CN117034114A - Data prediction method, device, equipment and storage medium based on artificial intelligence - Google Patents
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Abstract
The application belongs to the field of artificial intelligence and the field of financial science and technology, and relates to a data prediction method based on artificial intelligence, which comprises the following steps: collecting use data generated by a target user when using a target product; performing sample construction on the use based on the tag class data of the use data to obtain a use data sample; generating a virtual sample using the sample data based on the kernel probability density estimation strategy; generating a target sample based on the usage data sample and the virtual sample; training the initial classification model based on the target sample to obtain a target prediction model; and generating a product feedback result of the usage data based on the target prediction model. The application also provides a data prediction device, computer equipment and a storage medium based on the artificial intelligence. In addition, the present application relates to blockchain techniques in which virtual samples may be stored. The method and the device can be applied to the product feedback generation scene in the financial field, and the accuracy of the generated product feedback result is ensured based on the use of the target prediction model.
Description
Technical Field
The application relates to the technical field of artificial intelligence development and the technical field of finance, in particular to a data prediction method, a data prediction device, computer equipment and a storage medium based on artificial intelligence.
Background
With the rapid growth of business demands, financial and technological companies, such as insurance companies, banks, etc., are constantly developing new products that need to be released or promoted. In the life cycle of gradual evolution of traditional products, the update and release of the products are indispensable links, and the products provide related services for business users after being online. Gray level verification is needed in the process of releasing a new product to the outside or popularizing the product, basically, a mode of firstly small-scale or local test points and then all popularizing is adopted, product use feedback data of a user of the local test points are obtained, and then the service system uses built-in effect discrimination rules to analyze the product use feedback data of the user of the local test points so as to obtain a feedback result of the new product. Because the quantity of the feedback data used by the user's product generated by the local test points is small, the reliability and accuracy of the feedback result of the generated new product are low.
Disclosure of Invention
The embodiment of the application aims to provide a data prediction method, a device, computer equipment and a storage medium based on artificial intelligence, which are used for solving the technical problems that the reliability and the accuracy of the feedback result of a generated new product are lower due to the smaller quantity of the feedback data of the product use of the user generated by the local test point in a mode that the feedback data of the product use of the user of the local test point is analyzed by using a built-in effect discrimination rule to obtain the feedback result of the new product by the existing service system.
In order to solve the above technical problems, the embodiment of the present application provides an artificial intelligence based data prediction method, which adopts the following technical scheme:
acquiring use data generated by a target user when using a target product based on a preset buried point mode; the target product is a product for gray verification;
acquiring a tag class of the usage data, and constructing a sample of the usage data based on the tag class to obtain a corresponding usage data sample;
performing sample generation processing on the use data samples based on a preset kernel probability density estimation strategy to obtain virtual samples corresponding to the use sample data;
Generating a target sample based on the usage data sample and the virtual sample;
training a preset initial classification model based on the target sample to obtain a trained target prediction model;
and carrying out prediction processing on the use data based on the target prediction model to obtain a product feedback result corresponding to the target user.
Further, the step of constructing the sample of the usage data based on the tag class to obtain a corresponding usage data sample specifically includes:
acquiring the number of first use data with the tag class of the use data being negatively fed back;
screening second use data with the label category of positive feedback from the use data;
screening out third usage data which is the same as the number from the second usage data;
the usage data sample is constructed based on the first usage data and the third usage data.
Further, the step of predicting the usage data based on the target prediction model to obtain a product feedback result corresponding to the target user specifically includes:
classifying the specified use data of the specified user through the target prediction model to generate posterior probability of the specified use number relative to various classification results;
Acquiring a specified posterior probability with the maximum numerical value from all the posterior probabilities;
acquiring a specified classification result corresponding to the specified posterior probability;
and taking the specified classification result as a product feedback result corresponding to the specified user.
Further, before the step of collecting the usage data generated by the target user when using the target product based on the preset buried point mode, the method further includes:
obtaining a product code corresponding to the target product;
acquiring a preset buried point code;
the embedded point code is added to the product code.
Further, before the step of collecting the usage data generated by the target user when using the target product based on the preset buried point mode, the method further includes:
acquiring using frequency data of a historical version product corresponding to the target product;
analyzing the frequency data, and screening target frequency data which accords with preset use conditions from the frequency data;
determining a specific user corresponding to the target frequency of use data;
the specific user is taken as the target user.
