CN105844529A - Fitness data-based health insurance actuarial system and method - Google Patents
Fitness data-based health insurance actuarial system and method Download PDFInfo
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- CN105844529A CN105844529A CN201610165427.6A CN201610165427A CN105844529A CN 105844529 A CN105844529 A CN 105844529A CN 201610165427 A CN201610165427 A CN 201610165427A CN 105844529 A CN105844529 A CN 105844529A
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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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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Abstract
The invention provides a fitness data-based health insurance actuarial method. The method includes the following steps that: fitness data which are generated when a user wears a wearable fitness device are obtained from a fitness information platform, wherein the fitness data contain dates; the fitness data are classified according to the dates; fitness data corresponding a certain date are extracted from the classified fitness data; when heart rate data in the fitness data corresponding to the date are in a preset rate range, a fitness qualified marker is generated; and when all the classified fitness data are extracted, the number of the markers is calculated, so that the fitness qualification rate of the user can be obtained, and the health insurance premium of the user can be obtained through calculation according to the fitness qualification rate and a preset health insurance actuarial algorithm. With the fitness data-based health insurance actuarial method adopted, the health insurance premium can be dynamically adjusted according to the fitness data of the user, and the risk of health insurance can be decreased, and the profitability of an insurance company can be improved.
Description
Technical field
The present invention relates to Insurance Actuarial Science field, particularly relate to a kind of health insurance of based on workout data actuarial system
System and method.
Background technology
Recently as the fast development of the technology such as the Internet, cloud computing, mobile communication and Internet of Things, nothing
Not mobile device, RFID, wireless senser every point are per second is all producing data, hundreds of millions of use
The Internet service at family is at every moment producing the mutual of flood tide, and data volume to be processed is huge, data one
Straight the most all with annual 50% speed increment, and the real-time that data are processed by business demand and competitive pressure,
Effectiveness also been proposed requirements at the higher level, and traditional routine techniques means are unable to cope with at all, therefore, several
Become a recent hot technology according to technology (Big Data), cause and pay attention to widely.
Actuarial risk profile can be accelerated: by means of ever-increasing secret by big data technique
And public user information, big data technique helps people to extract from the big scale of construction, high complicated workout data
It is worth.
But, the Insurance Actuarial Science system of present stage is when being analyzed processing for medical data, not
Consider the active factor that health is produced by user's body-building at ordinary times, at the big data age of network, add insurance
The risk of company.
Summary of the invention
A kind of health insurance actuarial system based on workout data of offer and side are provided
Method, it is intended to solve to be not based in existing Insurance Actuarial Science system workout data and carry out actuarial technology and ask
Topic.
For achieving the above object, the invention provides a kind of health insurance actuarial system based on workout data,
Running on data center, described data center is connected with body-building information platform by network, and described body-building is believed
Breath platform is connected with described wearable body-building device by described network, and this system includes:
Acquisition module, is produced for obtaining when user wears wearable body-building device from body-building information platform
Workout data, described workout data includes date and heart rate data；
Sort module, for classifying to described workout data according to the described date；
Extraction module, for extracting workout data corresponding to a date from sorted workout data；
Generation module, for when the heart rate data in workout data corresponding to this date is all at default heart rate model
When enclosing, generate the labelling that a body-building is qualified；And
Computing module, for when sorted workout data all extracts, calculates the quantity of described labelling,
To obtain the body-building qualification rate of user and qualified according to default health insurance actuarial algorithm and described body-building
Rate calculates the health insurance premium of this user.
Preferably, described workout data include address name, body weight, height, the age, sex, the date,
The body-building time started, the body-building end time, body-building mileage, body-building region, gymnasium title,
Fitness project, heart rate data and body-building step number.
Preferably, the computing formula of described body-building qualification rate is: P=M/N, and wherein, P is body-building qualification rate,
M is the quantity of labelling, described N be this user workout data in the earliest date between the date the latest
Total natural law.
