CN110705957A - Suggestion result feedback method of man-machine cooperative type weak artificial intelligence cloud system - Google Patents
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Abstract
The invention provides a suggested result feedback method of a man-machine cooperative weak artificial intelligence cloud system, and belongs to the field of artificial intelligence. Under the scene of multi-person one-machine cooperative artificial intelligence cloud service, under the condition that the number of personal databases which are different from the suggested results of the professional databases and have the same suggested results with each other exceeds half of the total number of the personal databases, the method calculates the comprehensive weight of the personal databases which are different from the suggested results of the professional databases and have the same suggested results with each other, and then judges whether the comprehensive weight is larger than the professional data updating decision value or not. And if so, updating corresponding data of the professional database. Otherwise, the data of the professional database is not updated. After the steps are implemented, an artificial intelligence algorithm is adopted, a suggestion result is obtained based on a current professional database and fed back to a user, and therefore the problem of suggestion result feedback of a man-machine cooperation type weak artificial intelligence cloud system when a plurality of personal suggestion results are uneven and the professional suggestion results are the same is solved.
Description
Technical Field
The invention relates to a data updating method of a database, belonging to the field of artificial intelligence.
Background
Existing Artificial Intelligence (AI) can be simply classified into strong artificial intelligence and weak artificial intelligence according to the degree of intelligence.
The strong artificial intelligence holds that it is possible to make intelligent machines that can really reason about and solve problems. Such machines are considered conscious, self-conscious, can think about problems independently and make optimal solutions to the problems, have their own view of value and world view systems, and have various instincts like life and safety requirements. In a sense, such machines can be considered as a new civilization.
Weak artificial intelligence has been thought impossible to produce intelligent machines that can really reason about and solve the problem. Such machines simply look intelligent, but do not really have intelligence, nor are they self-conscious. Such machines only provide suggested results, still requiring a human to make final decisions.
The man-machine cooperative architecture is a development trend of the existing weak artificial intelligence cloud system, and the man-machine cooperative weak artificial intelligence cloud system takes an artificial intelligence algorithm as a core, relies on a professional database and a personal database, and provides artificial intelligence online service for users. The process of the man-machine cooperative weak artificial intelligence cloud system for obtaining the suggestion result is as follows: the cloud host forwards the questions sent by the user to the cloud server. After receiving the user problem, the cloud server obtains a professional suggestion result based on the artificial intelligence algorithm and the professional database, and meanwhile, the cloud server obtains a plurality of personal suggestion results based on the artificial intelligence algorithm and the plurality of personal databases. Thus, the feedback of the suggested results of the human-computer collaborative weak artificial intelligence cloud system necessarily involves the following problems:
when the plurality of personal suggestion results are uneven and the professional suggestion results are the same, how the man-machine cooperative weak artificial intelligence cloud system feeds back the user is achieved.
Disclosure of Invention
The invention provides a suggested result feedback method of a man-machine cooperative weak artificial intelligence cloud system, which aims to solve the problems of the man-machine cooperative weak artificial intelligence cloud system.
The suggested result feedback method of the man-machine cooperative weak artificial intelligence cloud system comprises the following steps:
under the scene of multi-person-one-machine cooperative artificial intelligence cloud service, an artificial intelligence algorithm is adopted, the same problem is processed on the basis of a professional database and a plurality of personal databases, and when the number of the personal databases which are different from the suggested results of the professional database and have the same suggested results with each other exceeds half of the total number of the personal databases, the comprehensive weight of the personal databases which are different from the suggested results of the professional database and have the same suggested results with each other is calculated;
judging whether the comprehensive weight is larger than a set professional data updating decision value or not;
when the judgment result is yes, updating corresponding data of the professional database based on the question and a suggestion result given by a personal database which is different from the professional database suggestion result and has the same suggestion result with each other;
when the judgment result is negative, the corresponding data of the professional database is not updated;
adopting an artificial intelligence algorithm, obtaining a suggestion result based on the current professional database, and feeding the suggestion result back to the user;
the database suggestion result is a suggestion result obtained based on the database by adopting an artificial intelligence algorithm.
Preferably, the method for calculating the comprehensive weight includes:
distributing the weights of the plurality of personal databases evenly, and calculating the total weight of the personal databases which are different from the professional database suggestion result and have the same suggestion result with each other;
for the personal databases which are different from the professional database suggestion results and have the same suggestion results with each other, obtaining the product of the weight of each database and the given weight proportion coefficient of the database, and taking the sum of all the products as the total weight of the personal databases which are different from the professional database suggestion results and have the same suggestion results with each other;
and taking the average value of the two total weights obtained by implementing the steps as the comprehensive weight.
