CN107480295A - The modification method of user data - Google Patents
The modification method of user data Download PDFInfo
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- CN107480295A CN107480295A CN201710758812.6A CN201710758812A CN107480295A CN 107480295 A CN107480295 A CN 107480295A CN 201710758812 A CN201710758812 A CN 201710758812A CN 107480295 A CN107480295 A CN 107480295A
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Abstract
The invention discloses a kind of modification method of user data, according to medical advice information and big data information, logical base is established；Personal information data are brought into logical base, when personal information data and logical base reference data mismatch when, then the data are false data；Valid data in personal information, are calculated to false data, and the optimal possibility result of false data is obtained with reference to logical base；The missing data of personal information is inferred according to the valid data of personal information simultaneously, the optimal possibility result of missing data is obtained with reference to logical base；It is inference data by optimal possibility result queue when optimal possibility result meets degree of association demand；And it is perfect to remind client to carry out inference data.By the present invention, the accuracy rate that the accuracy rate and missing data that false data is examined and corrected are filled is improved, ensures the validity of user data.
Description
Technical field
The present invention relates to technical field of data processing, more particularly, to a kind of modification method of user data.
Background technology
With the development of the social economy, the change of people's diet structure and habits and customs, causes obesity, hypertension, glycosuria
The problem of incidence of disease of the diseases such as disease linearly rises, therefore effective prevention of disease is paid close attention to the most as various circles of society.
According to the research report of the World Health Organization, the disease of the mankind 1/3 can be avoided by prevention and health care, 1/3
Disease early detection can be effectively controlled, and 1/3 disease can improve therapeutic effect by effective communication.For disease,
Treatment is not unique approach, is effectively prevented by health control, controls disease and lift the efficiency of disease treatment and is only the mankind
Healthy is basic.
Prior art is more by carrying out health evaluating to the userspersonal information collected, pre- with the health for establishing personalized
Anti- scheme.However, because the personal information data of user easily occur the problems such as omitting or be wrong in typing, cause what is collected
The problems such as shortage of data, false data or logic are not inconsistent be present in user data.Missing data and false data not only compromise number
According to integrality, the conclusion that also results in data analysis there is deviation.Often it is pre-charged with order to avoid there is such case
The data of these missings, but traditional big data supplement/verification method generally existing missing data filling and false data are tested
The problem of accuracy rate of card is low.
Therefore, in view of the above-mentioned problems, the invention provides a kind of modification method of user data, improve false data and examine
The accuracy rate filled with the accuracy rate and missing data of amendment, ensure the validity of user data.
The content of the invention
In view of this, the invention provides a kind of modification method of user data, improve what false data was examined and corrected
Accuracy rate and the accuracy rate of missing data filling, ensure the validity of user data.
In order to solve the above-mentioned technical problem, the present invention proposes a kind of modification method of user data, including：
According to medical advice information and big data information, logical base is established；Wherein, the logical base is regular factor and ginseng
Examine the degree of association between data；
The personal information data of user are obtained, the personal information data are brought into the logical base, examine described
Whether people's information data matches with the reference data in the logical base；
When the data of personal information reference data corresponding with the logical base matches, then the personal information
Data be True Data, and be valid data by the data markers；
When corresponding with the logical base reference data of the data of the personal information mismatches, then the personal information
Data be false data；
According to the valid data in the personal information, the false data is calculated, obtained at least one described
The possibility result of false data, the possibility result is brought into the logical base, obtain the possibility result with it is right
The degree of association of the user tag is answered, and by the degree of association according to priority ranking, obtaining the optimal of the false data can
Can property result；
Set degree of association threshold value of the optimal possibility result with the corresponding user tag, when acquisition it is described most preferably
When possibility result is more than or equal to the degree of association threshold value with the degree of association of the corresponding user tag, by the optimal possibility
Result queue is inference data；
When the optimal possibility result of acquisition is less than the degree of association threshold with the degree of association of the corresponding user tag
During value, the false data in the personal information is calculated according to the big data information, obtains the false data
Optimal possibility result, bring the optimal possibility result into the logical base, obtain the optimal possibility result with it is right
The degree of association of the user tag is answered, when the degree of association is more than or equal to the degree of association threshold value, by the optimal possibility
Result queue is inference data；When the degree of association is less than the degree of association threshold value, then according to the big data information to institute
The false data stated in personal information is calculated again, until the optimal possibility result and the corresponding user of acquisition
Untill the degree of association of label is more than or equal to the degree of association threshold value.
