CN113704811A - Data value management method - Google Patents
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- CN113704811A CN113704811A CN202110803420.3A CN202110803420A CN113704811A CN 113704811 A CN113704811 A CN 113704811A CN 202110803420 A CN202110803420 A CN 202110803420A CN 113704811 A CN113704811 A CN 113704811A
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- 238000007726 management method Methods 0.000 title claims abstract description 22
- 238000004140 cleaning Methods 0.000 claims abstract description 20
- 238000012545 processing Methods 0.000 claims abstract description 4
- 238000013507 mapping Methods 0.000 claims description 9
- 238000000034 method Methods 0.000 claims description 8
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000010606 normalization Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 abstract description 2
- 238000012549 training Methods 0.000 description 4
- 238000013523 data management Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 208000026350 Inborn Genetic disease Diseases 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 208000016361 genetic disease Diseases 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000001356 surgical procedure Methods 0.000 description 2
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- 125000001495 ethyl group Chemical group [H]C([H])([H])C([H])([H])* 0.000 description 1
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- G06F21/60—Protecting data
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
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Abstract
The application discloses a data value management method, which comprises the following steps: acquiring data input by a user through input equipment; carrying out data cleaning on the data, and deleting invalid data; carrying out standardization processing on the data after data cleaning to form standard data; calculating the data value of each piece of standard data according to the standard data and a data value formula; storing data of data value of the standard data; displaying the data value when the system retrieves the standard data; and when the system calls the standard data, deducting the data integral of a data user according to the data value. The application has the advantage of providing a data value management method which enables the data value to have definite measurement standard so as to promote data sharing.
Description
Technical Field
The application relates to the field of data management, in particular to a data value management method.
Background
In the near future, the medical industry will incorporate more high technologies such as artificial intelligence, sensing technology and the like, so that the medical service is made to be truly intelligent, and the prosperous development of the medical industry is promoted. Under the background of new Chinese medical improvement, intelligent medical treatment is going to live in the lives of common people. The data of the medical industry has the need of privacy protection, so that when artificial intelligence is applied to the research, model training and data prediction in the medical field, a plurality of medical institutions are often required to perform the research, model training and data prediction in a networking and data collaboration mode.
The problem that can exist is how to utilize a good mechanism to promote and stimulate each hospital to participate in data sharing and use under the condition of ensuring data safety without leakage, and to better make effective data contribution for disease research.
In the prior art, value measurement cannot be carried out on data, so that each party does not have power for uploading the data, and corresponding training data cannot be effectively obtained through machine model training based on federal learning.
Disclosure of Invention
In order to solve the defects of the prior art, the application provides a data value management method, which comprises the following steps: acquiring data input by a user through input equipment; carrying out data cleaning on the data, and deleting invalid data; carrying out standardization processing on the data after data cleaning to form standard data; calculating the data value of each piece of standard data according to the standard data and a data value formula; storing data of data value of the standard data; displaying the data value when the system retrieves the standard data; and when the system calls the standard data, deducting the data integral of a data user according to the data value.
Further, the data cleaning comprises the following steps: and judging whether the format of the data meets a preset condition, and if not, judging that the data is invalid.
Further, the data cleaning further comprises the following steps: and judging whether the null value item of the data is greater than or equal to a preset value, and if so, judging the data to be invalid.
Further, the data cleaning further comprises the following steps: and judging whether the value of the key item of the data is a null value, and if so, judging the data to be invalid.
Further, the data cleaning further comprises the following steps: and judging whether the generation time of the data exceeds a preset time, and if so, judging as invalid data.
Further, the normalization process comprises the following steps: a unified reference standard for data standardization is established and defined as an international value range; making an academy reference standard for data standardization, and defining the academy reference standard as a local value range; mapping the international value range and the local value range to establish a conversion mapping relation; and converting the medical data input according to the local value range into standardized data according to the international value range according to the conversion mapping relation.
Further, the calculating the data value of each standard data according to the standard data and the data value formula comprises the following steps: and calculating a corresponding integrity score according to the integrity degree of the standard data.
Further, the calculating the data value of each standard data according to the standard data and the data value formula comprises the following steps: and calculating a corresponding timeliness score according to the timeliness degree of the standard data.
