CN107357935B - Intelligent planting equipment control method based on program cloud - Google Patents
Intelligent planting equipment control method based on program cloud Download PDFInfo
- Publication number
- CN107357935B CN107357935B CN201710697982.8A CN201710697982A CN107357935B CN 107357935 B CN107357935 B CN 107357935B CN 201710697982 A CN201710697982 A CN 201710697982A CN 107357935 B CN107357935 B CN 107357935B
- Authority
- CN
- China
- Prior art keywords
- plant
- matching
- attribute
- operation method
- plant growing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2468—Fuzzy queries
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Fuzzy Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Automation & Control Theory (AREA)
- Cultivation Of Plants (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides an intelligent planting equipment control method based on program cloud, which comprises the following steps: the method comprises the steps of obtaining user information and plant information, wherein the plant information comprises a first plant attribute, and the first plant attribute is current growth state information and/or variety information of a plant. And acquiring the plant planting equipment bound by the user and the function information thereof based on the user information. And generating an optimal plant growing device operation method through cloud computing according to the function information and the first plant attributes. The program cloud-based intelligent planting equipment control method provided by the invention can be applied to intelligent plant planting equipment, so that different plants can be subjected to corresponding intelligent planting and cultivation, the survival rate of the plants is improved, the nutrition state of the plants is improved, and the cultivation mode is more scientific and reasonable.
Description
Technical Field
The invention relates to the field of intelligent planting, in particular to an intelligent planting equipment control method based on program cloud.
Background
With the continuous improvement of life quality, in order to avoid too stiff and serious office and home, many people like to put favorite plants around, and a vivid color is added for life. But the trouble is also caused, and people often ignore the flowers and plants when the work is busy.
Common household planting equipment needs manual control, so that operations such as watering, fertilizing and light supplementing are realized. In recent years, intelligent plant growing machines are gradually introduced to the market, and the intelligent plant growing machines can automatically water, fertilize and supplement light for plants. However, the planting characteristics of plants vary depending on the species. In addition, the amount of watering, fertilizing, lighting time and suitable pH environment of plants in different growth stages can be different, and the planting parameters can greatly influence the growth state of the plants.
Modern urban people rarely come into contact with plant cultivation, so that it is difficult to know exactly how to plant plants. Even if one knows how to plant the plant, because the urban man does not necessarily have enough time to focus on planting. Similarly, due to the technical limitation of intelligent plant planting equipment, at present, there is no planting program corresponding to a plurality of plants, so that users often adopt similar planting methods for most plants, and thus the plants cannot be guaranteed to be planted scientifically and the survival rate of the plants cannot be guaranteed.
Disclosure of Invention
The invention aims to provide an intelligent planting device control method based on program cloud, which can be applied to intelligent plant planting devices to enable different plants to obtain corresponding intelligent planting and cultivation, so that the survival rate of the plants is improved, and the nutritional state of the plants is improved.
In order to solve the technical problems, the invention adopts the following technical scheme:
an intelligent planting equipment control method based on program cloud comprises the following steps:
s1, acquiring user information and plant information, wherein the plant information comprises a first plant attribute, and the first plant attribute is the current growth state information and/or variety information of the plant;
s2, acquiring plant planting equipment bound by a user and function information thereof based on the user information;
and S3, generating an optimal plant growing device operation method through cloud computing according to the function information and the first plant attributes. The step of generating the optimal plant growing equipment operation method through cloud computing comprises generating the optimal plant growing equipment operation method through a single-target multi-attribute matching agent, wherein the single-target multi-attribute matching agent is used for matching a first plant attribute of a pre-grown plant with a plant attribute in a database.
Preferably, the method for matching the agents comprises the following steps:
and executing direct matching based on the plant growing equipment operation methods in the database, and generating the optimal plant growing equipment operation method corresponding to the plant information.
Preferably, the method for matching the agents comprises the following steps:
based on the operation method of the plant planting equipment in the database, fuzzy matching is executed, and the fuzzy matching comprises the following steps: calculating deviation degrees of attribute parameters of plant attributes in m plant planting equipment operation methods in a database and attribute parameters of first plant attributes, wherein m is a positive integer to obtain m deviation values; selecting the minimum value of the deviation degrees from the m deviation degree values as the minimum value of the deviation degrees, and taking out the minimum value of the deviation degrees to compare with a set threshold value; and if the minimum deviation degree value is less than or equal to the threshold value, generating the optimal plant growing equipment operation method, wherein the optimal plant growing equipment operation method is selected from the plant growing equipment operation methods corresponding to the minimum deviation degree value.