Further, in addition to:
When shortage of data be present in the personal information, according to the valid data in the personal information, to missing number
According to being calculated, the possibility result of at least one missing data is obtained, the possibility result is brought into the logical base,
The degree of association of the possibility result with the corresponding user tag is obtained, and the degree of association is obtained according to priority ranking
Obtain the optimal possibility result of the missing data；
Set degree of association threshold value of the optimal possibility result with the corresponding user tag, when acquisition it is described most preferably
When possibility result is more than or equal to the degree of association threshold value with the degree of association of the corresponding user tag, by the optimal possibility
Result queue is inference data；
When the optimal possibility result of acquisition is less than the degree of association threshold with the degree of association of the corresponding user tag
During value, the missing data in the personal information is calculated according to the big data information, obtains the missing data
Optimal possibility result, bring the optimal possibility result into the logical base, obtain the optimal possibility result with it is right
The degree of association of the user tag is answered, when the degree of association is more than or equal to the degree of association threshold value, by the optimal possibility
Result queue is inference data；When the degree of association is less than the degree of association threshold value, then according to the big data information to institute
The missing data stated in personal information is calculated again, until the optimal possibility result and the corresponding user of acquisition
Untill the degree of association of label is more than or equal to the degree of association threshold value.
Further, in addition to：
When the user does not finish to the inference data conducts oneself well reason, according to the valid data of the personal information and institute
Logical base is stated periodically the inference data are verified and changed.
Further, in addition to：
After the user modifies to the inference data to be improved, the latest data of acquisition is placed again into described patrol
Collect in storehouse, examine whether the perfect latest data matches with the reference data in the logical base；
When the reference data in the latest data and the logical base matches, then the latest data is labeled as
Valid data；
When the reference data in the latest data and the logical base mismatches, then the latest data is false number
According to.
Further, in addition to：
The more new state of medical advice information described in dynamic monitoring and the big data information, according to the doctor after renewal
Tutorial message and the big data information are learned, real-time update is carried out to the logical base；
The logical base after renewal carries out real-time verification and amendment to the data of the personal information.
Preferably, the medical advice information, further comprises：Fo Minghan cardiovascular events risk evaluation model, TIMI
Rating Model, Hamilton depressive scale and diabetes mellitus in China guideline of prevention and treatment.
Preferably, the big data information, further for：The big data of population in the world Information Statistics.
Preferably, the degree of association is divided into：Direct correlation and indirect association, the direct correlation include：Direct one-level is closed
Connection, the association of direct two level associate with direct three-level；The indirect association includes：Indirect one-level association, the association of indirect two level and
Connect three-level association；
Wherein, the priority relationship of the degree of association is：Direct one-level association is more than the association of direct two level and closed more than direct three-level
The United Nations General Assembly is more than the association of indirect two level in the association of indirect one-level and is more than the association of indirect three-level.
Further, the personal information, further comprises：Personal essential information, personal main suit's information, personal health letter
Breath and personal gene information；Wherein,
The personal essential information, including：The natural situation category information such as sex, age, height, body weight；
Personal main suit's information, including：Habits and customs, mood, medical history, present illness history, allergies, symptom and sign,
Family history, chemical factor, physical factor and social factor；
The personal health information, including：Biochemical indicator, image data, surgery situation, pathological section and major event；
The personal gene information, including：Disease, medicine and nutrition.
