CN112509699A

CN112509699A - Health identification code generation method and device, storage medium and electronic equipment

Info

Publication number: CN112509699A
Application number: CN202011581099.0A
Authority: CN
Inventors: 毋小艺; 刘婷婷; 李林峰
Original assignee: Yidu Cloud Beijing Technology Co Ltd
Current assignee: Yidu Cloud Beijing Technology Co Ltd
Priority date: 2020-12-28
Filing date: 2020-12-28
Publication date: 2021-03-16
Also published as: WO2022142721A1

Abstract

The disclosure belongs to the technical field of data processing, and relates to a method and a device for generating a health identification code, a storage medium and electronic equipment. The method comprises the following steps: acquiring multi-dimensional data for generating a health identification code, and determining identification code dimensions and feature data of the identification code dimensions by using the multi-dimensional data; constructing a health identification code generation model by using identification code dimension and characteristic data, and acquiring health filling data of a user; inputting the health filling data into a health identification code generation model so that the health identification code generation model outputs risk grade data for generating the health identification code and risk factors of the risk grade data; and generating the health identification code of the user according to the risk grade data and the risk factors. The method and the system ensure the accuracy, the reasonability and the real-time performance of the health identification code generation model, provide effective basis for decision of each scene, show the health state for the user in time during the epidemic situation of the infectious disease, and avoid the occurrence of infection in public places.

Description

Health identification code generation method and device, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for generating a health identifier, a computer-readable storage medium, and an electronic device.

Background

When an epidemic outbreak occurs, the whole country enters an emergency state. To reduce the potential for spread of epidemic conditions due to crowd gathering and intimate contact, all closed arenas were shut down. However, the state of home isolation caused by shutdown and production halt causes social economy and life to fall into a 'stop pendulum' state.

The occurrence of the health identification code can visually present the risk assessment of personal health, and an effective, feasible and safe guarantee is provided for the repeated work and production of the whole people. However, the accuracy and the real-time performance of the health identification codes in the current market are not enough, and the generated risk registration data are thin, so that the health states of all users cannot be displayed in time.

In view of the above, there is a need in the art to develop a new method and apparatus for generating a health identifier.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The present disclosure is directed to a method for generating a health identifier, a device for generating a health identifier, a computer-readable storage medium, and an electronic device, so as to overcome, at least to some extent, the problem that a health identifier cannot be generated according to a user risk level due to limitations of related technologies.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to an aspect of the present disclosure, there is provided a method for generating a health identifier, the method including:

acquiring multi-dimensional data for generating a health identification code, and determining an identification code dimension and feature data of the identification code dimension by using the multi-dimensional data;

constructing a health identification code generation model by using the identification code dimension and the characteristic data, and acquiring health filling data of a user;

inputting the health filling data into the health identification code generation model so that the health identification code generation model outputs risk level data for generating the health identification code and risk factors of the risk level data;

and generating the health identification code of the user according to the risk grade data and the risk factors.

In an exemplary embodiment of the present disclosure, the method further comprises:

acquiring complaint processing data corresponding to the risk factors, and calculating a proportion corresponding to the complaint processing data;

and obtaining a threshold corresponding to the proportion, and determining to update the health identification code generation model by using the complaint processing data according to a comparison result of the proportion and the threshold.

In an exemplary embodiment of the present disclosure, the threshold value includes an update threshold value and a determination threshold value; the determining to update the health identifier generation model using the complaint handling data according to the comparison result of the proportion and the threshold includes:

if the proportion is larger than or equal to the updating threshold, updating the health identification code generation model by using the complaint processing data;

if the proportion is larger than the judgment threshold and smaller than the update threshold, judging whether to update the health identification code generation model by the complaint processing data;

and if the proportion is less than or equal to the judgment threshold, updating the health identification code generation model without using the complaint processing data.

and updating the multidimensional data in real time, and updating the risk grade data and the risk factors of the risk grade data by using the updated multidimensional data.