Preferably, described default health insurance actuarial algorithm employing equation below: S=A+B+C+D, A=A1
× (2-P) × A2 × A3, A2=1+A21, wherein, S be health insurance premium, A be medical compensatory expense,
B is that prevention and health care takes, C is administration fee, D is reserve fund, A1 is medical fee base-line data, A2 is guarantor
The danger factor, A3 are body-building qualification rate for compensation ratio, P, parameter B in described formula, C, D, A1
And A3 is fixed value, A21 is the increment rate of Health service utilization.
Preferably, described default health insurance actuarial algorithm uses equation below: Z=X-k × P, wherein, Z
For health insurance premium, X is the standard premium that insurance company sets, and P is body-building qualification rate, and k is constant.
On the other hand, the present invention also provides for a kind of health insurance calculating method based on workout data, is applied to
Data center, described data center is connected with body-building information platform by network, described body-building information platform
Being connected with described wearable body-building device by described network, the method includes:
Produced workout data when user wears wearable body-building device, institute is obtained from body-building information platform
State workout data and include date and heart rate data；
According to the described date, described workout data is classified；
Workout data corresponding to a date is extracted from sorted workout data；
When the heart rate data in the workout data that this date is corresponding is all at default heart rate range, generate one
The labelling that body-building is qualified；And
When sorted workout data all extracts, calculate the quantity of described labelling, to obtain user's
Body-building qualification rate, and calculate this use according to default health insurance actuarial algorithm and described body-building qualification rate
The health insurance premium at family.
Preferably, described workout data include address name, body weight, height, the age, sex, the date,
The body-building time started, the body-building end time, body-building mileage, body-building region, gymnasium title,
Fitness project, heart rate and body-building step number.
Preferably, the computing formula of described body-building qualification rate is: P=M/N, and wherein, P is body-building qualification rate,
M is the quantity of labelling, described N be this user workout data in the earliest date between the date the latest
Total natural law.
Preferably, described default health insurance actuarial algorithm employing equation below: S=A+B+C+D, A=A1
× (2-P) × A2 × A3, A2=1+A21, wherein, S be health insurance premium, A be medical compensatory expense,
B is that prevention and health care takes, C is administration fee, D is reserve fund, A1 is medical fee base-line data, A2 is guarantor
The danger factor, A3 are body-building qualification rate for compensation ratio, P, parameter B in described formula, C, D, A1
And A3 is fixed value, A21 is the increment rate of Health service utilization.
Preferably, described default health insurance actuarial algorithm uses equation below: Z=X-k × P, wherein, Z
For health insurance premium, X is the standard premium that insurance company sets, and P is body-building qualification rate, and k is constant.
The present invention uses technique scheme, and the technique effect brought is: of the present invention based on body-building number
According to health insurance actuarial system and method, understand user for body-building qualification rate, and root in conjunction with user for body-building data
Adjust the premium of health insurance according to body-building qualification rate accordingly, reduce the risk of health insurance, improve insurance public affairs
The profitability of department.
Accompanying drawing explanation
Fig. 1 is present invention health insurance based on workout data actuarial systematic difference environment schematic；
Fig. 2 is the functional module of the preferred embodiment of present invention health insurance based on workout data actuarial system
Figure；
Fig. 3 is the flow chart of the preferred embodiment of present invention health insurance based on workout data calculating method.
The realization of the object of the invention, functional characteristics and advantage will in conjunction with the embodiments, do referring to the drawings further
Explanation.
Detailed description of the invention
By further illustrating the technological means and effect that the present invention taked by reaching predetermined goal of the invention,
Below in conjunction with accompanying drawing and preferred embodiment, detailed description of the invention, structure, feature and the merit thereof to the present invention
Effect, describes in detail as follows.Should be appreciated that specific embodiment described herein is only in order to explain this
Bright, it is not intended to limit the present invention.
With reference to shown in Fig. 1, Fig. 1 is present invention health insurance based on workout data actuarial systematic difference ring
Border schematic diagram.
Health insurance actuarial system 20 based on workout data in the present invention runs on data center 2.Described
Data center 2 is connected with body-building information platform 5 by network 3.
Described body-building information platform 5 is also by network 3 and one or more wearable body-building device 4 (Fig. 1
In illustrate as a example by three) communication connection.
