CN118197626A

CN118197626A - Information generation method, device, equipment and storage medium based on artificial intelligence

Info

Publication number: CN118197626A
Application number: CN202410366711.4A
Authority: CN
Inventors: 周友军
Original assignee: Ping An Health Insurance Company of China Ltd
Current assignee: Ping An Health Insurance Company of China Ltd
Priority date: 2024-03-28
Filing date: 2024-03-28
Publication date: 2024-06-14

Abstract

The application belongs to the field of artificial intelligence and the field of digital medical treatment, and relates to an information generation method based on artificial intelligence, which comprises the following steps: acquiring health data of a user; processing the health data based on a feature selection algorithm to obtain appointed health data; generating a risk probability value for the specified health data based on the assessment model; screening specified risk probability values greater than a probability threshold from all risk probability values; determining a specified risk level corresponding to the specified risk probability value; acquiring appointed health guide information corresponding to the appointed risk level; the specified health guide information is pushed to the user. The application also provides an information generating device, computer equipment and a storage medium based on the artificial intelligence. Furthermore, the specified risk level of the present application may be stored in the blockchain. The method and the system can be applied to the health information generation scene in the digital medical field, automatically and accurately realize the risk assessment of the health data based on the assessment model, and improve the processing efficiency and accuracy of the health assessment.

Description

Information generation method, device, equipment and storage medium based on artificial intelligence

Technical Field

The application relates to the technical field of artificial intelligence development and the field of digital medical treatment, in particular to an information generation method, an information generation device, computer equipment and a storage medium based on artificial intelligence.

Background

Along with the improvement of the life quality of people, the health is gradually valued by people; health is a necessary requirement for promoting the comprehensive development of people, is a basic condition for the development of economy and society, and is also a common pursuit of masses.

At present, the risk early warning processing of carrying out health risk identification on users and timely sending out corresponding health guidance becomes an important concern for realizing timely and effective treatment of health risks of users and improving life quality of users for some health insurance companies. However, the existing method for performing health risk identification and risk early warning on users generally requires a professional medical team to complete the process by means of clinical working experience and an evaluation scale, and the processing method requires more manpower resources, has low processing efficiency and cannot ensure the accuracy of the generated health risk identification result.

Disclosure of Invention

The embodiment of the application aims to provide an information generation method, device, computer equipment and storage medium based on artificial intelligence, so as to solve the technical problems that the existing method for carrying out health risk identification and risk early warning on users needs to consume more manpower resources, has low processing efficiency and cannot ensure the accuracy of the generated health risk identification result.

In order to solve the technical problems, the embodiment of the application provides an information generation method based on artificial intelligence, which adopts the following technical scheme:

Acquiring health data of a user;

processing the health data based on a preset feature selection algorithm to obtain corresponding appointed health data;

performing evaluation processing on the specified health data based on a preset evaluation model, and generating a risk probability value corresponding to the specified health data;

Screening specified risk probability values larger than a preset probability threshold from all the risk probability values;

Determining a specified risk level corresponding to the specified risk probability value;

acquiring appointed health guide information corresponding to the appointed risk level;

Pushing the specified health instruction information to the user.

Further, the step of processing the health data based on the preset feature selection algorithm to obtain corresponding specified health data specifically includes:

Acquiring a preset multiple feature selection algorithm;

Screening a preset number of appointed feature selection algorithms from the plurality of feature selection algorithms;

Processing the health data by using each specified feature selection algorithm to obtain first health data corresponding to each specified feature selection algorithm;

The specified health data is constructed based on all of the first health data.

Further, the step of constructing the specified health data based on all the first health data specifically includes:

Calling a preset similarity analysis algorithm;

repeating screening processing is carried out on all the first health data based on the similarity analysis algorithm, and repeated second health data are screened out from all the first health data;

And taking the second health data as the specified health data.

Further, the step of determining the specified risk level corresponding to the specified risk probability value specifically includes:

acquiring a preset grade mapping table;

Inquiring a target grade corresponding to the appointed risk probability value from the grade mapping table;

and taking the target grade as the appointed risk grade.

