CN115348281A - Automatic plant planting method and system based on self-optimization - Google Patents
Automatic plant planting method and system based on self-optimization Download PDFInfo
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Abstract
The invention belongs to the technical field of automatic planting, and discloses a plant automatic planting method and system based on self-optimization. The method is deployed locally and comprises the following steps: and acquiring a plurality of real-time parameters and actual growth charts uploaded by monitoring equipment. And judging whether any real-time parameter exceeds the standard parameter range in the planting strategy. And when the real-time parameters exceed the standard parameter range, judging whether the actual growth vigor of the plant is inferior to the standard growth vigor or not based on the actual growth vigor map and the standard growth vigor map in the planting strategy. When the actual growth vigor of the plant is inferior to the standard growth vigor, judging whether the real-time parameters exceed a preset deviation threshold value so as to determine whether to trigger control equipment to adjust corresponding parameter quantity; otherwise, the real-time parameters and the actual growth chart are generated into sub-strategies and stored. And circularly executing the processes until the plant maturation period is finished. The invention improves the reliability and real-time performance of automatic planting, optimizes the plant output and reduces the energy consumption.
Description
Technical Field
The invention relates to the technical field of automatic planting, in particular to a plant automatic planting method and system based on self optimization.
Background
Indoor automatic planting occupies a certain share in people's daily food supply gradually because of having advantages such as no weather influence, green, manpower saving.
The existing indoor automatic planting system generally comprises a planting cabinet, a cloud end, monitoring equipment and control equipment. Planting strategies corresponding to various plants are included in the cloud; various plants are planted in the planting cabinet; the monitoring equipment and the control equipment are arranged in the planting cabinet and connected with the cloud. In the automatic planting process, firstly, a planting strategy corresponding to the plant is taken out from the cloud, then parameters of soil, temperature, humidity, illumination and the like related to plant growth in a planting cabinet acquired by monitoring equipment are uploaded to the cloud, and the parameters are compared with standard parameters in the planting strategy; and finally, carrying out output control on the control equipment based on the comparison result so as to enable each growth parameter of the plants in the planting cabinet to be consistent with the standard parameter.
From the above, the current automatic planting is performed based on the cloud, and when network interruption or network signal fluctuation occurs, the planting cabinet, the cloud, the monitoring device and the control device in the automatic planting system are in an unconnected state, so that the regulation and control capability of the plant growth environment is lost. Meanwhile, the whole automatic planting process takes a standard planting strategy as the only basis to regulate and control the growth environment of the plants, but the difference in the actual planting process is ignored by the method. Therefore, in the planting process, optimal regulation and control of illumination, humidity, temperature, nutrition supply and the like can not be performed on specific plants, and finally, optimal yield can not be realized in planting.
Disclosure of Invention
The invention aims to provide a plant automatic planting method and system based on self-optimization, which are used for solving the technical problems that the reliability and real-time performance of planting regulation and control are poor and the optimized output cannot be realized in the existing automatic planting process.
In order to achieve the above purpose, the invention provides the following technical scheme:
a plant automatic planting method based on self optimization is deployed locally and comprises the following steps:
acquiring a plurality of real-time parameters and an actual growth chart uploaded by monitoring equipment;
judging whether any real-time parameter exceeds a standard parameter range in a planting strategy;
when the real-time parameters exceed the standard parameter range, judging whether the actual growth vigor of the plant is inferior to the standard growth vigor or not based on the actual growth vigor map and a standard growth vigor map in the planting strategy;
when the actual growth vigor of the plant is inferior to the standard growth vigor, judging whether the real-time parameters exceed a preset deviation threshold value so as to determine whether to trigger control equipment to adjust corresponding parameter quantity; otherwise, generating the real-time parameters and the actual growth chart into sub-strategies and storing the sub-strategies;
the above processes are circularly executed until the plant mature period is finished.
Further, before obtaining a plurality of real-time parameters and an actual growth chart uploaded by the monitoring device, the method further includes:
acquiring coding information corresponding to plant species;
sending a request to the cloud based on the coding information to acquire a corresponding planting strategy;
and acquiring and storing the planting strategy.
Further, the above-mentioned processes are executed in a loop until the plant maturation period is over, and the method further includes:
updating the planting strategy based on each sub-strategy to construct a standby strategy;
and storing the standby strategy for direct calling in the next planting.
