CN116703487A - Data analysis method, device, equipment and storage medium based on artificial intelligence - Google Patents
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Abstract
The embodiment of the application belongs to the field of artificial intelligence and the field of financial science and technology, and relates to a data analysis method based on artificial intelligence, which comprises the following steps: collecting service data of a target website from a service database; acquiring target index data from the service data based on the first index type; inputting target index data into an index analysis model; based on index analysis rules included in the index analysis model, invoking an index calculation formula corresponding to the index analysis rules to calculate target index data, and obtaining a grading value of the target website; and determining the rating result of the target website based on the rating value. The application also provides a data analysis device, computer equipment and a storage medium based on the artificial intelligence. In addition, the application also relates to a blockchain technology, and the rating result can be stored in the blockchain. The method and the device can be applied to website rating scenes in the financial field, realize automatic and rapid generation of the rating results of the target website, and ensure the accuracy of the rating results of the target website.
Description
Technical Field
The present application relates to the field of artificial intelligence technology and financial technology, and in particular, to an artificial intelligence-based data analysis method, apparatus, computer device, and storage medium.
Background
The insurance industry often cooperates with institutional agents when developing podcasts and expanding insurance. The car insurance agency mainly comprises a car dealer (car merchant) and other agents (generation, exhibition, economic company and the like), and the service popularization of the agency is mainly distributed in various sites of the whole country and mainly comprises 4S shops and comprehensive repair shops. When the vehicle owner maintains the vehicle or repairs the vehicle, the vehicle owner goes to the website, the agent can recommend the relevant insurance to the vehicle owner, and if the vehicle owner applies for insurance, premium income can be generated.
Currently, the insurance industry typically has business requirements to rate the website. The existing website grading mode often adopts an off-line manual scoring mode, indexes are manually input, and then the indexes are subjected to score calculation so as to obtain a grading result. The website grading mode based on the manpower needs to be high in time and labor cost, is low in efficiency, and cannot guarantee the accuracy of grading results.
Disclosure of Invention
The embodiment of the application aims to provide a data analysis method, a device, computer equipment and a storage medium based on artificial intelligence, which are used for solving the technical problems that the traditional website grading mode based on artificial intelligence needs to be high in time and labor cost, is low in efficiency and cannot guarantee the accuracy of grading results.
In order to solve the above technical problems, the embodiment of the present application provides an artificial intelligence based data analysis method, which adopts the following technical scheme:
collecting service data of a target website from a preset service database;
acquiring target index data from the service data based on a preset first index type;
inputting the target index data into a preset index analysis model;
based on index analysis rules included in the index analysis model, invoking an index calculation formula corresponding to the index analysis rules to calculate the target index data, and obtaining a grading value of the target network points;
and determining the grading result of the target website based on the grading value.
Further, the step of inputting the target index data into a preset index analysis model specifically includes:
acquiring a preset data verification rule;
verifying the target index data based on the data verification rule;
and if the target index data passes the verification, inputting the target index data into the index analysis model.
Further, the step of determining the rating result of the target website based on the rating value specifically includes:
Calling a preset grade mapping table;
inquiring the grade mapping table based on the grading value, and determining a grading interval corresponding to the grading value from the grade mapping table;
obtaining a target grade corresponding to the grading interval from the grade mapping table;
and taking the target grade as a grading result of the target network point.
Further, after the step of determining the rating result of the target website based on the rating value, the method further includes:
judging whether the evaluation result is an inferior website;
if yes, acquiring output value data and profit data corresponding to the target network points from a preset service library based on a preset second index type;
and processing the production value data and the profit data based on a preset production value mathematical model to generate a target production value input value corresponding to the target website.
Further, after the step of determining the rating result of the target website based on the rating value, the method further includes:
acquiring a historical evaluation order submitted by a user and corresponding to the target website;
acquiring evaluation data in the historical evaluation orders;
performing effective evaluation on the historical evaluation orders based on evaluation data in the historical evaluation orders, and determining effective evaluation orders from the historical evaluation orders;
And generating a target evaluation score of the target website based on the effective evaluation order.