Further, the calculating the data value of each standard data according to the standard data and the data value formula comprises the following steps: and calculating corresponding content value scores according to the data content of the standard data.
Further, the calculating the data value of each standard data according to the standard data and the data value formula comprises the following steps: and generating the data value according to the weighting operation result of the integrity score, the timeliness score and the value score.
The application has the advantages that: a data value management method is provided for enabling data values to have a definite metric to facilitate data sharing.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a schematic illustration of the steps of a data value management method according to one embodiment of the present application;
FIG. 2 is a schematic illustration of an upload data management interface of a data value management method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a usage data management interface for a method of data value management according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1, the data value management method of the present application includes the steps of: acquiring data input by a user through input equipment; cleaning data and deleting invalid data; carrying out standardization processing on the data after data cleaning to form standard data; calculating the data value of each standard data according to the standard data and a data value formula; storing data of data value of the standard data; displaying the data value when the system retrieves the standard data; and when the system calls the standard data, deducting the data integral of the data user according to the data value.
Specifically, the data cleaning comprises the following steps: and judging whether the format of the data meets a preset condition or not, and if not, judging the data to be invalid.
Specifically, the data cleaning further comprises the following steps: and judging whether the null value item of the data is greater than or equal to a preset value, and if so, judging the data to be invalid.
Specifically, the data cleaning further comprises the following steps: and judging whether the value of the key item of the data is a null value or not, and if so, judging the data to be invalid.
Specifically, the data cleaning further comprises the following steps: and judging whether the generation time of the data exceeds the preset time, and if so, judging the data to be invalid.
Specifically, the normalization process includes the steps of: a unified reference standard for data standardization is established and defined as an international value range; making an academy reference standard for data standardization, and defining the academy reference standard as a local value range; mapping the international value range and the local value range to establish a conversion mapping relation; and converting the medical data input according to the local value range into standardized data according to the international value range according to the conversion mapping relation.
More specifically, taking medical data as an example, the international value range is established by setting a national standard value and corresponding description of the national standard value. Specifically, the national standard value includes at least an arabic numeral and a chinese character.
When data standardization is performed, the national standard values or the corresponding national standard value descriptions thereof can be edited or deleted, and table files with the national standard values and the national standard value descriptions can be imported.
More specifically, the local value domain is formulated by setting a local value and a corresponding local value description. The place value includes at least Arabic numerals and the place value description includes at least Chinese characters.
Specifically, the step of calculating the data value of each standard datum according to the standard datum and the data value formula comprises the following steps: calculating a corresponding integrity score according to the integrity degree of the standard data; calculating a corresponding timeliness score according to the timeliness degree of the standard data; and calculating the corresponding content value score according to the data content of the standard data.
Then, a data merit is generated from the result of the weighted operation of the integrity score, the timeliness score, and the merit score.
As a specific example, = (30 cost +10 hospital grade coefficient +7 doctor staff factor +9 surgical grade coefficient +12 data integrity coefficient +11 data timeliness coefficient +21 data scarcity coefficient)/100
Hospital grade coefficient: judging by hospital grade, wherein the coefficient of the grade A is 1;
doctor job title coefficient: judging the coefficient according to the job title of the doctor of the business, wherein the coefficient of the principal and the subordinate doctor is 1;
surgical grading factor: the coefficients are graded according to surgery, such as a four-stage surgery coefficient of 1;
data integrity factor: calculating a coefficient according to the integrity of the data, namely whether the data has a null value or not, wherein the coefficient is 1 when 100 percent of the data has the null value;
data timeliness coefficient: calculating a coefficient according to the data timeliness, wherein the coefficient of the data within 1 year is 1;
data scarcity coefficient: calculating a coefficient according to the scarcity of the data, wherein the coefficient is 1 as usual;
the cost coefficient is as follows: calculating a coefficient according to medical fee generated by the data, wherein the coefficient is 1 when the number of 100000 yuan is more;
the details of the acquisition of each coefficient from the data for which the integral is calculated in association with other information tables of the hospital are as follows:
hospital grade:
third-level 1.0;
0.8 of tertiary ethyl and the like;
0.6 of second-level A and the like;
0.4 of second-level B and the like;
first order 0.2.
Doctor title:
the chief physician (height 1;
0.8 for the assistant chief physician (minor height);
0.6 of the attending physician;
hospitalized physician 0.4.