Further, after the updating the planting strategy based on each sub-strategy to construct a backup strategy, the method further includes:
and uploading the standby strategy to a cloud end and storing the standby strategy.
Further, before obtaining a plurality of real-time parameters and an actual growth chart uploaded by the monitoring device, the planting strategy is further optimized, including:
creating a plurality of samples which are respectively input into a machine learning model to output a plurality of corresponding predicted growth maps; the samples comprise environmental parameters and energy consumption parameters;
performing weighted assignment on each predicted growth pattern based on a weight model; the weight model is constructed based on a fuzzy mathematical method, and the weighting items comprise the germination rate of a seedling stage, the chlorophyll content of a growing stage and the fruit number of a mature stage;
and selecting the predicted growth vigor graph with the highest assignment and a corresponding sample thereof to construct a new planting strategy.
A plant automation planting system based on self-optimization, comprising:
the first acquisition module is used for acquiring a plurality of real-time parameters and an actual growth chart uploaded by the monitoring equipment;
the first judgment module is used for judging whether any real-time parameter exceeds a standard parameter range in a planting strategy;
the second judging module is used for judging whether the actual growth vigor of the plant is inferior to the standard growth vigor or not based on the actual growth vigor map and the standard growth vigor map in the planting strategy when the real-time parameter exceeds the standard parameter range;
the third judging module is used for judging whether the real-time parameters exceed a preset deviation threshold value to determine whether to trigger the control equipment to adjust corresponding parameter quantity when the actual growth condition of the plants is inferior to the standard growth condition; otherwise, generating the real-time parameters and the actual growth chart into sub-strategies and storing the sub-strategies;
and the first circulation module is used for sequentially and circularly calling the functional modules until the plant maturation period is finished.
Further, the method comprises the following steps:
the second acquisition module is used for acquiring coding information corresponding to the plant species;
the first request module is used for sending a request to the cloud based on the coding information so as to obtain a corresponding planting strategy;
and the first storage module is used for acquiring the planting strategy and storing the planting strategy to the local.
Further, the method comprises the following steps:
a first updating module for updating the planting strategy based on each of the sub-strategies to form a standby strategy;
and the second storage module is used for storing the standby strategy to the local for direct calling when the plants are planted next time.
Further, the method comprises the following steps:
and the third storage module is used for uploading the standby strategy to a cloud and storing the standby strategy.
Further, the method comprises the following steps:
the third acquisition module is used for creating a plurality of samples which are respectively input into the machine learning model so as to output a plurality of corresponding predicted growth charts; the samples comprise environmental parameters and energy consumption parameters;
the calculation module is used for carrying out weighted assignment on each predicted growth chart based on a weight model; the weight model is constructed based on a fuzzy mathematical method, and the weighting items comprise the germination rate of a seedling stage, the chlorophyll content of a growing stage and the fruit number of a mature stage;
and the second updating module is used for selecting the predicted growth vigor graph with the highest assignment and the corresponding sample thereof to construct a new planting strategy.
Has the beneficial effects that:
according to the technical scheme, the automatic plant planting method and the automatic plant planting system based on self optimization are provided, and are used for overcoming the defects of low instantaneity, poor stability and difficulty in optimizing yield in the conventional automatic plant planting method and system.
The method is deployed locally and comprises the following steps: and acquiring a plurality of real-time parameters and actual growth charts uploaded by monitoring equipment. And judging whether any real-time parameter exceeds the standard parameter range in the planting strategy. And when the real-time parameters exceed the standard parameter range, judging whether the actual growth vigor of the plant is inferior to the standard growth vigor or not based on the actual growth vigor graph and the standard growth vigor graph in the planting strategy. When the actual growth vigor of the plant is inferior to the standard growth vigor, judging whether the real-time parameters exceed a preset deviation threshold value so as to determine whether to trigger control equipment to adjust corresponding parameter quantity; otherwise, the real-time parameters and the actual growth chart are generated into sub-strategies and stored locally. The above processes are circularly executed until the plant mature period is finished.
The method has the advantages that the whole process is carried out on the basis of the local, compared with the existing automatic planting process carried out on the basis of the cloud, the method has good stability due to the fact that the method is not influenced by network signals any more, and meanwhile, the method also has good reliability due to the fact that the local part is directly connected with the planting cabinet, monitoring equipment and control equipment in the planting cabinet through signal lines.