Described body-building information platform 5 is used for providing body-building service, and records user and wear described wearable strong
Body device 4 carries out produced workout data during body-building.Described workout data includes, but not limited to use
Family name, body weight, height, the age, sex, the date, the body-building time started, the body-building end time,
Body-building mileage, body-building region, gymnasium title, fitness project (such as, run, swim,
Climb up), heart rate and body-building step number etc..
Specifically, described wearable body-building device 4 is worn on user's body, is used for recording user and is good for
Produced workout data during body.Described wearable body-building device 4 is believed with described body-building also by network 3
Breath platform 5 connects, for described workout data is uploaded to described body-building information platform 5.
Described body-building information platform 5 provide data introducting interface (such as, application programming interfaces, Application
Program Interface, API), the equipment or the system that access this api interface can be believed from described body-building
Breath platform 5 obtains wearable body-building device 4 and uses the workout data of Internet service.In described data
The heart 2 (i.e. authorizes and accesses described body-building information platform 5 on the basis of described body-building information platform 5 authorizes
The api interface provided) obtain described workout data, and resolve to be used to described workout data
Family uses the keyword that search engine is inputted.
Described network 3 can be wire communication network or wireless communication networks.Described network 3 is preferably nothing
Line communication network, includes but not limited to, GSM network, GPRS network, cdma network, TD-SCDMA
The wireless-transmission networks such as network, WiMAX network, TD-LTE network, FDD-LTE network.
Additionally, described data center 2 is connected with described wearable body-building device 4 by network 3.Need
Illustrating, described data center 2 is a certain station server in cloud platform or cloud platform, passes through data
The data transmission capabilities at center 2 and data storage capacities, can preferably manage and/or assist and these data
The wearable body-building device 4 that center 2 connects, is conducive to understanding user by described wearable body-building device 4
Produced workout data.
Described wearable body-building device 4 may be, but not limited to, intelligent watch, Intelligent bracelet etc. other
The wearable device with rhythm of the heart function of any appropriate.
With reference to shown in Fig. 2, it it is the preferred embodiment of present invention health insurance based on workout data actuarial system
Functional block diagram.In the present embodiment, described health insurance actuarial system 20 based on workout data is applied
In data center 2.This data center 2 include but not limited to, health insurance based on workout data essence
Calculation system 20, memory element 22, processing unit 24 and communication unit 26.
Described memory element 22 can be a kind of read-only memory unit ROM, electrically-erasable memory element
EEPROM, flash memory cell FLASH or solid hard disk etc..
Described processing unit 24 can be a kind of central processing unit (Central Processing Unit,
CPU), microcontroller (MCU), data processing chip or have at the information of data processing function
Reason unit.
Described communication unit 26 is a kind of wireless communication interface with long-distance radio communication function, such as,
Support GSM, GPRS, WCDMA, CDMA, TD-SCDMA, WiMAX, TD-LTE,
The communication interface of the mechanicss of communication such as FDD-LTE.
Described health insurance actuarial system 20 based on workout data includes, but are not limited to, acquisition module 200,
Sort module 210, extraction module 220, judge module 230, generation module 240 and computing module 250,
Module alleged by the present invention refers to a kind of to be performed and energy by the processing unit 24 of described data center 2
Enough completing the series of computation machine programmed instruction section of fixing function, it is stored in depositing of described data center 2
In storage unit 22.
Described acquisition module 200 wears wearable body-building device 4 for obtaining user from body-building information platform 5
Time produced workout data.
Specifically, described body-building information platform 5 provides api interface, accesses the equipment of this api interface
Or system can obtain described workout data from described body-building information platform 5.Described acquisition module 200
Call the api interface of described body-building information platform 5 offer to obtain described workout data.
It should be noted that owing to described workout data belongs to privacy information, in order to ensure information security,
When described workout data is sent to data center 2, enciphering and deciphering algorithm (such as, MD5 encryption and decryption can be passed through
Algorithm, RSA enciphering and deciphering algorithm, DES enciphering and deciphering algorithm, DSA enciphering and deciphering algorithm, AES encryption and decryption are calculated
Method etc.) first workout data is encrypted, it is transferred to described data center 2 afterwards.
