WO2020119113A1

WO2020119113A1 - Premium pricing method, device, equipment for medicines based on big data and storage medium

Info

Publication number: WO2020119113A1
Application number: PCT/CN2019/095814
Authority: WO
Inventors: 李云峰
Original assignee: 平安医疗健康管理股份有限公司
Priority date: 2018-12-13
Filing date: 2019-07-12
Publication date: 2020-06-18
Also published as: CN109615535A

Abstract

Disclosed are a premium pricing method, device and equipment for medicines based on big data and a storage medium. The method comprises the following steps: obtaining a target city identifier in a premium pricing request based on target medicines upon receiving such request, and obtaining, according to the target city identifier, the historical per capita annual cost of the target medicines in the target city as the standard per capita annual cost of the target medicines (S10); calculating a target risk premium of the target medicines according to preset compensation parameters corresponding to the target medicines and the standard per capita annual cost (S20); and calculating a premium of the target medicines according to preset cost parameters corresponding to the target medicines and the target risk premium (S30). The method is more adaptive to the premium pricing of medicines based on actual use, and can reduce the manual workload and save the human cost at the same time of improving the pricing efficiency and accuracy.

Description

基于大数据的药物保费定价方法、装置、设备及存储介质Drug premium pricing method, device, equipment and storage medium based on big data

本申请要求于2018年12月13日提交中国专利局、申请号为201811524256.7、发明名称为“基于大数据的药物保费定价方法、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。This application requires the priority of the Chinese patent application submitted to the China Patent Office on December 13, 2018, with the application number 201811524256.7 and the invention titled "Big Data-based Drug Premium Pricing Methods, Devices, Equipment, and Storage Media", all of which are The content is incorporated into the application by reference.

技术领域Technical field

本申请涉及数据分析技术领域，尤其涉及一种基于大数据的药物保费定价方法、装置、设备及计算机可读存储介质。The present application relates to the technical field of data analysis, and in particular to a method, device, device and computer-readable storage medium for pricing pharmaceutical premiums based on big data.

背景技术Background technique

随着市场上特殊药品(特药)的增多，特药的使用量也越来越大，而特药一般价格昂贵，如尼达尼布胶囊(乙磺酸尼达尼布软胶囊)是一种用于治疗特发性肺纤维化(IPF)的药物。为了减轻患者负担，急需将特药纳入医保范畴，如一些保险机构提出以将尼达尼布胶囊的医药费用纳入保险报销范围内。在将特药纳入医保范畴之前，人社局需要先确定特药的保险定价，而现有技术中，药品保险定价需要由专家收集数据，综合分析后才能得出结果。因此，如何解决现有人工定价保费方法存在的效率以及准确率低下的问题，成为了目前亟待解决的技术问题。With the increase of special medicines (special medicines) on the market, the use of special medicines is also increasing, and special medicines are generally expensive. For example, Nidanibu capsules (Nidanibu ethanesulfonate soft capsules) are A drug used to treat idiopathic pulmonary fibrosis (IPF). In order to reduce the burden on patients, special medicines are urgently needed to be included in the scope of medical insurance. For example, some insurance agencies have proposed to include the medical expenses of Nidanib capsules into the insurance reimbursement scope. Before special medicines are included in the medical insurance category, the Bureau of Human Resources and Social Security needs to determine the insurance pricing for special medicines. In the prior art, the insurance pricing for medicines needs to be collected by experts and comprehensively analyzed to obtain results. Therefore, how to solve the problem of low efficiency and low accuracy of the existing manual pricing premium method has become a technical problem to be solved urgently.

发明内容Summary of the invention

本申请的主要目的在于提供一种基于大数据的药物保费定价方法、装置、设备及计算机可读存储介质，旨在解决人工定价保费方法存在的效率以及准确率低下的技术问题。The main purpose of the present application is to provide a pharmaceutical premium pricing method, device, equipment and computer-readable storage medium based on big data, aiming to solve the technical problems of low efficiency and accuracy of manual pricing premium methods.

为实现上述目的，本申请提供一种基于大数据的药物保费定价方法，所述基于大数据的药物保费定价方法包括以下步骤：To achieve the above objective, the present application provides a method for pricing drug premiums based on big data. The method for pricing drug premiums based on big data includes the following steps:

在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用，作为所述目标药物的标准人均年费用；When receiving the premium pricing request based on the target drug, obtain the target city identifier in the premium pricing request, and obtain the historical drug per capita annual cost of the target drug in the target city according to the target city identifier as the target The standard annual cost of medicines per capita;

根据所述目标药物对应的预设赔付参数以及所述标准人均年费用，计算所述目标药物的目标风险保费；Calculate the target risk premium of the target drug according to the preset compensation parameter corresponding to the target drug and the standard annual per capita cost;

根据所述目标药物对应的预设成本参数以及所述目标风险保费，计算所述目标药物的保费定价。The premium pricing of the target drug is calculated according to the preset cost parameters corresponding to the target drug and the target risk premium.

可选地，所述在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用，作为所述目标药物的标准人均年费用的步骤包括：Optionally, when the premium pricing request based on the target drug is received, the target city identifier in the premium pricing request is obtained, and the historical drug per capita year of the target drug in the target city is obtained according to the target city identifier Costs, as the standard annual cost per capita of the target drugs, include:

在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识判断数据库中是否存在所述目标药物在目标城市的历史人均年费用；When receiving the premium pricing request based on the target drug, obtain the target city identifier in the premium pricing request, and determine whether the historical per capita annual cost of the target drug in the target city exists in the database according to the target city identifier;

若所述数据库中不存在所述历史人均年费用，则在所述数据库中获取与所述目标城市关联的参考城市；If the historical per capita annual fee does not exist in the database, obtain a reference city associated with the target city in the database;

获取所述目标药物在所述参考城市的参考历史人均年费用，作为所述目标药物的标准人均年费用。Obtain the reference historical per capita annual cost of the target drug in the reference city as the standard per capita annual cost of the target drug.

可选地，所述若所述数据库中不存在所述历史人均年费用，则根据所述目标城市标识在所述数据库中获取与所述目标城市关联的参考城市的步骤包括：Optionally, if the historical per capita annual fee does not exist in the database, the step of obtaining the reference city associated with the target city in the database according to the target city identifier includes:

若所述数据库中不存在所述历史人均年费用，则获取所述目标城市的目标城市参数，并获取所述数据库中其他城市的其他城市参数；If the historical per capita annual fee does not exist in the database, obtain the target city parameters of the target city, and obtain other city parameters of other cities in the database;

根据所述目标城市参数以及其他城市参数，计算所述目标城市与所述其他城市的差异度，其中所述差异度的计算依据以下公式：差异度

其中，n为预设构成城市参数的总个数，i为城市参数标号，A _i为目标城市参数值，B _i为其他城市参数值，k _i为参数i的权重值； Calculate the degree of difference between the target city and the other cities according to the target city parameters and other city parameters, where the difference degree is calculated according to the following formula: degree of difference

Where n is the total number of preset city parameters, i is the city parameter label, A _i is the target city parameter value, B _i is the other city parameter value, and k _i is the weight value of parameter i;

根据所述差异度判断所述数据库中是否存在与所述目标城市关联的参考城市，其中，所述目标城市与所述参考城市的差异度小于预设阈值；Judging whether there is a reference city associated with the target city in the database according to the degree of difference, wherein the degree of difference between the target city and the reference city is less than a preset threshold;

若所述数据库中存在所述参考城市，则获取所述参考城市。If the reference city exists in the database, the reference city is obtained.

可选地，所述根据所述差异度判断所述数据库中是否存在与所述目标城市关联的参考城市的步骤之后，还包括：Optionally, after the step of determining whether there is a reference city associated with the target city in the database according to the degree of difference, the method further includes:

若所述数据库中不存在所述参考城市，则获取所述目标药物的单价、使用方法以及所述目标药物在所述目标城市的预设优惠规则；If the reference city does not exist in the database, obtain the unit price of the target drug, the method of use, and the preset preferential rules of the target drug in the target city;

根据所述单价以及使用方法，计算所述目标药物在所述目标城市的理论人均年费用，作为所述目标药物的标准人均年费用，其中，所述使用方法包括每月所需盒数以及需要连续服用月数，所述理论人均年费用的计算依据以下公式：标准人均年费用F＝单价C*每月所需盒数D*需要连续服用月数E。Based on the unit price and the method of use, calculate the theoretical per capita annual cost of the target drug in the target city as the standard per capita annual cost of the target drug, where the method of use includes the number of boxes required per month and the need For the number of months of continuous consumption, the calculation of the theoretical per capita annual cost is based on the following formula: Standard annual per capita cost F = unit price C * number of boxes required per month D * number of months required for continuous consumption E.

