CN112801733A - Service provider grade evaluating method, storage medium and system based on block chain and artificial intelligence - Google Patents
Service provider grade evaluating method, storage medium and system based on block chain and artificial intelligence Download PDFInfo
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Abstract
The invention provides a service provider grade evaluating method, a storage medium and a system based on a block chain and artificial intelligence, wherein under the condition that a service provider to be shelved is shelved on other service provider platforms, first grading data of the service provider to be shelved on the other service provider platforms are recorded on the block chain, then the first grading data are obtained from the block chain and are input into a judgment model based on a convolutional neural network, the judgment model judges whether the grade of the service provider reflected by the first grading data is higher than a preset grade of the shelve, if so, the service provider to be shelved is allowed to be shelved on the service provider platform, then second grading data of the service provider to be shelved on the service provider platform is obtained, the second grading data are input into a judgment model based on the convolutional neural network, and the judgment model judges whether the grade of the service provider reflected by the second grading data is lower than the preset grade of the shelve, and if so, enabling the shelved service provider to be shelved from the service provider platform.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a service provider grade evaluating method, a storage medium and a system based on a block chain and artificial intelligence.
Background
With the rapid development of digital economy, most of the service providers choose the online service provider platform to put on shelf to increase their turnover. At present, a plurality of online facilitator platforms are provided, such as popular comment, American group and the like, the facilitator platforms are provided with scoring systems, and consumers can score the facilitator on the scoring systems, so that other consumers can select the facilitator by referring to the scoring. If the service quality or the product quality of the service provider is too poor, the score of the service provider is too low naturally, and the consumer can know that the service quality or the product quality of the service provider is too poor according to the too low score, so that the consumer can manually screen the service provider with too low score for selection when selecting the service provider, and a great amount of time and energy are consumed for the consumer.
Before a certain service provider platform prepares to put a certain service provider on the shelf, if the service provider is already put on the other service provider platform, the service provider platform can obtain the grading data of the service provider from the other service provider platform, and then judge whether the grading data reaches the default value of putting on the shelf, if so, the grading data reflects that the grade of the service provider is higher than the default degree of putting on the shelf, so the service provider is allowed to put on the service provider platform, if not, the grading data reflects that the grade of the service provider is not higher than the default degree of putting on the shelf, so the service provider is not allowed to put on the service provider platform, and the consumer does not need to manually screen the service provider with too low grade, however, the grading data is old data on the other service provider platform, and the service quality or product quality of the service provider after long-term operation is possibly deteriorated, therefore, after the service provider is on shelf on the service provider platform, a consumer who selects the service provider for consumption may score low scores, so that the service provider becomes too low in score, and therefore the service provider platform manages the service providers by using old data on other service provider platforms, and the service providers with too low scores still may exist, so that the consumer still needs to spend time and effort to manually screen the service providers with too low scores when selecting the service providers.
Disclosure of Invention
The invention aims to solve the technical problem of how to manually screen the service providers with low scores without consuming time and energy when a consumer selects the service providers.
In order to solve the technical problem, the invention provides a service provider grade evaluating method based on a block chain and artificial intelligence, which executes the following steps under the condition that a service provider to be put on shelf is put on shelf on other service provider platforms:
A. recording first scoring data of the to-be-shelved facilitator on other shelved facilitator platforms on a block chain;
B. acquiring the first grading data from the block chain and inputting the first grading data into a judgment model based on a convolutional neural network, judging whether the grade of a service provider reflected by the first grading data is higher than a preset grade for uploading by the judgment model, and if so, allowing the service provider to be uploaded to the platform of the service provider;
C. acquiring second grading data of a service provider on the service provider platform, wherein the service provider is placed on the service provider platform;
D. and inputting the second grading data into a judgment model based on a convolutional neural network, judging whether the grade of the service provider reflected by the second grading data is lower than a preset unloading degree or not by the judgment model, and if so, unloading the service provider on the service provider platform from the service provider platform.
Preferably, the second scoring data is an average of a plurality of scores of the facilitator on the platform, which have been set up on the platform.
Preferably, the first scoring data is an average of a plurality of scores of the facilitator to be shelved on other facilitator platforms that have been shelved.
