CN109636184A - A kind of appraisal procedure and system of the account assets of brand - Google Patents
A kind of appraisal procedure and system of the account assets of brand Download PDFInfo
- Publication number
- CN109636184A CN109636184A CN201811523012.7A CN201811523012A CN109636184A CN 109636184 A CN109636184 A CN 109636184A CN 201811523012 A CN201811523012 A CN 201811523012A CN 109636184 A CN109636184 A CN 109636184A
- Authority
- CN
- China
- Prior art keywords
- indexes
- index
- calculating
- evaluation index
- incremental data
- 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 41
- 238000011156 evaluation Methods 0.000 claims abstract description 89
- 239000013598 vector Substances 0.000 claims abstract description 57
- 230000008569 process Effects 0.000 claims abstract description 19
- 239000011159 matrix material Substances 0.000 claims description 17
- 238000010606 normalization Methods 0.000 claims description 6
- 239000002131 composite material Substances 0.000 claims description 4
- 241001269238 Data Species 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 238000012854 evaluation process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000006855 networking Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 235000010627 Phaseolus vulgaris Nutrition 0.000 description 1
- 244000046052 Phaseolus vulgaris Species 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
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
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Complex Calculations (AREA)
Abstract
The present invention relates to the appraisal procedures and system of a kind of account assets of brand, this method comprises: establishing the evaluation index system of account assets；Account number and number of fans of the bottom index in the whole brands for giving industry of evaluation index system are obtained, and calculates the incremental data of account number and number of fans；Fuzzy interval division is carried out to incremental data, establishes the standards of grading of evaluation index system；Establish the weight of indexs at different levels；The membership vector of indexs at different levels is calculated using multiply-add operator；According to standards of grading, weight and membership vector, the comprehensive score of multiply-add operator step-by-step calculation evaluation index system is utilized.Technical solution provided by the invention, fully utilize analytic hierarchy process (AHP) and Field Using Fuzzy Comprehensive Assessment, realize to the account assets of brand it is quantitative, objective, accurately assess, improving Brand Marketing precision and user for businessman improves brand consumption Experience Degree and has established decision basis, user satisfaction is high, experiences.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a method and a system for evaluating brand account number assets.
Background
With the rapid development of networking, the running tracks of various brands on the network are increased, and the networking data information of various brands is also increased in a large amount, and the information can be undoubtedly used as intangible internet digital assets of the brands in the current big data age.
But the numerous digital information can be dazzling and uncomfortable for the company or the consumer. Therefore, the statistics, analysis and judgment of the digital information related to each brand have good promotion effects on the operation of the company and the understanding of the company by consumers. The company can master own brand advantages and disadvantages in time by knowing own internet digital assets, maintain the advantages, make up the disadvantages, further improve own brand effect and earn more profits for the company; consumers can guide the consumers to more scientifically consume and buy more ideal products or services by knowing the brand internet digital assets of the company.
From a brand value evaluation perspective, the internet digital assets of a brand include: content assets, volume assets, account assets. The account assets refer to user account numbers and fan numbers of certain brands on internet channels such as a third-party social platform, a third-party search platform, a third-party live broadcast platform and a third-party document platform. To evaluate a certain brand of internet digital assets, it must be referred to how to evaluate the brand of account assets.
Currently, the most widely used comprehensive evaluation theory at home and abroad is the Analytic Hierarchy Process (AHP). The idea of AHP is to decompose complex problems by establishing a clear hierarchical structure, introduce a measure theory, standardize human judgment by relative scale through comparison, establish judgment matrices layer by layer, solve the weights of the judgment matrices, and finally calculate the comprehensive weights of the scheme. However, when the AHP method compares two by two, if the information is incomplete, the judgment is uncertain, so that the solution accuracy has a large deviation. A fuzzy Evaluation Method (fuzzy comprehensive Evaluation Method) is an analysis Method which is based on fuzzy set theory and carries out quantitative processing on various fuzzy information in analysis and Evaluation and carries out state judgment.
The fuzzy comprehensive evaluation is a comprehensive evaluation of various factors related to an evaluation object by applying a fuzzy transformation principle and considering the factors.
The basic principle is as follows:
(1) constructing a plurality of membership functions according to the evaluated criteria,
(2) a fuzzy relation matrix can be formed by evaluating different degrees (namely different degrees of membership) of indexes in all membership functions.
(3) And constructing a weight coefficient matrix.
(4) And finally, obtaining the membership matrix of the comprehensive index to each evaluation grade by fuzzy operation of the weight coefficient fuzzy matrix and the fuzzy relation matrix.
Although the AHP theory and the fuzzy comprehensive evaluation theory in the prior art are well developed and applied to multiple fields, how to apply the AHP theory and the fuzzy comprehensive evaluation theory to the field of account asset evaluation to realize evaluation of account assets is not yet related in the prior art. This makes brand users and brand merchants unable to make quantitative, objective, accurate assessments of specific brands, results in low brand marketing accuracy for merchants, and poor brand consumption experience for users.
