CN114219667A

CN114219667A - Medical data processing method, device, equipment and medium for insurance service

Info

Publication number: CN114219667A
Application number: CN202111558796.9A
Authority: CN
Inventors: 周英明; 刘保卫; 匡尚超; 马晶; 周华
Original assignee: North Health Medical Big Data Technology Co ltd
Current assignee: North Health Medical Big Data Technology Co ltd
Priority date: 2021-12-20
Filing date: 2021-12-20
Publication date: 2022-03-22

Abstract

The invention discloses a medical data processing method, a device, equipment and a medium for insurance service, wherein the method comprises the following steps: acquiring at least two pieces of target associated data associated with each user to be verified; all or part of at least one identification item in each target associated data is the same; for each user to be verified, determining at least one to-be-processed associated data group by performing combined processing on at least two pieces of target associated data of the current user to be verified; determining a similarity value of at least one to-be-processed associated data group of the current to-be-verified user aiming at each to-be-verified user, and determining target verification data according to the similarity value and the corresponding to-be-processed associated data group; and determining a target verification result based on each target verification data and corresponding pre-reported data so as to execute corresponding services based on the target verification result. The method and the system realize the effects of verifying the user information according to the data information of the user on each third-party platform, determining the service corresponding to the user and reducing the service operation risk.

Description

Medical data processing method, device, equipment and medium for insurance service

Technical Field

The embodiment of the invention relates to the technical field of data processing, in particular to a medical data processing method, a medical data processing device, medical data processing equipment and a medical data processing medium for insurance service.

Background

Before a user acquires a business service, a business organization needs to perform statistical verification on personal information or diagnosis and treatment information and the like related to the user to provide the corresponding business service for the user.

At present, generally, diagnosis and treatment information of a patient is collected in a plurality of medical institutions, accurate matching or field similarity calculation is performed based on keywords in the collected diagnosis and treatment information, and diagnosis and treatment information with high matching degree with a user is determined according to a calculation result. However, due to objective factors, the diagnosis and treatment information in the medical institution may have keyword loss, mismatching of diagnosis and treatment data formats, or inaccurate similarity calculation results, which results in low matching degree between the diagnosis and treatment information and the user, and the problem that reliable diagnosis and treatment information of the user cannot be obtained.

In order to solve the above problems, user information may be verified to reduce business operation risk and provide corresponding business service for the user.

Disclosure of Invention

The invention provides a medical data processing method, a medical data processing device, medical data processing equipment and a medical data processing medium for insurance service, which are used for realizing the effects of providing proper business service for users according to actual information of the users and reducing business operation risks.

In a first aspect, an embodiment of the present invention provides a medical data processing method for insurance services, which is characterized by including:

acquiring at least two pieces of target associated data associated with each user to be verified; the target associated data comprises at least one identification item and data to be verified corresponding to each identification item; all or part of at least one identification item in each target associated data is the same;

for each user to be verified, determining at least one to-be-processed associated data group by performing combined processing on at least two pieces of target associated data of the current user to be verified; wherein, the to-be-processed associated data group comprises two pieces of target associated data;

for each user to be verified, determining a similarity value of at least one to-be-processed associated data group of the current user to be verified, and determining target verification data according to the similarity value and the corresponding to-be-processed associated data group;

and determining a target verification result based on each target verification data and corresponding pre-reported data so as to execute corresponding service based on the target verification result.

In a second aspect, an embodiment of the present invention further provides a medical data processing apparatus for insurance services, including:

the target associated data acquisition module is used for acquiring at least two pieces of target associated data associated with each user to be verified; the target associated data comprises at least one identification item and data to be verified corresponding to each identification item; all or part of at least one identification item in each target associated data is the same;

the device comprises a to-be-processed associated data group determining module, a to-be-processed associated data group determining module and a to-be-processed associated data group determining module, wherein the to-be-processed associated data group determining module is used for determining at least one to-be-processed associated data group by performing combined processing on at least two pieces of target associated data of a current to-be-verified user aiming at each to-be-verified user; wherein, the to-be-processed associated data group comprises two pieces of target associated data;

the target verification data determining module is used for determining the similarity value of at least one to-be-processed associated data group of the current to-be-verified user aiming at each to-be-verified user, and determining target verification data according to the similarity value and the corresponding to-be-processed associated data group;

and the target verification result determining module is used for determining a target verification result based on each target verification data and corresponding pre-reported data so as to execute corresponding service based on the target verification result.

In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement a method for medical data processing for insurance services according to any one of the embodiments of the present invention.

In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are used for executing the medical data processing method for insurance services according to any one of the embodiments of the present invention.

According to the technical scheme of the embodiment, at least two pieces of target associated data associated with each user to be verified are obtained; and preprocessing the acquired target associated data to obtain target associated data with a uniform format, and matching the target associated data in pairs to obtain a to-be-processed associated data set. For each user to be verified, at least one to-be-processed associated data set is determined by performing combined processing on at least two pieces of target associated data of the current user to be verified, a common identification item in the to-be-processed associated data set is obtained based on the identification item in the to-be-processed associated data set and a corresponding original configuration weight value, a use weight value corresponding to the common identification item is determined again, and a similarity value of the to-be-processed associated data set is determined based on the use weight value. And for each user to be verified, determining the similarity value of at least one to-be-processed associated data group of the current user to be verified, determining target verification data according to the similarity value and the corresponding to-be-processed associated data group, comparing the pre-reported data of the user to be verified with the target verification data, judging whether the user to be verified meets the service standard, and correspondingly, taking the obtained comparison result as a target verification result. And determining a target verification result based on each target verification data and corresponding pre-reported data, so as to provide corresponding service for the user to be verified based on the target verification result. The problem that business services provided by each business organization for the user may have business operation risks due to the fact that data are omitted or reported when the business services are obtained for the user is solved, and the effects of determining corresponding business services according to data information of the user on each third-party platform and reducing the business operation risks are achieved.

Drawings

In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, a brief description is given below of the drawings used in describing the embodiments. It should be clear that the described figures are only views of some of the embodiments of the invention to be described, not all, and that for a person skilled in the art, other figures can be derived from these figures without inventive effort.