可选地，所述在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识判断数据库中是否存在所述目标药物在目标城市的历史人均年费用的步骤之后，还包括：Optionally, when the premium pricing request based on the target drug is received, the target city identifier in the premium pricing request is obtained, and whether the target drug exists in the target city in the database is determined according to the target city identifier After the steps of historical per capita annual cost, it also includes:

若所述数据库中存在所述历史人均年费用，则获取所述目标药物在所述目标城市的历史药物人均年费用，作为标准人均年费用。If the historical per capita annual cost exists in the database, the historical per capita annual cost of the target drug in the target city is obtained as the standard per capita annual cost.

可选地，所述基于大数据的药物保费定价方法还包括：Optionally, the drug premium pricing method based on big data further includes:

若接收到所述投保人操作触发的理赔请求时，获取所述投保人上传的审核材料，并根据所述目标药物对应的理赔参数，判断所述投保人是否符合理赔条件。If a claim request triggered by the policyholder's operation is received, the audit material uploaded by the policyholder is obtained, and according to the claim parameters corresponding to the target drug, it is determined whether the policyholder meets the claim conditions.

在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用；When receiving the premium pricing request based on the target drug, obtain the target city identifier in the premium pricing request, and obtain the historical drug per capita annual cost of the target drug in the target city according to the target city identifier;

根据所述目标药物对应的预设费用附加风险因子、预设时间趋势因子以及所述历史药物人均年费用，计算所述目标药物的目标人均年费用，作为所述目标药物的标准人均年费用，其中，所述目标人均年费用的计算依据以下公式：目标人均年费用Q＝历史药物人均年费用I*(1+预设费用附加风险因子M)*(1+预设时间趋势因子N)。Calculate the target annual per capita cost of the target drug as the standard annual per capita cost of the target drug according to the preset additional risk factor corresponding to the target drug, the preset time trend factor, and the per capita annual cost of the historical drug Wherein, the calculation of the target per capita annual cost is based on the following formula: target per capita annual cost Q = historical drug per capita annual cost I * (1 + preset cost additional risk factor M) * (1 + preset time trend factor N).

此外，为实现上述目的，本申请还提供一种基于大数据的药物保费定价装置，所述基于大数据的药物保费定价装置包括：In addition, in order to achieve the above object, the present application also provides a drug premium pricing device based on big data. The drug premium pricing device based on big data includes:

费用计算模块，用于在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用，作为所述目标药物的标准人均年费用；The cost calculation module is used to obtain the target city identifier in the premium pricing request when receiving the premium pricing request based on the target drug, and obtain the historical drug per capita year of the target drug in the target city according to the target city identifier Cost, as the standard per capita annual cost of the target drug;

风险计算模块，用于根据所述目标药物对应的预设赔付参数以及所述标准人均年费用，计算所述目标药物的目标风险保费；The risk calculation module is used to calculate the target risk premium of the target drug according to the preset compensation parameters corresponding to the target drug and the standard annual per capita cost;

保费定价模块，用于根据所述目标药物对应的预设成本参数以及所述目标风险保费，计算所述目标药物的保费定价。The premium pricing module is used to calculate the premium pricing of the target drug according to the preset cost parameter corresponding to the target drug and the target risk premium.

此外，为实现上述目的，本申请还提供一种基于大数据的药物保费定价设备，所述基于大数据的药物保费定价设备包括处理器、存储器、以及存储在所述存储器上并可被所述处理器执行的计算机可读指令，其中所述计算机可读指令被所述处理器执行时，实现如上述的基于大数据的药物保费定价方法的步骤。In addition, in order to achieve the above object, the present application also provides a drug premium pricing device based on big data. The drug premium pricing device based on big data includes a processor, a memory, and is stored on the memory and can be used by the Computer readable instructions executed by the processor, wherein when the computer readable instructions are executed by the processor, the steps of the big data-based pharmaceutical premium pricing method as described above are implemented.

此外，为实现上述目的，本申请还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机可读指令，其中所述计算机可读指令被处理器执行时，实现如上述的基于大数据的药物保费定价方法的步骤。In addition, in order to achieve the above object, the present application also provides a computer-readable storage medium having computer-readable instructions stored on the computer-readable storage medium, wherein when the computer-readable instructions are executed by a processor, the implementation is as described above Steps of a big data-based drug premium pricing method.

附图说明BRIEF DESCRIPTION

图1为本申请实施例方案中涉及的基于大数据的药物保费定价设备的硬件结构示意图；FIG. 1 is a schematic diagram of the hardware structure of a drug premium pricing device based on big data involved in an embodiment of the present application;

图2为本申请基于大数据的药物保费定价方法第一实施例的流程示意图；FIG. 2 is a schematic flowchart of a first embodiment of a drug premium pricing method based on big data in this application;

图3为本申请基于大数据的药物保费定价方法第二实施例的流程示意图；FIG. 3 is a schematic flow chart of a second embodiment of a pharmaceutical premium pricing method based on big data in this application;

图4为本申请基于大数据的药物保费定价装置第一实施例的功能模块示意图。4 is a schematic diagram of functional modules of a first embodiment of a drug premium pricing device based on big data in this application.

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。The implementation, functional characteristics and advantages of the present application will be further described in conjunction with the embodiments and with reference to the drawings.

具体实施方式detailed description

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。It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

本申请实施例涉及的基于大数据的药物保费定价方法主要应用于基于大数据的药物保费定价设备，该基于大数据的药物保费定价设备可以是PC、便携计算机、移动终端等具有显示和处理功能的设备。The pharmaceutical premium pricing method based on big data involved in the embodiments of the present application is mainly applied to pharmaceutical premium pricing equipment based on big data. The pharmaceutical premium pricing equipment based on big data may be a PC, a portable computer, a mobile terminal, etc. with display and processing functions device of.

参照图1，图1为本申请实施例方案中涉及的基于大数据的药物保费定价设备的硬件结构示意图。本申请实施例中，基于大数据的药物保费定价设备可以包括处理器1001(例如CPU)，通信总线1002，用户接口1003，网络接口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；用户接口1003可以包括显示屏(Display)、输入单元比如键盘(Keyboard)；网络接口1004可选的可以包括标准的有线接口、无线接口(如WI-FI接口)；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。Referring to FIG. 1, FIG. 1 is a schematic diagram of a hardware structure of a drug premium pricing device based on big data involved in an embodiment of the present application. In the embodiment of the present application, the drug premium pricing device based on big data may include a processor 1001 (such as a CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components; the user interface 1003 may include a display (Display), an input unit such as a keyboard (Keyboard); the network interface 1004 may optionally include a standard wired interface, a wireless interface (Such as WI-FI interface); the memory 1005 can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as a disk memory, and the memory 1005 can optionally be a storage device independent of the foregoing processor 1001 .

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基于大数据的药物保费定价设备的限定，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。Those skilled in the art may understand that the hardware structure shown in FIG. 1 does not constitute a limitation on the big data-based drug premium pricing equipment, and may include more or less components than the illustration, or a combination of certain components, or Different parts arrangement.

继续参照图1，图1中作为一种计算机可读存储介质的存储器1005可以包括操作***、网络通信模块以及计算机可读指令。With continued reference to FIG. 1, the memory 1005 in FIG. 1 as a computer-readable storage medium may include an operating system, a network communication module, and computer-readable instructions.

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基于大数据的药物保费定价方法。In FIG. 1, 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 big data-based drug premiums provided by the embodiments of the present application. Pricing method.

本申请实施例提供了一种基于大数据的药物保费定价方法。The embodiments of the present application provide a method for pricing drug premiums based on big data.

参照图2，图2为本申请基于大数据的药物保费定价方法第一实施例的流程示意图。Referring to FIG. 2, FIG. 2 is a schematic flowchart of a first embodiment of a drug premium pricing method based on big data of the present application.

本实施例中，所述基于大数据的药物保费定价方法包括以下步骤：In this embodiment, the drug premium pricing method based on big data includes the following steps:

步骤S10,在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用，作为所述目标药物的标准人均年费用；Step S10, when receiving the premium pricing request based on the target drug, obtain the target city identifier in the premium pricing request, and obtain the historical drug per capita annual cost of the target drug in the target city according to the target city identifier, as The standard per capita annual cost of the target drug;

目前，现有技术中，药品保险定价需要由专家收集数据，综合分析后才能得出结果。但是人工收集数据的方法存在效率以及准确率低下的问题。At present, in the prior art, drug insurance pricing needs to be collected by experts, and the results can only be obtained after comprehensive analysis. However, the method of manually collecting data has problems of low efficiency and low accuracy.

