CN111861768B - Service processing method and device based on artificial intelligence, computer equipment and medium - Google Patents
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Abstract
The invention relates to the field of data processing, and discloses a business processing method, a business processing device, a business processing computer device and a business processing storage medium based on artificial intelligence, wherein the business processing method comprises the following steps: when service application data of a user is received, health notification information and user basic information contained in the service application data are obtained, abnormal identification is carried out on the health notification information, if the health notification information is identified to be abnormal, an interactive questionnaire is generated by combining the health notification information and the user basic information through a classification tree model, a plurality of rounds of interactive questionnaire is adopted to obtain questionnaire investigation results based on the interactive questionnaire, and finally service evaluation is carried out on the questionnaire investigation results by combining historical product data to obtain service processing results. The invention also relates to the field of block chain, and the service application data and the obtained service processing result are stored in a block chain network.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a service processing method, apparatus, computer device, and medium based on artificial intelligence.
Background
With the enhancement of public health consciousness, more and more customers purchase health risks; after the clients propose the purchase application of health risks, online check and protection business processing is needed. At present, the online underwriting business mainly carries out risk screening on application materials through professional underwriting personnel, if a client has risk on a certain disease in health notification, the client needs to communicate with the client again, and the client is required to describe the disease in detail and supplement medical records or physical examination reports; the process of the verification is complicated, and the verification time is long; meanwhile, the verification operation is completed by leading a verification staff in the insurance company, and once the degree of expertise of the verification staff is insufficient, the verification result is inaccurate, and meanwhile, the verification service is high in cost and low in efficiency.
Disclosure of Invention
The embodiment of the invention provides a business processing method, a business processing device, computer equipment and a storage medium based on artificial intelligence so as to improve business processing efficiency.
In order to solve the above technical problems, an embodiment of the present application provides a service processing method based on artificial intelligence, including:
when service application data of a user are received, health notification information and user basic information contained in the service application data are obtained, wherein the health notification information contains user history health data;
Performing exception identification on the health notification information by comparing the historical health data of the user with preset conditions, and if the health notification information is identified to be abnormal, generating an interactive questionnaire by combining the health notification information and the user basic information through a classification tree model;
based on the interactive questionnaire, adopting a plurality of rounds of interactive questionnaires to obtain questionnaire investigation results;
and carrying out service evaluation on the questionnaire investigation result by combining the historical product data to obtain a service processing result.
Optionally, if it is identified that the health notification information is abnormal, generating, by using a classification tree model, an interactive questionnaire by combining the health notification information and the user basic information includes:
acquiring each node dimension of the classification tree model;
screening attribute information corresponding to the node dimension from the health notification information and the user basic information to serve as target information;
classifying the target information by using the classification tree model, and taking the classified terminal node as a target node;
and acquiring a questionnaire corresponding to the target node as the interactive questionnaire.
Optionally, based on the interactive questionnaire, obtaining the questionnaire survey result by adopting multiple rounds of interactive questionnaires includes:
Taking the problem displayed to the user as a current problem, and acquiring the current problem identifier;
acquiring an answer identifier of an answer selected by a user as a target answer identifier;
displaying the next question mark to the user according to the current question mark and the target answer mark;
taking the question corresponding to the next question mark as the current question, returning to the answer mark for obtaining the answer selected by the user, and continuing to execute the question as the target answer mark until the question in the interactive questionnaire is not displayed any more;
and acquiring each target answer identifier and a question identifier corresponding to the target answer identifier as the questionnaire survey result.
Optionally, the displaying the next question identifier to the user according to the current question identifier and the target answer identifier includes:
determining each jump problem identifier according to the current problem identifier;
acquiring a target answer identifier, determining a skip question identifier corresponding to the answer identifier by combining a preset trigger mechanism, and taking the skip question identifier corresponding to the answer identifier as the next question identifier;
and sending a jump instruction containing the next problem identification to a front-end interface through a preset page jump script, and driving the front-end interface to display the problem corresponding to the next problem identification.
Optionally, in combination with the historical product data, performing service evaluation on the questionnaire investigation result to obtain a service processing result includes:
acquiring each disease type corresponding to the questionnaire investigation result as a target type;
combining data in an ICD medical library to determine risk probability corresponding to each target type;
acquiring historical product data corresponding to the target type from a product rule base;
and determining the business processing result based on the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type.
Optionally, the determining the service processing result based on the target type, the risk probability corresponding to the target type, and the historical product data corresponding to the target type includes:
acquiring historical product data, claim settlement responsibilities corresponding to the historical product data and product pay-off benefits from each historical policy claim settlement data;
according to the historical product data and the corresponding claim liability of the historical product data, a basic circulation branch is combed, and the product pay benefits are used as a branch conclusion of the basic circulation branch;
Matching the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type with each basic circulation branch, taking the basic circulation branch successfully matched as a target circulation branch, and taking a branch conclusion corresponding to the target circulation branch as a target branch conclusion;
and determining a business processing result according to the target branch conclusion, wherein the business processing result comprises standard body underwriting, exclusionary underwriting, charging underwriting and refusing.