Grading the operation:
level four 1.0;
third-level 0.8;
0.6 of second level;
first-order 0.4;
no operation 0.2;
data integrity:
100% is 1.0;
90-99% is 0.9;
80-89% is 0.8;
70-79% is 0.7;
60% -69% is 0.6;
50% -59% is 0.5;
40-49% is 0.4;
30-39% is 0.3;
20-29% is 0.2;
10% -19% is 0.1;
1-10% is 0.05;
0 is 1% or less.
Data timeliness:
1 within 1 year;
0.8 in 2-4 years;
0.6 in 5-7 years;
0.4 in 7-9 years;
0.2 in 9-11 years;
0.1 in 11-13 years;
data scarcity:
ten rare monster diseases worldwide 1.7;
adult prevalence is less than one part per million, neonatal incidence is less than 1.6 for one-fiftieth of genetic disease;
adult prevalence is one part per million, and neonatal prevalence is one fiftieth of a ten million of genetic disease 1.5;
one ninety-ten-thousandth of the prevalence in adults and one forty-ten-thousandth of the prevalence in newborns 1.4;
the prevalence rate of adults is less than one in fifty ten thousand, and the prevalence rate in newborns is less than 1.0 in ten thousand.
The cost is as follows:
0-1000 yuan is 0.2;
1000-10000 yuan is 0.4;
10000-50000 yuan is 0.6;
50000-100000 yuan is 0.8;
1.0 is 100000 or more.
As shown in FIG. 2, through the interface, when the user uploads the data, the user can know the value data of the data, so that federal learning parties can be stimulated to upload the data.
It should be noted that the uploaded data is still stored locally, and other parties can only know the index of the data and cannot acquire the data itself.
As shown in fig. 3, credit-based deduction may be performed through an interactive interface and a credit value is awarded to a user who uploads data.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A data value management method is characterized in that:
the data value management method comprises the following steps:
acquiring data input by a user through input equipment;
carrying out data cleaning on the data, and deleting invalid data;
carrying out standardization processing on the data after data cleaning to form standard data;
calculating the data value of each piece of standard data according to the standard data and a data value formula;
storing data of data value of the standard data;
displaying the data value when the system retrieves the standard data;
and when the system calls the standard data, deducting the data integral of a data user according to the data value.
2. The data value management method of claim 1, wherein:
the data cleaning comprises the following steps:
and judging whether the format of the data meets a preset condition, and if not, judging that the data is invalid.
3. The data value management method of claim 2, wherein:
the data cleaning further comprises the following steps:
and judging whether the null value item of the data is greater than or equal to a preset value, and if so, judging the data to be invalid.
4. The data value management method of claim 3, wherein:
the data cleaning further comprises the following steps:
and judging whether the value of the key item of the data is a null value, and if so, judging the data to be invalid.
5. The data value management method of claim 4, wherein:
the data cleaning further comprises the following steps:
and judging whether the generation time of the data exceeds a preset time, and if so, judging as invalid data.
6. The data value management method of claim 5, wherein:
the normalization process comprises the following steps:
a unified reference standard for data standardization is established and defined as an international value range;
making an academy reference standard for data standardization, and defining the academy reference standard as a local value range;
mapping the international value range and the local value range to establish a conversion mapping relation;
and converting the medical data input according to the local value range into standardized data according to the international value range according to the conversion mapping relation.
7. The data value management method of claim 6, wherein:
the step of calculating the data value of each standard data according to the standard data and the data value formula comprises the following steps: and calculating a corresponding integrity score according to the integrity degree of the standard data.
8. The data value management method of claim 7, wherein:
the step of calculating the data value of each standard data according to the standard data and the data value formula comprises the following steps: and calculating a corresponding timeliness score according to the timeliness degree of the standard data.
9. The data value management method of claim 8, wherein:
the step of calculating the data value of each standard data according to the standard data and the data value formula comprises the following steps:
and calculating corresponding content value scores according to the data content of the standard data.
10. The data value management method of claim 9, wherein:
the step of calculating the data value of each standard data according to the standard data and the data value formula comprises the following steps: and generating the data value according to the weighting operation result of the integrity score, the timeliness score and the value score.
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