Preferably, the method for matching the agents comprises the following steps:
based on the operation method of the plant growing equipment in the database, generating and matching based on the neural network are executed, and the generating and matching steps are as follows: calculating to obtain an optimal plant growing equipment operation method through a neural network based on all minimum execution unit codes in the database and the attribute parameters of the first plant attributes; all minimal execution unit codes are taken from the plant growing apparatus operating methods in the database, the minimal execution unit codes being minimal execution unit codes that perform one-step growing activities.
Preferably, when the direct matching is not successful, the method further comprises the following steps:
based on the operation method of the plant planting equipment in the database, fuzzy matching is executed, and the fuzzy matching comprises the following steps: calculating deviation degrees of attribute parameters of plant attributes in m plant planting equipment operation methods in a database and attribute parameters of first plant attributes, wherein m is a positive integer to obtain m deviation values; selecting the minimum value of the deviation degrees from the m deviation degree values as the minimum value of the deviation degrees, and taking out the minimum value of the deviation degrees to compare with a set threshold value; if the minimum deviation degree is smaller than or equal to the threshold value, generating the optimal plant planting equipment operation method and ending the matching agent, wherein the optimal plant planting equipment operation method is selected from the plant planting equipment operation methods corresponding to the minimum deviation degree; if the minimum value of the deviation degree is larger than the threshold value, executing generation matching based on the neural network;
based on the operation method of the plant growing equipment in the database, generating and matching based on the neural network are executed, and the generating and matching steps are as follows: calculating to obtain an optimal plant growing equipment operation method through a neural network based on all minimum execution unit codes in the database and the attribute parameters of the first plant attributes; all minimal execution unit codes are taken from the plant growing apparatus operating methods in the database, the minimal execution unit codes being minimal execution unit codes that perform one-step growing activities.
Preferably, when the fuzzy matching is performed, a one-to-one corresponding weight coefficient is set for each attribute parameter.
Preferably, the calculation formula of the deviation value is Vn ═ X-P) QT, where Vn is the deviation value of the operation method of the nth plant growing apparatus, X is each attribute parameter of the first plant attribute, P is each attribute parameter of the operation method of the nth plant growing apparatus, Q sets a one-to-one corresponding weight coefficient for each attribute parameter, and QT is the transpose of Q.
Preferably, the minimum execution unit code comprises a watering code block, a fertilizing code block, a light supplementing code block, a temperature control code block, a pH value adjusting code block, a time delay code block and/or a safety inspection code block.
Preferably, step S1 further includes acquiring the geographic location information of the user.
Compared with the prior art, the invention has the beneficial effects that: the invention provides an intelligent planting equipment control method based on program cloud, which comprises the following steps: the method comprises the steps of obtaining user information and plant information, wherein the plant information comprises a first plant attribute, and the first plant attribute is current growth state information and/or variety information of a plant. And acquiring the plant planting equipment bound by the user and the function information thereof based on the user information. And generating an optimal plant growing device operation method through cloud computing according to the function information and the first plant attributes. According to the method, the server carries out cloud computing to generate the optimal plant planting equipment operation method according to the function information and the first plant attribute bound by the user, so that different plants can be intelligently planted and cultivated correspondingly, the survival rate of the plants is improved, and the nutritional state of the plants is improved. Aiming at the planting and cultivating mode which can not be realized by the bound plant planting equipment, the optimal planting step description can be output, so that a user can conveniently refer to the step description, and the plant planting and cultivating can be efficiently carried out.
Drawings
Fig. 1 is a control system and method for intelligent planting equipment based on program cloud according to an embodiment of the present invention;
fig. 2 is a control method of an intelligent planting device based on a program cloud according to another embodiment of the present invention.
Detailed Description
The above and further features and advantages of the present invention will be apparent from the following, complete description of the invention, taken in conjunction with the accompanying drawings, wherein the described embodiments are merely some, but not all embodiments of the invention.
As shown in fig. 1 and 2, in an embodiment of the present invention, a method for controlling an intelligent planting device based on a program cloud includes the following steps:
and S1, acquiring user information and plant information, wherein the plant information comprises a first plant attribute, and the first plant attribute is the current growth state information and/or variety information of the plant.
