WO2020119111A1 - 非疾病治疗项目的保费计算方法、装置、设备及存储介质 - Google Patents

非疾病治疗项目的保费计算方法、装置、设备及存储介质 Download PDF

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WO2020119111A1
WO2020119111A1 PCT/CN2019/095615 CN2019095615W WO2020119111A1 WO 2020119111 A1 WO2020119111 A1 WO 2020119111A1 CN 2019095615 W CN2019095615 W CN 2019095615W WO 2020119111 A1 WO2020119111 A1 WO 2020119111A1
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group
age
users
disease treatment
characteristic
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PCT/CN2019/095615
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English (en)
French (fr)
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李云峰
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平安医疗健康管理股份有限公司
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Publication of WO2020119111A1 publication Critical patent/WO2020119111A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

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  • the present application relates to the technical field of data processing, and in particular to a method, device, equipment and storage medium for calculating premiums for non-disease treatment items.
  • This application provides a non-disease treatment premium calculation method, device, equipment and storage medium, aiming to improve the efficiency, pertinence and accuracy of premium calculation.
  • the present application provides a method for calculating premiums for non-disease treatment items, the method includes:
  • the step of calculating the per capita premium of the non-disease treatment item based on the total cost and the total number of insured persons obtained in advance further includes:
  • the insurance item corresponding to the non-disease treatment item is started, the insurance item is promoted to target users with the group characteristic according to the group characteristic.
  • the step of extracting the group characteristics of the historical user according to the user information includes:
  • One or more age groups whose age distribution ratio is greater than the first threshold are used as group age characteristics, and/or one or more physical conditions whose body condition distribution ratio is greater than the second threshold are used as group body condition characteristics;
  • the step of calculating the total cost based on the number of users and the average treatment cost obtained in advance includes:
  • the step of promoting the insurance item to target users with the group characteristics includes:
  • the step of calculating the per capita premium of the non-disease treatment item based on the total cost and the total number of insured persons obtained in advance further includes:
  • the method further includes:
  • the expense reimbursement request is reviewed based on the expense reimbursement review standard.
  • the embodiments of the present application also provide a premium calculation device for non-disease treatment items.
  • the premium calculation device for non-disease treatment items includes:
  • An acquisition module for acquiring user information of historical users of target non-disease treatment items
  • An extraction module configured to extract the group characteristics of the historical user according to the user information
  • a prediction module used to predict the number of users of the target non-disease treatment item according to the group characteristics
  • the first calculation module is used to calculate the total cost based on the number of users and the average treatment cost obtained in advance;
  • the second calculation module is used to calculate the per capita premium of the non-disease treatment item based on the total cost and the total number of insured persons obtained in advance.
  • the embodiments of the present application also provide a premium calculation device for non-disease treatment items.
  • the premium calculation device for non-disease treatment items includes a processor, a memory, and computer-readable instructions stored in the memory. The computer When the readable instructions are executed by the processor, the steps of the non-disease treatment premium calculation method described above are realized.
  • embodiments of the present application also provide a computer storage medium having computer readable instructions stored on the computer storage medium, the computer readable instructions being executed by a processor to implement the non-disease treatment premium calculation method as described above A step of.
  • the present application provides a non-disease treatment premium calculation method, device, equipment and storage medium to obtain user information of a user who has received a target non-disease treatment item; extract the history according to the user information Group characteristics of users; predict the number of users of the target non-disease treatment item based on the group characteristics; calculate the total cost based on the number of users and the average pre-acquired treatment cost; based on the total cost and the total parameters acquired in advance
  • the number of insurers calculates the per capita premium of the non-disease treatment program.
  • the user portrait is obtained through the user information, and the number of users who obtain the target non-disease treatment item based on the user portrait improves the efficiency, pertinence, and accuracy of premium calculation.
  • FIG. 1 is a schematic diagram of the hardware structure of a non-disease treatment item premium calculation device involved in each embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a first embodiment of a method for calculating premiums for non-disease treatment items of this application;
  • FIG. 3 is a schematic flowchart of a second embodiment of a method for calculating premiums for non-disease treatment items of this application;
  • FIG. 4 is a schematic diagram of functional modules of a first embodiment of a premium calculation device for non-disease treatment items of this application.
  • the non-disease treatment item premium calculation device mainly involved in the embodiments of the present application refers to a network connection device capable of achieving network connection, and the non-disease treatment item premium calculation device may be a server, a cloud platform, or the like.
  • FIG. 1 is a schematic diagram of a hardware structure of a premium calculation device for non-disease treatment items related to embodiments of the present application.
  • the premium computing device for non-disease treatment items may include a processor 1001 (for example, the central processor Central Processing Unit, CPU), communication bus 1002, input port 1003, output port 1004, memory 1005.
  • processor 1001 for example, the central processor Central Processing Unit, CPU
  • communication bus 1002 input port 1003, output port 1004, memory 1005.
  • the communication bus 1002 is used to realize the connection communication between these components; the input port 1003 is used for data input; the output port 1004 is used for data output, and the memory 1005 can be a high-speed RAM memory or a non-volatile memory (non-volatile) memory), such as a disk storage, the storage 1005 may optionally be a storage device independent of the foregoing processor 1001.