In a specific automatic planting process, the method takes data uploaded by the monitoring equipment and standard data in a planting strategy as a basis, and performs output adjustment of the control equipment based on results after multiple judgments. Firstly, the method aims to improve the planting yield based on the actual difference among plants and the primary purpose of planting, and introduces the evaluation standard of the plant growth vigor. Namely, when the real-time parameters exceed the standard parameter range in the planting strategy, the plant growth condition is continuously judged. If the plant growth vigor is better than the standard growth vigor when the plant growth vigor exceeds the standard parameter range, the real-time parameter is more suitable for the current plant growth, and therefore the control equipment is not required to adjust. Meanwhile, when the plant growth vigor is better than the standard growth vigor, the real-time parameters and the actual growth vigor map are generated into sub strategies and stored locally. So as to be directly used in the next planting cycle to ensure high yield of the plants.
Secondly, based on the low energy consumption requirement in the automatic planting process, the randomness and the self-repairability in the plant growth process are considered, and a deviation threshold value is introduced. Namely, when the plant growth vigor is inferior to the standard growth vigor, whether the real-time parameters exceed a preset deviation threshold value is continuously judged. If the deviation does not exceed the preset deviation threshold, the growth condition of the plant is within a normal range, and the plant does not need to be regulated and controlled through control equipment. And when the preset deviation threshold value is exceeded, the growth of the plant is in an abnormal range, and the output adjustment of the control equipment is needed to intervene in the growth of the plant. Compared with the existing method that when a certain real-time parameter exceeds the standard parameter range of the planting strategy, the control equipment is started to carry out corresponding adjustment, the method for carrying out adjustment through judgment of the deviation threshold effectively avoids high energy consumption caused by frequent adjustment on the premise of ensuring normal growth of plants.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent.
The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
Drawings
The figures are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a self-optimization-based automatic plant growing method according to the present invention;
FIG. 2 is a flow chart of the local acquisition of the planting strategy;
FIG. 3 is a flowchart illustrating an embodiment of real-time parameter determination based on the deviation threshold;
FIG. 4 is a flow chart of the backup policy acquisition;
fig. 5 is a flow chart of the optimization of the planting strategy.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention. Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.
The use of "first," "second," and similar terms in the description and claims of the present application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. Similarly, the singular forms "a," "an," or "the" do not denote a limitation of quantity, but rather denote the presence of at least one, unless the context clearly dictates otherwise. The terms "comprises" or "comprising," and the like, mean that the elements or components listed in the preceding list of elements or components include the features, integers, steps, operations, elements and/or components listed in the following list of elements or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof. "upper", "lower", "left", "right", and the like are used only to indicate relative positional relationships, and when the absolute position of the object to be described is changed, the relative positional relationships may also be changed accordingly.
Because the planting process of the existing automatic planting system is carried out by depending on the cloud, the running stability of the whole planting system is easily influenced by the network state, and if the network is interrupted, the monitoring equipment, the control equipment and the cloud in the planting system are also mutually disconnected; thereby causing paralysis of the whole planting system. When network fluctuation occurs, the interaction accuracy and real-time performance of monitoring information, control information and the like in the planting system are interfered. Meanwhile, the existing automatic planting method only carries out targeted adjustment on each parameter quantity in the planting process by using the planting strategy, and the specific method is that once a certain real-time parameter exceeds the standard parameter range of the planting strategy, the control equipment is started to carry out corresponding adjustment. But neglects the result that the actual planting is the combined action of all parameters and the difference of the actual planting; it is difficult to optimize the actual planting in a targeted manner. And frequent regulation and control also causes that the energy consumption of the whole planting system is difficult to reduce. The invention aims to provide a plant automatic planting method and system based on self-optimization, which are used for simultaneously improving the technical problems of poor stability, low real-time performance, difficulty in realizing optimized output and energy consumption reduction in the conventional automatic planting process.
In the embodiment, the method is deployed locally, so that the whole self-optimization regulation and control is performed locally. Compared with the prior art of automatic planting based on cloud, on one hand, network interference does not exist, and therefore reliability is higher; on the other hand, the communication between the local terminal and the planting cabinet enables the information interaction process of automatic planting to have higher real-time performance.
As shown in fig. 1, the method specifically includes the following steps:
and S102, acquiring a plurality of real-time parameters and actual growth charts uploaded by monitoring equipment.