Further, the step of effectively evaluating the historical evaluation order based on the evaluation data in the historical evaluation order and determining an effective evaluation order from the historical evaluation order specifically includes:
calling a preset evaluation model;
inputting the evaluation data in each historical evaluation order into the evaluation model, and performing evaluation processing on the evaluation data in each historical evaluation order through the evaluation model to generate evaluation results corresponding to each historical evaluation order;
and determining the effective evaluation order from the historical evaluation orders based on the evaluation result.
Further, after the step of determining the rating result of the target website based on the rating value, the method further includes:
judging whether an original rating result corresponding to the target network point exists or not;
if the original rating result exists, judging whether the original rating result is the same as the rating result;
and if the original rating results are different, updating the original rating results by using the rating results.
In order to solve the technical problems, the embodiment of the application also provides a data analysis device based on artificial intelligence, which adopts the following technical scheme:
the acquisition module is used for acquiring the service data of the target network point from a preset service database;
the first acquisition module is used for acquiring target index data from the service data based on a preset first index type;
the input module is used for inputting the target index data into a preset index analysis model;
the analysis module is used for calling an index calculation formula corresponding to the index analysis rule to calculate the target index data based on the index analysis rule included in the index analysis model to obtain a scoring value of the target website;
and the first determining module is used for determining the rating result of the target website based on the rating value.
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:
collecting service data of a target website from a preset service database;
acquiring target index data from the service data based on a preset first index type;
Inputting the target index data into a preset index analysis model;
based on index analysis rules included in the index analysis model, invoking an index calculation formula corresponding to the index analysis rules to calculate the target index data, and obtaining a grading value of the target network points;
and determining the grading result of the target website based on the grading value.
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:
collecting service data of a target website from a preset service database;
acquiring target index data from the service data based on a preset first index type;
inputting the target index data into a preset index analysis model;
based on index analysis rules included in the index analysis model, invoking an index calculation formula corresponding to the index analysis rules to calculate the target index data, and obtaining a grading value of the target network points;
and determining the grading result of the target website based on the grading value.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
The embodiment of the application collects the business data of the target network point from a preset business database; then acquiring target index data from the service data based on a preset first index type; inputting the target index data into a preset index analysis model; subsequently, based on index analysis rules included in the index analysis model, invoking an index calculation formula corresponding to the index analysis rules to calculate the target index data, and obtaining a grading value of the target network points; and finally, determining the grading result of the target website based on the grading value. According to the method, after the target index data corresponding to the first index type is acquired from the service database, the target index data is calculated by using the index analysis model, and then the rating result of the target network point is rapidly determined according to the rating value, so that the automatic rapid generation of the rating result of the target network point is realized, and no manpower or material resources are needed, so that the manpower and material resources are saved, and the accuracy of the generated rating result of the target network point is ensured.
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 analysis method in accordance with the present application;
FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based data analysis device in accordance with the 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 analysis 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 analysis device based on artificial intelligence is generally disposed in the server/terminal device.
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 analysis 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 analysis method based on artificial intelligence provided by the embodiment of the application can be applied to any scene needing to be subjected to the organization grading, and can be applied to products of the scenes, such as the grading of agency institutions associated with insurance companies in the field of financial insurance. The artificial intelligence-based data analysis method comprises the following steps:
Step S201, collecting service data of a target network point from a preset service database.
In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the data analysis method based on artificial intelligence operates may acquire service data 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. The service database is a database which is built in advance by an insurance company and stores various service data of a target website.
Step S202, acquiring target index data from the service data based on a preset first index type.
In this embodiment, the first index type is specifically a production value index and a premium index, and the target index data is a production value of a target website and a premium of the target website stored in the service data. The first index type can be used for rapidly and conveniently acquiring the required target index data from service data.
Step S203, inputting the target index data into a preset index analysis model.
In this embodiment, the index analysis model includes an index analysis rule corresponding to the index data. The specific implementation process of inputting the target index data into the preset index analysis model will be described in further detail in the following specific embodiments, which will not be described herein.
And step S204, calling an index calculation formula corresponding to the index analysis rule to calculate the target index data based on the index analysis rule included in the index analysis model, and obtaining the grading value of the target website.
In this embodiment, the index analysis rule is specifically a calculation formula of the yield-to-guarantee ratio=yield value/premium, and the lower the yield-to-guarantee ratio is, the higher the relative profit is, and the index calculation formula corresponds to the yield-to-guarantee ratio calculation formula. The generated calculation result is the grading value of the target network point by substituting the output value of the target network point and the premium of the target network point into a calculation formula of the output and insurance ratio.
