CN113204635A - Intelligent consultation system for modern agricultural planting - Google Patents
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Abstract
The invention relates to the technical field of information query, and particularly discloses an intelligent consultation system for modern agricultural planting, which comprises a knowledge base, wherein data information in the agricultural field is stored in the knowledge base; further comprising: the intelligent question-answering module is used for judging whether the knowledge consultation belongs to the knowledge consultation field of the agricultural planting field or not according to the consultation problem of the user, judging whether the consultation problem belongs to the problem which is already received and recorded in the knowledge base or not if the consultation problem belongs to the knowledge consultation field of the agricultural planting field, and acquiring a corresponding answer from the knowledge base if the consultation problem belongs to the problem; the network searching module is used for searching corresponding answers from the network when the consultation question does not belong to the question which is already received and recorded in the knowledge base; and the personal assistant module is used for answering questions which do not belong to the agricultural planting knowledge consultation. By adopting the technical scheme of the invention, the consultation requirements of the user can be effectively met.
Description
Technical Field
The invention relates to the technical field of information query, in particular to an intelligent consultation system for modern agricultural planting.
Background
When crops are planted, particularly when agricultural crops are planted on a large scale, information in the aspect of agricultural planting needs to be familiar, such as the habit of the crops, the adaptability to the environment, various pest control measures and the like, so as to guide the agricultural production of farmers and finally obtain high yield of agricultural products. At present, most information inquiry related to agricultural planting is carried out by inquiring related literature data in libraries or inquiring related data through the Internet, but the information is fragmented and incomplete.
Therefore, there is a need to build an intelligent consulting system for modern agriculture planting, which can effectively meet the consulting requirements of users, so as to meet the requirements of users for consulting the relevant problems of agriculture planting.
Disclosure of Invention
The invention provides an intelligent consultation system for modern agricultural planting, which can effectively meet the consultation requirements of users.
In order to solve the technical problem, the present application provides the following technical solutions:
an intelligent consultation system for modern agricultural planting comprises a knowledge base, wherein data information of the agricultural field is stored in the knowledge base;
further comprising:
the intelligent question-answering module is used for judging whether the agricultural planting knowledge consultation belongs to the agricultural planting knowledge consultation according to the consultation questions of the user, judging whether the consultation questions belong to the collected and recorded questions in the knowledge base if the consultation belongs to the agricultural planting knowledge consultation, and acquiring corresponding answers from the knowledge base if the consultation belongs to the collected and recorded questions;
the network searching module is used for searching corresponding answers from the network when the consultation question does not belong to the question which is already received and recorded in the knowledge base;
and the personal assistant module is used for answering the relevant questions which do not belong to the agricultural planting knowledge consultation.
The basic scheme principle and the beneficial effects are as follows:
in the scheme, when a user consults a question, the intelligent question-answering module judges whether the question belongs to agricultural planting knowledge consultation or not, if so, the question is searched in the knowledge base, and if the question belongs to a question which is already received and recorded in the knowledge base, the corresponding answer is obtained from the knowledge base, so that the consultation of the user can be accurately answered.
Because the establishment of the complete knowledge base needs long-time accumulation, when the problem is not included in the knowledge base, the network searching module searches the corresponding answer from the network, so that the user can obtain the answer for consultation, the condition that the user cannot answer when the knowledge base is not perfect is avoided, the user experience is better, and the willingness of the user to continue consultation is enhanced.
Relevant questions which do not belong to the agro-farming knowledge consultation can be answered by the personal assistant module, for example, when the user asks for future weather conditions, the user can inform the user of the local weather.
In conclusion, the scheme can effectively meet the consultation requirement of the user, and reduces the probability that the user can not give answers after consultation.
Further, still include the propelling movement module, the propelling movement module is used for propelling movement agricultural news and agricultural market quotation etc. to the user.
And the user can conveniently know the relevant agricultural information in time.
The system further comprises an input module, which is used for acquiring the consultation information of the user and converting the voice information into the consultation problem in text format in real time when the consultation information of the user is voice information.
Not only supports the character input, also supports the voice input, and the user can select the proper input mode according to the actual situation, so that the user experience is better.
The intelligent question answering system further comprises a preprocessing module used for acquiring the consultation questions in the text format, replacing non-standard words in the consultation questions in the text format with standard words and sending the replaced consultation questions to the intelligent question answering module.
The system further comprises a standard word bank, wherein a plurality of associated non-standard words and standard words are stored in the standard word bank, and the non-standard words are also associated with the geographic position information.