Preferably, in the step C, user data of the service provider on the service provider platform that has been put on the service provider platform is further obtained, a consumer corresponding to each of the multiple scores of the service provider is analyzed according to the user data, and if the same consumer corresponds to multiple related scores, the second score data is calculated by taking the average value of the multiple related scores of the consumer as one score.
Preferably, in the step C, transaction amount data of the service provider on the service provider platform, which is already on the service provider platform, is also acquired; in the step D, it is first determined whether the transaction amount data is higher than a preset transaction degree, and if not, it is not determined whether the level of the service provider reflected by the second rating data is lower than a preset off-shelf degree.
Preferably, the preset transaction degree is that the transaction amount data is greater than or equal to a preset transaction value.
Preferably, the preset degree of the upper rack is that the first score data is greater than or equal to the preset value of the upper rack, and the preset degree of the lower rack is that the second score data is less than the preset value of the lower rack.
Preferably, the upper preset value is equal to the lower preset value.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method as described above.
The invention also provides a service provider grade evaluating system based on the block chain and the artificial intelligence, which comprises a computer readable storage medium and a processor which are connected with each other, wherein the computer readable storage medium is as described above.
The invention has the following beneficial effects: if the grade of the service provider reflected by the first scoring data of the service provider waiting to be on shelf on other service provider platforms is higher than the preset grade of the service provider on the shelf, it means that the score of the service provider to be shelved is not too low, so that the service provider to be shelved is shelved on the service provider platform, if the grade of the service provider reflected by the second scoring data of the service provider on the service provider platform is lower than the preset degree of off-shelf service provider, it means that the service provider on the service provider platform has too low score on the service provider platform, so that the service provider on the service provider platform is off-loaded from the service provider platform, which can ensure that there is no service provider with too low score on the service provider platform, therefore, when the consumer selects the service provider on the service provider platform, the consumer does not need to spend time and energy to manually screen the service provider with low score.
Detailed Description
Exemplary embodiments of the present application will be described in detail below. While exemplary embodiments of the present application are described below, it should be understood that the present application can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
To increase the business volume of the service provider, the service provider usually selects an online service provider platform (a service provider management platform which is hungry, American or specially constructed) to put on shelf. The service provider platforms are provided with scoring systems, consumers can score service providers on the scoring systems, so that other consumers can select service providers by referring to the scores, and in order to enable consumers not to spend time and effort to manually select service providers with too low scores when selecting service providers, the embodiment provides a service provider grade evaluating system based on a block chain and artificial intelligence. The service provider grade evaluating system based on the block chain and the artificial intelligence can evaluate and manage service providers to be put on shelves and on shelves, for example, a certain service provider is hungry to put on a platform and is ready to put on a beauty group platform, in this case, a computer program in a computer readable storage medium of the service provider grade evaluating system based on the block chain and the artificial intelligence is executed by a processor, and the service provider grade evaluating method based on the block chain and the artificial intelligence is realized as follows:
the method comprises the steps of firstly obtaining a plurality of scores of the service providers to be put on shelves, then calculating the average value of the scores to obtain first score data, then recording the first score data on a block chain so as not to be modified, when the grade of the service provider needs to be evaluated, obtaining the first score data from the block chain and inputting the first score data into a judgment model based on a convolutional neural network, and judging whether the first score data is larger than or equal to a preset value for putting on shelves or not by the judgment model. The Convolutional Neural Networks (CNN) are feed-forward Neural Networks (fed Neural Networks) including Convolutional calculation and having a deep structure, and are one of representative algorithms for deep learning, and a plurality of sets of scoring data and signals corresponding to whether the scoring data is greater than an upper preset value are input into the Convolutional Neural Networks for sample training for a plurality of times, so that the Convolutional Neural network-based judgment model can be obtained, and the training process is not repeated here. In this embodiment, the default value of shelving is 2 points, and the first score data calculated as to how hungry the waiter to be shelved is 3 points, that is, the first score data of how hungry the waiter to be shelved is greater than the default value of shelving, which means that the rank of the waiter reflected by the first score data is higher than the default degree of shelving, so that the waiter to be shelved is allowed to be shelved on the mei-qu platform.
