CN112732766A - Data sorting method and device, electronic equipment and storage medium - Google Patents
Data sorting method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN112732766A CN112732766A CN202011614607.0A CN202011614607A CN112732766A CN 112732766 A CN112732766 A CN 112732766A CN 202011614607 A CN202011614607 A CN 202011614607A CN 112732766 A CN112732766 A CN 112732766A
- Authority
- CN
- China
- Prior art keywords
- data
- attribute
- determining
- membership
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 76
- 238000003860 storage Methods 0.000 title claims abstract description 25
- 238000012163 sequencing technique Methods 0.000 claims abstract description 68
- 238000004891 communication Methods 0.000 claims description 19
- 238000004590 computer program Methods 0.000 claims description 17
- 238000013500 data storage Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 18
- 230000006399 behavior Effects 0.000 description 11
- 238000000605 extraction Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000002776 aggregation Effects 0.000 description 2
- 238000004220 aggregation Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013079 data visualisation Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
- G06F16/287—Visualization; Browsing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a data sorting method, a data sorting device, electronic equipment and a storage medium, wherein the method comprises the following steps: when a data sorting request is received, determining the membership degree and the non-membership degree of each attribute of each piece of data aiming at each piece of data in the system; determining first target role information and target login time information of a user currently logging in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing which is pre-stored aiming at each role information; and sequencing each piece of data according to the membership and the non-membership of each attribute of each piece of data, the target attribute sequencing and an intuitive fuzzy decision method. The embodiment of the invention can determine the data sorting results corresponding to different users at different login times, thereby providing a data sorting scheme based on the use habits of the users.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data sorting method and apparatus, an electronic device, and a storage medium.
Background
The popularization of computers has been deepened in the aspects of daily life, entertainment, work and the like, each system in the computer contains various data, the data becomes huge with the increase of the using time, and important data which a user really needs to know is usually displayed in a form of a data table. Data sheets provide an important data visualization technique that can provide data in a very compact format and can provide access to a wide audience through proper design decisions.
Most of current data table sorting methods are sorting through single characteristics such as data creation time and names, when the data amount is large, the data which a user wants cannot be provided clearly and clearly for the user according to the sorting of the single characteristics, and the process of searching the data by the user becomes very long. When the data volume is large, and it is very difficult for a user to find needed data when keywords of the data are not determined to be needed to be searched, a data sorting scheme based on the use habits of the user is urgently needed.
Disclosure of Invention
The embodiment of the invention provides a data sorting method, a data sorting device, electronic equipment and a storage medium, which are used for providing a data sorting scheme based on user use habits.
The embodiment of the invention provides a data sorting method, which comprises the following steps:
when a data sorting request is received, determining the membership degree and the non-membership degree of each attribute of each piece of data aiming at each piece of data in the system;
determining first target role information and target login time information of a user currently logging in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing which is pre-stored aiming at each role information;
and sequencing each piece of data according to the membership and the non-membership of each attribute of each piece of data, the target attribute sequencing and an intuitive fuzzy decision method.
Further, each attribute of the data includes at least one of:
the data editing method comprises the following steps of data newly building time, data saving time, data reference other data times, data reference other data weight values, data filling integrity, data name similarity, data role creating information, data editing times, data latest editing time, data viewing times and data latest viewing time.
Further, determining the degree of membership and the degree of non-membership of each attribute of the data comprises:
if the data attribute is data new establishment time, acquiring the new establishment time of the data, and determining the membership degree of the data according to the new establishment time of the data and the corresponding relation between each preset new establishment time period and the membership degree;
if the data attribute is data storage time, acquiring new creation time of the data, determining the storage time of the data according to the new creation time of the data and the current time, and determining the membership degree of the data according to the storage time of the data and the corresponding relation between each preset storage time period and the membership degree;
if the data attribute is the number of times that the data references other data, acquiring the number of times that the data references other data, and determining the membership degree of the data according to the number of times that the data references other data and the preset corresponding relation between each frequency range and the membership degree;
if the data attribute is that the data refers to other data weight values, determining the weight values of other data of the data according to a preset weight value corresponding to each data, and determining the membership degree of the data according to the corresponding relation between the weight values of other data referred by the data and the preset weight value range and the membership degree;
if the data attribute is the data filling integrity, determining the filling integrity of the data according to the filling condition of the data, and taking the filling integrity of the data as the membership of the data;
if the data attribute is data name similarity, determining the name similarity of the data and other data, and taking the highest name similarity as the membership of the data;
if the data attribute is data role creating information, acquiring first target role information of a user currently logging in the system and second target role information of the data, and determining the membership degree of the data according to the first target role information and the second target role information; if the first target role information is the same as the second target role information, determining that the membership degree of the data is the highest, and if the first target role information is different from the second target role information, the higher the grade of the second target role information is, the higher the membership degree of the data is;
if the data attribute is the data editing times, determining the editing times of the data, and determining the membership degree of the data according to the editing times of the data and the preset corresponding relation between each editing time range and the membership degree;
if the data attribute is the latest editing time of the data, acquiring the latest editing time of the data, and determining the membership degree of the data according to the latest editing time of the data and the corresponding relation between each preset editing time period and the membership degree;
if the data attribute is the data viewing times, acquiring the viewing times of the data, and determining the membership degree of the data according to the viewing times of the data and the corresponding relation between each preset viewing time range and the membership degree;
if the data attribute is the latest data viewing time, acquiring the latest data viewing time, and determining the membership degree of the data according to the latest data viewing time and the corresponding relation between each preset data viewing time period and the membership degree;
and regarding each data attribute, taking the difference value of 1 and the membership degree of the data as the non-membership degree of the data.