Fig. 1 is a schematic flow chart illustrating a medical data processing method for insurance services according to an embodiment of the present invention;

fig. 2 is a flowchart illustrating a medical data processing method for insurance services according to a second embodiment of the present invention;

fig. 3 is a flowchart illustrating a medical data processing method for insurance services according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of a medical data processing apparatus for insurance services according to a fourth embodiment of the present invention.

Fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

As the kinds of business and medical services are more and more, the coverage area is wider and wider, and in order to reduce the risk of business operation, user information needs to be verified before providing business services for users. In order to avoid the situation that information is missed or misreported when a user acquires corresponding business service, data information connection can be established between a business organization and medical organizations, so that user information can be checked in real time, and information related to the user, such as personal identity information, diagnosis and treatment records and the like, can be acquired from each medical organization. And determining whether the user meets the business service standard of medical business or business according to the information matching result through the related information acquired from each medical institution.

Example one

Fig. 1 is a flowchart illustrating a medical data processing method for insurance services according to an embodiment of the present invention, where the embodiment is applicable to a case where a business entity verifies user information when a user acquires a business service or a medical service, and provides a business service corresponding to the user according to the user information, and the method may be implemented by a medical data processing apparatus for insurance services, where the apparatus may be implemented in the form of software and/or hardware, and the hardware may be an electronic device, such as a mobile terminal or a PC terminal.

As shown in fig. 1, the method includes:

s110, acquiring at least two pieces of target associated data associated with each user to be verified; the target associated data comprises at least one identification item and data to be verified corresponding to each identification item; all or part of at least one identification item in the target related data is the same.

The user to be authenticated can be understood as a user needing to perform identity information authentication before providing a business service for the user, the business mechanism can be a commercial business mechanism or a medical business mechanism, and the business service can include a commercial business or a medical business and the like. The target associated data may be data information which is acquired from each medical institution and is matched with the user to be verified, the target data to be processed may be recorded information from different medical institutions, or may be information which is recorded by the same user in the same medical institution for multiple times, and the target associated data may be acquired in a manner of searching for information which has the same identification attribute as the user to be verified from an information base of each medical institution. The number of the target associated data can be one or more, and the more the number is, the more comprehensive the statistics of the user information is, so that the optimal business service is provided for the user, and meanwhile, the risk when the business mechanism provides the business service for the user is reduced. The identification item can be understood as an information category associated with the user, and can include information categories such as the name, sex, identification number, mobile phone number, home address, and medical records of the user. The data to be verified can be understood as information data corresponding to each identification item.

Specifically, before the business organization provides business services for the user, relevant information of the user needs to be verified, and the user needing to verify the information is used as the user to be authenticated. In order to obtain the target associated data of the user to be verified, the information associated with the user to be verified can be searched from the information base of each medical institution, and the information associated with the information of the user to be verified is used as the target associated data. The target associated data includes at least one identification item and data to be verified corresponding to each identification item, it can be understood that the target associated data is obtained from an information base of each medical institution, different medical institutions may have coincident information when recording associated information of users, for example, in a normal case, information such as names, sexes, and identification numbers of users may be recorded, but each medical institution may also have non-coincident information such as a home address, a birth address, or a life work and rest when recording associated information of users. The different information corresponds to different identification items, and correspondingly, at least one identification item in the target associated data which is acquired from each medical institution and is related to the user to be verified can be the same or partially the same.

Optionally, the obtaining at least two pieces of target association data associated with each user to be authenticated includes: acquiring a plurality of pieces of stored associated data to be used from at least two third-party platforms; and determining the associated data to be used corresponding to the same user identifier, and processing the associated data to be used according to the data format of the corresponding identification item to obtain the target associated data of the user to be verified, which is matched with the user identifier.

The third-party platform can be understood as each medical institution, and when the user visits a doctor in the medical institution, the medical institution can acquire the relevant information of the user and then store the information in the information base, so that if the business institution wants to acquire the relevant target associated data of the user to be verified, the relevant target associated data can be acquired through the third-party platform. The associated data to be used may be understood as user information obtained from a third party platform, and may or may not match the user to be authenticated. The user identifier may be understood as identification information of the user, and has uniqueness, and the user identifier may include multiple types, for example, identification number information of the user, mobile phone number information, or number information corresponding to the user, or identification information generated for each user based on an encryption algorithm, for example, the encryption algorithm may be a hash encryption algorithm, and the like.

Specifically, when a user visits a doctor on the third-party platform, the third-party platform records information related to the user, stores the acquired information in the information base, and when target associated data of each user to be verified needs to be acquired, the target associated data can be inquired and acquired in the information base of the third-party platform. The related information of all users of the platform is recorded in an information base of the third-party platform, in order to determine the corresponding relation of the related information of the users, a user identifier can be established, the related information of the users is determined through the user identifier, data corresponding to the user identifier information are used as the related data to be used, the related data to be used are processed, the related data to be used are divided into different identification items, meanwhile, the data formats corresponding to the identification items are processed in a unified mode, and the unification of the data formats is guaranteed. For example, the data may be checked and verified, duplicate data in the information or invalid spaces before and after the data may be deleted, the format may be verified with respect to fields such as a birth date, an identification number, a mobile phone number, and the like, and the data that does not meet the format requirement may be regarded as invalid data and nulled. Taking the identification number as an example, the identification number has 15 bits and 18 bits, and in order to ensure the uniformity of the data format, the 15-bit identification number may be converted into 18 bits according to a conversion rule, where the conversion rule may be based on the birth date. And after the data processing is finished, the target associated data of the user to be verified, which is matched with the user identification, can be obtained.

S120, aiming at each user to be verified, determining at least one associated data group to be processed by carrying out combined processing on at least two pieces of target associated data of the current user to be verified; wherein, the to-be-processed associated data group comprises two pieces of target associated data.

Wherein, the current user to be authenticated can be understood as the user to be authenticated who is performing or is about to perform information verification. The to-be-processed associated data set may be understood as an associated data set obtained by combining the target associated data acquired in each third-party platform, for example, the combination mode may be pairwise combination, that is, the to-be-processed associated data set may include at least two pieces of target associated data related to the user in each third-party platform.

Specifically, at least two pieces of target associated data of each user to be authenticated can be acquired from each third-party platform, the user to be authenticated, which needs to be verified currently, is taken as the current user to be authenticated, and at least two pieces of target associated data of the current user to be authenticated are acquired. The obtained at least two pieces of target associated data are combined, and at least one to-be-processed associated data group can be determined.