In an exemplary embodiment of the disclosure, the obtaining multidimensional data for generating the health identifier includes:

acquiring original multi-dimensional data used for generating a health identification code, and preprocessing the original multi-dimensional data to obtain multi-dimensional data to be used;

and constructing a multi-source data center by using the multi-dimensional data to be used, and acquiring the multi-dimensional data in the multi-source data center.

In an exemplary embodiment of the disclosure, the inputting the health-fill data into the health identifier generation model includes:

acquiring information data of the user, and generating identification data of the user by using the information data;

establishing an incidence relation between the identification data and the multi-dimensional data in the multi-source data center;

and acquiring target multi-dimensional data of the user in the multi-source data center according to the identification data and the incidence relation, and inputting the health filling data and the target multi-dimensional data into the health identification code generation model.

acquiring a scene passing rule, and judging whether the risk level data and the risk factors meet the scene passing rule;

and if the risk grade data and the risk factors meet the scene passing rule, generating a scene identifier corresponding to the scene passing rule.

In an exemplary embodiment of the disclosure, the identification dimension includes: health status data, past chronic history data, travel history data, exposure history data, occupational dimension data, and family dimension data.

According to an aspect of the present disclosure, there is provided a health identifier generation apparatus, the apparatus including:

the data acquisition module is configured to acquire multi-dimensional data for generating the health identification code and determine an identification code dimension and feature data of the identification code dimension by using the multi-dimensional data;

the model building module is configured to build the health identification code generation model by using the identification code dimension and the feature data and acquire health filling data of a user;

a grade output module configured to input the health fill-in data to the health identifier generation model to cause the health identifier generation model to output risk grade data for generating the health identifier and risk factors of the risk grade data;

an identification code generation module configured to generate a health identification code of the user according to the risk level data and the risk factors.

According to an aspect of the present disclosure, there is provided an electronic device including: a processor and a memory; wherein the memory has stored thereon computer readable instructions which, when executed by the processor, implement the method for generating a health identifier of any of the above-mentioned exemplary embodiments.

According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of generating a health identifier in any of the above-described exemplary embodiments.

As can be seen from the foregoing technical solutions, the method for generating a health identifier, the apparatus for generating a health identifier, the computer storage medium, and the electronic device in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:

in the method and the device provided by the exemplary embodiment of the disclosure, the health identifier generation model can be constructed through multi-dimensional data so as to meet the requirement of generating the user risk level data. On one hand, the health identification code generation model is generated by utilizing the multi-dimensional data, so that the accuracy, the reasonability and the real-time performance of the health identification code generation model are ensured, and further, the health identification code generation model is utilized to generate the corresponding health identification code, so that an effective basis is provided for the decision of each scene; on the other hand, the health identification code can be used for showing the health state of the user in time during the epidemic situation of the infectious disease, thereby avoiding panic and infection in public places.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

Fig. 1 schematically illustrates a flow chart of a method for generating a health identifier in an exemplary embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow diagram of a method of acquiring multidimensional data in an exemplary embodiment of the disclosure;

FIG. 3 schematically illustrates a flow chart of a method of entering health-fill data in a health identifier generation model in an exemplary embodiment of the disclosure;

FIG. 4 schematically illustrates a flow chart of a method of updating a health identifier generation model in an exemplary embodiment of the present disclosure;

FIG. 5 schematically illustrates a flow chart of a method for further updating the health identifier generation model in an exemplary embodiment of the present disclosure;

FIG. 6 schematically illustrates a flow chart of a method of generating a scene identification in an exemplary embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of a health identifier generation apparatus in an exemplary embodiment of the present disclosure;

fig. 8 schematically illustrates an electronic device for implementing a method for generating a health identifier in an exemplary embodiment of the present disclosure;

fig. 9 schematically illustrates a computer-readable storage medium for implementing a method for generating a health identifier in an exemplary embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/parts/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.; the terms "first" and "second", etc. are used merely as labels, and are not limiting on the number of their objects.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.