Described sort module 210 is for classifying to described workout data according to the date.Specifically, due to
Described workout data includes that date, described extraction module 220 extract the date in described workout data,
And according to the date, described workout data is classified.Workout data after classification is formed many according to the date
Workout data, such as, the workout data on January 1, the workout data on January 2 etc..User is led to
Spending the date can extract workout data corresponding to this date.
Described extraction module 220 is for extracting body-building number corresponding to a date from sorted workout data
According to.In the present embodiment, described extraction module 220 according to chronological order (such as, from January 1 to
December 31 days) extract workout data corresponding to date.
Described judge module 230 is used for judging that the heart rate data in the workout data that this date is corresponding is the most equal
At default heart rate range.In the present embodiment, described default heart rate range be 120 beats/min to 180 times/
Point.
Described generation module 240 is for all presetting when the heart rate data in workout data corresponding to this date
During heart rate range, generating the labelling that a body-building is qualified, this labelling indicates user's body-building number on this date
According to qualified.
Described computing module 250, for when sorted workout data all extracts, calculates described labelling
Quantity, to obtain the body-building qualification rate of user.The computing formula of described body-building qualification rate is: P=M/N,
Wherein, P is body-building qualification rate, and M is the quantity of labelling, described N be this user workout data in
Total natural law between the early date to date the latest, such as, in workout data, the earliest date is in January, 2015
1, the date was on January 4th, 2016 the latest, then the value of N is 370.
Described computing module 250 is additionally operable to according to described body-building qualification rate and default health insurance actuarial algorithm
Calculate the health insurance premium of this user.Described default health insurance actuarial algorithm includes equation below:
S=A+B+C+D, A=A1 × (2-P) × A2 × A3；Wherein, S is that health insurance premium, A are for curing
Medicine compensation, B be prevention and health care expense, C be administration fee (i.e. the administration fee of insurance company management health insurance),
D be reserve fund, A1 be medical fee base-line data, A2 be Insurance factor, A3 for compensating ratio, P is strong
Body qualification rate.Wherein, parameter B in described formula, C, D, A1 and A3 are fixed value.Insure because of
Son is the insured people increase degree to Health service utilization, and its computing formula is that A2=1+A21, A21 are for curing
(the go to a doctor difference of number of the most continuous 2 years healthcare structure is maximum with medical institutions for increment rate that the service for the treatment of utilizes
Load is gone to a doctor the ratio between number).
Knowable to above-mentioned formula, specifically, body-building qualification rate is the highest, it is meant that insurance benefits occurs
Probability reduces, and also implies that the minimizing of health insurance premium.Otherwise, body-building qualification rate is the lowest, it is meant that
The probability that insurance benefits occurs improves, and also implies that the increase of health insurance premium.
Described default health insurance actuarial algorithm includes equation below: Z=X-k × P, and wherein, Z is healthy
Danger premium, X be insurance company set standard premium, P is body-building qualification rate, k be constant (such as,
Can be numeral 5000).It should be noted that the calculation of described standard premium is existing, at this
Repeat no more.
Described health insurance essence additionally, above-mentioned health insurance actuarial algorithm is merely illustrative, in the present invention
Calculate algorithm and can also is that other existing Insurance Actuarial Science algorithm comprising body-building qualification rate.
With reference to shown in Fig. 3, it it is the preferred embodiment of present invention health insurance based on workout data calculating method
Flow chart.Shown in Fig. 2, in the present embodiment, described health insurance based on workout data essence
Calculation method is applied to data center 2, and the method comprises the following steps:
Step S10: described acquisition module 200 obtains user from body-building information platform 5 and wears wearable body-building
Produced workout data during device 4.
Specifically, described body-building information platform 5 provides api interface, accesses the equipment of this api interface
Or system can obtain described workout data from described body-building information platform 5.Described acquisition module 200
Call the api interface of described body-building information platform 5 offer to obtain described workout data.
