CN114004664A - APP software interaction method based on SaaS platform - Google Patents

APP software interaction method based on SaaS platform Download PDF

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CN114004664A
CN114004664A CN202210003428.6A CN202210003428A CN114004664A CN 114004664 A CN114004664 A CN 114004664A CN 202210003428 A CN202210003428 A CN 202210003428A CN 114004664 A CN114004664 A CN 114004664A
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elements
saas platform
merchant
app
merchant information
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鲍森
罗路
李彪
马杰
李瑞丰
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Hangzhou Cheling Network Technology Co ltd
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Abstract

The invention discloses an APP software interaction method based on a SaaS platform, which comprises the steps that the SaaS platform is connected with a car owner APP and a merchant APP; the method comprises the steps that a merchant APP uploads merchant information to a SaaS platform, a vehicle owner APP uploads vehicle owner preference information to the SaaS platform, the SaaS platform carries out interactive matching of the merchant information and the vehicle owner preference information, and a decision maker is arranged in the SaaS platform to push accurate merchant information to a vehicle owner APP of a vehicle owner; the invention can help the merchant to push the merchant information which accords with the preference of the vehicle owner to the vehicle owner, so that the vehicle owner can play more conveniently, and the merchant can deliver more accurate advertisements.

Description

APP software interaction method based on SaaS platform
Technical Field
The invention relates to an APP software interaction method based on a SaaS platform, and belongs to the field of software services.
Background
Software-as-a-Service (SaaS) is a completely innovative Software application model that began to emerge in the 21 st century as internet technology developed and application Software matured. SaaS can enable the merchant and the user to realize information interaction through corresponding APP, and can promote the merchant to push accurate merchant information to the owner APP of the owner. However, the effect of recommending merchants to the vehicle is poor at present, for example, the consumption of merchants is too high, the merchant product information and the merchant evaluation degree do not meet the requirements of vehicle owners, or the merchant needs to park and queue at the position and the parking position is not good, so that the interactive information (merchant information) pushed to the vehicle owners is not good in feedback, and the advertisement resources are wasted.
Disclosure of Invention
The invention aims to provide an APP software interaction method based on a SaaS platform. The invention can help the merchant to push the merchant information which accords with the preference of the vehicle owner to the vehicle owner, so that the vehicle owner can play more conveniently, and the merchant can deliver more accurate advertisements.
The technical scheme of the invention is as follows: the APP software interaction method based on the SaaS platform comprises the SaaS platform, wherein the SaaS platform is connected with a car owner APP and a merchant APP; the method comprises the steps that a merchant APP uploads merchant information to a SaaS platform, a vehicle owner APP uploads vehicle owner preference information to the SaaS platform, the SaaS platform carries out interactive matching of the merchant information and the vehicle owner preference information, and a decision maker is arranged in the SaaS platform and pushes accurate merchant information to a vehicle owner APP of a vehicle owner; wherein the setting of the decision maker comprises the following steps:
s1, establishing a tower structure module, wherein the tower structure module comprises a target layer, a standard layer and an index layer; the target layer is used for expressing the most suitable merchant information acquired by the vehicle owner, the standard layer is a judgment standard for realizing the basis for the vehicle owner to acquire the most suitable merchant information, and the index layer is an optional index for realizing the vehicle owner to acquire the most suitable merchant information;
s2, establishing a judgment matrix about the standard layer for the target layer, and establishing a judgment matrix about the index layer for the standard layer; calculating a weight vector of each judgment matrix to obtain the weight vector of each element in each judgment matrix;
and S3, after the weight vector is obtained, calculating the combination weight formed by different element combinations from top to bottom from the target layer, thereby obtaining the decision makers with different combination weight types.
According to the APP software interaction method based on the SaaS platform, the element of the target layer is the optimal merchant information selectable by the vehicle owner; elements of the standard layer include security, economy, and convenience; the elements of the index layer comprise merchant product information and merchant evaluation degree corresponding to safety, charging price corresponding to economy, merchant rights and interests, parking space availability, distance utilization rate and parking position corresponding to convenience.