And S2, acquiring the plant growing equipment bound by the user and the function information thereof based on the user information.
And S3, generating an optimal plant growing device operation method through cloud computing according to the function information and the first plant attributes. The step of generating the optimal plant growing equipment operation method through cloud computing comprises generating the optimal plant growing equipment operation method through a single-target multi-attribute matching agent, wherein the single-target multi-attribute matching agent is used for matching a first plant attribute of a pre-grown plant with a plant attribute in a database.
In another embodiment provided by the invention, a program cloud-based intelligent planting device control method comprises the following steps:
the user inputs plant information and user information on the client, the client uploads the plant information and the user information to the server, and the plant information comprises a first plant attribute. The client can be a mobile client such as a mobile phone, a tablet computer and a notebook computer, and can also be a fixed client such as a desktop computer. The plant name may be the name and/or custom number of the plant. The first plant attribute is the current growth state information and/or variety information of the plant. The first botanical attribute includes botanical height, width, weight, variety, and/or leaf color, among others. The user information may include a user name and/or a bound plant growing device.
And the server acquires the plant planting equipment bound by the user and the function information thereof based on the user information.
And according to the function information and the first plant attributes, the server generates an optimal plant growing device operation method through cloud computing.
And the server downloads the optimal operation method of the plant growing equipment. The optimal plant growing apparatus operating method may include optimal growing program instructions and/or optimal growing step instructions.
In another embodiment of the present invention, uploading the geographical positioning information of the user to the server is further included. And according to the uploaded geographical positioning information, the server obtains the weather of the user, so that a more reasonable and optimal operation method of the plant growing equipment is output. Control program instructions or plant step instructions for more appropriate moisture and humidity may be advantageously calculated.
In another embodiment of the present invention, the plant growing apparatus receives and executes optimal growing program instructions. The plant planting equipment executes the planting program instruction, so that the planting can be executed more accurately, and the plants can be planted and cultivated more reasonably. The language form of the optimal planting program instruction can be a mark-up language form, an executable software form, a source code form or a binary stream form.
In another embodiment of the present invention, the client of the user receives and displays the optimal planting step specification. When the plant planting device bound by the user cannot completely execute all planting program instructions, the user can know how to plant and cultivate the plants by outputting the description of the optimal planting step so as to cultivate the plants more comprehensively. When the user is according to the best planting step explanation, can cultivate by oneself according to the volume.
In another embodiment of the present invention, the user information is obtained through a client; the client acquires the user information of the user by acquiring and identifying the biological characteristic information of the user. The unnecessary operation of the user can be reduced, the efficiency of inputting relevant information by the user is improved, and the user experience is improved.
In another embodiment of the present invention, the step of generating an optimal plant growing apparatus operating method through cloud computing comprises generating the optimal plant growing apparatus operating method through a single target multi-attribute matching agent, the single target multi-attribute matching agent being for matching a first plant attribute of the pre-grown plant with a plant attribute in a database. By means of a single-target multi-attribute matching mode, planting rationality of the optimal plant planting equipment operation method is improved, multiple plant attributes of plants are comprehensively considered, and the optimal plant planting equipment operation method matched with the current plant state is obtained.
In another embodiment of the invention, the way of matching agents comprises the following steps: and executing direct matching based on the plant growing equipment operation methods in the database, and generating the optimal plant growing equipment operation method corresponding to the plant information.
In another embodiment of the invention, the way of matching agents comprises the following steps: based on the operation method of the plant planting equipment in the database, fuzzy matching is executed, and the fuzzy matching comprises the following steps: calculating deviation degrees of attribute parameters of plant attributes in m plant planting equipment operation methods in a database and attribute parameters of first plant attributes, wherein m is a positive integer to obtain m deviation values; selecting the minimum value of the deviation degrees from the m deviation degree values as the minimum value of the deviation degrees, and taking out the minimum value of the deviation degrees to compare with a set threshold value; and if the minimum deviation degree value is less than or equal to the threshold value, generating the optimal plant growing equipment operation method, wherein the optimal plant growing equipment operation method is selected from the plant growing equipment operation methods corresponding to the minimum deviation degree value.
In another embodiment of the invention, the way of matching agents comprises the following steps: based on the operation method of the plant growing equipment in the database, generating and matching based on the neural network are executed, and the generating and matching steps are as follows: calculating to obtain an optimal plant growing equipment operation method through a neural network based on all minimum execution unit codes in the database and the attribute parameters of the first plant attributes; all minimal execution unit codes are taken from the plant growing apparatus operating methods in the database, the minimal execution unit codes being minimal execution unit codes that perform one-step growing activities.