  • the hardware structure shown in FIG. 1 does not constitute a limitation on the device of the present application, and may include more or less components than those illustrated, or combine certain components, or arrange different components.
  • the memory 1005 in FIG. 1 as a readable storage medium may include an operating system, a network communication module, an application program module, and computer-readable instructions.
  • the network communication module is mainly used to connect to a server and perform data communication with the server; and the processor 1001 can call computer-readable instructions stored in the memory 1005 and execute the premiums for non-disease treatment items provided by embodiments of the present application Calculation method.
  • the embodiments of the present application provide a method for calculating the premium of non-disease treatment items.
  • FIG. 2 is a schematic flowchart of a first embodiment of a method for calculating premiums of non-disease treatment items of this application.
  • the non-disease treatment item premium calculation method is applied to the non-disease treatment item premium calculation device, and the method includes:
  • Step S101 Obtain the user information of the historical user of the target non-disease treatment item
  • the non-disease treatment items include: various cosmetics, bodybuilding items, non-functional cosmetic procedures, orthopedic surgery, weight loss, muscle gain, heightening items, health checkups, preventive health care diagnosis and treatment items, medical consultation, medical identification Wait.
  • the user information of the historical user of the target non-disease treatment item is obtained from the medical institution.
  • the historical user refers to a user who has received the target non-disease treatment program.
  • the historical user needs to reserve corresponding user information when receiving a non-disease treatment item in a medical institution.
  • the user information includes name, contact information, age, and physical health status. Therefore, user information reserved by the user can be collected from the medical institution.
  • Step S102 Extract the group characteristics of the historical user according to the user information
  • the group characteristics of the historical users are extracted from the user information through statistical analysis. Specifically, the user information is analyzed, and the distribution of age and the distribution of physical condition are counted. The user's age is extracted from the user information, the user's age is counted using an age distribution map, and the user's physical condition is extracted from the user information, and the user's physical condition is counted into a physical condition distribution map. Calculate the age distribution ratio of each age range according to the age distribution, and/or calculate the body condition distribution ratio of each body condition grade interval according to the body condition distribution.
  • the user's age is divided into several age groups, for example, 10-20 years old as the first age group, 20-25 years old as the second age group, and more than 25 years old as the third age group.
  • the user's physical condition is divided into several levels according to the user's physical condition distribution map.
  • One or more age groups whose age distribution ratio is greater than the first threshold value are used as group age characteristics, and/or one or more physical conditions whose body condition distribution ratio is greater than the second threshold value are used as group body condition characteristics.
  • the first threshold may be set to 50%, 60%, 80%, etc.
  • the second threshold may be set to 50%, 60%, 80%, etc.
  • the first threshold and the second threshold may be the same or different. After setting the first threshold and the second threshold, based on the first threshold and the second threshold as filtering conditions, one or more age groups whose age distribution ratio is greater than the first threshold are used as the history The group age characteristic of the user, and/or one or more body conditions whose distribution ratio of the body condition is greater than the second threshold is used as the group body condition characteristic of the historical user.
  • the supplementary information of the historical user may be obtained from other platforms according to the user information.
  • the supplementary information such as the user's network, interests, and hobbies can be obtained through a social platform; the consumption habits of the historical user can be obtained through a shopping platform; and the economic level of the historical user can be obtained through a financial platform.
  • supplementary group characteristics of the historical user are obtained, and the supplementary group characteristics include interests, consumption habits, economic level, occupation, and the like.
  • the group age feature and/or the physical condition feature filtered out from the user information based on the first threshold and the second threshold are used as the group feature.
  • Step S103 predict the number of users of the target non-disease treatment item according to the group characteristics
  • a preset number of ordinary user samples are randomly collected, and the ordinary user sample includes user information, the number of characteristic users with the group characteristics is obtained from the ordinary user sample, and the number of characteristic users is divided by When the preset number is multiplied by 100%, the user with the specific group characteristics accounts for the first percentage of the total number of people. Multiply the first percentage by the total number of people to obtain the number of users of the target non-disease treatment program. Since the collection of ordinary user samples is random, if you want to obtain more accurate prediction results, you can adjust the number of users already obtained to obtain a more accurate number of users.
  • Step S104 Calculate the total cost based on the number of users and the average treatment cost obtained in advance;
  • the corresponding actual treatment cost is obtained from the user information, and the average treatment cost is obtained by dividing the sum of the actual treatment cost by the amount of the user information; the number of users is multiplied by the average
  • a preset number of user samples may be extracted from the user information, and the average treatment cost may be calculated according to the preset number of user samples to reduce the burden.
  • Step S105 Calculate the per capita premium of the non-disease treatment item based on the total cost and the total number of insured persons obtained in advance.
  • the final premium may be obtained after adaptive adjustments are made to the per capita premium according to the factors that affect the per capita premium, such as taxes, profits, and operating costs.
  • the characteristic premium weight of the applicant is obtained based on the relevant information of the applicant; the characteristic premium weight is multiplied by the per capita premium to obtain the premium corresponding to the applicant.
  • the applicant's own conditions will have a huge impact on the success or failure of the non-disease treatment program. For example, if the non-disease treatment item is a weight loss item, but if it is difficult for the user to lose weight due to their own physical fitness.