The monitoring equipment is arranged in the planting cabinet and comprises a temperature sensor, a humidity sensor, a gas sensor, an illumination sensor and the like. Respectively used for obtaining the temperature, the humidity, the gas composition, the light intensity and the like in the planting cabinet.
At this time, the real-time parameters comprise temperature, humidity and CO 2 Content, light intensity, brightness, divergence, etc.
The actual growth chart in this embodiment may be a photograph, a time-lapse video, or a data chart created based on plant parameters (such as leaf size, color, chlorophyll content, plant height, growth speed, etc.). When the actual growth chart is a photo or a time-delay video, the monitoring equipment further comprises a high-frequency camera or a time-delay camera.
And step S104, judging whether any real-time parameter exceeds the standard parameter range in the planting strategy.
The planting strategy corresponds to the plant species and is a universal planting and evaluation standard of the plants. According to the growth process of the plants, the planting strategy is divided according to the cultivation stages of the plants, namely, a seedling stage, a growth stage and a mature stage; the standard parameter ranges in the planting strategy corresponding to each cultivation stage include temperature range, humidity range, illumination range, fertilizer concentration range, CO 2 Content ranges, etc.
As a specific embodiment, as shown in fig. 2, before step S102, a planting strategy is also obtained, which includes:
and S100.2, acquiring coding information corresponding to the plant type.
In this embodiment, the specific presentation form of the encoded information may be a two-dimensional code, a barcode, or the like.
S100.4, sending a request to a cloud based on the coding information to acquire a corresponding planting strategy;
and S100.6, acquiring the planting strategy and storing the planting strategy to the local.
And S106, when the real-time parameters exceed the standard parameter range, judging whether the actual growth vigor of the plants is inferior to the standard growth vigor or not based on the actual growth vigor map and the standard growth vigor map in the planting strategy.
The existing automatic planting strategies mostly adopt a single factor control method for one-to-one corresponding comparison and regulation of real-time parameters and standard parameters in the planting strategies. However, it can be seen from actual planting that the growth of plants is a result of the combined action of all parameters, and the growth of plants is different. Therefore, in the present example, based on the above two considerations, high yield was targeted, and plant growth was introduced as an evaluation criterion.
Step S108, when the actual growth vigor of the plants is inferior to the standard growth vigor, judging whether the real-time parameters exceed a preset deviation threshold value so as to determine whether to trigger control equipment to adjust corresponding parameter quantity; otherwise, generating the real-time parameters and the actual growth chart into a sub-strategy and storing the sub-strategy.
In this embodiment, the control device includes an LED lamp, a circulating water pump, a fan, and the like, and the LED lamp is used to adjust light intensity, brightness, and the like; the circulating water pump is used for adjusting humidity, fertilizer content concentration and the like; the fan is used for adjusting CO 2 Concentration, concentration of fine particulate matter (PM 2.5, PM10, etc.), and the like.
In this step, when the actual growth vigor of the plants is inferior to the standard growth vigor, it is indicated that the current real-time parameters are more in line with the growth of the plants in the planting cabinet. It is stored for use in the next planting cycle to increase plant yield; meanwhile, the corresponding control equipment regulation and control are avoided, and the planting energy consumption is reduced.
Meanwhile, in the step, the control equipment is adjusted only when the preset deviation threshold value is exceeded. The regulation and control method considers the random factors and the self-repairing factors in the actual growth of the plants, avoids energy consumption loss caused by frequent regulation and control on the premise of ensuring the normal growth of the plants, and improves the energy conservation of the whole system.
As an optional implementation, setting the deviation threshold value includes a first deviation threshold value and a second deviation threshold value; specifically, the first deviation threshold is smaller than the second deviation threshold. As shown in fig. 3, at this time, the determining whether the real-time parameter exceeds the preset deviation threshold to determine whether to trigger the control device to perform corresponding parameter adjustment includes:
step S108.0, counting once when the real-time parameter exceeds a preset first deviation threshold value, and judging whether the real-time parameter exceeds a preset second deviation threshold value;
step S108.2, when the real-time parameter exceeds a preset second deviation threshold value, triggering control equipment to adjust corresponding parameter quantity;
and step S108.4, repeating the process until the real-time parameters are restored to the standard parameter range.