It should be noted that owing to described workout data belongs to privacy information, in order to ensure information security,
When described workout data is sent to data center 2, enciphering and deciphering algorithm (such as, MD5 encryption and decryption can be passed through
Algorithm, RSA enciphering and deciphering algorithm, DES enciphering and deciphering algorithm, DSA enciphering and deciphering algorithm, AES encryption and decryption are calculated
Method etc.) first workout data is encrypted, it is transferred to described data center 2 afterwards.
Step S11: described workout data is classified by described sort module 210 according to the date.Specifically,
Owing to described workout data includes that date, described extraction module 220 extract the day in described workout data
Phase, and according to the date, described workout data is classified.Workout data after classification is according to date shape
Become many workout data, such as, the workout data on January 1, the workout data on January 2 etc..With
Family can extract workout data corresponding to this date by the date.
Step S12: described extraction module 220 extracts a date corresponding being good for from sorted workout data
Body data.In the present embodiment, described extraction module 220 according to chronological order (such as, from January 1
Day to December 31 days) extract workout data corresponding to date.
Step S13: described judge module 230 judges that the heart rate data in workout data corresponding to this date is
No all at default heart rate range.In the present embodiment, described default heart rate range be 120 beats/min to 180
Beat/min.If the heart rate data in the workout data that this date is corresponding is equal to or exceedes preset value, flow process is entered
Enter step S14.Otherwise, if the heart rate data in workout data corresponding to this date is respectively less than preset value, stream
Journey returns step S12.
Step S14: described generation module 240 generates a qualified labelling of body-building, and this labelling indicates user
Workout data on this date is qualified.
Step S15: when sorted workout data all extracts, described computing module 250 calculates described
The quantity of labelling, to obtain the body-building qualification rate of user.The computing formula of described body-building qualification rate is:
P=M/N, wherein, P is body-building qualification rate, and M is the quantity of labelling, and described N is the body-building of this user
In data, the earliest date is to the total natural law between the date the latest, and such as, in workout data, the earliest date is 2015
In on January 1, in, the date is on January 4th, 2016 the latest, then the value of N is 370.
Step S16: described computing module 250 is calculated according to described body-building qualification rate and default health insurance actuarial
Method calculates the health insurance premium of this user.Described default health insurance actuarial algorithm includes equation below:
S=A+B+C+D, A=A1 × (2-P) × A2 × A3；Wherein, S is that health insurance premium, A are for curing
Medicine compensation, B be prevention and health care expense, C be administration fee (i.e. the administration fee of insurance company management health insurance),
D be reserve fund, A1 be medical fee base-line data, A2 be Insurance factor, A3 for compensating ratio, P is strong
Body qualification rate.Wherein, parameter B in described formula, C, D, A1 and A3 are fixed value.Insure because of
Son is the insured people increase degree to Health service utilization, and its computing formula is that A2=1+A21, A21 are for curing
(the go to a doctor difference of number of the most continuous 2 years healthcare structure is maximum with medical institutions for increment rate that the service for the treatment of utilizes
Load is gone to a doctor the ratio between number).
Knowable to above-mentioned formula, body-building qualification rate is the highest, it is meant that occur the probability of insurance benefits to reduce,
Also implying that the minimizing of health insurance premium, say, that user for body-building is the most, health insurance premium is the lowest.
Otherwise, body-building qualification rate is the lowest, it is meant that occurs the probability of insurance benefits to improve, also implies that health
The increase of danger premium, say, that user for body-building is the fewest, and health insurance premium is the highest.
Described default health insurance actuarial algorithm includes equation below: Z=X-k × P, and wherein, Z is healthy
Danger premium, X be insurance company set standard premium, P is body-building qualification rate, k be constant (such as,
Can be numeral 5000).It should be noted that the calculation of described standard premium is existing, at this
Repeat no more.
Described health insurance essence additionally, above-mentioned health insurance actuarial algorithm is merely illustrative, in the present invention
Calculate algorithm and can also is that other existing Insurance Actuarial Science algorithm comprising body-building qualification rate.
These are only the preferred embodiments of the present invention, not thereby limit the scope of the claims of the present invention, every
Utilize equivalent structure or equivalence flow process conversion that description of the invention and accompanying drawing content made, or directly or
Connect and be used in other relevant technical fields, be the most in like manner included in the scope of patent protection of the present invention.