  • the cost reimbursement review standard for the insurance item is set.
  • the cost reimbursement review standards for the insurance items are set; the review standards include required materials, expense reimbursement thresholds, and reimbursement time limits.
  • the expense reimbursement request is reviewed based on the expense reimbursement review standard. Review the expense reimbursement request as required to prevent fraudulent insurance from happening and enable the insurance program to truly help those in need.
  • the embodiment of the present application obtains user information of historical users of target non-disease treatment items through the above solution; extracts group characteristics of the historical users according to the user information; and predicts users of the target non-disease treatment items according to the group characteristics Quantity; calculate the total cost based on the number of users and the average pre-acquired treatment cost; calculate the per capita premium of the non-disease treatment item based on the total cost and the total pre-acquired number of participants.
  • the user portrait is obtained through the user information, and the number of users who obtain the target non-disease treatment item based on the user portrait improves the efficiency, pertinence, and accuracy of premium calculation.
  • the second embodiment of the present application proposes a method for calculating the premium of non-disease treatment items.
  • the The number of people calculating the per capita premium of the non-disease treatment program also includes:
  • Step S106 after the insurance item corresponding to the non-disease treatment item is started, the insurance item is promoted to target users with the group characteristic according to the group characteristic.
  • the insurance item needs to be promoted. Specifically, a large number of user samples are collected first, from the collected large number of user samples, the characteristic user samples with the group characteristics are screened, the population corresponding to the characteristic user samples is taken as the target user, and the target user is obtained Contact information, the contact information includes mailbox, telephone and other information. Send marketing information related to the reimbursement item to the target user according to the contact information. In addition, corresponding print, voice or video advertisements can also be placed in places where the target users gather more.
  • the insurance item corresponding to the non-disease treatment item is started, the insurance item is promoted to target users with the group characteristic according to the group characteristic. Therefore, through the user portrait, the insurance item is recommended to the target user, which enhances the pertinence of the project promotion.
  • FIG. 4 is a schematic diagram of functional modules of a first embodiment of a premium calculation device for non-disease treatment items of this application.
  • the non-disease treatment item premium calculation device of the present application is a virtual device, and is stored in the memory 1005 of the non-disease treatment item premium calculation device shown in FIG. 1 to implement all functions of the non-disease treatment item premium calculation method.
  • the non-disease treatment premium calculation device includes:
  • the obtaining module 10 is used to obtain user information of historical users of target non-disease treatment items
  • An extraction module 20 configured to extract the group characteristics of the historical user according to the user information
  • the prediction module 30 is used to predict the number of users of the target non-disease treatment item according to the group characteristics
  • the first calculation module 40 is used to calculate the total cost based on the number of users and the average treatment cost obtained in advance;
  • the second calculation module 50 is used to calculate the per capita premium of the non-disease treatment item based on the total cost and the total number of insured persons obtained in advance.
  • the acquisition module is also used to:
  • the insurance item corresponding to the non-disease treatment item is started, the insurance item is promoted to target users with the group characteristic according to the group characteristic.
  • extraction module is also used to:
  • One or more age groups whose age distribution ratio is greater than the first threshold are used as group age characteristics, and/or one or more physical conditions whose body condition distribution ratio is greater than the second threshold are used as group body condition characteristics;
  • the first calculation module is also used to:
  • the acquisition module is also used to:
  • the second calculation module is also used to:
  • prediction module is also used to:
  • the expense reimbursement request is reviewed based on the expense reimbursement review standard.
  • the present application also provides a computer storage medium, and the computer-readable storage medium may be a non-volatile readable storage medium.
  • Computer-readable instructions are stored on the computer storage medium. When the computer-readable instructions are executed by the processor, the steps of the non-disease treatment premium calculation method described above are implemented, which will not be repeated here.
  • a premium calculation method, device, equipment and storage medium for non-disease treatment items proposed in this application obtain user information of users who have received a target non-disease treatment item; and extract the user information according to the user information Group characteristics of historical users; predict the number of users of the target non-disease treatment item based on the group characteristics; calculate the total cost based on the number of users and the average treatment cost obtained in advance; based on the total cost and the total amount obtained in advance The number of insured persons calculates the per capita premium of the non-disease treatment program.
  • a user portrait is obtained through user information, and the number of users who obtain a target non-disease treatment item based on the user portrait improves the efficiency, pertinence, and accuracy of premium calculation.