Further, after receiving the data sorting request, before determining, for each piece of data in the system, the membership degree and the non-membership degree of each attribute of the data, the method further includes:
and judging whether the data sorting request carries keywords or not, and if not, carrying out subsequent steps.
Further, if the data sorting request carries a keyword, the method further includes:
and determining the similarity between each piece of data in the system and the keyword according to an edit distance algorithm, selecting target data with the similarity larger than a preset similarity threshold, and sequencing according to the similarity of the target data.
Further, the method further comprises:
judging whether target data with the same similarity exists or not, and if so, determining the membership degree and the non-membership degree of each attribute of the target data aiming at each target data with the same similarity; and performing subsequent sorting according to the membership degree and the non-membership degree of each attribute of each piece of target data.
In another aspect, an embodiment of the present invention provides a data sorting apparatus, where the apparatus includes:
the first determining module is used for determining the membership degree and the non-membership degree of each attribute of each piece of data in the system aiming at each piece of data when a data sorting request is received;
the second determining module is used for determining first target role information and target login time information of a user currently logging in the system and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing which is pre-stored aiming at each role information;
and the first sequencing module is used for sequencing each piece of data according to the membership and non-membership of each attribute of each piece of data, the target attribute sequencing and an intuitive fuzzy decision method.
Further, the apparatus further comprises:
and the judging module is used for judging whether the data sorting request carries keywords or not, and if not, triggering the first determining module.
Further, the apparatus further comprises:
and the second sequencing module is used for determining the similarity between each piece of data in the system and the keyword according to an editing distance algorithm, selecting target data with the similarity larger than a preset similarity threshold value, and sequencing according to the similarity of the target data.
Further, the determining module is further configured to determine whether there is target data with the same similarity, and if so, trigger the first determining module for each piece of target data with the same similarity.
In another aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete mutual communication through the communication bus;
a memory for storing a computer program;
a processor for implementing any of the above method steps when executing a program stored in the memory.
In yet another aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the method steps of any one of the above.
The embodiment of the invention provides a data sorting method, a data sorting device, electronic equipment and a storage medium, wherein the method comprises the following steps: when a data sorting request is received, determining the membership degree and the non-membership degree of each attribute of each piece of data aiming at each piece of data in the system; determining first target role information and target login time information of a user currently logging in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing which is pre-stored aiming at each role information; and sequencing each piece of data according to the membership and the non-membership of each attribute of each piece of data, the target attribute sequencing and an intuitive fuzzy decision method.
The technical scheme has the following advantages or beneficial effects:
in the embodiment of the invention, after a data sorting request is received, the membership degree and the non-membership degree of each attribute of each piece of data in the system can be determined, the target attribute sorting is determined according to the first target role information and the target login time information of a user currently logging in the system, and each piece of data is sorted according to the membership degree and the non-membership degree of each attribute of each piece of data, the target attribute sorting and an intuitive fuzzy decision method. Therefore, the data sorting results corresponding to different users at different login times can be determined, and a data sorting scheme based on the use habits of the users is provided.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a data sorting process provided in embodiment 1 of the present invention;
fig. 2 is a schematic diagram of time period division provided in embodiment 2 of the present invention;
fig. 3 is a structural diagram of a data sorting system according to embodiment 3 of the present invention;
fig. 4 is a schematic diagram of user role division provided in embodiment 3 of the present invention;
fig. 5 is a schematic structural diagram of a data sorting apparatus according to embodiment 4 of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the attached drawings, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
Example 1:
fig. 1 is a schematic diagram of a data sorting process provided in an embodiment of the present invention, including the following steps:
s101: when a data sorting request is received, for each piece of data in the system, the membership degree and the non-membership degree of each attribute of the data are determined.
S102: determining first target role information and target login time information of a user currently logging in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing which is pre-stored aiming at each role information.
S103: and sequencing each piece of data according to the membership and the non-membership of each attribute of each piece of data, the target attribute sequencing and an intuitive fuzzy decision method.
The data sorting method provided by the embodiment of the invention is applied to electronic equipment, and the electronic equipment can be equipment such as a PC (personal computer), a tablet computer and the like.
After receiving a data sorting request, the electronic device acquires the membership and the non-membership of each attribute of each piece of data in the system, wherein each attribute of the data comprises at least one of the following items: the data editing method comprises the following steps of data newly building time, data saving time, data reference other data times, data reference other data weight values, data filling integrity, data name similarity, data role creating information, data editing times, data latest editing time, data viewing times and data latest viewing time. And the electronic equipment can determine first target role information of a user currently logging in the system according to the user name of the currently logging in system, wherein the target role information comprises an administrator, an operator, a super administrator and the like. The electronic device can store role information corresponding to each user name, and when a user logs in the system, the first target role information of the user currently logging in the system can be determined according to the user name of the currently logging in the system. In addition, when the user logs in the system, the electronic device may recognize target login time information of the user logging in the system.
The electronic device prestores a corresponding relation between each login time period and the attribute sequence aiming at each role information, so that the target attribute sequence can be determined according to the first target role information and the target login time information of the user currently logging in the system and the corresponding relation between each login time period and the attribute sequence prestored aiming at each role information. And after the electronic equipment determines the membership degree and the non-membership degree of each attribute of each piece of data and the target attribute sequencing, finishing the sequencing of each piece of data according to an intuitive fuzzy decision method.