In the APP software interaction method based on the SaaS platform, in step S2, when the determination matrix is established, importance comparison in the determination matrix is represented by scale; when two elements are compared, the relationship of the importance of the two elements is expressed by a scale of 1-9, wherein the scale 1 represents that the two elements have equal importance, the scale 3 represents that the former is slightly more important than the latter, the scale 5 represents that the former is obviously more important than the latter, the scale 7 represents that the former is strongly more important than the latter, the scale 9 represents that the former is extremely more important than the latter, and the scales 2, 4, 6 and 8 represent intermediate values of adjacent judgments of the two elements; note the book
Figure 757852DEST_PATH_IMAGE001
Presentation element
Figure 579178DEST_PATH_IMAGE002
And elements
Figure 239835DEST_PATH_IMAGE003
Compared with elements
Figure 146611DEST_PATH_IMAGE004
The importance level of;
Figure 36070DEST_PATH_IMAGE005
presentation element
Figure 977481DEST_PATH_IMAGE003
And elements
Figure 543460DEST_PATH_IMAGE002
Compared with elements
Figure 734270DEST_PATH_IMAGE003
The importance level of.
In the APP software interaction method based on the SaaS platform, the step of calculating the weight vector of the judgment matrix includes;
s2.1, calculating the product of each row of elements in the judgment matrix;
Figure 427420DEST_PATH_IMAGE006
；
s2.2, calculating each product
Figure 223337DEST_PATH_IMAGE007
Is/are as follows
Figure 976530DEST_PATH_IMAGE008
Root of inferior square;
Figure 113025DEST_PATH_IMAGE009
；
s2.3, dividing each square root
Figure 344286DEST_PATH_IMAGE010
Carrying out normalization processing;
Figure 994710DEST_PATH_IMAGE011
；
to obtain
Figure 918804DEST_PATH_IMAGE012
I.e. the weight vector of the decision matrix.
According to the APP software interaction method based on the SaaS platform, the consistency check is carried out on the weight vector of the judgment matrix obtained after calculation, the weight vector is used for checking the rationality of the judgment matrix, and the violation of common sense is avoided; the steps of the consistency check are as follows:
first, a consistency index is calculated
Figure 5578DEST_PATH_IMAGE013
：
Figure 40530DEST_PATH_IMAGE014
；
In the formula
Figure 545460DEST_PATH_IMAGE015
Judging the maximum weight value of the matrix;
according to the average random consistency index
Figure 889723DEST_PATH_IMAGE016
Calculating a consistency ratio
Figure 745683DEST_PATH_IMAGE017
：
Figure 318747DEST_PATH_IMAGE018
；
When the consistency ratio
Figure 943764DEST_PATH_IMAGE017
When the ratio is less than 0.1, the consistency of the matrix is judged to be feasible, and when the consistency ratio is less than
Figure 193348DEST_PATH_IMAGE017
If it is greater than 0.1, it is judged that the matrix consistency is not feasible, and the consistency ratio is corrected
Figure 271026DEST_PATH_IMAGE017
Less than 0.1.
In the APP software interaction method based on the SaaS platform, the combination weight is calculated as follows:
is provided with a standard layer
Figure 913360DEST_PATH_IMAGE019
An element
Figure 392883DEST_PATH_IMAGE020
The weight vectors of the elements of the evaluation target layer of the standard layer are respectively
Figure 78948DEST_PATH_IMAGE021
Wherein the elements in the standard layer
Figure 643921DEST_PATH_IMAGE022
In the corresponding index layer are
Figure 355525DEST_PATH_IMAGE023
Sub-elements
Figure 955134DEST_PATH_IMAGE024
Evaluation of elements in the standard layer in the index layer
Figure 562833DEST_PATH_IMAGE022
Respectively are
Figure 864370DEST_PATH_IMAGE025
Then the elements in the index layer
Figure 582927DEST_PATH_IMAGE026
The combined weight of the elements in the evaluation target layer is as follows:
Figure 37042DEST_PATH_IMAGE027
。
in the APP software interaction method based on the SaaS platform, the step of setting a decision maker in the SaaS platform to push accurate merchant information to the owner APP of the owner specifically includes:
after the merchant APP uploads the merchant information, the SaaS platform scores each item of merchant information according to a built-in scoring criterion to obtain each index score of the merchant information, when a vehicle owner matches the merchant information through the vehicle owner APP, each index score of the merchant information is correspondingly multiplied by a weight vector in the combined weight respectively through a decision maker to match each item of merchant information, and then the sum is added to obtain a total score, wherein the highest total score in the merchant information is the most appropriate merchant information of the vehicle owner.
Compared with the prior art, the decision maker is arranged in the SaaS platform to push accurate merchant information which accords with the preference of the owner to the owner APP of the owner, so that the owner can play more conveniently, and the merchant can deliver more accurate advertisements. For the arrangement of the decision maker, the complex problem of pushing the most suitable merchant information to the vehicle owner is divided into a plurality of levels by using the tower structure module, then the establishment of a judgment matrix and the calculation of a weight vector are carried out on the complex problem, various types of combination weights are given according to the weight vector, each type of combination weight corresponds to a decision scheme, and the decision scheme is the importance degree of a target level, so that the optimal merchant information is determined according to the maximum weight principle. The invention reasonably expresses the merchant information in a weight vector mode, and provides the most preferable merchant information for the vehicle owner conveniently, so that the vehicle owner can obtain the best merchant information conveniently, and the adaptability of the vehicle owner and the merchant is improved.
Drawings
Figure 1 is a schematic view of a tower structure module of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples, which are not to be construed as limiting the invention.
Example (b): the APP software interaction method based on the SaaS platform comprises the SaaS platform, wherein the SaaS platform is arranged on the Alice cloud and is connected with a car owner APP and a merchant APP; the method comprises the steps that a merchant APP uploads merchant information to a SaaS platform, a vehicle owner APP uploads vehicle owner preference information to the SaaS platform, the SaaS platform carries out interactive matching of the merchant information and the vehicle owner preference information, and a decision maker is arranged in the SaaS platform and pushes accurate merchant information to a vehicle owner APP of a vehicle owner; wherein the setting of the decision maker comprises the following steps:
s1, establishing a tower structure module, wherein the tower structure module comprises a target layer, a standard layer and an index layer as shown in figure 1; the target layer is used for expressing the most suitable merchant information acquired by the vehicle owner, the standard layer is a judgment standard for realizing the basis for the vehicle owner to acquire the most suitable merchant information, and the index layer is an optional index for realizing the vehicle owner to acquire the most suitable merchant information; the elements of the target layer are the optimal and most suitable merchant information obtained by the vehicle owner; the elements of the destination layer are the optimal merchant information selectable by the owner; elements of the standard layer include security, economy, and convenience; the elements of the index layer include merchant product information (such as quality, hygiene and the like) corresponding to safety, and also can be determined from use feedback of commodities, such as high quality, low price, moderate price, high price and low quality) and merchant information evaluation degree (evaluation after users play), a charging price corresponding to economy and merchant rights and interests (such as whether parking is free time, whether parking is discounted, whether available coupons are available and the like) corresponding to convenience, parking space availability, distance utilization rate (the distance between a merchant information position and a vehicle owner is longer, the utilization rate is lower) and parking positions (ground, underground, mechanical parking spaces and the like).
S2, establishing a judgment matrix about the standard layer for the target layer, and establishing a judgment matrix about the index layer for the standard layer; calculating a weight vector of each judgment matrix to obtain the weight vector of each element in each judgment matrix;
when a judgment matrix is established, importance comparison in the judgment matrix is expressed by scale; when two elements are compared, the relationship of the importance of the two elements is expressed by a scale of 1-9, wherein the scale 1 represents that the two elements have equal importance, the scale 3 represents that the former is slightly more important than the latter, the scale 5 represents that the former is obviously more important than the latter, the scale 7 represents that the former is strongly more important than the latter, the scale 9 represents that the former is extremely more important than the latter, and the scales 2, 4, 6 and 8 represent intermediate values of adjacent judgments of the two elements; note the book
Figure 815642DEST_PATH_IMAGE028
Presentation element
Figure 604476DEST_PATH_IMAGE029
And elements
Figure 861145DEST_PATH_IMAGE030
Compared with elements
Figure 169766DEST_PATH_IMAGE031
The level of importance of (a) is,
Figure 119268DEST_PATH_IMAGE032
presentation element
Figure 129818DEST_PATH_IMAGE030
And elements
Figure 455757DEST_PATH_IMAGE029
Elements of
Figure 618885DEST_PATH_IMAGE030
The importance level of.
In this embodiment, a destination layer is denoted by a, a standard layer is denoted by B (B1 denotes safety, B2 denotes economy, B3 denotes convenience), and an index layer is denoted by C (C1 merchant product information, C2 denotes merchant information evaluation degree, C3 denotes charging price, C4 denotes equity, C5 denotes parking space availability, C6 denotes distance utilization, and C7 denotes parking position). The comparison of importance between elements is set according to owner selection, and this value can be preset using questionnaire surveys when the owner uses the owner APP.
In this embodiment, taking a certain owner as an example, the owner makes a selection on the importance relationship among the elements when using the owner APP, and thus, according to the selection of the owner, a judgment matrix about a standard layer is established in a decision maker of the SaaS platform for a destination layer as follows:
Figure 739288DEST_PATH_IMAGE033
TABLE 1 (judgment matrix of A-B)
Then, a judgment matrix about the index layer is established for the standard layer, and the judgment matrix B1-C and the judgment matrix B3-C of the embodiment are shown in the following tables 2 and 3:
Figure 50184DEST_PATH_IMAGE034
table 2 (judgment matrix of B1-C)
Figure 181080DEST_PATH_IMAGE035
Table 3 (judgment matrix of B3-C)
And after obtaining the judgment matrix, performing weight vector calculation on the judgment matrix to obtain the weight vector of each element in each judgment matrix, wherein the calculation step comprises the following steps:
s2.1, calculating the product of each row of elements in the judgment matrix;
Figure 198715DEST_PATH_IMAGE036
；
s2.2, calculating each product
Figure 755598DEST_PATH_IMAGE037
Is/are as follows
Figure 491473DEST_PATH_IMAGE038
Root of inferior square;
Figure 142903DEST_PATH_IMAGE039
；
s2.3, dividing each square root
Figure 280623DEST_PATH_IMAGE040
Carrying out normalization processing;
Figure 8408DEST_PATH_IMAGE041
；
to obtain
Figure 965999DEST_PATH_IMAGE042
I.e. the weight vector of the decision matrix.
In this embodiment, according to the weight vector calculation step, weight vectors are calculated for the determination matrix a-B, the determination matrix B1-C, and the determination matrix B3-C:
s2.1, the product of each row element in the judgment matrix of A-B is as follows:
Figure 421120DEST_PATH_IMAGE043
；
the product of each row element of the decision matrix of B1-C is:
Figure 413347DEST_PATH_IMAGE044
；
the product of each row element of the decision matrix of B3-C is:
Figure 46454DEST_PATH_IMAGE045
；
s2.2, calculating each product
Figure 756921DEST_PATH_IMAGE046
Is/are as follows
Figure 15733DEST_PATH_IMAGE047
Root of inferior square;
Figure 862466DEST_PATH_IMAGE048
；
Figure 932053DEST_PATH_IMAGE049
；
Figure 864237DEST_PATH_IMAGE050
；
s2.3, dividing each square root
Figure 474210DEST_PATH_IMAGE040
And (3) carrying out normalization treatment:
Figure 690297DEST_PATH_IMAGE051
；
Figure 665206DEST_PATH_IMAGE052
；
Figure 350265DEST_PATH_IMAGE053
。
carrying out consistency check on the weight vector of the judgment matrix obtained after calculation, and using the weight vector to check the rationality of the judgment matrix and avoid violating common sense (for example, A is extremely important compared with B, B is extremely important compared with C, and C is extremely important compared with A); the steps of the consistency check are as follows:
first, a consistency index is calculated
Figure 701612DEST_PATH_IMAGE054
：
Figure 772205DEST_PATH_IMAGE055
；
In the formula
Figure 918016DEST_PATH_IMAGE056
Judging the maximum weight value of the matrix;
according to the average random consistency index
Figure 824792DEST_PATH_IMAGE016
Calculating a consistency ratio
Figure 714250DEST_PATH_IMAGE057
：
Figure 639350DEST_PATH_IMAGE058
；
In this embodiment, the average random consistency index
Figure 221641DEST_PATH_IMAGE016
(available from AHP lookup tables) is shown in Table 4 below:
Figure 615713DEST_PATH_IMAGE059
TABLE 4
When the consistency ratio
Figure 308863DEST_PATH_IMAGE017
When the ratio is less than 0.1, the consistency of the matrix is judged to be feasible, and when the consistency ratio is less than
Figure 354048DEST_PATH_IMAGE017
If it is greater than 0.1, it is judged that the matrix consistency is not feasible, and the consistency ratio is corrected
Figure 107240DEST_PATH_IMAGE017
Less than 0.1. Through calculation, the consistency indexes of the judgment matrix of A-B, the judgment matrix of B1-C and the judgment matrix of B3-C
Figure 723029DEST_PATH_IMAGE016
0.005, 0 and 0.02, respectively, which meet the consistency test.
And S3, after the weight vector is obtained, calculating the combination weight formed by different element combinations from top to bottom from the target layer, thereby obtaining the decision makers with different combination weight types.
The combining weights are calculated as follows:
is provided with a standard layer
Figure 219870DEST_PATH_IMAGE019
An element
Figure 125421DEST_PATH_IMAGE060
The weight vectors of the elements of the evaluation target layer of the standard layer are respectively
Figure 49515DEST_PATH_IMAGE021
In the standard layerElement(s)
Figure 418179DEST_PATH_IMAGE022
In the corresponding index layer are
Figure 249869DEST_PATH_IMAGE023
Sub-elements
Figure 754800DEST_PATH_IMAGE061
Evaluation of elements in the standard layer in the index layer
Figure 99062DEST_PATH_IMAGE022
Respectively are
Figure 689443DEST_PATH_IMAGE062
Then the elements in the index layer
Figure 528086DEST_PATH_IMAGE026
The combined weight of the elements in the evaluation target layer is as follows:
Figure 153103DEST_PATH_IMAGE027
。
in this embodiment, the weight vector quantities of the judgment matrix A-B, the judgment matrix B1-C and the judgment matrix B3-C calculated in step 2 are respectively:
Figure 402687DEST_PATH_IMAGE063
，
Figure 480365DEST_PATH_IMAGE064
，
and
Figure 857120DEST_PATH_IMAGE065
。
the combining weight is:
Figure 336642DEST_PATH_IMAGE066
。
as shown in table 5:
Figure 22708DEST_PATH_IMAGE067
TABLE 5
And setting a decision maker special for the vehicle owner in the SaaS platform according to the combination weight of the vehicle owner.
In this embodiment, taking an example that the car owner has a car, the expected playing time is 2 pm. At present, the SaaS platform has the following information of merchants near the destination:
Figure 587681DEST_PATH_IMAGE068
TABLE 6
After the user uploads the merchant information, the SaaS platform scores each item of merchant information according to a built-in scoring criterion to obtain each index score of the merchant information. The scoring criteria may be set according to actual needs, taking table 6 as an example:
in the security, the merchant product information is set to be 40 for high price and low quality, 60 for medium price and low price and 100 for high quality and low price; the merchant information evaluation degree is set to be high at 100, medium at 60 and low at 40; the low price setting 100 of the economy, the medium setting 60, the high setting 20; the right-of-time setting for convenience is 100, the general setting is 60, the queuing setting is 40, the smaller of the space availability ratios is 40, the general setting is 60, the sufficient setting is 100, the merchant information position is underground setting 100, the ground setting is 60, and the mechanical setting is 40.
When the owner matches the merchant information through the owner APP, matching each item of merchant information through the decision maker, correspondingly multiplying each item of index score of the merchant information with the weight vector in the combined weight respectively, and adding to obtain a total score; the total score calculation formula of the owner in this embodiment is as follows:
Figure 768127DEST_PATH_IMAGE069
；
therefore, according to the merchant information in the table 5, scoring is performed by using the scoring criterion, then the total score of each piece of merchant information is calculated by using the total score calculation formula of the vehicle owner, and after the score of the merchant information 1 is calculated to be 69.23, the score of the merchant information 2 is calculated to be 99.03, the score of the merchant information 3 is calculated to be 75.95, and the score of the merchant information 4 is calculated to be 89.08, it can be seen that the total score of the merchant information 2 in the merchant information is the highest, and the merchant information 2 is the merchant information most suitable to be pushed to the vehicle owner.
In summary, the decision maker is arranged in the SaaS platform to push accurate merchant information meeting the preference of the owner to the owner APP of the owner, so that the owner can play more conveniently, and the merchant can deliver more accurate advertisements. For the arrangement of the decision maker, the complex problem of pushing the most suitable merchant information to the vehicle owner is divided into a plurality of levels by using the tower structure module, then the establishment of a judgment matrix and the calculation of a weight vector are carried out on the complex problem, various types of combination weights are given according to the weight vector, each type of combination weight corresponds to a decision scheme, and the decision scheme is the importance degree of a target level, so that the optimal merchant information is determined according to the maximum weight principle. The invention reasonably expresses the merchant information in a weight vector mode, and provides the most preferable merchant information for the vehicle owner conveniently, so that the vehicle owner can obtain the best merchant information conveniently, and the adaptability of the vehicle owner and the merchant is improved.

Claims (7)

1. The APP software interaction method based on the SaaS platform comprises the SaaS platform, wherein the SaaS platform is connected with a car owner APP and a merchant APP; trade company APP uploads trade company's information to SaaS platform, car owner APP uploads car owner preference information to SaaS platform, SaaS platform carries out the mutual matching of trade company's information and car owner preference information, its characterized in that: the method comprises the steps that a decision maker is arranged in a SaaS platform to push accurate merchant information to an owner APP of an owner; wherein the setting of the decision maker comprises the following steps:
s1, establishing a tower structure module, wherein the tower structure module comprises a target layer, a standard layer and an index layer; the target layer is used for expressing the most suitable merchant information acquired by the vehicle owner, the standard layer is a judgment standard for realizing the basis for the vehicle owner to acquire the most suitable merchant information, and the index layer is an optional index for realizing the vehicle owner to acquire the most suitable merchant information;
s2, establishing a judgment matrix about the standard layer for the target layer, and establishing a judgment matrix about the index layer for the standard layer; calculating a weight vector of each judgment matrix to obtain the weight vector of each element in each judgment matrix;
and S3, after the weight vector is obtained, calculating the combination weight formed by different element combinations from top to bottom from the target layer, thereby obtaining the decision makers with different combination weight types.
2. The APP software interaction method based on the SaaS platform as claimed in claim 1, characterized in that: the elements of the destination layer are the optimal merchant information selectable by the owner; elements of the standard layer include security, economy, and convenience; the elements of the index layer comprise merchant product information and merchant evaluation degree corresponding to safety, charging price corresponding to economy, merchant rights and interests, parking space vacancy rate, distance utilization rate and parking position corresponding to convenience.
3. The APP software interaction method based on the SaaS platform as claimed in claim 1, characterized in that: in step S2, when the determination matrix is established, importance comparison in the determination matrix is expressed by scale; when two elements are compared, the relationship of the importance of the two elements is expressed by a scale of 1-9, wherein the scale 1 represents that the two elements have equal importance, the scale 3 represents that the former is slightly more important than the latter, the scale 5 represents that the former is obviously more important than the latter, the scale 7 represents that the former is strongly more important than the latter, the scale 9 represents that the former is extremely more important than the latter, and the scales 2, 4, 6 and 8 represent intermediate values of adjacent judgments of the two elements; note the book
Figure 364755DEST_PATH_IMAGE001
Presentation element
Figure 603975DEST_PATH_IMAGE002
And elements
Figure 384849DEST_PATH_IMAGE003
Compared with elements
Figure 776647DEST_PATH_IMAGE004
The importance level of;
Figure 617564DEST_PATH_IMAGE005
presentation element
Figure 338221DEST_PATH_IMAGE003
And elements
Figure 922786DEST_PATH_IMAGE002
Compared with elements
Figure 169091DEST_PATH_IMAGE003
The importance level of.
4. The APP software interaction method based on the SaaS platform as claimed in claim 3, characterized in that: the step of calculating the weight vector of the judgment matrix comprises the following steps;
s2.1, calculating the product of each row of elements in the judgment matrix;
Figure 915330DEST_PATH_IMAGE006
；
s2.2, calculating each product
Figure 129143DEST_PATH_IMAGE007
Is/are as follows
Figure 517399DEST_PATH_IMAGE008
Root of inferior square;
Figure 618210DEST_PATH_IMAGE009
；
s2.3, dividing each square root
Figure 800930DEST_PATH_IMAGE010
Carrying out normalization processing;
Figure 236459DEST_PATH_IMAGE011
；
to obtain
Figure 162827DEST_PATH_IMAGE012
I.e. the weight vector of the decision matrix.
5. The APP software interaction method based on the SaaS platform as claimed in claim 4, wherein: carrying out consistency check on the weight vector of the judgment matrix obtained after calculation, and using the weight vector to check the rationality of the judgment matrix and avoid violating the common sense; the steps of the consistency check are as follows:
first, a consistency index is calculated
Figure 118144DEST_PATH_IMAGE013
：
Figure 737345DEST_PATH_IMAGE014
；
In the formula
Figure 925749DEST_PATH_IMAGE016
Judging the maximum weight value of the matrix;
according to the average random consistency index
Figure 655808DEST_PATH_IMAGE017
Calculating a consistency ratio
Figure 200053DEST_PATH_IMAGE018
：
Figure 990154DEST_PATH_IMAGE019
；
When the consistency ratio
Figure 665855DEST_PATH_IMAGE020
When the ratio is less than 0.1, the consistency of the matrix is judged to be feasible, and when the consistency ratio is less than
Figure 668446DEST_PATH_IMAGE020
If it is greater than 0.1, it is judged that the matrix consistency is not feasible, and the consistency ratio is corrected
Figure 129515DEST_PATH_IMAGE020
Less than 0.1.
6. The APP software interaction method based on the SaaS platform as claimed in claim 4, wherein: the combining weights are calculated as follows:
is provided with a standard layer
Figure 215151DEST_PATH_IMAGE021
An element
Figure 722356DEST_PATH_IMAGE022
The weight vectors of the elements of the evaluation target layer of the standard layer are respectively
Figure 669583DEST_PATH_IMAGE023
Wherein the elements in the standard layer
Figure 578633DEST_PATH_IMAGE024
In the corresponding index layer are
Figure 444958DEST_PATH_IMAGE025
Sub-elements
Figure 829672DEST_PATH_IMAGE026
Evaluation of elements in the standard layer in the index layer
Figure 439645DEST_PATH_IMAGE024
Respectively are
Figure 344147DEST_PATH_IMAGE027
Then the elements in the index layer
Figure 381373DEST_PATH_IMAGE028
The combined weight of the elements in the evaluation target layer is as follows:
Figure 253383DEST_PATH_IMAGE029
。
7. the APP software interaction method based on the SaaS platform as claimed in claim 1, characterized in that: the method is characterized in that a decision maker arranged in the SaaS platform specifically pushes accurate merchant information to an owner APP of an owner:
after the merchant APP uploads the merchant information, the SaaS platform scores each item of merchant information according to a built-in scoring criterion to obtain each index score of the merchant information, when a vehicle owner matches the merchant information through the vehicle owner APP, each item of merchant information is matched through a decision maker, each index score of the merchant information is respectively multiplied by a weight vector in the combined weight correspondingly and then added to obtain a total score, and the highest total score in the merchant information is the most appropriate merchant information of the vehicle owner.
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