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Abstract

一种非疾病治疗项目的保费计算方法、装置、设备及存储介质，该方法包括：获取已接受目标非疾病治疗项目的历史用户的用户信息(S101)；根据所述用户信息提取所述历史用户的群体特征(S102)；根据所述群体特征预测所述目标非疾病治疗项目的用户数量(S103)；基于所述用户数量和预先获取的平均治疗费用，计算总费用(S104)；根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费(S105)。该方法通过用户信息获取用户画像，基于所述用户画像获得目标非疾病治疗项目的用户数量，提高了保费计算的效率、针对性和准确性。

Description

非疾病治疗项目的保费计算方法、装置、设备及存储介质
本申请要求于2018年12月13日提交中国专利局、申请号为201811529120.5、发明名称为“非疾病治疗项目的保费计算方法、装置、设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及数据处理技术领域，尤其涉及一种非疾病治疗项目的保费计算方法、装置、设备及存储介质。
背景技术
随着人们生活水平的提高和保险意识的提升，人们需要越来越健全的保险体系。然而在非疾病治疗的项目中，却没有可供人们购买的非疾病治疗项目保险，故需要将非疾病医疗项目纳入保险则是一种给人们提供更完善的保险体系。
在建立新的保险项目时，必须设立准确合理的保费。一般地，保费计算需要由专家收集大量数据，并对这些大量数据进行准确分析才能得出对应的保费，而这种计算保费的方法效率低，针对性不强，准确性不高。
发明内容
本申请提供一种非疾病治疗项目的保费计算方法、装置、设备及存储介质，旨在提高保费计算的效率、针对性和准确性。
为实现上述目的，本申请提供一种非疾病治疗项目的保费计算方法，所述方法包括：
获取目标非疾病治疗项目的历史用户的用户信息；
根据所述用户信息提取所述历史用户的群体特征；
根据所述群体特征预测所述目标非疾病治疗项目的用户数量；
基于所述用户数量和预先获取的平均治疗费用，计算总费用；
根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。
优选地，所述根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费的步骤之后还包括：
当所述非疾病治疗项目对应的保险项目启动后，根据所述群体特征，向具有所述群体特征的目标用户推广所述保险项目。
优选地，所述根据所述用户信息提取所述历史用户的群体特征的步骤包括：
对所述用户信息进行分析，统计年龄分布情况以及身体状况分布情况；
根据所述年龄分布情况计算各年龄段区间的年龄分布比例，和/或根据所述身体状况分布情况计算各身体状况等级区间的身体状况分布比例；
将年龄分布比例大于第一阈值的一个或多个年龄段作为群体年龄特征，和/或将身体状况分布比例大于第二阈值的一种或多种身体状况作为群体身体状况特征；
将所述群体年龄特征和/或所述群体身体状况特征作为所述历史用户的群体特征。
优选地，所述基于所述用户数量和预先获取的平均治疗费用，计算总费用的步骤包括：
从所述用户信息中获取对应的实际治疗费用，将所述实际治疗费用的总和除以所述用户信息的数量则得到平均治疗费用；
将所述用户数量乘以所述平均治疗费用则获得总费用。
优选地，所述向具有所述群体特征的目标用户推广所述保险项目的步骤包括：
收集用户样本；
从所述用户样本中筛选具有所述群体特征的特征用户样本，将所述特征用户样本对应的用户作为目标用户；
向所述目标用户发送与所述保险项目相关的推广信息。
优选地，所述根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费的步骤之后还包括：
当接收到保险申请时，根据申请人的相关信息，获得申请人的特征保费权
将所述特征保费权重乘以所述人均保费，则获得所述申请人对应的保费。
优选地，所述方法还包括：
设置所述目标疾病的费用报销审核标准；
当接收到所述目标疾病的费用报销请求时，基于所述费用报销审核标准对所述费用报销请求进行审核。
此外，本申请实施例还提供一种非疾病治疗项目的保费计算装置，所述非疾病治疗项目的保费计算装置包括：
获取模块，用于获取目标非疾病治疗项目的历史用户的用户信息；
提取模块，用于根据所述用户信息提取所述历史用户的群体特征；
预测模块，用于根据所述群体特征预测所述目标非疾病治疗项目的用户数量；
第一计算模块，用于基于所述用户数量和预先获取的平均治疗费用，计算总费用；
第二计算模块，用于根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。
此外，本申请实施例还提供一种非疾病治疗项目的保费计算设备，所述非疾病治疗项目的保费计算设备包括处理器，存储器以及存储在所述存储器中的计算机可读指令，所述计算机可读指令被所述处理器运行时，实现上所述的非疾病治疗项目的保费计算方法的步骤。
此外本申请实施例还提供一种计算机存储介质，所述计算机存储介质上存储有计算机可读指令，所述计算机可读指令被处理器运行时实现如上所述的非疾病治疗项目的保费计算方法的步骤。