Optionally, before the determining at least one to-be-processed associated data set by performing combined processing on at least two pieces of target associated data of the current to-be-authenticated user, the method further includes: and determining an original configuration weight value corresponding to the identification item in each to-be-processed associated data so as to determine to-be-verified data according to the original configuration weight value.

The number and the type of the identification items can be determined according to actual requirements, and the number and the type of the identification items which can be set by each third-party platform can be the same or different. For example, 10 identification items such as name, gender, identification number, mobile phone number, home address, work unit, work property, work and rest habits can be set. The original configuration weight value can be understood as an initial weight value set for a plurality of preset identification items, the original configuration weight value corresponding to each identification item can be distributed averagely, or can be set according to the importance degree of the identification item, the weight value of the identification item with high importance degree is set to be higher, the weight value of the identification item with low importance degree is set to be lower, and the sum of the original configuration weight values of all the identification items is 1.

Specifically, in consideration of the fact that the identification items set by the third-party platforms are not identical, in order to determine the weight value corresponding to each identification item, before determining at least one to-be-processed associated data set by performing combined processing on at least two pieces of target associated data of the current to-be-verified user, a plurality of identification items may be set in advance, and an original configuration weight value is set for each identification item, so that when the number and the category of the identification items set by different third-party platforms are not identical, the weight values are redistributed to the identification items. After the original configuration weight value corresponding to each identification item is determined, the to-be-verified data corresponding to the identification item can be determined according to each identification item and the corresponding original configuration weight value.

Illustratively, each identification item is divided into two categories, one category is an explicit information data identification item with unique information, and is marked as CI1, CI2, … and CIn; the other is fuzzy data identification items, which are marked as FI1, FI2, … and FIm, and each identification item is provided with a corresponding original configuration weight value, and the sum of the original configuration weight values is 1. The definite information data identification items CI1, CI2 and … respectively have original configuration weight values cw1, cw2, … and cwn; the original configuration weight values of the fuzzy data identification items FI1, FI2, … and FIm are fw1, fw2, … and fwm respectively, and the sum of the original configuration weight values corresponding to the identification items is cw1+ cw2+ … + cwn + fw1+ fw2+ … + fwm equal to 1.

Optionally, the determining at least one to-be-processed associated data group by performing combined processing on at least two pieces of target associated data of the current to-be-verified user includes: combining the at least two pieces of target associated data pairwise to obtain a plurality of associated data groups to be used; and determining a common identification item in the current to-be-used associated data group aiming at each group of to-be-used associated data group, and determining a to-be-processed associated data group corresponding to the to-be-used associated data based on the common identification item and corresponding to-be-verified data.

The to-be-used associated data set can be understood as an associated data set obtained by combining each pair of target associated data, and the common identification item can be understood as the same identification item contained in the to-be-used associated data set, for example, the to-be-used associated data set contains target associated data 1 and target associated data 2, the target associated data 1 and the target associated data 2 simultaneously contain the name and the identification number of the user to be verified, and the common identification item is the name and the identification number.

Specifically, when determining the associated data sets to be processed, at least two target associated data sets may be combined in pairs, that is, when identifying the target associated data set of the user to be verified, the target associated data sets acquired from each third-party platform may be matched in pairs to obtain a plurality of associated data sets to be used. It can be understood that each target associated data set may include a plurality of identification items, where the categories of the identification items may be completely the same or may not be completely the same, and the same identification items in the associated data sets to be used, which are obtained by pairwise matching of the target associated data sets, are used as common identification items. And then obtaining a to-be-processed associated data group corresponding to the to-be-used associated data according to the common identification item and the to-be-verified data corresponding to the common identification item.

S130, aiming at each user to be verified, determining the similarity value of at least one to-be-processed associated data group of the current user to be verified, and determining target verification data according to the similarity value and the corresponding to-be-processed associated data group.

The similarity value can be used for representing the similarity between target associated data groups contained in the associated data groups to be processed, the association degree of data between the associated data groups to be processed can be determined according to the similarity, and the higher the similarity value is, the stronger the association between the associated data groups to be processed is. The target authentication data may be understood as information data corresponding to common identification items of users to be authenticated, and the number of the target authentication data may be plural and match the number of the common identification items.

Specifically, target associated data of each user to be verified is obtained from each third-party platform, at least one associated data group to be processed can be obtained based on each target associated data, in order to determine target verification data corresponding to the current user to be verified, similarity calculation can be performed on each associated data group to be processed, and whether each piece of data to be verified is the target verification data of the current user to be verified or not is determined according to a similarity calculation result. It can be understood that each target associated data group includes a plurality of identification items, and similarity matching is performed on to-be-verified data corresponding to each identification item, so that whether the to-be-verified data belong to the same to-be-verified user can be determined, and further whether the to-be-verified data corresponding to each identification item are target verification data can be determined.

S140, determining a target verification result based on each target verification data and corresponding pre-reported data, and executing corresponding service based on the target verification result.

The pre-reported data may be understood as data associated with each user to be verified, which needs to be uploaded in the system when the third-party platform performs a service for each user to be verified. The target verification result may be understood as a result obtained by comparing the target verification data with the pre-reported data, that is, a verification result obtained by performing information verification on each user to be verified, to determine whether each target verification data is consistent with the pre-reported data, and the target verification result may include two types, one type is that each target verification data belongs to the same user to be verified, and the other type is that each target verification data does not belong to the same user to be verified.

Specifically, when each user to be verified acquires service services on each third-party platform, a service organization is required to provide corresponding data such as personal information, the data information provided by the user to be verified is used as pre-reported data, a target verification result corresponding to the current user to be verified is determined by comparing target verification data of the current user to be verified with the corresponding pre-reported data, and then a corresponding target service is provided for the current user to be verified based on the target verification result.

Optionally, the determining a target verification result based on each target verification data and corresponding pre-reported data includes: and calling target verification data to be compared corresponding to the pre-reported data from each target verification data, and determining a target verification result by comparing the target verification data to be compared with the pre-reported data.

The target verification data to be compared can be understood as data which needs to be compared with the pre-reported data in each target verification data.