Further, the step of obtaining the specified health guidance information corresponding to the specified risk level specifically includes:

Calling a preset health database;

Acquiring specified risk information corresponding to the specified risk probability value;

acquiring first health guide information corresponding to the specified risk information from the health database;

Screening second health guide information corresponding to the appointed risk level from the first health guide information;

and taking the second health guide information as the appointed health guide information.

Further, before the step of performing the evaluation processing on the specified health data based on the preset evaluation model and generating the risk probability value corresponding to the specified health data, the method further includes:

Acquiring historical health data in a preset time period;

Labeling the historical health data to obtain corresponding health sample data;

Dividing the health sample data into a training set and a testing set;

training a preset machine learning model based on the training set to obtain a corresponding appointed model;

testing the designated model based on a preset model evaluation index and the test set;

And if the test passes, taking the designated model as the evaluation model.

Further, after the step of determining the specified risk level corresponding to the specified risk probability value, the method further includes:

Acquiring policy information of the user;

Determining a specified policy corresponding to the specified health data based on the policy information;

And adjusting the designated policy based on the designated risk level.

In order to solve the technical problems, the embodiment of the application also provides an information generating device based on artificial intelligence, which adopts the following technical scheme:

the first acquisition module is used for acquiring health data of a user;

The first processing module is used for processing the health data based on a preset feature selection algorithm to obtain corresponding appointed health data;

The second processing module is used for carrying out evaluation processing on the specified health data based on a preset evaluation model and generating a risk probability value corresponding to the specified health data;

The screening module is used for screening specified risk probability values larger than a preset probability threshold value from all the risk probability values;

the first determining module is used for determining a specified risk level corresponding to the specified risk probability value;

The second acquisition module is used for acquiring the appointed health guidance information corresponding to the appointed risk level;

And the pushing module is used for pushing the appointed health guide information to the 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 health data of a user;

Pushing the specified health instruction information to the 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 health data of a user;

Pushing the specified health instruction information to the user.

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

Firstly, acquiring health data of a user; then processing the health data based on a preset feature selection algorithm to obtain corresponding appointed health data; then, carrying out evaluation processing on the appointed health data based on a preset evaluation model, and generating a risk probability value corresponding to the appointed health data; subsequently screening out appointed risk probability values larger than a preset probability threshold value from all the risk probability values; further determining a specified risk level corresponding to the specified risk probability value, and acquiring specified health guidance information corresponding to the specified risk level; and finally pushing the appointed health guidance information to the user. According to the embodiment of the application, the acquired health data of the user are processed by using a feature selection algorithm to obtain the appointed health data, the appointed health data are further evaluated based on a preset evaluation model, a risk probability value corresponding to the appointed health data is generated, then an appointed risk grade corresponding to the appointed risk probability value larger than a preset probability threshold is determined, and then appointed health guidance information corresponding to the appointed risk grade is automatically acquired and pushed to the user. The application realizes automatic accurate evaluation of the health data of the user based on the use of the evaluation model, effectively improves the processing efficiency of the health evaluation, improves the accuracy of the generated health evaluation result, and improves the generation intelligence and the data accuracy of the health guidance information.

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 information generation method according to the present application;

FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based information generating apparatus according to 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 (Mov i ng P i cture Experts G roup Aud i o Layer I I I, dynamic video expert compression standard audio plane 3), MP4 (Mov i ng P i ctu re Experts G roup Aud i o Layer I V, 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 information generating method based on artificial intelligence provided by the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the information generating 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 information generation 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 information generating method based on the artificial intelligence provided by the embodiment of the application can be applied to any scene needing to carry out health risk assessment and health guidance of insurance clients, and can be applied to products of the scenes, such as health risk assessment and health guidance of insurance clients in the field of financial insurance. The information generation method based on artificial intelligence comprises the following steps:

step S201, health data of a user is acquired.

In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the information generating method based on artificial intelligence operates may acquire the health 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 connections, wifi connections, bluetooth connections, wimax connections, Z i gbee connections, UWB (u l t ra W i deband) connections, and other now known or later developed wireless connection means. Health data of the user may be collected using sensor technology, and may include at least height, weight, blood pressure, blood glucose, activity, sleep, etc. Or the medical data of the user can be downloaded from the medical server as supplement to the health data of the user, and the medical data can comprise personal health files, prescriptions, examination reports and the like.