Aiming at the problems in the related art, the present disclosure provides a method for generating a health identifier. Fig. 1 shows a flow chart of a method for generating a health identifier, as shown in fig. 1, the method for generating a health identifier at least comprises the following steps:

and S110, acquiring multi-dimensional data for generating the health identification code, and determining the dimension of the identification code and the feature data of the dimension of the identification code by using the multi-dimensional data.

And S120, constructing a health identification code generation model by using the identification code dimension and the characteristic data, and acquiring filled data of the user.

And S130, inputting the filled data into a health identification code generation model so that the health identification code generation model outputs risk level data of the user and risk factors of the risk level data.

And S140, generating the health identification code of the user according to the risk grade data and the risk factors.

In an exemplary embodiment of the present disclosure, a health identifier generation model may be constructed from multi-dimensional data to meet the requirements of generating user risk level data. On one hand, the health identification code generation model is generated by utilizing the multi-dimensional data, so that the accuracy, the reasonability and the real-time performance of the health identification code generation model are ensured, and further, the health identification code generation model is utilized to generate the corresponding health identification code, so that an effective basis is provided for the decision of each scene; on the other hand, the health identification code can be used for showing the health state of the user in time during the epidemic situation of the infectious disease, thereby avoiding panic and infection in public places.

The following describes each step of the method for generating the health identifier in detail.

In step S110, multidimensional data for generating the health identifier is acquired, and the identifier dimension and feature data of the identifier dimension are determined using the multidimensional data.

In the exemplary embodiment of the present disclosure, there are various manners of acquiring the multidimensional data, which may be acquired from a database or downloaded from other sources, and this exemplary embodiment is not particularly limited to this.

In an alternative embodiment, fig. 2 shows a flow chart of a method for acquiring multidimensional data, as shown in fig. 2, the method at least comprises the following steps: in step S210, original multidimensional data for the health identifier is obtained, and the original multidimensional data is preprocessed to obtain multidimensional data to be used.

Wherein the raw multi-dimensional data can be used to build a multi-source data center. And can be obtained through an interface or a database reading mode.

Specifically, the original multidimensional data includes census data, medical System data, entry and exit management data, telecommunication data, bluetooth data in public places, Global Positioning System (GPS) activity track data, Application program (APP) end active self-screening/reporting data, family relation data, high risk occupation and other multidimensional data. In addition, the original multi-dimensional data may also include data of other dimensions, which is not particularly limited in this exemplary embodiment.

To facilitate subsequent building of the multi-source data center, the original multi-dimensional data for building the multi-source data center may be in the same or similar format. The obtained original multidimensional data may not be directly constructed, so that the original multidimensional data may be preprocessed.

For example, the preprocessing mode may be parsing and cleaning data in json format or xml format in the original multidimensional data.

Specifically, the parsing may be to parse a json format or an xml format into a json-like format, such as a dictionary, and then perform field setting on data in the format to unify data formats. Further, the data with punctuation in the data can be processed by de-punctuation, or the data with none can be assigned or processed in a unified way, or the time format can be processed in a unified way, or the content in the data, such as the representation modes of men and women, can be processed in a unified way, and the like. In addition, the data cleansing process may also have other cleansing methods, and this exemplary embodiment is not particularly limited thereto.

After the raw data are preprocessed, multidimensional data to be used can be obtained, namely the multidimensional data to be used is data which can be used for constructing a multi-source data center.

In step S220, a multi-source data center is constructed by using the multi-dimensional data to be used, and the multi-dimensional data is obtained in the multi-source data center.

And generating a corresponding database according to different data sources aiming at the acquired multi-dimensional data to be used. For example, a medical system database, an entry and exit management database, a telecommunications data database, and the like may be generated. Further, different databases are utilized to jointly form a multi-source data center, that is, the multi-source data center may be a data center including a plurality of different databases.

After the multi-source data center is built, multi-dimensional data can be acquired in the multi-source data center.

In the exemplary embodiment, the multi-dimensional data, specifically the data source of the multi-dimensional data, is obtained from the built multi-source data center, and the uniform format of the obtained multi-dimensional data is ensured, so that the subsequent operations of feature extraction and the like are facilitated.