In order to solve the above technical problem, an embodiment of the present application further provides an artificial intelligence based service processing device, including:
the information acquisition module is used for acquiring health notification information and user basic information contained in service application data when the service application data of a user are received, wherein the health notification information contains user history health data;
the questionnaire generation module is used for carrying out abnormal recognition on the health notification information by comparing the historical health data of the user with preset conditions, and if the health notification information is recognized to be abnormal, generating an interactive questionnaire by combining the health notification information and the user basic information through a classification tree model;
The interactive questionnaire module is used for obtaining questionnaire investigation results by adopting a plurality of rounds of interactive questionnaires based on the interactive questionnaires;
and the result determining module is used for carrying out service evaluation on the questionnaire investigation result by combining the historical product data to obtain a service processing result.
Optionally, the questionnaire generating module includes:
a dimension acquisition unit, configured to acquire each node dimension of the classification tree model;
the information screening unit is used for screening attribute information corresponding to the node dimension from the health notification information and the user basic information to serve as target information;
the information classification unit is used for classifying the target information by using the classification tree model, and taking the classified terminal node as a target node;
and the questionnaire determining unit is used for acquiring the questionnaire corresponding to the target node as the interactive questionnaire.
Optionally, the interactive question-answering module includes:
the current problem identification acquisition unit is used for taking the problem displayed to the user as the current problem and acquiring the current problem identification;
the target answer identification acquisition unit is used for acquiring an answer identification of the answer selected by the user as a target answer identification;
The next question mark determining unit is used for displaying the next question mark to the user according to the current question mark and the target answer mark;
the loop answer unit is used for taking the question corresponding to the next question mark as the current question, returning to the answer mark for obtaining the answer selected by the user, and continuing to execute the answer mark as the target answer mark until the question in the interactive questionnaire is not displayed any more;
and the questionnaire result determining unit is used for acquiring each target answer identifier and a question identifier corresponding to the target answer identifier as the questionnaire investigation result.
Optionally, the next problem identification determining unit includes:
a jump identification obtaining subunit, configured to determine each jump problem identification according to the current problem identification;
the target identification determining subunit is used for acquiring a target answer identification, combining a preset trigger mechanism, determining a skip question identification corresponding to the answer identification, and taking the skip question identification corresponding to the answer identification as the next question identification;
and the display subunit is used for sending a jump instruction containing the next problem identifier to the front-end interface through a preset page jump script, and driving the front-end interface to display the problem corresponding to the next problem identifier.
Optionally, the result determining module includes:
a target type determining unit, configured to obtain each disease type corresponding to the questionnaire investigation result as a target type;
the risk probability obtaining unit is used for combining the data in the ICD medical library to determine the risk probability corresponding to each target type;
the historical product data acquisition unit is used for acquiring historical product data corresponding to the target type from a product rule base;
and the business result determining unit is used for determining the business processing result based on the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type.
Optionally, the service result determining unit includes:
the historical data acquisition subunit is used for acquiring historical product data, the claim settlement responsibility corresponding to the historical product data and the product benefit from each historical policy claim settlement data;
a historical data analysis subunit, configured to group a basic circulation branch according to historical product data and claim responsibilities corresponding to the historical product data, and take product pay benefits as a branch conclusion of the basic circulation branch;
The branch matching subunit is used for matching the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type with each basic circulation branch, taking the basic circulation branch successfully matched as a target circulation branch, and taking a branch conclusion corresponding to the target circulation branch as a target branch conclusion;
and the business result generation subunit is used for determining business processing results according to the target branch conclusion, wherein the business processing results comprise standard body underwriting, exclusionary underwriting, charging underwriting and refusing underwriting.
In order to solve the above technical problem, the embodiments of the present application further provide a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the steps of the above artificial intelligence based service processing method are implemented when the processor executes the computer program.
To solve the above technical problem, embodiments of the present application further provide a computer readable storage medium storing a computer program, where the computer program implements the steps of the artificial intelligence based service processing method described above when executed by a processor.
According to the service processing method, device, computer equipment and storage medium based on artificial intelligence, when service application data of users are received, health notification information and user basic information contained in the service application data are acquired, abnormal identification is carried out on the health notification information, if the health notification information is identified to be abnormal, an interactive questionnaire is generated by combining the health notification information and the user basic information through a classification tree model, personalized questionnaire is generated according to the service application data of each user, efficiency of acquiring effective information of the users is improved, accuracy and efficiency of service processing are improved, meanwhile, based on the interactive questionnaire, a plurality of interactive questionnaire investigation results are obtained, finally service evaluation is carried out on the questionnaire investigation results through combination of historical product data, service processing results are obtained, rapid intelligent service processing is realized, and service processing efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled 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 business processing method of the present application;
FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based business processing device according to the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device according to 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 and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. 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 present 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.
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, 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.