Disclosure of Invention
In view of this, the present invention aims to overcome the defects of the prior art, and provides a method and a system for evaluating a brand account asset, so as to solve the problems that the brand marketing accuracy of a merchant is low and the brand consumption experience of a user is poor due to the fact that evaluation of the brand account asset cannot be realized in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for evaluating account assets of a brand, comprising:
step S1, establishing an evaluation index system of the account assets;
s2, acquiring account numbers and fan numbers of all brands of the lowest indexes of the evaluation index system in a given industry, and respectively calculating incremental data of the account numbers and the fan numbers;
step S3, carrying out fuzzy interval division on the incremental data, and establishing a scoring standard of the evaluation index system;
s4, establishing weights of indexes at all levels by using an analytic hierarchy process;
step S5, calculating membership degree vectors of indexes at all levels by using multiplication and addition operators;
and step S6, calculating the comprehensive score of the evaluation index system step by using a multiplication and addition operator according to the score standard, the weight and the membership degree vector.
Preferably, the step S3 includes:
step S31, fuzzy interval division is carried out on the incremental data, the division result is expressed by vectors, and a fuzzy set vector (G) of any index corresponding to n grading grades is obtained1，G2....Gn) Wherein n is more than or equal to 1;
step S32, determining fuzzy set vector (G) according to actual experience value1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score; or,
g is prepared from1＝C(G1),g2＝C(G2)....gn＝C(Gn) Determined as a fuzzy set vector (G)1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score;
wherein, C (G)i) Represents GiI is more than or equal to 1 and less than or equal to n.
Preferably, the step S4 includes:
step S41, a questionnaire is issued to experts to count a judgment matrix of the importance degree of each expert on every two indexes in the evaluation index system and direct weight distribution of the two indexes;
s42, according to the credibility of experts, weighting and summarizing to obtain the weight distribution between the two indexes;
and step S43, weighting and summarizing according to the reliability of experts to obtain a judgment matrix of three or more indexes, and calculating according to an analytic hierarchy process to obtain weight distribution among the three or more indexes.
Preferably, the step S5 includes:
step S51, standardizing the incremental data of the account number and the fan number according to the formula (1):
wherein, Δ x*The incremental data after the normalization processing is shown, Δ x shows the incremental data before the normalization processing, minData shows the minimum value of the incremental data, and maxData shows the maximum value of the incremental data;
step S52, calculating Delta x according to formula (2)*For trapezoidal fuzzy set Gi＝[a,b,c,d]And the membership of i is more than or equal to 1 and less than or equal to nThereby obtaining Δ x*The membership degree vector of the corresponding index is as follows:
wherein,
wherein, a, b, c, d are each trapezoidal fuzzy set G obtained by performing fuzzy interval division on the incremental data in the step S31iDividing points;
step S53, assuming that there are m next-level indexes under any index in the middle-level indexes, and the membership vector of the jth index of the m next-level indexes is recorded as:j-th finger of the m next-level indicesThe target weight is WjAnd j is more than or equal to 1 and less than or equal to m, calculating the membership vector of any index in the intermediate level indexes according to a formula (3):
wherein, the intermediate level index refers to indexes of other levels except for the lowest level index.
Preferably, the step S6 includes:
step S61, assuming that the membership vector of any index in the intermediate level indexes is (a)1，a2....an) Whereincorresponding fuzzy set vector (G)1，G2....Gn) Has a representative value of (g)1，g2....gn) Then, the increment score Δ S of the level index is calculated according to formula (4):
ΔS＝a1g1+a2g2+.....angn(4)，
step S62, setting the total y-level indexes of the evaluation index system, wherein m lower-level indexes exist under any index in the middle-level indexes, and calculating the comprehensive score of the evaluation index system according to the formula (5):
wherein,the increment score of the j index representing the x level at the current moment; wxjA weight representing a jth index of the xth stage;represents the composite score of the evaluation index system at the last moment,and representing the comprehensive score of the evaluation index system at the current moment.
Preferably, the account number and fan number of all brands of the lowest-layer indexes of the evaluation index system in a given industry are obtained through at least one of the following modes:
the crawler program is captured from the internet, manually entered and provided by a third-party data platform.
In addition, the invention also provides an evaluation system of account assets of the brand, which comprises the following steps:
the establishing unit is used for establishing an evaluation index system of the account assets;
the incremental data calculation unit is used for acquiring the account number and the fan number of all brands of the bottom-layer indexes of the evaluation index system in a given industry and calculating the incremental data of the account number and the fan number;
the scoring standard establishing unit is used for carrying out fuzzy interval division on the incremental data and establishing a scoring standard of the evaluation index system;
the weight establishing unit is used for establishing the weight of each level of index by utilizing an analytic hierarchy process;
the membership calculation unit is used for calculating membership vectors of indexes at all levels by utilizing multiplication and addition operators;
and the comprehensive scoring unit is used for calculating the comprehensive scoring of the evaluation index system step by utilizing a multiplication and addition operator according to the scoring standard, the weight and the membership degree vector.
Preferably, the scoring criteria establishing unit includes:
a dividing unit for dividing the incrementDividing the data into fuzzy intervals, expressing the division result by vector to obtain fuzzy set vector (G) of any index corresponding to n grading grades1，G2....Gn) Wherein n is more than or equal to 1;
a determination unit for determining a fuzzy set vector (G) based on the actual empirical value1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score; or,
g is prepared from1＝C(G1),g2＝C(G2)....gn＝C(Gn) Determined as a fuzzy set vector (G)1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score;
wherein, C (G)i) Represents GiI is more than or equal to 1 and less than or equal to n.
Preferably, the weight establishing unit includes:
the statistical unit is used for issuing questionnaires to experts to perform statistics on a judgment matrix of the importance degree of each expert on every two indexes in the evaluation index system and direct weight distribution of the two indexes;
the weighting unit is used for weighting and summarizing according to the credibility of experts to obtain weight distribution between the two indexes;
and the method is also used for weighting and summarizing according to the reliability of experts to obtain a judgment matrix of three or more indexes, and calculating the weight distribution among the three or more indexes according to an analytic hierarchy process.
Preferably, the incremental data calculation unit obtains the account number and fan number of all brands of the lowest-level index of the evaluation index system in a given industry by at least one of the following ways:
the crawler program is captured from the internet, manually entered and provided by a third-party data platform.
By adopting the technical scheme, the invention at least has the following beneficial effects:
the technical scheme provided by the invention considers that the vast and complicated data information on the Internet and the true and false doping can cause interference on the evaluation result, comprehensively utilizes the advantages of an analytic hierarchy process on the distribution weight and the advantages of a fuzzy comprehensive evaluation process on the processing uncertainty, realizes the quantitative, objective and accurate evaluation of the account assets of the brand, carries out the data description with the appearance on the value evaluation of the abstract account assets, has stronger robustness and anti-interference performance compared with a general weighted average model, lays a decision basis for improving the brand marketing accuracy of a merchant and the brand consumption experience of a user, and has high user satisfaction and good experience.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of a method for evaluating brand account assets according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a method for calculating a composite score for an account asset according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of an evaluation system for brand account assets according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Referring to fig. 1, an embodiment of the present invention provides a method for evaluating a brand account number asset, including:
step S1, establishing an evaluation index system of the account assets;
s2, acquiring account numbers and fan numbers of all brands of the lowest indexes of the evaluation index system in a given industry, and respectively calculating incremental data of the account numbers and the fan numbers;
step S3, carrying out fuzzy interval division on the incremental data, and establishing a scoring standard of the evaluation index system;
s4, establishing weights of indexes at all levels by using an analytic hierarchy process;
step S5, calculating membership degree vectors of indexes at all levels by using multiplication and addition operators;
and step S6, calculating the comprehensive score of the evaluation index system step by using a multiplication and addition operator according to the score standard, the weight and the membership degree vector.
The technical scheme provided by the embodiment considers the vast and complicated data information on the Internet and the true and false doping, and can cause interference on the evaluation result, comprehensively utilizes the advantages of an analytic hierarchy process in weight distribution and the advantages of a fuzzy comprehensive evaluation process in processing uncertainty, realizes quantitative, objective and accurate evaluation on the account assets of the brand, carries out the data description with the appearance on the value evaluation of the abstract account assets, has stronger robustness and anti-interference performance compared with a general weighted average model, lays a decision basis for improving the brand marketing accuracy of a merchant and the brand consumption experience of a user, and has high user satisfaction and good experience.
It is understood that in a specific practice, the evaluation index system of the account assets may include multiple levels of indexes, and each level of indexes may include multiple next-level indexes except for the lowest-level indexes.
For convenience of understanding, taking the evaluation index system of the account asset including three levels of indexes as an example, the following is illustrated by a table one:
watch 1
It should be noted that the above table one is only an example for convenience of explaining the evaluation index system of the account asset mentioned in this embodiment, and does not represent that the evaluation index system of the account asset mentioned in this embodiment only has the index system shown in table one, nor represents those indexes shown in table one.
It can be understood that the evaluation index system of the account asset may include only one level of index, or may include two levels of indexes, three levels of indexes, or more.
Preferably, the account number and fan number of all brands of the lowest-layer indexes of the evaluation index system in a given industry are obtained through at least one of the following modes:
the crawler program is captured from the internet, manually entered and provided by a third-party data platform.
It should be noted that, in the step S2, the account number and the fan number of all brands of the lowest-layer index of the evaluation index system in a given industry are limited to be obtained, because only the lowest-layer index has the account number and the fan number, and the other-layer index has no account number and no fan number. The technical scheme provided by the embodiment is that the lowest-layer indexes calculate respective membership degrees according to incremental data of account numbers and fan numbers, other-layer indexes calculate own index scores according to the membership degrees and weights of indexes of the next layer, and then the index scores are accumulated layer by layer to obtain the final score of the account assets.