After obtaining the multidimensional data, the multidimensional data can be utilized to extract the corresponding identification code dimension and feature data of the identification code dimension.

In an alternative embodiment, the identification code dimension includes: health status data, past chronic history data, travel history data, exposure history data, occupational dimension data, and family dimension data.

Further, there is feature data corresponding to these identification dimensions. For example, the characteristic data in the health condition data may be symptom characteristic data, and specifically, may correspond to specific symptom data such as fever, cough, rhinorrhea, and the like; the characteristic data in the prior chronic history data can be various chronic diseases, such as acute or chronic respiratory diseases, unhealed cancers, diabetes, cardiovascular and cerebrovascular diseases and other specific chronic disease data; the characteristic data in the travel history data may include data related to a place of travel to or the like; the data of the close contact history can comprise data of whether the patient has the close contact history with the confirmed patient and which contact mode; the occupational dimension data comprises data such as whether the occupations are high-risk occupations and what high-risk occupations are, wherein the high-risk occupations can comprise occupations of isolation point work, aviation unit work, transnational transportation work, doctors and the like; the family dimension data comprises data such as the number of family members and personal information of the family members. In addition, other dimension data or other corresponding feature data may also be available, and this is not particularly limited in this exemplary embodiment.

In step S120, a health identifier generation model is constructed using the identifier dimensions and the feature data, and the filled data of the user is obtained.

In an exemplary embodiment of the present disclosure, the health identifier generation model may be constructed after the identifier dimensions and corresponding feature data are acquired. However, to enrich the health identifier generation model and adapt it to various regions, knowledge of the infectious disease and input rules provided by local clinical experts may be further acquired.

Taking the novel coronavirus pneumonia as an example, the knowledge of infectious diseases comprises the infection route, susceptible population, epidemiological history, symptom expression, confirmed cases, clinical typing and the like of the novel coronavirus pneumonia.

Specifically, routes of infection include transmission via respiratory droplets and contact, and there is the potential for transmission via aerosols when exposed to high concentrations of aerosols for extended periods of time in a relatively closed environment; the susceptible population is all; the epidemiological history comprises the traveling history or the living history of areas with high risk and surrounding areas or disease cases reporting community in 14 days before the disease occurs, or the history of contact with a novel coronavirus infector (positive nucleic acid detection person) in 14 days before the disease occurs, or the contact between the areas with high risk and surrounding areas or the patients with fever or respiratory symptoms in the disease cases reporting community, or the aggregated disease occurs; symptoms include fever, hypodynamia, dry cough, and a few symptoms such as nasal obstruction, watery nasal discharge, pharyngalgia, myalgia, and diarrhea; clinical typing includes light, ordinary, heavy and critical types. There are also slight differences in the construction of knowledge of the new coronavirus pneumonia in different countries and regions, and this exemplary embodiment is not particularly limited thereto.

For example, the input rules provided by the local clinical specialist may be to determine that the patient is at a high risk when the patient is on the first day of a common symptom such as cough; when the symptoms of cough disappeared the next day, the patient was further rated as at intermediate risk on that day. The patient may also be determined to be at high risk if the patient has symptoms of cough for 2 consecutive days within 5 days. In addition, other rules may exist according to the rule division of different countries and regions, and this exemplary embodiment is not particularly limited thereto.

After acquiring the identification code dimension and feature data, and the data of knowledge and rules of infectious diseases to be estimated for the health identification code generation model, the health identification code generation model can be constructed.

The health identifier generation model may be a model in which a plurality of risk prediction rules exist. Specifically, taking the novel coronavirus pneumonia as an example, the symptom characteristic data can be divided into 4 typical symptoms and 8 common symptoms. Specifically, 4 typical symptoms are fever, cough, dyspnea, and loss of taste and smell, respectively; the 8 common symptoms include nasal obstruction, angina, myalgia, diarrhea, runny nose, hypodynamia, chest distress and vomiting. The risk estimation rule can be that when the user has three or more common symptoms within 7 days, the risk level of the user on the health condition data is determined to be high risk; when the user has 1-2 common symptoms within 7 days, determining the risk grade of the user on the health condition data as medium risk; when the user has 1 typical symptom within 7 days, determining the risk level of the user on the health condition data as high risk; when the user is free of any typical symptoms and common symptoms, the risk level of the user on the health condition data is determined to be risk-free.