Further, after the step of predicting the usage data based on the target prediction model to obtain a product feedback result corresponding to the target user, the method further includes:
Carrying out statistical analysis on the feedback result of the product to obtain a corresponding analysis result;
acquiring a target adjustment strategy corresponding to the analysis result from a preset strategy library;
adjusting the target product based on the target adjustment strategy to obtain an adjusted target product;
and storing the adjusted target product.
Further, after the step of adjusting the target product based on the target adjustment policy to obtain an adjusted target product, the method further includes:
judging whether the adjusted target product accords with a preset expected effect or not;
if yes, acquiring a release strategy corresponding to the adjusted target product;
and carrying out release processing on the adjusted target product based on the release strategy.
In order to solve the technical problems, the embodiment of the application also provides a data prediction device based on artificial intelligence, which adopts the following technical scheme:
the acquisition module is used for acquiring the use data generated by a target user when using a target product based on a preset buried point mode; the target product is a product for gray verification;
the construction module is used for acquiring the label category of the use data and carrying out sample construction on the use data based on the label category to obtain a corresponding use data sample;
The first acquisition module is used for generating and processing the use data samples based on a preset kernel probability density estimation strategy to obtain virtual samples corresponding to the use sample data;
a generation module for generating a target sample based on the usage data sample and the virtual sample;
the training module is used for training a preset initial classification model based on the target sample to obtain a trained target prediction model;
and the prediction module is used for predicting the use data based on the target prediction model to obtain a product feedback result corresponding to the target user.
In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:
acquiring use data generated by a target user when using a target product based on a preset buried point mode; the target product is a product for gray verification;
acquiring a tag class of the usage data, and constructing a sample of the usage data based on the tag class to obtain a corresponding usage data sample;
performing sample generation processing on the use data samples based on a preset kernel probability density estimation strategy to obtain virtual samples corresponding to the use sample data;
Generating a target sample based on the usage data sample and the virtual sample;
training a preset initial classification model based on the target sample to obtain a trained target prediction model;
and carrying out prediction processing on the use data based on the target prediction model to obtain a product feedback result corresponding to the target user.
In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:
acquiring use data generated by a target user when using a target product based on a preset buried point mode; the target product is a product for gray verification;
acquiring a tag class of the usage data, and constructing a sample of the usage data based on the tag class to obtain a corresponding usage data sample;
performing sample generation processing on the use data samples based on a preset kernel probability density estimation strategy to obtain virtual samples corresponding to the use sample data;
generating a target sample based on the usage data sample and the virtual sample;
training a preset initial classification model based on the target sample to obtain a trained target prediction model;
And carrying out prediction processing on the use data based on the target prediction model to obtain a product feedback result corresponding to the target user.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
firstly, acquiring use data generated by a target user when using a target product based on a preset buried point mode; then, acquiring a label class of the usage data, and constructing a sample of the usage data based on the label class to obtain a corresponding usage data sample; then, carrying out sample generation processing on the use data samples based on a preset kernel probability density estimation strategy to obtain virtual samples corresponding to the use sample data; generating a target sample based on the usage data sample and the virtual sample; training a preset initial classification model based on the target sample to obtain a trained target prediction model; and finally, carrying out prediction processing on the use data based on the target prediction model to obtain a product feedback result corresponding to the target user. After the usage data generated by a target user when using a target product is acquired, the usage data is firstly subjected to sample construction according to the label type of the usage data to obtain the usage data sample, and then the usage data sample is intelligently processed by using a preset kernel probability density estimation strategy to obtain a virtual sample, so that the expansion of the usage data sample according to the automatically generated virtual sample is realized to obtain the target sample. And the target prediction model obtained by training the target sample is used for predicting the use data, so that a product feedback result corresponding to the target user can be quickly and accurately generated, the accuracy of the generated product feedback result is ensured, and the reliability of the generated product feedback result is improved.