As a specific embodiment, in order to rapidly restore the real-time parameters to the standard parameter range, the adverse effect on the plants is reduced; after step S108.6, the method further includes:
step S108.6, recording the recovery time of the real-time parameter to the standard parameter range;
and S108.8, correcting the first deviation threshold and the second deviation threshold based on the counting times exceeding the first deviation threshold and the recovery time.
In a specific implementation, the first deviation threshold and the second deviation threshold are corrected by weight adjustment.
As an alternative embodiment, the deviation threshold may be three, four, five, etc. according to actual requirements, and the specific values of the different deviation thresholds are gradually expanded when the step-by-step judgment is performed.
And step S110, circularly executing the processes until the plant maturation period is finished.
When the actual growth vigor of the plant is inferior to the standard growth vigor, a preferred planting strategy (i.e., a standby strategy) of the plant in the environment of the planting cabinet is further established in order to facilitate repeated calling of the set of real-time parameters, as shown in step S108. As shown in fig. 4, the method specifically includes:
step S112.2, updating the planting strategy based on each sub-strategy to form a standby strategy;
in specific implementation, if the sub-strategy corresponds to the seedling stage in the general planting strategy, replacing the standard parameter range and the standard growth chart corresponding to the seedling stage in the general planting strategy.
As an alternative embodiment, in order to facilitate data integration to achieve universal stepwise optimization of the planting strategy, the step S112.2 further includes:
and step S112.2', uploading the standby strategy to the cloud end and storing the standby strategy.
Step S112.4, storing the backup strategy for direct recall at the next planting of the plant.
As an alternative implementation, the differences in different planting periods are considered, and in the next planting period, a general planting strategy is still obtained from the cloud. At this time, the planting strategy and the standby strategy are stored locally, and the method further includes the following steps before step S104:
step S103, judging the similarity of the real-time parameters and the actual growth pattern and the standard parameter range and the standard growth pattern corresponding to the planting strategy and the standby strategy, and selecting the planting strategy or the standby strategy to carry out the planting based on high similarity.
In order to realize double optimization of plant yield and energy consumption, the planting strategy is optimized on the basis of environmental data, energy consumption data and plant generation condition data. As shown in fig. 5, the optimization of the planting strategy includes:
step S202, creating a plurality of samples, and inputting the samples into a machine learning model respectively to output a plurality of corresponding prediction growth charts; the samples include environmental parameters and energy consumption parameters.
The samples in this step are randomly generated by a computer. As an alternative embodiment, the samples may also be generated based on the backup strategy in step S112.2'.
S204, carrying out weighted assignment on each predicted growth pattern based on a weight model; the weight model is constructed based on a fuzzy mathematical method, and the weighting items comprise the germination rate of the seedling stage, the chlorophyll content of the growing stage and the fruit number of the mature stage.
As consistently selectable embodiments, the weighting terms also include leaf size in the growing period, plant height, fruit size in the mature period, fruit sugar content, and the like.
And S206, selecting the predicted growth vigor graph with the highest assignment and a sample corresponding to the predicted growth vigor graph to construct a new planting strategy.
The programs described above may be run on a processor or may also be stored in memory (or referred to as computer-readable media), which includes both non-transitory and non-transitory, removable and non-removable media, that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
These computer programs may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks, and corresponding steps may be implemented by different modules.
The embodiment also provides a plant automatic planting system based on self optimization, and the system is built based on the method. It includes in proper order: the device comprises a first acquisition module, a first judgment module, a second judgment module, a third judgment module and a first circulation module.
The first acquisition module is used for acquiring a plurality of real-time parameters and an actual growth chart uploaded by the monitoring equipment. The first judgment module is used for judging whether any real-time parameter exceeds the standard parameter range in the planting strategy. And the second judging module is used for judging whether the actual growth vigor of the plant is inferior to the standard growth vigor or not based on the actual growth vigor map and the standard growth vigor map in the planting strategy when the real-time parameter exceeds the standard parameter range. The third judging module is used for judging whether the real-time parameters exceed a preset deviation threshold value to determine whether to trigger the control equipment to adjust corresponding parameter quantity when the actual growth vigor of the plants is inferior to the standard growth vigor; otherwise, generating the real-time parameters and the actual growth chart into a sub-strategy and storing the sub-strategy. The first circulation module is used for sequentially and circularly calling the functional modules until the plant maturation period is finished.