Compared with prior art, the modification method of a kind of user data of the invention, realizes following beneficial effect：
(1) modification method of a kind of user data of the present invention, by establishing logical base, according to big data information with
And the valid data of user profile, user data is verified, finds the false data in personal information in time, and to user
False data in data and missing data carry out amendment in time and perfect, ensure the completeness and efficiency of user data.
(2) modification method of a kind of user data of the present invention, the medical advice information that upgrades in time and big data letter
Breath, is adjusted in real time to logical base, and then improves the accuracy rate for verifying and filling to user data, further improves user
The validity of data.
Certainly, implementing any product of the present invention specific needs while must not reach all the above technique effect.
By referring to the drawings to the present invention exemplary embodiment detailed description, further feature of the invention and its
Advantage will be made apparent from.
Brief description of the drawings
It is combined in the description and the accompanying drawing of a part for constitution instruction shows embodiments of the invention, and even
It is used for the principle for explaining the present invention together with its explanation.
Fig. 1 is the schematic flow sheet of the modification method for the user data that the embodiment of the present invention 1 provides；
Fig. 2 is the schematic flow sheet of the modification method for the user data that the embodiment of the present invention 2 provides.
Embodiment
The various exemplary embodiments of the present invention are described in detail now with reference to accompanying drawing.It should be noted that：Unless have in addition
Body illustrates that the unlimited system of part and the positioned opposite of step, numerical expression and the numerical value otherwise illustrated in these embodiments is originally
The scope of invention.
The description only actually at least one exemplary embodiment is illustrative to be never used as to the present invention below
And its application or any restrictions that use.
It may be not discussed in detail for technology, method and apparatus known to person of ordinary skill in the relevant, but suitable
In the case of, the technology, method and apparatus should be considered as part for specification.
In shown here and discussion all examples, any occurrence should be construed as merely exemplary, without
It is as limitation.Therefore, other examples of exemplary embodiment can have different values.
It should be noted that：Similar label and letter represents similar terms in following accompanying drawing, therefore, once a certain Xiang Yi
It is defined, then it need not be further discussed in subsequent accompanying drawing in individual accompanying drawing.
Embodiment 1
As shown in figure 1, the modification method for the user data that the present embodiment 1 is provided, comprises the following steps：
Step 101, according to medical advice information and big data information, establish logical base；Wherein, the logical base is conventional
The degree of association between the factor and reference data.
Specifically, regular factor storehouse is established according to medical advice information and big data information, and obtained and regular factor phase
Corresponding reference data, according to the relevance level relation between reference data and the matching convention factor, establish regular factor with
Logical base between reference data.
Further, the degree of association between the regular factor and reference data is divided into：Direct correlation and indirect association, institute
Stating direct correlation includes：Direct one-level association, the association of direct two level associate with direct three-level；The indirect association includes：Indirectly
One-level association, the association of indirect two level associate with indirect three-level；Wherein, the priority relationship of the degree of association is：Direct one-level association is big
Associate to associate more than indirect two level more than the association of indirect one-level more than the association of direct three-level in direct two level and be more than indirect three-level pass
Connection.
Step 102, the personal information data for obtaining user, the personal information data are brought into the logical base, are examined
Test whether the personal information data match with the reference data in the logical base.
Obtain user personal information data, personal information data are brought into logical base, by personal information data with
The reference data of the matching convention factor is matched, and examines personal information data whether in the zone of reasonableness of reference data.Its
Described in personal information data further include：Personal essential information, personal main suit's information, personal health information and personal base
Because of information；Wherein,
The personal essential information, including：The natural situation category information such as sex, age, height, body weight；
Personal main suit's information, including：Habits and customs, mood, medical history, present illness history, allergies, symptom and sign,
Family history, chemical factor, physical factor and social factor；
The personal health information, including：Biochemical indicator, image data, surgery situation, pathological section and major event；
The personal gene information, including：Disease, medicine and nutrition.