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 E interface display perts Group Audio Layer III, moving Picture expert compression standard audio layer 3), MP4 players (Moving Picture E interface display perts Group Audio Layer IV, moving Picture expert compression standard audio layer 4), laptop and desktop computers, and so on.
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 service processing method based on artificial intelligence provided in the embodiment of the present application is executed by a server, and accordingly, the service processing device based on artificial intelligence is disposed in the server.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. Any number of terminal devices, networks and servers may be provided according to implementation requirements, and the terminal devices 101, 102 and 103 in the embodiments of the present application may specifically correspond to application systems in actual production.
Referring to fig. 2, fig. 2 shows an artificial intelligence based service processing method according to an embodiment of the present invention, and the method is applied to the server in fig. 1 for illustration, and is described in detail as follows:
s201: and when receiving service application data of the user, acquiring health notification information and user basic information contained in the service application data, wherein the health notification information contains user history health data.
Specifically, before the user makes an application, the application front end provides a service application form to fill in for the user, where the service application form includes health notification information and user basic information, for example, it asks the user whether there is a hospitalization history within 3 years, whether there is the following diseases, etc., after the user fills in these, the user sends the filled service application form to the server through the network transmission protocol, and when the server receives the service application data of the user, the service application data includes health notification information and user basic information.
Wherein the health notification information contains user historical health data including, but not limited to: historical disease diagnosis records of users, familial genetic medical records, chronic diseases, and the like.
The user basic information comprises basic information of the user, such as a user name, an identity card number, a contact way, an age and the like.
S202: and comparing the historical health data of the user with preset conditions, carrying out abnormal recognition on the health notification information, and if the health notification information is recognized to be abnormal, generating an interactive questionnaire by combining the health notification information and the user basic information through a classification tree model.
Specifically, after the health notification information is obtained, historical health in the health notification information filled by the user is analyzed to obtain an analysis result, the analysis result is compared with preset conditions, whether the health notification information has an abnormal type or not is judged, if so, the health notification is judged to have an abnormal state, and an interactive questionnaire is generated according to the health notification and the user information through a classification tree model, wherein the user information comprises, but is not limited to, the age, the gender and the like of the user.
In this embodiment, the health notification information may specifically be selected by selecting questions, or may be filled in by filling in a blank or question-answering manner, and when the health notification information is set as a filling manner of the selected questions, whether an abnormality exists may be determined directly according to a selection result in combination with a preset condition, and when the health notification information is set as a filling manner of filling in a blank or question-answering manner, the analysis may be performed by extracting keywords or recognizing semantics of the content filled in by the user.
The keyword extraction method includes, but is not limited to: a keyword extraction algorithm TF-IDF2 based on statistical characteristics, a keyword extraction algorithm PageRankTextRank algorithm based on a word graph model, an LDA Bayesian model and the like.
The semantic recognition concrete modes include, but are not limited to: neural network models, bert models, and Trandform models, etc.
The preset condition may specifically be preset abnormality types, which may be set according to actual needs, and in this embodiment, the abnormality types are classified into a plurality of abnormality types according to the classification of diseases, for example, abnormality types in which hypertension is present as chronic cardiovascular and cerebrovascular diseases, abnormality types in which cervical spondylosis is present as degenerative basic disease, and the like, which are not specifically limited herein.
Wherein the classification tree model includes, but is not limited to: bayesian networks, neural networks, K-nearest neighbor (KNN) classification algorithms, support vector machines (Support Vector Machine, SVM), and classification decision tree models, among others.
Preferably, the classification Tree model of the present embodiment adopts a classification Decision Tree model, and a Decision Tree (Decision Tree) is a graphical method for intuitively applying probability analysis on the basis of knowing occurrence probabilities of various situations, by constructing a Decision Tree to obtain probabilities that the expected value of the net present value is greater than or equal to zero, evaluate risk of the project, and determine feasibility of the project. Since such decision branches are drawn in a pattern much like the branches of a tree, the decision tree is called decision tree. In machine learning, a decision tree is a prediction model, which represents a mapping relationship between object attributes and object values, a classification decision tree is also called a classification tree (Classification Tree), belongs to a class of decision trees, and nodes where variables are located are called root nodes (root nodes), and the bottom four nodes are called leaf nodes (leaf nodes) or terminal nodes (terminal nodes). The objective is to divide an observation into "high risk" or "low risk" each leaf node being the value of a dependent variable, and the other nodes except the leaf node being independent variables.
It should be noted that, in order to improve the service processing efficiency, the classification tree model of the present application is obtained by training according to historical product data, each terminal node corresponds to a disease influence factor of a type, and the questions related to the disease influence factors are extracted from a questionnaire library to be associated, so as to obtain a questionnaire corresponding to the terminal node, where the disease influence factors may specifically be "whether smoking, whether smoking frequency is high or low", etc.
In this embodiment, the factors related to the severity of various diseases are analyzed through the classification tree model, so that corresponding interactive questionnaires are generated according to the current user information and the health notification.