Step S202, processing the health data based on a preset feature selection algorithm to obtain corresponding appointed health data.

In this embodiment, the specific implementation process of processing the health data based on the preset feature selection algorithm to obtain the corresponding specified health data will be described in further detail in the following specific embodiments, which will not be described herein.

Step S203, performing an evaluation process on the specified health data based on a preset evaluation model, and generating a risk probability value corresponding to the specified health data.

In this embodiment, the specified health data may be subjected to evaluation processing by the evaluation model by inputting the specified health data into the evaluation model, and a risk probability value corresponding to the specified health data may be output. The risk probability value corresponding to the specified health data may specifically include a risk probability value that the user has various preset diseases. In addition, for the model construction process of the above evaluation model, the present application will be described in further detail in the following specific embodiments, which will not be described herein.

Step S204, screening out the specified risk probability values larger than a preset probability threshold from all the risk probability values.

In this embodiment, the probability threshold is a probability value set according to an actual medium-high risk discrimination requirement, and the value of the probability threshold is not specifically limited. The specified risk probability value greater than the preset probability threshold value refers to a risk probability value corresponding to a middle risk or a high risk in the risk probability values.

Step S205, determining a specified risk level corresponding to the specified risk probability value.

In this embodiment, the specific implementation process of determining the specified risk level corresponding to the specified risk probability value is described in further detail in the following specific embodiment, which will not be described herein.

Step S206, acquiring the specified health guidance information corresponding to the specified risk level.

In this embodiment, the specific implementation process of obtaining the specified health guidance information corresponding to the specified risk level is described in further detail in the following specific embodiment, which is not described herein.

Step S207, pushing the specified health guidance information to the user.

In this embodiment, the communication information of the user may be obtained, and the specified health instruction information may be further pushed to the communication terminal corresponding to the user based on the communication information. Wherein, the communication information comprises telephone numbers, mail addresses and the like.

Firstly, acquiring health data of a user; then processing the health data based on a preset feature selection algorithm to obtain corresponding appointed health data; then, carrying out evaluation processing on the appointed health data based on a preset evaluation model, and generating a risk probability value corresponding to the appointed health data; subsequently screening out appointed risk probability values larger than a preset probability threshold value from all the risk probability values; further determining a specified risk level corresponding to the specified risk probability value, and acquiring specified health guidance information corresponding to the specified risk level; and finally pushing the appointed health guidance information to the user. According to the method, the obtained health data of the user are processed through the feature selection algorithm to obtain the appointed health data, the appointed health data are further evaluated based on the preset evaluation model, the risk probability value corresponding to the appointed health data is generated, then the appointed risk grade of the appointed risk probability value which is larger than the preset probability threshold is determined, and then the appointed health guidance information corresponding to the appointed risk grade is automatically obtained and pushed to the user. The application realizes automatic accurate evaluation of the health data of the user based on the use of the evaluation model, effectively improves the processing efficiency of the health evaluation, improves the accuracy of the generated health evaluation result, and improves the generation intelligence and the data accuracy of the health guidance information.

In some alternative implementations, step S202 includes the steps of:

and acquiring a preset multiple feature selection algorithm.

In this embodiment, the feature selection algorithm may include a univariate feature selection algorithm, a pearson correlation coefficient, mutual information and maximum information coefficient, feature ordering based on a learning model, a random forest algorithm, and the like.

And screening a preset number of appointed characteristic selection algorithms from the plurality of characteristic selection algorithms.

In this embodiment, the above specified feature selection algorithm may perform random screening from the multiple feature selection algorithms. Or the algorithm accuracy and the algorithm processing efficiency of various feature selection algorithms can be obtained, the algorithm accuracy and the algorithm processing efficiency of the various feature selection algorithms are weighted and summed to obtain the corresponding algorithm scores of the various feature selection algorithms, and the algorithm with the highest preset number of algorithm scores is selected from the various feature selection algorithms to serve as the appointed feature selection algorithm, so that the obtained appointed feature selection algorithm can have better processing performance. The preset number of values is not limited, and can be set according to actual use requirements.