Further, the preprocessing module is used for acquiring the geographic position information of the user, judging whether the non-standard words exist in the consultation problem or not based on the pre-stored standard word bank and the position information of the user, and if the non-standard words exist in the consultation problem, the preprocessing module is also used for extracting the standard words related to the non-standard words from the standard word bank and replacing the non-standard words in the consultation problem.
For example, yam is referred to as potato in some regions, and how to search for answers according to yam when a user consults cannot give useful answers to the user. By associating the non-standard words with the geographic position information, the yams can be replaced with the potatoes in the corresponding regions, and the true intention of the user can be accurately understood.
Further, the input module comprises a collecting unit, a recognition unit and a display unit, wherein the collecting unit is used for acquiring the voice information of the user in real time;
the recognition unit is used for recognizing the voice information in real time according to a preset recognition rule, and when various recognition results appear, the voice information is converted into a plurality of initial texts according to the recognition similarity;
the display unit is used for synchronously displaying a plurality of initial text messages according to the sequence of similarity from high to low;
the display unit is further used for displaying initial text information selected by clicking of the user as an initial text with the highest sequence when the click selection of the user is obtained;
and when the acquisition unit does not acquire the voice information of the user again within the preset time, the identification unit is also used for outputting the initial text with the highest current sequence as the consultation problem in the text format.
Compared with the consultation of daily problems, the consultation related to agricultural planting has higher possibility of relating to extraordinary terms, the traditional voice recognition can be based on big data, context correlation analysis and the like on the identification of the extraordinary terms, and has higher recognition accuracy, and the traditional voice recognition accuracy is still required to be improved for the consultation related to agricultural planting which is easy to appear the extraordinary terms. In the preferred scheme, a display unit synchronously displays a plurality of initial text messages according to the sequence of similarity from high to low; when the user feels that the recognition result with the highest sequence is inaccurate, other initial text information can be directly clicked for switching. The probability that the user manually deletes the incorrect word or sentence and then modifies the word or sentence is reduced, and the operation is more convenient.
Furthermore, the acquisition unit is also used for acquiring expressions when the user inputs the expressions; the recognition unit also recognizes the speech speed of the user in real time based on the voice information, when the display unit synchronously displays a plurality of pieces of initial text information from high to low according to the similarity, the subsequent speech speed of the user is compared with the previous speech speed, whether the speech speed of the user is slowed down is judged, if so, the recognition unit is also used for judging whether the user is currently thinking according to the expression of the user, and if not, the sequencing adjustment information is not generated;
the standard word bank also stores rarely-used words; if the user is judged to be thinking currently according to the expression of the user, the recognition unit also judges whether the initial text information contains uncommon words in the standard word bank after recognizing the subsequent voice information of the user in real time, if so, the sorting adjustment information is not generated, and if not, the sorting adjustment information is generated;
the display unit is also used for changing the order of the initial text information for display after receiving the ordering adjustment information.
After the display unit synchronously displays a plurality of pieces of initial text information from high to low in similarity, a user may find that the initial text information with the highest sequence is inconsistent with the actual expression of the client, some users may select to continue inputting, and the initial text information is modified after all the input is finished. However, the user's speech rate is slow, and the current thinking may be how to correctly express a certain uncommon word, such as "potato scab" when the user consults potato-related problems, but the word is not familiar with the slow speech rate. In the preferred scheme, the initial text information is exchanged to be displayed when the uncommon vocabulary is not contained, so that the accuracy is higher.
Drawings
FIG. 1 is a logic block diagram of an intelligent advisory system for modern farming, according to an embodiment.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
As shown in fig. 1, the intelligent consulting system for modern agriculture planting of the embodiment comprises a knowledge base, an input module, a preprocessing module, an intelligent question-answering module, a network searching module and a personal assistant module.
The knowledge base stores data information of agricultural fields. In this embodiment, the data information is stored in the knowledge base in the form of questions, relationships, and answers, where a relationship refers to a relationship between a question and an answer.
In this embodiment, the knowledge base includes:
information class knowledge base: the method mainly comprises the contents of agricultural essentials, agricultural policies and the like, and the main acquisition mode of the knowledge base is to crawl the title and address of a preset agricultural technology website.
Agricultural encyclopedia knowledge base: mainly covers information such as agricultural common sense, plant encyclopedia, plant diseases and insect pests encyclopedia and the like. The existing electronic book documents are already arranged in a question-answer mode and can be directly used as the warehousing linguistic data of a knowledge base.
Agricultural skill knowledge base: the method mainly provides information technology service, agricultural technology service and the like for farmers. The agricultural technology comprises crop seed selection, crop planting, agricultural prevention and treatment of crop related diseases and insect pests and related treatment methods, such as citrus diseases and insect pests, strawberry planting methods and the like, and the display content comprises image-text introduction, voice, video and other modes. A few knowledge bases can be constructed through the electronic book, and more knowledge base contents continuously improve the knowledge bases in a mode of adopting the experience of agricultural experts.