In the embodiment of the invention, after a data sorting request is received, the membership degree and the non-membership degree of each attribute of each piece of data in the system can be determined, the target attribute sorting is determined according to the first target role information and the target login time information of a user currently logging in the system, and each piece of data is sorted according to the membership degree and the non-membership degree of each attribute of each piece of data, the target attribute sorting and an intuitive fuzzy decision method. Therefore, the data sorting results corresponding to different users at different login times can be determined, and a data sorting scheme based on the use habits of the users is provided.
Example 2:
on the basis of the above embodiment, in an embodiment of the present invention, determining the degree of membership and the degree of non-membership of each attribute of the data includes:
if the data attribute is data new establishment time, acquiring the new establishment time of the data, and determining the membership degree of the data according to the new establishment time of the data and the corresponding relation between each preset new establishment time period and the membership degree;
if the data attribute is data storage time, acquiring new creation time of the data, determining the storage time of the data according to the new creation time of the data and the current time, and determining the membership degree of the data according to the storage time of the data and the corresponding relation between each preset storage time period and the membership degree;
if the data attribute is the number of times that the data references other data, acquiring the number of times that the data references other data, and determining the membership degree of the data according to the number of times that the data references other data and the preset corresponding relation between each frequency range and the membership degree;
if the data attribute is that the data refers to other data weight values, determining the weight values of other data of the data according to a preset weight value corresponding to each data, and determining the membership degree of the data according to the corresponding relation between the weight values of other data referred by the data and the preset weight value range and the membership degree;
if the data attribute is the data filling integrity, determining the filling integrity of the data according to the filling condition of the data, and taking the filling integrity of the data as the membership of the data;
if the data attribute is data name similarity, determining the name similarity of the data and other data, and taking the highest name similarity as the membership of the data;
if the data attribute is data role creating information, acquiring first target role information of a user currently logging in the system and second target role information of the data, and determining the membership degree of the data according to the first target role information and the second target role information; if the first target role information is the same as the second target role information, determining that the membership degree of the data is the highest, and if the first target role information is different from the second target role information, the higher the grade of the second target role information is, the higher the membership degree of the data is;
if the data attribute is the data editing times, determining the editing times of the data, and determining the membership degree of the data according to the editing times of the data and the preset corresponding relation between each editing time range and the membership degree;
if the data attribute is the latest editing time of the data, acquiring the latest editing time of the data, and determining the membership degree of the data according to the latest editing time of the data and the corresponding relation between each preset editing time period and the membership degree;
if the data attribute is the data viewing times, acquiring the viewing times of the data, and determining the membership degree of the data according to the viewing times of the data and the corresponding relation between each preset viewing time range and the membership degree;
if the data attribute is the latest data viewing time, acquiring the latest data viewing time, and determining the membership degree of the data according to the latest data viewing time and the corresponding relation between each preset data viewing time period and the membership degree;
and regarding each data attribute, taking the difference value of 1 and the membership degree of the data as the non-membership degree of the data.
The following is illustrated as an example:
data new establishment time: in the unit of hour, if the system is an internal system of a company, according to the diagram shown in fig. 2, 24 hours a day is divided into 12 equal parts, each equal part represents a time period, according to the working hours of employees on the company, the situation of newly creating data by a general user is between the time period 5 and the time period 10, and the occupied ratio T of the newly created data of all data in the system between eight morning hours and eight evening hours is calculated1Since the employee is rarely likely to newly create data in the time period 1 to the time period 3, the new data in the time period accounts for T2The remaining ratio between the period 4 and the periods 11 and 12 is T3Wherein T is1+T2+T3＝1；T1>T3>T2. T above1、T2、T3I.e. the degree of membership of the data.
Data retention time: taking the day as a unit, subtracting the new construction time from the current time to obtain a result of a day x when updating the sorting table every time, and setting the probability of the use data with the day x being less than or equal to 20 as D1Setting the probability of using data with the number of days x being more than or equal to 100 as D2The set days are 20<x<Usage data between 100 is summarized as D3Wherein D is1+D2+D3＝1；D1>D3>D2(ii) a Above D1、D2、D3I.e. the degree of membership of the data.
Number of times data references other data: obtaining the number y of times of referencing other data, if the piece of data can reference the total numberFor X other data, different intervals are set according to the system condition, and then the use probability is Q when the reference number is 80% X ≦ y1Then the probability of use is Q when the quote number is y ≦ 10%. X2When the quoted number is 10% X<y<Use probability of Q at 80%. X3Wherein Q is1+Q2+Q3＝1；Q1>Q3>Q2(ii) a Above Q1、Q2、Q3I.e. the degree of membership of the data.
Reference other data weight values: acquiring and quoting other data, setting a weight value for each other data in the system, if the total number of the weight values of the data capable of quoting the other data is Y, the weight value of the data quoting the other data is C, and setting different intervals according to the system condition, then when the quoting weight value is 80% Y ≦ C, the use probability is C1Then the probability of use is C when the quote weight is C ≦ 10% Y2When the quoted weight is 10% Y<c<Use probability of C at 80% Y3In which C is1+C2+C3＝1；C1>C3>C2) (ii) a C above1、C2、C3I.e. the degree of membership of the data.
Data filling integrity: calculating the data filling integrity F according to the filling condition of the data1；F1I.e. the degree of membership of the data.
Data name similarity: adopting an edit Distance algorithm (Levenshtein Distance) to obtain the similarity percentage of the current data name and other data names, and finally taking the highest percentage N in the percentages1；N1I.e. the degree of membership of the data. The similarity percentage between the current data name and other data names may be the similarity percentage between the current data name and other data names of unreferenced pages.