Claims (10)
1. a health insurance actuarial system based on workout data, run on data center, it is characterised in that described data center is connected with body-building information platform by network, described body-building information platform is connected with wearable body-building device by described network, and this system includes:
Acquisition module, for obtaining produced workout data when user wears wearable body-building device from body-building information platform, described workout data includes date and heart rate data；
Sort module, for classifying to described workout data according to the described date；
Extraction module, for extracting workout data corresponding to a date from sorted workout data；
Generation module, for when the heart rate data in workout data corresponding to this date is all at default heart rate range, generating the labelling that a body-building is qualified；And
Computing module, for when sorted workout data all extracts, calculates the quantity of described labelling, to obtain the body-building qualification rate of user, and calculates the health insurance premium of this user according to described body-building qualification rate and default health insurance actuarial algorithm.
2. health insurance actuarial system based on workout data as claimed in claim 1, it is characterized in that, described workout data includes address name, body weight, height, age, sex, body-building time started, body-building end time, body-building mileage, body-building region, gymnasium title, fitness project, heart rate data and body-building step number.
3. health insurance actuarial system based on workout data as claimed in claim 1, it is characterized in that, the computing formula of described body-building qualification rate is: P=M/N, wherein, P is body-building qualification rate, M is the quantity of labelling, described N be this user workout data in the earliest date to the total natural law between the date the latest.
4. health insurance actuarial system based on workout data as claimed in claim 3, it is characterized in that, described default health insurance actuarial algorithm uses equation below: S=A+B+C+D, A=A1 × (2-P) × A2 × A3, A2=1+A21, wherein, S be health insurance premium, A be medical compensatory expense, B be prevention and health care expense, C be administration fee, D be reserve fund, A1 be medical fee base-line data, A2 be Insurance factor, A3 be body-building qualification rate for compensating than, P, parameter B in described formula, C, D, A1 and A3 are fixed value, and A21 is the increment rate of Health service utilization.
5. health insurance actuarial system based on workout data as claimed in claim 3, it is characterised in that described default health insurance actuarial algorithm uses equation below: Z=X-k × P, wherein, Z is health insurance premium, and X is the standard premium that insurance company sets, P is body-building qualification rate, and k is constant.
6. a health insurance calculating method based on workout data, it is applied to data center, it is characterised in that described data center is connected with body-building information platform by network, described body-building information platform is connected with wearable body-building device by described network, and the method includes:
Obtaining produced workout data when user wears wearable body-building device from body-building information platform, described workout data includes date and heart rate data；
According to the described date, described workout data is classified；
Workout data corresponding to a date is extracted from sorted workout data；
When the heart rate data in the workout data that this date is corresponding is all at default heart rate range, generate the labelling that a body-building is qualified；And
When sorted workout data all extracts, calculate the quantity of described labelling, to obtain the body-building qualification rate of user, and calculate the health insurance premium of this user according to described body-building qualification rate and default health insurance actuarial algorithm.
7. health insurance calculating method based on workout data as claimed in claim 6, it is characterized in that, described workout data includes address name, body weight, height, age, sex, date, body-building time started, body-building end time, body-building mileage, body-building region, gymnasium title, fitness project, heart rate data and body-building step number.
8. health insurance calculating method based on workout data as claimed in claim 6, it is characterized in that, the computing formula of described body-building qualification rate is: P=M/N, wherein, P is body-building qualification rate, M is the quantity of labelling, described N be this user workout data in the earliest date to the total natural law between the date the latest.
9. health insurance calculating method based on workout data as claimed in claim 8, it is characterized in that, described default health insurance actuarial algorithm uses equation below: S=A+B+C+D, A=A1 × (2-P) × A2 × A3, A2=1+A21, wherein, S be health insurance premium, A be medical compensatory expense, B be prevention and health care expense, C be administration fee, D be reserve fund, A1 be medical fee base-line data, A2 be Insurance factor, A3 be body-building qualification rate for compensating than, P, parameter B in described formula, C, D, A1 and A3 are fixed value, and A21 is the increment rate of Health service utilization.