In another embodiment of the present invention, when the direct matching is not successful, the method further comprises the following steps:
based on the operation method of the plant planting equipment in the database, fuzzy matching is executed, and the fuzzy matching comprises the following steps: calculating deviation degrees of attribute parameters of plant attributes in m plant planting equipment operation methods in a database and attribute parameters of first plant attributes, wherein m is a positive integer to obtain m deviation values; selecting the minimum value of the deviation degrees from the m deviation degree values as the minimum value of the deviation degrees, and taking out the minimum value of the deviation degrees to compare with a set threshold value; if the minimum deviation degree is smaller than or equal to the threshold value, generating the optimal plant planting equipment operation method and ending the matching agent, wherein the optimal plant planting equipment operation method is selected from the plant planting equipment operation methods corresponding to the minimum deviation degree; if the minimum value of the deviation degree is larger than the threshold value, executing generation matching based on the neural network;
based on the operation method of the plant growing equipment in the database, generating and matching based on the neural network are executed, and the generating and matching steps are as follows: calculating to obtain an optimal plant growing equipment operation method through a neural network based on all minimum execution unit codes in the database and the attribute parameters of the first plant attributes; all minimal execution unit codes are taken from the plant growing apparatus operating methods in the database, the minimal execution unit codes being minimal execution unit codes that perform one-step growing activities.
In another embodiment of the present invention, when the step of fuzzy matching is performed, a one-to-one corresponding weight coefficient is set for each attribute parameter. The parameters of the plant attributes of the pre-planted plants can be modified by modifying the weighting coefficients, thereby improving the planting condition parameters of the plants which are particularly concerned with.
In another embodiment of the present invention, the deviation value is calculated by the formula Vn ═ Q (X-P) QTWhere Vn is a deviation value of an nth plant growing apparatus operation method, X is each attribute parameter of a first plant attribute, and P is each attribute parameter of an nth plant growing apparatus operation methodThe number Q is a weight coefficient corresponding to each attribute parameterTIs the transpose of Q.
The optimal plant planting equipment operation method can be effectively obtained through three matching generation modes of a direct matching program, a fuzzy matching program and a neural network generation program. The advantage of direct matching is that the calculation results are quickly available. The optimal plant planting equipment operation method is easier to obtain through simulation matching, the matching range is large, and meanwhile the calculation load is small. The generation program of the neural network can be calculated to obtain the generation program of the neural network of any plant growing equipment. The matching generation mode of direct matching, fuzzy matching and the generation program of the neural network is combined, the matching efficiency can be improved on the basis of keeping the rationality of the planting mode, and the load of matching calculation is reduced.
In another embodiment of the present invention, the minimum execution unit code includes a watering code block, a fertilizing code block, a light supplementing code block, a temperature control code block, a ph value adjusting code block, a time delay code block and/or a safety inspection code block. The codes of various minimum execution units can be reasonably matched according to the specific conditions of plants, and the plants are planted and cultivated in an omnibearing manner.
Specifically, in another embodiment of the present invention, the method for matching the agents includes the following steps:
the server performs direct matching based on a method of operation of the plant growing apparatus in a database of the server. Where, X ═ { X1, X2, … Xk, … Xn } is each attribute parameter of the first botanical attribute, and Xn is the nth attribute parameter of the first botanical attribute. S ═ { S1, S2, … Sk, … Sn } is a plant growing device operation method that has been released in the server database, and Sn is the nth plant growing device operation method. Each plant growing apparatus operation method has an attribute parameter P, where P ═ { P1, P2, … Pr, … Pm }, Pm being the mth attribute parameter of a certain plant growing apparatus operation method. And the matching program directly carries out retrieval matching in the database, and if X is contained in Sn of P, the Sn is directly regarded as the best program output and the matching agent is closed. And if the matching is successful, the server generates an optimal plant planting equipment operation method corresponding to the plant information, otherwise, fuzzy matching is carried out.