Drawings
In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of an artificial intelligence based data prediction method in accordance with the present application;
FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based data prediction device in accordance with the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the data prediction method based on artificial intelligence provided by the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the data prediction device based on artificial intelligence is generally disposed in the server/terminal device.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow chart of one embodiment of an artificial intelligence based data prediction method in accordance with the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The data prediction method based on the artificial intelligence provided by the embodiment of the application can be applied to any scene needing product feedback prediction, and can be applied to products of the scenes, for example, the product feedback prediction in the field of financial insurance. The data prediction method based on artificial intelligence comprises the following steps:
Step S201, collecting using data generated by a target user when using a target product based on a preset buried point mode; the target product is a product for gray verification.
In this embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the data prediction method based on artificial intelligence operates may acquire the usage data generated when the target user uses the target product through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. Specifically, in the business scenario of product gray verification of financial insurance, the target product may refer to a system product or a business product; the system products may include insurance systems, banking systems, transaction systems, order systems, and the like. Business products may include insurance products, investment products, securities trading products, and so forth. The buried point method may be a data acquisition method based on a buried point code. The use data is feedback information of the product use effect input by the target user when the target product is used.
Step S202, obtaining the label category of the usage data, and constructing a sample of the usage data based on the label category to obtain a corresponding usage data sample.
In this embodiment, the tag data of the usage data may be a tag type generated by a business person analyzing the usage data. The tag categories may include negative feedback and positive feedback. The specific implementation process of performing sample construction on the usage data based on the tag class to obtain the corresponding usage data sample will be described in further detail in the following specific embodiments, which will not be described herein.
Step S203, performing sample generation processing on the usage data samples based on a preset kernel probability density estimation policy, to obtain virtual samples corresponding to the usage sample data.
In this embodiment, the above-mentioned kernel probability density estimation strategy is a strategy for performing sample supplementation using kernel probability density estimation. The assumption of the kernel probability density estimation is that if a certain data sample is present in the observation, the probability density of the data sample is large and the probability density of the sample closer to the data sample is also large, while the probability density of the sample farther from the data sample is small. For data containing n samples, the kernel density estimation function f h (x) As shown in formula 1.1, wherein h>0 is a smoothing parameter, called bandwidth,sigma is the standard deviation of sample x, K h (.) is a scaling kernel function, specifically a gaussian kernel function as shown in equation 1.2:
based on the probability distribution f h (x) Virtual samples can be generated for any sample x in the sample data according to equation 1.3, where r x Is an inertia factor, can rapidly calculate a virtual sample, and Y satisfies the following conditions For sample covariance:
x new ＝x+r x *hY 1.3
r x ＝r max -(r max -r min )*(1+x) -1 1.4
wherein r is max 、r min Is the maximum and minimum of the inertia factor, so that the coincidence nuclear density estimation function f can be rapidly calculated according to the use data samples h (x) Is a virtual sample of (a). The problem of insufficient small sample data sets can be well solved by expanding the sample data by using a mode based on the kernel probability density estimation.
Step S204, generating a target sample based on the usage data sample and the virtual sample.
In this embodiment, the usage data samples and the virtual samples may be integrated to generate corresponding target samples. Wherein the target samples include the usage data samples and the virtual samples.
Step S205, training a preset initial classification model based on the target sample to obtain a trained target prediction model.
In this embodiment, the initial classification model is specifically a naive bayes model. The Naive Bayes Classification (NBC) is a method based on bayes theorem and assuming mutual independence between feature conditions, first, a given training set is used to learn joint probability distribution from input to output on the premise of independence between feature words, and then, based on the learned model, input X is used to calculate output Y that maximizes the posterior probability. The training generation process of the target prediction model may refer to the training generation process of the naive bayes model, which is not described herein.
And S206, carrying out prediction processing on the use data based on the target prediction model to obtain a product feedback result corresponding to the target user.
In this embodiment, the specific implementation process of predicting the usage data based on the target prediction model to obtain the product feedback result corresponding to the target user will be described in further detail in the following specific embodiments, which will not be described herein.
