CN113283813B - Method and device for ordering target importance based on DEMATEL - Google Patents

Method and device for ordering target importance based on DEMATEL Download PDF

Info

Publication number
CN113283813B
CN113283813B CN202110769979.9A CN202110769979A CN113283813B CN 113283813 B CN113283813 B CN 113283813B CN 202110769979 A CN202110769979 A CN 202110769979A CN 113283813 B CN113283813 B CN 113283813B
Authority
CN
China
Prior art keywords
matrix
raw material
material supply
information
importance
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.)
Active
Application number
CN202110769979.9A
Other languages
Chinese (zh)
Other versions
CN113283813A (en
Inventor
王涛
林木
王彦锋
王维平
何华
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
National University of Defense Technology
Original Assignee
National University of Defense Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by National University of Defense Technology filed Critical National University of Defense Technology
Priority to CN202110769979.9A priority Critical patent/CN113283813B/en
Publication of CN113283813A publication Critical patent/CN113283813A/en
Application granted granted Critical
Publication of CN113283813B publication Critical patent/CN113283813B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • 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
    • G06F16/2455—Query execution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Mathematical Analysis (AREA)
  • Economics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Human Resources & Organizations (AREA)
  • General Engineering & Computer Science (AREA)
  • Computational Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Mathematical Optimization (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Educational Administration (AREA)
  • Computational Linguistics (AREA)
  • Game Theory and Decision Science (AREA)
  • Algebra (AREA)
  • Computing Systems (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Development Economics (AREA)

Abstract

The application relates to a method and a device for ordering target importance based on DEMATEL. The method comprises the following steps: acquiring a target library to be analyzed in a data table format, constructing a comparison matrix according to the raw material supply sequencing task target and the comparison result label, correcting the comparison matrix to obtain a correction matrix, and obtaining influence degree information of the raw material supply sequencing task target through a DEMATEL algorithm; acquiring expert information for correcting the importance difference of the task target, and combining infinite norms of the comprehensive influence matrix to obtain a differentiation coefficient; and obtaining the importance information of the task targets for sorting according to the influence information and the differentiation coefficient. The invention provides an importance weight method capable of calculating indirect influence of superposition between targets, and provides an improvement strategy aiming at the problem that a DEMATEL algorithm does not originally support importance ranking, and then introduces expert opinions, so that the flexibility of the ranking method is improved, and the method has better applicability.