Step S205, determining a rating result of the target website based on the rating value.
In this embodiment, the foregoing specific implementation process of determining the rating result of the target website based on the rating value will be described in further detail in the following specific embodiments, which will not be described herein.
The application collects the business data of the target network point from the preset business database; then acquiring target index data from the service data based on a preset first index type; inputting the target index data into a preset index analysis model; subsequently, based on index analysis rules included in the index analysis model, invoking an index calculation formula corresponding to the index analysis rules to calculate the target index data, and obtaining a grading value of the target network points; and finally, determining the grading result of the target website based on the grading value. According to the method, after the target index data corresponding to the first index type is acquired from the service database, the target index data is calculated by using the index analysis model, and then the rating result of the target network point is rapidly determined according to the rating value, so that the automatic rapid generation of the rating result of the target network point is realized, and no manpower or material resources are needed, so that the manpower and material resources are saved, and the accuracy of the generated rating result of the target network point is ensured.
In some alternative implementations, step S203 includes the steps of:
and acquiring a preset data verification rule.
In this embodiment, the data verification rule may be generated and stored based on the verification requirement in actual service use, and then the data verification rule is used to verify the service data. The content of the data verification rule is not specifically limited, and may include, for example: primary key non-null verification, uniqueness verification, rate class range verification, amount class range verification, interest class range verification, and the like.
And verifying the target index data based on the data verification rule.
In this embodiment, the target index data is verified by using a data verification rule to determine whether the data is true or false, so as to reduce data errors and be beneficial to avoiding the influence of human subjective factors.
And if the target index data passes the verification, inputting the target index data into the index analysis model.
In this embodiment, the target index data is verified, and when the target index data is verified, it is proved that the target index data has accuracy, and the target index data can be input into the index analysis model for analysis processing.
The method comprises the steps of obtaining a preset data verification rule; then verifying the target index data based on the data verification rule; and if the target index data passes the verification, inputting the target index data into the index analysis model. Before analyzing and processing the target index data, the application intelligently checks the target index data by using the data checking rule to judge whether the data is true or false, thereby reducing data errors and being beneficial to avoiding the influence of human subjective factors. In addition, only after the target index data is verified, the accuracy of the target index data is proved, the target index data can be input into the index analysis model for analysis and processing, and the standardization and the accuracy of the analysis and processing process of the target index data are ensured.
In some alternative implementations of the present embodiment, step S205 includes the steps of:
and calling a preset level mapping table.
In this embodiment, the level mapping table is a data table created according to an actual service level division requirement and storing a plurality of scoring intervals and level data corresponding to each scoring interval one by one.
And inquiring the grade mapping table based on the grade value, and determining a grade interval corresponding to the grade value from the grade mapping table.
In this embodiment, the score interval is an interval corresponding to a numerical value including the score value.
And obtaining a target grade corresponding to the grading interval from the grade mapping table.
In this embodiment, the target level corresponding to the scoring section may be acquired from the level map based on the correspondence between the scoring section and the level data.
And taking the target grade as a grading result of the target network point.
The application calls the preset grade mapping table; then, inquiring the grade mapping table based on the grading value, and determining a grading interval corresponding to the grading value from the grade mapping table; and obtaining a target grade corresponding to the grading interval from the grade mapping table, and taking the target grade as a grading result of the target website. The application processes the grading value of the generated target website based on the use of the grade mapping table, can quickly and accurately generate the grading result of the target website without the help of manpower, thereby saving manpower and material resources, realizing the automatic generation of website grading, and ensuring the accuracy of the generated grading result of the target website.
In some alternative implementations, after step S205, the electronic device may further perform the following steps:
and judging whether the grading result is an inferior website.
In this embodiment, the rating result may include a high quality website or a poor quality website. The insurance company can rate each website, the website with low production and protection ratio is high-quality website, and the website with high production and protection ratio is poor website. To increase profits, the high-quality dot duty ratio is increased and the low-quality dot duty ratio is reduced. Profit margin= ((premium-yield value))/yield value=1/yield-to-guarantee ratio-1. The improvement of the website rating core is to reduce the website yield and the yield ratio, and the relation between the profit margin and the yield ratio can be seen that the higher the profit margin is, the lower the yield and the yield ratio is.