The server executes fuzzy matching, the server has the plant planting equipment operation methods of the bound plant planting equipment, deviation degree calculation and summarization are carried out on each attribute parameter of the plant attributes in each plant planting equipment operation method and each attribute parameter of the first plant attribute, and the minimum deviation degree is taken out to be compared with a set threshold value. In the fuzzy matching program, X ═ { X1, X2, … Xk, … Xn } is each attribute parameter of the first vegetation attribute, and Xn is the nth attribute parameter of the first vegetation attribute. S ═ { S1, S2, … Sk, … Sn } is a plant growing device operation method that has been released in the server database, and Sn is the nth plant growing device operation method. Each plant growing apparatus operation method has an attribute parameter P, where P ═ { P1, P2, … Pr, … Pm }, Pm being the mth attribute parameter of a certain plant growing apparatus operation method. The fuzzy matching sets a weight coefficient Q for each Pn. Wherein, Q ═ { Q1, Q2, … Qr, … Qm }, and Qm is the weighting coefficient corresponding to the mth attribute parameter. The degree of deviation Vn ═ Q (X-P) for each optimum program can be calculatedT. And selecting the Vmin with the minimum deviation degree in V and the deviation degree smaller than the threshold Y, outputting the program Sn corresponding to the Vmin as the optimal program, closing the matching agent, and if the value with the minimum deviation degree in V is still larger than the threshold Y, determining that the fuzzy matching fails. And if the minimum deviation degree value is smaller than or equal to the threshold value, the server generates an optimal plant growing equipment operation method, the optimal plant growing equipment operation method is selected from the plant growing equipment operation methods corresponding to the minimum deviation degree value, and if not, a generation program based on the neural network is executed.
The server executes a generating program based on the neural network, the generating program obtains an optimal plant growing device operation method through calculation of the neural network based on all the minimum execution unit codes and the attribute parameters of the first plant attributes, and the server generates the optimal plant growing device operation method. The matching agent starts to execute a generating program based on the neural network, and the generating program firstly obtains all minimum execution unit codes of the plant planting equipment from a database based on the plant planting equipment bound by the user. The neural network includes an input layer, an output layer, and an intermediate hidden layer. The set of minimum execution unit codes is set as D { (D1, D2, … Dr, … Dm }, each attribute parameter of the first plant attribute of the input layer of the neural network is X ═ { X1, X2, … Xk, … Xn }, a parameter matrix M ═ { M1, M2, … Mr, … Mm } is obtained after passing through a plurality of intermediate hidden layers, finally, the output is DM { (D1M 1, D2M2, … DrMr, … DmM }, and the DM matrix is input into a code combination generating module to generate an optimal program Sn output and close the matching agent. All the minimum execution unit codes are taken from the plant growing equipment operation methods of the plant growing equipment bound in the server, and the minimum execution unit codes are execution unit codes for executing one-step growing activities. The minimum execution unit code comprises a watering code block, a fertilizing code block, a light supplementing code block, a temperature control code block, a pH value adjusting code block, a time delay code block, a safety inspection code block and the like. The delay code block is used for being matched with other code blocks, and if the light supplement starts, the light supplement is kept on for a long time and then is closed. And the safety inspection code block is used for inspecting whether the whole planting equipment operates normally or not and whether potential safety hazards exist or not.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the invention, may occur to those skilled in the art and are intended to be included within the scope of the invention.
Claims (8)
1. An intelligent planting equipment control method based on program cloud is characterized by comprising the following steps:
s1, acquiring user information and plant information, wherein the plant information comprises a first plant attribute, and the first plant attribute is the current growth state information and/or variety information of the plant;
s2, acquiring plant planting equipment bound by a user and function information thereof based on the user information;
s3, generating an optimal plant growing device operation method through cloud computing according to the function information and the first plant attributes; the step of generating the optimal plant growing equipment operation method through cloud computing comprises the steps of generating the optimal plant growing equipment operation method through a single-target multi-attribute matching agent, wherein the single-target multi-attribute matching agent is used for matching a first plant attribute of a pre-grown plant with a plant attribute in a database;
the method for matching agents comprises the following steps:
based on a plant growing equipment operation method in a database, fuzzy matching is performed, and the fuzzy matching comprises the following steps: calculating deviation degrees of attribute parameters of plant attributes in m plant planting equipment operation methods in the database and attribute parameters of first plant attributes, wherein m is a positive integer, and m deviation values are obtained; selecting the minimum value of the deviation degrees from the m deviation degree values as the minimum value of the deviation degrees, and taking out the minimum value of the deviation degrees to compare with a set threshold value; and if the minimum deviation degree value is less than or equal to the threshold value, generating the optimal plant growing equipment operation method, wherein the optimal plant growing equipment operation method is selected from the plant growing equipment operation methods corresponding to the minimum deviation degree value.