相比现有技术，本申请提供一种非疾病治疗项目的保费计算方法、装置、设备及存储介质，获取已接受目标非疾病治疗项目的用户的用户信息；根据所述用户信息提取所述历史用户的群体特征；根据所述群体特征预测所述目标非疾病治疗项目的用户数量；基于所述用户数量和预先获取的平均治疗费用，计算总费用；根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。由此，通过用户信息获取用户画像，基于所述用户画像获得目标非疾病治疗项目的用户数量，提高了保费计算的效率、针对性和准确性。
附图说明
图1是本申请各实施例涉及的非疾病治疗项目的保费计算设备的硬件结构示意图；
图2是本申请非疾病治疗项目的保费计算方法第一实施例的流程示意图；
图3是本申请非疾病治疗项目的保费计算方法第二实施例的流程示意图；
图4是本申请非疾病治疗项目的保费计算装置第一实施例的功能模块示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请实施例主要涉及的非疾病治疗项目的保费计算设备是指能够实现网络连接的网络连接设备，所述非疾病治疗项目的保费计算设备可以是服务器、云平台等。
参照图1，图1是本申请各实施例涉及的非疾病治疗项目的保费计算设备的硬件结构示意图。本申请实施例中，非疾病治疗项目的保费计算设备可以包括处理器1001（例如中央处理器Central Processing Unit、CPU），通信总线1002，输入端口1003，输出端口1004，存储器1005。其中，通信总线1002用于实现这些组件之间的连接通信；输入端口1003用于数据输入；输出端口1004用于数据输出，存储器1005可以是高速RAM存储器，也可以是稳定的存储器（non-volatile memory），例如磁盘存储器，存储器1005可选的还可以是独立于前述处理器1001的存储装置。本领域技术人员可以理解，图1中示出的硬件结构并不构成对本申请设备的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
继续参照图1，图1中作为一种可读存储介质的存储器1005可以包括操作系统、网络通信模块、应用程序模块以及计算机可读指令。在图1中，网络通信模块主要用于连接服务器，与服务器进行数据通信；而处理器1001可以调用存储器1005中存储的计算机可读指令，并执行本申请实施例提供的非疾病治疗项目的保费计算方法。
本申请实施例提供了一种非疾病治疗项目的保费计算方法。
参照图2，图2是本申请非疾病治疗项目的保费计算方法第一实施例的流程示意图。
本实施例中，所述非疾病治疗项目的保费计算方法应用于非疾病治疗项目的保费计算设备，所述方法包括：
步骤S101，获取目标非疾病治疗项目的历史用户的用户信息；
本实施例中，所述非疾病治疗项目包括：各种美容、健美项目、非功能性整容、矫形手术、减肥、增肌、增高项目、健康体检、预防保健性诊疗项目、医疗咨询、医疗鉴定等。
本实施例中，从医疗机构中获取所述目标非疾病治疗项目的历史用户的用户信息。所述历史用户是指已接受过所述目标非疾病治疗项目的用户。一般地，所述历史用户在医疗机构接受非疾病治疗项目时，需要预留相应的用户信息，所述用户信息包括姓名、联系方式、年龄、身体健康状况等。故可以从所述医疗机构中收集用户预留的用户信息。
步骤S102，根据所述用户信息提取所述历史用户的群体特征；
本实施例中，通过统计分析法从所述用户信息中提取所述历史用户的群体特征。具体地，对所述用户信息进行分析，统计年龄分布情况以及身体状况分布情况。从所述用户信息中提取用户年龄，将所述用户年龄用年龄分布图进行统计，并从所述用户信息中提取用户身体状况，将所述用户身体状况统计成身体状况分布图。根据所述年龄分布情况计算各年龄段区间的年龄分布比例，和/或根据所述身体状况分布情况计算各身体状况等级区间的身体状况分布比例。根据用户年龄分布图将用户年龄划分成若干个年龄段，例如将10-20岁作为第一年龄段，将20-25岁作为第二年龄段，将大于25岁作为第三年龄段等。根据用户身体状况分布图将用户身体状况划分成若干个等级。将年龄分布比例大于第一阈值的一个或多个年龄段作为群体年龄特征，和/或将身体状况分布比例大于第二阈值的一种或多种身体状况作为群体身体状况特征。可将所述第一阈值设置为50%，60%，80%等，所述第二阈值设置为50%，60%，80%等，在对目标非疾病治疗项目进行保费计算的过程中，所述第一阈值与所述第二阈值可以相同也可以不相同。设置所述第一阈值和所述第二阈值后，则基于所述第一阈值和所述第二阈值作为筛选条件，将年龄分布比例大于第一阈值的一个或多个年龄段作为所述历史用户的群体年龄特征，和/或将身体状况分布比例大于第二阈值的一种或多种身体状况作为所述历史用户的群体身体状况特征。
进一步地，还可以根据所述用户信息从其它平台获取所述历史用户的补充信息。例如通过社交平台获取所述用户的关系网、兴趣爱好等补充信息，通过购物平台获取所述历史用户的消费习惯；通过金融平台获取所述历史用户的经济水平。再根据所述补充信息获得所述历史用户的补充群体特征，所述补充群体特征包括兴趣爱好、消费习惯、经济水平、职业等。
本实施例中，将基于所述第一阈值和所述第二阈值从所述用户信息中筛选出来的所述群体年龄特征和/或所述身体状况特征作为群体特征。
步骤S103，根据所述群体特征预测所述目标非疾病治疗项目的用户数量；
本实施例中，随机收集预设数量的普通用户样本，所述普通用户样本包括用户信息，从所述普通用户样本中获取具有所述群体特征的特征用户数量，将所述特征用户数量除以所述预设数量再乘以百分百，则得到具体所述群体特征的用户占总人数的第一百分比。将所述第一百分比乘以总人数则获得了所述目标非疾病治疗项目的用户数量。由于普通用户样本的收集具有随机性，故若要获得更准确的预测结果，则可将已获得的用户数量进行适应性调整就能得到更精确的用户数量。
