm.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
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.
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 first, 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 smart attribution analysis method, the method comprising:
acquiring an initial data set, and performing data replacement processing on abnormal values in the initial data set to obtain a standard data set;
dividing the standard dataset into an attributed phenomenon dataset and an attribution factor dataset corresponding to the attributed phenomenon dataset by using a pre-constructed variable library;
importing the attributed phenomenon data set and the attribution factor data set into a preset attributed phenomenon prediction model library, and calculating the prediction success rate of each attributed phenomenon prediction model in the attributed phenomenon prediction model library;
determining an optimal attribution phenomenon prediction model from the attribution phenomenon prediction model library according to the model prediction success rate;
and selecting a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model, and calculating the contribution degree of each attribution factor data in the attribution factor data set to the attributed phenomenon data by using the model interpretation algorithm.
2. The intelligent attribution analysis method of claim 1, wherein the performing of data replacement processing on the outliers in the initial data set to obtain a standard data set comprises:
calculating a local reachable density ratio between each initial data in the initial data set and neighborhood data of the initial data;
when the local density ratio is smaller than or equal to a preset density ratio threshold, determining the initial data to be an abnormal value;
and performing data replacement processing on the abnormal value by using a preset correct data set to obtain a standard data set.
3. The intelligent attribution analysis method of claim 1, wherein the utilizing a pre-built variable library to divide the standard dataset into an attributed phenomenon dataset and an attribution factor dataset corresponding to the attributed phenomenon dataset comprises:
comparing the variable data in the standard data set with a pre-constructed variable database, determining the variable data in the standard data set which is consistent with the variable database as attributed phenomenon data, and determining the variable data in the standard data set which is inconsistent with the variable database as attributed factor data;
calculating a degree of association of the attributed phenomenon data and the attribution factor data, and determining attribution factor data with the degree of association larger than a preset threshold as target attribution factor data corresponding to the attributed phenomenon data;
and aggregating the attributed phenomenon data and the target attribution factor data to obtain an attributed phenomenon data set and an attribution factor data set corresponding to the attributed phenomenon data set.
4. The intelligent attribution analysis method according to claim 3, wherein the calculating the degree of correlation of the attributed phenomenon data with the attribution factor data comprises:
wherein r (X, Y) is the degree of correlation, X is the attributed phenomenon data, Y is the Yth attribution factor data, Cov (X, Y) is the covariance between the attributed phenomenon data and the attribution factor data, σxIs the standard deviation, σ, of the attributed phenomenon datayIs the standard deviation of the attribution factor data.
5. The intelligent attribution analysis method according to claim 1, wherein the calculating the prediction success rate of each attribution phenomenon prediction model in the to-be-attributed phenomenon prediction model library comprises:
dividing the attributed data set and the attribution factor data set into a training sample and a testing sample according to a preset proportion;
performing model training on each attribution phenomenon prediction model in the preset attribution phenomenon prediction model library according to the training samples to obtain a plurality of initial prediction models;
performing model prediction on the test sample by using each initial prediction model to obtain test data of each initial test model;
performing a difference calculation on the test data of each initial test model and the attributed phenomenon data of the test sample;
and determining the test data with the difference value smaller than a preset threshold value as correct prediction data, and calculating the proportion of the correct prediction data in the test data of each initial test model to obtain the prediction success rate of each attribution phenomenon prediction model.
6. The intelligent attribution analysis method of claim 1, wherein the calculating, with the model interpretation algorithm, the contribution of each attribution factor data in the attribution factor data set to the attributed phenomenon data comprises:
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;
performing data disturbance on the attribution factor data according to the disturbance range to obtain new data of the attribution factors;
training by adopting the new data of each attribution factor based on the model interpretation algorithm to obtain a target linear regression model, and obtaining the weight corresponding to each attribution factor data;
and multiplying each attribution factor data by the corresponding weight to obtain the contribution degree of each attribution factor data.
7. The intelligent attribution analysis method according to any one of claims 1 to 6, wherein the training with the new data of each attribution factor based on the model interpretation algorithm to obtain a target linear regression model comprises:
respectively calculating the distance between each attribution factor data and the new data of each attribution factor, and taking the distance value as the weight of the new data of each attribution factor;
the optimal attribution phenomenon model is utilized to carry out attribution phenomenon prediction on the new data of each attribution factor to obtain attribution phenomenon prediction data, and the attribution phenomenon prediction data are used as label data corresponding to the new data of each attribution factor;
and training by adopting the label data and the new data of each attribution factor with the weight based on a preset model interpretation algorithm to obtain a target linear regression model.
8. An intelligent attribution analysis device, the device comprising:
the standard data set acquisition module is used for acquiring an initial data set and performing data replacement processing on abnormal values in the initial data set to obtain a standard data set;
a standard data set dividing module for dividing the standard data set into an attributed phenomenon data set and an attribution factor data set corresponding to the attributed phenomenon data set by using a pre-constructed variable library;
the model prediction success rate calculation module is used for importing the attributed phenomenon data set and the attribution factor data set into a preset attributed phenomenon prediction model library and calculating the prediction success rate of each attributed phenomenon prediction model in the attributed phenomenon prediction model library;
the optimal attribution phenomenon prediction model determining module is used for determining an optimal attribution phenomenon prediction model from the attribution phenomenon prediction model base according to the model prediction success rate;
and the attribution factor data contribution calculation module is used for selecting a corresponding model interpretation algorithm according to the type of the optimal attribution phenomenon prediction model and calculating the contribution of each attribution factor data in the attribution factor data set to the attributed phenomenon data by using the model interpretation algorithm.
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 memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the intelligent attribution analysis method of any one 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 intelligent attribution analysis method according to any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115099540A (en) * | 2022-08-25 | 2022-09-23 | 中国工业互联网研究院 | Carbon neutralization treatment method based on artificial intelligence |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112215696A (en) * | 2020-09-28 | 2021-01-12 | 北京大学 | Personal credit evaluation and interpretation method, device, equipment and storage medium based on time sequence attribution analysis |
| CN113327136A (en) * | 2021-06-23 | 2021-08-31 | 中国平安财产保险股份有限公司 | Attribution analysis method and device, electronic equipment and storage medium |
| CN113469519A (en) * | 2021-06-29 | 2021-10-01 | 平安银行股份有限公司 | Attribution analysis method and device of business event, electronic equipment and storage medium |
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112215696A (en) * | 2020-09-28 | 2021-01-12 | 北京大学 | Personal credit evaluation and interpretation method, device, equipment and storage medium based on time sequence attribution analysis |
| CN113327136A (en) * | 2021-06-23 | 2021-08-31 | 中国平安财产保险股份有限公司 | Attribution analysis method and device, electronic equipment and storage medium |
| CN113469519A (en) * | 2021-06-29 | 2021-10-01 | 平安银行股份有限公司 | Attribution analysis method and device of business event, electronic equipment and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115099540A (en) * | 2022-08-25 | 2022-09-23 | 中国工业互联网研究院 | Carbon neutralization treatment method based on artificial intelligence |
| CN115099540B (en) * | 2022-08-25 | 2022-11-08 | 中国工业互联网研究院 | Carbon neutralization treatment method based on artificial intelligence |
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