Step 103, when the data of personal information reference data corresponding with the logical base matches, then it is described
The data of personal information are True Data, and are valid data by the data markers.
As the reference frame for calculating other data.
Step 104, when corresponding with the logical base reference data of the data of the personal information mismatches, then it is described
The data of personal information are false data.
Then need to carry out secondary checking to false data.
Step 105, the valid data in the personal information, are calculated to the false data, are obtained at least
The possibility result of one false data, the possibility result is brought into the logical base, obtains the possibility
As a result with the degree of association of the corresponding user tag, and the degree of association is obtained into the false data according to priority ranking
Optimal possibility result.
Specifically, the valid data in userspersonal information, are calculated to false data, obtain multiple falsenesses
The possibility result of data.These possibility results are brought into logical base, filter out possibility result with it is right in logical base
The data that reference data matches, and the degree of association of the possibility result and matching convention factor to match is obtained, based on direct
One-level, which associates to associate to associate to associate more than indirect two level more than indirect one-level more than the association of direct three-level more than direct two level, to be more than
The rule of indirect three-level association, obtains degree of association highest data in the possibility result to match, and the data are denoted as into this
The optimal possibility result of false data.
Step 106, degree of association threshold value of the optimal possibility result with the corresponding user tag is set, when acquisition
When the optimal possibility result is more than or equal to the degree of association threshold value with the degree of association of the corresponding user tag, by described in most
Good possibility result queue is inference data.
Specifically, when the false data of user profile is excessive, cause by the valid data of user infer to obtain can
Can property result authenticity it is relatively low, it is specific real at one to be further ensured that the validity of possibility result that deduction obtains
Apply in example, setting degree of association threshold value is direct three-level association, when the pass of optimal possibility result regular factor related to logical base
Connection degree associates more than or equal to direct three-level, is inference data by optimal possibility result queue.
Step 107, when acquisition the optimal possibility result with the degree of association of the corresponding user tag less than described in
During degree of association threshold value, the false data in the personal information is calculated according to the big data information, obtains the void
The optimal possibility result of false data, bring the optimal possibility result into the logical base, obtain the optimal possibility
As a result with the degree of association of the corresponding user tag, when the degree of association is more than or equal to the degree of association threshold value, will described in most
Good possibility result queue is inference data；When the degree of association is less than the degree of association threshold value, then according to the big data
Information calculated again to the false data in the personal information, until acquisition the optimal possibility result with it is corresponding
Untill the degree of association of the user tag is more than or equal to the degree of association threshold value.
In a specific embodiment, setting degree of association threshold value is the association of direct three-level, when passing through personal information data
In the degree of association of optimal possibility result regular factor related to logical base that is inferred to of valid data closed less than direct three-level
Connection, the then validity of optimal possibility result for inferring to obtain are relatively low.Further pass through the big data of population in the world Information Statistics
False data in the personal information is calculated, obtains the optimal possibility result of the false data, and band herein
Enter the checking for carrying out calculating data in logical base, checking and modification method with reference to described in the present embodiment circulate successively, until obtaining
Take the optimal possibility result of the condition of satisfaction and label it as inference data.
The inference data are marked, and periodically remind user to carry out improving modification to the inference data,
In some optional embodiments, when the user does not finish to the inference data conducts oneself well reason, according to the personal information
Valid data and the logical base periodically the inference data are verified and changed, obtain more accurate data, and
Data after renewal are continued to be labeled as inference data, and it is perfect periodically to remind user to carry out the data.
Embodiment 2
As shown in Fig. 2 the modification method for the user data that the present embodiment 2 is provided, comprises the following steps：
Step 201, according to medical advice information and big data information, establish logical base；Wherein, the logical base is conventional
The degree of association between the factor and reference data.