本实施例中，为了解决上述问题，提供一种基于大数据的药物保费定价方法，本实施例中，以尼达尼布胶囊为例进行说明。本实施例中尼达尼布胶囊的保费定价方法可以是由计算终端(服务器、PC等)实现的。服务器在接收到基于尼达尼布胶囊的保费定价请求时，首先需要获取尼达尼布胶囊的人均年费用(特药人均费用_时间调整)；值得说明的是，由于不同城市的气候、消费水平、人群特征等都有所区别，因此本案是针对不同的城市获取对应尼达尼布胶囊的人均年费用。服务器可选取预设个数的城市，根据城市的中医院***(或医药***)，获取所述尼达尼布胶囊对应的历史人均年费用数据，包括参考城市中尼达尼布胶囊的历史年度总费用、使用人数，根据该历史年度总费用、使用人数可计算得到参考城市中尼达尼布胶囊的历史人均年费用(历史人均年费用＝历史年度总费用/使用人数)，例如参考城市NB市中尼达尼布胶囊的2015年的历史年度总费用为384800元、使用人数为68人，则NB市中尼达尼布胶囊的历史人均年费用为56600元。然后将城市名称与所述尼达尼布胶囊对应的历史人均年费用数据关联存储至数据库中。在接收到基于尼达尼布胶囊的保费定价请求时，首先获取所述保费定价请求中的目标城市，并根据所述目标城市在数据库中查找匹配，判断是否存在所述尼达尼布胶囊在所述目标城市的人均年费用，即历史人均年费用。若存在所述目标药物在所述目标城市的历史人均年费用时，则将所述历史人均年费用作为所述目标药物的标准人均年费用。In this embodiment, in order to solve the above-mentioned problems, a method for pricing drug premiums based on big data is provided. In this embodiment, Nidanibu capsules are taken as an example for description. In this embodiment, the premium pricing method of Nidabu capsules may be implemented by a computing terminal (server, PC, etc.). When the server receives a premium pricing request based on Nidanibu capsules, it first needs to obtain the per capita annual cost of Nidanibu capsules (per capita cost of special drugs_time adjustment); it is worth noting that due to the climate and consumption of different cities The level and characteristics of the population are all different, so this case is for each city to obtain the corresponding per capita annual cost of Nidanib capsules. The server may select a preset number of cities, and obtain historical per capita annual cost data corresponding to the Nida Nibu capsule according to the city's TCM system (or medical system), including reference to the historical year of the Nida Nibu capsule in the city The total cost and the number of users can be calculated according to the historical annual total cost and the number of users. The historical average annual cost of Nida Nibu capsules in the reference city (historical average annual cost = historical annual total cost/number of users), for example, reference city NB In 2015, the total annual cost of Nida Nibu Capsules in the city was 384,800 yuan and the number of users was 68. The historical per capita annual cost of Zhong Nida Nibu Capsules in NB City was 56,600 yuan. Then, the city name and historical per capita annual cost data corresponding to the Nida Nibu capsule are associated and stored in the database. When receiving a premium pricing request based on Nida Nibu capsules, first obtain the target city in the premium pricing request, and find a match in the database according to the target city to determine whether the Nida Nibu capsule exists in The per capita annual cost of the target city is the historical per capita annual cost. If there is a historical per capita annual cost of the target drug in the target city, then the historical per capita annual cost is used as the standard per capita annual cost of the target drug.

步骤S20，根据所述目标药物对应的预设赔付参数以及所述标准人均年费用，计算所述目标药物的目标风险保费；Step S20: Calculate the target risk premium of the target drug according to the preset compensation parameter corresponding to the target drug and the standard annual per capita cost;

本实施例中，服务器在得到尼达尼布胶囊的人均年费用时，将或获取预设的尼达尼布胶囊保险计划，该保险计划包括预设赔付参数，所述预设赔付参数包括赔付限额(包括上限和下限)和赔付比例，例如In this embodiment, when obtaining the per capita annual cost of Nidabu capsules, the server will obtain or obtain a preset Nidabu capsule insurance plan, the insurance plan includes preset payment parameters, and the preset payment parameters include payment Limits (including upper and lower limits) and compensation ratios, such as

赔付下限(万)Lower limit of compensation (ten thousand)	赔付上限(万)Payout limit (ten thousand)	赔付比例Payout ratio
00	11	95％95%
11	33	95％95%
33	55	95％95%
55	20000002000000	95％95%

根据尼达尼布胶囊的人均年费用和该保险计划的上述内容，服务器可计算预计的人均赔付。例如，尼达尼布胶囊的人均年费用为65090，则预计人均赔付＝0.95*1+(3-1)*0.95+(5-3)*0.95+(65090-5)*0.95＝61835.5。Based on the per capita annual cost of Nidabu capsules and the above contents of the insurance plan, the server can calculate the estimated per capita compensation. For example, the per capita annual cost of Nidanibu capsules is 65090, then the estimated per capita compensation = 0.95*1+(3-1)*0.95+(5-3)*0.95+(65090-5)*0.95=61835.5.

服务器在得到尼达尼布胶囊的预计人均赔付时，可根据尼达尼布胶囊的预计人均赔付计算保险计划的风险保费；对于风险保费，是指正好用以支付赔款，因此若要计算风险保费，还需要先获取赔付事件发生的概率，即保障人使用尼达尼布胶囊的使用率。该使用率可根据尼达尼布胶囊的经验发生率与一预设的发生率附加风险因子计算得到(使用率＝经验发生率*(1+发生率附加风险因子))；其中，经验发生率可理解为尼达尼布胶囊在理想状态下使用率，发生率附加风险因子为一调整系数，可根据实际情况进行设置。The server can calculate the risk premium of the insurance plan based on the estimated per capita compensation of Nida Nibu Capsule when the server receives the estimated per capita compensation of Nida Nibu Capsule; for the risk premium, it means that it is just used to pay the compensation, so if you want to calculate the risk premium , You also need to first obtain the probability of the occurrence of the compensation event, that is, the use rate of the guarantee person using Nidanibu capsules. The usage rate can be calculated based on the empirical incidence rate of Nidanibu capsules and a predetermined incidence rate additional risk factor (utilization rate = empirical incidence rate * (1 + incidence rate additional risk factor)); where, the empirical incidence rate It can be understood as the use rate of Nidanibu capsules in an ideal state, and the additional risk factor of the incidence rate is an adjustment factor, which can be set according to the actual situation.

具体实施例中，对于经验发生率，可以是根据参考城市的疾病记录和用药记录计算得到的历史发生率。其中疾病记录包括IPF患病率、诊断率、治疗率等，用药记录包括尼达尼布胶囊用药率；参考城市的尼达尼布胶囊的历史发生率＝IPF患病率*诊断率*治疗率*尼达尼布胶囊用药率。In a specific embodiment, the experience incidence rate may be a historical incidence rate calculated according to the disease records and medication records of the reference city. The disease records include the prevalence of IPF, the diagnosis rate, the treatment rate, etc., and the medication records include the use rate of Nidabu capsules; the historical incidence of Nidabu capsules in the reference city = IPF prevalence rate * diagnosis rate * treatment rate *Nidanibu capsule usage rate.

当然，经验发生率，也可以是通过预设的方式获得(特别是在未能获取到参考城市的相关记录的情况下)的预测发生率。具体的，服务器遍历相关文献，并从文献中获取IPF的理论发病率(适用疾病发病率)，然后再将理论发病率与一预设的预估发生率调整因子相乘，从而得到预设发生率。Of course, the experience incidence rate can also be the predicted incidence rate obtained by a preset method (especially when the relevant record of the reference city cannot be obtained). Specifically, the server traverses the relevant literature and obtains the theoretical incidence of IPF (applicable disease incidence) from the literature, and then multiplies the theoretical incidence by a preset estimated incidence adjustment factor to obtain the preset occurrence rate.

在得到经验发生率时(历史发生率或预测发生率)，则可根据计算得到尼达尼布胶囊的使用率(使用率＝经验发生率/(1+发生率附加风险因子)。然后再根据尼达尼布胶囊的预计人均赔付、使用率计算得到尼达尼布胶囊保险计划的风险保费，即风险保费＝预计人均赔付*使用率。When the experience incidence rate (historical incidence rate or predicted incidence rate) is obtained, the usage rate of Nidanibu capsules can be obtained according to the calculation (utilization rate=experience incidence rate/(1+incidence rate additional risk factor). Then based on The estimated per capita compensation and utilization rate of Nida Nibu Capsule can be calculated as the risk premium of Nida Nibu Capsule Insurance Plan, that is, the risk premium = estimated per capita compensation * utilization rate.

更多实施例中，当尼达尼布胶囊的保险计划是针对第一次投保(首年)的客户时，该风险保费还需要考虑市面上的竞品因素(即市面上是否存在其它治疗药物供患者选择)，该竞品因素可用一预设的竞品分摊系数(0＜竞品分摊系数＜＝1)进行表征，此时风险保费_首年＝预计人均赔付*使用率*竞品分摊系数。In more embodiments, when the insurance plan of Nidanibu Capsule is for customers who are insured for the first time (the first year), the risk premium also needs to consider the competitive factors on the market (that is, whether there are other therapeutic drugs on the market) For patient selection), the competitive factor can be characterized by a preset competitive product allocation coefficient (0 <competitive product allocation coefficient <= 1), at this time the risk premium _ first year = estimated per capita compensation * utilization rate * competitive product allocation coefficient.