In order to visually display the risk level of the user predicted by the health identification code generation model, health identification codes with different colors can be correspondingly used. The health identifier may include 5 colors, purple, blue, red, yellow, and green, respectively. The purple health identification codes correspond to patients confirmed to be diagnosed in a hospital, the blue health identification codes correspond to patients cured from the hospital, the red health identification codes correspond to any one or more identification code dimensions and represent users with high risk or any three or more identification code dimensions and represent users with medium risk, the yellow health identification codes correspond to any 1-2 identification code dimensions and represent users with medium risk, and the green health identification codes correspond to users with no risk in all identification code dimensions.

After the health identification code generation model is constructed by utilizing the identification code dimension and the characteristic data, the filled data uploaded by the user can be obtained so as to determine the risk level of the user.

When the user uploads the filled data, a button corresponding to risk prediction, such as a my health code button, can be clicked on an application program corresponding to the health identification code generation model, and basic information of the individual and other source data are input to perform application calculation.

In order to ensure timeliness of risk assessment, a user can be required to fill in the data in forms of personal health conditions, family health conditions, travel records, close contact records, nucleic acid detection conditions and the like every day, and after completion, submission can be confirmed.

In step S130, the filled data is input to the health identifier generation model, so that the health identifier generation model outputs the risk level data of the user and the risk factors of the risk level data.

In an exemplary embodiment of the present disclosure, after receiving the filled-in data uploaded by the user, the filled-in data may be input into the health identifier generation model.

In an alternative embodiment, fig. 3 is a flow chart illustrating a method for inputting filling data in a health identifier generation model, as shown in fig. 3, the method at least includes the following steps: in step S310, information data of the user is acquired, and identification data of the user is generated using the information data.

The information data may include identification card data, passport data, birth card data, birth date, and the like of the user.

After the information data is obtained, there are different ways of generating the identification data for adults and minors. The identification data may be a unique identification of the user, i.e. a user corresponds to a unique identification data.

Specifically, for adults, the priority of the identification card data is higher, and the identification data of the user can be generated according to the identification card data and the birth date; in the case where the user is abroad, the passport data of the user within the validity period may be acquired to generate the identification data of the user using the passport data and the date of birth. And the identity card cannot be applied for by the young, so that the corresponding identification data can be generated by using the birth card data and the birth date. Of course, when the minor is abroad, the passport data within the validity period may be acquired to generate the corresponding identification data using the passport data and the date of birth. In addition, other generation manners are also possible, and this exemplary embodiment is not particularly limited to this.

In step S320, an association relationship between the identification data and the multi-dimensional data in the multi-source data center is established.

After the identification data of each user is generated, an incidence relation between the identification data and the multi-dimensional data in the multi-source data center can be established, so that the user can check effective users when logging in an application program corresponding to the health identification code generation model, or relevant data in the multi-source data center can be obtained when the user requires to estimate the risk level.

In step S330, target multidimensional data of the user is obtained in the multi-source data center according to the identification data and the association relationship, and the health filing data and the target multidimensional data are input into the health identification code generation model.

After the identification data and the association relation are obtained, the target multi-dimensional data of the user can be obtained in the multi-source data center. The target multi-dimensional data is data of all dimensions related to the user stored in the multi-source data center.

Furthermore, identification data filled by the user and target multi-dimensional data acquired in the multi-source data center can be input into the health identification code generation model together.

In the exemplary embodiment, the target multi-dimensional data of the user is acquired and input to the health identification code generation model together with the identification data, so that the identification code dimension of the user risk assessment is enriched, and the accuracy and the real-time performance of the user risk assessment are ensured.

After the filling data and the target multi-dimensional data are input into the health identification code generation model, the risk level data and the risk factors of the user output by the health identification code generation model can be obtained.