Creating data role information: the roles of system users in the system are mainly divided into a super administrator, an administrator and an operator, the role CR of the current login user is obtained, the role DR of the user creating the data is obtained, and if CR is equal to DR, the use probability R of the data is obtained1Is 1; when the CD is not equal to the DR,if DR is super administrator, the probability of using the piece of data is R1If DR is administrator, the probability of using the piece of data is R2If DR is the operator, the probability of use of the bar is R3；1>R1>R2>R3(ii) a R is as defined above1、R2、R3I.e. the degree of membership of the data.
The data editing times are as follows: obtaining the data editing times n, setting different intervals according to the system condition, and then when n is more than or equal to 5, the using probability is E1Then the probability of use is E when n ≦ 12When the reference number is 1<y<The use probability of 5 is E3；1>E1>E3>E2(ii) a Above E1、E2、E3I.e. the degree of membership of the data.
The latest editing time: taking the day as a unit, subtracting the latest editing time (when the editing times is 0, namely the new building time) from the current time every time the sequencing list is updated to obtain a result of a day m, and setting the probability of using data with the day m being less than or equal to 20 as DT1Setting the probability of using data with the number of days m being more than or equal to 100 as DT2The set days are 20<m<Usage data between 100 is summarized as DT3Wherein DT1+DT2+DT3＝1；DT1>DT3>DT2(ii) a The above DT1、DT2、DT3I.e. the degree of membership of the data.
And (3) data viewing times: obtaining the number i of times of viewing detailed contents of the data, setting different intervals according to the system condition, and then when i is more than or equal to 5, the use probability is CD1Then when i ≦ 1 the probability of use is CD2When the reference number is 1<i<The 5-hour use probability is CD3；1>CD1>CD3>CD2(ii) a The above CD1、CD2、CD3I.e. the degree of membership of the data.
Data recent viewing time: taking a day as a unit, subtracting the latest detailed content viewing time (when the number of times of latest detailed content viewing is 0, namely the new creation time) from the current time every time the sorting table is updated, obtaining a result of a day j, and settingThe probability of using data with the number of days j less than or equal to 20 is CDT1Setting the probability of using data with the number of days j being more than or equal to 100 as CDT2The set days are 20<j<Usage data between 100 is summarized as CDT3Wherein CDT1+CDT2+CDT3＝1；CDT1>CDT3>CDT2(ii) a CDT as described above1、CDT2、CDT3I.e. the degree of membership of the data.
And regarding each data attribute, taking the difference value of 1 and the membership degree of the data as the non-membership degree of the data.
The degree of membership and the degree of non-membership for each attribute of the data are shown in the following table:
example 3:
on the basis of the foregoing embodiments, in an embodiment of the present invention, after receiving a data sorting request, for each piece of data in a system, before determining a membership degree and a non-membership degree of each attribute of the piece of data, the method further includes:
and judging whether the data sorting request carries keywords or not, and if not, carrying out subsequent steps.
In the embodiment of the invention, after receiving a data sorting request, an electronic device firstly judges whether the data sorting request carries a keyword or not before determining the membership degree and the non-membership degree of each attribute of the data for each piece of data in a system, and if the data sorting request does not carry the keyword, the electronic device carries out subsequent sorting for each piece of data in the system, determines the membership degree and the non-membership degree of each attribute of the data, and a subsequent sorting process.
If the data sorting request carries keywords, the method further comprises the following steps:
and determining the similarity between each piece of data in the system and the keyword according to an edit distance algorithm, selecting target data with the similarity larger than a preset similarity threshold, and sequencing according to the similarity of the target data.
In the embodiment of the invention, if the data sorting request carries the keyword, the electronic equipment determines the similarity between each piece of data in the system and the keyword according to an editing distance algorithm. And storing a preset similarity threshold in the electronic equipment, then selecting target data with the similarity greater than the preset similarity threshold, and sequencing according to the similarity of the target data. Generally, the target data are sorted in the order of similarity from large to small.
In order to further make the data sorting more accurate, in an embodiment of the present invention, the method further includes:
judging whether target data with the same similarity exists or not, and if so, determining the membership degree and the non-membership degree of each attribute of the target data aiming at each target data with the same similarity; and performing subsequent sorting according to the membership degree and the non-membership degree of each attribute of each piece of target data.
The electronic equipment determines the similarity between each piece of data in the system and the keywords according to an edit distance algorithm, then judges whether target data with the same similarity exists, and if yes, determines the membership degree and the non-membership degree of each attribute of the target data aiming at each piece of target data with the same similarity; and performing subsequent sorting according to the membership degree and the non-membership degree of each attribute of each piece of target data.
That is to say, the data sorting method provided by the embodiment of the present invention includes a data sorting method without a keyword and a sorting method related to the keyword. Specifically, the keyword-free sorting method is to sort each piece of data according to the membership and non-membership of each attribute of each piece of data, target attribute sorting and an intuitive fuzzy decision method. The method for sorting the keywords comprises the steps of determining the similarity between each piece of data in the system and the keywords according to an editing distance algorithm, selecting target data with the similarity larger than a preset similarity threshold value, and sorting according to the similarity of the target data. And in the process of sequencing related keywords, if target data with the same similarity exists, sequencing the target data with the same similarity by adopting a keyword-free sequencing method, so that the data sequencing is more accurate.
The embodiment of the invention aims to provide a sorting method for matching user requirements under a large number of data tables, which can effectively improve the reliability and accuracy of data sorting under the condition of no keyword.
Aiming at the above purpose, the technical scheme adopted by the embodiment of the invention is as follows:
(1) the context information acquisition module acquires context information of a user:
the context information acquisition module records user context information, including user role information and login time information, wherein the user roles are mainly divided into a super manager, an administrator and an operator, the obtained information is temporarily stored in the step (4), and data characteristics and a sequencing mode are determined according to the two types of context information.