步骤S30，根据所述目标药物对应的预设成本参数以及所述目标风险保费，计算所述目标药物的保费定价。Step S30: Calculate the premium pricing of the target drug according to the preset cost parameter corresponding to the target drug and the target risk premium.

本实施例中，所述预设成本参数包括保险计划的运营费用、税费比例即目标利润等，保险计划的运营费用可理解为运营成本，其中包括***费用和人力成本，其中***费用又包括接口费用和专线费用，人力成本则为线下网点人数与平均工资的乘积。In this embodiment, the preset cost parameters include the operating expenses of the insurance plan, the tax rate and the target profit, etc. The operating expenses of the insurance plan can be understood as operating costs, including system fees and labor costs, and the system fees include Interface fees and dedicated line fees, and labor costs are the product of the number of offline outlets and the average salary.

对于税费比例及利润目标，则可以是根据实际情况进行设置。For the tax rate and profit target, it can be set according to the actual situation.

服务器在得到保险计划的风险保费时，可根据业务***获取得到相关的运营费用、税费比例及利润目标信息，然后计算得到尼达尼布胶囊保险计划的保费定价，即When the server obtains the risk premium of the insurance plan, it can obtain the related operating expenses, tax rate and profit target information according to the business system, and then calculate the premium pricing of the Nidabu capsule insurance plan, that is

保费定价＝(风险保费+运营费用)/(1-税费比例-利润率目标)。Premium pricing = (risk premium + operating expenses)/(1-tax rate-profit target).

本实施例提供一种基于大数据的药物保费定价方法，即在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用，作为所述目标药物的标准人均年费用；根据所述目标药物对应的预设赔付参数以及所述标准人均年费用，计算所述目标药物的目标风险保费；根据所述目标药物对应的预设成本参数以及所述目标风险保费，计算所述目标药物的保费定价。通过上述方式，本申请根据目标药物对应的实际成本参数，并结合目标城市对应的实际历史药物费用和实际赔付参数，计算得到目标药物的保费定价，制定出更符合药物实际使用情况和保险机构运营情况的保费定价，在提高定价的合理性和准确性的同时，也降低了人工工作量，节约了人力成本。This embodiment provides a drug premium pricing method based on big data, that is, when a premium pricing request based on a target drug is received, the target city identifier in the premium pricing request is obtained, and the target city identifier is obtained according to the target city identifier The annual per capita cost of the historical drug of the target drug in the target city is used as the standard per capita annual cost of the target drug; the target risk of the target drug is calculated according to the preset compensation parameter corresponding to the target drug and the standard annual per capita cost Premium; calculate the premium pricing of the target drug based on the preset cost parameters corresponding to the target drug and the target risk premium. Through the above-mentioned methods, this application calculates the premium pricing of the target drug based on the actual cost parameters corresponding to the target drug, combined with the actual historical drug costs and actual compensation parameters corresponding to the target city, and formulates more in line with the actual use of the drug and the operation of the insurance institution The premium pricing of the situation, while improving the rationality and accuracy of the pricing, also reduces the labor workload and saves labor costs.

参照图3，图3为本申请基于大数据的药物保费定价方法第二实施例的流程示意图。Referring to FIG. 3, FIG. 3 is a schematic flowchart of a second embodiment of a drug premium pricing method based on big data of the present application.

基于上述图2所示实施例，本实施例中，所述步骤S10具体包括：Based on the embodiment shown in FIG. 2 above, in this embodiment, the step S10 specifically includes:

步骤S11，在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识判断数据库中是否存在所述目标药物在目标城市的历史人均年费用；Step S11: When receiving the premium pricing request based on the target drug, obtain the target city identifier in the premium pricing request, and determine whether the historical per capita year of the target drug in the target city exists in the database according to the target city identifier cost;

本实施例中，在接收到基于尼达尼布胶囊的保费定价请求时，首先获取所述保费定价请求中的目标城市，并根据所述目标城市在数据库中查找匹配，判断是否存在所述尼达尼布胶囊在所述目标城市的人均年费用，即历史人均年费用。其中，所述历史人均年费用可以从目标城市所在的中医院***(或医药***)获取。In this embodiment, when receiving a premium pricing request based on Nida Nibu capsules, first obtain the target city in the premium pricing request, and find a match in the database according to the target city to determine whether the The per capita annual cost of Danib capsules in the target cities is the historical per capita annual cost. Wherein, the historical per capita annual cost can be obtained from the TCM system (or medical system) where the target city is located.

步骤S12，若所述数据库中不存在所述历史人均年费用，则在所述数据库中获取与所述目标城市关联的参考城市；Step S12: If the historical per capita annual fee does not exist in the database, obtain a reference city associated with the target city in the database;

本实施例中，若所述数据库中不存在所述尼达尼布胶囊在所述目标城市的人均年费用，即未采集目标城市有关尼达尼布胶囊的历史人均年费用。可以通过根据与目标城市具有相似特点的参考城市的历史数据得出目标城市的人均年费用(其中，该特点的“相似”可以是通过上述气候、消费水平、人群特征等维度进行衡量)。In this embodiment, if the per capita annual cost of the Nida Nibu capsule in the target city does not exist in the database, that is, the historical per capita annual cost of the Nida Nibu capsule in the target city is not collected. The per capita annual cost of the target city can be derived from the historical data of reference cities that have similar characteristics to the target city (where the "similarity" of this characteristic can be measured by the dimensions of the above-mentioned climate, consumption level, crowd characteristics, etc.).

步骤S13，获取所述目标药物在所述参考城市的参考历史人均年费用，作为所述目标药物的标准人均年费用。Step S13: Obtain the reference historical per capita annual cost of the target drug in the reference city as the standard per capita annual cost of the target drug.

本实施例中，获取所述参考城市的参考历史人均年费用，将所述参考历史人均年费用作为目标药物的标准人均年费用。具体实施例中，还可以将所述参考历史人均年费用乘以对应设置的风险因子，然后将得到的费用数据作为标准人均年费用，以减小费用误差。In this embodiment, the reference historical per capita annual cost of the reference city is obtained, and the reference historical per capita annual cost is used as the standard per capita annual cost of the target drug. In a specific embodiment, the reference historical per capita annual cost can be multiplied by the correspondingly set risk factor, and then the obtained cost data can be used as the standard per capita annual cost to reduce the cost error.

本实施例中，进一步地，步骤S11之后，还包括：In this embodiment, further, after step S11, it also includes:

本实施例中，若所述数据库中存在所述历史人均年费用，即获取所述历史药物人均年费用，即可作为所述目标药物的标准人均年费用。In this embodiment, if the historical per capita annual cost exists in the database, that is, the historical per capita annual cost of the historical drug is obtained, which can be used as the standard per capita annual cost of the target drug.

基于上述图3所示实施例，本实施例中，所述步骤S12具体包括：Based on the embodiment shown in FIG. 3 above, in this embodiment, the step S12 specifically includes:

本实施例中，若数据库中不存在所述目标城市对应的历史人均年费用，则可通过获取与所述目标城市相关的关联城市对应的参考历史人均年费用。其中，获取关联城市的步骤为：获取数据库中目标城市以及其他城市对应的城市参数。其中，所述城市参数包括气候参数、人均消费参数以及人均年龄中的一种或者多种等。In this embodiment, if the historical per capita annual cost corresponding to the target city does not exist in the database, the reference historical per capita annual cost corresponding to the related city related to the target city may be obtained. Among them, the step of obtaining associated cities is: obtaining the city parameters corresponding to the target city and other cities in the database. Wherein, the city parameters include one or more of climate parameters, per capita consumption parameters, and per capita age.