Wherein the risk level data may be data characterizing the user as being at high risk, medium risk or no risk, so as to visually present the risk level of the user using the risk level data. The risk factors can be factors on all dimensions of the determined risk level data of the user so as to show the reason for obtaining the risk level to the user, and the user can conveniently check and examine the risk level.

In step S140, a health identifier of the user is generated according to the risk level data and the risk factors.

In an exemplary embodiment of the present disclosure, in order to visually display the risk level of the user predicted by the health identifier generation model, health identifiers with different colors may be correspondingly used. The health identifier may include 5 colors, purple, blue, red, yellow, and green, respectively. The purple health identification codes correspond to patients confirmed to be diagnosed in a hospital, the blue health identification codes correspond to patients cured from the hospital, the red health identification codes correspond to any one or more identification code dimensions and represent users with high risk or any three or more identification code dimensions and represent users with medium risk, the yellow health identification codes correspond to any 1-2 identification code dimensions and represent users with medium risk, and the green health identification codes correspond to users with no risk in all identification code dimensions.

In addition, in order to enable the user to determine the reason of the generated health identification code and generate the health identification code by using the risk factors, the main risk factors or all the risk factors can be displayed.

In order to further improve the real-time performance of the user risk level data, real-time data of all dimensions can be dynamically captured to update the risk level data so as to update the generated health identification code.

In an alternative embodiment, the multidimensional data is updated in real time, and the risk level data and the risk factors of the risk level data are updated by using the updated multidimensional data.

The real-time updating of the multidimensional data may be by dynamically capturing real-time data of various data sources such as a medical system, bluetooth, a base station, and the like, or by other means of updating the multidimensional data, which is not particularly limited in this exemplary embodiment.

After the data of a certain dimension is updated, if the risk level data of the user is affected, the risk level data of the user and the corresponding risk factors can be determined again according to the updated dimension data and the health identification code generation model.

For example, after the user actively reports the filled data in the morning, the risk level data of the user is determined to be data representing no risk, and a green health identification code is issued to the user. However, when bluetooth detects that the user is in close contact with a diagnosed patient at 13 o' clock, the updated data and health identifier generation model can be used to re-determine the risk level data of the user as data representing high risk, and re-issue a red health identifier to the user. In addition, the updated risk factors can be displayed to the user again, so that the user can take measures such as isolation or medical treatment.

It is worth noting that in practical application, the whole using process is 10 minutes at most, which is far shorter than the time of the existing risk assessment, so that the function of updating the risk level data can be realized efficiently.

After the risk grade data and the risk factors are displayed for the user, a complaint channel can be introduced for improving the humanization of the health identification code generation model. The complaint channel can support red health identification codes and yellow health identification codes, namely complaints of users with high risk levels and medium risk levels.

Specifically, a user clicks a complaint button on a health code result interface, selects risk factors to be complained and fills out complaint reasons, and submits the complaint factors to a background for auditing by special personnel. In the complaint processing background, the professional personnel checks and judges the data and logic, marks the characteristic data with problems, gives the passing and failing processing, and writes the processing reason, and the health code after passing can be set into other colors.

In order to perform intelligent iterative upgrade on the health identification code generation model by using the complaint processing data of the user, the complaint processing data of the user can be regularly processed. Generally, the periodic processing time period is one week or two weeks, and other time periods may be set according to actual situations, which is not particularly limited in the present exemplary embodiment.

In an alternative embodiment, fig. 4 shows a flow chart of a method for updating a health identifier generation model, as shown in fig. 4, the method at least comprises the following steps: in step S410, complaint processing data corresponding to the risk factors is acquired, and a ratio corresponding to the complaint processing data is calculated.

The complaint handling data includes complaints acquired weekly and data relating to complaint handling. Specifically, the complaint processing data may include data of multiple dimensions, such as complaint reasons, complaint processing conditions, audit states, feature data labels, and processed health code change conditions.

After the complaint processing data is obtained, the proportion of the complaint persons in the dimension of the identification code to all persons holding the health identification code, namely the proportion corresponding to the complaint processing data can be correspondingly calculated.