Processing the health data by using each specified characteristic selection algorithm to obtain

In this embodiment, the feature extraction processing may be performed on the health data by using each of the specified feature selection algorithms, so as to obtain the processing performed on the health data by using each of the specified feature selection algorithms.

The specified health data is constructed based on all of the first health data.

In this embodiment, the specific implementation process of constructing the specified health data based on all the first health data will be described in further detail in the following specific embodiments, which will not be described herein.

The method comprises the steps of obtaining a preset multiple feature selection algorithm; then screening a preset number of appointed feature selection algorithms from the plurality of feature selection algorithms; then, the health data are respectively processed by using each appointed characteristic selection algorithm to obtain first health data respectively corresponding to each appointed characteristic selection algorithm; the specified health data is then constructed based on all of the first health data. According to the application, the specified feature selection algorithms with the preset number are screened out from the multiple feature selection algorithms, and then the feature selection processing is respectively carried out on the health data by each specified feature selection algorithm based on the specified feature selection algorithm, so that the required specified health data can be quickly and accurately constructed according to the obtained first health data respectively corresponding to each specified feature selection algorithm, and the data accuracy of the obtained specified health data is ensured.

In some optional implementations of the present embodiment, the constructing the specified health data based on all of the first health data includes:

and calling a preset similarity analysis algorithm.

In this embodiment, the selection of the similarity analysis algorithm is not limited, and any one of euclidean distance, jaccard similarity, cosine similarity, pearson similarity, and the like may be used.

And carrying out repeated screening treatment on all the first health data based on the similarity analysis algorithm, and screening repeated second health data from all the first health data.

In this embodiment, the similarity analysis algorithm may be used to calculate the similarity between all the first health data to obtain corresponding similarity values, and then select a specified similarity greater than a preset similarity threshold from all the similarity values, so as to extract specific health data matching the specified similarity from all the first health data, and use the specific health data as the second health data. The similarity threshold is not limited, and may be set according to an actual similarity determination requirement.

And taking the second health data as the specified health data.

The method and the device call a preset similarity analysis algorithm; then, based on the similarity analysis algorithm, carrying out repeated screening treatment on all the first health data, and screening repeated second health data from all the first health data; the second health data is then taken as the specified health data. After the first health data corresponding to each specified characteristic selection algorithm are obtained, repeated screening processing is carried out on all the first health data by calling a similar analysis algorithm, and repeated second health data are screened out from all the first health data and are used as final specified health data.

In some alternative implementations, step S205 includes the steps of:

And acquiring a preset grade mapping table.

In this embodiment, the level mapping table is a data table that is pre-constructed according to an actual risk discrimination requirement and stores a correspondence between a risk probability value interval and a risk level.

And inquiring a target grade corresponding to the appointed risk probability value from the grade mapping table.

In this embodiment, a specified risk probability value interval matched with the specified risk probability value may be determined from the level mapping table, and then a risk level corresponding to the specified risk probability value interval may be queried from the level mapping table and used as the target level.

And taking the target grade as the appointed risk grade.

The method comprises the steps of obtaining a preset grade mapping table; then inquiring a target grade corresponding to the appointed risk probability value from the grade mapping table; and taking the target grade as the appointed risk grade. The application can realize quick and convenient inquiry of the appointed risk level corresponding to the appointed risk probability value based on the use of the level mapping table, improves the determination efficiency of the appointed risk level and ensures the accuracy of the obtained data of the appointed risk level.

In some alternative implementations, step S206 includes the steps of:

and calling a preset health database.

In this embodiment, the health database is pre-constructed according to actual business requirements, and stores health guidance information corresponding to each disease with different risk levels.

And acquiring the specified risk information corresponding to the specified risk probability value.

In this embodiment, the risk probability value corresponding to the specified health data generated after the specified health data is evaluated by the evaluation model refers to a risk probability value that the user has various diseases. The specified risk information refers to a disease corresponding to the specified risk probability value.

And acquiring first health guide information corresponding to the specified risk information from the health database.

In this embodiment, the specified risk information may be used to perform data matching on the health database to determine a specified disease corresponding to the specified risk information in the health database, and then all health guidance information corresponding to the specified disease may be queried from the health database to obtain the first health guidance information.