In this embodiment, according to the current user information and the health notification, a specific implementation manner of the corresponding interactive questionnaire is generated, and reference may be made to the description of the subsequent embodiment, so that no redundant description is provided herein.
S203: based on the interactive questionnaire, a plurality of rounds of interactive questionnaires are adopted to obtain questionnaire investigation results.
Specifically, each interactive questionnaire preset by the server contains more questions, but some questions are not associated or even connected, so as to save question answering time and improve efficiency.
The multi-round interactive question and answer means that the next question is dynamically determined according to the question and answer of the previous round, so that the questions are more targeted, and the influence of excessive irrelevant questions on efficiency and caused data redundancy are avoided.
For a specific implementation of obtaining a questionnaire result by adopting a multi-round interactive questionnaire based on the interactive questionnaire, reference may be made to the description of the subsequent examples, and in order to avoid repetition, a description is omitted here.
S204: and carrying out service evaluation on the questionnaire investigation result by combining the historical product data to obtain a service processing result.
Specifically, the service application of the user is evaluated according to the questionnaire investigation result in combination with the historical product data to obtain the service processing result, and the specific evaluation mode may be evaluating in combination with the historical policy claim data, or may refer to the specific description of the subsequent embodiment, so that the repetition is avoided and will not be repeated here.
The historical product data refers to each type of historical product data, claim settlement responsibility, benefit settlement and the like in the historical policy claim settlement data.
In this embodiment, when service application data of a user is received, health notification information and user basic information contained in the service application data are acquired, abnormal identification is performed on the health notification information, if abnormal health notification information is identified, an interactive questionnaire is generated by combining the health notification information and the user basic information through a classification tree model, so that personalized questionnaires are generated according to the service application data of each user, efficiency of acquiring effective information of the user is improved, accuracy and efficiency of service processing are improved, meanwhile, based on the interactive questionnaire, a plurality of rounds of interactive questionnaire are adopted to obtain questionnaire investigation results, finally intelligent service evaluation is performed according to the questionnaire investigation results to obtain service processing results, rapid intelligent service processing is realized, and service processing efficiency is improved.
In an embodiment, the service application data and the obtained service processing result are stored in the blockchain network node, and sharing of the service application data and the obtained service processing result among different platforms is realized through blockchain storage, so that the data can be prevented from being tampered.
Blockchains are novel application modes of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanisms, encryption algorithms, 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, and an application services layer.
In some optional implementations of this embodiment, in step S202, if it is identified that the health notification information is abnormal, generating, by combining the health notification information and the user basic information through the classification tree model, an interactive questionnaire includes:
acquiring each node dimension of the classification tree model;
screening attribute information corresponding to the node dimension from the health notification information and the user basic information to serve as target information;
Classifying the target information by using a classification tree model, and taking the classified terminal node as a target node;
and acquiring a questionnaire corresponding to the target node as an interactive questionnaire.
Specifically, each node dimension of the classification tree model is obtained, then attribute information corresponding to the node dimension is screened out from health notification information and user basic information to be used as target information, the target information is classified by adopting the classification tree model, a terminal node corresponding to the target information is used as a target node, and a questionnaire corresponding to the target node is obtained to be used as an interactive questionnaire.
The node dimension refers to the dimension required by the classification tree model in each construction (classification) process, for example, gender, age, whether chronic diseases exist or not, and the like.
Wherein the end point is the lowest leaf node in the classification tree model.
It should be noted that, in step S202, after the classification tree model is constructed, a corresponding interactive questionnaire is generated in advance for each terminal node of the classification tree model, and after the target node, that is, the corresponding terminal node is determined, a questionnaire corresponding to the terminal node is obtained as the interactive questionnaire.
It is easy to understand that, from the health notification information and the user basic information, the attribute information corresponding to the node dimension is screened out as the target information, and specifically, the target information may be obtained by fuzzy matching, or may be obtained by attribute identification indexing, and the like, and the specific limitation is not imposed here.
It should be noted that, the classification tree model of the server generates the health notification information corresponding to the user basic information and the user basic information in the data of the historical insurance policy claims, and finally determines the combination of different user basic information and health notification according to the data of the claims corresponding to the user basic information in the data of the historical insurance policy claims, and then generates the corresponding questionnaire to collect the specific disease history data of the user according to the degree of the disease severity, for example, whether the operation treatment is performed, whether the pathology is benign or malignant, etc. as the score mode.
It will be readily appreciated that the type of questionnaire selected may also need to be differentiated according to age, sex of the input, and the need to answer different questions may also be different for children and adults, men and women.
In the embodiment, the interactive questionnaire is generated by combining the health notification information and the user basic information through the classification tree model, so that the generated interactive questionnaire has stronger pertinence, and the accuracy of the questionnaire survey results obtained later is improved.