In step S420, a threshold corresponding to the ratio is acquired, and it is determined to update the health identification code generation model using the complaint handling data according to the comparison result between the ratio and the threshold.

Wherein the threshold comprises an update threshold and a judgment threshold. When the update threshold and the judgment threshold corresponding to the ratio are obtained, the ratio may be compared with the update threshold and/or the judgment threshold, and whether to update the health identification code generation model is determined according to the comparison result.

In an alternative embodiment, fig. 5 shows a flow chart of a method for further updating the health identifier generation model, as shown in fig. 5, the method at least comprises the following steps: in step S510, if the ratio is greater than or equal to the update threshold, the health identifier generation model is updated using the complaint handling data.

The update threshold may be set to 10%, or may be set to other values according to actual situations, and this exemplary embodiment is not particularly limited to this.

When the proportion is 12%, namely the number of complaints is 12% of the number of persons holding the health identification code, and the update threshold is 10%, the proportion can be determined to be larger than the update threshold, so that the corresponding risk assessment rule in the health identification code generation model can be adjusted, and the number of persons and data influenced after adjustment can be tested for being audited by experts. After the expert review passes, the health identifier generation model can be updated with complaint handling data.

For example, when complaints are only cough symptoms, a high risk level is determined, and the number of people with red health identifiers exceeds the update threshold, the rule may be adjusted to yellow health identifiers. Further, after testing the risk assessment rule and expert review passes, the risk assessment rule may be adjusted to update the health identifier generation model.

In step S520, if the ratio is greater than the determination threshold and smaller than the update threshold, it is determined whether to update the health identifier generation model using the complaint handling data.

The determination threshold may be set to 5%, or may be set to other values, which is not particularly limited in this exemplary embodiment.

When the proportion is 8%, the judgment threshold is 5%, and the update threshold is 10%, it can be determined that the proportion is greater than the judgment threshold and less than the update threshold, and therefore it can be manually determined whether the complaint has a value of updating the health identification code generation model. Only when the manual judgment is valuable, the operations such as testing, subsequent expert review and the like are carried out; and if the manual judgment is worthless, updating the health identification code generation model without using the complaint processing data.

In step S530, if the ratio is less than or equal to the determination threshold, the health identifier generation model is not updated using the complaint processing data.

When the proportion is 2% and the judgment threshold is 5%, the proportion is determined to be less than or equal to the judgment threshold, so that the complaint is worthless for updating the health identification code generation model, and the complaint processing data is not required to be used for updating the health identification code generation model.

In the exemplary embodiment, the health identification code generation model can be intelligently and iteratively updated according to the updating threshold and the judging threshold, so that the accuracy and the humanization of risk assessment are improved.

When the health identification code generation model is put into practical use, different traffic rules can be dynamically set according to different practical scenes such as market activities, store business, dining of restaurants, school development, gymnasium construction and company reworking, so that scene identification of corresponding scenes is printed on the basis of outputting risk level data.

In an alternative embodiment, fig. 6 shows a flowchart of a method for generating a scene identifier, as shown in fig. 6, the method at least includes the following steps: in step S610, a scene passing rule is obtained, and it is determined whether the risk level data and the risk factors satisfy the scene passing rule.

The scene passing rule may be a communication rule generated according to different actual scenes. For example, when the risk level data characterizes no risk, all scenarios may be passed; in the scene of dining in a restaurant, a user who has a yellow health identification code due to a chronic past medical history can also pass through the system. In addition, there may be other scenario passing rules, which are not limited in this exemplary embodiment.

Therefore, after the scene passing rule of the corresponding scene is acquired, the risk level data and the risk factors can be used for judging that the corresponding scene passing rule is met when the user is used.

In step S620, if the risk level data and the risk factors satisfy the scene passing rule, a scene identifier corresponding to the scene passing rule is generated.

For example, when the risk level data indicates that the user with the middle risk level meets the scene passing rule that the user can enter a restaurant to eat by holding the yellow health identifier, it can be further determined whether the reason that the user holds the yellow health identifier is due to the chronic medical history. When the reason why the user holds the yellow health identification code is that the chronic medical history indicates that the user hits the scene passing rule of the scene dining, the generated scene identification can be displayed on the yellow health identification code of the user so as to facilitate the passing of the user.