And screening second health guide information corresponding to the specified risk level from the first health guide information.

In this embodiment, after the first health guidance information is obtained, the specified risk level may be further used to screen the first health guidance information, so as to screen the second health guidance information corresponding to the specified risk level.

In this embodiment, the health database is used to intelligently provide the user with the specified health guidance information corresponding to the specified risk level of the user, so that the health level of the user can be improved according to the use of the specified health guidance information, and the health of the user is guaranteed.

The method and the device call a preset health database; then acquiring appointed risk information corresponding to the appointed risk probability value; then, first health guide information corresponding to the appointed risk information is obtained from the health database; and subsequently, screening second health guide information corresponding to the appointed risk level from the first health guide information, and taking the second health guide information as the appointed health guide information. According to the method and the device for acquiring the specified risk information, the specified risk information corresponding to the specified risk probability value is acquired, and then the specified risk information and the specified risk level are used for carrying out data query on the health database, so that the specified health guidance information corresponding to the specified risk level is acquired rapidly, the acquisition efficiency of the specified health guidance information is improved, and the accuracy of the acquired specified health guidance information is ensured.

In some optional implementations of this embodiment, before step S203, the electronic device may further perform the following steps:

and acquiring historical health data in a preset time period.

In this embodiment, the data type of the history health data is the same as the number type of the health data of the user. The value of the preset time period is not limited, and may be, for example, within the previous year from the current time.

And marking the historical health data to obtain corresponding health sample data.

In this embodiment, the relevant disease label labeling process may be performed on the disease corresponding to the historical health data, so as to obtain corresponding health sample data.

The health sample data is divided into a training set and a testing set.

In this embodiment, the health sample data may be divided into a training set and a testing set according to a preset division ratio. The value of the dividing ratio is not particularly limited, and may be set according to actual use requirements, for example, may be set to 8:2.

Training a preset machine learning model based on the training set to obtain a corresponding appointed model.

In this embodiment, the machine learning model may be trained by using the training set, so as to learn and train the neural network model by using the training set, and construct a model having a function of predicting a mapping relationship between the historical health data and the disease label, that is, the specified model.

And testing the specified model based on a preset model evaluation index and the test set.

In this embodiment, the test set may be used to test the specified model, and calculate a specified model evaluation index of the specified model that matches the model evaluation index, so as to determine whether the specified model evaluation index is greater than a preset index threshold, if the specified model evaluation index is greater than the index threshold, then determine that the specified model passes the test, otherwise determine that the specified model fails the test. The selection of the model evaluation index is not limited, and may be set according to actual use requirements, for example, an accuracy index may be adopted. In addition, the value of the index threshold is not limited, and can be set according to actual use requirements.

And if the test passes, taking the designated model as the evaluation model.

The method comprises the steps of obtaining historical health data in a preset time period; then, marking the historical health data to obtain corresponding health sample data; dividing the health sample data into a training set and a testing set; training a preset machine learning model based on the training set to obtain a corresponding appointed model; further testing the specified model based on a preset model evaluation index and the test set; and if the test passes, taking the designated model as the evaluation model. According to the application, the obtained historical health data in the preset time period is labeled to obtain the health sample data, the health sample data is divided into the training set and the testing set, the preset machine learning model is trained and tested by using the data set, the testing set and the preset model evaluation index, and the model passing the test is used as a final evaluation model, so that the model construction process of the evaluation model is completed, the model effect and the prediction accuracy of the generated evaluation model are effectively ensured, and the construction efficiency of the evaluation model is improved.

In some optional implementations of this embodiment, after step S204, the electronic device may further perform the following steps:

and acquiring the policy information of the user.

In this embodiment, the policy information of the user may be queried from a preset policy database by acquiring the user information of the user and further querying the policy information based on the user information. The policy information may refer to a policy in a valid state that is matched by a user.

And determining a specified policy corresponding to the specified health data based on the policy information.

In this embodiment, the content analysis may be performed on the policy information by using the specified health data to screen out a policy having the underwriting term content corresponding to the specified health data from the policy information and as the specified policy.

And adjusting the designated policy based on the designated risk level.