(2) The operation module selects a specific operation behavior of a user in the system, and makes different responses according to the operation:
the operation module comprises four operation behaviors of creating data, editing data, checking data and searching and sequencing data, wherein the searching and sequencing data is divided into keyword searching and sequencing and irrelevant keyword searching and sequencing, when a user inputs keywords to search the data, the behavior is relevant keyword searching and sequencing, and when the user directly enters a data table page without searching the keywords, the behavior is non-keyword searching and sequencing.
(3) The characteristic extraction module extracts characteristic values of the data table and the user operation behaviors:
(3a) extracting characteristic attributes from the newly-built data behavior and newly-built data acquired in the step (2);
(3b) re-extracting characteristic attributes from the edited data behavior and the edited data acquired in the step (2) according to the modified new data, the editing times and the latest editing time;
(3c) re-extracting characteristic attributes from the behavior of viewing the detailed data content acquired in the step (2) according to the frequency of viewing the detailed data content and the time of recently viewing the detailed data content;
(4) storing data into a database:
storing the characteristic attributes obtained in the step (3) into a database XX1.db, and storing corresponding conventional filling data into a database XX2.db;
(5) the sorting module sorts the data table according to the user requirement:
and inputting the sorting module according to the context information of the user logging in the system and the searching behavior of the user with or without keywords to obtain a sorting result.
As described above, the automatic sorting method for matching user requirements under a large number of data tables according to the embodiment of the present invention has the following advantages: 1) an automatic sequencing method model provided according to the requirements of a user under the condition of a large amount of data is designed, so that the user can quickly and accurately find required contents when the data is large; 2) a feature extraction mode aiming at a large amount of data is designed, a large amount of redundant features are removed, the matching speed is higher, and the response capability after operation is improved.
The following sets forth in detail embodiments of the invention:
fig. 3 is a diagram of a data sorting system according to an embodiment of the present invention, including a PC device of a user and a server.
The user at the PC device comprises: the device comprises a context information acquisition module and an operation module.
The context information acquisition module: collecting context information of user login, including user role information and login time information;
an operation module: the method comprises the following steps of constructing data by a user, editing the data, checking the data and searching the sorted data, wherein the searching of the sorted data is divided into keyword searching and irrelevant keyword searching;
the server includes: the system comprises a database, a feature extraction module and a sequencing module.
A database: storing three data modes, namely conventional newly-built data content, characteristic data corresponding to each piece of data and historical context information of a user;
a feature extraction module: corresponding features are extracted mainly aiming at data generated by three operations of creating, editing and viewing detailed data by a user, and the three features are stored in a database;
a sorting module: and determining that the irrelevant key words exist according to the operation of searching the sequencing data of the user, and then matching the context information of the current login of the user with the data in the database to obtain a sequencing result.
The context information acquisition module specifically comprises the following modules:
and after the user successfully logs in the system, acquiring the current user role information and the login time. As shown in fig. 4, the current user roles are mainly divided into a hypervisor, an administrator, and an operator; as shown in fig. 2, 24 hours a day is divided into 12 equal parts, and two hours are taken as an interval, so as to obtain an interval value of the current user login time. And temporarily storing the obtained context information into a database, and entering a user operation module after the context information is acquired.
Step 2, operating the module as follows:
the operation module is mainly divided into four operation behaviors: the method comprises the following steps of data creating operation, data editing operation, data viewing operation and data searching and sorting operation.
Newly building data, wherein when the data is built, a user fills in the data according to requirements, and after the data is successfully built, the data is stored in a database and enters a feature extraction module;
editing data, namely, modifying original data by a user on the basis of the existing data, storing the data into a database after the data is successfully edited, and entering a feature extraction module;
the data checking operation is carried out, a user clicks detailed information of the checked data on the basis of the existing data, and the data enters a feature extraction module after a window is closed;
the operation of searching the sequencing data is mainly divided into key word searching and irrelevant key word searching. A user initiates a non-keyword ordering request to enter an ordering module under the condition of not knowing keywords; and searching the keywords by the user under the condition of knowing the keywords, obtaining a keyword search result, and transmitting the search result to a sequencing module for sequencing.
Step 3, a feature extraction module specifically comprises the following steps:
it is known from step 2 that feature extraction is mainly extracted from three operation behaviors of a user, namely, a new data operation, a data editing operation and a data viewing operation, and 11 feature attributes are mainly obtained through screening, which include: the data editing method comprises the following steps of data newly building time, data saving time, data reference other data times, data reference other data weight values, data filling integrity, data name similarity, data role creating information, data editing times, data latest editing time, data viewing times and data latest viewing time.
Step 4, a sorting module specifically comprises the following steps:
an intuitive fuzzy number sorting in an intuitive fuzzy multi-attribute decision problem is mainly adopted in a sorting module, each piece of data in a database can be sorted as a decision method, 11 attributes of each piece of data can be obtained according to the step 3, the membership degree and the non-membership degree in the attributes and the priority relation among the attributes are determined according to different conditions, and an intuitive fuzzy decision matrix is established for the collected decision information; then, calculating the closeness of the corresponding attribute of the data according to the membership degree and the non-membership degree; and finally, calculating the associated attribute weight vector by using an intuitive fuzzy weighted average operator (IFPWA), further calculating the aggregation result of the strip data, and sequencing the final aggregation result.