Preferably, the database weight scaling factor is an adoption rate of a previously proposed result of the corresponding database within a predetermined period of time.
Preferably, the professional data updating decision value is a ratio of the number of suggested results given by the plurality of personal databases, which are different from and identical to the professional database suggested results, to the total number of suggested results within a predetermined period of time.
Preferably, the updating the corresponding data of the professional database includes:
storing the questions and the suggested results given by the personal database which are different from the suggested results of the professional database and have the same suggested results with each other as professional data into the professional database or replacing original corresponding data in the professional database;
and training the professional database after the data is updated.
The invention relates to a method for feeding back suggested results of a man-machine cooperative weak artificial intelligence cloud system, which is characterized in that when a scene of multi-person one-machine cooperative artificial intelligence cloud service is faced, an artificial intelligence algorithm is adopted, the same problem is processed on the basis of a professional database and a plurality of personal databases, and the number of the personal databases which are different from the suggested results of the professional databases and have the same suggested results exceeds half of the total number of the personal databases, the comprehensive weight of the personal databases which are different from the suggested results of the professional databases and have the same suggested results is calculated, and then whether the comprehensive weight is larger than a set professional data updating decision value or not is judged. And when the judgment result is yes, updating corresponding data of the professional database based on the question and a suggestion result given by a personal database which is different from the professional database suggestion result and has the same suggestion result with each other. And when the judgment result is negative, not updating the corresponding data of the professional database. And when the data of the professional database is updated or the professional database does not need to update the data, obtaining a suggestion result based on the current professional database by adopting an artificial intelligence algorithm, and feeding the suggestion result back to the user, so as to solve the problem of suggestion result feedback of the man-machine cooperative weak artificial intelligence cloud system when a plurality of personal suggestion results are uneven and the professional suggestion results are the same.
Drawings
The suggested result feedback method of the man-machine cooperative weak artificial intelligence cloud system according to the present invention will be described in more detail below based on embodiments and with reference to the accompanying drawings, in which:
FIG. 1 is a flowchart illustrating an implementation of a proposed result feedback method of a human-computer collaborative weak artificial intelligence cloud system according to an embodiment;
FIG. 2 is a flow chart of an implementation of a method for calculating comprehensive weights of personal databases different from professional database recommendation results and identical to each other recommendation results;
FIG. 3 is a flowchart illustrating an embodiment of updating corresponding data of a professional database;
fig. 4 is a system framework diagram of the man-machine cooperative weak artificial intelligence cloud system according to the embodiment.
Detailed Description
The following will further describe the suggested result feedback method of the man-machine cooperative weak artificial intelligence cloud system according to the present invention with reference to the accompanying drawings.
Example this embodiment is described in detail below in conjunction with figure 1 ~ and figure 4.
The proposed result feedback method for the man-machine collaborative weak artificial intelligence cloud system in this embodiment is applicable to a scenario of a multi-person one-machine collaborative artificial intelligence cloud service, adopts an artificial intelligence algorithm and processes the same problem based on one professional database and a plurality of personal databases, and the number of the personal databases which are different from the proposed results of the professional databases and have the same proposed results with each other exceeds half of the total number of the personal databases, and includes:
step S1, calculating the comprehensive weight of the personal database which is different from the professional database proposal result and has the same proposal result with each other;
step S2, judging whether the comprehensive weight is larger than a preset professional data updating decision value or not, if so, executing S3, otherwise, executing S4;
step S3, updating corresponding data of the professional database based on the question and the suggestion result given by the personal database which is different from the professional database suggestion result and has the same suggestion result with each other;
step S4, corresponding data of the professional database are not updated;
and step S5, obtaining a suggestion result based on the current professional database by adopting an artificial intelligence algorithm, and feeding the suggestion result back to the user.
In this embodiment, the suggested result of the professional database and the suggested result given by the database both refer to the suggested result obtained based on the database by using an artificial intelligence algorithm. In the embodiment, the multiple persons and one machine in the multiple person and one machine collaborative artificial intelligence cloud service refers to multiple personal databases and one professional database. When the artificial intelligence algorithm is adopted and the same problem is processed based on a professional database and a plurality of personal databases, the suggested results of the professional databases are relatively authoritative, and the suggested results of the personal databases are only used for reference due to the fact that the suggested results are obtained based on personal experience. However, personal experience may increase over time. When the number of the personal databases which are different from the professional database recommendation results and are the same as each other in view of the same problem exceeds half of the total number of the personal databases, the final recommendation result of the artificial intelligence cloud service needs to be reconsidered. The method for feeding back the suggested result of the human-computer cooperative weak artificial intelligence cloud system is suitable for final feedback of the suggested result of the human-computer cooperative weak artificial intelligence cloud system under the above conditions.