Specifically, before providing corresponding service for each user to be authenticated, the relevant information of each user to be authenticated needs to be verified to determine whether the service can be provided for the current user to be authenticated or not, and to provide more appropriate service for the current user to be authenticated. In order to ensure the authenticity of the information of each user to be verified, target verification data to be compared corresponding to the pre-reported data are taken from each target verification data to be compared and compared according to the pre-reported data corresponding to each service. If the obtained comparison result is that the information is consistent, the target verification data of the user to be verified can be matched with the service standard of the service, and corresponding service is provided for the user. If inconsistent data exists in the obtained comparison result, it indicates that the current user does not conform to the service standard of the service, and the service cannot be provided for the user. Correspondingly, according to the information provided by the current user to be verified, the appropriate service can be provided for the user.

Example two

As an optional embodiment of the foregoing embodiment, fig. 2 is a flowchart of a medical data processing method for insurance services according to a second embodiment of the present invention, and optionally, for each user to be authenticated, a similarity value of at least one to-be-processed associated data group of the current user to be authenticated is determined, and target authentication data is determined to be refined according to the similarity value and the corresponding to-be-processed associated data group.

As shown in fig. 2, the method includes:

s210, acquiring at least two pieces of target associated data associated with each user to be verified; the target associated data comprises at least one identification item and data to be verified corresponding to each identification item; all or part of at least one identification item in the target related data is the same.

S220, aiming at each user to be verified, determining at least one associated data group to be processed by carrying out combined processing on at least two pieces of target associated data of the current user to be verified; wherein, the to-be-processed associated data group comprises two pieces of target associated data.

S230, aiming at each to-be-processed associated data group, determining an original configuration weight value of a current common identification item in the current to-be-processed associated data group, and a weight sum of original configuration weight values of all common identification items in the current to-be-processed associated data group, and determining a use weight value of the current common identification item.

Each to-be-processed associated data group is composed of target associated data, each target associated data comprises a plurality of identification items, the number and the type of the identification items contained in each target associated data are not identical, when each identification item is preset, a corresponding original configuration weight value is set for each identification item, after the target associated data are matched pairwise to obtain the to-be-processed associated data group, the to-be-processed associated data group comprises a common identification item and a non-common identification item, and in order to obtain a more objective and reliable similarity value of each to-be-processed associated data group, the weight value of the common identification item needs to be determined again. The use of a weighting value is understood to mean the actual weighting value determined for each common identifier in each pending association data set. The current common identification item may be understood as an identification item common to the target associated data in the current to-be-processed associated data group.

Specifically, the target associated data of the current user to be verified are matched pairwise to obtain at least one associated data group to be processed, and generally, the similarity value of each associated data group to be processed can be determined based on the identification item in the associated data group to be processed and the corresponding original configuration weight value. However, in consideration of the situations that key fields are missing or missing when each target associated data is statistically recorded, it is necessary to determine common identification items in each to-be-processed associated data set and to re-determine actual weight values corresponding to the common identification items, that is, the use weight values of the current common identification items.

For example, if the number of the preset identification items is 10, an original configuration weight value is set for each identification item, the weight values corresponding to the identification items may be equal or unequal, and the sum of the original configuration weight values of the identification items is 1. The association data group to be processed comprises two target association data, wherein the identification item in the first target association data comprises: CI1, CI2, CI3, CI4, FI1 and FI2, and the corresponding original configuration weight values are respectively as follows: cw1, cw2, cw3, cw4, fw1, fw 2; the identification item in the second target association data includes: CI1, CI2, CI4 and FI1, the corresponding original configuration weight values are: cw1, cw2, cw4, fw1, and the common identifiers in the associated data group to be processed can be determined as: CI1, CI2, CI4 and FI1, wherein the use weight values corresponding to the common identification items are respectively as follows: cw1_ new, cw2_ new, cw4_ new and fw1_ new. When similarity calculation is performed on the associated data group to be processed, calculation is performed based on the common identification items and the use weight values corresponding to the common identification items, that is, the weight values corresponding to the common identification items need to be determined again. Taking the use weight value cw1_ new corresponding to the common item CI1 as an example, cw1_ new may be determined by the following formula:

similarly, the determination methods of the respective use weight values cw2_ new, cw4_ new, and fw1_ new are the same as the determination method of cw1_ new. When calculating the similarity value of the current to-be-processed associated data set, only the common identification items need to be calculated, and therefore, the weight values corresponding to the common identification items need to be re-determined, that is, the use weight values of the current common identification items are determined, and the sum of the use weight values is still 1, that is:

cw1_new+cw2_new+cw4_new+fw1_new＝1

s240, aiming at each to-be-processed associated data group, determining a first similarity value of to-be-verified data of the same common identification item in the current to-be-processed associated data group.

The first similarity value may be understood as a similarity value corresponding to the same common identification item, the number of the first similarity values may be multiple, and the first similarity value may correspond to the number of the common identification items in each to-be-processed associated data set, and the first similarity value may be calculated based on the common identification items and the corresponding use weight values.

Specifically, each to-be-processed associated data group includes a plurality of common identification items, the plurality of common identification items include two types of identification items, the identification item corresponding to the explicit information and the identification item corresponding to the fuzzy information, data unique to the name, the sex, the mobile phone number, the identification number and the like is used as the explicit information, and data unique to the home address, the work unit, the residential address, the birth address and the like is used as the fuzzy information. That is, the explicit information may be understood as explicit data having uniqueness, and the fuzzy information may be understood as data having a large factor such as personal habits, and generally requires semantic recognition. And calculating the similarity value of the to-be-verified data of each common identification item to obtain a first similarity value corresponding to each common identification item.

It should be noted that the similarity value calculation method for the explicit information identification item is different from the similarity value calculation method for the fuzzy information identification item. The definite information identification item adopts a similarity value determination method which is not 0, namely 1, and the similarity value of the fuzzy information identification item is jointly calculated by a plurality of similarity value calculation methods.

For example, taking two target related data as an example, if the data to be verified in the common identification item is the same, the first similarity value corresponding to the common identification item is 1, and if the data to be verified in the common identification item is different, the first similarity value corresponding to the common identification item is 0. For example, a cosine similarity algorithm, a Jaccard similarity algorithm, a similarity algorithm based on euclidean distance, or the like may be adopted, and a corresponding weight value is set for each similarity algorithm, so as to determine a first similarity value of the common identification item in the fuzzy data based on the identification item corresponding to the fuzzy data and the corresponding weight value.