In other embodiments, if the first score data is equal to the default value of top rack, it also means that the level of the service provider reflected by the first score data is higher than the default level of top rack, thus allowing the service provider to be top rack on the mei qun platform; if the first scoring data is smaller than the upper preset value, the first scoring data means that the grade of the service provider reflected by the first scoring data is not higher than the upper preset degree, and therefore the service provider to be placed on the shelf is not allowed to be placed on the mei-qu platform.
After the facilitator is put on the American group platform, transaction data, user data and a plurality of scores of the facilitator on the American group platform are obtained, then a consumer corresponding to a plurality of scores of the facilitator on the American group platform is analyzed according to the user data, if the same consumer corresponds to a plurality of related scores, the average value of the plurality of related scores of the consumer is used as one score, then the average value of the plurality of scores of the facilitator on the American group platform is calculated to obtain second score data, for example, a consumer A has 10 related scores for the facilitator on the American group platform, 10 related scores are all 5 scores, other different consumers have 5 scores for the facilitator on the American group platform, 5 scores are all 1 score, the average value of the 10 related scores is calculated to be 5 scores, then the average value of the 10 related scores is used as one score, i.e. only one score of 5 points, then the average of multiple scores of the facilitator on the mei-qu platform is calculated, and the second score data is (5+1+1+1+ 1)/(1+5) ═ 1.67 points. Then, judging whether the transaction amount data is greater than or equal to a transaction preset value, in this embodiment, the transaction preset value is 10 times, and the transaction amount data of the facilitator on the mei-qu platform is 15 times, that is, the transaction amount data of the facilitator on the mei-qu platform is greater than the transaction preset value, which means that the transaction amount data of the facilitator on the mei-qu platform is up to a transaction preset degree, so that the calculated second score data can accurately reflect the operation condition of the facilitator, so that the second score data is input into a judgment model based on a convolutional neural network, the judgment model judges whether the second score data is less than a lower-shelf preset value, in this embodiment, the lower-shelf preset value is also 2 points and is equal to an upper-shelf preset value, and the service quality or product quality of the facilitator after the mei platform operates for a long time is changed from good to bad, which results in that the second score data of the facilitator on the mei platform is 1.67 points, namely, the second scoring data of the facilitator on the mei-qu platform is smaller than the lower preset value, which means that the grade of the facilitator reflected by the second scoring data is as low as the lower preset degree, so that the facilitator is lowered from the mei-qu platform, and thus the facilitator can be ensured not to have too low scoring facilitators on the mei-qu platform, and thus, the consumer does not need to spend time and energy to manually screen the too low scoring facilitator when selecting the facilitator on the mei-qu platform. In other embodiments, if the service quality or the product quality of the facilitator is maintained better after the mei-zong platform operates for a long time, the consumer will give a higher score, so that the second score data is greater than or equal to the lower preset value, which means that the grade of the facilitator reflected by the second score data is not as low as the lower preset degree, and thus the facilitator is not placed from the mei-zong platform.
In other embodiments, if the transaction amount data is equal to the transaction preset value, it also means that the transaction amount data is up to the transaction preset degree, that is, the second scoring data can accurately reflect the operation condition of the facilitator who has been put on the mei-gang platform, and therefore, it can also be determined whether the facilitator grade reflected by the second scoring data is less than the put-off preset degree, so as to determine whether to put the facilitator who has been put on the mei-gang platform off the mei-gang platform; if the transaction amount data is smaller than the transaction preset value, the transaction amount data is not as high as the transaction preset degree, namely the second scoring data cannot accurately reflect the operation condition of the service provider on the U.S. Pat. platform, so that whether the grade of the service provider reflected by the second scoring data is smaller than the off-shelf preset degree is not judged, and whether the service provider on the U.S. Pat. platform is off-shelf from the U.S. Pat. platform is not judged.
Optionally, the upper preset value and the lower preset value may be 1.5 minutes, 2.5 minutes, 3 minutes or any other settable value (5 minutes); the transaction preset value may be 20 times, 30 times, 50 times or any other settable value.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the protection scope of the present application, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application.