The suggested result feedback method of the man-machine collaborative weak artificial intelligence cloud system only considers the situation that the number of the personal databases which are different from the suggested results of the professional databases and have the same suggested results with each other exceeds half of the total number of the personal databases, and does not consider the situation that the number of the personal databases which are different from the suggested results of the professional databases and have the same suggested results with each other does not exceed half of the total number of the personal databases, and in this situation, the corresponding professional data of the professional databases do not need to be updated.
A frame diagram of the man-machine cooperative weak artificial intelligence cloud system based on the suggested result feedback method of the man-machine cooperative weak artificial intelligence cloud system according to the embodiment is shown in fig. 4.
In the method for feeding back the recommendation result of the human-computer collaborative weak artificial intelligence cloud system according to this embodiment, a method for calculating the comprehensive weight of a personal database that is different from the recommendation result of the professional database and has the same recommendation result as each other includes:
step S11, evenly distributing the weights of the plurality of personal databases, and calculating the total weight of the personal databases which are different from the professional database proposal results and have the same proposal results with each other;
step S12, for the personal databases which are different from the professional database suggestion results and have the same suggestion results with each other, obtaining the product of the weight of each database and the given weight proportion coefficient of the database, and taking the sum of all the products as the total weight of the personal databases which are different from the professional database suggestion results and have the same suggestion results with each other;
step S13 is to set the average of the total weight obtained by the step S11 and the total weight obtained by the step S12 as the total weight.
In the suggested result feedback method for the human-computer collaborative weak artificial intelligence cloud system according to this embodiment, the database weight scaling factor is an adopted rate of a suggested result given by a corresponding database in advance in a predetermined period of time.
In the method for feeding back suggested results of a human-computer collaborative weak artificial intelligence cloud system according to this embodiment, the professional data update decision value is a ratio of the number of suggested results given by the plurality of personal databases, which are different from and identical to suggested results of the professional databases, to a total number of suggested results within a predetermined time period.
The proposed result feedback method of the human-computer collaborative weak artificial intelligence cloud system provided by this embodiment proposes a concept of comprehensive weight, and the comprehensive weight considers both the case of evenly distributing weights of a plurality of personal databases participating in processing the same problem and the problem of the adoption rate of proposed results of the personal databases, so that the calculated comprehensive weight of the personal databases having different proposed results from professional databases and the same proposed results as each other can better reflect the authority of the proposed results of the personal databases having different proposed results from professional databases and the same proposed results as each other and the necessity of updating data of the professional databases.
The suggested result feedback method of the human-computer collaborative weak artificial intelligence cloud system in this embodiment provides a professional data update decision value that is a ratio of the number of the suggested results given by the plurality of personal databases, which are different from and identical to the suggested results of the professional databases, to the total number of the suggested results within a predetermined period of time. For example, 10 personal databases and 1 professional database together process 10 questions within 10 days, and the number of the personal databases which are different from the professional database recommendation results and have the same recommendation results as each other is 3, 4, 6, 2, 5, 3, 5, 2, 5 and 4 in the process of processing the questions 10 times, so that the corresponding professional data update decision value is (3 +4+6+2+5+3+5+2+5+ 4)/100. The predetermined period of time here refers to a history period of a certain length. Therefore, the professional data update decision value provided by the suggested result feedback method of the man-machine cooperative weak artificial intelligence cloud system can truly reflect the occupation situation of the personal databases which are different from the suggested results of the professional databases and have the same suggested results in a certain length of historical time period, and further the necessity of updating the professional database data is ensured.
In the method for feeding back a suggested result of a human-computer collaborative weak artificial intelligence cloud system according to this embodiment, the updating of the corresponding data of the professional database includes:
step S31, storing the questions and the suggested results given by the personal database which are different from the suggested results of the professional database and have the same suggested results with each other as professional data into the professional database or replacing the original corresponding data in the professional database;
step S32, training the professional database after data updating;
step S33, judging whether the training of the professional database is finished, if so, executing step S34, otherwise, executing step S31;
and step S34, end.