2. The intelligent planting device control method based on program cloud according to claim 1, wherein the agent matching manner further comprises the following steps:
and executing direct matching based on the plant growing equipment operation methods in the database, and generating the optimal plant growing equipment operation method corresponding to the plant information.
3. The program cloud based intelligent planting device control method of claim 1, wherein the agent matching manner comprises the following steps:
based on the operation method of the plant growing equipment in the database, generating and matching based on the neural network are executed, and the generating and matching steps are as follows: calculating to obtain an optimal plant growing equipment operation method through a neural network based on all minimum execution unit codes in the database and the attribute parameters of the first plant attributes; all the minimum execution unit codes are taken from the plant growing equipment operation methods in the database, and the minimum execution unit codes are the minimum execution unit codes for executing one-step growing activities.
4. The intelligent planting device control method based on program cloud according to claim 2, further comprising the following steps after the direct matching is not successful:
based on a plant growing equipment operation method in a database, fuzzy matching is performed, and the fuzzy matching comprises the following steps: calculating deviation degrees of attribute parameters of plant attributes in m plant planting equipment operation methods in the database and attribute parameters of first plant attributes, wherein m is a positive integer, and m deviation values are obtained; selecting the minimum value of the deviation degrees from the m deviation degree values as the minimum value of the deviation degrees, and taking out the minimum value of the deviation degrees to compare with a set threshold value; if the minimum deviation degree value is smaller than or equal to the threshold value, generating an optimal plant growing equipment operation method and ending the matching agent, wherein the optimal plant growing equipment operation method is selected from the plant growing equipment operation methods corresponding to the minimum deviation degree value; if the minimum value of the deviation degree is larger than the threshold value, executing generation matching based on the neural network;
based on the operation method of the plant growing equipment in the database, generating and matching based on the neural network are executed, and the generating and matching steps are as follows: calculating to obtain an optimal plant growing equipment operation method through a neural network based on all minimum execution unit codes in the database and the attribute parameters of the first plant attributes; all the minimum execution unit codes are taken from the plant growing equipment operation methods in the database, and the minimum execution unit codes are the minimum execution unit codes for executing one-step growing activities.
5. The intelligent planting device control method based on program cloud according to claim 1 or 4, wherein in the step of fuzzy matching, a one-to-one corresponding weight coefficient is set for each attribute parameter.
6. The program cloud based intelligent planting device control method according to claim 1 or 4, wherein the calculation formula of the deviation value is Vn = (X-P) QTWherein Vn is a deviation value of the operation method of the nth plant growing equipment, X is each attribute parameter of the first plant attribute, P is each attribute parameter of the operation method of the nth plant growing equipment, Q sets a one-to-one corresponding weight coefficient for each attribute parameter, and Q is a weight coefficient of the operation method of the nth plant growing equipmentTIs the transpose of Q.
7. The program cloud based intelligent planting device control method according to claim 3 or 4, wherein the minimum execution unit code comprises a watering code block, a fertilizing code block, a light supplementing code block, a temperature control code block, a pH value adjusting code block, a time delay code block and/or a safety inspection code block.