As can be seen from the functional modules, the system takes the local client as a control main body for automatic planting, so that network interference is avoided, and communication is more convenient; therefore, the method has good reliability and real-time performance. Meanwhile, a growth judgment standard and a deviation threshold judgment standard are introduced through the first judgment module, the second judgment module and the third judgment module based on a plant high-yield principle, a planting low-energy-consumption principle and a plant difference principle. Therefore, the system can carry out targeted adjustment on plant planting according to the current planting environment in the actual planting process, and the plant output is improved; and the problem of high energy consumption caused by frequent regulation and control is avoided.
In order to realize the acquisition of the planting strategy on the cloud, the system further comprises: the device comprises a second acquisition module, a first request module and a first storage module. The second acquisition module is used for acquiring coding information corresponding to the plant species. The first request module is used for sending a request to the cloud based on the coding information to obtain a corresponding planting strategy. The first storage module is used for acquiring the planting strategy and storing the planting strategy to the local.
In order to facilitate repeated calling of the real-time parameters with better actual plant growth conditions, the system further comprises: the device comprises a first updating module and a second storage module. The first updating module is used for updating the planting strategy based on each sub-strategy to form a standby strategy. The second storage module is used for storing the standby strategy to the local for direct calling when the plants are planted next time.
In order to realize data integration so as to gradually optimize the planting strategy of the cloud, the system further comprises a third storage module. And the third storage module is used for uploading the new planting strategy to a cloud and storing the new planting strategy.
Similarly, the system further comprises the following modules in sequence to optimize the planting strategy:
the third acquisition module is used for creating a plurality of samples which are respectively input into the machine learning model so as to output a plurality of corresponding predicted growth charts; the samples include environmental parameters and energy consumption parameters.
The calculation module is used for carrying out weighted assignment on each predicted growth tendency map based on a weight model; the weight model is constructed based on a fuzzy mathematical method, and the weighting items comprise the germination rate of the seedling stage, the chlorophyll content of the growing stage and the fruit number of the mature stage.
And the second updating module is used for selecting the predicted growth vigor graph with the highest value and the corresponding sample thereof to construct a new planting strategy.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be defined by the appended claims.
Claims (10)
1. A plant automatic planting method based on self optimization is deployed locally and comprises the following steps:
acquiring a plurality of real-time parameters and an actual growth chart uploaded by monitoring equipment;
judging whether any real-time parameter exceeds a standard parameter range in a planting strategy;
when the real-time parameters exceed the standard parameter range, judging whether the actual growth vigor of the plant is inferior to the standard growth vigor or not based on the actual growth vigor map and a standard growth vigor map in the planting strategy;
when the actual growth of the plant is inferior to the standard growth, judging whether the real-time parameters exceed a preset deviation threshold value or not so as to determine whether to trigger control equipment to adjust corresponding parameter quantity or not; otherwise, generating the real-time parameters and the actual growth chart into sub-strategies and storing the sub-strategies;
and circularly executing the processes until the plant maturation period is finished.
2. The automatic plant planting method based on self optimization according to claim 1, wherein before the obtaining of the real-time parameters and the actual growth chart uploaded by the monitoring device, the method further comprises:
acquiring coding information corresponding to plant species;
sending a request to the cloud based on the coding information to obtain a corresponding planting strategy;
and acquiring and storing the planting strategy.
3. The self-optimization-based automatic plant growing method according to claim 1, wherein the above processes are executed circularly until the plant mature period is over, further comprising:
updating the planting strategy based on each sub-strategy to construct a standby strategy;
and storing the standby strategy for direct calling in the next planting.
4. The self-optimization-based automatic plant growing method according to claim 3, wherein after the updating the growing strategy based on each sub-strategy to construct a backup strategy, the method further comprises:
and uploading the standby strategy to a cloud end and storing the standby strategy.
5. The automatic plant planting method based on self optimization according to claim 1, wherein before the acquisition of the real-time parameters and the actual growth chart uploaded by the monitoring equipment, the planting strategy is further optimized, and the method comprises the following steps:
creating a plurality of samples which are respectively input into a machine learning model to output a plurality of corresponding predicted growth charts; the samples comprise environmental parameters and energy consumption parameters;
carrying out weighted assignment on each predicted growth chart based on a weight model; the weight model is constructed based on a fuzzy mathematical method, and the weighting items comprise the germination rate of a seedling stage, the chlorophyll content of a growing stage and the fruit number of a mature stage;
and selecting the predicted growth vigor graph with the highest assignment and a corresponding sample thereof to construct a new planting strategy.