In some optional implementations of this embodiment, in step S203, based on the interactive questionnaire, a plurality of rounds of interactive questionnaires are adopted, and obtaining the questionnaire survey results includes:
Taking the problem displayed to the user as a current problem, and acquiring a current problem identifier;
acquiring an answer identifier of an answer selected by a user as a target answer identifier;
according to the current question mark and the target answer mark, displaying the next question mark to the user;
taking the question corresponding to the next question mark as the current question, and returning to the answer mark for obtaining the answer selected by the user, and continuing to execute the answer mark as the target answer mark until the question in the interactive questionnaire is not displayed any more;
and acquiring each target answer identifier and a question identifier corresponding to the target answer identifier as a questionnaire investigation result.
Specifically, the questions displayed to the user are used as current questions, the next question identification displayed to the user is determined according to the current question identification and the answer identification of the answer selected by the user, the questions are continuously displayed according to the next question identification selected by the user, and multiple rounds of interactive question and answer are performed according to the mode, so that a final questionnaire survey result is obtained.
The problem identifier refers to an identifier for uniquely identifying the problem, and the identifier can be one or more of a number, a character, a symbol, a text and the like.
The answer identifier refers to an identifier for uniquely identifying the selectable answer, and the answer identifier can be one or more of numbers, characters, symbols, characters and the like.
It should be noted that, the interactive questionnaire includes a plurality of questions, and in this embodiment, a mapping is constructed for each selected answer and the next question according to the degree of association between questions, and the questions to be answered subsequently are determined according to the answer selected by the user, so as to avoid the influence of processing unnecessary questions on efficiency.
According to the current question mark and the target answer mark, the next question mark is displayed to the user, and the specific implementation process can refer to the description of the subsequent embodiment, so that repetition is avoided and repeated description is omitted.
In the embodiment, the number of answer questions is reduced by adopting multiple rounds of interactive questions and answers, unnecessary question waste questionnaire investigation time is avoided, and meanwhile, data redundancy can be reduced, so that a questionnaire investigation result can be obtained quickly and efficiently, and the business processing efficiency is improved.
In some optional implementations of the present embodiment, presenting the next question identifier to the user according to the current question identifier and the target answer identifier includes:
determining each jump problem identifier according to the current problem identifier;
acquiring a target answer identifier, determining a skip question identifier corresponding to the answer identifier by combining a preset trigger mechanism, and taking the skip question identifier corresponding to the answer identifier as a next question identifier;
And sending a jump instruction containing the next problem identification to the front-end interface through a preset page jump script, and driving the front-end interface to display the problem corresponding to the next problem identification.
Specifically, in the interactive questionnaire, except for the last question, each question corresponds to at least one skip question mark, each answer mark is associated with one skip question mark, each skip question mark is determined according to the current question mark, then a target answer mark is obtained, the skip question mark corresponding to the answer mark is determined by combining a preset trigger mechanism, the skip question mark corresponding to the answer mark is used as the next question mark, a skip instruction containing the next question mark is sent to a front-end interface through a preset page skip script, and the front-end interface is driven to display the question corresponding to the next question mark by using the skip instruction.
The preset page jump script is a script file for jumping a page to a page containing a problem corresponding to the next problem identifier by using a jump instruction, and common jump instructions include, but are not limited to: in this embodiment, considering that the problems involved are relatively large, the page rendering efficiency is reduced by adopting these common skip instructions, so that the embodiment sets a tag Div for each problem, the initial state is set to be hidden, and the adopted showDiv function displays the problem corresponding to the next problem identifier to be displayed, thereby avoiding the simultaneous loading of excessive problems or the occupation of a large amount of resources caused by frequent skip, saving server resources, and being beneficial to improving the interactive question-answering efficiency.
The preset trigger mechanism may specifically be to trigger skip display according to the association of the answer identifier and the skip question identifier as a trigger condition.
It should be understood that the trigger mechanism should be set according to the association relationship and the front-back logic relationship between the problems, or according to the actual needs, which is not limited herein.
For example, in a specific embodiment, the skip question identifier corresponding to the question K includes a question 6, a question 7, and a question 10, where the question K has three answers, and answer identifiers are respectively: the method comprises the steps of selecting an answer B by a user, clicking and determining, jumping to the question 7 according to a preset trigger mechanism, and displaying the question 7 on a front-end interface, wherein the jump question associated with the answer A is identified as the question 6, the jump question associated with the answer B is identified as the question 7, and the jump question associated with the answer C is identified as the question 10.
In this embodiment, according to the current question identifier and the target answer identifier, the next question identifier is determined and the question corresponding to the next question identifier is displayed to the user, so that time waste and data redundancy caused by too many irrelevant questions are avoided, and the service processing efficiency is improved.
In some optional implementations of this embodiment, in step S204, performing service evaluation on the questionnaire survey results in combination with the historical product data, where obtaining service processing results includes:
acquiring each disease type corresponding to a questionnaire investigation result as a target type;
combining data in the ICD medical library to determine risk probability corresponding to each target type;
acquiring historical product data corresponding to the target type from a product rule base;
and determining a business processing result based on the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type.