In the exemplary embodiment, passing convenience under different scenes is provided for users with different risk level data according to the scene passing rule, scene management can be dynamically balanced, personalized requirements on the risk level data under different scenes are met, and the method becomes an effective means for reworking and production.

In addition, in an exemplary embodiment of the present disclosure, a health identifier generation apparatus is also provided. Fig. 7 is a schematic structural diagram of a health identifier generation apparatus, and as shown in fig. 7, the health identifier generation apparatus 700 may include: a data acquisition module 710, a model construction module 720, a grade output module 730 and an identification code generation module 740. Wherein:

a data acquisition module 710 configured to acquire multi-dimensional data for generating the health identifier, and determine an identifier dimension and feature data of the identifier dimension using the multi-dimensional data; a model building module 720 configured to build a health identifier generation model using the identifier dimensions and the feature data, and obtain health filling data of the user; a grade output module 730 configured to input the health filling data to the health identifier generation model, so that the health identifier generation model outputs risk grade data for generating the health identifier and risk factors of the risk grade data; and an identification code generation module 740 configured to generate a health identification code of the user according to the risk level data and the risk factors.

The specific details of the apparatus for generating a health identifier have been described in detail in the method for generating a corresponding health identifier, and therefore are not described herein again.

It should be noted that although in the above detailed description several modules or units of the health identifier generating apparatus 700 are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

An electronic device 800 according to such an embodiment of the invention is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.

As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: the at least one processing unit 810, the at least one memory unit 820, a bus 830 connecting different system components (including the memory unit 820 and the processing unit 810), and a display unit 840.

Wherein the storage unit stores program code that is executable by the processing unit 810 to cause the processing unit 810 to perform steps according to various exemplary embodiments of the present invention as described in the "exemplary methods" section above in this specification.

The storage unit 820 may include readable media in the form of volatile storage units, such as a random access storage unit (RAM)821 and/or a cache storage unit 822, and may further include a read only storage unit (ROM) 823.

Storage unit 820 may also include a program/utility 824 having a set (at least one) of program modules 825, such program modules 825 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 830 may be any of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 800 may also communicate with one or more external devices 1000 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 800, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 800 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 860. As shown, a network adapter 840 communicates with the other modules of the electronic device 800 over the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when said program product is run on the terminal device.

Referring to fig. 9, a program product 900 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method for generating a health identifier, the method comprising:

inputting the health filling data into the health identification code generation model so that the health identification code model outputs risk level data of the user and risk factors of the risk level data;

and generating the health identification code of the user according to the risk grade data and the risk information.

2. The method for generating health identifier according to claim 1, wherein the method further comprises:

3. The method for generating health status information according to claim 2, wherein the threshold value includes an update threshold value and a determination threshold value;

the determining to update the health identifier generation model using the complaint handling data according to the comparison result of the proportion and the threshold includes:

4. The method for generating health identifier according to claim 1, wherein the method further comprises:

5. The method for generating health identifier according to claim 1, wherein said obtaining multidimensional data for generating health identifier comprises:

6. The method for generating health identifier according to claim 5, wherein said inputting said health-reporting data into said health identifier generation model comprises:

7. The method for generating health identifier according to claim 1, wherein the method further comprises:

8. The method for generating health identifier according to claim 1, wherein said identifier dimension comprises: health status data, past chronic history data, travel history data, exposure history data, occupational dimension data, and family dimension data.

9. An apparatus for generating a health identifier, comprising:

and the identification code generation module is configured to generate the health identification code of the user according to the risk grade data and the risk information.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a transmitter, carries out the method for generating a health identifier according to any one of claims 1 to 8.

11. An electronic device, comprising:

a transmitter;

a memory for storing executable instructions of the transmitter;

wherein the transmitter is configured to perform the method of generating a health identifier of any of claims 1-8 via execution of the executable instructions.