根据所述目标城市参数以及其他城市参数，计算所述目标城市与所述其他城市的差异度，其中所述差异度依据以下公式：差异度

其中，n为预设构成城市参数的总个数，i为城市参数标号，A _i为目标城市参数值，B _i为其他城市参数值，k _i为参数i的权重值； Calculate the degree of difference between the target city and the other cities according to the target city parameters and other city parameters, where the degree of difference is based on the following formula: degree of difference

本实施例中，所述目标城市参数与所述其他城市参数越接近，则所述目标城市与所述其他城市的差异度则越大。根据公式：差异度

其中，i为预设构成城市参数的总个数，A _i为目标城市参数值，B _i为其他城市参数值，k _i为参数i的权重值。在城市参数具有多个参数时，可以根据预设权重进行计算，若气候参数、人均消费参数以及人均年龄的权重分别为0.2、0.3和0.5，即差异度S＝[0.2*|目标城市气候参数值A ₁-其他城市气候参数值B ₁|/A ₁+0.3*|目标城市人均消费参数值A ₂-其他城市人均消费参数值B ₂|/A ₂+0.5*|目标城市人均年龄值A ₃-其他城市人均年龄值B ₃|/A ₃]。其中，差值均取绝对值，防止出现负数。然后获取差异度小于预设阈值的其他城市作为所述目标城市的参考城市，并获取所述参考城市对应的参考历史人均年费用，作为所述标准人均年费用。 In this embodiment, the closer the target city parameter is to the other city parameter, the greater the degree of difference between the target city and the other city. According to the formula: degree of difference

Where i is the total number of preset city parameters, A _i is the parameter value of the target city, B _i is the parameter value of other cities, and k _i is the weight value of the parameter i. When the city parameter has multiple parameters, it can be calculated according to the preset weights. If the weights of the climate parameters, per capita consumption parameters and per capita age are 0.2, 0.3 and 0.5, respectively, the degree of difference S=[0.2*|target city climate parameters Value A ₁ -Climate parameter value of other cities B ₁ |/A ₁ +0.3*|Per capita consumption parameter value of target cities A ₂ -Per capita consumption parameter value of other cities B ₂ |/A ₂ +0.5*|Per capita age value of target cities A ₃ -Per capita age in other cities B ₃ |/A ₃ ]. Among them, the absolute value of the difference is taken to prevent negative numbers. Then obtain other cities with a degree of difference less than a preset threshold as the reference city of the target city, and obtain the reference historical annual per capita cost corresponding to the reference city as the standard annual per capita cost.

本实施例中，判断各个城市与所述目标城市的各个差异度，并判断各个差异度中是否存在小于预设阈值的差异度。其中，预设阈值可以是根据用户实际需要进行设置的数值，还可以是***根据***数据自动设置。In this embodiment, each degree of difference between each city and the target city is determined, and it is determined whether there is a degree of difference smaller than a preset threshold in each degree of difference. The preset threshold may be a value set according to actual needs of the user, or may be automatically set by the system according to system data.

本实施例中，若服务器不存在相似的参考城市，或者未能获取到参考城市的历史药物费用(即参考城市也不存在相关历史费用)，则可通过预测的方式获取经验人均年费用。具体的，服务器首先将根据尼达尼布胶囊的使用方法、预设说明书单价和优惠慈善计划计算理论人均年费用。In this embodiment, if the server does not have a similar reference city, or fails to obtain the historical drug cost of the reference city (that is, the reference city also does not have related historical costs), the annual per capita cost of experience can be obtained by prediction. Specifically, the server will first calculate the theoretical per capita annual fee based on the usage method of Nidanibu capsules, the unit price of the preset manual, and the preferential charity plan.

本实施例中，所述理论人均年费用的计算具体步骤为：依据计算公式标准人均年费用F＝单价C*每月所需盒数D*需要连续服用月数E；例如，尼达尼布胶囊每盒价格为2250元，每月需使用3盒，优惠慈善计划为参保人员连续购买4个月后免费供药，则尼达尼布胶囊的理论人均年费用为2250*3*4＝27000元。在得到理论人均年费用时，可将该理论人均年费用与一预设的预估费用调整因子相乘，并将得到的乘积作为尼达尼布胶囊的预估人均年费用，其中该预估费用调整因子可根据实际情况进行设置。In this embodiment, the specific steps of calculating the theoretical per capita annual cost are: according to the calculation formula, the standard per capita annual cost F = unit price C * the number of boxes required per month D * the number of months required to continue taking E; for example, Nidanibu The price of each capsule is 2250 yuan, and 3 capsules are used each month. The preferential charity plan provides the insured person with free medicine after 4 months of continuous purchase. The theoretical annual cost of Nida Nibu capsules is 2250*3*4= 27,000 yuan. When the theoretical per capita annual cost is obtained, the theoretical per capita annual cost can be multiplied by a preset estimated cost adjustment factor, and the obtained product can be used as the estimated per capita annual cost of Nida Nibu capsules, where the estimated The cost adjustment factor can be set according to the actual situation.

本实施例中，若在所述数据库中查找到所述目标城市的参考城市，即该参考城市的城市参数与所述目标城市的城市参数的差异度小于预设阈值，也就是说，所述参考城市的参考历史人均年费用可以作为所述目标城市的人均年费用参考值。In this embodiment, if the reference city of the target city is found in the database, that is, the difference between the city parameter of the reference city and the city parameter of the target city is less than a preset threshold, that is, the The reference historical per capita annual cost of the reference city can be used as the reference value of the per capita annual cost of the target city.

进一步地，步骤S10具体包括：Further, step S10 specifically includes:

本实施例中，无论是上述通过参考城市的历史数据得出的历史人均年费用、还是通过说明书预测得到的预估人均年费用，都属于经验范畴的经验人均年费用，为了使得计算结果能够符合实际情况，还需要引入预设的费用附加风险因子、时间趋势因子，以表征下一保险周期可能带来的风险情况，并根据该经验人均年费用、费用附加风险因子、时间趋势因子，以及预设公式，：目标人均年费用Q＝历史药物人均年费用I*(1+预设费用附加风险因子M)*(1+预设时间趋势因子N)，计算尼达尼布胶囊的人均年费用，即：In this embodiment, whether it is the historical annual per capita cost obtained by referring to the historical data of the city or the estimated annual per capita cost predicted by the manual, it belongs to the experience per capita annual cost, in order to make the calculation result consistent with In actual situations, it is necessary to introduce preset cost additional risk factors and time trend factors to characterize the risk situation that may be brought by the next insurance cycle, and according to this experience, per capita annual cost, additional cost risk factors, time trend factors, and predictive Set the formula: target annual cost per capita Q = historical drug per capita annual cost I * (1 + preset cost additional risk factor M) * (1 + preset time trend factor N), calculate the annual cost per capita of Nidabu capsule ,which is:

尼达尼布胶囊的人均年费用＝经验人均年费用*(1+费用附加风险因子)*(1+时间趋势因子)，例如，尼达尼布胶囊的人均年费用＝56600*(1+15％)*(1+0)＝65090。Per capita annual cost of Nidabu capsule = Experience per capita annual cost * (1 + cost additional risk factor) * (1 + time trend factor), for example, per capita annual cost of Nidabu capsule = 56600 * (1+15 %)*(1+0)=65090.

进一步地，步骤S30之后，还包括：Further, after step S30, it also includes:

本实施例中，服务器在计算得到尼达尼布胶囊保险计划的保费定价时，可将该保费定价反馈至对应的业务终端(或投保人终端)，以使得业务人员根据该保费定价进行收费(或使得投保人进行缴费)。投保人投保后，若出现理赔事件(购买使用了尼达尼布胶囊)，可通过对应终端向服务器发送理赔请求。服务器在接收到理赔请求时，将根据尼达尼布胶囊的适用病种获取对应的理赔审核材料，如特发性肺纤维化审核必备的病理学诊断或者血小板减少或者导致出血诊断；然后服务器可根据上述材料判断投保人是否满足理赔条件，若满足，则可获取投保人购买尼达尼布胶囊的实际花费，并结合投保计划中的赔付比例计算理赔费用进行理赔。In this embodiment, when the server calculates the premium pricing of the Nidanibu Capsule Insurance Plan, it can feed back the premium pricing to the corresponding business terminal (or insured terminal), so that the business personnel can charge according to the premium pricing ( Or make the policyholder pay the fee). After applying for insurance, the insured can send a claim request to the server through the corresponding terminal if a claim event occurs (purchased and used Nida Nibu capsule). When the server receives the claim request, it will obtain the corresponding claim review materials according to the applicable disease type of Nidanibu capsules, such as the pathological diagnosis or thrombocytopenia or hemorrhage diagnosis necessary for the idiopathic pulmonary fibrosis review; then the server According to the above materials, it can be judged whether the insured person meets the claim conditions. If it is satisfied, the actual cost of the insured person to purchase Nida Nibu capsules can be obtained, and the claim expenses can be calculated in combination with the claim ratio in the insurance plan.

此外，本申请实施例还提供一种基于大数据的药物保费定价装置。In addition, the embodiments of the present application also provide a drug premium pricing device based on big data.

参照图4，图4为本申请基于大数据的药物保费定价装置第一实施例的功能模块示意图。Referring to FIG. 4, FIG. 4 is a schematic diagram of functional modules of a first embodiment of a drug premium pricing device based on big data in this application.

本实施例中，所述基于大数据的药物保费定价装置包括：In this embodiment, the drug premium pricing device based on big data includes:

费用计算模块10，用于在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用，作为所述目标药物的标准人均年费用；The fee calculation module 10 is configured to obtain the target city identifier in the premium pricing request when receiving the premium pricing request based on the target drug, and obtain the historical drug per capita of the target drug in the target city according to the target city identifier Annual cost, as the standard per capita annual cost of the target drug;

风险计算模块20，用于根据所述目标药物对应的预设赔付参数以及所述标准人均年费用，计算所述目标药物的目标风险保费；The risk calculation module 20 is configured to calculate the target risk premium of the target drug according to the preset compensation parameters corresponding to the target drug and the standard annual per capita cost;

保费定价模块30，用于根据所述目标药物对应的预设成本参数以及所述目标风险保费，计算所述目标药物的保费定价。The premium pricing module 30 is configured to calculate the premium pricing of the target drug according to the preset cost parameter corresponding to the target drug and the target risk premium.