In the suggested result feedback method for the man-machine cooperative weak artificial intelligence cloud system according to this embodiment, in the process of updating the corresponding data of the professional database, it is determined through step S33 whether training of the professional database is completed, and when training is not completed, training of the professional database is continued until training of the professional database is completed, thereby ensuring the safety of updating the professional data of the professional database.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (5)
1. The proposal result feedback method of the man-machine cooperative weak artificial intelligence cloud system is characterized by comprising the following steps:
under the scene of multi-person-one-machine cooperative artificial intelligence cloud service, an artificial intelligence algorithm is adopted, the same problem is processed on the basis of a professional database and a plurality of personal databases, and when the number of the personal databases which are different from the suggested results of the professional database and have the same suggested results with each other exceeds half of the total number of the personal databases, the comprehensive weight of the personal databases which are different from the suggested results of the professional database and have the same suggested results with each other is calculated;
judging whether the comprehensive weight is larger than a set professional data updating decision value or not;
when the judgment result is yes, updating corresponding data of the professional database based on the question and a suggestion result given by a personal database which is different from the professional database suggestion result and has the same suggestion result with each other;
when the judgment result is negative, the corresponding data of the professional database is not updated;
adopting an artificial intelligence algorithm, obtaining a suggestion result based on the current professional database, and feeding the suggestion result back to the user;
the database suggestion result is a suggestion result obtained based on the database by adopting an artificial intelligence algorithm.
2. The method for feedback of suggested results of human-computer cooperative weak artificial intelligence cloud system of claim 1, wherein the method for calculating the synthetic weight comprises:
distributing the weights of the plurality of personal databases evenly, and calculating the total weight of the personal databases which are different from the professional database suggestion result and have the same suggestion result with each other;
for the personal databases which are different from the professional database suggestion results and have the same suggestion results with each other, obtaining the product of the weight of each database and the given weight proportion coefficient of the database, and taking the sum of all the products as the total weight of the personal databases which are different from the professional database suggestion results and have the same suggestion results with each other;
and taking the average value of the two total weights obtained by implementing the steps as the comprehensive weight.
3. The method for feeding back suggested results of a human-computer cooperative weak artificial intelligence cloud system of claim 2, wherein the database weight scaling factor is an adopted rate of suggested results previously given by a corresponding database within a predetermined period of time.
4. The method for feeding back suggested results of human-computer collaborative weak artificial intelligence cloud system according to claim 3, wherein the professional data update decision value is a ratio of the number of suggested results given by the plurality of personal databases, different from and identical to the suggested results of the professional databases, to the total number of suggested results within a predetermined period of time.
5. The method for feeding back suggested results of a man-machine cooperative weak artificial intelligence cloud system of any one of claims 1 to 4, wherein the updating of the corresponding data of the professional database comprises:
storing the questions and the suggested results given by the personal database which are different from the suggested results of the professional database and have the same suggested results with each other as professional data into the professional database or replacing original corresponding data in the professional database;
and training the professional database after the data is updated.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104699708A (en) * | 2013-12-09 | 2015-06-10 | 中国移动通信集团北京有限公司 | Self-learning method and device for customer service robot |
| CN106844686A (en) * | 2017-01-26 | 2017-06-13 | 武汉奇米网络科技有限公司 | Intelligent customer service question and answer robot and its implementation based on SOLR |
| CN107223261A (en) * | 2016-12-07 | 2017-09-29 | 深圳前海达闼云端智能科技有限公司 | Man-machine hybrid decision method and device |
| JP2018013826A (en) * | 2016-07-19 | 2018-01-25 | 株式会社トプコン | Medical information processing system and medical information processing method |
| US20180253664A1 (en) * | 2015-09-18 | 2018-09-06 | Safran Aircraft Engines | Decision aid system and method for the maintenance of a machine with learning of a decision model supervised by expert opinion |
| CN110188205A (en) * | 2019-05-08 | 2019-08-30 | 三角兽（北京）科技有限公司 | A kind of update method and device of intelligent customer service system knowledge base |
-
2019
- 2019-08-31 CN CN201910819829.7A patent/CN110705957A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104699708A (en) * | 2013-12-09 | 2015-06-10 | 中国移动通信集团北京有限公司 | Self-learning method and device for customer service robot |
| US20180253664A1 (en) * | 2015-09-18 | 2018-09-06 | Safran Aircraft Engines | Decision aid system and method for the maintenance of a machine with learning of a decision model supervised by expert opinion |
| JP2018013826A (en) * | 2016-07-19 | 2018-01-25 | 株式会社トプコン | Medical information processing system and medical information processing method |
| CN107223261A (en) * | 2016-12-07 | 2017-09-29 | 深圳前海达闼云端智能科技有限公司 | Man-machine hybrid decision method and device |
| CN106844686A (en) * | 2017-01-26 | 2017-06-13 | 武汉奇米网络科技有限公司 | Intelligent customer service question and answer robot and its implementation based on SOLR |
| CN110188205A (en) * | 2019-05-08 | 2019-08-30 | 三角兽（北京）科技有限公司 | A kind of update method and device of intelligent customer service system knowledge base |
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