8. The intelligent planting device control method based on program cloud according to any one of claims 1-4, wherein in step S1, the method further comprises obtaining geographic positioning information of the user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710697982.8A CN107357935B (en) | 2017-08-15 | 2017-08-15 | Intelligent planting equipment control method based on program cloud |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710697982.8A CN107357935B (en) | 2017-08-15 | 2017-08-15 | Intelligent planting equipment control method based on program cloud |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107357935A CN107357935A (en) | 2017-11-17 |
| CN107357935B true CN107357935B (en) | 2020-02-07 |
Family
ID=60287530
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710697982.8A Expired - Fee Related CN107357935B (en) | 2017-08-15 | 2017-08-15 | Intelligent planting equipment control method based on program cloud |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107357935B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107969297A (en) * | 2017-12-20 | 2018-05-01 | 佛山市洛克威特科技有限公司 | A kind of method of intelligence plantation strawberry |
| CN108257038A (en) * | 2018-01-12 | 2018-07-06 | 杭州智控网络有限公司 | A kind of plant planting system and method based on big data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104077725A (en) * | 2014-07-14 | 2014-10-01 | 内蒙古德辰信息网络科技有限责任公司 | Potato planting Internet-of-things monitoring, control and information service cloud platform integrated system |
| CN104699042A (en) * | 2015-02-05 | 2015-06-10 | 彭琦 | Internet of Things based facility agricultural management and control system and control method thereof |
| CN104866970A (en) * | 2015-05-26 | 2015-08-26 | 徐吉祥 | Intelligent plantation management method and intelligent plantation equipment |
| CN106331127A (en) * | 2016-08-30 | 2017-01-11 | 深圳前海弘稼科技有限公司 | Planting equipment and management method, device and system thereof and server |
| CN106506699A (en) * | 2016-12-27 | 2017-03-15 | 杭州云锄科技有限公司 | A kind of wisdom agricultural Internet of Things plantation platform |
| CN106709811A (en) * | 2016-12-08 | 2017-05-24 | 党兴仁 | Vegetable growth management method and system |
-
2017
- 2017-08-15 CN CN201710697982.8A patent/CN107357935B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104077725A (en) * | 2014-07-14 | 2014-10-01 | 内蒙古德辰信息网络科技有限责任公司 | Potato planting Internet-of-things monitoring, control and information service cloud platform integrated system |
| CN104699042A (en) * | 2015-02-05 | 2015-06-10 | 彭琦 | Internet of Things based facility agricultural management and control system and control method thereof |
| CN104866970A (en) * | 2015-05-26 | 2015-08-26 | 徐吉祥 | Intelligent plantation management method and intelligent plantation equipment |
| CN106331127A (en) * | 2016-08-30 | 2017-01-11 | 深圳前海弘稼科技有限公司 | Planting equipment and management method, device and system thereof and server |
| CN106709811A (en) * | 2016-12-08 | 2017-05-24 | 党兴仁 | Vegetable growth management method and system |
| CN106506699A (en) * | 2016-12-27 | 2017-03-15 | 杭州云锄科技有限公司 | A kind of wisdom agricultural Internet of Things plantation platform |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107357935A (en) | 2017-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kang et al. | From parallel plants to smart plants: intelligent control and management for plant growth | |
| González-Amarillo et al. | An IoT-based traceability system for greenhouse seedling crops | |
| CN109874477B (en) | Agricultural park fertilizer applicator hosting method and system | |
| JP2016146039A (en) | Information processing device, program and information processing method | |
| CN103605353A (en) | Agricultural intelligent optimization monitoring system and optimization monitoring method based on cloud services | |
| CN107357935B (en) | Intelligent planting equipment control method based on program cloud | |
| Nada et al. | Irrigation expert system for trees | |
| Jia | Intelligent garden planning and design based on agricultural internet of things | |
| Kolmanič et al. | An algorithm for automatic dormant tree pruning | |
| Bashar | Agricultural machine automation using iot through android | |
| CN106960392B (en) | Planting technology service method for small and medium-sized planters | |
| CN104620879B (en) | Tree pruning optimizing method based canopy light distribution analog | |
| JP2016146046A (en) | Predictor, prediction method and program | |
| Avigal et al. | Simulating polyculture farming to learn automation policies for plant diversity and precision irrigation | |
| JP2018173911A (en) | Information processing device, program, information processing method, method and data structure for producing design information | |
| Tripathy et al. | Smart Farming based on Deep Learning Approaches | |
| CN114066061A (en) | Resource optimization configuration method, device and equipment based on neural network model | |
| Yunindanova | Preparing for Indonesian Agricultural Transformation in The Society Era 5.0 | |
| CN109191320A (en) | A kind of agricultural production management guidance method and system | |
| Kumar et al. | Monitoring and accelerating plant growth using IoT and Hydroponics | |
| MORIYUKI et al. | Profit models based on the growth dynamics of lettuce populations in a plant factory | |
| Cong et al. | Automated Hydroponic System for Indoor Plant Growth in Urban Area | |
| Lopez et al. | Using L-PEACH for dynamic simulation of source-sink behavior of peach trees: Effects of date of thinning on fruit growth | |
| Ali et al. | Design and Implementation of Smart Greenhouse and Experiment in Strawberry Cultivation | |
| Calzita et al. | Intelligent Aeroponic System for Real-Time Control and Monitoring of Lactuca Sativa Production |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200207 Termination date: 20210815 |