步骤S104，基于所述用户数量和预先获取的平均治疗费用，计算总费用；
本实施例中，从所述用户信息中获取对应的实际治疗费用，将所述实际治疗费用的总和除以所述用户信息的数量则得到平均治疗费用；将所述用户数量乘以所述平均治疗费用则获得总费用：总费用=用户数量×平均治疗费用。在其它实施例中，也可以从所述用户信息中提取预设数量的用户样本，根据预设数量的用户样本来计算所述平均治疗费用，以减轻负担。
步骤S105，根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。
本实施例中，若将所述非疾病治疗项目纳入医保系统，则可从人社局获得当地的总参保人数。将所述总费用除以所述总参保人数则获得了人均保费：人均保费=总费用÷总参保人数。
在其它实施例中，还可根据税费、利润、运营成本等会对所述人均保费造成影响的影响因素对所述人均保费进行适应性调整之后获得最终保费。
进一步地，当接收到保险申请时，根据申请人的相关信息，获得申请人的特征保费权重；将所述特征保费权重乘以所述人均保费，则获得所述申请人对应的保费。具体地，由于申请人的自身条件会对所述非疾病治疗项目的成败带来巨大的影响。例如若所述非疾病治疗项目是减肥项目，但是若由于用户是由于自身体质原因导致的难以减肥成功。从所述相关信息中提取特征信息，所述特征信息包括用户年龄和用户身体状况；根据所述用户年龄从预设的用户年龄与年龄的保费权重映射关系表中获取对应的年龄保费权重，和/或所述用户身体状况从预设的用户身体状况与身体状况的保费权重映射关系表中获取对应的身体状况保费权重，将所述特征保费权重乘以所述人均保费，则获得所述申请人对应的保费。
进一步地，将所述目标项目纳入保险系统后，向参保人收取保费，并设置所述保险项目的费用报销审核标准。具体地，设置所述保险项目的费用报销审核标准；所述审核标准包括所需资料、费用报销阈值以及报销时限等。当接收到所述保险项目的费用报销请求时，基于所述费用报销审核标准对所述费用报销请求进行审核。按要求对所述费用报销请求进行审核，防止骗保的事情发生，使所述保险项目真正帮助有需要的人。
本申请实施例通过上述方案，获取目标非疾病治疗项目的历史用户的用户信息；根据所述用户信息提取所述历史用户的群体特征；根据所述群体特征预测所述目标非疾病治疗项目的用户数量；基于所述用户数量和预先获取的平均治疗费用，计算总费用；根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费费。由此，通过用户信息获取用户画像，基于所述用户画像获得目标非疾病治疗项目的用户数量，提高了保费计算的效率、针对性和准确性。
如图3所示，本申请第二实施例提出一种非疾病治疗项目的保费计算方法，基于上述图2所示的第一实施例，所述根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费的步骤之后还包括：
步骤S106，当所述非疾病治疗项目对应的保险项目启动后，根据所述群体特征，向具有所述群体特征的目标用户推广所述保险项目。
本实施例中，当所述保险项目启动后，需要将所述保险项目进行推广。具体地，先收集大量用户样本，从已收集的所述大量用户样本中，筛选具有所述群体特征的特征用户样本，将所述特征用户样本对应的人群作为目标用户，并获取所述目标用户的联系方式，所述联系方式包括邮箱、电话等信息。根据所述联系方式向所述目标用户发送与所述报销项目相关的推销信息。此外，还可以在所述目标用户多聚集的地方投放相应的平面、语音或视频广告。
本申请实施例通过上述方案，当所述非疾病治疗项目对应的保险项目启动后，根据所述群体特征，向具有所述群体特征的目标用户推广所述保险项目。由此通过用户画像，向目标用户推荐所述保险项目，增强了项目推广的针对性。
此外，本实施例还提供一种非疾病治疗项目的保费计算装置。参照图4，图4为本申请非疾病治疗项目的保费计算装置第一实施例的功能模块示意图。
本申请非疾病治疗项目的保费计算装置为虚拟装置，存储于图1所示的非疾病治疗项目的保费计算设备的存储器1005中，用于实现非疾病治疗项目的保费计算方法的所有功能，用于收集目标非疾病治疗项目的历史用户的用户信息；用于根据所述用户信息提取所述用户的群体特征；用于根据所述群体特征预测所述目标非疾病治疗项目的用户数量；用于基于所述用户数量和预先获取的平均治疗费用，计算总费用；用于根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。
具体地，本实施例中，所述非疾病治疗项目的保费计算装置包括：
获取模块10，用于获取目标非疾病治疗项目的历史用户的用户信息；
提取模块20，用于根据所述用户信息提取所述历史用户的群体特征；
预测模块30，用于根据所述群体特征预测所述目标非疾病治疗项目的用户数量；
第一计算模块40，用于基于所述用户数量和预先获取的平均治疗费用，计算总费用；
第二计算模块50，用于根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。
进一步地，所述获取模块还用于：
当所述非疾病治疗项目对应的保险项目启动后，根据所述群体特征，向具有所述群体特征的目标用户推广所述保险项目。
进一步地，所述提取模块还用于：
对所述用户信息进行分析，统计年龄分布情况以及身体状况分布情况；