Firstly, acquiring use data generated by a target user when using a target product based on a preset buried point mode; then, acquiring a label class of the usage data, and constructing a sample of the usage data based on the label class to obtain a corresponding usage data sample; then, carrying out sample generation processing on the use data samples based on a preset kernel probability density estimation strategy to obtain virtual samples corresponding to the use sample data; generating a target sample based on the usage data sample and the virtual sample; training a preset initial classification model based on the target sample to obtain a trained target prediction model; and finally, carrying out prediction processing on the use data based on the target prediction model to obtain a product feedback result corresponding to the target user. After the usage data generated by a target user when using a target product is acquired, the usage data is firstly subjected to sample construction according to the label type of the usage data to obtain the usage data sample, and then the usage data sample is intelligently processed by using a preset kernel probability density estimation strategy to obtain a virtual sample, so that the expansion of the usage data sample according to the automatically generated virtual sample is realized to obtain the target sample. And the target prediction model obtained by training the target sample is used for predicting the use data, so that a product feedback result corresponding to the target user can be quickly and accurately generated, the accuracy of the generated product feedback result is ensured, and the reliability of the generated product feedback result is improved.
In some alternative implementations, step S202 includes the steps of:
and acquiring the number of the first use data with the tag class negatively fed back in the use data.
In this embodiment, the tag data of the usage data may be a tag type generated by a business person analyzing the usage data. The tag categories may include negative feedback and positive feedback. The usage data of the negative feedback represents that the real feedback of the user to the target product is negative feedback, and the usage data of the negative feedback represents a negative sample. The usage data of positive feedback represents that the actual feedback of the user to the target product is positive feedback, and the usage data of positive feedback represents a positive sample.
And screening second use data with the label category of positive feedback from the use data.
And screening out third usage data which is the same as the number from the second usage data.
In this embodiment, a plurality of usage data equal to the number may be selected from the second usage data by adopting a random screening manner as the third usage data.
The usage data sample is constructed based on the first usage data and the third usage data.
In this embodiment, the first usage data and the third usage data may be integrated to construct a usage data sample including the first usage data and the third usage data. The constructed using data samples are balanced data sets containing positive samples and negative samples with the same quantity, so that the distribution condition of sample data of different categories in the using data samples can be effectively ensured. The method is beneficial to training and obtaining the target prediction model by using the data sample, and can effectively improve the classification effect and generalization capability of the target prediction model.
The method comprises the steps of obtaining the number of first use data with the label category of negative feedback in the use data; then screening second use data with the label category of positive feedback from the use data; then screening out third usage data which is the same as the number from the second usage data; the usage data sample is then constructed based on the first usage data and the third usage data. According to the application, the intelligent screening processing is carried out on the usage data, so that the first usage data and the third usage data with the same quantity are screened from the usage data to construct the final usage data sample, and the balanced distribution condition of different types of sample data in the usage data sample can be effectively ensured. The method is beneficial to training and obtaining the target prediction model by using the data sample, and can effectively improve the classification effect and generalization capability of the target prediction model.
In some alternative implementations of the present embodiment, step S206 includes the steps of:
and classifying the specified use data of the specified user through the target prediction model, and generating posterior probabilities of the specified use number relative to various classification results.
In this embodiment, the specified user is any one of all the target users. Specifically, assume that there is a sample data set d= { D 1 ，d 2 ，...，d n Characteristic attribute set of corresponding sample data is x= { X } 1 ，x 2 ，...x n The class variable is y= { Y } 1 ，y 2 ，...y n "D can be divided into y n Category. Wherein x is 1 ，x 2 ，...x n Independent and random, the prior probability P of Y prior Posterior probability P of Y =p (Y) post =p (y|x), which is available by a naive bayes algorithm, the posterior probability can be obtained by the prior probability P prior The = P (Y), evidence P (X), class conditional probability P (x|y) is calculated:the naive bayes are based on the independence of each feature, and given that the class is y, the above formula 1 can be further expressed as the following formula: />The posterior probability can be calculated from the above two formulas as: />Since the size of P (X) is fixed, the molecular moiety of the above formula may be compared when comparing the posterior probability. Thus, one sample data belonging to the class y i Naive bayes computation of (c): />The content of the classification result is not specifically limited, and may be set according to actual service requirements, for example, may include the results of bug existence, yi Kadu, normal operation, and the like. The naive Bayes classification result close to the real feedback of the user can be accurately obtained by performing prediction processing on the usage data by using a target prediction model obtained by training the naive Bayes model through a target sample.
And acquiring the specified posterior probability with the maximum numerical value from all the posterior probabilities.
In this embodiment, the specified posterior probability with the largest value can be obtained from all the posterior probabilities by comparing the values of all the posterior probabilities.