6. A plant automation planting system based on self optimization is characterized by comprising:
the first acquisition module is used for acquiring a plurality of real-time parameters and an actual growth chart uploaded by the monitoring equipment;
the first judgment module is used for judging whether any real-time parameter exceeds a standard parameter range in a planting strategy;
the second judging module is used for judging whether the actual growth vigor of the plant is inferior to the standard growth vigor or not based on the actual growth vigor graph and the standard growth vigor graph in the planting strategy when the real-time parameter exceeds the standard parameter range;
the third judging module is used for judging whether the real-time parameters exceed a preset deviation threshold value to determine whether to trigger the control equipment to adjust corresponding parameter quantity when the actual growth condition of the plants is inferior to the standard growth condition; otherwise, generating the real-time parameters and the actual growth chart into sub-strategies and storing the sub-strategies;
and the first circulation module is used for sequentially and circularly calling the functional modules until the plant maturation period is finished.
7. The self-optimization based plant automation planting system of claim 6, comprising:
the second acquisition module is used for acquiring coding information corresponding to the plant species;
the first request module is used for sending a request to the cloud based on the coding information so as to obtain a corresponding planting strategy;
and the first storage module is used for acquiring the planting strategy and storing the planting strategy to the local.
8. The self-optimization based plant automation planting system of claim 6, comprising:
a first updating module for updating the planting strategy based on each of the sub-strategies to form a standby strategy;
and the second storage module is used for storing the standby strategy to the local for direct calling when the plants are planted next time.
9. The self-optimization-based plant automated planting system of claim 8, comprising:
and the third storage module is used for uploading the standby strategy to a cloud and storing the standby strategy.
10. The self-optimization-based plant automated planting system of claim 6, comprising:
the third acquisition module is used for creating a plurality of samples which are respectively input into the machine learning model so as to output a plurality of corresponding predicted growth charts; the samples comprise environmental parameters and energy consumption parameters;
the calculation module is used for carrying out weighted assignment on each predicted growth chart based on a weight model; the weight model is constructed based on a fuzzy mathematical method, and the weighting items comprise the germination rate of a seedling stage, the chlorophyll content of a growing stage and the fruit number of a mature stage;
and the second updating module is used for selecting the predicted growth vigor graph with the highest assignment and the corresponding sample thereof to construct a new planting strategy.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115901890A (en) * | 2022-12-28 | 2023-04-04 | 苏州数言信息技术有限公司 | Sensor suitable for water culture system and test method |
| CN115950928A (en) * | 2022-12-28 | 2023-04-11 | 苏州数言信息技术有限公司 | PH and EC testing method and system suitable for hydroponic system |
| CN116627193A (en) * | 2023-07-21 | 2023-08-22 | 山东梦芯信息科技有限公司 | Intelligent management and control platform and method for greenhouse |
| CN117522083A (en) * | 2024-01-05 | 2024-02-06 | 山西农众物联科技有限公司 | Cultivation control method and system for sensing data identification of Internet of things |
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- 2022-06-30 CN CN202210766003.0A patent/CN115348281A/en not_active Withdrawn
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115901890A (en) * | 2022-12-28 | 2023-04-04 | 苏州数言信息技术有限公司 | Sensor suitable for water culture system and test method |
| CN115950928A (en) * | 2022-12-28 | 2023-04-11 | 苏州数言信息技术有限公司 | PH and EC testing method and system suitable for hydroponic system |
| CN115901890B (en) * | 2022-12-28 | 2024-08-06 | 苏州数言信息技术有限公司 | Sensor suitable for hydroponic system and testing method |
| CN116627193A (en) * | 2023-07-21 | 2023-08-22 | 山东梦芯信息科技有限公司 | Intelligent management and control platform and method for greenhouse |
| CN117522083A (en) * | 2024-01-05 | 2024-02-06 | 山西农众物联科技有限公司 | Cultivation control method and system for sensing data identification of Internet of things |
| CN117522083B (en) * | 2024-01-05 | 2024-03-12 | 山西农众物联科技有限公司 | Cultivation control method and system for sensing data identification of Internet of things |
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Application publication date: 20221115 |