In this embodiment, the preset policy adjustment policy may be invoked, and the specific policy may be adjusted according to the specific risk level by using the policy adjustment policy. The policy adjustment policy is a processing policy constructed according to the association relationship between actual disease risk assessment and pricing adjustment of the policy.

The application obtains the policy information of the user; then determining a specified policy corresponding to the specified health data based on the policy information; and subsequently, adjusting the designated policy based on the designated risk level. After determining the appointed risk level corresponding to the appointed risk probability value, the application also determines the appointed policy corresponding to the appointed health data according to the policy information of the user, and further intelligently uses the appointed risk level to adjust the appointed policy so as to complete corresponding adjustment processing on the appointed policy of the user automatically and accurately according to the actual risk assessment result of the user. The intelligent processing and the processing efficiency of policy adjustment for the user are effectively improved, and the satisfaction degree of the user is improved.

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 invention.

It is emphasized that the specified risk level may also be stored in a blockchain node in order to further ensure privacy and security of the specified risk level.

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 (B l ockcha i n), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information 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 (ART I F I C I A L I NTE L L I GENCE, A I) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand 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-On-y 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 information generating 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 information generating apparatus 300 according to the present embodiment includes: a first acquisition module 301, a first processing module 302, a second processing module 303, a screening module 304, a first determination module 305, a second acquisition module 306, and a push module 307. Wherein:

A first obtaining module 301, configured to obtain health data of a user;

the first processing module 302 is configured to process the health data based on a preset feature selection algorithm to obtain corresponding specified health data;

The second processing module 303 is configured to perform an evaluation process on the specified health data based on a preset evaluation model, and generate a risk probability value corresponding to the specified health data;

A screening module 304, configured to screen out specified risk probability values greater than a preset probability threshold from all the risk probability values;

a first determining module 305, configured to determine a specified risk level corresponding to the specified risk probability value;

A second obtaining module 306, configured to obtain specified health guidance information corresponding to the specified risk level;

A pushing module 307, configured to push the specified health instruction information to the user.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based information generating method in the foregoing embodiment one by one, which is not described herein again.

In some alternative implementations of the present embodiment, the first processing module 302 includes:

The first acquisition sub-module is used for acquiring a plurality of preset feature selection algorithms;

the first screening submodule is used for screening a preset number of appointed characteristic selection algorithms from the plurality of characteristic selection algorithms;

the processing sub-module is used for respectively processing the health data by using each appointed characteristic selection algorithm to obtain first health data respectively corresponding to each appointed characteristic selection algorithm;

and the construction sub-module is used for constructing the specified health data based on all the first health data.

In some alternative implementations of the present embodiment, the building sub-module includes:

The calling unit is used for calling a preset similarity analysis algorithm;

the screening unit is used for carrying out repeated screening processing on all the first health data based on the similarity analysis algorithm, and screening repeated second health data from all the first health data;

And the determining unit is used for taking the second health data as the specified health data.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based information generating method in the foregoing embodiment, and are not described herein again.

In some alternative implementations of the present embodiment, the first determining module 305 includes:

the second acquisition sub-module is used for acquiring a preset grade mapping table;

the inquiring submodule is used for inquiring a target grade corresponding to the appointed risk probability value from the grade mapping table;

and the first determining submodule is used for taking the target grade as the specified risk grade.

In some optional implementations of the present embodiment, the second obtaining module 306 includes:

the calling sub-module is used for calling a preset health database;

the third acquisition sub-module is used for acquiring the specified risk information corresponding to the specified risk probability value;

a fourth obtaining sub-module, configured to obtain first health guidance information corresponding to the specified risk information from the health database;

A second screening sub-module, configured to screen second health guidance information corresponding to the specified risk level from the first health guidance information;

And a second determination submodule, configured to use the second health guidance information as the specified health guidance information.

In some optional implementations of the present embodiment, the artificial intelligence based information generating apparatus further includes:

The third acquisition module is used for acquiring historical health data in a preset time period;

The third processing module is used for marking the historical health data to obtain corresponding health sample data;

the dividing module is used for dividing the health sample data into a training set and a testing set;

The training module is used for training a preset machine learning model based on the training set to obtain a corresponding appointed model;

The test module is used for testing the specified model based on a preset model evaluation index and the test set;

and the second determining module is used for taking the designated model as the evaluation model if the test passes.