进一步地，所述费用计算模块10包括：Further, the fee calculation module 10 includes:

第一费用获取单元，用于在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用；The first fee obtaining unit is configured to obtain the target city identifier in the premium pricing request when receiving the premium pricing request based on the target drug, and obtain the historical drug of the target drug in the target city according to the target city identifier Annual expenses per capita;

费用计算单元，用于根据所述目标药物对应的预设费用附加风险因子、预设时间趋势因子以及所述历史药物人均年费用，计算所述目标药物的目标人均年费用，作为所述目标药物的标准人均年费用，其中，所述目标人均年费用的计算依据以下公式：目标人均年费用Q＝历史药物人均年费用I*(1+预设费用附加风险因子M)*(1+预设时间趋势因子N)。A cost calculation unit for calculating the target annual cost of the target drug as the target drug based on the preset cost additional risk factor corresponding to the target drug, the preset time trend factor and the historical drug per capita annual cost The standard annual cost per capita, wherein the calculation of the target annual cost per capita is based on the following formula: Target annual cost per capita Q= Annual cost per capita of historical drugs I*(1+preset cost additional risk factor M)*(1+preset Time trend factor N).

进一步地，所述费用计算模块10还包括：Further, the fee calculation module 10 further includes:

城市判断单元，用于在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识判断数据库中是否存在所述目标药物在目标城市的历史人均年费用；The city judgment unit is used to obtain the target city ID in the premium pricing request when receiving the premium pricing request based on the target drug, and determine whether the target drug exists in the target city in the database according to the target city ID Historical per capita annual cost;

城市获取单元，用于若所述数据库中不存在所述历史人均年费用，则在所述数据库中获取与所述目标城市关联的参考城市；A city obtaining unit, configured to obtain a reference city associated with the target city in the database if the historical per capita annual fee does not exist in the database;

第二费用获取单元，用于获取所述目标药物在所述参考城市的参考历史人均年费用，作为所述目标药物的标准人均年费用。The second cost obtaining unit is used to obtain the reference historical annual per capita cost of the target drug in the reference city as the standard annual per capita cost of the target drug.

第三费用获取单元，用于若所述数据库中存在所述历史人均年费用，则获取所述目标药物在所述目标城市的历史药物人均年费用，作为标准人均年费用。The third cost obtaining unit is used to obtain the historical drug per capita annual cost of the target drug in the target city if the historical per capita annual cost exists in the database as a standard per capita annual cost.

进一步地，所述城市获取单元包括：Further, the city acquiring unit includes:

参数获取子单元，用于若所述数据库中不存在所述历史人均年费用，则获取所述目标城市的目标城市参数，并获取所述数据库中其他城市的其他城市参数；A parameter obtaining subunit, used to obtain the target city parameters of the target city and obtain other city parameters of other cities in the database if the historical per capita annual fee does not exist in the database;

差异度计算子单元，用于根据所述目标城市参数以及其他城市参数，计算所述目标城市与所述其他城市的差异度，其中所述差异度的计算依据以下公式：差异度

其中，i为预设构成城市参数的总个数，A _i为目标城市参数值，B _i为其他城市参数值，k _i为参数i的权重值。 The difference degree calculation subunit is used to calculate the difference degree between the target city and the other city according to the target city parameter and other city parameters, wherein the calculation of the difference degree is based on the following formula: difference degree

Where i is the total number of preset city parameters, A _i is the parameter value of the target city, B _i is the parameter value of other cities, and k _i is the weight value of the parameter i.

城市判断子单元，用于根据所述差异度判断所述数据库中是否存在与所述目标城市关联的参考城市，其中，所述目标城市与所述参考城市的差异度小于预设阈值；A city judgment subunit, configured to judge whether there is a reference city associated with the target city in the database according to the degree of difference, wherein the degree of difference between the target city and the reference city is less than a preset threshold;

城市获取子单元，用于若所述数据库中存在所述参考城市，则获取所述参考城市。The city obtaining subunit is used to obtain the reference city if the reference city exists in the database.

进一步地，所述城市获取单元还包括：Further, the city acquiring unit further includes:

药物参数获取子单元，用于若所述数据库中不存在所述参考城市，则获取所述目标药物的单价、使用方法以及所述目标药物在所述目标城市的预设优惠规则；A drug parameter acquisition subunit, used to obtain the unit price of the target drug, the method of use and the preset preferential rules of the target drug in the target city if the reference city does not exist in the database;

费用计算子单元，用于根据所述目标药物对应的预设费用附加风险因子、预设时间趋势因子以及所述历史药物人均年费用，计算所述目标药物的目标人均年费用，作为所述目标药物的标准人均年费用，其中，所述目标人均年费用的计算依据以下公式：目标人均年费用Q＝历史药物人均年费用I*(1+预设费用附加风险因子M)*(1+预设时间趋势因子N)。The cost calculation subunit is used to calculate the target annual cost of the target drug according to the preset additional risk factor corresponding to the target drug, the preset time trend factor and the historical drug per capita, as the target The standard per capita annual cost of drugs, wherein the calculation of the target per capita annual cost is based on the following formula: target per capita annual cost Q = historical drug per capita annual cost I*(1+preset cost additional risk factor M)*(1+pre- Set the time trend factor N).

进一步地，所述费用计算模块10还用于：Further, the fee calculation module 10 is also used to:

进一步地，所述基于大数据的药物保费定价装置还包括：Further, the drug premium pricing device based on big data further includes:

理赔审核模块，用于若接收到所述投保人操作触发的理赔请求时，获取所述投保人上传的审核材料，并根据所述目标药物对应的理赔参数，判断所述投保人是否符合理赔条件。Claims review module, used to obtain the review materials uploaded by the policyholder when receiving a claim request triggered by the policyholder’s operation, and determine whether the policyholder meets the claims conditions according to the claim parameters corresponding to the target drug .

其中，上述基于大数据的药物保费定价装置中各个模块与上述基于大数据的药物保费定价方法实施例中各步骤相对应，其功能和实现过程在此处不再一一赘述。Wherein, each module in the big data-based drug premium pricing device corresponds to each step in the above-mentioned big data-based drug premium pricing method embodiment, and its functions and implementation processes will not be repeated here one by one.

此外，本申请实施例还提供一种计算机可读存储介质，所述计算机可读存储介质可以为非易失性可读存储介质。In addition, embodiments of the present application also provide a computer-readable storage medium, and the computer-readable storage medium may be a non-volatile readable storage medium.

本申请计算机可读存储介质上存储有计算机可读指令，其中所述计算机可读指令被处理器执行时，实现如上述的基于大数据的药物保费定价方法的步骤。The computer-readable storage medium of the present application stores computer-readable instructions, wherein when the computer-readable instructions are executed by a processor, the steps of the big data-based pharmaceutical premium pricing method as described above are implemented.

其中，计算机可读指令被执行时所实现的方法可参照本申请基于大数据的药物保费定价方法的各个实施例，此处不再赘述。For the method implemented when the computer-readable instructions are executed, reference may be made to the various embodiments of the pharmaceutical premium pricing method based on big data in this application, and details are not described here.

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***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***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***中还存在另外的相同要素。It should be noted that in this article, the terms "include", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, It also includes other elements that are not explicitly listed, or include elements inherent to this process, method, article, or system. Without more restrictions, the element defined by the sentence "include one..." does not exclude that there are other identical elements in the process, method, article or system that includes the element.

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。The sequence numbers of the above embodiments of the present application are for description only, and do not represent the advantages and disadvantages of the embodiments.

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(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。Through the description of the above embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware, but in many cases the former is better Implementation. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence or part that contributes to the existing technology, and the computer software product is stored in a storage medium (such as ROM/RAM as described above) , Magnetic disk, optical disk), including several instructions to make a terminal device (which can be a mobile phone, computer, server, air conditioner, or network equipment, etc.) to perform the method described in each embodiment of the present application.

以上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等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。The above are only the preferred embodiments of the present application, and do not limit the scope of the patent of the present application. Any equivalent structure or equivalent process transformation made by the description and drawings of this application, or directly or indirectly used in other related technical fields , The same reason is included in the scope of patent protection in this application.