Specifically, regular factor storehouse is established according to medical advice information and big data information, and obtained and regular factor phase
Corresponding reference data, according to the relevance level relation between reference data and the matching convention factor, establish regular factor with
Logical base between reference data.
Further, the degree of association between the regular factor and reference data is divided into：Direct correlation and indirect association, institute
Stating direct correlation includes：Direct one-level association, the association of direct two level associate with direct three-level；The indirect association includes：Indirectly
One-level association, the association of indirect two level associate with indirect three-level；Wherein, the priority relationship of the degree of association is：Direct one-level association is big
Associate to associate more than indirect two level more than the association of indirect one-level more than the association of direct three-level in direct two level and be more than indirect three-level pass
Connection.
In some specific embodiments, the medical advice information, further comprise：Fo Minghan cardiovascular event risks
Assessment models, TIMI Rating Models, Hamilton depressive scale and diabetes mellitus in China guideline of prevention and treatment.The big data information, enters
One step is：The big data of population in the world Information Statistics.
Further, the more new state of medical advice information described in dynamic monitoring and the big data information, according to renewal
The medical advice information and the big data information afterwards, real-time update is carried out to the logical base, ensures the logical base
It is ageing and authoritative.
Step 202, the personal information data for obtaining user, the personal information data are brought into the logical base, are examined
Test whether the personal information data match with the reference data in the logical base.
Obtain user personal information data, personal information data are brought into logical base, by personal information data with
The reference data of the matching convention factor is matched, and examines personal information data whether in the zone of reasonableness of reference data.Its
Described in personal information data further include：Personal essential information, personal main suit's information, personal health information and personal base
Because of information；Wherein,
The personal essential information, including：The natural situation category information such as sex, age, height, body weight；
Personal main suit's information, including：Habits and customs, mood, medical history, present illness history, allergies, symptom and sign,
Family history, chemical factor, physical factor and social factor；
The personal health information, including：Biochemical indicator, image data, surgery situation, pathological section and major event；
The personal gene information, including：Disease, medicine and nutrition.
Step 203, when the data of personal information reference data corresponding with the logical base matches, then it is described
The data of personal information are True Data, and are valid data by the data markers.
As the reference frame for calculating other data.
Further, logical base carries out real-time update according to the renewal of medical advice information and big data information, after renewal
Logical base real-time verification and amendment are carried out to the data of personal information.
Step 204, when corresponding with the logical base reference data of the data of the personal information mismatches, then it is described
The data of personal information are false data.
Then need to carry out secondary checking to false data.
Step 205, the valid data in the personal information, are calculated to the false data, are obtained at least
The possibility result of one false data, the possibility result is brought into the logical base, obtains the possibility
As a result with the degree of association of the corresponding user tag, and the degree of association is obtained into the false data according to priority ranking
Optimal possibility result.
Specifically, the valid data in userspersonal information, are calculated to false data, obtain multiple falsenesses
The possibility result of data.These possibility results are brought into logical base, filter out possibility result with it is right in logical base
The data that reference data matches, and the degree of association of the possibility result and matching convention factor to match is obtained, based on direct
One-level, which associates to associate to associate to associate more than indirect two level more than indirect one-level more than the association of direct three-level more than direct two level, to be more than
The rule of indirect three-level association, obtains degree of association highest data in the possibility result to match, and the data are denoted as into this
The optimal possibility result of false data.
Step 206, degree of association threshold value of the optimal possibility result with the corresponding user tag is set, when acquisition
When the optimal possibility result is more than or equal to the degree of association threshold value with the degree of association of the corresponding user tag, by described in most
Good possibility result queue is inference data.
Specifically, when the false data of user profile is excessive, cause by the valid data of user infer to obtain can
Can property result authenticity it is relatively low, it is specific real at one to be further ensured that the validity of possibility result that deduction obtains
Apply in example, setting degree of association threshold value is direct three-level association, when the pass of optimal possibility result regular factor related to logical base
Connection degree associates more than or equal to direct three-level, is inference data by optimal possibility result queue.