A fourth obtaining module, configured to obtain policy information of the user;

The third determining module is used for determining a specified policy corresponding to the specified health data based on the policy information;

and the adjustment module is used for adjusting the appointed policy based on the appointed risk level.

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 calculation and/or information processing according to preset or stored instructions, and the hardware thereof includes, but is not limited to, a microprocessor, an application specific integrated circuit (APP L I CAT I on SPEC I F I C I NTEGRATED C I rcu I t, AS IC), a programmable gate array (Fie l d-Programmab L E GATE AR RAY, FPGA), a digital Processor (D I G I TA L S I GNA L Processor, DSP), an embedded device, and the like.

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 memory card (SMART MED I A CARD, SMC), a secure digital (Secu RE D I G I TA L, SD) card, a flash memory card (F L ASH 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 information generating method based on artificial intelligence, and the like. 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 (Cent ra lProcess i ng Un i t, 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 computer readable instructions for executing the artificial intelligence based information generating 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.

In the embodiment of the application, firstly, health data of a user is obtained; then processing the health data based on a preset feature selection algorithm to obtain corresponding appointed health data; then, carrying out evaluation processing on the appointed health data based on a preset evaluation model, and generating a risk probability value corresponding to the appointed health data; subsequently screening out appointed risk probability values larger than a preset probability threshold value from all the risk probability values; further determining a specified risk level corresponding to the specified risk probability value, and acquiring specified health guidance information corresponding to the specified risk level; and finally pushing the appointed health guidance information to the user. According to the embodiment of the application, the acquired health data of the user are processed by using a feature selection algorithm to obtain the appointed health data, the appointed health data are further evaluated based on a preset evaluation model, a risk probability value corresponding to the appointed health data is generated, then an appointed risk grade corresponding to the appointed risk probability value larger than a preset probability threshold is determined, and then appointed health guidance information corresponding to the appointed risk grade is automatically acquired and pushed to the user. The application realizes automatic accurate evaluation of the health data of the user based on the use of the evaluation model, effectively improves the processing efficiency of the health evaluation, improves the accuracy of the generated health evaluation result, and improves the generation intelligence and the data accuracy of the health guidance information.

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 information generating method as described above.

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

1. An information generation method based on artificial intelligence is characterized by comprising the following steps:

Acquiring health data of a user;

Pushing the specified health instruction information to the user.

2. The method for generating information based on artificial intelligence according to claim 1, wherein the step of processing the health data based on a preset feature selection algorithm to obtain corresponding specified health data specifically comprises:

Acquiring a preset multiple feature selection algorithm;

The specified health data is constructed based on all of the first health data.

3. The method for generating artificial intelligence based information according to claim 2, wherein the step of constructing the specified health data based on all the first health data comprises:

Calling a preset similarity analysis algorithm;

And taking the second health data as the specified health data.

4. The method for generating artificial intelligence based information according to claim 1, wherein the step of determining a specified risk level corresponding to the specified risk probability value, comprises:

acquiring a preset grade mapping table;

and taking the target grade as the appointed risk grade.

5. The method for generating artificial intelligence based information according to claim 1, wherein the step of acquiring the specified health guidance information corresponding to the specified risk level comprises:

Calling a preset health database;

6. The artificial intelligence based information generating method according to claim 1, further comprising, before the step of performing an evaluation process on the specified health data based on a preset evaluation model to generate a risk probability value corresponding to the specified health data:

Acquiring historical health data in a preset time period;

Labeling the historical health data to obtain corresponding health sample data;

Dividing the health sample data into a training set and a testing set;

And if the test passes, taking the designated model as the evaluation model.

7. The artificial intelligence based information generating method according to claim 1, further comprising, after the step of determining a specified risk level corresponding to the specified risk probability value:

Acquiring policy information of the user;

And adjusting the designated policy based on the designated risk level.

8. An artificial intelligence based information generating apparatus, comprising:

the first acquisition module is used for acquiring health data of a 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 information generating 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 information generating method according to any of claims 1 to 7.