Description

Method and device for ordering target importance based on DEMATEL
Technical Field
The application relates to the technical field of computers, in particular to a method and a device for ordering target importance based on DEMATEL.
Background
In the industrial production planning, a plurality of raw materials are used for producing a plurality of products, the supply sequence of the raw materials is related to factors such as the production flow of the products, the delivery period of the products and the like, when the expenditure of the raw materials is short, the factors such as the product value and the like are comprehensively considered, the supply of the raw materials of unimportant products is delayed, and the supply of the raw materials of the important products is ensured. Therefore, it is of great significance to order the supply of raw materials for product production.
Generally, in the same system or system, because the importance of the task object is preset, it is relatively easy to obtain a comparison result of the importance between the two, and in different systems or systems, there may be a certain association, that is, there may be a certain association between the first task object of the first system or system and the second task object of the second system or system, but in this multi-system multi-object technical scenario, it is difficult to directly order the importance.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, and a storage medium for ranking the importance of an object based on DEMATEL, which can improve the applicability of the algorithm.
A method for ordering importance of targets based on DEMATEL, the method comprising:
obtaining a target library to be analyzed of product production raw material supply sequencing; the target library to be analyzed comprises a plurality of raw material supply sequencing task targets and a comparison result of importance degrees between every two raw material supply sequencing task targets; a single said raw material supply sequencing task objective represents a supply requirement for a certain raw material for a certain product; the target importance of the raw material supply sequencing task is obtained by pre-calculation; the comparison result is marked by a label; the raw material supply sequencing task target is in a code form; the target library to be analyzed is data in a data table format;
constructing a comparison matrix according to the raw material supply sequencing task target and the label; the values of elements in the comparison matrix are values of labels, the values of the labels are integers in a preset range, and comparison results and importance degrees of the targets of the two raw material supply sequencing tasks are represented according to signs and sizes of the integers;
correcting the comparison matrix to obtain a correction matrix, normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix, obtaining a comprehensive influence matrix according to the standard direct influence matrix, and obtaining influence degree information of the raw material supply sequencing task target according to the comprehensive influence matrix;
acquiring preset expert information, and acquiring a differentiation coefficient according to the expert information and the infinite norm of the comprehensive influence matrix; the expert information is a correction coefficient for difference of target importance of the raw material supply sequencing task;
and obtaining importance information of the raw material supply sequencing task targets according to the influence information and the differentiation coefficient, and sequencing the raw material supply sequencing task targets according to the importance information.
In one embodiment, the method further comprises the following steps: correcting the comparison matrix through a superposition constant matrix to obtain a correction matrix; the elements of the correction matrix are all non-negative.
In one embodiment, the method further comprises the following steps: normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix as follows:
Figure 790971DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 449486DEST_PATH_IMAGE002
it is shown that the specification directly affects the matrix,
Figure 552571DEST_PATH_IMAGE003
，
Figure 836791DEST_PATH_IMAGE004
representing the correction matrix
Figure 607300DEST_PATH_IMAGE005
The infinite norm of (a) of (b),
Figure 233454DEST_PATH_IMAGE006
representing the comparison matrix
Figure 823835DEST_PATH_IMAGE007
The elements (A) and (B) in (B),mrepresenting the number of said raw material supply sequencing task objectives,
Figure 662478DEST_PATH_IMAGE008
is shown as
Figure 818653DEST_PATH_IMAGE009
An object relative to
Figure 68238DEST_PATH_IMAGE010
The results of the comparison of the importance of the individual objects,
Figure 145915DEST_PATH_IMAGE011
，
Figure 584987DEST_PATH_IMAGE012
。
in one embodiment, the method further comprises the following steps: obtaining a comprehensive influence matrix according to the standard direct influence matrix as follows:
Figure 64510DEST_PATH_IMAGE013
wherein the content of the first and second substances,
Figure 501307DEST_PATH_IMAGE014
the composite impact matrix is represented by a matrix of complex influences,
Figure 49969DEST_PATH_IMAGE015
representing an identity matrix;
obtaining the influence degree information of the raw material supply sequencing task target according to the comprehensive influence matrix, wherein the influence degree information comprises:
Figure 230415DEST_PATH_IMAGE016
wherein the content of the first and second substances,
Figure 361182DEST_PATH_IMAGE017
representing the synthetic influence matrix
Figure 968881DEST_PATH_IMAGE018
Of (1).
In one embodiment, the method further comprises the following steps: acquiring preset expert information, and obtaining differentiation coefficients according to the expert information and the infinite norm of the comprehensive influence matrix, wherein the differentiation coefficients are as follows:
Figure 755571DEST_PATH_IMAGE019
wherein the content of the first and second substances,
Figure 746833DEST_PATH_IMAGE020
the difference coefficient is represented by a difference coefficient,
Figure 200948DEST_PATH_IMAGE021
the expert information is represented by a representation of the expert information,
Figure 979549DEST_PATH_IMAGE022
representing the synthetic influence matrix
Figure 519114DEST_PATH_IMAGE023
Infinite norm of (d).
In one embodiment, the method further comprises the following steps: according to the influence degree information and the differentiation coefficient, obtaining the importance degree information of the raw material supply sequencing task target as follows:
Figure 395989DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 345491DEST_PATH_IMAGE026
representing the importance information.
In one embodiment, the method further comprises the following steps: after the importance information of the raw material supply sequencing task target is obtained according to the influence information and the differentiation coefficient, whether the importance information meets a preset condition or not is judged, when the importance information does not meet the preset condition, the expert information is adjusted, and the differentiation coefficient and the importance information are recalculated according to the updated expert information until the importance information meets the preset condition.
A DEMATEL-based target importance ranking apparatus, the apparatus comprising:
the target library to be analyzed acquisition module is used for acquiring a target library to be analyzed of product production raw material supply sequencing; the target library to be analyzed comprises a plurality of raw material supply sequencing task targets and a comparison result of importance degrees between every two raw material supply sequencing task targets; a single said raw material supply sequencing task objective represents a supply requirement for a certain raw material for a certain product; the target importance of the raw material supply sequencing task is obtained by pre-calculation; the comparison result is marked by a label; the raw material supply sequencing task target is in a code form; the target library to be analyzed is data in a data table format;
the comparison matrix construction module is used for constructing a comparison matrix according to the raw material supply sequencing task target and the label; the values of elements in the comparison matrix are values of labels, the values of the labels are integers in a preset range, and comparison results and importance degrees of the targets of the two raw material supply sequencing tasks are represented according to signs and sizes of the integers;
the influence degree information acquisition module is used for correcting the comparison matrix to obtain a correction matrix, normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix, obtaining a comprehensive influence matrix according to the standard direct influence matrix, and obtaining influence degree information of the raw material supply sequencing task target according to the comprehensive influence matrix;
the difference coefficient acquisition module is used for acquiring preset expert information and acquiring difference coefficients according to the expert information and the infinite norm of the comprehensive influence matrix; the expert information is a correction coefficient for difference of target importance of the raw material supply sequencing task;
and the sorting module is used for obtaining the importance information of the raw material supply sorting task targets according to the influence information and the differentiation coefficient, and sorting the raw material supply sorting task targets according to the importance information.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
obtaining a target library to be analyzed of product production raw material supply sequencing; the target library to be analyzed comprises a plurality of raw material supply sequencing task targets and a comparison result of importance degrees between every two raw material supply sequencing task targets; a single said raw material supply sequencing task objective represents a supply requirement for a certain raw material for a certain product; the target importance of the raw material supply sequencing task is obtained by pre-calculation; the comparison result is marked by a label; the raw material supply sequencing task target is in a code form; the target library to be analyzed is data in a data table format;
constructing a comparison matrix according to the raw material supply sequencing task target and the label; the values of elements in the comparison matrix are values of labels, the values of the labels are integers in a preset range, and comparison results and importance degrees of the targets of the two raw material supply sequencing tasks are represented according to signs and sizes of the integers;
correcting the comparison matrix to obtain a correction matrix, normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix, obtaining a comprehensive influence matrix according to the standard direct influence matrix, and obtaining influence degree information of the raw material supply sequencing task target according to the comprehensive influence matrix;
acquiring preset expert information, and acquiring a differentiation coefficient according to the expert information and the infinite norm of the comprehensive influence matrix; the expert information is a correction coefficient for difference of target importance of the raw material supply sequencing task;
and obtaining importance information of the raw material supply sequencing task targets according to the influence information and the differentiation coefficient, and sequencing the raw material supply sequencing task targets according to the importance information.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
obtaining a target library to be analyzed of product production raw material supply sequencing; the target library to be analyzed comprises a plurality of raw material supply sequencing task targets and a comparison result of importance degrees between every two raw material supply sequencing task targets; a single said raw material supply sequencing task objective represents a supply requirement for a certain raw material for a certain product; the target importance of the raw material supply sequencing task is obtained by pre-calculation; the comparison result is marked by a label; the raw material supply sequencing task target is in a code form; the target library to be analyzed is data in a data table format;
constructing a comparison matrix according to the raw material supply sequencing task target and the label; the values of elements in the comparison matrix are values of labels, the values of the labels are integers in a preset range, and comparison results and importance degrees of the targets of the two raw material supply sequencing tasks are represented according to signs and sizes of the integers;
correcting the comparison matrix to obtain a correction matrix, normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix, obtaining a comprehensive influence matrix according to the standard direct influence matrix, and obtaining influence degree information of the raw material supply sequencing task target according to the comprehensive influence matrix;
acquiring preset expert information, and acquiring a differentiation coefficient according to the expert information and the infinite norm of the comprehensive influence matrix; the expert information is a correction coefficient for difference of target importance of the raw material supply sequencing task;
and obtaining importance information of the raw material supply sequencing task targets according to the influence information and the differentiation coefficient, and sequencing the raw material supply sequencing task targets according to the importance information.
The target importance ranking method and device based on DEMATEL, the computer equipment and the storage medium acquire the target library to be analyzed in a data table format, wherein the target library to be analyzed comprises a plurality of raw material supply ranking task targets and a comparison result of importance between every two raw material supply ranking task targets, the comparison result is marked through a label, and the raw material supply ranking task targets are abstracted into a code form. And constructing a comparison matrix according to the raw material supply sequencing task target and the label, wherein the value of the label is an integer in a preset range, and the comparison result and the importance degree of every two raw material supply sequencing task targets are represented according to the signs and the sizes of the integers. Correcting the comparison matrix to obtain a correction matrix, normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix, obtaining a comprehensive influence matrix according to the standard direct influence matrix, and obtaining influence information of a raw material supply sequencing task target according to the comprehensive influence matrix; acquiring preset expert information, and obtaining a differentiation coefficient according to the expert information and the infinite norm of the comprehensive influence matrix, wherein the expert information is used for correcting the importance difference of the raw material supply sequencing task target; and obtaining importance information of the raw material supply sequencing task targets according to the influence information and the differentiation coefficient, and sequencing the raw material supply sequencing task targets according to the importance information. The invention provides an importance weight method capable of calculating indirect influence superposed among targets, and provides an improvement strategy aiming at the problem that a DEMATEL algorithm does not support importance ranking natively, wherein the improved correction matrix can obtain the influence of ranking targets by adopting the DEMATEL algorithm, and then introduces expert opinions to correct importance weight coefficients, so that the importance of the ranking targets is obtained according to the influence degree and expert information, the flexibility of the importance ranking method is improved, and the method has better applicability.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating a method for ranking target importance based on DEMATEL in one embodiment;
FIG. 2 is a block diagram of an embodiment of a DEMATEL-based target importance ranking apparatus;
FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The method for ordering the target importance based on DEMATEL can be applied to the following application environments. The terminal executes a target importance ranking method based on DEMATEL, acquires a target library to be analyzed in a data table format, constructs a comparison matrix according to a raw material supply ranking task target and a label, corrects the comparison matrix to obtain a correction matrix, normalizes the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix, obtains a comprehensive influence matrix according to the standard direct influence matrix, and obtains influence information of the raw material supply ranking task target according to the comprehensive influence matrix; acquiring preset expert information, and obtaining a differentiation coefficient according to the expert information and the infinite norm of the comprehensive influence matrix, wherein the expert information is used for correcting the importance difference of the raw material supply sequencing task target; and obtaining importance information of the raw material supply sequencing task targets according to the influence information and the differentiation coefficient, and sequencing the raw material supply sequencing task targets according to the importance information. The terminal may be, but is not limited to, various personal computers, notebook computers, and tablet computers.
In one embodiment, as shown in fig. 1, a method for ranking target importance based on DEMATEL is provided, which includes the following steps:
and 102, acquiring a target library to be analyzed in product production raw material supply sequencing.
The target library to be analyzed comprises a plurality of raw material supply sequencing task targets and a comparison result of importance degrees between every two raw material supply sequencing task targets; the single raw material supply sequencing task target represents the supply requirement of a certain raw material for a certain product; the target importance of the raw material supply sequencing task is obtained by pre-calculation; the comparison result is marked by a label; the raw material supply sequencing task target is in a code form; the target library to be analyzed is data in a data table format;
the raw material supply sequencing task target can be obtained from different systems or systems, generally speaking, in the same system or system, because the importance of the raw material supply sequencing task target is preset, the comparison result of the importance of the raw material supply sequencing task target and the raw material supply sequencing task target can be obtained easily, in different systems or systems, a certain correlation may exist, namely, a first raw material supply sequencing task target of a first system or system and a second raw material supply sequencing task target of a second system or system exist.
Specifically, the raw material supply sequencing task target can be a task, and the sequencing in the invention is to reasonably sequence the execution sequence of the raw material supply sequencing task target so as to ensure the smooth execution of each raw material supply sequencing task target.
Generally, when processing is performed, the raw material supply sorting task targets are stored in a database in the form of a form, a plurality of raw material supply sorting task target records form a table in the database, when sorting is performed, data can be extracted from the table in the database, so that a target library to be analyzed in a data table format is obtained, and when specific processing is performed, the data table format can be an Excel format, a Word format, an Access format and the like. It should be noted that, when the target library to be analyzed is extracted from the table in the database, only the label corresponding to the target of the raw material supply and sorting task and the label corresponding to the comparison result of the importance between every two targets of the raw material supply and sorting task need to be extracted, the specific content of the target of the raw material supply and sorting task does not need to be identified, and the specific content of the target of the raw material supply and sorting task does not need to be analyzed.
And 104, constructing a comparison matrix according to the raw material supply sequencing task target and the label.
And the values of the elements in the comparison matrix are values of the labels, the values of the labels are integers in a preset range, and the comparison result and the importance degree of the target of the supply and sequencing task of every two raw materials are represented according to the signs and the sizes of the integers.
The scale values in the comparison matrix have positive and negative values. In one particular embodiment, the ranked targets database contains m targets, each target being noted
Figure 841194DEST_PATH_IMAGE027
(ii) a Paired comparison matrix
Figure 432713DEST_PATH_IMAGE028
Of (2) element(s)
Figure 579529DEST_PATH_IMAGE029
Is shown as
Figure 699932DEST_PATH_IMAGE030
An object relative to
Figure 10827DEST_PATH_IMAGE031
Comparing the importance of the objects, by 7-level scaling
Figure 609299DEST_PATH_IMAGE032
Given, positive values represent
Figure 626934DEST_PATH_IMAGE033
Ratio of
Figure 980554DEST_PATH_IMAGE034
More importantly, negative values represent
Figure 965697DEST_PATH_IMAGE034
Ratio of
Figure 367859DEST_PATH_IMAGE035
More importantly, 0 represents
Figure 240000DEST_PATH_IMAGE036
And
Figure 967785DEST_PATH_IMAGE037
"equally important", scale value
Figure 987694DEST_PATH_IMAGE038
Corresponding to "slightly important, significantly important", respectively. The positive and negative value calibration method accords with the cognitive habits of people.
In a specific embodiment, the comparison result is obtained by calculating the importance in advance according to factors such as product value, product delivery deadline, raw material demand, product process flow and the like in the raw material supply sequencing task target, and then performing classification according to the importance value.
The product comprises A, B and C, the raw materials comprise a and B, and then the raw material supply sequencing task aims at:
raw material supply sequencing task object
Figure 442815DEST_PATH_IMAGE039
: aa: represents the supply requirement for using feedstock a for product a;
raw material supply sequencing task object
Figure 435041DEST_PATH_IMAGE040
: ab: represents the supply requirement for using feedstock b for product a;
raw material supply sequencing task object
Figure 68148DEST_PATH_IMAGE041
: ba: represents the supply requirement for using feedstock a for product B;
raw material supply sequencing task object
Figure 309774DEST_PATH_IMAGE042
: bb: represents the supply requirement for using feedstock B for product B;
raw material supply sequencing task object
Figure 319318DEST_PATH_IMAGE043
: ca: represents the supply requirement for using feedstock a for product C;
raw material supply sequencing task object
Figure 415319DEST_PATH_IMAGE043
: cb: represents the supply requirements for using feedstock b for product C;
designing an importance calculation formula according to product value, product delivery time limit and raw material demand in raw material supply sequencing task targets, obtaining the value of the importance of each raw material supply sequencing task target according to the importance calculation formula, comparing every two raw material supply sequencing task targets, obtaining the difference value of the importance values of the two raw material supply sequencing task targets, carrying out grade division according to the difference value, and carrying out 7-grade scale division through 7 grades
Figure 16064DEST_PATH_IMAGE044
And carrying out correspondence to obtain a label value corresponding to the comparison result. For example,
Figure 213827DEST_PATH_IMAGE045
: aa has an importance value of 15,
Figure 495904DEST_PATH_IMAGE046
: the importance value of Ba is 25, the difference between the importance values of Aa and Ba is 10,
Figure 462723DEST_PATH_IMAGE046
ratio of
Figure 234370DEST_PATH_IMAGE047
Of slight importance, in comparison with the matrix
Figure 903118DEST_PATH_IMAGE048
In
Figure 254465DEST_PATH_IMAGE049
Is-1.
And 106, correcting the comparison matrix to obtain a correction matrix, normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix, obtaining a comprehensive influence matrix according to the standard direct influence matrix, and obtaining influence information of the raw material supply sequencing task target according to the comprehensive influence matrix.
DEMATEL is a methodology of system science, a method of system analysis that uses graph theory and matrix tools. The influence degree and the influenced degree of each element on other elements can be calculated through the logical relation and the direct influence matrix among the elements in the system, so that the cause degree and the center degree of each element are calculated and used as the basis of a structural model, and the cause-effect relation among the elements and the position of each element in the system are determined.
Because the element values of the direct influence matrix in the DEMATEL algorithm are non-negative, the paired comparison matrix constructed by the invention does not meet the premise, the paired comparison matrix is corrected through a superposition constant, and the improved correction matrix can be subjected to later analysis by using the DEMATEL algorithm.
And step 108, acquiring preset expert information, and obtaining a differentiation coefficient according to the expert information and the infinite norm of the comprehensive influence matrix.
The expert information is a correction coefficient for sorting the difference of the target importance. The adjustment of the differentiation coefficient can be realized through expert information.
And 110, obtaining importance information of the raw material supply sequencing task targets according to the influence information and the differentiation coefficient, and sequencing the raw material supply sequencing task targets according to the importance information.
The invention can quantize the qualitative importance to obtain the specific importance value of each sequencing target, thereby providing information and data support for management and decision.
In the target importance ranking method based on DEMATEL, a target library to be analyzed in a data table format is obtained, wherein the target library to be analyzed comprises a plurality of raw material supply ranking task targets and a comparison result of importance between every two raw material supply ranking task targets, the comparison result is marked through a label, and the raw material supply ranking task targets are abstracted into a code form. And constructing a comparison matrix according to the raw material supply sequencing task target and the label, wherein the value of the label is an integer in a preset range, and the comparison result and the importance degree of every two raw material supply sequencing task targets are represented according to the signs and the sizes of the integers. Correcting the comparison matrix to obtain a correction matrix, normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix, obtaining a comprehensive influence matrix according to the standard direct influence matrix, and obtaining influence information of a raw material supply sequencing task target according to the comprehensive influence matrix; acquiring preset expert information, and obtaining a differentiation coefficient according to the expert information and the infinite norm of the comprehensive influence matrix, wherein the expert information is used for correcting the importance difference of the raw material supply sequencing task target; and obtaining importance information of the raw material supply sequencing task targets according to the influence information and the differentiation coefficient, and sequencing the raw material supply sequencing task targets according to the importance information. The invention provides an importance weight method capable of calculating indirect influence superposed among targets, and provides an improvement strategy aiming at the problem that a DEMATEL algorithm does not support importance ranking natively, wherein the improved correction matrix can obtain the influence of ranking targets by adopting the DEMATEL algorithm, and then introduces expert opinions to correct importance weight coefficients, so that the importance of the ranking targets is obtained according to the influence degree and expert information, the flexibility of the importance ranking method is improved, and the method has better applicability.
In one embodiment, the method further comprises the following steps: correcting the comparison matrix through the superposition constant matrix to obtain a correction matrix; the elements of the correction matrix are all non-negative.
In one embodiment, the method further comprises the following steps: normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix as follows:
Figure 872528DEST_PATH_IMAGE050
wherein the content of the first and second substances,
Figure 283918DEST_PATH_IMAGE051
the representation specification directly affects the matrix and,
Figure 190694DEST_PATH_IMAGE052
，
Figure 63841DEST_PATH_IMAGE053
representation correction matrix
Figure 270831DEST_PATH_IMAGE054
The infinite norm of (a) of (b),
Figure 321964DEST_PATH_IMAGE055
representing a comparison matrix
Figure 512774DEST_PATH_IMAGE056
The elements (A) and (B) in (B),mindicating the number of raw material supply sequencing task targets,
Figure 205923DEST_PATH_IMAGE057
is shown as
Figure 251108DEST_PATH_IMAGE058
An object relative to
Figure 738721DEST_PATH_IMAGE059
The results of the comparison of the importance of the individual objects,
Figure 416827DEST_PATH_IMAGE060
，
Figure 382509DEST_PATH_IMAGE061
。
in one embodiment, the method further comprises the following steps: the obtained comprehensive influence matrix according to the standard direct influence matrix is as follows:
Figure 32934DEST_PATH_IMAGE062
wherein the content of the first and second substances,
Figure 753765DEST_PATH_IMAGE063
a composite impact matrix is represented that is,
Figure 371697DEST_PATH_IMAGE064
representing an identity matrix;
the influence degree information of the raw material supply sequencing task target obtained according to the comprehensive influence matrix is as follows:
Figure 406649DEST_PATH_IMAGE065
wherein the content of the first and second substances,
Figure 911580DEST_PATH_IMAGE066
representing a composite impact matrix
Figure 740996DEST_PATH_IMAGE067
Of (1).
In one embodiment, the method further comprises the following steps: acquiring preset expert information, and obtaining differentiation coefficients according to the expert information and the infinite norm of the comprehensive influence matrix:
Figure 393694DEST_PATH_IMAGE068
wherein the content of the first and second substances,
Figure 216025DEST_PATH_IMAGE069
the coefficient of difference is represented by a difference coefficient,
Figure 575462DEST_PATH_IMAGE070
the information of the expert is represented by,
Figure 638096DEST_PATH_IMAGE071
representing a composite impact matrix
Figure 450194DEST_PATH_IMAGE072
Infinite norm of (d).
In one embodiment, the method further comprises the following steps: according to the influence degree information and the differentiation coefficient, the importance degree information of the raw material supply sequencing task target is obtained as follows:
Figure 368789DEST_PATH_IMAGE073
wherein the content of the first and second substances,
Figure 789275DEST_PATH_IMAGE074
representing importance information.
In one embodiment, the method further comprises the following steps: after the importance information of the raw material supply sequencing task target is obtained according to the influence information and the differentiation coefficient, whether the importance information meets the preset condition or not is judged, when the importance information does not meet the preset condition, the expert information is adjusted, the differentiation coefficient and the importance information are recalculated according to the updated expert information until the importance information meets the preset condition.
The expert information is a correction coefficient for sorting the difference of the target importance. The adjustment of the differentiation coefficient can be realized through expert information. For example, if the importance of the ranking target a is 30% and the importance of the ranking target B is 5% obtained through one calculation, but the deviation between the importance result and the actual result is large, if the preset condition is that the difference between the importance of any two ranking targets is not greater than 10%, the result of the importance information can be adjusted by adjusting the value of the expert information k, so that the result of the importance conforms to the preset condition.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 2, there is provided a DEMATEL-based target importance ranking apparatus, including: an object library to be analyzed obtaining module 202, a comparison matrix constructing module 204, an influence information obtaining module 206, a differentiation coefficient obtaining module 208, and a sorting module 210, wherein:
a target library to be analyzed obtaining module 202, configured to obtain a target library to be analyzed; the target library to be analyzed comprises a plurality of raw material supply sequencing task targets and a comparison result of importance degrees between every two raw material supply sequencing task targets; the comparison result is marked by a label; the raw material supply sequencing task target is in a code form; the target library to be analyzed is data in a data table format;
a comparison matrix construction module 204, configured to construct a comparison matrix according to the raw material supply ordering task target and the label; the values of the elements in the comparison matrix are values of labels, the values of the labels are integers in a preset range, and comparison results and importance degrees of the targets of the two raw material supply sequencing tasks are represented according to signs and sizes of the integers;
the influence degree information acquisition module 206 is configured to correct the comparison matrix to obtain a correction matrix, normalize the correction matrix through a DEMATEL algorithm to obtain a normative direct influence matrix, obtain a comprehensive influence matrix according to the normative direct influence matrix, and obtain influence degree information of the raw material supply ordering task target according to the comprehensive influence matrix;
a differentiation coefficient obtaining module 208, configured to obtain preset expert information, and obtain a differentiation coefficient according to the expert information and an infinite norm of the comprehensive influence matrix; expert information supplies correction coefficients for differentiation of the target importance of the sorting task for the raw materials;
the sorting module 210 is configured to obtain importance information of the raw material supply sorting task targets according to the influence information and the differentiation coefficient, and sort the raw material supply sorting task targets according to the importance information.
The influence degree information obtaining module 206 is further configured to modify the comparison matrix to obtain a modification matrix through the superposition constant matrix; the elements of the correction matrix are all non-negative.
The influence degree information obtaining module 206 is further configured to normalize the modification matrix by a DEMATEL algorithm to obtain a normalized direct influence matrix as follows:
Figure 150986DEST_PATH_IMAGE075
wherein the content of the first and second substances,
Figure 65852DEST_PATH_IMAGE076
the representation specification directly affects the matrix and,
Figure 399882DEST_PATH_IMAGE077
，
Figure 7581DEST_PATH_IMAGE078
representation correction matrix
Figure 856588DEST_PATH_IMAGE079
The infinite norm of (a) of (b),
Figure 824413DEST_PATH_IMAGE080
representing a comparison matrix
Figure 12949DEST_PATH_IMAGE081
The elements (A) and (B) in (B),mindicating the number of raw material supply sequencing task targets,
Figure 588287DEST_PATH_IMAGE082
is shown as
Figure 862273DEST_PATH_IMAGE083
An object relative to
Figure 384521DEST_PATH_IMAGE084
The results of the comparison of the importance of the individual objects,
Figure 489881DEST_PATH_IMAGE085
，
Figure 423070DEST_PATH_IMAGE086
。
the influence degree information obtaining module 206 is further configured to obtain a comprehensive influence matrix according to the normative direct influence matrix as follows:
Figure 981091DEST_PATH_IMAGE087
wherein the content of the first and second substances,
Figure 307030DEST_PATH_IMAGE088
a composite impact matrix is represented that is,
Figure 204579DEST_PATH_IMAGE089
representing an identity matrix;
the influence degree information of the raw material supply sequencing task target obtained according to the comprehensive influence matrix is as follows:
Figure 121719DEST_PATH_IMAGE090
wherein the content of the first and second substances,
Figure 635877DEST_PATH_IMAGE091
representing a composite impact matrix
Figure 483616DEST_PATH_IMAGE092
Of (1).
The differentiation coefficient obtaining module 208 is further configured to obtain preset expert information, and obtain differentiation coefficients according to the expert information and the infinite norm of the comprehensive influence matrix:
Figure 297988DEST_PATH_IMAGE093
wherein the content of the first and second substances,
Figure 589292DEST_PATH_IMAGE094
the coefficient of difference is represented by a difference coefficient,
Figure 325167DEST_PATH_IMAGE095
the information of the expert is represented by,
Figure 524067DEST_PATH_IMAGE096
representing a composite impact matrix
Figure 396208DEST_PATH_IMAGE097
Infinite norm of (d).
The sorting module 210 is further configured to obtain, according to the influence information and the differentiation coefficient, importance information of a raw material supply sorting task target as:
Figure 127590DEST_PATH_IMAGE098
wherein the content of the first and second substances,
Figure 333443DEST_PATH_IMAGE099
representing importance information.
The sorting module 210 is further configured to determine whether the importance information satisfies a preset condition after obtaining the importance information of the raw material supply sorting task target according to the influence information and the differentiation coefficient, adjust the expert information when the importance information does not satisfy the preset condition, and recalculate the differentiation coefficient and the importance information according to the updated expert information until the importance information satisfies the preset condition.
For specific definition of the apparatus for sorting the target importance based on the DEMATEL, reference may be made to the above definition of the method for sorting the target importance based on the DEMATEL, which is not described herein again. The modules in the above DEMATEL-based target importance ranking apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a DEMATEL-based target importance ranking method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (8)

1. A method for ordering target importance based on DEMATEL is characterized by comprising the following steps:
obtaining a target library to be analyzed of product production raw material supply sequencing; the target library to be analyzed comprises a plurality of raw material supply sequencing task targets and a comparison result of importance degrees between every two raw material supply sequencing task targets; a single said raw material supply sequencing task object representing a supply requirement for a raw material for a product; the target importance of the raw material supply sequencing task is obtained by pre-calculation; the comparison result is marked by a label; the raw material supply sequencing task target is in a code form; the target library to be analyzed is data in a data table format;
constructing a comparison matrix according to the raw material supply sequencing task target and the label; the values of elements in the comparison matrix are values of labels, the values of the labels are integers in a preset range, and comparison results and importance degrees of the targets of the two raw material supply sequencing tasks are represented according to signs and sizes of the integers;
correcting the comparison matrix to obtain a correction matrix, normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix, obtaining a comprehensive influence matrix according to the standard direct influence matrix, and obtaining influence degree information of the raw material supply sequencing task target according to the comprehensive influence matrix;
acquiring preset expert information, and obtaining differentiation coefficients according to the expert information and the infinite norm of the comprehensive influence matrix, wherein the differentiation coefficients are as follows:
Figure 295396DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 688331DEST_PATH_IMAGE002
the difference coefficient is represented by a difference coefficient,
Figure 56996DEST_PATH_IMAGE003
representing the expert information, the expert information providing a correction coefficient for the differentiation of the target importance of the sorting task for the raw material,
Figure 888686DEST_PATH_IMAGE004
representing the synthetic influence matrix
Figure 659196DEST_PATH_IMAGE005
Infinite norm of (d);
and obtaining importance information of the raw material supply sequencing task targets according to the influence information and the differentiation coefficient, and sequencing the raw material supply sequencing task targets according to the importance information.
2. The method of claim 1, wherein modifying the comparison matrix to obtain a modification matrix comprises: correcting the comparison matrix through a superposition constant matrix to obtain a correction matrix; the elements of the correction matrix are all non-negative.
3. The method of claim 2, wherein normalizing the correction matrix by a DEMATEL algorithm results in a normalized direct influence matrix, comprising:
normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix as follows:
Figure 973765DEST_PATH_IMAGE006
wherein the content of the first and second substances,
Figure 829725DEST_PATH_IMAGE007
it is shown that the specification directly affects the matrix,
Figure 465106DEST_PATH_IMAGE008
，
Figure 824543DEST_PATH_IMAGE009
representing the correction matrix
Figure 824860DEST_PATH_IMAGE010
The infinite norm of (a) of (b),
Figure 151805DEST_PATH_IMAGE011
representing the comparison matrix
Figure 590877DEST_PATH_IMAGE012
The elements (A) and (B) in (B),mrepresenting the number of said raw material supply sequencing task objectives,
Figure 70399DEST_PATH_IMAGE013
is shown as
Figure 241618DEST_PATH_IMAGE014
An object relative to
Figure 806591DEST_PATH_IMAGE015
The results of the comparison of the importance of the individual objects,
Figure 783775DEST_PATH_IMAGE016
，
Figure 868536DEST_PATH_IMAGE017
。
4. the method of claim 3, wherein obtaining a composite impact matrix according to the normative direct impact matrix, and obtaining impact information of the raw material supply sequencing task objective according to the composite impact matrix comprises:
obtaining a comprehensive influence matrix according to the standard direct influence matrix as follows:
Figure 210656DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 262926DEST_PATH_IMAGE019
the composite impact matrix is represented by a matrix of complex influences,
Figure 981483DEST_PATH_IMAGE020
representing an identity matrix;
obtaining the influence degree information of the raw material supply sequencing task target according to the comprehensive influence matrix, wherein the influence degree information comprises:
Figure 232336DEST_PATH_IMAGE021
wherein the content of the first and second substances,
Figure 260203DEST_PATH_IMAGE022
representing the synthetic influence matrix
Figure 534190DEST_PATH_IMAGE023
Of (1).
5. The method according to claim 4, wherein obtaining importance information of the raw material supply sequencing task target according to the influence information and the differentiation coefficient comprises:
according to the influence degree information and the differentiation coefficient, obtaining the importance degree information of the raw material supply sequencing task target as follows:
Figure 161797DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 111299DEST_PATH_IMAGE026
representing the importance information.
6. The method according to any one of claims 1 to 5, further comprising, after obtaining the importance information of the raw material supply ranking task objective according to the influence information and the differentiation coefficient:
judging whether the importance information meets a preset condition, adjusting the expert information when the importance information does not meet the preset condition, and recalculating the differentiation coefficient and the importance information according to the updated expert information until the importance information meets the preset condition.
7. A DEMATEL-based target importance ranking apparatus, the apparatus comprising:
the target library to be analyzed acquisition module is used for acquiring a target library to be analyzed of product production raw material supply sequencing; the target library to be analyzed comprises a plurality of raw material supply sequencing task targets and a comparison result of importance degrees between every two raw material supply sequencing task targets; a single said raw material supply sequencing task object representing a supply requirement for a raw material for a product; the target importance of the raw material supply sequencing task is obtained by pre-calculation; the comparison result is marked by a label; the raw material supply sequencing task target is in a code form; the target library to be analyzed is data in a data table format;
the comparison matrix construction module is used for constructing a comparison matrix according to the raw material supply sequencing task target and the label; the values of elements in the comparison matrix are values of labels, the values of the labels are integers in a preset range, and comparison results and importance degrees of the targets of the two raw material supply sequencing tasks are represented according to signs and sizes of the integers;
the influence degree information acquisition module is used for correcting the comparison matrix to obtain a correction matrix, normalizing the correction matrix through a DEMATEL algorithm to obtain a standard direct influence matrix, obtaining a comprehensive influence matrix according to the standard direct influence matrix, and obtaining influence degree information of the raw material supply sequencing task target according to the comprehensive influence matrix;
the differentiation coefficient acquisition module is used for acquiring preset expert information, and obtaining differentiation coefficients according to the expert information and the infinite norm of the comprehensive influence matrix:
Figure 611595DEST_PATH_IMAGE027
wherein the content of the first and second substances,
Figure 671955DEST_PATH_IMAGE028
the difference coefficient is represented by a difference coefficient,
Figure 835083DEST_PATH_IMAGE029
representing the expert information, the expert information providing a correction coefficient for the differentiation of the target importance of the sorting task for the raw material,
Figure 752224DEST_PATH_IMAGE030
representing the synthetic influence matrix
Figure 266382DEST_PATH_IMAGE031
Infinite norm of (d);
and the sorting module is used for obtaining the importance information of the raw material supply sorting task targets according to the influence information and the differentiation coefficient, and sorting the raw material supply sorting task targets according to the importance information.
8. The apparatus of claim 7, wherein the ordering module is further configured to:
according to the influence degree information and the differentiation coefficient, obtaining the importance degree information of the raw material supply sequencing task target as follows:
Figure 928493DEST_PATH_IMAGE032
wherein the content of the first and second substances,
Figure 485376DEST_PATH_IMAGE033
the information on the degree of importance is represented,
Figure 955672DEST_PATH_IMAGE034
、
Figure 623414DEST_PATH_IMAGE035
the degree of influence information is represented by the degree of influence information,mthe number of the raw material supply sequencing task targets is shown.
CN202110769979.9A 2021-07-08 2021-07-08 Method and device for ordering target importance based on DEMATEL Active CN113283813B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110769979.9A CN113283813B (en) 2021-07-08 2021-07-08 Method and device for ordering target importance based on DEMATEL

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110769979.9A CN113283813B (en) 2021-07-08 2021-07-08 Method and device for ordering target importance based on DEMATEL

Publications (2)

Publication Number Publication Date
CN113283813A CN113283813A (en) 2021-08-20
CN113283813B true CN113283813B (en) 2021-09-28

Family

ID=77286538

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110769979.9A Active CN113283813B (en) 2021-07-08 2021-07-08 Method and device for ordering target importance based on DEMATEL

Country Status (1)

Country Link
CN (1) CN113283813B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105303265A (en) * 2015-11-20 2016-02-03 天津大学 Comprehensive evaluation method of development level of active power distribution network
AU2019101535A4 (en) * 2019-12-07 2020-01-23 Karamoozian, Amirhossein Mr Risk assessment in construction projects by considering interdependencies between risk factors
CN111695787A (en) * 2020-05-22 2020-09-22 南京理工大学 Vision-based automatic ticket selling and checking system service capability evaluation method
CN112766726A (en) * 2021-01-20 2021-05-07 国网经济技术研究院有限公司 Method for making power grid operation standard cost adjustment coefficient
CN112967075A (en) * 2021-03-29 2021-06-15 北京工商大学 DEMATEL-ISM (DeModel-industrial scientific medical science) -based grain and oil quality safety block chain risk analysis and optimization method

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104392393A (en) * 2014-11-20 2015-03-04 三峡大学 DEMATEL-ANP-VIKOR mixed selection method of power system security risk reduction schemes

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105303265A (en) * 2015-11-20 2016-02-03 天津大学 Comprehensive evaluation method of development level of active power distribution network
AU2019101535A4 (en) * 2019-12-07 2020-01-23 Karamoozian, Amirhossein Mr Risk assessment in construction projects by considering interdependencies between risk factors
CN111695787A (en) * 2020-05-22 2020-09-22 南京理工大学 Vision-based automatic ticket selling and checking system service capability evaluation method
CN112766726A (en) * 2021-01-20 2021-05-07 国网经济技术研究院有限公司 Method for making power grid operation standard cost adjustment coefficient
CN112967075A (en) * 2021-03-29 2021-06-15 北京工商大学 DEMATEL-ISM (DeModel-industrial scientific medical science) -based grain and oil quality safety block chain risk analysis and optimization method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
制造企业绿色供应链管理绩效评价研究――以南康家具企业为例;贾扬蕾等;《江西理工大学学报》;20161215(第06期);28-32 *

Also Published As

Publication number Publication date
CN113283813A (en) 2021-08-20

Similar Documents

Publication Publication Date Title
CN108711110B (en) Insurance product recommendation method, apparatus, computer device and storage medium
Akram et al. Novel intuitionistic fuzzy soft multiple-attribute decision-making methods
WO2020133398A1 (en) Application recommendation method and apparatus, server and computer-readable storage medium
CN108182633B (en) Loan data processing method, loan data processing device, loan data processing program, and computer device and storage medium
CN113762350A (en) Abnormal data detection method and device, computer equipment and storage medium
WO2021223686A1 (en) Model training task processing method and apparatus, electronic device, and storage medium
CN110931090A (en) Disease data processing method and device, computer equipment and storage medium
CN112231224A (en) Business system testing method, device, equipment and medium based on artificial intelligence
CN111210356B (en) Medical insurance data analysis method and device, computer equipment and storage medium
CN109034941B (en) Product recommendation method and device, computer equipment and storage medium
CN110866656A (en) Power material demand prediction method and device, computer equipment and storage medium
CN114816711A (en) Batch task processing method and device, computer equipment and storage medium
CN115062501A (en) Chip packaging design optimization method based on adaptive subproblem selection strategy
CN113283813B (en) Method and device for ordering target importance based on DEMATEL
CN108764553B (en) User scale prediction method and device and computer equipment
CN110991538A (en) Sample classification method and device, storage medium and computer equipment
CN114626887A (en) Passenger flow volume prediction method and device, computer equipment and storage medium
CN112464660B (en) Text classification model construction method and text data processing method
Cao et al. An algorithm for protein helix assignment using helix geometry
JP2010146222A (en) Document classification apparatus, document classification method, and program
WO2020124977A1 (en) Method and apparatus for processing production data, computer device, and storage medium
CN110727710A (en) Data analysis method and device, computer equipment and storage medium
CN115273099A (en) Method for accurately matching intelligent recommendation of human sentry
CN111310127B (en) Method and device for acquiring raw material quality range based on food product quality range
CN112106045B (en) Information processing apparatus, information processing system, information processing method, and computer-readable recording 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