When the irrelevant keyword is searched and sorted, the role and login time of the user are obtained from the context module, and first, the login time is found in the corresponding time period T in fig. 2, if the time period T is between the time period 5 and the time period 10, the target attribute sorting among 11 attributes in step 3 is, for example: attribute 6> attribute 7> attribute 1> attribute 9> attribute 4> attribute 3> attribute 11> attribute 8> attribute 10> attribute 2> attribute 5; if the time period T is not between time period 5 and time period 10, the target attribute ordering among the 11 attributes in step 3 is, for example: attribute 6> attribute 7> attribute 9> attribute 4> attribute 3> attribute 11> attribute 8> attribute 10> attribute 2> attribute 1> attribute 5; and sequencing each piece of data according to the membership and the non-membership of each attribute of each piece of data, the target attribute sequencing and an intuitive fuzzy decision method. It should be noted that the above target attribute ordering is only an example, and the specific attribute ordering may be determined according to different application scenarios of the user.
When the keywords are searched and sorted, a data set is obtained by adopting an edit distance algorithm according to the filled keywords, the similarity rate of the keywords in the data set is firstly sorted from high to low once, and then the data sets with the same similarity rate are sorted according to an irrelevant keyword searching and sorting method.
In summary, the embodiment of the invention provides a small amount of representative data characteristics after the user logs in, so as to ensure the accuracy of the data sequence displayed according to the actual requirement of the user and avoid the process that the user searches for the target data in a large amount of data.
Example 4:
fig. 5 is a schematic structural diagram of a data sorting apparatus according to an embodiment of the present invention, including:
a first determining module 51, configured to determine, for each piece of data in the system, a membership degree and a non-membership degree of each attribute of the data when a data sorting request is received;
a second determining module 52, configured to determine first target role information and target login time information of a user currently logging in the system, and determine a target attribute rank according to a correspondence between each login time period and an attribute rank pre-stored for each role information;
and a first sorting module 53, configured to sort each piece of data according to the membership and non-membership of each attribute of each piece of data, the target attribute sorting, and an intuitive fuzzy decision method.
The device further comprises:
a judging module 54, configured to judge whether the data sorting request carries a keyword, and if not, trigger the first determining module 51.
The device further comprises:
and a second sorting module 55, configured to determine similarity between each piece of data in the system and the keyword according to an edit distance algorithm, select target data with similarity greater than a preset similarity threshold, and sort according to the similarity of the target data.
The determining module 54 is further configured to determine whether there is target data with the same similarity, and if so, trigger the first determining module for each target data with the same similarity.
Example 5:
on the basis of the foregoing embodiments, an embodiment of the present invention further provides an electronic device, as shown in fig. 6, including: the system comprises a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302 and the memory 303 complete mutual communication through the communication bus 304;
the memory 303 has stored therein a computer program which, when executed by the processor 301, causes the processor 301 to perform the steps of:
when a data sorting request is received, determining the membership degree and the non-membership degree of each attribute of each piece of data aiming at each piece of data in the system;
determining first target role information and target login time information of a user currently logging in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing which is pre-stored aiming at each role information;
and sequencing each piece of data according to the membership and the non-membership of each attribute of each piece of data, the target attribute sequencing and an intuitive fuzzy decision method.
Based on the same inventive concept, the embodiment of the present invention further provides an electronic device, and as the principle of the electronic device for solving the problem is similar to the data sorting method, the implementation of the electronic device may refer to the implementation of the method, and repeated details are not repeated.
The electronic device provided by the embodiment of the invention can be a desktop computer, a portable computer, a smart phone, a tablet computer, a Personal Digital Assistant (PDA), a network side device and the like.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface 302 is used for communication between the above-described electronic apparatus and other apparatuses.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a central processing unit, a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an application specific integrated circuit, a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like.
When the processor executes the program stored in the memory, the embodiment of the invention realizes that after a data sorting request is received, the membership degree and the non-membership degree of each attribute of each piece of data in the system can be determined, the target attribute sorting is determined according to the first target role information and the target login time information of a user currently logging in the system, and each piece of data is sorted according to the membership degree and the non-membership degree of each attribute of each piece of data, the target attribute sorting and an intuitive fuzzy decision method. Therefore, the data sorting results corresponding to different users at different login times can be determined, and a data sorting scheme based on the use habits of the users is provided.
Example 6:
on the basis of the foregoing embodiments, an embodiment of the present invention further provides a computer storage readable storage medium, in which a computer program executable by an electronic device is stored, and when the program is run on the electronic device, the electronic device is caused to execute the following steps:
when a data sorting request is received, determining the membership degree and the non-membership degree of each attribute of each piece of data aiming at each piece of data in the system;
determining first target role information and target login time information of a user currently logging in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing which is pre-stored aiming at each role information;
and sequencing each piece of data according to the membership and the non-membership of each attribute of each piece of data, the target attribute sequencing and an intuitive fuzzy decision method.
Based on the same inventive concept, embodiments of the present invention further provide a computer-readable storage medium, and since a principle of solving a problem when a processor executes a computer program stored in the computer-readable storage medium is similar to a data sorting method, implementation of the computer program stored in the computer-readable storage medium by the processor may refer to implementation of the method, and repeated details are not repeated.
The computer readable storage medium may be any available medium or data storage device that can be accessed by a processor in an electronic device, including but not limited to magnetic memory such as floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc., optical memory such as CDs, DVDs, BDs, HVDs, etc., and semiconductor memory such as ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), Solid State Disks (SSDs), etc.