And acquiring a specified classification result corresponding to the specified posterior probability.
And taking the specified classification result as a product feedback result corresponding to the specified user.
Classifying the specified use data of a specified user through the target prediction model to generate posterior probability of the specified use number relative to various classification results; then acquiring the specified posterior probability with the maximum numerical value from all the posterior probabilities; then acquiring a specified classification result corresponding to the specified posterior probability; and taking the specified classification result as a product feedback result corresponding to the specified user. According to the application, when the specified use data of the specified user is input into the target prediction model, the specified classification result corresponding to the specified posterior probability with the highest numerical value output by the target prediction model is intelligently used as the product feedback result corresponding to the specified user, so that the generation efficiency of the product feedback result is improved, and the accuracy of the generated product feedback result is ensured.
In some alternative implementations, before step S201, the electronic device may further perform the following steps:
and obtaining a product code corresponding to the target product.
In this embodiment, the product code corresponding to the target product may be queried from the product information base by querying the product information base. The product information base is a pre-constructed database storing product codes of a plurality of products. The product code is code data of a product which is written and generated by a product developer in the process of developing the product.
And acquiring a preset embedded point code.
In this embodiment, the embedded point code is code data written and generated according to a service function for collecting usage data generated by a user during the usage of a product.
The embedded point code is added to the product code.
In this embodiment, the embedded point code is added to the product code, so as to provide a function of embedded point acquisition for product usage data of a target product.
The application obtains the product code corresponding to the target product; then acquiring a preset buried point code; the embedded point code is then added to the product code. The embedded point code is added into the product code of the target product based on the embedded point code, so that the function of collecting the use data generated by the target user when the target product is used can be realized later, and the acquisition efficiency and the acquisition intelligence of the use data of the target product are improved.
In some alternative implementations, before step S201, the electronic device may further perform the following steps:
and acquiring the use frequency data of the historical version product corresponding to the target product.
In this embodiment, the product database may be queried to find the usage frequency data of the historical version product corresponding to the target product from the product database. The usage frequency data refers to the frequency data of the user usage history version product. The product database is pre-constructed and stores use frequency data generated by a user in the use process of the product.
Analyzing the frequency data, and screening target frequency data which accords with preset use conditions from the frequency data.
In this embodiment, the target usage frequency data meeting the preset usage condition refers to usage frequency data greater than a preset frequency threshold. The value of the preset frequency threshold is not specifically limited, and may be set according to actual use requirements. Specifically, the preset frequency threshold may be set according to average usage data of the products corresponding to the frequent usage population of the products.
A particular user corresponding to the target frequency of use data is determined.
In this embodiment, the user information corresponding to the target usage frequency data may be obtained, so that the matched specific user may be determined based on the user information.
The specific user is taken as the target user.
The method comprises the steps of obtaining using frequency data of a historical version product corresponding to the target product; then analyzing the frequency data, and screening target frequency data which accords with preset use conditions from the frequency data; and subsequently determining a specific user corresponding to the target frequency data, and taking the specific user as the target user. According to the method, the use frequency data of the historical version product corresponding to the target product is analyzed, so that the target use frequency data which accords with the preset use condition is screened out from the use frequency data, and then the users corresponding to the target use frequency data are used as target users for acquiring the use data in a group. The target user is a frequent use crowd which is intelligently screened out according to the use frequency data of the historical version product corresponding to the target product and is matched with the target product, so that the use data generated by the target user when the target product is used is predicted by using the target prediction model, a persuasive product feedback result for the target product is generated, and the effectiveness and the accuracy of the generated product feedback result can be effectively ensured.
In some optional implementations of this embodiment, after step S206, the electronic device may further perform the following steps:
and carrying out statistical analysis on the feedback result of the product to obtain a corresponding analysis result.
In this embodiment, statistical analysis is performed on the product feedback results to generate an analysis result including feedback results of all the categories existing in the product feedback results.
And acquiring a target adjustment strategy corresponding to the analysis result from a preset strategy library.
In this embodiment, the policy repository is a product adjustment policy created in advance according to actual product adjustment requirements and stored with feedback results corresponding to various types. The product feedback result and all feedback results contained in the strategy library can be matched to obtain a successfully matched appointed feedback result, and then an adjustment strategy corresponding to the appointed feedback result is extracted from the strategy library to obtain the target adjustment strategy.