Claims (10)
1. A service provider grade evaluating method based on a block chain and artificial intelligence is characterized in that under the condition that a service provider to be shelved is shelved on other service provider platforms, the following steps are executed:
A. recording first scoring data of the to-be-shelved facilitator on other shelved facilitator platforms on a block chain;
B. acquiring the first grading data from the block chain and inputting the first grading data into a judgment model based on a convolutional neural network, judging whether the grade of a service provider reflected by the first grading data is higher than a preset grade for uploading by the judgment model, and if so, allowing the service provider to be uploaded to the platform of the service provider;
C. acquiring second grading data of a service provider on the service provider platform, wherein the service provider is placed on the service provider platform;
D. and inputting the second grading data into a judgment model based on a convolutional neural network, judging whether the grade of the service provider reflected by the second grading data is lower than a preset unloading degree or not by the judgment model, and if so, unloading the service provider on the service provider platform from the service provider platform.
2. The method of claim 1 wherein said second scoring data is a mean of a plurality of scores of a facilitator that has been staged on said facilitator platform.
3. The method of claim 1 or 2, wherein the first scoring data is a mean of a plurality of scores of the facilitator to be shelved on other facilitator platforms that have been shelved.
4. The method as claimed in claim 2, wherein in the step C, user data of the service provider on the service provider platform, which is already on the service provider platform, is further obtained, the consumer corresponding to each of the plurality of scores of the service provider is analyzed according to the user data, and if the same consumer corresponds to a plurality of related scores, the average value of the plurality of related scores of the consumer is used as one score to calculate the second score data.
5. The method of claim 1 or 4, wherein: in the step C, transaction amount data of the service provider on the service provider platform, which is already on the shelf of the service provider platform, is also obtained; in the step D, it is first determined whether the transaction amount data is higher than a preset transaction degree, and if not, it is not determined whether the level of the service provider reflected by the second rating data is lower than a preset off-shelf degree.
6. The method of claim 5, wherein the predetermined degree of trading is the trading volume data being greater than or equal to a predetermined trading value.
7. The method of claim 1, wherein the predetermined degree of the upper stage is that the first score data is greater than or equal to a predetermined value of the upper stage, and the predetermined degree of the lower stage is that the second score data is less than a predetermined value of the lower stage.
8. The method of claim 7, wherein said upper shelf preset value is equal to said lower shelf preset value.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
10. A block chain and artificial intelligence based service provider rating system comprising a computer readable storage medium and a processor coupled to each other, wherein the computer readable storage medium is as claimed in claim 9.
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| CN202110041375.2A CN112801733A (en) | 2021-01-13 | 2021-01-13 | Service provider grade evaluating method, storage medium and system based on block chain and artificial intelligence |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140244291A1 (en) * | 2010-12-02 | 2014-08-28 | Hartford Fire Insurance Company | Outcomes based service provider networks |
| CN111415216A (en) * | 2020-02-11 | 2020-07-14 | 广州探途网络技术有限公司 | Commodity recommendation method and device, server and storage medium |
| CN111581224A (en) * | 2020-05-09 | 2020-08-25 | 全球能源互联网研究院有限公司 | Power Internet of things service credibility evaluation method and system based on intelligent contract |
| CN112036941A (en) * | 2020-08-25 | 2020-12-04 | 山东爱城市网信息技术有限公司 | Block chain-based store consumption evaluation method, equipment and medium |
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- 2021-01-13 CN CN202110041375.2A patent/CN112801733A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140244291A1 (en) * | 2010-12-02 | 2014-08-28 | Hartford Fire Insurance Company | Outcomes based service provider networks |
| CN111415216A (en) * | 2020-02-11 | 2020-07-14 | 广州探途网络技术有限公司 | Commodity recommendation method and device, server and storage medium |
| CN111581224A (en) * | 2020-05-09 | 2020-08-25 | 全球能源互联网研究院有限公司 | Power Internet of things service credibility evaluation method and system based on intelligent contract |
| CN112036941A (en) * | 2020-08-25 | 2020-12-04 | 山东爱城市网信息技术有限公司 | Block chain-based store consumption evaluation method, equipment and medium |
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