根据所述年龄分布情况计算各年龄段区间的年龄分布比例，和/或根据所述身体状况分布情况计算各身体状况等级区间的身体状况分布比例；
将年龄分布比例大于第一阈值的一个或多个年龄段作为群体年龄特征，和/或将身体状况分布比例大于第二阈值的一种或多种身体状况作为群体身体状况特征；
将所述群体年龄特征和/或所述群体身体状况特征作为所述历史用户的群体特征。
进一步地，所述第一计算模块还用于：
从所述用户信息中获取对应的实际治疗费用，将所述实际治疗费用的总和除以所述用户信息的数量则得到平均治疗费用；
将所述用户数量乘以所述平均治疗费用则获得总费用。
进一步地，所述获取模块还用于：
收集用户样本；
从所述用户样本中筛选具有所述群体特征的特征用户样本，将所述特征用户样本对应的用户作为目标用户；
向所述目标用户发送与所述保险项目相关的推广信息。
进一步地，所述第二计算模块还用于：
当接收到保险申请时，根据申请人的相关信息，获得申请人的特征保费权重；
将所述特征保费权重乘以所述人均保费，则获得所述申请人对应的保费。
进一步地，所述预测模块还用于：
设置所述目标疾病的费用报销审核标准；
当接收到所述目标疾病的费用报销请求时，基于所述费用报销审核标准对所述费用报销请求进行审核。
此外，本申请还提供计算机存储介质，所述计算机可读存储介质可以为非易失性可读存储介质。所述计算机存储介质上存储有计算机可读指令，所述计算机可读指令被处理器运行时实现上所述的非疾病治疗项目的保费计算方法的步骤，在此不再赘述。
相比现有技术，本申请提出的一种非疾病治疗项目的保费计算方法、装置、设备及存储介质，获取已接受目标非疾病治疗项目的用户的用户信息；根据所述用户信息提取所述历史用户的群体特征；根据所述群体特征预测所述目标非疾病治疗项目的用户数量；基于所述用户数量和预先获取的平均治疗费用，计算总费用；根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。由此，通过用户信息获取用户画像，基于所述用户画像获得目标非疾病治疗项目的用户数量，提高了保费计算的效率、针对性和准确性。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备执行本申请各个实施例所述的方法。
以上所述仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或流程变换，或直接或间接运用在其它相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种非疾病治疗项目的保费计算方法，其特征在于，所述方法应用于保费计算设备，所述方法包括：
    获取目标非疾病治疗项目的历史用户的用户信息；
    根据所述用户信息提取所述历史用户的群体特征；
    根据所述群体特征预测所述目标非疾病治疗项目的用户数量；
    基于所述用户数量和预先获取的平均治疗费用，计算总费用；
    根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费的步骤之后还包括：
    当所述非疾病治疗项目对应的保险项目启动后，根据所述群体特征，向具有所述群体特征的目标用户推广所述保险项目。
  3. 根据权利要求1所述的方法，其特征在于，所述根据所述用户信息提取所述历史用户的群体特征的步骤包括：
    对所述用户信息进行分析，统计年龄分布情况以及身体状况分布情况；
    根据所述年龄分布情况计算各年龄段区间的年龄分布比例，和/或根据所述身体状况分布情况计算各身体状况等级区间的身体状况分布比例；
    将年龄分布比例大于第一阈值的一个或多个年龄段作为群体年龄特征，和/或将身体状况分布比例大于第二阈值的一种或多种身体状况作为群体身体状况特征；
    将所述群体年龄特征和/或所述群体身体状况特征作为所述历史用户的群体特征。
  4. 根据权利要求1所述的方法，其特征在于，所述基于所述用户数量和预先获取的平均治疗费用，计算总费用的步骤包括：
    从所述用户信息中获取对应的实际治疗费用，将所述实际治疗费用的总和除以所述用户信息的数量则得到平均治疗费用；
    将所述用户数量乘以所述平均治疗费用则获得总费用。
  5. 根据权利要求2所述的方法，其特征在于，所述向具有所述群体特征的目标用户推广所述保险项目的步骤包括：
    收集用户样本；
    从所述用户样本中筛选具有所述群体特征的特征用户样本，将所述特征用户样本对应的用户作为目标用户；
    向所述目标用户发送与所述保险项目相关的推广信息。
  6. 根据权利要求1所述的方法，其特征在于，所述根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费的步骤之后还包括：
    当接收到保险申请时，根据申请人的相关信息，获得申请人的特征保费权重；
    将所述特征保费权重乘以所述人均保费，则获得所述申请人对应的保费。
  7. 根据权利要求6所述的方法，其特征在于，所述方法还包括：
    设置所述目标疾病的费用报销审核标准；
    当接收到所述目标疾病的费用报销请求时，基于所述费用报销审核标准对所述费用报销请求进行审核。
  8. 一种非疾病治疗项目的保费计算装置，其特征在于，所述非疾病治疗项目的保费计算装置包括：
    获取模块，用于获取目标非疾病治疗项目的历史用户的用户信息；
    提取模块，用于根据所述用户信息提取所述历史用户的群体特征；
    预测模块，用于根据所述群体特征预测所述目标非疾病治疗项目的用户数量；