The input module is used for acquiring the consultation information in the text format of the user.
The preprocessing module is used for acquiring the consultation questions in the text format, converting nonstandard sentences in the consultation questions in the text format into standard sentences and sending the converted consultation questions to the intelligent question-answering module.
In this embodiment, the system further includes a standard word bank, where a plurality of associated non-standard words and standard words are stored in the standard word bank, where the non-standard words are also associated with the geographic location information.
Specifically, the preprocessing module is used for acquiring geographic position information of the user, judging whether a non-standard word exists in the consultation problem or not based on a pre-stored standard word bank and the position information of the user, and if the non-standard word exists, extracting a standard word associated with the non-standard word from the standard word bank to replace the non-standard word in the consultation problem.
The intelligent question-answering module is used for judging whether the agricultural planting knowledge consultation belongs to the agricultural planting knowledge consultation according to the consultation questions of the user, and matching corresponding answers from the knowledge base if the agricultural planting knowledge consultation belongs to the agricultural planting knowledge consultation; and if the matching is successful, outputting the corresponding answer.
In this embodiment, whether the agricultural planting knowledge consultation belongs to is judged according to the consultation problem of the user, a keyword matching mode may be adopted, for example, in the consultation problem, no keywords related to agriculture appear, and the consultation is regarded as not belonging to the agricultural planting knowledge consultation.
In this embodiment, when the corresponding answer is matched from the knowledge base based on the consultation question, the existing four steps of text vectorization, intention classification, main body extraction, and content similarity calculation may be performed.
For example, the knowledge base contains information about citrus charring, such as "round, slightly concave, gray-white in the center, brown at the edges, which is one of the symptoms of citrus charring. "
When the user encounters the citrus with the charcoal maggot disease, the input consultation problem is as follows: "how do the oranges get ill, the leaves are white and round, and the edges of the leaves are black? "
That is, the algorithm converts the text into a vector sequence for subsequent calculation, and secondly, identifies that the user statement is about pest and disease damage, and makes a clear intention. Thirdly, the main body of the sentence is identified as 'orange', and the 'orange' is identified as 'orange'.
And finally, when the intention and the main body of the user question are judged to be consistent with the questions in the knowledge picture, comparing the similarity of the text contents, and returning the answer of the knowledge base question with the highest similarity to the user.
And the network searching module is used for searching the corresponding answers from the network and outputting the answers when the corresponding answers are not successfully matched in the knowledge base. In this embodiment, a search is performed by a search engine.
The personal assistant module is used for answering relevant questions which do not belong to the agricultural planting knowledge consultation. For example, "how do tomorrow? "answer tomorrow weather. Also, for example, when the user inputs sentences such as "hello", "speak a joke", and the like, an anthropomorphic answer can be made. The personal assistant module can be realized based on the existing intelligent assistant, and all manufacturers of large intelligent sound boxes and mobile phone manufacturers have similar intelligent assistants, which belong to the prior art and are not described again.
The input module can acquire the consultation information in the user text format through an APP, a website or a WeChat public number.
Example two
The difference between the embodiment and the first embodiment is that the embodiment further includes a pushing module, and the pushing module is used for pushing information such as agricultural news and agricultural market quotations to the user. The information such as agricultural news and agricultural market quotations can be crawled from a preset website through a web crawler.
EXAMPLE III
The difference between this embodiment and the first embodiment is that, in this embodiment, the input module is configured to convert the voice information into a text format of the consultation problem in real time when the consultation information input by the user is the voice information.
In this embodiment, the input module specifically includes an acquisition unit, an identification unit, and a display unit.
The acquisition unit is used for acquiring user voice information in real time; in this embodiment, the collecting unit is further configured to collect an expression when the user inputs the input;
the recognition unit is used for recognizing the voice information in real time according to a preset recognition rule, and when various recognition results appear, the voice information is converted into a plurality of initial texts according to the recognition similarity;
the display unit is configured to perform synchronous display on a plurality of pieces of initial text information in an order from high to low in similarity, in this embodiment, 3 pieces of initial text information are displayed, where a font size in the initial text information is positively correlated with a ranking, that is, the higher the ranking is, the larger the font size is.