And adjusting the target product based on the target adjustment strategy to obtain an adjusted target product.
In this embodiment, the target adjustment policy may be executed to adjust the target product by executing the adjustment step included in the target adjustment policy, so as to obtain an adjusted target product. The target prediction model predicts the use data, the obtained real product feedback result corresponding to the target user is self-adaptively adjusted to new target products, and the expected effect of the ideal target products can be approximately obtained after continuous training and continuous adjustment.
And storing the adjusted target product.
In this embodiment, the storage manner of the adjusted target product is not limited, and may be set according to actual use requirements, for example, a manner of blockchain storage, cloud disk storage, local database storage, and the like may be adopted.
According to the application, the corresponding analysis result is obtained by carrying out statistical analysis on the feedback result of the product; then, a target adjustment strategy corresponding to the analysis result is obtained from a preset strategy library; then adjusting the target product based on the target adjustment strategy to obtain an adjusted target product; and storing the adjusted target product. According to the application, after the product feedback result is subjected to statistical analysis to obtain an analysis result, a target adjustment strategy corresponding to the analysis result is queried based on the use of the strategy library, and then the target product is automatically and intelligently adjusted according to the target adjustment strategy, so that a final target product meeting the requirements and expected effects of a user is obtained, the product efficiency of the generated adjusted target product is guaranteed, and the use experience of related users can be effectively improved by promoting and releasing the adjusted target product.
In some optional implementations of this embodiment, after the step of adjusting the target product based on the target adjustment policy to obtain an adjusted target product, the electronic device may further execute the following steps:
and judging whether the adjusted target product accords with a preset expected effect or not.
In this embodiment, the desired effect may be generated according to the actual product business effect requirement.
If yes, acquiring a release strategy corresponding to the adjusted target product.
In this embodiment, for different types of products, a release policy corresponding to the product is constructed in advance according to the characteristics of the product. The release policy may include information such as release time, release mode, release platform, etc.
And carrying out release processing on the adjusted target product based on the release strategy.
In this embodiment, the release policy is used to release the adjusted target product, so that the release standardization of the target product can be ensured, and the related user can use the adjusted target product after the adjusted target product is released.
Judging whether the adjusted target product meets a preset expected effect or not; if yes, acquiring a release strategy corresponding to the adjusted target product; and then carrying out release processing on the adjusted target product based on the release strategy. After the adjusted target product is detected to meet the preset expected effect, the method intelligently performs release processing on the adjusted target product according to the release strategy corresponding to the acquired adjusted target product, so that the release standardization of the adjusted target product can be ensured.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
It is emphasized that to further guarantee the privacy and security of the virtual samples, the virtual samples may also be stored in nodes of a blockchain.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an artificial intelligence-based data prediction apparatus, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 3, the artificial intelligence based data prediction apparatus 300 according to the present embodiment includes: the system comprises an acquisition module 301, a construction module 302, a first acquisition module 303, a generation module 304, a training module 305 and a prediction module 306. Wherein:
the acquisition module 301 is configured to acquire usage data generated when a target user uses a target product based on a preset buried point mode; the target product is a product for gray verification;
the construction module 302 is configured to obtain a tag class of the usage data, and construct a sample of the usage data based on the tag class to obtain a corresponding usage data sample;
the first obtaining module 303 is configured to perform sample generation processing on the usage data sample based on a preset kernel probability density estimation policy, so as to obtain a virtual sample corresponding to the usage sample data;
a generating module 304, configured to generate a target sample based on the usage data sample and the virtual sample;
The training module 305 is configured to train a preset initial classification model based on the target sample, so as to obtain a trained target prediction model;
and the prediction module 306 is configured to perform prediction processing on the usage data based on the target prediction model, so as to obtain a product feedback result corresponding to the target user.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data prediction method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some alternative implementations of the present embodiment, the building block 302 includes:
the first acquisition sub-module is used for acquiring the quantity of the first use data with the tag class of the use data being negatively fed back;
the first screening submodule is used for screening second use data with the label category of positive feedback from the use data;
a second screening sub-module, configured to screen third usage data with the same number from the second usage data;
and the construction sub-module is used for constructing and obtaining the use data sample based on the first use data and the third use data.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data prediction method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some alternative implementations of the present embodiment, the prediction module 306 includes:
the generation sub-module is used for classifying the specified use data of the specified user through the target prediction model, and generating posterior probabilities of the specified use number relative to various classification results;
the second acquisition sub-module is used for acquiring the specified posterior probability with the maximum value from all the posterior probabilities;
the third acquisition sub-module is used for acquiring a specified classification result corresponding to the specified posterior probability;
and the determining submodule is used for taking the specified classification result as a product feedback result corresponding to the specified user.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data prediction method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some optional implementations of this embodiment, the artificial intelligence based data prediction apparatus further includes:
the second acquisition module is used for acquiring a product code corresponding to the target product;
the third acquisition module is used for acquiring a preset buried point code;
and the adding module is used for adding the embedded point code into the product code.