The computer program is stored in a computer-readable storage medium provided in the embodiment of the present invention, and when executed by a processor, the computer program can determine membership and non-membership of each attribute of each piece of data in a system after receiving a data sorting request, determine target attribute sorting according to first target role information and target login time information of a user currently logging in the system, and sort each piece of data according to the membership and non-membership of each attribute of each piece of data, the target attribute sorting, and an intuitive fuzzy decision method. Therefore, the data sorting results corresponding to different users at different login times can be determined, and a data sorting scheme based on the use habits of the users is provided.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method of data ordering, the method comprising:
when a data sorting request is received, determining the membership degree and the non-membership degree of each attribute of each piece of data aiming at each piece of data in the system;
determining first target role information and target login time information of a user currently logging in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing which is pre-stored aiming at each role information;
and sequencing each piece of data according to the membership and the non-membership of each attribute of each piece of data, the target attribute sequencing and an intuitive fuzzy decision method.
2. The method of claim 1, wherein each attribute of data comprises at least one of:
the data editing method comprises the following steps of data newly building time, data saving time, data reference other data times, data reference other data weight values, data filling integrity, data name similarity, data role creating information, data editing times, data latest editing time, data viewing times and data latest viewing time.
3. The method of claim 2, wherein determining the degree of membership and the degree of non-membership of each attribute of the data comprises:
if the data attribute is data new establishment time, acquiring the new establishment time of the data, and determining the membership degree of the data according to the new establishment time of the data and the corresponding relation between each preset new establishment time period and the membership degree;
if the data attribute is data storage time, acquiring new creation time of the data, determining the storage time of the data according to the new creation time of the data and the current time, and determining the membership degree of the data according to the storage time of the data and the corresponding relation between each preset storage time period and the membership degree;
if the data attribute is the number of times that the data references other data, acquiring the number of times that the data references other data, and determining the membership degree of the data according to the number of times that the data references other data and the preset corresponding relation between each frequency range and the membership degree;
if the data attribute is that the data refers to other data weight values, determining the weight values of other data of the data according to a preset weight value corresponding to each data, and determining the membership degree of the data according to the corresponding relation between the weight values of other data referred by the data and the preset weight value range and the membership degree;
if the data attribute is the data filling integrity, determining the filling integrity of the data according to the filling condition of the data, and taking the filling integrity of the data as the membership of the data;
if the data attribute is data name similarity, determining the name similarity of the data and other data, and taking the highest name similarity as the membership of the data;
if the data attribute is data role creating information, acquiring first target role information of a user currently logging in the system and second target role information of the data, and determining the membership degree of the data according to the first target role information and the second target role information; if the first target role information is the same as the second target role information, determining that the membership degree of the data is the highest, and if the first target role information is different from the second target role information, the higher the grade of the second target role information is, the higher the membership degree of the data is;
if the data attribute is the data editing times, determining the editing times of the data, and determining the membership degree of the data according to the editing times of the data and the preset corresponding relation between each editing time range and the membership degree;
if the data attribute is the latest editing time of the data, acquiring the latest editing time of the data, and determining the membership degree of the data according to the latest editing time of the data and the corresponding relation between each preset editing time period and the membership degree;
if the data attribute is the data viewing times, acquiring the viewing times of the data, and determining the membership degree of the data according to the viewing times of the data and the corresponding relation between each preset viewing time range and the membership degree;
if the data attribute is the latest data viewing time, acquiring the latest data viewing time, and determining the membership degree of the data according to the latest data viewing time and the corresponding relation between each preset data viewing time period and the membership degree;
and regarding each data attribute, taking the difference value of 1 and the membership degree of the data as the non-membership degree of the data.
4. The method of claim 1, wherein after receiving a data ordering request, for each piece of data in the system, before determining the degree of membership and the degree of non-membership for each attribute of the data, the method further comprises:
and judging whether the data sorting request carries keywords or not, and if not, carrying out subsequent steps.
5. The method of claim 4, wherein if the data ordering request carries a key, the method further comprises:
and determining the similarity between each piece of data in the system and the keyword according to an edit distance algorithm, selecting target data with the similarity larger than a preset similarity threshold, and sequencing according to the similarity of the target data.
6. The method of claim 5, wherein the method further comprises:
judging whether target data with the same similarity exists or not, and if so, determining the membership degree and the non-membership degree of each attribute of the target data aiming at each target data with the same similarity; and performing subsequent sorting according to the membership degree and the non-membership degree of each attribute of each piece of target data.
7. An apparatus for sorting data, the apparatus comprising:
the first determining module is used for determining the membership degree and the non-membership degree of each attribute of each piece of data in the system aiming at each piece of data when a data sorting request is received;
the second determining module is used for determining first target role information and target login time information of a user currently logging in the system and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing which is pre-stored aiming at each role information;
and the first sequencing module is used for sequencing each piece of data according to the membership and non-membership of each attribute of each piece of data, the target attribute sequencing and an intuitive fuzzy decision method.