The incremental data for calculating the account number and the fan count in step S2 is the prior art, for example, it is known that the account number and the fan count at the previous time are N1The account number and the bean vermicelli number at the current moment are N2Then, at the current moment, the incremental data Δ x of the account number and the fan number is equal to N2-N1。
For the convenience of understanding the account asset assessment method provided by the embodiment, referring to fig. 2, it is assumed that the assessment index system of the account asset is in three levels.
And step S2, for the three-level evaluation index system of the account assets, calculating the incremental data accounts of the account number and the incremental data fans of the fan number of the lowest-level index.
And step S3, carrying out fuzzy interval division on the incremental data, and establishing a scoring standard of the evaluation index system.
S4, establishing weights of indexes at all levels by using an analytic hierarchy process; for example, for an account asset module, the ith index in the first level index is weighted by Ws1iThe weight of the jth secondary index of the ith index in the first-stage indexes is Ws2ijThe weight of the kth tertiary index of the jth secondary index of the ith index in the first-stage indexes is Ws3ijk。
And step S5, calculating membership degree vectors of indexes at all levels by using multiplication and addition operators.
And step S6, calculating the comprehensive score of the evaluation index system step by using a multiplication and addition operator according to the score standard, the weight and the membership degree vector.
Preferably, the step S3 includes:
step S31, fuzzy interval division is carried out on the incremental data, the division result is expressed by vectors, and a fuzzy set vector (G) of any index corresponding to n grading grades is obtained1，G2....Gn) Wherein n is more than or equal to 1;
step S32, determining fuzzy set vector (G) according to actual experience value1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score; or,
g is prepared from1＝C(G1),g2＝C(G2)....gn＝C(Gn) Determined as a fuzzy set vector (G)1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score;
wherein, C (G)i) Represents GiI is more than or equal to 1 and less than or equal to n.
For step S31, assuming there are 3 scoring levels, the corresponding level term vector can be expressed as (low, medium, high), and the corresponding fuzzy set vector can be expressed as (G)1，G2，G3)。
In the step S31, fuzzy interval division is performed on the incremental data, and the specific implementation method is as follows:
step S311 sets a fuzzy aggregation number numMF of the fuzzy interval division, and calculates a division point number q of 2 × numMF-1.
Step S312, reading data Datas of the fuzzy interval to be divided, and calculating the minimum value minData and the maximum value maxData of the data Datas;
it should be noted that: if the data is normalized, minData is 0, maxData is 1;
and the data Datas of the fuzzy interval to be divided is the incremental data.
Step S313, if the data sets are empty or the data sets are all the same, then the interval [0, 1] is divided into numNF trapezoidal fuzzy sets on average (note: the data sets are empty or the data sets are all the same, and the result is the same for all the interval divisions, so a simple average division method is adopted):
(1) the parameters of the first trapezoidal fuzzy set are set as [0, 0, 1/q quantile and 2/q quantile ];
(2) for k is 1: q-3 do (middle trapezoidal fuzzy set parameter set);
[ k/q quantile, (k +1)/q quantile, (k +2)/q quantile, (k +3)/q quantile ];
(3) the parameters of the last trapezoidal fuzzy set are set to be [ (q-2)/q quantile, (q-1)/q quantile, 1, 1 ].
Step S314, if the number of different data in the data is less than or equal to the number q of the points, at this time, the interval is divided into numNF triangular fuzzy sets (note: since the data is less, the interval is divided into more detailed triangular fuzzy sets):
(1) the parameter of the first triangular fuzzy set is set as [ minData, minData, minData, 1/(numMF-1) quantile ];
(2) for j ═ 0: numNF-3 do (middle trapezoidal fuzzy set parameter set)
[ j/(numMF-1) quantiles, (j +1)/(numMF-1) quantiles, (j +2)/(numMF-1) quantiles ];
(3) the parameters of the last triangular fuzzy set are set as [ (numMF-2)/(numMF-1) quantile, maxData, maxData, maxData ].
Step S315, if the number of different data in the data is greater than the number q of the points, at this time, numMF trapezoidal fuzzy sets are set as follows:
t is 0; (index for controlling quantile for rejecting abnormally large or abnormally small value)
while t < 10 (maximum rejection below 10% quantile and above 90% quantile, this order can be adjusted by itself)
quantile 99; (initial settings are 99% quantile, i.e. numerical rejection less than 1% quantile and greater than 99% quantile)
(100-quantile-t 0.1)/100 quantile;
high ═ 0.1/100 quantile + t; (setting new minimum Low and maximum high intervals)
Data with if between [ low, high ] is > the number of points q
(1) The parameters of the first trapezoidal fuzzy set are set as [ low, low, 1/q quantile and 2/q quantile ];
(2) for k ═ 1: q-3 do (middle trapezoidal fuzzy set parameter set)
[ k/q quantile, (k +1)/q quantile, (k +2)/q quantile, (k +3)/q quantile ];
(3) the parameters of the last trapezoidal fuzzy set are set as [ (q-2)/q quantiles, (q-1)/q quantiles, high, high ];
else
t＝t+1。
preferably, the step S4 includes:
step S41, a questionnaire is issued to experts to count a judgment matrix of the importance degree of each expert on every two indexes in the evaluation index system and direct weight distribution of the two indexes;
s42, according to the credibility of experts, weighting and summarizing to obtain the weight distribution between the two indexes;
for convenience of understanding, taking the evaluation index system of the account asset including three levels of indexes as an example, the following is illustrated by table two:
watch two
The second table is the weight data given by the experts, and the weights of the corresponding indexes are obtained by using the data, for example, the next-layer index weights of the third-level index service number and the subscription number are respectively:
and (3) authentication: unauthenticated 3/(3+7): 0.3:0.7 (service number)
And (3) authentication: unauthenticated 4/(4+5):5/(4+ 5): 0.44:0.56 (subscription number)
Thus, the dimension is unified, and the sum of the weights is equal to 1.