Step 207, when acquisition the optimal possibility result with the degree of association of the corresponding user tag less than described in
During degree of association threshold value, the false data in the personal information is calculated according to the big data information, obtains the void
The optimal possibility result of false data, bring the optimal possibility result into the logical base, obtain the optimal possibility
As a result with the degree of association of the corresponding user tag, when the degree of association is more than or equal to the degree of association threshold value, will described in most
Good possibility result queue is inference data；When the degree of association is less than the degree of association threshold value, then according to the big data
Information calculated again to the false data in the personal information, until acquisition the optimal possibility result with it is corresponding
Untill the degree of association of the user tag is more than or equal to the degree of association threshold value.
In a specific embodiment, setting degree of association threshold value is the association of direct three-level, when passing through personal information data
In the degree of association of optimal possibility result regular factor related to logical base that is inferred to of valid data closed less than direct three-level
Connection, the then validity of optimal possibility result for inferring to obtain are relatively low.Further pass through the big data of population in the world Information Statistics
False data in the personal information is calculated, obtains the optimal possibility result of the false data, and band herein
Enter the checking for carrying out calculating data in logical base, checking and modification method with reference to described in the present embodiment circulate successively, until obtaining
Take the optimal possibility result of the condition of satisfaction and label it as inference data.
Step 208, when shortage of data be present in the personal information, according to the valid data in the personal information,
Missing data is calculated, obtains the possibility result of at least one missing data, the possibility result is brought into described
In logical base, the degree of association of the possibility result with the corresponding user tag is obtained, and by the degree of association according to preferential
Level sequence, obtain the optimal possibility result of the missing data.
Step 209, degree of association threshold value of the optimal possibility result with the corresponding user tag is set, when acquisition
When the optimal possibility result is more than or equal to the degree of association threshold value with the degree of association of the corresponding user tag, by described in most
Good possibility result queue is inference data.
Step 210, when acquisition the optimal possibility result with the degree of association of the corresponding user tag less than described in
During degree of association threshold value, the missing data in the personal information is calculated according to the big data information, obtains described lack
The optimal possibility result of data is lost, the optimal possibility result is brought into the logical base, obtains the optimal possibility
As a result with the degree of association of the corresponding user tag, when the degree of association is more than or equal to the degree of association threshold value, will described in most
Good possibility result queue is inference data；When the degree of association is less than the degree of association threshold value, then according to the big data
Information calculated again to the missing data in the personal information, until acquisition the optimal possibility result with it is corresponding
Untill the degree of association of the user tag is more than or equal to the degree of association threshold value.And by the optimal possibility result queue to push away
By data.
The inference data are marked, and periodically remind user to carry out improving modification to the inference data,
In some optional embodiments, when the user does not finish to the inference data conducts oneself well reason, according to the personal information
Valid data and the logical base periodically the inference data are verified and changed, obtain more accurate data, and
Data after renewal are continued to be labeled as inference data, and it is perfect periodically to remind user to carry out the data.
In other optional embodiments, after the user modifies to the inference data to be improved, it will obtain
Latest data be placed again into the logical base, examine the perfect latest data whether with the reference in the logical base
Data match.
When the reference data in the latest data and the logical base matches, then the latest data is labeled as
Valid data.
When the reference data in the latest data and the logical base mismatches, then the latest data is false number
According to then repeat step 206 and step 207 carry out corresponding reckoning processing to the false data.
In a specific embodiment, table 1 is logical base (this table 1 illustrate only a part for logical base), and table 2 is use
Family personal information；
The logical base of table 1
The userspersonal information of table 2
Above-mentioned user message table is that certain 58 years old women fills in data, and the userspersonal information in table 2 is updated into table 1 one by one
In logical base in：
1) according to being verified in logical base, other data and patrol that " diet is light " that the user fills in is filled in diet class
In volume storehouse there is conflict in the reference data of related copula, thus draw a conclusion " diet is light " be false data, need to be with reference to this hair
Bright described data correcting method, valid data/large database concept information and combination logical base pair in userspersonal information
The false data is calculated and verified.
