CN111353858A - Method and equipment for calculating online credit of consumer goods - Google Patents

Method and equipment for calculating online credit of consumer goods Download PDF

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CN111353858A
CN111353858A CN202010161386.XA CN202010161386A CN111353858A CN 111353858 A CN111353858 A CN 111353858A CN 202010161386 A CN202010161386 A CN 202010161386A CN 111353858 A CN111353858 A CN 111353858A
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indexes
score
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许应成
蔡华利
宁秀丽
裴飞
刘碧松
高晓红
李莹
吴倩
李亚
叶如意
苏雪妍
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Abstract

The invention relates to the field of credit metering, in particular to a consumer product online credit calculation method and device. The calculation method comprises the steps of dividing the credit of the consumer goods, comparing every two key degrees of the indexes in the quality of the consumer goods, presenting a comparison result through scores, further constructing a judgment matrix, carrying out consistency check for determining the judgment matrix to be reasonable, and obtaining the weight of each index through normalization processing of the judgment matrix after the consistency check is passed. Because the credit of each index corresponds to the index score in the preset credit score table, the index score and the index weight are multiplied to obtain a score which is reasonable and has persuasion, and all the index scores are added to obtain the online credit value of the consumer product. By the technical scheme, the online credit of the consumer goods can be digitally processed, and the online credit is effectively measured, so that the quantized online credit of the consumer goods is more intuitively expressed and compared.

Description

Method and equipment for calculating online credit of consumer goods
Technical Field
The invention relates to the field of credit metering, in particular to a consumer product online credit calculation method and device.
Background
With the rapid development of the internet, the traditional consumption mode is changed silently, more and more people start to choose online shopping, and the network consumption mode is increasingly popular among people. However, the problems of uneven online consumption quality, low service quality level, asymmetric information and the like reduce the satisfaction of consumers, and meanwhile, great challenges are brought to online consumption quality supervision. The online credit measurement of the consumer goods is beneficial to maintaining the stability of transaction, preventing network deception, strengthening the credit condition of both the buyer and the seller of the electronic commerce transaction and promoting the increase of the online transaction times. Currently, there is no effective way to gauge consumer product online goodwill.
Disclosure of Invention
Technical problem to be solved
The invention aims to provide a consumer product online credit calculation method, consumer product online credit calculation equipment, electronic equipment and a non-transitory computer readable storage medium, so as to solve the problem that the prior art cannot measure the online credit of a consumer product.
(II) technical scheme
In order to solve the technical problem, the invention provides a method for calculating the online credit of a consumer product, which is characterized by comprising the following steps:
determining a plurality of indexes according to the consumer categories, and constructing a judgment matrix according to the pairwise comparison result of all the indexes;
judging whether the indexes pass consistency check according to the judgment matrix, and calculating the normalized eigenvector of the indexes to obtain the index weight;
acquiring an index score according to a preset credit score table;
and multiplying the index weight and the index score to obtain an index score, and adding all the index scores to obtain the online reputation value of the consumer product.
In some embodiments, preferably, the determining that the indicator passes the consistency check includes:
calculating the maximum eigenvalue lambda of the judgment matrixmax；
By using
Figure BDA0002405916710000021
Calculating a consistency ratio CI, wherein n is the order number of the judgment matrix;
obtaining a consistency index RI according to the order number of the judgment matrix and the order index corresponding relation;
by using
Figure BDA0002405916710000022
Calculating a consistency ratio CR;
and when the CR is smaller than the threshold value, judging that the index passes the consistency test, otherwise, judging that the index does not pass the consistency test.
In some embodiments, preferably, the calculating a normalized feature vector of the index to obtain the index weight includes:
and normalizing the feature vectors of the judgment matrix, wherein the normalized feature vectors correspond to the index weights.
In some embodiments, preferably, after obtaining the online reputation value of the consumer product, the online reputation calculation method of the consumer product further includes:
and acquiring the consumer product grade corresponding to the online credit value of the consumer product according to a preset credit grade table.
In some embodiments, preferably, after determining the plurality of indicators according to the consumer goods category, the method for calculating online reputation of consumer goods further comprises:
and if the indexes do not pass the consistency check, revising the pairwise comparison result of the indexes until the indexes pass the consistency check.
In some embodiments, preferably, the metrics constitute a tag tree;
and constructing a judgment matrix in the consistency test of all indexes to meet the following conditions: establishing a judgment matrix according to the pairwise comparison results of all the first-level indexes; except the first-stage index, when the indexes of other stages are tested for consistency, the results of pairwise comparison of all indexes of the next stage subordinate to the index of the previous stage are compared to construct a judgment matrix;
and/or the presence of a gas in the gas,
after all indexes pass the consistency test, the index weights of all indexes meet the following conditions: the sum of the index weights of all the first-level indexes is equal to 1; except the first-stage index, the index weight of the index at the upper stage in other stages is equal to the sum of the index weights of all indexes at the lower stage.
In some embodiments, preferably, the index score is an index score corresponding to a last-stage index of the tag tree;
the obtaining of the index score according to the preset reputation score table comprises: acquiring a preset credit score table; acquiring an index score corresponding to the final-stage index in the preset credit score table;
the step of obtaining the index score by multiplying the index weight and the index score comprises the following steps: and multiplying the index weight of the final-stage index by the corresponding index score to obtain the index score.
The invention also provides a consumer product online credit calculation device which executes the consumer product online credit calculation method;
the method comprises the following steps:
the first module is used for determining a plurality of indexes according to consumer categories and constructing a judgment matrix according to pairwise comparison results of all the indexes;
the second module is used for judging whether the indexes pass consistency check according to the judgment matrix, and calculating the normalized eigenvector of the indexes to obtain the index weight;
the acquisition module is used for acquiring index scores according to a preset credit score table;
and the calculation module is used for multiplying the index weight and the index score to obtain an index score, and summing all the index scores to obtain the online reputation value of the consumer product.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the computer program, the online reputation calculation method of the consumer goods is realized.
The present invention also provides a non-transitory computer readable storage medium having stored thereon computer instructions, which when executed by a computer, implement the above-mentioned consumer product online reputation calculation method.
(III) advantageous effects
The technical scheme provided by the invention provides an online reputation calculation method for a consumer product, which comprises the following steps: determining a plurality of indexes according to the consumer categories, and constructing a judgment matrix according to the pairwise comparison result of all the indexes; judging whether the indexes pass consistency check according to the judgment matrix, and calculating the normalized eigenvector of the indexes to obtain the index weight; acquiring an index score according to a preset credit score table; and multiplying the index weight and the index score to obtain an index score, and adding all the index scores to obtain the online reputation value of the consumer product. In the scheme, index division is carried out on credit of the consumer goods, pairwise comparison is carried out on key degrees of the indexes in quality of the consumer goods, comparison results are presented through scores, a judgment matrix is further constructed, consistency check is carried out for determining that the judgment matrix is reasonable, and after the consistency check is passed, normalization processing is carried out on the judgment matrix, so that the weight of each index is obtained. Because the credit of each index corresponds to the index score in the preset credit score table, the index score and the index weight are multiplied to obtain a score which is reasonable and has persuasion, and all the index scores are added to obtain the online credit value of the consumer product. By the technical scheme, the online credit of the consumer goods can be digitally processed, and the online credit is effectively measured, so that the quantized online credit of the consumer goods is more intuitively expressed and compared.
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FIG. 1 is a diagram illustrating a method for calculating an online reputation of a consumer product according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a consumer product online reputation calculation method in another embodiment of the present invention;
fig. 3 is a schematic physical structure diagram of an electronic device provided in an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In order to solve the problem of online credit measurement of the existing consumer goods, the invention provides a consumer goods online credit calculation method, consumer goods online credit calculation equipment, electronic equipment and a non-transitory computer readable storage medium.
The invention provides a method for calculating the online credit of a consumer product, which comprises the following steps of:
step 10, determining a plurality of indexes according to consumer product types, and establishing a judgment matrix according to the comparison result of the importance degrees of all the indexes in the quality of the consumer product;
step 20, judging whether the index passes consistency check according to the judgment matrix, and calculating a normalized eigenvector of the index to obtain an index weight;
step 30, acquiring an index score according to a preset credit score table;
and step 40, multiplying the index weight and the index score to obtain an index score, and adding all the index scores to obtain the online reputation value of the consumer product.
In the scheme, index division is carried out on credit of the consumer goods, pairwise comparison is carried out on key degrees of the indexes in quality of the consumer goods, comparison results are presented through scores, a judgment matrix is further constructed, consistency check is carried out for determining that the judgment matrix is reasonable, and after the consistency check is passed, normalization processing is carried out on the judgment matrix, so that the weight of each index is obtained. Because the credit of each index corresponds to the index score in the preset credit score table, the index score and the index weight are multiplied to obtain a score which is reasonable and has persuasion, and all the index scores are added to obtain the online credit value of the consumer product. By the technical scheme, the online credit of the consumer goods can be digitally processed, and the online credit is effectively measured, so that the quantized online credit of the consumer goods is more intuitively expressed and compared.
In the online reputation calculation method for the consumer product, the online reputation calculation method can be developed through the following steps, as shown in fig. 2:
step 110, determining a plurality of indexes according to consumer categories;
determining the consumer goods or some consumer goods needing on-line credit calculation, and selecting proper indexes in an index system according to the characteristics of the consumer goods category. The index system is the existing, common or general index in the industry. For example, table 1, it can be seen from table 1 that the indices form a multi-level label tree. If the index is constructed as a label tree, any index on the label tree is the index.
TABLE 1
Figure BDA0002405916710000061
Step 120, establishing a judgment matrix according to the pairwise comparison results of all indexes;
the way and principle of pairwise comparison are various. Such as: for different consumer product qualities, the importance degree of the index can be used as a comparison result of pairwise comparison, and the comparison results form numerical sorting according to the importance degree, so that a judgment matrix is formed.
When the index is only one level, a judgment matrix can be directly constructed according to the index; when the indexes are multi-level, it is preferable to establish a judgment matrix for the indexes of different levels respectively.
For example, table 2:
TABLE 2 significance Scale of values
Figure BDA0002405916710000062
Figure BDA0002405916710000071
And step 130, judging that the index passes consistency check according to the constructed judgment matrix.
Calculating the maximum eigenvalue lambda of the judgment matrixmax；
By using
Figure BDA0002405916710000072
Calculating a consistency ratio CI, wherein n is the order number of the judgment matrix;
according to the order of the judgment matrix, inquiring and acquiring a consistency index RI in the order index corresponding relation (namely an industry universal consistency index table, table 3);
TABLE 3
Order of matrix 1 2 3 4 5 6 7
RI 0 0 0.52 0.89 1.12 1.26 1.36
Order of matrix 8 9 10 1 12 13 14
RI 1.41 1.46 1.49 1.52 1.54 1.56 1.58
By using
Figure BDA0002405916710000073
Calculating a consistency ratio CR;
and when the CR is smaller than the threshold value, judging that the index passes the consistency test, otherwise, judging that the index does not pass the consistency test.
When the judgment index fails the consistency check, the judgment matrix needs to be adjusted, revised, and then the step 130 is performed again until the consistency check is passed.
And 140, normalizing the feature vectors of the feature matrix passing the consistency test, wherein the normalized feature vectors are each index weight.
In some embodiments, when performing step 120, step 130, and step 140, the indexes of the consumer product form a multi-level label tree, and then the feature matrix formed by the comparison result of each level of indexes is subjected to a consistency check, which includes:
A. the comparison results of all the first-level indexes which are compared pairwise form a characteristic matrix, and step 120 is executed;
B. performing consistency check on the feature matrix, executing step 130, and if the feature matrix passes the consistency check, executing step 140 on the feature matrix passing the consistency check; if the consistency check is not passed, readjusting the characteristic matrix, and then executing the step again until the consistency check is passed;
C. and (3) forming a feature matrix by the comparison results of pairwise comparison of the next-level indexes to which each previous-level index belongs, executing the step 120, performing consistency check, executing the step 130, after the consistency check, executing the step 140 on the feature matrix passing the consistency check, and readjusting the feature matrix not passing the consistency check until the consistency check is passed.
And (3) respectively executing the step (120), the step (130) and the step (140) on each level of indexes according to the content in the step (C), wherein the obtained index weight meets the following requirements: the sum of the index weights of all the first-level indexes is equal to 1; except the first-stage index, the index weight of the index at the upper stage in other stages is equal to the sum of the index weights of all indexes at the lower stage.
Step 150, obtaining an index score of the final-stage index according to a preset credit score table;
and index scores corresponding to different reputations from poor to good of each final-stage index are set in a preset reputation score table.
In the preset credit score table of the step, the corresponding index score is searched according to the online credit of the final-stage index.
The last level indicator means that the indicator is not split into more detailed sub-indicators. That is, when only the 1-stage index is included, the last stage is the 1 st stage, when only the 2-stage index is included, the last stage is the 2 nd stage, when only the 3-stage index is included, the last stage is the 3 rd stage, and so on.
Step 160, multiplying the index of the final stage by the index weight and the index score to obtain the index score of the final stage index;
since the index weight and the index score are obtained separately for each final index (i.e., index), the index score is obtained by multiplying the index weight and the index score.
And 170, calculating the sum of the index scores of all the final-stage indexes to obtain the online reputation value of the consumer product.
And adding the index scores of each final index in the step 160 to obtain the online reputation value of the consumer product.
And step 180, acquiring the consumer product grade corresponding to the online credit value of the consumer product according to a preset credit grade table.
In step 170, the online reputation value of the consumer product is obtained, that is, the online reputation of the consumer product is effectively measured, and in addition, the value of the effective measurement is considered to be more visual, so that the reputation value is graded in advance, the reputation value is divided into a plurality of data segments, and different data segments correspond to different reputation grades, so that the reputation grade of the consumer product is more visually displayed, and the reputation grade is effectively and visually combined with the comprehensive measurement of a plurality of indexes.
An example of a reputation ranking table that classifies the ranking of consumer goods according to their online reputation scores is:
the online reputation rating of the consumer goods is divided into A, B, C, D four equal parts from high to low, each of which can be further subdivided into levels. Each level of distinction of the same class is distinguished by the number of letters.
The larger the number of letters (three at most), the better the online reputation evaluation of the consumer product is, as follows:
a, etc. can be subdivided into AAA, AA, and A classes.
B, etc. can be subdivided into BBB, BB, and B stages.
C, etc. can be subdivided into CCC grades, CC and C grades.
D, etc. is the lowest and the differences are not subdivided.
The partitioning method may incorporate existing and common quality assessment criteria for consumer products.
Next, a specific example is given:
step 210, specifying metrology targets and ranges
Taking infant clothes as samples, the consumer product is mainly used for infants of 0-3 years old.
Step 220, selecting the index
The infant has the characteristics of small resistance and small age, and the indexes of consumer product quality credit, merchant credit, sales platform credit and public opinion attention 4 are selected at this time.
Step 230, constructing a judgment matrix
And comparing the indexes of the same level pairwise to obtain a judgment matrix. The judgment matrix of the first-level index is shown in Table 4
TABLE 4 infant clothing online reputation evaluation primary index judgment matrix
Figure BDA0002405916710000101
Step 240, calculating weight vector
Calculating and judging the maximum eigenvalue lambda of the matrixmaxAnd the corresponding characteristic vector is subjected to normalization processing. Calculating the first-level index judgment matrix to obtain the maximum eigenvalue lambdamaxAt 4.05, the corresponding feature vector is normalized to (0.58,0.20,0.13, 0.09).
Step 250, consistency check
① calculating the conformity ratio CI
Figure BDA0002405916710000102
② looking up the table to obtain a consistency index RI of 0.89
③ calculating the consistency ratio CR
Figure BDA0002405916710000103
CR <0.1 (threshold), the consistency check passes, the normalized eigenvector calculated is each index weight. And if the consistency check is not passed, adjusting the judgment matrix.
And sequentially carrying out the operations on the judgment moments of the secondary indexes, and if the consistency check is not passed, adjusting the judgment matrix. And finally determining the weight of all secondary indexes.
Because the second-level index is set at this time, if the third-level index and the fourth-level index are set in other embodiments, the operation is also performed.
Step 260, obtaining index score
After the weights of the indexes are determined, the indexes (generally directly corresponding to the final-stage indexes) are divided into a plurality of grades in a preset credit score table, and quantitative numerical values are given to the grades. Such as it can be divided into seven levels of best, good, better, general, poor, worst, and then the scores are set as 100, 85, 70, 55, 40, 25, 10.
And finally, multiplying the index weight of each final-stage index by the corresponding index score to obtain the score of each secondary index (the final-stage index in the example), and summing to obtain the final credit value. The preset reputation score table is shown in table 5.
TABLE 5 Online reputation Preset reputation score sheet for infant consumables
Figure BDA0002405916710000111
Step 270, level division
The infant clothes ratings were classified and the classification results are shown in table 6.
TABLE 6 infant clothes grade dividing method
Figure BDA0002405916710000121
Therefore, the online reputation level of the evaluated infant garment product is BBB level such as B level.
The invention also provides a computing device for executing the computing method, so as to execute the computing method, the computing device can be a hardware device or a software program, and the module setting can be determined according to the requirements of the method steps.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the online reputation calculation method of the consumer goods is realized when the processor executes the computer program.
The electronic device of the embodiment of the present invention may further include a communication interface and a bus. Referring to fig. 3, an entity structure diagram of an electronic device provided in an embodiment of the present invention includes: at least one memory 401, at least one processor 402, a communication interface 403, and a bus 404.
Wherein, the memory 401, the processor 402 and the communication interface 403 complete mutual communication through the bus 404, and the communication interface 403 is used for information transmission between the electronic device and the consumer goods online reputation computing device; the memory 401 stores therein a computer program operable on the processor 402, and the processor 402 executes the computer program to implement the steps of the consumer product online reputation calculation method according to the embodiments described above.
It is understood that the electronic device at least includes a memory 401, a processor 402, a communication interface 403 and a bus 404, and the memory 401, the processor 402 and the communication interface 403 are connected in communication with each other through the bus 404, and can complete communication with each other, for example, the processor 402 reads program instructions of a calculation method from the memory 401. In addition, the communication interface 403 may also implement communication connection between the electronic device and a target data device, and may complete mutual information transmission, such as data reading through the communication interface 403.
When the electronic device is running, the processor 402 invokes the program instructions in the memory 401 to perform the methods provided by the method embodiments described above.
The program instructions in the memory 401 may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Alternatively, all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, where the program may be stored in a computer-readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The invention also provides a non-transitory computer readable storage medium, on which computer instructions are stored, and the computer instructions are executed by a computer to realize the online reputation calculation method of the consumer goods.
It is to be understood that the above-described embodiments of the apparatus, the electronic device and the storage medium are merely illustrative, and that elements described as separate components may or may not be physically separate, may be located in one place, or may be distributed on different network elements. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the technical solutions mentioned above may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a usb disk, a removable hard disk, a ROM, a RAM, a magnetic or optical disk, etc., and includes several instructions for causing a computer device (such as a personal computer, a server, or a network device, etc.) to execute the methods of the method embodiments or some parts of the method embodiments.
In addition, it should be understood by those skilled in the art that in the specification of the embodiments of the present invention, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In the description of the embodiments of the invention, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description. Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, to simplify the disclosure of embodiments of the invention and to aid in the understanding of one or more of the various inventive aspects.
However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of an embodiment of this invention.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A method for calculating online reputation of a consumer product, comprising:
determining a plurality of indexes according to the consumer categories, and constructing a judgment matrix according to the pairwise comparison result of all the indexes;
judging whether the indexes pass consistency check according to the judgment matrix, and calculating the normalized eigenvector of the indexes to obtain the index weight;
acquiring an index score according to a preset credit score table;
and multiplying the index weight and the index score to obtain an index score, and adding all the index scores to obtain the online reputation value of the consumer product.
2. The method of claim 1, wherein the determining that the indicator passes the consistency check comprises:
calculating the maximum eigenvalue lambda of the judgment matrixmax；
By using
Figure FDA0002405916700000011
Calculating a consistency ratio CI, wherein n is the order number of the judgment matrix;
obtaining a consistency index RI according to the order number of the judgment matrix and the order index corresponding relation;
by using
Figure FDA0002405916700000012
Calculating a consistency ratio CR;
and when the CR is smaller than the threshold value, judging that the index passes the consistency test, otherwise, judging that the index does not pass the consistency test.
3. The online reputation calculation method of a consumer product of claim 2, wherein the calculating a normalized feature vector of an index to derive an index weight comprises:
and normalizing the characteristic vectors of the judgment matrix passing the consistency test, wherein the normalized characteristic vectors correspond to each index weight.
4. The method of claim 3, wherein after obtaining the online reputation value of the consumer product, the method further comprises:
and acquiring the consumer product grade corresponding to the online credit value of the consumer product according to a preset credit grade table.
5. The consumer product online reputation calculation method of any of claims 1-4, wherein after determining a plurality of metrics according to consumer product category, the consumer product online reputation calculation method further comprises:
and if the indexes do not pass the consistency check, revising the pairwise comparison result of the indexes until the indexes pass the consistency check.
6. The consumer product online reputation calculation method of any of claims 1-5, wherein the metrics constitute a tag tree;
and constructing a judgment matrix in the consistency test of all indexes to meet the following conditions: establishing a judgment matrix according to the pairwise comparison results of all the first-level indexes; except the first-stage index, when the indexes of other stages are tested for consistency, the results of pairwise comparison of all indexes of the next stage subordinate to the index of the previous stage are compared to construct a judgment matrix;
and/or the presence of a gas in the gas,
after all indexes pass the consistency test, the index weights of all indexes meet the following conditions: the sum of the index weights of all the first-level indexes is equal to 1; except the first-stage index, the index weight of the index at the upper stage in other stages is equal to the sum of the index weights of all indexes at the lower stage.
7. The method of claim 6, wherein the index score is an index score corresponding to a last-stage index of the tag tree;
the obtaining of the index score according to the preset reputation score table comprises: acquiring a preset credit score table; acquiring an index score corresponding to the final-stage index in the preset credit score table;
the step of obtaining the index score by multiplying the index weight and the index score comprises the following steps: and multiplying the index weight of the final-stage index by the corresponding index score to obtain the index score.
8. A consumer goods online reputation calculation apparatus characterized in that it performs the consumer goods online reputation calculation method of any one of claims 1-7;
the method comprises the following steps:
the first module is used for determining a plurality of indexes according to consumer categories and constructing a judgment matrix according to pairwise comparison results of all the indexes;
the second module is used for judging whether the indexes pass consistency check according to the judgment matrix, and calculating the normalized eigenvector of the indexes to obtain the index weight;
the acquisition module is used for acquiring index scores according to a preset credit score table;
and the calculation module is used for multiplying the index weight and the index score to obtain an index score, and summing all the index scores to obtain the online reputation value of the consumer product.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the consumer product online reputation calculation method according to any one of claims 1-7.
10. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein the computer instructions, when executed by a computer, implement the consumer product online reputation calculation method of any of claims 1-7.
CN202010161386.XA 2020-03-10 2020-03-10 Method and equipment for calculating online credit of consumer goods Pending CN111353858A (en)

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