If yes, acquiring the production value data and the profit data corresponding to the target network point from a preset service library based on a preset second index type.
In this embodiment, the second index type is a production value index and a profit index, and the service library may refer to a service library of an insurance company having a proxy cooperation relationship with the target website. The website agency can develop customers and promote premium, and meanwhile, the expense is increased, because the insurance company needs to pay cost as cooperative chips such as coupons (car washes, oiling coupons and the like), business cost and the like when cooperating with the organization, the cost is called as a production value, and the cooperation mode with the website is to change the premium for the production value. The profit data refers to profit brought by the website to the insurance company under the production value input by the insurance company.
And processing the production value data and the profit data based on a preset production value mathematical model to generate a target production value input value corresponding to the target website.
In this embodiment, the mathematical model of the output value is a mathematical model constructed by a model algorithm that calculates the point of the maximum slope in the marginal effect curve by calculus. Marginal utility is an important rule found in modern economics. The basic content is that the utility increment, i.e. the marginal utility, obtained by the consumer from each consumption unit of a certain item is continuously increased, is decremented, while the consumption amount of other items remains unchanged for a certain period of time. Agency output input and profit increase accord with marginal utility phenomenon, when output input gradually increases, the relative profit brought is gradually reduced, when output input is too high, the profit increase brought is almost 0. The marginal effect curve refers to a yield value-profit curve, and the yield value mathematical model can find the optimal configuration of the yield value input resources according to the yield value-profit curve. Specifically, the output value data and the profit data are imported into a mathematical model of the output value, the output value data is taken as a model reference, the model is output as the output value when the profit-output value slope is maximum, and the profit corresponding to the output value increases the fastest when the slope is maximum.
Judging whether the evaluation result is an inferior website or not; if yes, acquiring output value data and profit data corresponding to the target network points from a preset service library based on a preset second index type; and processing the production value data and the profit data based on a preset production value mathematical model to generate a target production value input value corresponding to the target network point. When the rating result of the target website is detected to be poor in quality, the application further processes the yield value data and profit data of the target website through the use of the yield value mathematical model to generate the target yield value input value corresponding to the target website, and the generated target yield value input value is the optimal yield value input value of the target website, so that an optimal production scheme can be provided for the target website by utilizing the target yield value input value, and an insurance company can bring the most profit through the least yield value input according to the optimal yield value, thereby realizing the purposes of cost reduction and efficiency improvement. On the other hand, the ratio of the high-quality net points can be improved by enabling the yield and the guarantee ratio of the net points to be the lowest, and the risk of excessively high input yield of the inferior net points is avoided.
In some alternative implementations, after step S205, the electronic device may further perform the following steps:
And acquiring a historical evaluation order submitted by the user and corresponding to the target network point.
In this embodiment, the service database may be queried to obtain the historical evaluation orders submitted by the user and corresponding to the target website.
And acquiring evaluation data in the historical evaluation orders.
In this embodiment, the evaluation data in the historical evaluation order may include at least personal identification information of the user, order creation time, personal action track information of the user, and the like.
And carrying out effective evaluation on the historical evaluation orders based on evaluation data in the historical evaluation orders, and determining effective evaluation orders from the historical evaluation orders.
In this embodiment, in order to prevent the behavior of filling false well-being and manufacturing false sales volume for the website from occurring, if false order evaluation information is incorporated into well-being calculation for calculating the website, the well-being result of the website is unreal and unfair, and the consumer cannot reasonably select by checking the well-being value of the website. Therefore, before the evaluation degree calculation is carried out on the network points, the historical evaluation orders with false evaluation suspicions and high risk levels are screened and removed, the actual evaluation historical evaluation orders are reserved for the evaluation degree calculation of the network points, and the generated evaluation degree of the network points can be more fair and accurate. The method includes the steps of determining a specific implementation process of the valid evaluation order from the historical evaluation order, and performing further detailed description on the specific implementation process in the following specific embodiment, which will not be described in detail here.
And generating a target evaluation score of the target website based on the effective evaluation order.
In this embodiment, the target evaluation score corresponds to the wellness degree of the target website, and the effective evaluation order may be calculated according to a preset order wellness degree generation rule, so as to generate a true and accurate wellness degree of the target website, that is, the target evaluation score.
The application obtains the historical evaluation order corresponding to the target website submitted by the user; then acquiring evaluation data in the historical evaluation orders; then, based on evaluation data in the historical evaluation orders, carrying out effective evaluation on the historical evaluation orders, and determining effective evaluation orders from the historical evaluation orders; and generating a target evaluation score of the target website based on the effective evaluation order. Before the evaluation degree calculation is performed on the network points, the evaluation degree calculation is performed on the network points by intelligently screening and filtering out the historical evaluation orders with false evaluation suspicions and reserving the valid evaluation orders with real evaluation, so that the evaluation degree of the generated network points is more correct and accurate, the generated target evaluation scores of the target network points are ensured to be true and reliable, the reasonable network point selection can be performed by subsequent consumers through checking the evaluation degree values of the network points, and the use experience of the consumers is improved.
In some optional implementations of this embodiment, the effectively evaluating the historical evaluation order based on the evaluation data in the historical evaluation order, and determining the effective evaluation order from the historical evaluation order includes the following steps:
and calling a preset evaluation model.
In this embodiment, the evaluation model is a model that is generated by training a machine learning model according to a large amount of evaluation order sample data and is capable of accurately predicting a risk level corresponding to the evaluation data in the evaluation order. The risk levels may include an order risk level and a customer risk level, and the risk levels may include a low risk level or a high risk level. In addition, the selection of the machine learning model is not particularly limited, and an existing classification model may be employed. The training process of the evaluation model comprises the following steps: training a machine learning model by taking evaluation data of historical evaluation orders with low risk levels of order risk levels and client risk levels as positive samples; and training the machine learning model by taking the evaluation data of the historical evaluation orders with high risk levels of the order risk levels and the client risk levels as positive samples, so that the finally obtained evaluation model can accurately judge the risk level of the evaluation data in the evaluation orders.
And inputting the evaluation data in each historical evaluation order into the evaluation model, and performing evaluation processing on the evaluation data in each historical evaluation order through the evaluation model to generate evaluation results corresponding to each historical evaluation order.
In this embodiment, the evaluation results may include an order risk level and a customer risk level corresponding to the historical evaluation order.
And determining the effective evaluation order from the historical evaluation orders based on the evaluation result.
In this embodiment, an evaluation order with a low risk level for both the order risk level and the customer risk level existing in the historical evaluation order may be used as the effective evaluation order. The effective evaluation order is a historical evaluation order corresponding to evaluation data required by the rating calculation of the target website in the subsequent step.
The method and the device call a preset evaluation model; then, inputting the evaluation data in each historical evaluation order into the evaluation model, and performing evaluation processing on the evaluation data in each historical evaluation order through the evaluation model to generate evaluation results corresponding to each historical evaluation order; and determining the effective evaluation order from the historical evaluation orders based on the evaluation result. According to the application, before the evaluation model is used for performing the evaluation calculation on the network points, the historical evaluation orders with false evaluation suspicions can be rapidly and accurately screened and removed, so that only the actual evaluation orders with true evaluation are reserved for performing the evaluation calculation on the network points, the workload of the evaluation calculation is effectively reduced, the evaluation of the generated network points is more accurate, the generated target evaluation scores of the target network points are ensured to be real, rapid and reliable, the reasonable network point selection can be performed by a subsequent consumer through checking the evaluation values of the network points, and the use experience of the consumer is further improved.
In some optional implementations of this embodiment, after step S205, the electronic device may further perform the following steps:
and judging whether an original rating result corresponding to the target mesh point exists or not.
In this embodiment, after the rating of the website is completed, the rating result of the website is stored.
If the original rating result exists, judging whether the original rating result is identical to the rating result or not.
And if the original rating results are different, updating the original rating results by using the rating results.
In this embodiment, if the original rating result is the same as the rating result, the original rating result is not modified, and only the time information of the day is added to the original rating result, so that the evaluation update processing for the target website can be distinguished later.
The method comprises the steps of judging whether an original rating result corresponding to the target network point exists or not; if the original rating result exists, judging whether the original rating result is the same as the rating result; and if the original rating results are different, updating the original rating results by using the rating results. After the rating results of the target network points are generated, the generated new rating results are intelligently used for updating the original rating results of the target network points, so that the replacement of the rating results of the network points is not needed, the workload of staff is reduced, and the working experience of the staff is improved.
It is emphasized that to further ensure privacy and security of the rating results, the rating results may also be stored in a blockchain node.
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 analysis 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 analysis apparatus 300 according to the present embodiment includes: the device comprises an acquisition module 301, a first acquisition module 302, an input module 303, an analysis module 304 and a first determination module 305. Wherein:
the acquisition module 301 is configured to acquire service data of a target website from a preset service database;
a first obtaining module 302, configured to obtain target index data from the service data based on a preset first index type;
an input module 303, configured to input the target index data into a preset index analysis model;
the analysis module 304 is configured to invoke an index calculation formula corresponding to the index analysis rule to calculate the target index data based on the index analysis rule included in the index analysis model, so as to obtain a scoring value of the target website;
a first determining module 305 is configured to determine a rating result of the target website based on the rating value.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based data analysis method in the foregoing embodiment one by one, and are not described herein again.
In some alternative implementations of the present embodiment, the input module 303 includes:
the first acquisition sub-module is used for acquiring a preset data verification rule;
the verification sub-module is used for verifying the target index data based on the data verification rule;
and the input sub-module is used for inputting the target index data into the index analysis model if the target index data passes the verification.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based data analysis method in the foregoing embodiment one by one, and are not described herein again.
In some alternative implementations of the present embodiment, the first determining module 305 includes:
the first calling sub-module is used for calling a preset grade mapping table;
the first determining submodule is used for inquiring the grade mapping table based on the grade value and determining a grade interval corresponding to the grade value from the grade mapping table;
The second obtaining submodule is used for obtaining the target grade corresponding to the grading interval from the grade mapping table;
and the second determining submodule is used for taking the target grade as a grading result of the target network point.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data analysis method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some optional implementations of the present embodiments, the artificial intelligence based data analysis apparatus further includes:
the first judging module is used for judging whether the grading result is an inferior website;
the second acquisition module is used for acquiring the production value data and the profit data corresponding to the target network points from a preset service library based on a preset second index type if the target network points are the same;
the first generation module is used for processing the production value data and the profit data based on a preset production value mathematical model and generating a target production value input value corresponding to the target website.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based data analysis method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of the present embodiments, the artificial intelligence based data analysis apparatus further includes:
the third acquisition module is used for acquiring a historical evaluation order submitted by a user and corresponding to the target website;
a fourth obtaining module, configured to obtain evaluation data in the historical evaluation order;
the second determining module is used for effectively evaluating the historical evaluation orders based on evaluation data in the historical evaluation orders and determining effective evaluation orders from the historical evaluation orders;
and the second generation module is used for generating a target evaluation score of the target website based on the effective evaluation order.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based data analysis method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of the present embodiment, the second determining module:
the second calling sub-module is used for calling a preset evaluation model;
the evaluation sub-module is used for inputting the evaluation data in each historical evaluation order into the evaluation model, and performing evaluation processing on the evaluation data in each historical evaluation order through the evaluation model to generate evaluation results respectively corresponding to each historical evaluation order;
And the third determination submodule is used for determining the effective evaluation order from the historical evaluation orders based on the evaluation result.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based data analysis method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of the present embodiments, the artificial intelligence based data analysis apparatus further includes:
the second judging module is used for judging whether an original rating result corresponding to the target network point exists or not;
the third judging module is used for judging whether the original rating result is the same as the rating result or not if the original rating result exists;
and the updating module is used for updating the original rating result by using the rating result if the original rating result is different.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based data analysis method in the foregoing embodiment one by one, and are 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 analysis 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 analysis 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, the business data of the target network point are acquired from a preset business database; then acquiring target index data from the service data based on a preset first index type; inputting the target index data into a preset index analysis model; subsequently, based on index analysis rules included in the index analysis model, invoking an index calculation formula corresponding to the index analysis rules to calculate the target index data, and obtaining a grading value of the target network points; and finally, determining the grading result of the target website based on the grading value. According to the method, after the target index data corresponding to the first index type is acquired from the service database, the target index data is calculated by using the index analysis model, and then the rating result of the target network point is rapidly determined according to the rating value, so that the automatic rapid generation of the rating result of the target network point is realized, and no manpower or material resources are needed, so that the manpower and material resources are saved, and the accuracy of the generated rating result of the target network point is ensured.
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 analysis 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, the business data of the target network point are acquired from a preset business database; then acquiring target index data from the service data based on a preset first index type; inputting the target index data into a preset index analysis model; subsequently, based on index analysis rules included in the index analysis model, invoking an index calculation formula corresponding to the index analysis rules to calculate the target index data, and obtaining a grading value of the target network points; and finally, determining the grading result of the target website based on the grading value. According to the method, after the target index data corresponding to the first index type is acquired from the service database, the target index data is calculated by using the index analysis model, and then the rating result of the target network point is rapidly determined according to the rating value, so that the automatic rapid generation of the rating result of the target network point is realized, and no manpower or material resources are needed, so that the manpower and material resources are saved, and the accuracy of the generated rating result of the target network point is ensured.
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. A data analysis method based on artificial intelligence, comprising the steps of:
collecting service data of a target website from a preset service database;
acquiring target index data from the service data based on a preset first index type;
inputting the target index data into a preset index analysis model;
based on index analysis rules included in the index analysis model, invoking an index calculation formula corresponding to the index analysis rules to calculate the target index data, and obtaining a grading value of the target network points;
and determining the grading result of the target website based on the grading value.
2. The artificial intelligence based data analysis method according to claim 1, wherein the step of inputting the target index data into a preset index analysis model specifically comprises:
acquiring a preset data verification rule;
verifying the target index data based on the data verification rule;
and if the target index data passes the verification, inputting the target index data into the index analysis model.
3. The artificial intelligence based data analysis method according to claim 1, wherein the step of determining the rating result of the target website based on the rating value specifically comprises:
Calling a preset grade mapping table;
inquiring the grade mapping table based on the grading value, and determining a grading interval corresponding to the grading value from the grade mapping table;
obtaining a target grade corresponding to the grading interval from the grade mapping table;
and taking the target grade as a grading result of the target network point.
4. The artificial intelligence based data analysis method according to claim 1, further comprising, after the step of determining the rating result of the target website based on the rating value:
judging whether the evaluation result is an inferior website;
if yes, acquiring output value data and profit data corresponding to the target network points from a preset service library based on a preset second index type;
and processing the production value data and the profit data based on a preset production value mathematical model to generate a target production value input value corresponding to the target website.
5. The artificial intelligence based data analysis method according to claim 1, further comprising, after the step of determining the rating result of the target website based on the rating value:
acquiring a historical evaluation order submitted by a user and corresponding to the target website;
Acquiring evaluation data in the historical evaluation orders;
performing effective evaluation on the historical evaluation orders based on evaluation data in the historical evaluation orders, and determining effective evaluation orders from the historical evaluation orders;
and generating a target evaluation score of the target website based on the effective evaluation order.
6. The artificial intelligence based data analysis method according to claim 5, wherein the step of effectively evaluating the historical evaluation orders based on the evaluation data in the historical evaluation orders and determining the effective evaluation orders from the historical evaluation orders specifically comprises:
calling a preset evaluation model;
inputting the evaluation data in each historical evaluation order into the evaluation model, and performing evaluation processing on the evaluation data in each historical evaluation order through the evaluation model to generate evaluation results corresponding to each historical evaluation order;
and determining the effective evaluation order from the historical evaluation orders based on the evaluation result.
7. The artificial intelligence based data analysis method according to claim 1, further comprising, after the step of determining the rating result of the target website based on the rating value:
Judging whether an original rating result corresponding to the target network point exists or not;
if the original rating result exists, judging whether the original rating result is the same as the rating result;
and if the original rating results are different, updating the original rating results by using the rating results.
8. An artificial intelligence based data analysis device comprising:
the acquisition module is used for acquiring the service data of the target network point from a preset service database;
the first acquisition module is used for acquiring target index data from the service data based on a preset first index type;
the input module is used for inputting the target index data into a preset index analysis model;
the analysis module is used for calling an index calculation formula corresponding to the index analysis rule to calculate the target index data based on the index analysis rule included in the index analysis model to obtain a scoring value of the target website;
and the first determining module is used for determining the rating result of the target website based on the rating value.
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 analysis 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 analysis method of any of claims 1 to 7.
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