And step S43, weighting and summarizing according to the reliability of experts to obtain a judgment matrix of three or more indexes, and calculating according to an analytic hierarchy process to obtain weight distribution among the three or more indexes.
Taking the evaluation index system of the above example in table two as an example, the determination matrices of the three secondary indexes can be shown in table three as follows:
watch III
It should be noted that, according to the analytic hierarchy process, it is the prior art to assign weights to indexes at different levels, and the present application utilizes the prior art in the implementation scheme of weight assignment, which has been disclosed in the prior art, and is not described herein again.
Preferably, the step S5 includes:
step S51, standardizing the incremental data of the account number and the fan number according to the formula (1):
wherein, Δ x*The incremental data after the normalization processing is shown, Δ x shows the incremental data before the normalization processing, minData shows the minimum value of the incremental data, and maxData shows the maximum value of the incremental data;
step S52, calculating Delta x according to formula (2)*For trapezoidal fuzzy set Gi＝[a,b,c,d]And the membership of i is more than or equal to 1 and less than or equal to nThereby obtaining Δ x*The membership degree vector of the corresponding index is as follows:
wherein,
wherein, a, b, c, d are each trapezoidal fuzzy set G obtained by performing fuzzy interval division on the incremental data in the step S31iDividing points;
step S53, assume that there are m next-level indexes under any index in the middle-level indexesIn the notation, the membership vector of the j index of the m next-level indexes is recorded as:the j index of the m next-level indexes has the weight WjAnd j is more than or equal to 1 and less than or equal to m, calculating the membership vector of any index in the intermediate level indexes according to a formula (3):
wherein, the intermediate level index refers to indexes of other levels except for the lowest level index.
Preferably, the step S6 includes:
step S61, assuming that the membership vector of any index in the intermediate level indexes is (a)1，a2....an) Whereincorresponding fuzzy set vector (G)1，G2....Gn) Has a representative value of (g)1，g2....gn) Then, the increment score Δ S of the level index is calculated according to formula (4):
ΔS＝a1g1+a2g2+.....angn(4)，
step S62, setting the total y-level indexes of the evaluation index system, wherein m lower-level indexes exist under any index in the middle-level indexes, and calculating the comprehensive score of the evaluation index system according to the formula (5):
wherein,the increment score of the j index representing the x level at the current moment; wxjA weight representing a jth index of the xth stage;represents the composite score of the evaluation index system at the last moment,and representing the comprehensive score of the evaluation index system at the current moment.
In addition, referring to fig. 3, the present invention further provides an evaluation system 100 for brand account assets, comprising:
the establishing unit 101 is used for establishing an evaluation index system of the account assets;
the incremental data calculation unit 102 is configured to obtain the account number and the fan number of all brands of the lowest-layer index of the evaluation index system in a given industry, and calculate incremental data of the account number and the fan number respectively;
a scoring standard establishing unit 103, configured to perform fuzzy interval division on the incremental data, and establish a scoring standard of the evaluation index system;
a weight establishing unit 104, configured to establish a weight of each level of the index by using an analytic hierarchy process;
a membership calculation unit 105, configured to calculate membership vectors of each level of the index using a multiplication and addition operator;
and the comprehensive scoring unit 106 is used for calculating the comprehensive scoring of the evaluation index system step by utilizing a multiplication and addition operator according to the scoring standard, the weight and the membership degree vector.
The technical scheme provided by the embodiment considers the vast and complicated data information on the Internet and the true and false doping, and can cause interference on the evaluation result, comprehensively utilizes the advantages of an analytic hierarchy process in weight distribution and the advantages of a fuzzy comprehensive evaluation process in processing uncertainty, realizes quantitative, objective and accurate evaluation on the account assets of the brand, carries out the data description with the appearance on the value evaluation of the abstract account assets, has stronger robustness and anti-interference performance compared with a general weighted average model, lays a decision basis for improving the brand marketing accuracy of a merchant and the brand consumption experience of a user, and has high user satisfaction and good experience.
Preferably, the scoring criterion establishing unit 103 includes:
a dividing unit for dividing the increment data into fuzzy intervals and expressing the division result by a vector to obtain a fuzzy set vector (G) of any index corresponding to n grading grades1，G2....Gn) Wherein n is more than or equal to 1;
a determination unit for determining a fuzzy set vector (G) based on the actual empirical value1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score; or,
g is prepared from1＝C(G1),g2＝C(G2)....gn＝C(Gn) Determined as a fuzzy set vector (G)1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score;
wherein, C (G)i) Represents GiI is more than or equal to 1 and less than or equal to n.
Preferably, the weight establishing unit 104 includes:
the statistical unit is used for issuing questionnaires to experts to perform statistics on a judgment matrix of the importance degree of each expert on every two indexes in the evaluation index system and direct weight distribution of the two indexes;
the weighting unit is used for weighting and summarizing according to the credibility of experts to obtain weight distribution between the two indexes;
and the method is also used for weighting and summarizing according to the reliability of experts to obtain a judgment matrix of three or more indexes, and calculating the weight distribution among the three or more indexes according to an analytic hierarchy process.
Preferably, the incremental data calculation unit 102 obtains the account number and fan number of all brands of the lowest index of the evaluation index system in a given industry by at least one of the following ways:
the crawler program is captured from the internet, manually entered and provided by a third-party data platform.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims. The terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The term "plurality" means two or more unless expressly limited otherwise.
Claims (10)
1. A method for evaluating account assets of a brand, comprising:
step S1, establishing an evaluation index system of the account assets;
s2, acquiring account numbers and fan numbers of all brands of the lowest indexes of the evaluation index system in a given industry, and respectively calculating incremental data of the account numbers and the fan numbers;
step S3, carrying out fuzzy interval division on the incremental data, and establishing a scoring standard of the evaluation index system;
s4, establishing weights of indexes at all levels by using an analytic hierarchy process;
step S5, calculating membership degree vectors of indexes at all levels by using multiplication and addition operators;
and step S6, calculating the comprehensive score of the evaluation index system step by using a multiplication and addition operator according to the score standard, the weight and the membership degree vector.
2. The method according to claim 1, wherein the step S3 includes:
step S31, fuzzy interval division is carried out on the incremental data, the division result is expressed by vectors, and a fuzzy set vector (G) of any index corresponding to n grading grades is obtained1，G2....Gn) Wherein n is more than or equal to 1;
step S32, determining fuzzy set vector (G) according to actual experience value1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score; or,
g is prepared from1＝C(G1),g2＝C(G2)....gn＝C(Gn) Determined as a fuzzy set vector (G)1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score;
wherein, C (G)i) Represents GiI is more than or equal to 1 and less than or equal to n.
3. The method according to claim 1, wherein the step S4 includes:
step S41, a questionnaire is issued to experts to count a judgment matrix of the importance degree of each expert on every two indexes in the evaluation index system and direct weight distribution of the two indexes;
s42, according to the credibility of experts, weighting and summarizing to obtain the weight distribution between the two indexes;
and step S43, weighting and summarizing according to the reliability of experts to obtain a judgment matrix of three or more indexes, and calculating according to an analytic hierarchy process to obtain weight distribution among the three or more indexes.
4. The method according to claim 2, wherein the step S5 includes:
step S51, standardizing the incremental data of the account number and the fan number according to the formula (1):
wherein, Δ x*The incremental data after the normalization processing is shown, Δ x shows the incremental data before the normalization processing, minData shows the minimum value of the incremental data, and maxData shows the maximum value of the incremental data;
step S52, calculating Delta x according to formula (2)*For trapezoidal fuzzy set Gi＝[a,b,c,d]And the membership of i is more than or equal to 1 and less than or equal to nThereby obtaining Δ x*The membership degree vector of the corresponding index is as follows:
wherein,
wherein, a, b, c, d are each trapezoidal fuzzy set G obtained by performing fuzzy interval division on the incremental data in the step S31iDividing points;
step S53, assuming that there are m next-level indexes under any index in the middle-level indexes, and the membership vector of the jth index of the m next-level indexes is recorded as:the j index of the m next-level indexes has the weight WjAnd j is more than or equal to 1 and less than or equal to m, calculating the membership vector of any index in the intermediate level indexes according to a formula (3):
wherein, the intermediate level index refers to indexes of other levels except for the lowest level index.
5. The method according to claim 4, wherein the step S6 includes:
step S61, assuming that the membership vector of any index in the intermediate level indexes is (a)1，a2....an) Whereini is more than or equal to 1 and less than or equal to n, and corresponding fuzzy set vector (G)1，G2....Gn) Has a representative value of (g)1，g2....gn) Then, the increment score Δ S of the level index is calculated according to formula (4):
ΔS＝a1g1+a2g2+.....angn(4)，
step S62, setting the total y-level indexes of the evaluation index system, wherein m lower-level indexes exist under any index in the middle-level indexes, and calculating the comprehensive score of the evaluation index system according to the formula (5):
wherein,representing the j-th index of the x-th stage at the current momentAn incremental score; wxjA weight representing a jth index of the xth stage;represents the composite score of the evaluation index system at the last moment,and representing the comprehensive score of the evaluation index system at the current moment.
6. The method according to any one of claims 1 to 5, wherein the lowest index of the assessment index system is obtained through at least one of the following modes:
the crawler program is captured from the internet, manually entered and provided by a third-party data platform.
7. A system for evaluating brand account assets, comprising:
the establishing unit is used for establishing an evaluation index system of the account assets;
the incremental data calculation unit is used for acquiring the account number and the fan number of all brands of the bottom-layer indexes of the evaluation index system in a given industry and calculating the incremental data of the account number and the fan number;
the scoring standard establishing unit is used for carrying out fuzzy interval division on the incremental data and establishing a scoring standard of the evaluation index system;
the weight establishing unit is used for establishing the weight of each level of index by utilizing an analytic hierarchy process;
the membership calculation unit is used for calculating membership vectors of indexes at all levels by utilizing multiplication and addition operators;
and the comprehensive scoring unit is used for calculating the comprehensive scoring of the evaluation index system step by utilizing a multiplication and addition operator according to the scoring standard, the weight and the membership degree vector.
8. The system of claim 7, wherein the scoring criteria establishing unit comprises:
a dividing unit for dividing the increment data into fuzzy intervals and expressing the division result by a vector to obtain a fuzzy set vector (G) of any index corresponding to n grading grades1，G2....Gn) Wherein n is more than or equal to 1;
a determination unit for determining a fuzzy set vector (G) based on the actual empirical value1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score; or,
g is prepared from1＝C(G1),g2＝C(G2)....gn＝C(Gn) Determined as a fuzzy set vector (G)1，G2....Gn) Representative value of (g)1，g2....gn) And will be (g)1，g2....gn) As a scoring criterion for calculating an index score;
wherein, C (G)i) Represents GiI is more than or equal to 1 and less than or equal to n.
9. The system according to claim 7, wherein the weight establishing unit comprises:
the statistical unit is used for issuing questionnaires to experts to perform statistics on a judgment matrix of the importance degree of each expert on every two indexes in the evaluation index system and direct weight distribution of the two indexes;
the weighting unit is used for weighting and summarizing according to the credibility of experts to obtain weight distribution between the two indexes;
and the method is also used for weighting and summarizing according to the reliability of experts to obtain a judgment matrix of three or more indexes, and calculating the weight distribution among the three or more indexes according to an analytic hierarchy process.
10. The system according to any one of claims 7 to 9, wherein the incremental data calculation unit obtains account numbers and fan numbers of all brands of the lowest indexes of the evaluation index system in a given industry by at least one of the following ways:
the crawler program is captured from the internet, manually entered and provided by a third-party data platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811523012.7A CN109636184B (en) | 2018-12-13 | 2018-12-13 | Method and system for evaluating account assets of brands |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811523012.7A CN109636184B (en) | 2018-12-13 | 2018-12-13 | Method and system for evaluating account assets of brands |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109636184A true CN109636184A (en) | 2019-04-16 |
| CN109636184B CN109636184B (en) | 2023-07-21 |
Family
ID=66073394
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811523012.7A Active CN109636184B (en) | 2018-12-13 | 2018-12-13 | Method and system for evaluating account assets of brands |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109636184B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111127103A (en) * | 2019-12-24 | 2020-05-08 | 北京阿尔山区块链联盟科技有限公司 | Value evaluation method and system for digital assets |
| CN111160783A (en) * | 2019-12-30 | 2020-05-15 | 北京阿尔山区块链联盟科技有限公司 | Method and system for evaluating digital asset value and electronic equipment |
| CN112116238A (en) * | 2020-09-16 | 2020-12-22 | 深圳市维度统计咨询股份有限公司 | Satisfaction evaluation method based on index weight system design |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104217369A (en) * | 2013-06-05 | 2014-12-17 | 国家电网公司 | Large power grid construction economic evaluation method |
| GB2516894A (en) * | 2013-08-05 | 2015-02-11 | Ibm | User evaluation |
| CN104978665A (en) * | 2015-06-16 | 2015-10-14 | 北京畅游天下网络技术有限公司 | Brand evaluation method and brand evaluation device |
| CN107832950A (en) * | 2017-11-09 | 2018-03-23 | 国家电网公司 | A kind of power distribution network investment effect evaluation method based on improvement Interval Fuzzy evaluation |
| CN108038592A (en) * | 2017-11-22 | 2018-05-15 | 华北电力大学 | A kind of power distribution network investment effect evaluation method based on fuzzy interval analytic hierarchy process (AHP) |
| CN108241932A (en) * | 2018-01-24 | 2018-07-03 | 国网山东省电力公司泰安供电公司 | A kind of method for building up of electricity provider evaluation model |
| CN108629511A (en) * | 2018-05-04 | 2018-10-09 | 上海微小卫星工程中心 | A kind of satellite synthetic effectiveness evaluation method based on multifactor fuzzy theory reasoning and Analytic hierarchy process |
-
2018
- 2018-12-13 CN CN201811523012.7A patent/CN109636184B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104217369A (en) * | 2013-06-05 | 2014-12-17 | 国家电网公司 | Large power grid construction economic evaluation method |
| GB2516894A (en) * | 2013-08-05 | 2015-02-11 | Ibm | User evaluation |
| CN104978665A (en) * | 2015-06-16 | 2015-10-14 | 北京畅游天下网络技术有限公司 | Brand evaluation method and brand evaluation device |
| CN107832950A (en) * | 2017-11-09 | 2018-03-23 | 国家电网公司 | A kind of power distribution network investment effect evaluation method based on improvement Interval Fuzzy evaluation |
| CN108038592A (en) * | 2017-11-22 | 2018-05-15 | 华北电力大学 | A kind of power distribution network investment effect evaluation method based on fuzzy interval analytic hierarchy process (AHP) |
| CN108241932A (en) * | 2018-01-24 | 2018-07-03 | 国网山东省电力公司泰安供电公司 | A kind of method for building up of electricity provider evaluation model |
| CN108629511A (en) * | 2018-05-04 | 2018-10-09 | 上海微小卫星工程中心 | A kind of satellite synthetic effectiveness evaluation method based on multifactor fuzzy theory reasoning and Analytic hierarchy process |
Non-Patent Citations (2)
| Title |
|---|
| 李贺: "企业微博营销效果评估研究", 《中国优秀硕士学位论文全文数据库（电子期刊）》 * |
| 李贺: "企业微博营销效果评估研究", 《中国优秀硕士学位论文全文数据库（电子期刊）》, 15 April 2015 (2015-04-15), pages 30 - 36 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111127103A (en) * | 2019-12-24 | 2020-05-08 | 北京阿尔山区块链联盟科技有限公司 | Value evaluation method and system for digital assets |
| CN111127103B (en) * | 2019-12-24 | 2023-10-24 | 北京阿尔山区块链联盟科技有限公司 | Value evaluation method and system for digital asset |
| CN111160783A (en) * | 2019-12-30 | 2020-05-15 | 北京阿尔山区块链联盟科技有限公司 | Method and system for evaluating digital asset value and electronic equipment |
| CN111160783B (en) * | 2019-12-30 | 2023-10-24 | 北京阿尔山区块链联盟科技有限公司 | Digital asset value evaluation method and system and electronic equipment |
| CN112116238A (en) * | 2020-09-16 | 2020-12-22 | 深圳市维度统计咨询股份有限公司 | Satisfaction evaluation method based on index weight system design |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109636184B (en) | 2023-07-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109636467A (en) | A kind of comprehensive estimation method and system of the internet digital asset of brand | |
| CN109657962A (en) | A kind of appraisal procedure and system of the volume assets of brand | |
| CN106485562B (en) | Commodity information recommendation method and system based on user historical behaviors | |
| US8121886B2 (en) | Confidence based selection for survey sampling | |
| CN106952159B (en) | Real estate collateral risk control method, system and storage medium | |
| CN108399453A (en) | A kind of Electric Power Customer Credit Rank Appraisal method and apparatus | |
| CN109636184A (en) | A kind of appraisal procedure and system of the account assets of brand | |
| Li et al. | Dynamic fuzzy multiple criteria decision making for performance evaluation | |
| CN111126865B (en) | Technology maturity judging method and system based on technology big data | |
| Treerattanapitak et al. | Exponential fuzzy C-means for collaborative filtering | |
| US20150012471A1 (en) | System for user psychosocial profiling | |
| US11900457B2 (en) | Methods for prediction and rating aggregation | |
| CN111523768A (en) | Entropy weight-TOPSIS-based generalized demand side resource quality evaluation method | |
| CN111199493A (en) | Arrearage risk identification method based on customer payment information and credit investigation information | |
| CN118396720B (en) | Online and offline fusion sales system for household appliances | |
| Chen et al. | Optimization-based probabilistic decision support for assessing building information modelling (BIM) maturity considering multiple objectives | |
| CN110738565A (en) | Real estate finance artificial intelligence composite wind control model based on data set | |
| CN109377110B (en) | Evaluation method and system for brand content assets | |
| CN110633401A (en) | Prediction model of store data and establishment method thereof | |
| CN117592873A (en) | Data asset value assessment method based on data characteristics | |
| CN101324939A (en) | System and method for forecasting new business market based on data development | |
| CN116992135A (en) | Heat supply charging intelligent recommendation method based on arrearage risk assessment and user portrayal | |
| CN115689708A (en) | Screening method, risk assessment method, device, equipment and medium of training data | |
| CN108388911A (en) | A kind of mobile subscriber's Dynamic Fuzzy Clustering Algorithm method towards mixed attributes | |
| CN110851716B (en) | Personalized group recommendation method based on maximum harmony |
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 |