Claims

一种基于大数据的药物保费定价方法，其特征在于，所述基于大数据的药物保费定价方法包括以下步骤：A pharmaceutical premium pricing method based on big data, characterized in that the pharmaceutical premium pricing method based on big data includes the following steps:

在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用，作为所述目标药物的标准人均年费用；When receiving the premium pricing request based on the target drug, obtain the target city identifier in the premium pricing request, and obtain the historical drug per capita annual cost of the target drug in the target city according to the target city identifier as the target The standard annual cost of medicines per capita;

根据所述目标药物对应的预设赔付参数以及所述标准人均年费用，计算所述目标药物的目标风险保费；Calculate the target risk premium of the target drug according to the preset compensation parameter corresponding to the target drug and the standard annual per capita cost;

根据所述目标药物对应的预设成本参数以及所述目标风险保费，计算所述目标药物的保费定价。The premium pricing of the target drug is calculated according to the preset cost parameters corresponding to the target drug and the target risk premium.
如权利要求1所述的基于大数据的药物保费定价方法，其特征在于，所述在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用，作为所述目标药物的标准人均年费用的步骤包括：The drug premium pricing method based on big data according to claim 1, characterized in that, when receiving the premium pricing request based on the target drug, the target city identifier in the premium pricing request is obtained, and according to the The target city identifier acquires the historical drug per capita annual cost of the target drug in the target city, and the steps as the standard per capita annual cost of the target drug include:

在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识判断数据库中是否存在所述目标药物在目标城市的历史人均年费用；When receiving the premium pricing request based on the target drug, obtain the target city identifier in the premium pricing request, and determine whether the historical per capita annual cost of the target drug in the target city exists in the database according to the target city identifier;

若所述数据库中不存在所述历史人均年费用，则在所述数据库中获取与所述目标城市关联的参考城市；If the historical per capita annual fee does not exist in the database, obtain a reference city associated with the target city in the database;

获取所述目标药物在所述参考城市的参考历史人均年费用，作为所述目标药物的标准人均年费用。Obtain the reference historical per capita annual cost of the target drug in the reference city as the standard per capita annual cost of the target drug.
如权利要求2所述的基于大数据的药物保费定价方法，其特征在于，所述若所述数据库中不存在所述历史人均年费用，则根据所述目标城市标识在所述数据库中获取与所述目标城市关联的参考城市的步骤包括：The drug premium pricing method based on big data according to claim 2, wherein if the historical annual per capita cost does not exist in the database, the The steps of the reference city associated with the target city include:

若所述数据库中不存在所述历史人均年费用，则获取所述目标城市的目标城市参数，并获取所述数据库中其他城市的其他城市参数；If the historical per capita annual fee does not exist in the database, obtain the target city parameters of the target city, and obtain other city parameters of other cities in the database;

根据所述目标城市参数以及其他城市参数，计算所述目标城市与所述其他城市的差异度，其中所述差异度的计算依据以下公式：差异度
其中，n为预设构成城市参数的总个数，i为城市参数标号，A _i为目标城市参数值，B _i为其他城市参数值，k _i为参数i的权重值； Calculate the degree of difference between the target city and the other cities according to the target city parameters and other city parameters, where the difference degree is calculated according to the following formula: degree of difference
Where n is the total number of preset city parameters, i is the city parameter label, A _i is the target city parameter value, B _i is the other city parameter value, and k _i is the weight value of parameter i;

根据所述差异度判断所述数据库中是否存在与所述目标城市关联的参考城市，其中，所述目标城市与所述参考城市的差异度小于预设阈值；Judging whether there is a reference city associated with the target city in the database according to the degree of difference, wherein the degree of difference between the target city and the reference city is less than a preset threshold;

若所述数据库中存在所述参考城市，则获取所述参考城市。If the reference city exists in the database, the reference city is obtained.
如权利要求3所述的基于大数据的药物保费定价方法，其特征在于，所述根据所述差异度判断所述数据库中是否存在与所述目标城市关联的参考城市的步骤之后，还包括：The pharmaceutical premium pricing method based on big data according to claim 3, wherein after the step of determining whether there is a reference city associated with the target city in the database according to the degree of difference, the method further includes:

若所述数据库中不存在所述参考城市，则获取所述目标药物的单价、使用方法以及所述目标药物在所述目标城市的预设优惠规则；If the reference city does not exist in the database, obtain the unit price of the target drug, the method of use, and the preset preferential rules of the target drug in the target city;

根据所述单价以及使用方法，计算所述目标药物在所述目标城市的理论人均年费用，作为所述目标药物的标准人均年费用，其中，所述使用方法包括每月所需盒数以及需要连续服用月数，所述理论人均年费用的计算依据以下公式：标准人均年费用F＝单价C*每月所需盒数D*需要连续服用月数E。Based on the unit price and the method of use, calculate the theoretical per capita annual cost of the target drug in the target city as the standard per capita annual cost of the target drug, where the method of use includes the number of boxes required per month and the need For the number of months of continuous consumption, the calculation of the theoretical per capita annual cost is based on the following formula: Standard annual per capita cost F = unit price C * number of boxes required per month D * number of months required for continuous consumption E.
如权利要求2所述的基于大数据的药物保费定价方法，其特征在于，所述在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识判断数据库中是否存在所述目标药物在目标城市的历史人均年费用的步骤之后，还包括：The drug premium pricing method based on big data according to claim 2, characterized in that, when receiving the premium pricing request based on the target drug, the target city identifier in the premium pricing request is obtained, and according to the The target city identifier determines whether there is a historical per capita annual cost of the target drug in the target city in the database, which also includes:

若所述数据库中存在所述历史人均年费用，则获取所述目标药物在所述目标城市的历史药物人均年费用，作为标准人均年费用。If the historical per capita annual cost exists in the database, the historical per capita annual cost of the target drug in the target city is obtained as the standard per capita annual cost.
如权利要求1所述的基于大数据的药物保费定价方法，其特征在于，所述基于大数据的药物保费定价方法还包括：The drug premium pricing method based on big data according to claim 1, wherein the drug premium pricing method based on big data further comprises:

若接收到所述投保人操作触发的理赔请求时，获取所述投保人上传的审核材料，并根据所述目标药物对应的理赔参数，判断所述投保人是否符合理赔条件。If a claim request triggered by the policyholder's operation is received, the audit material uploaded by the policyholder is obtained, and according to the claim parameters corresponding to the target drug, it is determined whether the policyholder meets the claim conditions.
如权利要求1所述的基于大数据的药物保费定价方法，其特征在于，所述在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用，作为所述目标药物的标准人均年费用的步骤包括：The drug premium pricing method based on big data according to claim 1, characterized in that, when receiving the premium pricing request based on the target drug, the target city identifier in the premium pricing request is obtained, and according to the The target city identifier acquires the historical drug per capita annual cost of the target drug in the target city, and the steps as the standard per capita annual cost of the target drug include:

在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用；When receiving the premium pricing request based on the target drug, obtain the target city identifier in the premium pricing request, and obtain the historical drug per capita annual cost of the target drug in the target city according to the target city identifier;

根据所述目标药物对应的预设费用附加风险因子、预设时间趋势因子以及所述历史药物人均年费用，计算所述目标药物的目标人均年费用，作为所述目标药物的标准人均年费用，其中，所述目标人均年费用的计算依据以下公式：目标人均年费用Q＝历史药物人均年费用I*(1+预设费用附加风险因子M)*(1+预设时间趋势因子N)。Calculate the target annual per capita cost of the target drug as the standard annual per capita cost of the target drug according to the preset additional risk factor corresponding to the target drug, the preset time trend factor, and the per capita annual cost of the historical drug Wherein, the calculation of the target per capita annual cost is based on the following formula: target per capita annual cost Q = historical drug per capita annual cost I * (1 + preset cost additional risk factor M) * (1 + preset time trend factor N).
一种基于大数据的药物保费定价装置，其特征在于，所述基于大数据的药物保费定价装置包括：A medicine premium pricing device based on big data, characterized in that the medicine premium pricing device based on big data includes:

费用计算模块，用于在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识获取所述目标药物在目标城市的历史药物人均年费用，作为所述目标药物的标准人均年费用；The cost calculation module is used to obtain the target city identifier in the premium pricing request when receiving the premium pricing request based on the target drug, and obtain the historical drug per capita year of the target drug in the target city according to the target city identifier Cost, as the standard per capita annual cost of the target drug;

风险计算模块，用于根据所述目标药物对应的预设赔付参数以及所述标准人均年费用，计算所述目标药物的目标风险保费；The risk calculation module is used to calculate the target risk premium of the target drug according to the preset compensation parameters corresponding to the target drug and the standard annual per capita cost;

保费定价模块，用于根据所述目标药物对应的预设成本参数以及所述目标风险保费，计算所述目标药物的保费定价。The premium pricing module is used to calculate the premium pricing of the target drug according to the preset cost parameter corresponding to the target drug and the target risk premium.
如权利要求8所述的基于大数据的药物保费定价装置，其特征在于，所述费用计算模块还包括：The drug premium pricing device based on big data according to claim 8, wherein the cost calculation module further comprises:

城市判断单元，用于在接收到基于目标药物的保费定价请求时，获取所述保费定价请求中的目标城市标识，并根据所述目标城市标识判断数据库中是否存在所述目标药物在目标城市的历史人均年费用；The city judgment unit is used to obtain the target city ID in the premium pricing request when receiving the premium pricing request based on the target drug, and determine whether the target drug exists in the target city in the database according to the target city ID Historical per capita annual cost;

城市获取单元，用于若所述数据库中不存在所述历史人均年费用，则在所述数据库中获取与所述目标城市关联的参考城市；A city obtaining unit, configured to obtain a reference city associated with the target city in the database if the historical per capita annual fee does not exist in the database;

第二费用获取单元，用于获取所述目标药物在所述参考城市的参考历史人均年费用，作为所述目标药物的标准人均年费用。The second cost obtaining unit is used to obtain the reference historical annual per capita cost of the target drug in the reference city as the standard annual per capita cost of the target drug.