For example, the first similarity values corresponding to the explicit information identification items CI1, CI2, and CI4 among the common identification items are: sim _ CI1, sim _ CI2, and sim _ CI 4. The fuzzy information identification item in the common identification item is FI1, the similarity value calculation method of FI1 can be 3 similarity value calculation methods of SimMethod1, SimMethod2 and SimMethod3, and corresponding weight values are set for the similarity value calculation methods, and are respectively as follows: fw _ method1, fw _ method2, and fw _ method3, the first similarity value calculation result corresponding to FI1 is:

sim_FI1＝fw_method1×RES_FI1_method1+fw_method2×RES_FI1_method2

+fw_method3×RES_FI1_method3

and S250, aiming at each to-be-processed associated data group, determining the similarity value of the current to-be-processed associated data group according to the first similarity value and the corresponding use weight value of each common identification item in the current to-be-processed associated data group.

The similarity value of the currently to-be-processed associated data set may be understood as a similarity value determined based on each first similarity value and the corresponding weight value.

Specifically, the description is continued with the determination method of each usage weight value in the above steps, and the similarity value of the current to-be-processed associated data set may be determined based on the first similarity value corresponding to each common identification item and the corresponding usage weight value.

Based on each common identification item and the corresponding use weight value, a first similarity value corresponding to each common identification item can be determined, and the obtained similarity value calculation results are respectively as follows: sim _ CI1, sim _ CI2, sim _ CI4, and sim _ FI 1.

The similarity value sim of the associated data set to be processed can be calculated according to the following formula:

sim＝sim_CI1×cw1_new+sim_CI2×cw2_new+sim_CI4×cw4_new

+sim_FI1×fw1_new

optionally, the determining a similarity value of at least one to-be-processed associated data group of the current to-be-verified user, and determining target verification data according to the similarity value and the corresponding to-be-processed associated data group includes: for each to-be-processed associated data group, if the similarity value of the current to-be-processed associated data group is greater than a first preset similarity threshold value, and the number of identification items of common identification items in the current to-be-processed associated data group is greater than a preset identification item number threshold value, determining that the current to-be-processed associated data group corresponds to the same to-be-verified user, and obtaining first verification data; and determining the target verification data according to the first verification data of each to-be-processed associated data set.

The first preset similarity threshold may be understood as an upper limit value of a similarity threshold preset for each to-be-processed associated data set, and may be set to 0.8, for example. The preset identification item number threshold may be understood as the number of common identification items set in advance, and may be set to 3, for example. The first verification data may be understood as information data corresponding to the common identification item in the processing related data group that is to satisfy the first preset similarity threshold and the preset identification item number threshold.

Specifically, in order to determine whether the information data corresponding to the identification item in the current to-be-processed associated data group is the information data of the same to-be-verified user, a first preset similarity threshold and an identification item number threshold may be preset, the obtained similarity value of the current to-be-processed associated data group is compared with the first preset similarity threshold, if the similarity value of the current to-be-processed associated data group is greater than the first preset similarity threshold and the number of the identification items included in the current to-be-processed associated data group is greater than the preset identification item number threshold, the information data corresponding to the common identification item in the current to-be-processed associated data group may be determined to be the data of the same to-be-verified user, and the obtained first verification data may be used as the target verification data.

Optionally, if the similarity value of the current to-be-processed associated data group is smaller than a second preset similarity threshold, it is determined that the current to-be-processed associated data group does not belong to the same to-be-verified user, and first early warning information is fed back.

The second preset similarity threshold may be understood as a lower limit value of the similarity threshold preset for each to-be-processed associated data set, and may be set to 0.6, for example. The first warning information may be understood as a prompt message sent when the current to-be-processed associated data group does not belong to the same to-be-verified user, for example, the first warning information may be "the information record similarity of the user is low, and there is a high risk".

Specifically, a second preset similarity threshold is preset, if the similarity value of the current to-be-processed associated data group is smaller than the first preset similarity threshold, the similarity value of the current to-be-processed associated data group is compared with the second preset similarity value, and if the similarity value is smaller than the second preset similarity threshold, it is indicated that the similarity of the information data corresponding to the identification item in the current to-be-processed associated data group is low, and the information data do not belong to the same to-be-verified user, and the risk is high when the to-be-verified user is served, so that the first early warning information is fed back according to the comparison result.

Optionally, if the similarity value of the current to-be-processed associated data set is between the second preset similarity threshold and the first preset similarity threshold, determining that the current to-be-processed associated data set is fuzzy data, and feeding back second early warning information.

The fuzzy data can be understood as information data which can not determine whether the information data in the current to-be-processed associated data set is the information data of the same to-be-verified user. The second warning information can be understood as prompt information which is fed back to related personnel aiming at the fuzzy data and is used for prompting manual intervention confirmation.

Specifically, if the similarity value of the current to-be-processed associated data group is between the second preset similarity threshold and the first preset similarity threshold, it cannot be determined whether the current to-be-processed associated data group belongs to the same to-be-verified user, at this time, second early warning information may be fed back according to the comparison result, and the staff may confirm whether the current to-be-processed associated data group belongs to the same to-be-verified user.

Optionally, if the similarity value of the current to-be-processed associated data set is greater than a preset similarity threshold value, and the number of the common identification items is less than a preset identification item number threshold value, determining that the current to-be-processed associated data set is fuzzy data, and feeding back second early warning information.

Specifically, a situation that the similarity value of the current to-be-processed associated data group is greater than a preset similarity threshold value but the number of the common identification items is smaller than a preset identification item number threshold value may also occur, when such a situation occurs, whether the current to-be-processed associated data group belongs to the same to-be-verified user cannot be definitely determined, the current to-be-processed associated data group may be used as fuzzy data, second early warning information is fed back, and then, a worker manually determines whether the current to-be-processed associated data group belongs to the same to-be-verified user.

S260, determining the target verification data according to the first verification data of each to-be-processed associated data set.