In some optional implementations of this embodiment, the artificial intelligence based data prediction apparatus further includes:
a fourth obtaining module, configured to obtain usage frequency data of a historical version product corresponding to the target product;
the screening module is used for analyzing the using frequency data and screening target using frequency data which accords with preset using conditions from the using frequency data;
the first determining module is used for determining a specific user corresponding to the target frequency of use data;
and the second determining module is used for taking the specific user as the target user.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data prediction method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some optional implementations of this embodiment, the artificial intelligence based data prediction apparatus further includes:
the analysis module is used for carrying out statistical analysis on the feedback result of the product to obtain a corresponding analysis result;
a fifth obtaining module, configured to obtain a target adjustment policy corresponding to the analysis result from a preset policy library;
the adjusting module is used for adjusting the target product based on the target adjusting strategy to obtain an adjusted target product;
And the storage module is used for storing the adjusted target product.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data prediction method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some optional implementations of this embodiment, the artificial intelligence based data prediction apparatus further includes:
the judging module is used for judging whether the adjusted target product accords with a preset expected effect or not;
a sixth obtaining module, configured to obtain, if yes, a release policy corresponding to the adjusted target product;
and the release module is used for carrying out release processing on the adjusted target product based on the release strategy.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data prediction method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of an artificial intelligence-based data prediction method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as executing computer readable instructions of the artificial intelligence based data prediction method.
The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, firstly, the use data generated when a target user uses a target product is collected based on a preset buried point mode; then, acquiring a label class of the usage data, and constructing a sample of the usage data based on the label class to obtain a corresponding usage data sample; then, carrying out sample generation processing on the use data samples based on a preset kernel probability density estimation strategy to obtain virtual samples corresponding to the use sample data; generating a target sample based on the usage data sample and the virtual sample; training a preset initial classification model based on the target sample to obtain a trained target prediction model; and finally, carrying out prediction processing on the use data based on the target prediction model to obtain a product feedback result corresponding to the target user. After the usage data generated by a target user when using a target product is acquired, the usage data is firstly subjected to sample construction according to the label type of the usage data to obtain the usage data sample, and then the usage data sample is intelligently processed by using a preset kernel probability density estimation strategy to obtain a virtual sample, so that the expansion of the usage data sample according to the automatically generated virtual sample is realized to obtain the target sample. And the target prediction model obtained by training the target sample is used for predicting the use data, so that a product feedback result corresponding to the target user can be quickly and accurately generated, the accuracy of the generated product feedback result is ensured, and the reliability of the generated product feedback result is improved.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the artificial intelligence-based data prediction method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, firstly, the use data generated when a target user uses a target product is collected based on a preset buried point mode; then, acquiring a label class of the usage data, and constructing a sample of the usage data based on the label class to obtain a corresponding usage data sample; then, carrying out sample generation processing on the use data samples based on a preset kernel probability density estimation strategy to obtain virtual samples corresponding to the use sample data; generating a target sample based on the usage data sample and the virtual sample; training a preset initial classification model based on the target sample to obtain a trained target prediction model; and finally, carrying out prediction processing on the use data based on the target prediction model to obtain a product feedback result corresponding to the target user. After the usage data generated by a target user when using a target product is acquired, the usage data is firstly subjected to sample construction according to the label type of the usage data to obtain the usage data sample, and then the usage data sample is intelligently processed by using a preset kernel probability density estimation strategy to obtain a virtual sample, so that the expansion of the usage data sample according to the automatically generated virtual sample is realized to obtain the target sample. And the target prediction model obtained by training the target sample is used for predicting the use data, so that a product feedback result corresponding to the target user can be quickly and accurately generated, the accuracy of the generated product feedback result is ensured, and the reliability of the generated product feedback result is improved.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.