Specifically, through the answer condition of the user related to the questionnaire investigation result, the disease type which exists or is likely to exist by the user is determined as the target type, the risk probability corresponding to each target type is determined by combining the data in the ICD medical library, meanwhile, the historical product data corresponding to all the target types are obtained from the product rule library, and further, the applied business is evaluated by combining the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type, so that the business processing result is determined.
The method comprises the steps of determining the existence or possible existence of a user through answer conditions of the user related to questionnaire survey results, specifically, adopting a fuzzy matching mode, enabling a server to record common disease names in advance and configure the common disease names into a dictionary table, and reading the configured disease names from the configured dictionary table when the questionnaire is conducted. When some unrecorded diseases (such as new diseases, unknown cause diseases, undiagnosed diseases and less common diseases) are encountered, the user is allowed to input, the user input is identified by an intelligent semantic identification mode, guidance, notification and fuzzy matching are performed, and the quick positioning and confirmation of the disease type are realized.
The ICD (International Classification of Diseases ) medical library refers to a database for sorting and collecting various disease related information according to the ICD classification.
The product rule base refers to a database containing various insurance products, such as insurance policy amounts, dangerous seed responsibilities, claim amounts, and the like.
In this embodiment, according to the questionnaire investigation result, the possible disease types are analyzed, and then the service processing result is determined by combining the historical data, which is beneficial to improving the accuracy of the service processing result.
In some optional implementations of this embodiment, determining the business processing result based on the target type, the risk probability corresponding to the target type, and the historical product data corresponding to the target type includes:
acquiring historical product data, the claim settlement responsibility corresponding to the historical product data and the product payment benefits from each historical policy claim settlement data;
according to the historical product data and the corresponding claim settling responsibility of the historical product data, the basic circulation branches are combined, and the product pay benefits are used as branch conclusions of the basic circulation branches;
matching the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type with each basic circulation branch, taking the basic circulation branch successfully matched as a target circulation branch, and taking a branch conclusion corresponding to the target circulation branch as a target branch conclusion;
and determining service processing results according to the target branch conclusion, wherein the service processing results comprise standard body underwriting, exclusionary underwriting, charging underwriting and refusing.
Specifically, through analyzing each historical product data, the claim responsibility and the claim payment income in the historical insurance policy claim data, a basic circulation branch possibly related to each target type is combed, the benefit of the claim payment is further taken as a branch conclusion of the basic circulation branch, then the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type are used for matching with each basic circulation branch, the successfully matched basic circulation branch is taken as a target circulation branch, the branch conclusion corresponding to the target circulation branch is taken as a target branch conclusion, and the service processing result is determined according to the target branch conclusion.
Where base flow branches refer to processing branches that each disease type may involve, for example, product data generated in connection with other disease types, liabilities for claims related to the product data, and corresponding benefits for claims, etc.
In this embodiment, different basic circulation branches are carded through the data of the historical insurance policy claim, so that the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type are matched with each basic circulation branch to obtain the target circulation branch, and then the service processing result is determined based on the branch conclusion corresponding to the target circulation branch, so that the generated service processing result is more objective, and the accuracy of the service processing result 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.
Fig. 3 shows a schematic block diagram of an artificial intelligence based service processing apparatus in one-to-one correspondence with the artificial intelligence based service processing method of the above embodiment. As shown in fig. 3, the artificial intelligence based service processing apparatus includes an information acquisition module 31, a questionnaire generation module 32, an interactive questionnaire module 33, and a result determination module 34. The functional modules are described in detail as follows:
The information obtaining module 31 is configured to obtain, when receiving service application data of a user, health notification information and user base information included in the service application data, where the health notification information includes user history health data;
the questionnaire generating module 32 is configured to compare the historical health data of the user with preset conditions, perform abnormal recognition on the health notification information, and if it is recognized that the health notification information is abnormal, generate an interactive questionnaire by combining the health notification information and the user basic information through the classification tree model;
an interactive question-answering module 33, configured to obtain a question-questionnaire result by adopting multiple rounds of interactive question-answering based on an interactive question-questionnaire;
the result determining module 34 is configured to perform service evaluation on the questionnaire survey result in combination with the historical product data, so as to obtain a service processing result.
Optionally, the questionnaire generation module 32 comprises:
the dimension acquisition unit is used for acquiring each node dimension of the classification tree model;
the information screening unit is used for screening attribute information corresponding to the node dimension from the health notification information and the user basic information to serve as target information;
the information classification unit is used for classifying the target information by using the classification tree model, and taking the classified terminal node as a target node;
And the questionnaire determining unit is used for acquiring the questionnaire corresponding to the target node as an interactive questionnaire.