According to the technical scheme of the embodiment, for each to-be-processed associated data group, the original configuration weight value of the current common identification item in the current to-be-processed associated data group and the weight sum of the original configuration weight values of all the common identification items in the current to-be-processed associated data group are determined, the use weight value of the current common identification item is determined, the corresponding weight value is reset for the common identification item, the proportion of each common identification item in the similarity value calculation process is adjusted, and the obtained similarity value is more reliable. And for each to-be-processed associated data group, determining a first similarity value of to-be-verified data of the same common identification item in the current to-be-processed associated data group, and further calculating the similarity value of the to-be-processed associated data group based on the common identification item and the corresponding use weight value. Aiming at each to-be-processed associated data group, determining the similarity value of the current to-be-processed associated data group according to a first similarity value and a corresponding use weight value of each common identification item in the current to-be-processed associated data group, adopting a strictly equal similarity value calculation method for the identification item of definite information, adopting a plurality of similarity value calculation methods for fuzzy information, adopting a weight value corresponding to each similarity value calculation method for the fuzzy information, obtaining a more objective and reliable similarity value by adopting different similarity calculation methods for different identification items, then comparing the similarity value of the current to-be-processed associated data group with a preset similarity threshold value, and sending corresponding early warning information according to whether the to-be-processed associated data belongs to fuzzy data or not. The problem that the similarity value obtained by a single similarity value calculation method is not accurate enough is solved, and the effect of improving the accuracy of calculating the similarity value of the to-be-processed associated data set is achieved.

EXAMPLE III

In a specific example, as shown in fig. 3, relevant data (i.e., target related data of each user to be authenticated) of each user to be authenticated is collected from each third-party platform, and the data collected from each third-party platform is aggregated, where the data includes a user name, identity information data, a mobile phone number, personal information data, diagnosis data, treatment data, cost data, and the like. And then, carrying out data preprocessing on the acquired target associated data, and carrying out operations such as examination, verification, conversion and the like on each target associated data, so as to delete repeated information and correct errors in the data. For example, deleting invalid spaces before and after the character string, performing format check on fields such as a birth date, an identification card number, a mobile phone number and the like, regarding data which does not meet format requirements as invalid data and performing blank operation, converting a 15-bit identification card number into 18 bits according to rules, unifying a birth date format and the like. Meanwhile, a plurality of identification items (i.e., identification items) are preset according to the common information category or expert experience of each medical institution collected user, for example, the identification items may include an identification number, a name, a gender, a mobile phone number, a birth address, and the like of each user to be verified, and a corresponding initial weight value (i.e., an original configuration weight value) is set for each identification item, where the identification items may be set according to a specified field.

It can be understood that the collected target associated data of each user to be verified may include a plurality of identification items, and when calculating the similarity values between the information of the users to be verified, different similarity value calculation methods need to be adopted for different categories of identification classes. All the identification items are divided into two types, one type is a definite information identification item type which can comprise definite content identification items such as names, sexes, mobile phone numbers, identity card numbers and the like, and the identification items are marked as CI1, CI2, … and CIn; the other is fuzzy data identification item class, which includes identification items needing semantic understanding, such as birth address, residence address, work unit, etc., and are denoted as FI1, FI2, … and FIm, and such information is greatly influenced by personal habits and other factors. For the definite information, the similarity needs to be calculated by adopting a strict equality method, namely the similarity is not 0 or 1, if the data to be verified in the two pieces of target associated data are completely the same, the similarity value is 1, otherwise, if the data to be verified are not completely the same, the similarity value is 0. For fuzzy data identification item classes, each identification item needs to adopt two or more similarity value calculation methods, corresponding weight values are set for each similarity value calculation method, weighting and summing are carried out on multiple similarity calculation results to serve as the final similarity of the identification item, and the similarity value calculation method of the identification item of the fuzzy data class can select a cosine similarity algorithm, a Jaccard algorithm, a similarity algorithm based on Euclidean distance and the like. Wherein, the weight values corresponding to the definite information class identification items are recorded as cw1, cw2, … and cwn; the weight values corresponding to the fuzzy identification items are denoted as fw1, fw2, … and fwm, and the sum of ownership weight values is 1, that is, cw1+ cw2+ … + cwn + fw1+ fw2+ … + fwm is 1.

It should be noted that when pairwise matching is performed on the target associated data acquired from each third-party platform, a field may be missing, and it is necessary to determine an identification item common to the two records as a new identification item and perform weight distribution again. For example, if one piece of target related data includes the identification items CI1, CI2, CI3, CI4, FI1 and FI2, and another piece of target related data includes the identification items CI1, CI2, CI4 and FI1, when the similarity value of each identification item is calculated, calculation is performed based on CI1, CI2, CI4 and FI1 (that is, only the common identification item needs to be calculated). For each common identification item, a corresponding weight value is reset (i.e., a use weight value), wherein the number of use weight values matches the number of common identification items. Taking the use weight value corresponding to CI1 as an example, the use weight value cw1 corresponding to CI1 is:

similarly, the use weight values corresponding to CI2, CI4 and FI1 are cw2_ new, cw4_ new and fw1_ new, respectively, and the determination method of each use weight value is the same as the determination method of cw 1.

Based on each common identification item, the use weight value corresponding to each common identification item, and the similarity value calculation method of each common identification item, the similarity value between the associated data sets to be processed can be determined. Illustratively, if the number of the common identification items is 4, which are CI1, CI2, CI4 and FI1, and the weights corresponding to the common identification items are cw1_ new, cw2_ new, cw4_ new and fw1_ new, respectively, according to the similarity calculation method rule of the explicit information identification items, the similarity calculation methods corresponding to the CI1, CI2 and CI4 identification items are strictly equal, that is, not 0, that is, 1, and the to-be-verified data corresponding to the identification items in the two pieces of target associated data are the same, the similarity value is 1, and if the two pieces of target associated data are not completely the same, the similarity value is 0. The calculation results of the similarity values of the definite information identification items are respectively recorded as sim _ CI1, sim _ CI2, sim _ CI4 and FI 1. Wherein, FI1 is an identification item of the fuzzy data class, the corresponding similarity value calculation method is a common calculation of multiple similarity value methods, taking 3 similarity value calculation methods as an example, each similarity value calculation method is SimMethod1, SimMethod2 and SimMethod3, the weight values corresponding to each similarity value method are respectively set as fw _ method1, fw _ method2 and fw _ method3, then the identification item similarity value calculation results in the fuzzy data are respectively RES _ FI1_ method1, RES _ FI1_ method2 and RES _ FI1_ method3, based on which, the similarity value corresponding to FI1 can be determined as:

sim_FI1＝fw_method1×RES_FI1_method1+fw_method2×RES_FI1_method2

+fw_method3×RES_FI1_method3

determining a final similarity value (i.e. the similarity value of the associated data group to be processed) according to the similarity value of the identification item of the explicit information and the similarity value of the identification item of the fuzzy data as follows:

sim＝sim_CI1×cw1_new+sim_CI2×cw2_new+sim_CI4×cw4_new+

sim_FI1×fw1_new

a first preset similarity threshold, a second preset similarity threshold and a recognition item number threshold are preset, for example, the first preset similarity threshold is 0.8, the second preset similarity threshold is 0.6 and the preset recognition item number threshold is 3, respectively.

And after the similarity value of the current to-be-processed associated data group is obtained, comparing the similarity value with a preset first preset similarity threshold value of 0.8, and if the similarity value of the current to-be-processed associated data group is greater than the preset first preset similarity threshold value of 0.8, indicating that the current to-be-processed associated data group belongs to the same to-be-verified user.

If the similarity value of the current to-be-processed associated data group is smaller than a preset first preset similarity threshold value of 0.8, comparing the similarity value of the current to-be-processed associated data group with a second preset similarity threshold value of 0.6, and if the similarity value of the current to-be-processed associated data group is smaller than the preset second preset similarity threshold value of 0.6, indicating that the current to-be-processed associated data group does not belong to the same user to be verified, and feeding back first early warning information based on the comparison result.

If the similarity value of the current to-be-processed associated data group is between the second preset similarity threshold and the first preset similarity threshold, whether the current to-be-processed associated data group belongs to the same to-be-verified user cannot be determined, at this time, second early warning information can be fed back according to the comparison result, and whether the current to-be-processed associated data group belongs to the same to-be-verified user can be determined by a worker.

And after determining whether each to-be-processed associated data group belongs to the same to-be-verified user, obtaining a target verification result, and generating a business database based on the target verification result. When the fact that the current user to be verified needs to carry out business service is detected, patient matching is carried out on the current user to be verified in a business database (namely, whether the associated data group to be processed is the same user to be verified is determined), and whether corresponding business service is provided for the current user to be verified is determined according to a matching result. And if the current user to be verified is not detected, the corresponding service is not required to be provided.

Example four

Fig. 4 is a medical data processing device for insurance services according to a fourth embodiment of the present invention, the device including: a target associated data acquisition module 410, a pending associated data set determination module 420, a target verification data determination module 430 and a target verification result determination module 440.

The target associated data acquiring module 410 is configured to acquire at least two pieces of target associated data associated with each user to be verified; the target associated data comprises at least one identification item and data to be verified corresponding to each identification item; all or part of at least one identification item in each target associated data is the same;

a to-be-processed associated data set determining module 420, configured to determine, for each to-be-verified user, at least one to-be-processed associated data set by performing combined processing on at least two pieces of target associated data of a current to-be-verified user; wherein, the to-be-processed associated data group comprises two pieces of target associated data;

a target verification data determining module 430, configured to determine, for each to-be-verified user, a similarity value of at least one to-be-processed associated data group of the current to-be-verified user, and determine target verification data according to the similarity value and the corresponding to-be-processed associated data group;

and a target verification result determining module 440, configured to determine a target verification result based on each target verification data and corresponding pre-reported data, so as to execute a corresponding service based on the target verification result.

On the basis of any optional technical solution in the embodiment of the present invention, optionally, the target associated data obtaining module includes:

the to-be-used associated data acquisition submodule is used for acquiring a plurality of pieces of stored to-be-used associated data from at least two third party platforms;

and the target associated data determining submodule is used for determining the associated data to be used corresponding to the same user identifier, and processing the associated data to be used according to the data format of the corresponding identification item to obtain the target associated data of the user to be verified, which is matched with the user identifier.

On the basis of any optional technical solution in the embodiment of the present invention, optionally, the data processing apparatus is further configured to:

and determining an original configuration weight value corresponding to the identification item in each to-be-processed associated data so as to determine to-be-verified data according to the original configuration weight value.

On the basis of any optional technical solution in the embodiment of the present invention, optionally, the module for determining the to-be-processed associated data set includes:

the to-be-used associated data group determining submodule is used for pairwise combining the at least two pieces of target associated data to obtain a plurality of groups of to-be-used associated data groups;

and the to-be-processed associated data group determining submodule is used for determining a common identification item in the current to-be-used associated data group aiming at each group of to-be-used associated data group, and determining the to-be-processed associated data group corresponding to the to-be-used associated data based on the common identification item and the corresponding to-be-verified data.

On the basis of any optional technical solution in the embodiment of the present invention, optionally, the target verification data determining module includes:

the weight value determining submodule is used for determining an original configuration weight value of a current common identification item in the current to-be-processed associated data set and a weight sum of original configuration weight values of all common identification items in the current to-be-processed associated data set aiming at each to-be-processed associated data set, and determining a use weight value of the current common identification item;

the first similarity value determining submodule is used for determining a first similarity value of to-be-verified data of the same common identification item in the current to-be-processed associated data group aiming at each to-be-processed associated data group;

and the similarity value determining submodule is used for determining the similarity value of the current to-be-processed associated data set according to the first similarity value and the corresponding use weight value of each common identification item in the current to-be-processed associated data set.

On the basis of any optional technical solution in the embodiment of the present invention, optionally, the similarity value determining sub-module includes:

the first verification data determining sub-module is used for determining that the current to-be-processed associated data group corresponds to the same to-be-verified user to obtain first verification data if the similarity value of the current to-be-processed associated data group is larger than a first preset similarity threshold value and the number of identification items of all identification items in the current to-be-processed associated data group is larger than a preset identification item number threshold value;

and the target verification data determining submodule is used for determining the target verification data according to the first verification data of each to-be-processed associated data set.

a to-be-verified user determining unit, configured to determine that the current to-be-processed associated data set does not belong to the same to-be-verified user and feed back first warning information if the similarity value of the current to-be-processed associated data set is smaller than a second preset similarity threshold; or the like, or, alternatively,

a first fuzzy data determining unit, configured to determine that the current to-be-processed associated data set is fuzzy data and feed back second early warning information if the similarity value of the current to-be-processed associated data set is between the second preset similarity threshold and a first preset similarity threshold; or the like, or, alternatively,

and the second fuzzy information determining unit is used for determining that the current to-be-processed associated data set is fuzzy data and feeding back second early warning information if the similarity value of the current to-be-processed associated data set is greater than a preset similarity threshold value and the number of the common identification items is less than a preset identification item number threshold value.