For example, the initial text information obtained by the recognition of the ju zi is the tangerine, the orange and the chrysanthemum, wherein the tangerine has the highest user input rate and the chrysanthemum has the lowest user input rate in the recognition rule. Therefore, when displaying, the display unit displays according to the following sequence:
orange "
Orange "
"Chrysanthemum seed"
The recognition unit also recognizes the speech speed of the user in real time based on the voice information, when the display unit synchronously displays a plurality of pieces of initial text information from high to low according to the similarity, the subsequent speech speed of the user is compared with the previous speech speed, whether the speech speed of the user is slowed down is judged, if so, the recognition unit is also used for judging whether the user is currently thinking according to the expression of the user, and if not, the sequencing adjustment information is not generated. Whether a user thinks currently can be determined according to changes of the forehead, eyes and other parts, and the method can be realized through the existing face recognition related technology, and is not repeated here. In this embodiment, the slowing means that the speed reduction amplitude exceeds the first threshold value when the speed of subsequent speech is compared with the speed of previous speech.
In this embodiment, the standard word library stores uncommon words. If the user is judged to be thinking currently according to the expression of the user, the recognition unit is used for recognizing the subsequent voice information of the user in real time and then judging whether the initial text information contains the uncommon vocabulary in the standard word bank or not, if the initial text information contains the uncommon vocabulary, the sequencing adjustment information is not generated, and if the initial text information does not contain the uncommon vocabulary, the sequencing adjustment information is generated. In this embodiment, the uncommon vocabulary is manually entered.
The display unit is also used for changing the order of the initial text information for display after receiving the ordering adjustment information. In this embodiment, the initial text information in the second order is exchanged with the text information in the highest order.
The display unit is further used for displaying the initial text information clicked and selected by the user as the initial text with the highest sequence when the click selection of the user is obtained.
And when the acquisition unit does not acquire the voice information of the user again within the preset time, the identification unit is also used for outputting the initial text with the highest current sequence as the consultation problem in the text format.
The above are merely examples of the present invention, and the present invention is not limited to the field related to this embodiment, and the common general knowledge of the known specific structures and characteristics in the schemes is not described herein too much, and those skilled in the art can know all the common technical knowledge in the technical field before the application date or the priority date, can know all the prior art in this field, and have the ability to apply the conventional experimental means before this date, and those skilled in the art can combine their own ability to perfect and implement the scheme, and some typical known structures or known methods should not become barriers to the implementation of the present invention by those skilled in the art in light of the teaching provided in the present application. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (8)
1. An intelligent consultation system for modern agricultural planting comprises a knowledge base, wherein data information in the field of agricultural planting is stored in the knowledge base;
it is characterized by also comprising:
the intelligent question-answering module is used for judging whether the inquiry question belongs to knowledge inquiry in the aspect of agricultural planting according to the inquiry question of the user, judging whether the inquiry question belongs to a problem which is already received and recorded in the knowledge base if the inquiry question belongs to the knowledge inquiry in the aspect of agricultural planting, and acquiring a corresponding answer from the knowledge base if the inquiry question belongs to the problem which is already received and recorded in the knowledge base;
the network searching module is used for searching corresponding answers from the network when the consultation question does not belong to the question which is already received and recorded in the knowledge base;
and the personal assistant module is used for answering the relevant questions which do not belong to the agricultural planting knowledge consultation.
2. The intelligent advisory system for modern farming as claimed in claim 1, wherein: still include the propelling movement module, the propelling movement module is used for to user propelling movement agricultural news and agricultural market quotation etc..
3. The intelligent advisory system for modern farming as claimed in claim 2, wherein: the system also comprises an input module which is used for acquiring the consultation information of the user and converting the voice information into the consultation problem in text format in real time when the consultation information of the user is the voice information.
4. The intelligent advisory system for modern farming as claimed in claim 3, wherein: the intelligent question answering system further comprises a preprocessing module used for obtaining the consultation questions in the text format, replacing non-standard words in the consultation questions in the text format with standard words and sending the replaced consultation questions to the intelligent question answering module.
5. The intelligent advisory system for modern farming as claimed in claim 4, wherein: the system also comprises a standard word bank, wherein a plurality of associated non-standard words and standard words are stored in the standard word bank, and the non-standard words are also associated with the geographic position information.
6. The intelligent advisory system for modern farming according to claim 5, wherein: the preprocessing module is used for acquiring the geographic position information of the user, judging whether non-standard words exist in the consultation problem or not based on the pre-stored standard word bank and the position information of the user, and if the non-standard words exist in the consultation problem, the preprocessing module is also used for extracting the standard words related to the non-standard words from the standard word bank and replacing the non-standard words in the consultation problem.