Claims (10)
1. An artificial intelligence-based data prediction method is characterized by comprising the following steps:
acquiring use data generated by a target user when using a target product based on a preset buried point mode; the target product is a product for gray verification;
acquiring a tag class of the usage data, and constructing a sample of the usage data based on the tag class to obtain a corresponding usage data sample;
performing sample generation processing on the use data samples based on a preset kernel probability density estimation strategy to obtain virtual samples corresponding to the use sample data;
generating a target sample based on the usage data sample and the virtual sample;
training a preset initial classification model based on the target sample to obtain a trained target prediction model;
and carrying out prediction processing on the use data based on the target prediction model to obtain a product feedback result corresponding to the target user.
2. The method for predicting data based on artificial intelligence according to claim 1, wherein the step of constructing the sample of the usage data based on the tag class to obtain a corresponding usage data sample specifically comprises:
Acquiring the number of first use data with the tag class of the use data being negatively fed back;
screening second use data with the label category of positive feedback from the use data;
screening out third usage data which is the same as the number from the second usage data;
the usage data sample is constructed based on the first usage data and the third usage data.
3. The method for predicting data based on artificial intelligence according to claim 1, wherein the step of predicting the usage data based on the target prediction model to obtain a product feedback result corresponding to the target user specifically comprises:
classifying the specified use data of the specified user through the target prediction model to generate posterior probability of the specified use number relative to various classification results;
acquiring a specified posterior probability with the maximum numerical value from all the posterior probabilities;
acquiring a specified classification result corresponding to the specified posterior probability;
and taking the specified classification result as a product feedback result corresponding to the specified user.
4. The artificial intelligence based data prediction method according to claim 1, further comprising, before the step of collecting usage data generated by the target user when using the target product based on the preset buried point mode:
Obtaining a product code corresponding to the target product;
acquiring a preset buried point code;
the embedded point code is added to the product code.
5. The artificial intelligence based data prediction method according to claim 1, further comprising, before the step of collecting usage data generated by the target user when using the target product based on the preset buried point mode:
acquiring using frequency data of a historical version product corresponding to the target product;
analyzing the frequency data, and screening target frequency data which accords with preset use conditions from the frequency data;
determining a specific user corresponding to the target frequency of use data;
the specific user is taken as the target user.
6. The artificial intelligence based data prediction method according to claim 1, further comprising, after the step of predicting the usage data based on the target prediction model to obtain a product feedback result corresponding to the target user:
carrying out statistical analysis on the feedback result of the product to obtain a corresponding analysis result;
acquiring a target adjustment strategy corresponding to the analysis result from a preset strategy library;
Adjusting the target product based on the target adjustment strategy to obtain an adjusted target product;
and storing the adjusted target product.
7. The artificial intelligence based data prediction method according to claim 6, further comprising, after the step of adjusting the target product based on the target adjustment policy to obtain an adjusted target product:
judging whether the adjusted target product accords with a preset expected effect or not;
if yes, acquiring a release strategy corresponding to the adjusted target product;
and carrying out release processing on the adjusted target product based on the release strategy.
8. An artificial intelligence based data prediction apparatus, comprising:
the acquisition module is used for acquiring the use data generated by a target user when using a target product based on a preset buried point mode; the target product is a product for gray verification;
the construction module is used for acquiring the label category of the use data and carrying out sample construction on the use data based on the label category to obtain a corresponding use data sample;
the first acquisition module is used for generating and processing the use data samples based on a preset kernel probability density estimation strategy to obtain virtual samples corresponding to the use sample data;
A generation module for generating a target sample based on the usage data sample and the virtual sample;
the training module is used for training a preset initial classification model based on the target sample to obtain a trained target prediction model;
and the prediction module is used for predicting the use data based on the target prediction model to obtain a product feedback result corresponding to the target user.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the artificial intelligence based data prediction method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the artificial intelligence based data prediction method of any of claims 1 to 7.
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