On the basis of any optional technical solution in the embodiment of the present invention, optionally, the target verification result determining module is configured to:

and calling target verification data to be compared corresponding to the pre-reported data from each target verification data, and determining a target verification result by comparing the target verification data to be compared with the pre-reported data.

The medical data processing device for insurance services provided by the embodiment of the invention can execute the medical data processing method for insurance services provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.

EXAMPLE five

Fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary electronic device 40 suitable for use in implementing embodiments of the present invention. The electronic device 40 shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.

As shown in fig. 5, electronic device 40 is embodied in the form of a general purpose computing device. The components of electronic device 40 may include, but are not limited to: one or more processors or processing units 401, a system memory 402, and a bus 403 that couples the various system components (including the system memory 402 and the processing unit 401).

Bus 403 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Electronic device 40 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 40 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 402 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)404 and/or cache memory 405. The electronic device 40 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 406 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 403 by one or more data media interfaces. Memory 402 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 408 having a set (at least one) of program modules 407 may be stored, for example, in memory 402, such program modules 407 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 407 generally perform the functions and/or methods of the described embodiments of the invention.

The electronic device 40 may also communicate with one or more external devices 409 (e.g., keyboard, pointing device, display 410, etc.), with one or more devices that enable a user to interact with the electronic device 40, and/or with any devices (e.g., network card, modem, etc.) that enable the electronic device 40 to communicate with one or more other computing devices. Such communication may be through input/output (I/O) interface 411. Also, the electronic device 40 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 412. As shown, the network adapter 412 communicates with the other modules of the electronic device 40 over the bus 403. It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with electronic device 40, 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.

The processing unit 401 executes various functional applications and data processing, for example, implements a medical data processing method for insurance services provided by an embodiment of the present invention, by executing a program stored in the system memory 402.

EXAMPLE six

An embodiment of the present invention also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a method for medical data processing for insurance services, the method including:

Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer 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 computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, 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. In the context of this document, a computer 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.

A computer readable signal medium may include a propagated data signal with computer 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 computer readable signal medium may also be any computer readable medium that is not a computer 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 computer 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.

Computer program code for carrying out operations for embodiments 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, Smalltalk, 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method of processing medical data for insurance services, comprising:

2. The method of claim 1, wherein the obtaining at least two pieces of target association data associated with each user to be authenticated comprises:

acquiring a plurality of pieces of stored associated data to be used from at least two third-party platforms;

and determining the associated data to be used corresponding to the same user identifier, and processing the associated data to be used according to the data format of the corresponding identification item to obtain the target associated data of the user to be verified, which is matched with the user identifier.

3. The method according to claim 1, before said determining at least one to-be-processed associated data set by combined processing of at least two pieces of target associated data of a user to be currently authenticated, further comprising:

4. The method according to claim 1, wherein the determining at least one to-be-processed associated data set by combined processing of at least two pieces of target associated data of a current to-be-authenticated user comprises:

combining the at least two pieces of target associated data pairwise to obtain a plurality of associated data groups to be used;

and determining a common identification item in the current to-be-used associated data group aiming at each group of to-be-used associated data group, and determining a to-be-processed associated data group corresponding to the to-be-used associated data based on the common identification item and corresponding to-be-verified data.

5. The method according to claim 1, wherein the determining the similarity value of at least one to-be-processed associated data set of the current to-be-authenticated user comprises:

aiming at each to-be-processed associated data group, determining an original configuration weight value of a current common identification item in the current to-be-processed associated data group and a weight sum of original configuration weight values of all common identification items in the current to-be-processed associated data group, and determining a use weight value of the current common identification item;

determining a first similarity value of to-be-verified data of the same common identification item in the current to-be-processed associated data group aiming at each to-be-processed associated data group;

and for each to-be-processed associated data group, determining the similarity value of the current to-be-processed associated data group according to the first similarity value and the corresponding use weight value of each common identification item in the current to-be-processed associated data group.

6. The method according to claim 5, wherein the determining a similarity value of at least one to-be-processed associated data set of the current to-be-authenticated user, and determining the target authentication data according to the similarity value and the corresponding to-be-processed associated data set comprises:

for each to-be-processed associated data group, if the similarity value of the current to-be-processed associated data group is greater than a first preset similarity threshold value, and the number of identification items of common identification items in the current to-be-processed associated data group is greater than a preset identification item number threshold value, determining that the current to-be-processed associated data group corresponds to the same to-be-verified user, and obtaining first verification data;

and determining the target verification data according to the first verification data of each to-be-processed associated data set.

7. The method of claim 5, further comprising:

if the similarity value of the current to-be-processed associated data group is smaller than a second preset similarity threshold value, determining that the current to-be-processed associated data group does not belong to the same to-be-verified user, and feeding back first early warning information; or the like, or, alternatively,

if the similarity value of the current to-be-processed associated data set is between the second preset similarity threshold and the first preset similarity threshold, determining that the current to-be-processed associated data set is fuzzy data, and feeding back second early warning information; or the like, or, alternatively,

and if the similarity value of the current to-be-processed associated data set is greater than a preset similarity threshold value and the number of the common identification items is less than a preset identification item number threshold value, determining that the current to-be-processed associated data set is fuzzy data and feeding back second early warning information.

8. The method of claim 1, wherein determining a target verification result based on each target verification data and corresponding pre-reported data comprises:

9. A medical data processing apparatus for insurance services, comprising:

10. An electronic device, characterized in that the electronic device comprises:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of medical data processing for insurance services of any of claims 1-8.

11. A storage medium containing computer-executable instructions for performing the method for medical data processing for insurance services according to any one of claims 1 to 8 when executed by a computer processor.