Optionally, the interactive question-answering module 33 includes:
the current problem identification acquisition unit is used for taking the problem displayed to the user as the current problem and acquiring the current problem identification;
the target answer identification acquisition unit is used for acquiring an answer identification of the answer selected by the user as a target answer identification;
the next question mark determining unit is used for displaying the next question mark to the user according to the current question mark and the target answer mark;
the loop answer unit is used for taking the question corresponding to the next question mark as the current question, and returning to the answer mark for obtaining the answer selected by the user, and continuing to execute the answer mark as the target answer mark until the question in the interactive questionnaire is not displayed any more;
and the questionnaire result determining unit is used for acquiring each target answer identifier and the question identifier corresponding to the target answer identifier as a questionnaire investigation result.
Optionally, the next problem identification determining unit includes:
a jump identification obtaining subunit, configured to determine each jump problem identification according to the current problem identification;
the target mark determining subunit is used for acquiring target answer marks, determining jump question marks corresponding to the answer marks by combining a preset trigger mechanism, and taking the jump question marks corresponding to the answer marks as next question marks;
And the display subunit is used for sending a jump instruction containing the next problem identifier to the front-end interface through a preset page jump script to drive the front-end interface to display the problem corresponding to the next problem identifier.
Optionally, the result determination module 34 includes:
the target type determining unit is used for acquiring each disease type corresponding to the questionnaire investigation result as a target type;
the risk probability obtaining unit is used for combining the data in the ICD medical library to determine the risk probability corresponding to each target type;
the historical product data acquisition unit is used for acquiring historical product data corresponding to the target type from the product rule base;
the business result determining unit is used for determining business processing results based on the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type.
Optionally, the service result determining unit includes:
the historical data acquisition subunit is used for acquiring historical product data, the liability of the corresponding historical product data and the product pay benefits from each historical policy claim data;
the historical data analysis subunit is used for combing basic circulation branches according to the historical product data and the claim liability corresponding to the historical product data, and paying benefits of the products as branch conclusions of the basic circulation branches;
The branch matching subunit is used for matching the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type with each basic circulation branch, taking the basic circulation branch successfully matched as a target circulation branch, and taking a branch conclusion corresponding to the target circulation branch as a target branch conclusion;
and the business result generation subunit is used for determining business processing results according to the target branch conclusion, wherein the business processing results comprise standard body underwriting, exclusionary underwriting, charging underwriting and refusing.
For specific limitations on the artificial intelligence based service processing apparatus, reference may be made to the above limitation on the artificial intelligence based service processing method, and no further description is given here. The above-described modules in the artificial intelligence-based business processing apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
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 is noted that only a computer device 4 having a component connection memory 41, a processor 42, a network interface 43 is shown in the figures, but it is 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, a hard disk, a multimedia card, a card type memory (e.g., SD or D interface display memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an 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 for storing an operating system and various application software installed on the computer device 4, such as program codes for controlling electronic files, etc. 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 a program code stored in the memory 41 or process data, such as a program code for executing control of an electronic file.
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.
The present application also provides another embodiment, namely, a computer readable storage medium storing an interface display program, where the interface display program is executable by at least one processor, so that the at least one processor performs the steps of the artificial intelligence based service processing 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 (such as ROM/RAM, magnetic disk, optical disk), comprising several 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 described in the embodiments of the present application.
It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present 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, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.
Claims (7)
1. The service processing method based on the artificial intelligence is applied to the check and protection service of disease insurance, and is characterized by comprising the following steps:
when service application data of a user are received, health notification information and user basic information contained in the service application data are obtained, wherein the health notification information contains user history health data;
Performing exception identification on the health notification information by comparing the historical health data of the user with preset conditions, and if the health notification information is identified to be abnormal, generating an interactive questionnaire by combining the health notification information and the user basic information through a classification tree model;
based on the interactive questionnaire, adopting a plurality of rounds of interactive questionnaires to obtain questionnaire investigation results;
carrying out service evaluation on the questionnaire investigation result by combining historical product data to obtain a service processing result, wherein the historical product data is historical policy claim settlement data;
if it is identified that the health notification information is abnormal, generating an interactive questionnaire by combining the health notification information and the user basic information through a classification tree model includes:
acquiring each node dimension of the classification tree model;
screening attribute information corresponding to the node dimension from the health notification information and the user basic information to serve as target information;
classifying the target information by using the classification tree model, and taking the classified terminal node as a target node;
acquiring a questionnaire corresponding to a target node as the interactive questionnaire;
Based on the interactive questionnaire, adopting a plurality of rounds of interactive questionnaires to obtain questionnaire investigation results, wherein the obtaining of the questionnaire investigation results comprises the following steps:
taking the problem displayed to the user as a current problem, and acquiring the current problem identifier;
acquiring an answer identifier of an answer selected by a user as a target answer identifier;
displaying the next question mark to the user according to the current question mark and the target answer mark;
taking the question corresponding to the next question mark as the current question, returning to the answer mark for obtaining the answer selected by the user, and continuing to execute the question as the target answer mark until the question in the interactive questionnaire is not displayed any more;
and acquiring each target answer identifier and a question identifier corresponding to the target answer identifier as the questionnaire survey result.