    第一计算模块，用于基于所述用户数量和预先获取的平均治疗费用，计算总费用；
    第二计算模块，用于根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。
  9. 根据权利要求8所述的装置，其特征在于，所述获取模块还用于：
    当所述非疾病治疗项目对应的保险项目启动后，根据所述群体特征，向具有所述群体特征的目标用户推广所述保险项目。
  10. 根据权利要求8所述的装置，其特征在于，所述提取模块还用于：
    对所述用户信息进行分析，统计年龄分布情况以及身体状况分布情况；
    根据所述年龄分布情况计算各年龄段区间的年龄分布比例，和/或根据所述身体状况分布情况计算各身体状况等级区间的身体状况分布比例；
    将年龄分布比例大于第一阈值的一个或多个年龄段作为群体年龄特征，和/或将身体状况分布比例大于第二阈值的一种或多种身体状况作为群体身体状况特征；
    将所述群体年龄特征和/或所述群体身体状况特征作为所述历史用户的群体特征。
  11. 根据权利要求8所述的装置，其特征在于，所述第一计算模块还用于：
    从所述用户信息中获取对应的实际治疗费用，将所述实际治疗费用的总和除以所述用户信息的数量则得到平均治疗费用；
    将所述用户数量乘以所述平均治疗费用则获得总费用。
  12. 根据权利要求9所述的装置，其特征在于，所述获取模块还用于：
    收集用户样本；
    从所述用户样本中筛选具有所述群体特征的特征用户样本，将所述特征用户样本对应的用户作为目标用户；
    向所述目标用户发送与所述保险项目相关的推广信息。
  13. 根据权利要求8所述的装置，其特征在于，所述第二计算模块还用于：
    当接收到保险申请时，根据申请人的相关信息，获得申请人的特征保费权重；
    将所述特征保费权重乘以所述人均保费，则获得所述申请人对应的保费。
  14. 根据权利要求13所述的装置，其特征在于，所述预测模块还用于：
    设置所述目标疾病的费用报销审核标准；
    当接收到所述目标疾病的费用报销请求时，基于所述费用报销审核标准对所述费用报销请求进行审核。
  15. 一种非疾病治疗项目的保费计算设备，其特征在于，所述非疾病治疗项目的保费计算设备包括处理器，存储器以及存储在所述存储器中的计算机可读指令，所述计算机可读指令被所述处理器运行时，实现如下步骤：
    获取目标非疾病治疗项目的历史用户的用户信息；
    根据所述用户信息提取所述历史用户的群体特征；
    根据所述群体特征预测所述目标非疾病治疗项目的用户数量；
    基于所述用户数量和预先获取的平均治疗费用，计算总费用；
    根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。
  16. 根据权利要求15所述的设备，其特征在于，所述计算机可读指令被所述处理器运行时，还实现如下步骤：
    当所述非疾病治疗项目对应的保险项目启动后，根据所述群体特征，向具有所述群体特征的目标用户推广所述保险项目。
  17. 根据权利要求15所述的设备，其特征在于，所述计算机可读指令被所述处理器运行时，还实现如下步骤：
    对所述用户信息进行分析，统计年龄分布情况以及身体状况分布情况；
    根据所述年龄分布情况计算各年龄段区间的年龄分布比例，和/或根据所述身体状况分布情况计算各身体状况等级区间的身体状况分布比例；
    将年龄分布比例大于第一阈值的一个或多个年龄段作为群体年龄特征，和/或将身体状况分布比例大于第二阈值的一种或多种身体状况作为群体身体状况特征；
    将所述群体年龄特征和/或所述群体身体状况特征作为所述历史用户的群体特征。
  18. 一种计算机存储介质，其特征在于，所述计算机存储介质上存储有计算机可读指令，所述计算机可读指令被处理器运行时实现如下步骤：
    获取目标非疾病治疗项目的历史用户的用户信息；
    根据所述用户信息提取所述历史用户的群体特征；
    根据所述群体特征预测所述目标非疾病治疗项目的用户数量；
    基于所述用户数量和预先获取的平均治疗费用，计算总费用；
    根据所述总费用和预先获取的总参保人数计算所述非疾病治疗项目的人均保费。
  19. 根据权利要求18所述的计算机存储介质，其特征在于，所述计算机可读指令被处理器运行时还实现如下步骤：
    当所述非疾病治疗项目对应的保险项目启动后，根据所述群体特征，向具有所述群体特征的目标用户推广所述保险项目。
  20. 根据权利要求18所述的计算机存储介质，其特征在于，所述计算机可读指令被处理器运行时还实现如下步骤：
    对所述用户信息进行分析，统计年龄分布情况以及身体状况分布情况；
    根据所述年龄分布情况计算各年龄段区间的年龄分布比例，和/或根据所述身体状况分布情况计算各身体状况等级区间的身体状况分布比例；
    将年龄分布比例大于第一阈值的一个或多个年龄段作为群体年龄特征，和/或将身体状况分布比例大于第二阈值的一种或多种身体状况作为群体身体状况特征；
    将所述群体年龄特征和/或所述群体身体状况特征作为所述历史用户的群体特征。
PCT/CN2019/095615 2018-12-13 2019-07-11 非疾病治疗项目的保费计算方法、装置、设备及存储介质 WO2020119111A1 (zh)

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