7. The intelligent advisory system for modern farming as claimed in claim 6, wherein: the input module comprises a collection unit, a recognition unit and a display unit, wherein the collection unit is used for acquiring user voice information in real time;
the recognition unit is used for recognizing the voice information in real time according to a preset recognition rule, and when various recognition results appear, the voice information is converted into a plurality of initial texts according to the recognition similarity;
the display unit is used for synchronously displaying a plurality of initial text messages according to the sequence of similarity from high to low;
the display unit is further used for displaying initial text information selected by clicking of the user as an initial text with the highest sequence when the click selection of the user is obtained;
and when the acquisition unit does not acquire the voice information of the user again within the preset time, the identification unit is also used for outputting the initial text with the highest current sequence as the consultation problem in the text format.
8. The intelligent advisory system for modern farming as claimed in claim 7, wherein: the acquisition unit is also used for acquiring expressions when the user inputs the expressions; the recognition unit also recognizes the speech speed of the user in real time based on the voice information, when the display unit synchronously displays a plurality of pieces of initial text information from high to low according to the similarity, the subsequent speech speed of the user is compared with the previous speech speed, whether the speech speed of the user is slowed down is judged, if so, the recognition unit is also used for judging whether the user is currently thinking according to the expression of the user, and if not, the sequencing adjustment information is not generated;
the standard word bank also stores rarely-used words; if the user is judged to be thinking currently according to the expression of the user, the recognition unit also judges whether the initial text information contains uncommon words in the standard word bank after recognizing the subsequent voice information of the user in real time, if so, the sorting adjustment information is not generated, and if not, the sorting adjustment information is generated;
the display unit is also used for changing the order of the initial text information for display after receiving the ordering adjustment information.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116578667A (en) * | 2023-07-13 | 2023-08-11 | 湖南惠农科技有限公司 | Agricultural information service terminal based on agricultural big data management |
| WO2023160531A1 (en) * | 2022-02-24 | 2023-08-31 | 青岛海尔电冰箱有限公司 | Food material management method and system, and computer storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107180029A (en) * | 2016-03-09 | 2017-09-19 | 阿里巴巴集团控股有限公司 | A kind of information processing method and device based on consultation service |
| CN107862000A (en) * | 2017-10-22 | 2018-03-30 | 北京市农林科学院 | A kind of agricultural technology seeks advice from interactive method |
| CN108052627A (en) * | 2017-12-19 | 2018-05-18 | 中国科学院合肥物质科学研究院 | A kind of agrotechnical Knowledge Service Platform and method |
| CN110597969A (en) * | 2019-08-12 | 2019-12-20 | 中国农业大学 | Agricultural knowledge intelligent question and answer method and system and electronic equipment |
| CN111581338A (en) * | 2020-03-25 | 2020-08-25 | 北京市农林科学院 | Agricultural technical service robot man-machine fusion consultation question-answering method and system |
| CN111831801A (en) * | 2020-05-27 | 2020-10-27 | 北京市农林科学院 | Man-machine conversation method and system |
| CN112231460A (en) * | 2020-10-27 | 2021-01-15 | 中国科学院合肥物质科学研究院 | Construction method of question-answering system based on agricultural encyclopedia knowledge graph |
-
2021
- 2021-06-08 CN CN202110638379.9A patent/CN113204635A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107180029A (en) * | 2016-03-09 | 2017-09-19 | 阿里巴巴集团控股有限公司 | A kind of information processing method and device based on consultation service |
| CN107862000A (en) * | 2017-10-22 | 2018-03-30 | 北京市农林科学院 | A kind of agricultural technology seeks advice from interactive method |
| CN108052627A (en) * | 2017-12-19 | 2018-05-18 | 中国科学院合肥物质科学研究院 | A kind of agrotechnical Knowledge Service Platform and method |
| CN110597969A (en) * | 2019-08-12 | 2019-12-20 | 中国农业大学 | Agricultural knowledge intelligent question and answer method and system and electronic equipment |
| CN111581338A (en) * | 2020-03-25 | 2020-08-25 | 北京市农林科学院 | Agricultural technical service robot man-machine fusion consultation question-answering method and system |
| CN111831801A (en) * | 2020-05-27 | 2020-10-27 | 北京市农林科学院 | Man-machine conversation method and system |
| CN112231460A (en) * | 2020-10-27 | 2021-01-15 | 中国科学院合肥物质科学研究院 | Construction method of question-answering system based on agricultural encyclopedia knowledge graph |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023160531A1 (en) * | 2022-02-24 | 2023-08-31 | 青岛海尔电冰箱有限公司 | Food material management method and system, and computer storage medium |
| CN116578667A (en) * | 2023-07-13 | 2023-08-11 | 湖南惠农科技有限公司 | Agricultural information service terminal based on agricultural big data management |
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