10. health insurance calculating method based on workout data as claimed in claim 8, it is characterised in that described default health insurance actuarial algorithm uses equation below: Z=X-k × P, wherein, Z is health insurance premium, and X is the standard premium that insurance company sets, P is body-building qualification rate, and k is constant.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610165427.6A CN105844529A (en) | 2016-03-19 | 2016-03-19 | Fitness data-based health insurance actuarial system and method |
| PCT/CN2016/105116 WO2017161896A1 (en) | 2016-03-19 | 2016-11-08 | Health insurance actuarial system and method based on fitness data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610165427.6A CN105844529A (en) | 2016-03-19 | 2016-03-19 | Fitness data-based health insurance actuarial system and method |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017161896A1 (en) * | 2016-03-19 | 2017-09-28 | 深圳市前海安测信息技术有限公司 | Health insurance actuarial system and method based on fitness data |
| CN108257030A (en) * | 2017-11-08 | 2018-07-06 | 中国平安人寿保险股份有限公司 | A kind of premium method of adjustment, device, terminal device and storage medium |
| CN109447831A (en) * | 2018-12-28 | 2019-03-08 | 武汉大学 | A kind of medical insurance Contract Design method based on wearable device |
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| CN110148060A (en) * | 2019-04-15 | 2019-08-20 | 深圳壹账通智能科技有限公司 | Premium method of adjustment, device, equipment and storage medium based on health data |
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Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1758281A (en) * | 2005-10-13 | 2006-04-12 | 徐汇丰 | Database system for estimating risk of insured tender of life ensurance compony |
| US20110040582A1 (en) * | 2009-08-17 | 2011-02-17 | Kieran Mullins | Online system and method of insurance underwriting |
| KR20130113893A (en) * | 2012-04-08 | 2013-10-16 | 삼성전자주식회사 | User terminal device and system for performing user customized health management, and methods thereof |
| CN105844093A (en) * | 2016-03-19 | 2016-08-10 | 深圳市前海安测信息技术有限公司 | Social data based actuarial system and method |
| CN105844529A (en) * | 2016-03-19 | 2016-08-10 | 深圳市前海安测信息技术有限公司 | Fitness data-based health insurance actuarial system and method |
-
2016
- 2016-03-19 CN CN201610165427.6A patent/CN105844529A/en not_active Withdrawn
- 2016-11-08 WO PCT/CN2016/105116 patent/WO2017161896A1/en active Application Filing
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017161896A1 (en) * | 2016-03-19 | 2017-09-28 | 深圳市前海安测信息技术有限公司 | Health insurance actuarial system and method based on fitness data |
| CN108257030A (en) * | 2017-11-08 | 2018-07-06 | 中国平安人寿保险股份有限公司 | A kind of premium method of adjustment, device, terminal device and storage medium |
| CN109711857A (en) * | 2018-08-20 | 2019-05-03 | 中国平安人寿保险股份有限公司 | The preferential amount calculation method of premium, unit and readable storage medium storing program for executing |
| WO2020062326A1 (en) * | 2018-09-30 | 2020-04-02 | 上海笛乐护斯健康科技有限公司 | Smart health management and life safety insurance system, and implementation method therefor |
| CN109615545A (en) * | 2018-12-13 | 2019-04-12 | 平安医疗健康管理股份有限公司 | Special medicine medical insurance pricing method, device, equipment and medium based on data analysis |
| CN109447831A (en) * | 2018-12-28 | 2019-03-08 | 武汉大学 | A kind of medical insurance Contract Design method based on wearable device |
| CN110136005A (en) * | 2019-04-02 | 2019-08-16 | 阿里巴巴集团控股有限公司 | Mutual assistance program member checking method, device, electronic equipment and storage medium |
| CN110148060A (en) * | 2019-04-15 | 2019-08-20 | 深圳壹账通智能科技有限公司 | Premium method of adjustment, device, equipment and storage medium based on health data |
| CN111192684A (en) * | 2019-11-25 | 2020-05-22 | 泰康保险集团股份有限公司 | Health data processing method and device, electronic equipment and storage medium |
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