2) due to the hyperpietic that the user is age >=50 year old, the pressure value of the user should >=135, therefore draw knot
By the user " blood pressure 80~120 " is false data, ibid, it is necessary to which the false data is calculated and verified.
3) because the user is diabetic, the user does not fill in corresponding fasting blood sugar, causes the shortage of data,
According to the valid data of userspersonal information/big data information, the data of the missing are calculated, it is " empty to extrapolate the user
Abdomen blood glucose >=7.0 ", the data of deduction are verified with reference to the data correcting method of the present invention.
4) because the parameter label is hyperlipidemia patient and is hyperlipidemia family history, user does not fill in lipid examination and referred to
Scale value, cause the shortage of data, according to the valid data of userspersonal information/big data information, the data of the missing are carried out
Calculate, extrapolate the user and " HDL-C≤35mg/dl ", the data of deduction are tested with reference to the data correcting method of the present invention
Card.
Each embodiment, a kind of modification method of user data of the invention, existing beneficial effect more than
It is：
(1) modification method of a kind of user data of the present invention, by establishing logical base, according to big data information with
And the valid data of user profile, user data is verified, finds the false data in personal information in time, and to user
False data in data and missing data carry out amendment in time and perfect, ensure the completeness and efficiency of user data.
(2) modification method of a kind of user data of the present invention, the medical advice information that upgrades in time and big data letter
Breath, is adjusted in real time to logical base, and then improves the accuracy rate for verifying and filling to user data, further improves user
The validity of data.
It should be understood by those skilled in the art that, embodiments of the invention can be provided as method, apparatus or computer program
Product.Therefore, the present invention can use the reality in terms of complete hardware embodiment, complete software embodiment or combination software and hardware
Apply the form of example.Moreover, the present invention can use the computer for wherein including computer usable program code in one or more
The computer program production that usable storage medium is implemented on (including but is not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
Although some specific embodiments of the present invention are described in detail by example, the skill of this area
Art personnel it should be understood that example above merely to illustrating, the scope being not intended to be limiting of the invention.The skill of this area
Art personnel to above example it should be understood that can modify without departing from the scope and spirit of the present invention.This hair
Bright scope is defined by the following claims.
Claims (9)
- A kind of 1. modification method of user data, it is characterised in that including：According to medical advice information and big data information, logical base is established；Wherein, the logical base is regular factor and reference number The degree of association between；The personal information data of user are obtained, the personal information data are brought into the logical base, examine the personal letter Whether breath data match with the reference data in the logical base；When the data of personal information reference data corresponding with the logical base matches, then the number of the personal information According to for True Data, and it is valid data by the data markers；When corresponding with the logical base reference data of the data of the personal information mismatches, then the number of the personal information According to for false data；According to the valid data in the personal information, the false data is calculated, obtains at least one falseness The possibility result of data, the possibility result is brought into the logical base, obtain the possibility result and corresponding institute State the degree of association of user tag, and by the degree of association according to priority ranking, obtain the optimal possibility of the false data As a result；Degree of association threshold value of the optimal possibility result with the corresponding user tag is set, described when acquisition most preferably may Property result when being more than or equal to the degree of association threshold value with the degree of association of the corresponding user tag, by the optimal possibility result Labeled as inference data；When the optimal possibility result of acquisition is less than the degree of association threshold value with the degree of association of the corresponding user tag, Calculate that obtaining the optimal of the false data can to the false data in the personal information according to the big data information Can property result, bring the optimal possibility result into the logical base, obtain the optimal possibility result with it is corresponding described in The degree of association of user tag, when the degree of association is more than or equal to the degree of association threshold value, by the optimal possibility result mark It is designated as inference data；When the degree of association is less than the degree of association threshold value, then according to the big data information to the individual False data in information is calculated again, until the optimal possibility result and the corresponding user tag of acquisition Untill the degree of association is more than or equal to the degree of association threshold value.