第三费用获取单元，用于若所述数据库中存在所述历史人均年费用，则获取所述目标药物在所述目标城市的历史药物人均年费用，作为标准人均年费用。The third cost obtaining unit is used to obtain the historical drug per capita annual cost of the target drug in the target city if the historical per capita annual cost exists in the database as a standard per capita annual cost.
如权利要求9所述的基于大数据的药物保费定价装置，其特征在于，The drug premium pricing device based on big data according to claim 9, characterized in that

所述城市获取单元包括：The city acquisition unit includes:

参数获取子单元，用于若所述数据库中不存在所述历史人均年费用，则获取所述目标城市的目标城市参数，并获取所述数据库中其他城市的其他城市参数；A parameter obtaining subunit, used to obtain the target city parameters of the target city and obtain other city parameters of other cities in the database if the historical per capita annual fee does not exist in the database;

差异度计算子单元，用于根据所述目标城市参数以及其他城市参数，计算所述目标城市与所述其他城市的差异度，其中所述差异度的计算依据以下公式：差异度
其中，i为预设构成城市参数的总个数，A _i为目标城市参数值，B _i为其他城市参数值，k _i为参数i的权重值。 The difference degree calculation subunit is used to calculate the difference degree between the target city and the other city according to the target city parameter and other city parameters, wherein the calculation of the difference degree is based on the following formula: difference degree
Where i is the total number of preset city parameters, A _i is the parameter value of the target city, B _i is the parameter value of other cities, and k _i is the weight value of the parameter i.

城市判断子单元，用于根据所述差异度判断所述数据库中是否存在与所述目标城市关联的参考城市，其中，所述目标城市与所述参考城市的差异度小于预设阈值；A city judgment subunit, configured to judge whether there is a reference city associated with the target city in the database according to the degree of difference, wherein the degree of difference between the target city and the reference city is less than a preset threshold;

城市获取子单元，用于若所述数据库中存在所述参考城市，则获取所述参考城市。The city obtaining subunit is used to obtain the reference city if the reference city exists in the database.
如权利要求9所述的基于大数据的药物保费定价装置，其特征在于，所述城市获取单元还包括：The drug premium pricing device based on big data according to claim 9, wherein the city acquiring unit further comprises:

药物参数获取子单元，用于若所述数据库中不存在所述参考城市，则获取所述目标药物的单价、使用方法以及所述目标药物在所述目标城市的预设优惠规则；A drug parameter acquisition subunit, used to obtain the unit price of the target drug, the method of use and the preset preferential rules of the target drug in the target city if the reference city does not exist in the database;

费用计算子单元，用于根据所述目标药物对应的预设费用附加风险因子、预设时间趋势因子以及所述历史药物人均年费用，计算所述目标药物的目标人均年费用，作为所述目标药物的标准人均年费用，其中，所述使用方法包括每月所需盒数以及需要连续服用月数，所述理论人均年费用的计算依据以下公式：标准人均年费用F＝单价C*每月所需盒数D*需要连续服用月数E。The cost calculation subunit is used to calculate the target annual cost of the target drug according to the preset additional risk factor corresponding to the target drug, the preset time trend factor and the historical drug per capita, as the target The standard per capita annual cost of a drug, wherein the method of use includes the number of boxes required per month and the number of months required to be taken continuously. The calculation of the theoretical per capita annual cost is based on the following formula: standard per capita annual cost F = unit price C * monthly The required number of boxes D* requires continuous consumption of months E.
如权利要求8所述的基于大数据的药物保费定价装置，其特征在于，所述基于大数据的药物保费定价装置还包括：The drug premium pricing device based on big data according to claim 8, wherein the drug premium pricing device based on big data further comprises:

理赔审核模块，用于若接收到所述投保人操作触发的理赔请求时，获取所述投保人上传的审核材料，并根据所述目标药物对应的理赔参数，判断所述投保人是否符合理赔条件。Claims review module, used to obtain the review materials uploaded by the policyholder when receiving a claim request triggered by the policyholder’s operation, and determine whether the policyholder meets the claims conditions according to the claim parameters corresponding to the target drug .
一种基于大数据的药物保费定价设备，其特征在于，所述基于大数据的药物保费定价设备包括处理器、存储器、以及存储在所述存储器上并可被所述处理器执行的计算机可读指令，其中所述计算机可读指令被所述处理器执行时，实现如权利要求1所述的基于大数据的药物保费定价方法的步骤。A medicine premium pricing device based on big data, characterized in that the medicine premium pricing device based on big data includes a processor, a memory, and a computer readable stored on the memory and executable by the processor Instructions, wherein the computer readable instructions, when executed by the processor, implement the steps of the big data-based pharmaceutical premium pricing method of claim 1.
一种基于大数据的药物保费定价设备，其特征在于，所述基于大数据的药物保费定价设备包括处理器、存储器、以及存储在所述存储器上并可被所述处理器执行的计算机可读指令，其中所述计算机可读指令被所述处理器执行时，实现如权利要求2所述的基于大数据的药物保费定价方法的步骤。A medicine premium pricing device based on big data, characterized in that the medicine premium pricing device based on big data includes a processor, a memory, and a computer readable stored on the memory and executable by the processor Instructions, wherein the computer readable instructions, when executed by the processor, implement the steps of the big data-based pharmaceutical premium pricing method of claim 2.
一种基于大数据的药物保费定价设备，其特征在于，所述基于大数据的药物保费定价设备包括处理器、存储器、以及存储在所述存储器上并可被所述处理器执行的计算机可读指令，其中所述计算机可读指令被所述处理器执行时，实现如权利要求3所述的基于大数据的药物保费定价方法的步骤。A medicine premium pricing device based on big data, characterized in that the medicine premium pricing device based on big data includes a processor, a memory, and a computer readable stored on the memory and executable by the processor Instructions, wherein when the computer-readable instructions are executed by the processor, the steps of the big data-based pharmaceutical premium pricing method of claim 3 are implemented.
一种基于大数据的药物保费定价设备，其特征在于，所述基于大数据的药物保费定价设备包括处理器、存储器、以及存储在所述存储器上并可被所述处理器执行的计算机可读指令，其中所述计算机可读指令被所述处理器执行时，实现如权利要求4所述的基于大数据的药物保费定价方法的步骤。A medicine premium pricing device based on big data, characterized in that the medicine premium pricing device based on big data includes a processor, a memory, and a computer readable stored on the memory and executable by the processor Instructions, wherein when the computer-readable instructions are executed by the processor, the steps of the big data-based pharmaceutical premium pricing method of claim 4 are implemented.
一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机可读指令，其中所述计算机可读指令被处理器执行时，实现如权利要求1所述的基于大数据的药物保费定价方法的步骤。A computer-readable storage medium, characterized in that computer-readable instructions are stored on the computer-readable storage medium, wherein when the computer-readable instructions are executed by a processor, the computer-readable storage medium according to claim 1 is implemented Data on the steps of the drug premium pricing method.
一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机可读指令，其中所述计算机可读指令被处理器执行时，实现如权利要求2所述的基于大数据的药物保费定价方法的步骤。A computer-readable storage medium, characterized in that computer-readable instructions are stored on the computer-readable storage medium, wherein when the computer-readable instructions are executed by a processor, the computer-readable storage medium according to claim 2 is implemented Data on the steps of the drug premium pricing method.
一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机可读指令，其中所述计算机可读指令被处理器执行时，实现如权利要求3所述的基于大数据的药物保费定价方法的步骤。A computer-readable storage medium, characterized in that computer-readable instructions are stored on the computer-readable storage medium, wherein when the computer-readable instructions are executed by a processor, the computer-readable storage medium according to claim 3 is implemented Data on the steps of the drug premium pricing method.
一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机可读指令，其中所述计算机可读指令被处理器执行时，实现如权利要求4所述的基于大数据的药物保费定价方法的步骤。A computer-readable storage medium, characterized in that computer-readable instructions are stored on the computer-readable storage medium, wherein when the computer-readable instructions are executed by a processor, the computer-readable storage medium according to claim 4 is implemented Data on the steps of the drug premium pricing method.