2. The artificial intelligence based business processing method of claim 1, wherein said presenting the next question mark to the user based on the current question mark and the target answer mark comprises:
determining each jump problem identifier according to the current problem identifier;
acquiring a target answer identifier, determining a jump question identifier corresponding to the target answer identifier by combining a preset trigger mechanism, and taking the jump question identifier corresponding to the target answer identifier as the next question identifier;
And sending a jump instruction containing the next problem identification to a front-end interface through a preset page jump script, and driving the front-end interface to display the problem corresponding to the next problem identification.
3. The artificial intelligence based business processing method of claim 1 or 2, wherein the business evaluation of the questionnaire results in combination with the historical product data, the obtaining of the business processing results comprises:
acquiring each disease type corresponding to the questionnaire investigation result as a target type;
combining data in an ICD medical library to determine risk probability corresponding to each target type;
acquiring historical product data corresponding to the target type from a product rule base;
and determining the business processing result based on the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type.
4. The artificial intelligence based business processing method of claim 3, wherein the determining the business processing result based on the target type, the risk probability corresponding to the target type, and the historical product data corresponding to the target type comprises:
Acquiring historical product data, claim settlement responsibilities corresponding to the historical product data and product pay-off benefits from each historical policy claim settlement data;
according to the historical product data and the corresponding claim liability of the historical product data, a basic circulation branch is combed, and the product pay benefits are used as a branch conclusion of the basic circulation branch;
matching the target type, the risk probability corresponding to the target type and the historical product data corresponding to the target type with each basic circulation branch, taking the basic circulation branch successfully matched as a target circulation branch, and taking a branch conclusion corresponding to the target circulation branch as a target branch conclusion;
and determining a business processing result according to the target branch conclusion, wherein the business processing result comprises standard body underwriting, exclusionary underwriting, charging underwriting and refusing.
5. An artificial intelligence based service processing device is applied to a disease insurance check service, and is characterized in that the artificial intelligence based service processing device comprises:
the information acquisition module is used for acquiring health notification information and user basic information contained in service application data when the service application data of a user are received, wherein the health notification information contains user history health data;
The questionnaire generation module is used for carrying out abnormal recognition on the health notification information by comparing the historical health data of the user with preset conditions, and if the health notification information is recognized to be abnormal, generating an interactive questionnaire by combining the health notification information and the user basic information through a classification tree model;
the interactive questionnaire module is used for obtaining questionnaire investigation results by adopting a plurality of rounds of interactive questionnaires based on the interactive questionnaires;
the result determining module is used for carrying out service evaluation on the questionnaire investigation result by combining historical product data to obtain a service processing result, wherein the historical product data is historical policy claim settlement data;
the questionnaire generation module comprises:
a dimension acquisition unit, configured to acquire each node dimension of the classification tree model;
the information screening unit is used for screening attribute information corresponding to the node dimension from the health notification information and the user basic information to serve as target information;
the information classification unit is used for classifying the target information by using the classification tree model, and taking the classified terminal node as a target node;
The questionnaire determining unit is used for obtaining a questionnaire corresponding to the target node and taking the questionnaire as the interactive questionnaire;
the interactive question-answering module comprises:
the current problem identification acquisition unit is used for taking the problem displayed to the user as the current problem and acquiring the current problem identification;
the target answer identification acquisition unit is used for acquiring an answer identification of the answer selected by the user as a target answer identification;
the next question mark determining unit is used for displaying the next question mark to the user according to the current question mark and the target answer mark;
the loop answer unit is used for taking the question corresponding to the next question mark as the current question, and returning to the answer mark for obtaining the answer selected by the user, and continuing to execute the answer mark as the target answer mark until the question in the interactive questionnaire is not displayed any more;
and the questionnaire result determining unit is used for acquiring each target answer identifier and the question identifier corresponding to the target answer identifier as a questionnaire investigation result.
6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the artificial intelligence based business processing method of any of claims 1 to 4 when executing the computer program.
7. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the artificial intelligence based business processing method of any one of claims 1 to 4.
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| CN112381658A (en) * | 2020-11-11 | 2021-02-19 | 中国人寿保险股份有限公司 | Dynamic generation method and system for informing questionnaire information during electronic insurance application |
| CN112330472B (en) * | 2020-11-17 | 2023-10-27 | 中国平安财产保险股份有限公司 | Service data processing method, device, computer equipment and storage medium |
| CN112632995A (en) * | 2020-12-02 | 2021-04-09 | 北京健康之家科技有限公司 | User service request processing method and device, server and storage medium |
| CN113727121A (en) * | 2020-12-18 | 2021-11-30 | 北京沃东天骏信息技术有限公司 | Network live broadcast method and device, electronic equipment and computer readable medium |
| CN112700336A (en) * | 2020-12-29 | 2021-04-23 | 中国人寿保险股份有限公司 | Intelligent insurance underwriting method and system |
| CN113112991A (en) * | 2021-04-08 | 2021-07-13 | 深圳市友杰智新科技有限公司 | Health meter forming method, device, equipment and storage medium |
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