- 2. the modification method of user data according to claim 1, it is characterised in that also include:When shortage of data be present in the personal information, according to the valid data in the personal information, missing data is entered Row calculates, obtains the possibility result of at least one missing data, the possibility result is brought into the logical base, obtains The degree of association of the possibility result and the corresponding user tag, and the degree of association is obtained into institute according to priority ranking State the optimal possibility result of missing data；When the optimal possibility result of acquisition is more than or equal to the degree of association threshold with the degree of association of the corresponding user tag It is inference data by the optimal possibility result queue during value；When the optimal possibility result of acquisition is less than the degree of association threshold value with the degree of association of the corresponding user tag, Calculate that obtaining the optimal of the missing data can to the missing data in the personal information according to the big data information Can property result, bring the optimal possibility result into the logical base, obtain the optimal possibility result with it is corresponding described in The degree of association of user tag, when the degree of association is more than or equal to the degree of association threshold value, by the optimal possibility result mark It is designated as inference data；When the degree of association is less than the degree of association threshold value, then according to the big data information to the individual Missing data in information is calculated again, until the optimal possibility result and the corresponding user tag of acquisition Untill the degree of association is more than or equal to the degree of association threshold value.
- 3. the modification method of user data according to claim 2, it is characterised in that also include：When the user does not finish to the inference data conducts oneself well reason, according to the valid data of the personal information and described patrol Storehouse is collected periodically the inference data are verified and changed.
- 4. the modification method of user data according to claim 3, it is characterised in that also include：After the user modifies to the inference data to be improved, the latest data of acquisition is placed again into the logical base In, examine whether the perfect latest data matches with the reference data in the logical base；When the reference data in the latest data and the logical base matches, then by the latest data labeled as effective Data；When the reference data in the latest data and the logical base mismatches, then the latest data is false data.
- 5. the modification method of user data according to claim 1, it is characterised in that also include：The more new state of medical advice information described in dynamic monitoring and the big data information, referred to according to the medical science after renewal Information and the big data information are led, real-time update is carried out to the logical base；The logical base after renewal carries out real-time verification and amendment to the data of the personal information.
- 6. the modification method of user data according to claim 5, it is characterised in that the medical advice information, enter one Step includes：Fo Minghan cardiovascular events risk evaluation model, TIMI Rating Models, Hamilton depressive scale and diabetes mellitus in China Guideline of prevention and treatment.
- 7. the modification method of user data according to claim 5, it is characterised in that the big data information, further For：The big data of population in the world Information Statistics.
- 8. the modification method of user data according to claim 1, it is characterised in thatThe degree of association is divided into：Direct correlation and indirect association, the direct correlation include：Direct one-level association, direct two level Association associates with direct three-level；The indirect association includes：Indirect one-level association, the association of indirect two level associate with indirect three-level；Wherein, the priority relationship of the degree of association is：It is big more than the association of direct three-level that direct one-level association is more than the association of direct two level It is more than the association of indirect two level in the association of indirect one-level and is more than the association of indirect three-level.
- 9. the modification method of user data according to claim 1, it is characterised in that the personal information, further wrap Include：Personal essential information, personal main suit's information, personal health information and personal gene information；Wherein,The personal essential information, including：Sex, age, height and body weight；Personal main suit's information, including：Habits and customs, mood, medical history, present illness history, allergies, symptom and sign, family History, chemical factor, physical factor and social factor；The personal health information, including：Biochemical indicator, image data, surgery situation, pathological section and major event；The personal gene information, including：Disease, medicine and nutrition.
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