8. The apparatus of claim 7, wherein the apparatus further comprises:
and the judging module is used for judging whether the data sorting request carries keywords or not, and if not, triggering the first determining module.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any one of claims 1 to 6 when executing a program stored in the memory.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011614607.0A CN112732766B (en) | 2020-12-30 | 2020-12-30 | Data sorting method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011614607.0A CN112732766B (en) | 2020-12-30 | 2020-12-30 | Data sorting method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112732766A true CN112732766A (en) | 2021-04-30 |
| CN112732766B CN112732766B (en) | 2024-08-30 |
Family
ID=75611223
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011614607.0A Active CN112732766B (en) | 2020-12-30 | 2020-12-30 | Data sorting method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112732766B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116578602A (en) * | 2023-06-15 | 2023-08-11 | 深圳计算科学研究院 | Time sequence ordering method and device |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102591876A (en) * | 2011-01-14 | 2012-07-18 | 阿里巴巴集团控股有限公司 | Sequencing method and device of search results |
| US20150127406A1 (en) * | 2013-11-05 | 2015-05-07 | Bank Of America Corporation | Roles based access |
| CN104812027A (en) * | 2014-01-23 | 2015-07-29 | 北京邮电大学 | Network selection method based on intuitionistic fuzzy set multi-attribute decision-making |
| CN105989066A (en) * | 2015-02-09 | 2016-10-05 | 阿里巴巴集团控股有限公司 | Information processing method and device |
| US20170147580A1 (en) * | 2015-11-24 | 2017-05-25 | Sap Se | User-dependent ranking of data items |
| US20170316005A1 (en) * | 2016-04-29 | 2017-11-02 | Appdynamics Llc | Real-time ranking of monitored entities |
| US20170344608A1 (en) * | 2016-05-24 | 2017-11-30 | International Business Machines Corporation | Sorting tables in analytical databases |
| CN109146184A (en) * | 2018-08-28 | 2019-01-04 | 成都信息工程大学 | Based on the Interval Intuitionistic Fuzzy multiple attributive decision making method for improving entropy and scoring function |
| CN110335110A (en) * | 2019-06-03 | 2019-10-15 | 平安科技（深圳）有限公司 | Sort method, device, computer equipment and the storage medium of product |
| CN110928527A (en) * | 2018-09-20 | 2020-03-27 | 北京国双科技有限公司 | Sorting method and device |
| CN111949898A (en) * | 2020-08-28 | 2020-11-17 | 平安国际智慧城市科技股份有限公司 | Search result ordering method, device, equipment and computer readable storage medium |
-
2020
- 2020-12-30 CN CN202011614607.0A patent/CN112732766B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102591876A (en) * | 2011-01-14 | 2012-07-18 | 阿里巴巴集团控股有限公司 | Sequencing method and device of search results |
| US20150127406A1 (en) * | 2013-11-05 | 2015-05-07 | Bank Of America Corporation | Roles based access |
| CN104812027A (en) * | 2014-01-23 | 2015-07-29 | 北京邮电大学 | Network selection method based on intuitionistic fuzzy set multi-attribute decision-making |
| CN105989066A (en) * | 2015-02-09 | 2016-10-05 | 阿里巴巴集团控股有限公司 | Information processing method and device |
| US20170147580A1 (en) * | 2015-11-24 | 2017-05-25 | Sap Se | User-dependent ranking of data items |
| US20170316005A1 (en) * | 2016-04-29 | 2017-11-02 | Appdynamics Llc | Real-time ranking of monitored entities |
| US20170344608A1 (en) * | 2016-05-24 | 2017-11-30 | International Business Machines Corporation | Sorting tables in analytical databases |
| CN109146184A (en) * | 2018-08-28 | 2019-01-04 | 成都信息工程大学 | Based on the Interval Intuitionistic Fuzzy multiple attributive decision making method for improving entropy and scoring function |
| CN110928527A (en) * | 2018-09-20 | 2020-03-27 | 北京国双科技有限公司 | Sorting method and device |
| CN110335110A (en) * | 2019-06-03 | 2019-10-15 | 平安科技（深圳）有限公司 | Sort method, device, computer equipment and the storage medium of product |
| CN111949898A (en) * | 2020-08-28 | 2020-11-17 | 平安国际智慧城市科技股份有限公司 | Search result ordering method, device, equipment and computer readable storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116578602A (en) * | 2023-06-15 | 2023-08-11 | 深圳计算科学研究院 | Time sequence ordering method and device |
| CN116578602B (en) * | 2023-06-15 | 2024-03-12 | 深圳计算科学研究院 | Time sequence ordering method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112732766B (en) | 2024-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108304512B (en) | Video search engine coarse sorting method and device and electronic equipment | |
| CN102760138B (en) | Classification method and device for user network behaviors and search method and device for user network behaviors | |
| CN109032381B (en) | Input method and device based on context, storage medium and terminal | |
| CN108897842A (en) | Computer readable storage medium and computer system | |
| CN110458324B (en) | Method and device for calculating risk probability and computer equipment | |
| WO2018192348A1 (en) | Data processing method and device, and server | |
| CN110706015B (en) | Feature selection method for advertisement click rate prediction | |
| CN112818230B (en) | Content recommendation method, device, electronic equipment and storage medium | |
| CN113449168A (en) | Method, device and equipment for capturing theme webpage data and storage medium | |
| CN114328983A (en) | Document fragmenting method, data retrieval device and electronic equipment | |
| US9940372B2 (en) | Triggering method for instant search | |
| CN105989066A (en) | Information processing method and device | |
| CN112732766B (en) | Data sorting method and device, electronic equipment and storage medium | |
| CN111783107A (en) | Multi-source trusted data access method, device and equipment | |
| CN114090877A (en) | Position information recommendation method and device, electronic equipment and storage medium | |
| CN107133321B (en) | Method and device for analyzing search characteristics of page | |
| CN110851708B (en) | Negative sample extraction method, device, computer equipment and storage medium | |
| CN111858366A (en) | Test case generation method, device, equipment and storage medium | |
| CN107590176B (en) | Evaluation index obtaining method and device and electronic equipment | |
| CN114491232B (en) | Information query method and device, electronic equipment and storage medium | |
| CN116383340A (en) | Information searching method, device, electronic equipment and storage medium | |
| CN114519153A (en) | Webpage history record display method, device, equipment and storage medium | |
| CN104156492A (en) | Method and device for prompting search content | |
| CN115203253A (en) | Billboard data query method and device, computer terminal and storage medium | |
| CN113722356A (en) | Processing method and device for reporting event, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |