JP2022053489A - Sprinkler system - Google Patents

Abstract

To achieve optimal watering based on lawn conditions, weather conditions, and lawn grass varieties.SOLUTION: A sprinkler system that sprinkles water on planted lawn comprises: an information acquisition means for acquiring ground image information obtained by imaging the lawn to be sprinkled from the ground; with reference to three or more levels of association between ground image information for reference obtained by imaging the lawn from the ground in the past and a watering plan for the lawn glass, and based on the ground image information for reference in response to the ground image information acquired by the information acquisition means, a search means for searching a watering plan for the lawn by giving priority to the one with the higher degree of association; and a sprinkling means for sprinkling water on the lawn based on the sprinkling plan searched by the search means.SELECTED DRAWING: Figure 1

Description

The present invention relates to a sprinkler system that sprinkles water on a planted lawn.

Traditionally, lawns planted in gardens are often sprinkled with sprinklers, for example. If the amount of water sprinkled is too large, the water will be wasted, and if the amount of water sprinkled is small, the lawn will die. In addition, the sprinkler watering plan needs to be optimized based on the weather conditions and turf varieties.

However, the current situation is that no technique has been proposed for optimizing the process from setting a sprinkler watering plan to watering based on lawn conditions, weather conditions, and turf varieties.

Therefore, the present invention has been devised in view of the above-mentioned problems, and its purpose is to realize optimum watering based on the lawn condition, the weather condition, and the turf variety. Sprinkler system Lawn sprinkler plan Sprinkler plan Sprinkler system is to be provided.

In order to solve the above-mentioned problems, in the sprinkler system that sprinkles the planted lawn, the information acquisition means for acquiring the ground image information of the lawn to be sprinkled from the ground and the past image of the lawn from the ground. Refer to the three or more levels of association between the reference ground image information and the watering plan for the lawn, and based on the reference ground image information according to the ground image information acquired by the information acquisition means, the above association degree It is characterized by comprising a search means for searching a watering plan for a lawn by giving priority to a higher one, and a watering means for sprinkling water on the lawn based on the watering plan searched by the search means.

Even if you do not have any special skills or experience, it is possible to determine the watering plan of lawn trees with high accuracy.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the sprinkler system for sprinkler planning to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a watering plan sprinkler system 1 to which a watering plan sprinkler system to which the present invention is applied is mounted. The watering plan sprinkler system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, a database 3 connected to the search device 2, and a sprinkler device 7.

The information acquisition unit 9 is a device for a person using this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of taking an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the search device 2 described later. The information acquisition unit 9 outputs the detected information to the search device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be configured by a communication interface for acquiring data about the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and the body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the body sensor may acquire biometric data of not only humans but also animals. Further, the information acquisition unit 9 may be configured as a device for acquiring information such as drawings by scanning or reading from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects odors and scents.

The information acquisition unit 9 may be attached to or built into the sprinkler device 7.

FIG. 2 shows a configuration example of the sprinkler device 7. The sprinkler device 7 includes a sprinkler main body 7 connected to a water pipe 71. It goes without saying that the sprinkler main body 7 itself is equipped with a well-known sprinkler configuration and is provided with a nozzle mechanism necessary for sprinkling water. The sprinkler device 7 is embedded in the lawn, and water is supplied to the lawn by sprinkling water.

The database 3 stores a data set of reference information such as reference ground image information and reference aerial image information, which will be described later, and a watering plan for the lawn.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be the one to be converted. The user can obtain a search solution by the search device 2.

FIG. 3 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via an operation button, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 according to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technology.

The display unit 23 is configured by a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program will be read and executed by the control unit 24.

Incidentally, the present invention is not limited to the case where the search device 2 makes various discriminations, controls, proposals, and estimates based on the data set recorded in the database 3, and is, for example, a terminal device of the system. It may be composed of artificial intelligence mounted on an edge device.

In such a case, the estimation process may be performed by, for example, a sprinkler 7 or an edge device provided in an unmanned aerial vehicle such as a drone.

In the watering plan sprinkler system 1, for example, as shown in FIG. 4, it is premised that three or more levels of association between the reference aerial image information and the watering plan are set and acquired in advance.

The reference aerial image information is an image of the lawn taken from the air or the sky. In order to acquire this reference aerial image information, it is possible to obtain an image of the lawn from the sky by an aircraft, a helicopter, or an unmanned aerial vehicle such as a drone. Further, the reference aerial image information may be composed of satellite images captured by artificial satellites. The reference aerial image information may be composed of a so-called spectral image color-coded for each spectrum, in addition to the normal RGB image.

The watering plan shows a plan for watering by the sprinkler system 7. The sprinkling plan consists of the timing (day, time) of sprinkling, the amount of sprinkling, the sprinkling time from the start to the end of sprinkling, etc. It may be configured in any form. Further, the watering plan may consist only of immediate watering or no watering.

Based on the imaging time of the reference aerial image information, a specialist who observes the lawn on which the reference aerial image information is captured may ask for a suitable watering plan and obtain learning data, or actually. After watering with various time-series patterns, check the condition of the lawn, the withering condition of the lawn, and the growth condition, so that the time-series pattern of watering corresponding to the more suitable growth degree can be learned as a watering plan. You may.

That is, through this reference aerial image information and the data set of the watering plan, the relationship between the characteristics of various images generated in the reference aerial image information and the watering plan can be understood. In other words, the image features of the reference aerial image information and the watering plan are the data set. Therefore, by collecting the reference aerial image information and the data set of the watering plan, it is possible to know what kind of image features are present and how the watering plan was determined in the past.

When constructing this data set, an aerial image for reference is acquired by taking an image from the sky of a lawn area for which a watering plan is known in advance, and the known watering plan and a data set are created.

In the example of FIG. 3, it is assumed that the input data is, for example, reference aerial image information P01 to P03. The reference aerial image information as such input data is linked to the output. In this output, the watering plan as the output solution is displayed.

The reference aerial image information is associated with each other through three or more levels of association with the watering plan as the output solution. The reference aerial image information is arranged on the left side through this degree of association, and the watering plan is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to which watering plan is higher for the reference aerial image information arranged on the left side. In other words, this degree of association is an indicator of what watering plan each reference aerial image information is likely to be associated with, and is used to select the most probable watering plan from the reference aerial image information. It shows the accuracy in. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the watering plan as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the watering plan as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to whether the reference aerial image information or the watering plan in that case is adopted in determining the actual search solution, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 3 is created.

For example, it is assumed that a certain reference aerial image information is determined to be watering plan A (for example, watering 30 liters immediately). In such a situation, it is assumed that the reference aerial image information of a similar pattern is similarly determined to be watering plan A. In such a case, the degree of linkage of the watering plan A becomes stronger. On the other hand, in the exact same pattern (classification) of the aerial image information for reference, there were many cases where it was determined to be watering plan B (for example, 20 liters of watering after 1 hour), and few were determined to be watering plan A. It shall be. In such a case, the degree of association of the watering plan B becomes stronger, and the degree of association of the watering plan A becomes low.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference aerial image information P01, analysis is performed from the data of the determination result of the past watering plan. In the case of reference aerial image information P01, if there are many cases of watering plan A, the degree of linkage connected to this watering plan A is set higher, and if there are many cases of watering plan B, this watering plan B is used. Set a higher degree of association that leads to. For example, in the example of the reference aerial image information P01, the sprinkling plan A and the sprinkling plan B are linked, but from the previous case, the degree of linkage of w13 connected to the sprinkling plan A is set to 7 points, and the connection degree of w14 connected to the sprinkling plan B is set to 7 points. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 4 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 5, reference aerial image information is input as input data, each watering plan is output as output data, and at least one hidden layer is provided between the input node and the output node. It may be machine-learned. On the contrary, the sprinkling plan may be configured to be input and output aerial image information for reference.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the sprinkling plan will be predicted using the above-mentioned learned data when actually determining a new sprinkling plan from now on. In such a case, the aerial image information of the lawn for which the watering plan is to be newly determined is acquired. The method for acquiring the aerial image information is the same as the above-mentioned reference aerial image information.

The newly acquired aerial image information is input by the above-mentioned information acquisition unit 9. The information acquisition unit 9 may acquire such aerial image information as electronic data.

Based on the aerial image information newly acquired in this way, a watering plan that is likely to be actually determined for the aerial image information is searched for. In such a case, the degree of association shown in FIG. 4 (Table 1) acquired in advance is referred to. For example, when the newly acquired aerial image information is the same as or similar to P02, the watering plan B is associated with w15 and the watering plan C is associated with the association degree w16 via the degree of association. In such a case, the watering plan B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the watering plan C, which has a low degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, the sprinkler plan to be determined can be searched from the newly acquired aerial image information, and can be linked to the sprinkler control by the sprinkler device 7. The sprinkler device 7 will sprinkle water according to the sprinkler plan. For example, as a result of a solution search, if the lawn is about to die due to intense sunlight and immediate watering is required, and if immediate watering is selected as the search solution based on the aerial image information of the lawn that is about to die. The sprinkler device 7 will realize immediate watering. The watering plan can also be displayed to the user, if desired. By seeing this search result, the user can grasp the condition of the lawn based on the watering plan.

As an alternative to the above-mentioned reference aerial image information, trained data may be constructed between the reference ground image information and the watering plan. The reference ground image information is an image obtained by actually photographing the lawn from the ground. The reference ground image information may be composed of a so-called spectral image color-coded for each spectrum, in addition to the normal RGB image. The reference ground image information may be provided in the sprinkler device 7.

As an alternative to the reference aerial image information, the reference sensor detection information obtained by detecting the lawn condition with the sensor and the degree of association with the watering plan at three or more levels may be learned in advance. The reference sensor detection information is information obtained from a well-known sensor that detects an object. It may be arranged so that the height according to the degree of growth of the lawn can be detected. For example, by arranging and setting the sensor, it is possible to create a state in which the sensor responds or does not respond according to the height and wilting of the lawn, so it is possible to identify the growth status of the lawn itself through the sensor. It becomes.

The reference sensor detection information obtained in this way and the above-mentioned watering plan are learned through three or more levels of association with each other, and the sensor detection information is input at the time of solution search to obtain a search solution. It will be possible to search for watering plans in the same way.

In the example of FIG. 6, it is assumed that the input data is, for example, reference aerial image information P01 to P03 and reference time zone information P14 to 17. The intermediate node shown in FIG. 6 is a combination of reference time zone information and reference aerial image information as such input data. Each intermediate node is further linked to the output. In this output, each watering plan as an output solution is displayed.

In the example of FIG. 6, it is premised that a combination of the reference aerial image information and the reference time zone information is formed. The reference time zone information is information about the time zone. Specifically, it is the information of the time zone at the time when the aerial image information for reference is captured. Since watering may be refrained or sprinkled depending on which time zone of which day the aerial image is being imaged, this is added as an explanatory variable.

In the example of FIG. 6, it is assumed that the input data is, for example, reference aerial image information P01 to P03 and reference time zone information P14 to 17. The intermediate node shown in FIG. 6 is a combination of reference time zone information and reference aerial image information as such input data. Each intermediate node is further linked to the output. In this output, the watering plan as an output solution is displayed.

Each combination (intermediate node) of the reference aerial image information and the reference time zone information is associated with each other through three or more levels of association with the watering plan as the output solution. The reference aerial image information and the reference time zone information are arranged on the left side through this degree of association, and the watering plan is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to each watering plan with respect to the reference aerial image information and the reference time zone information arranged on the left side. In other words, this degree of association is an index showing what kind of watering plan the reference aerial image information and the reference time zone information are likely to be associated with, and the reference aerial image information and the reference time. It shows the accuracy in selecting each watering plan that is most probable from the band information. In addition to the aerial image information, it is possible to improve the accuracy by discriminating according to the image actually captured from the ground. Therefore, the optimum watering plan will be searched for by combining the reference aerial image information and the reference time zone information.

In the example of FIG. 6, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference aerial image information, the reference time zone information, and the watering plan in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference aerial image information P01 and the reference time zone information P16, the watering plan is analyzed from the past data. If there are many cases of watering plan A, the degree of association that leads to this watering plan A is set higher, and if there are many cases of watering plan B and there are few cases of watering plan A, the linkage that leads to watering plan B is set. Set the degree high and the degree of association leading to watering plan A low. For example, in the example of the intermediate node 61a, the output of the sprinkling plan A and the sprinkling plan B is linked. The degree is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 6, the node 61b is a node of the combination of the reference time zone information P14 with respect to the reference aerial image information P01, the degree of association of the watering plan C is w15, and the watering plan E. The degree of association is w16. The node 61c is a node that is a combination of the reference time zone information P15 and P17 with respect to the reference aerial image information P02, and the degree of association of the watering plan B is w17 and the degree of association of the watering plan D is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used in the actual search for determining the watering plan. In such a case, the time zone information is acquired in addition to the aerial image information from the company that newly determines the watering plan. This time zone information corresponds to the above-mentioned reference time zone information, and the acquisition method thereof is also the same.

Based on the newly acquired aerial image information and time zone information in this way, the optimum watering plan is searched for. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired aerial image information is the same as or similar to P02 and the time zone information is the same as or similar to P17, the node 61d is set to the node 61d via the degree of association. The node 61d is associated with the sprinkling plan C at w19 and the sprinkling plan D at the degree of association w20. In such a case, the watering plan C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the watering plan D, which has a low degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, an example of the degree of association w1 to w12 extending from the input is shown in Table 2 below.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

FIG. 7 shows an example in which the combination of the above-mentioned reference aerial image information and the reference weather information and the watering plan for the combination are set to three or more levels of association.

Reference weather information is information on weather and weather, and includes data on all weather and climate such as temperature, humidity, weather (sunny, cloudy, rain), typhoon movement, wind direction, and rainfall. Reference weather information may be obtained from data from the Japan Meteorological Agency or private weather forecasting companies. Since it may be better to switch the watering plan depending on the weather and weather, this is added as an explanatory variable.

In the example of FIG. 7, it is assumed that the input data is, for example, reference aerial image information P01 to P03 and reference weather information P18 to 21. The intermediate node shown in FIG. 6 is a combination of reference aerial image information and reference weather information as such input data. Each intermediate node is further linked to the output. In this output, the watering plan as the output solution is displayed. It is desirable that the reference weather information be composed of the weather information at the time of acquisition of the reference aerial image information.

Each combination of reference aerial image information and reference weather information (intermediate node) is associated with each other through three or more levels of association with the watering plan as this output solution. Reference aerial image information and reference weather information are arranged on the left side through this degree of association, and watering plans are arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the watering plan with respect to the reference aerial image information and the reference weather information arranged on the left side. In other words, this degree of association is an index showing what kind of watering plan each reference aerial image information and reference weather information are likely to be associated with, and is based on the reference aerial image information and reference weather information. It shows the accuracy in selecting each of the most probable watering plans. In addition to aerial image information, watering plans can be narrowed down in relation to actual weather. Therefore, the optimum watering plan will be searched for by combining these reference aerial image information and reference weather information.

In the example of FIG. 7, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the search device 2 accumulates past data as to which of the reference aerial image information, the reference weather information, and the watering plan in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference aerial image information P01 and the reference weather information P20, the watering plan is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of the watering plan A and the watering plan B is linked. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 7, the node 61b is a node in which the reference weather information P18 is combined with the reference aerial image information P01, the degree of association of the watering plan C is w15, and the association of the watering plan E. The degree is w16. The node 61c is a node that is a combination of the reference weather information P19 and P21 with respect to the reference aerial image information P02, and the degree of association of the watering plan B is w17 and the degree of association of the watering plan D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining a new lawn watering plan from now on, the above-mentioned learned data will be used. In such a case, in addition to the above-mentioned aerial image information, the weather information of the lawn for determining the watering plan is newly acquired. The weather information corresponds to the reference weather information, and may be acquired from data stored in, for example, the Japan Meteorological Agency or other organizations. Further, this weather information may be composed of information related to the weather forecast.

Based on the newly acquired aerial image information and weather information in this way, the watering plan is searched for. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired aerial image information is the same as or similar to P02 and the weather information is P21, the node 61d is associated via the degree of association. The node 61d is associated with the watering plan C by w19 and the watering plan D by the degree of association w20. In such a case, the watering plan C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the watering plan D, which has a low degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

As the reference information, the reference product type information may be applied. The reference variety information is information on turf varieties. Since it is necessary to switch the watering plan depending on the variety, this is added as an explanatory variable. The degree of association between the combination of the reference variety information and the reference aerial image information and the watering plan at three or more stages is acquired in advance as learning data. Then, when searching for a solution, information on the varieties of the turf to be actually sprinkled is acquired. Then, the solution search is performed while referring to the above-mentioned degree of association through the reference variety information corresponding to this variety information, which is the same as the above.

Further, as the reference information, the reference sensing information may be applied. The reference sensing information is information indicating the degree of proximity of an animal body such as a human or an animal. Normally, it is detected through a sensor that detects when an animal body is in close proximity, such as an infrared sensor or a proximity sensor. When an animal body invades the lawn of the originally restricted area, it may be threatened and repelled through watering by the sprinkler device 7, so this is added as an explanatory variable.

The degree of association between the combination of the reference sensing information and the reference aerial image information and the watering plan in three or more stages is acquired in advance as learning data. Then, when searching for a solution, sensing information is acquired. Sensing information indicates the degree of proximity of an animal body such as a human or an animal. Then, the solution search is performed while referring to the above-mentioned degree of association through the reference sensing information corresponding to this sensing information, which is the same as the above.

Further, as the reference information, the reference smoke detection information may be applied. The reference smoke detection information is information indicating whether or not smoke has been detected. Normally, it is determined whether or not smoke is detected through a sensor that detects smoke, but this may be used to acquire reference smoke detection information. When a fire or the like occurs in a building where a lawn is installed and smoke is emitted, it may be desired to discharge water to the fire by the prinkler device 7, so this is added as an explanatory variable.

The degree of association between the combination of the reference sensing information and the reference smoke detection information and the watering plan at three or more stages is acquired in advance as learning data. Then, when searching for a solution, smoke detection information is acquired. Smoke detection information may be acquired through a sensor that detects smoke. Then, the solution search is performed while referring to the above-mentioned degree of association through the reference smoke detection information corresponding to the smoke detection information, which is the same as the above.

The present invention is not limited to the above-described embodiment. As a search solution, it may be applied to a lawn condition management system that manages the lawn condition instead of the watering plan. In such a case, three or more levels of association between the reference ground image information or the reference aerial image information and the categorized information categorizing the roughness of the turf are formed. The degree of roughness of the turf indicates, for example, the amount of weeds, the state of care of the turf, and the degree of the turf being trampled by the animal body, and may be categorized in advance. In such a case, the determination may be made based on the feature amount learned in the past. At this time, artificial intelligence is used to learn the ground image information for reference or the aerial image information for reference, the amount of weeds, and the maintenance status of turf. Using a prediction model machine-learned using ground image information for reference or aerial image information for reference and the amount of weeds and the maintenance status of turf as teacher data, the input is ground image information or aerial image information, and the output is weeds. The categorized information may be specified based on the result of determination as the amount of the image, the maintenance status of the turf, and the like. For this specification, a table or the like that links each type with the amount of weeds and the care status of the turf may be prepared in advance, and the specification may be made by referring to this.

Then, the categorization information that categorizes the estimated roughness of the turf may be notified to the external system. This enables the turf manager to grasp how rough the turf is.

Similarly, according to the present invention, three or more levels of association between the reference ground image information or the reference aerial image information and the categorized information categorizing the growth state of the turf may be formed. .. This growth state indicates the degree of the length, amount, density, etc. of the turf, and may be categorized in advance. In such a case, the determination may be made based on the feature amount learned in the past. At this time, artificial intelligence is used to learn the reference ground image information or the reference aerial image information and the growth state of the turf. Results of determining the input as ground image information or aerial image information and the output as growth status using a machine-learned prediction model using reference ground image information or reference aerial image information and turf growth status as teacher data. You may try to specify the categorized information based on.

Next, the watering plan is searched based on this categorized categorization information. In such a case, create a template that links the categorization information and the watering plan to each other, and when the categorization information is requested, refer to the template and create the watering plan linked to the categorization information. It may be output. Further, the categorization information and the watering plan may be associated with each other by the degree of association of three or more stages described above, and the watering plan may be obtained based on this.

The present invention is not limited to the case where the sprinkler device 7 is installed on the lawn, and may be installed in the building. In such a case, if a fire breaks out in the building and smoke is emitted, water can be immediately discharged via the sprinkler device 7. When the sprinkler device 7 is installed in the building, it is of course possible to perform various watering controls through the contents obtained through the ground image information, the sensing information, and the like.

Further, the present invention is not limited to the above-described embodiment, and as shown in FIG. 8, for example, the reference information as the keynote and the degree of association between the watering plan and the three or more stages are used. May be good. In such a case, the solution search will be performed based on the degree of association between the reference information according to the newly acquired information and the watering plan in three or more stages. As the underlying reference information, all the above-mentioned reference information can be applied.

Similarly, in these cases, when the information corresponding to the reference information used as the learning data is input, the solution search is performed based on the above-mentioned method.

The search solution obtained through the degree of association may be further modified based on other reference information, or the weighting may be changed.

When any of the above-mentioned reference information is used as the keynote reference information, the other reference information here corresponds to any reference information other than the keynote reference information.

For example, as one of the other reference information, it is assumed that the watering plan B has been previously determined in a certain reference information F. When such information corresponding to the reference information F is newly acquired, it is set in advance to perform a process of increasing the weighting of the watering plan B, in other words, a process of connecting to the watering plan B. I will do it.

For example, other reference information G is an analysis result that suggests a search solution C as a watering plan, and reference information F is an analysis result that suggests a search solution D as a watering plan. Suppose there is. After the setting with the reference information in this way, if the actually acquired information is the same as or similar to the reference information G, a process of increasing the weighting of the watering plan C is performed. On the other hand, when the actually acquired information is the same as or similar to the reference information F, a process of increasing the weighting of the watering plan D is performed. That is, the degree of association itself leading to the watering plan may be controlled based on the reference information F to H. Alternatively, after the watering plan is determined only by the above-mentioned degree of association, the search solution obtained may be modified based on the reference information F to H. In the latter case, how to modify the watering plan as a search solution based on the reference information F to H will reflect what was designed on the system side each time.

Further, the reference information is not limited to the case where it is composed of any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the more the case leads to the demand degree suggested by the reference information, the higher the modification of the discriminant type as the search solution obtained through the degree of association may be made.

Similarly, as shown in FIG. 9, even in the case of forming a degree of association with the watering plan for a combination having the reference information as the keynote and other reference information, the reference information as the keynote is still available. Any reference information described above is applicable. Other reference information includes any reference information other than the underlying reference information.

At this time, if the reference information that is the basis is the reference weight information, the other reference information includes any other reference information.

In such a case as well, the watering plan can be estimated by performing a solution search in the same manner. At this time, as shown in FIG. 9 described above, the watering plan should be modified through further reference information (reference information F, G, H, etc.) for the search solution obtained through the degree of association. You may do it.

Further, as shown in FIG. 10, a degree of association may be formed between only the reference information as the keynote and the watering plan. Any of the above-mentioned reference information can be applied to this underlying reference information. The solution search method of FIG. 10 is omitted below by quoting the explanations of FIGS. 4 to 5.

1 Sprinkler system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 Node

Claims (11)

In a sprinkler system that sprinkles water on the planted lawn
Information acquisition means for acquiring ground image information obtained by imaging the lawn to be sprinkled from the ground,
Reference ground image information according to the ground image information acquired by the above information acquisition means by referring to the three or more levels of association between the reference ground image information obtained by imaging the lawn from the ground in the past and the watering plan for the lawn. Based on the above, the search means for searching the watering plan for the lawn by giving priority to the one with the higher degree of association,
A sprinkler system comprising a sprinkler means for sprinkling water on the lawn based on the sprinkler plan searched by the search means. In a sprinkler system that sprinkles water on the planted lawn
Information acquisition means for acquiring ground image information obtained by imaging the lawn to be sprinkled from the ground,
Refer to the three or more levels of association between the reference ground image information obtained by imaging the lawn from the ground in the past and the categorized information typifying the growth status of the lawn, and use the ground image information acquired by the above information acquisition means. Based on the corresponding ground image information for reference, priority is given to those with a higher degree of association, and an estimation means for estimating the growth condition of turf, and
Based on the turf growth condition estimated by the above estimation means, a search means for searching a watering plan for the lawn and a search means.
A sprinkler system comprising a sprinkler means for sprinkling water on the lawn based on the sprinkler plan searched by the search means. The above information acquisition means acquires time zone information regarding the current time zone, and
The search means refers to a combination having the reference ground image information and the reference time zone information regarding the time zone, and the degree of association with the watering plan for the lawn in three or more stages, and is further acquired by the information acquisition means. The sprinkler system according to claim 1, wherein the sprinkler system according to claim 1 is characterized in that a watering plan for a lawn is searched by giving priority to the one having a higher degree of association based on the reference time zone information according to the given time zone information. The above information acquisition means acquires weather information regarding the state of the weather and
The search means has three or more levels of linkage between the reference ground image information, a combination having reference weather information regarding the state of the weather at the time of acquisition of the reference ground image information, and a watering plan for the lawn. A claim characterized in that, based on reference weather information according to the weather information acquired by the above information acquisition means, priority is given to those having a higher degree of association, and a watering plan for the lawn is searched for. The sprinkler system described in 1. The above information acquisition means acquires variety information on turf varieties and obtains information.
The search means refers to a combination having the above-mentioned reference ground image information and the reference varieties information regarding the turf varieties, and the degree of association with the watering plan for the lawn in three or more stages, and is further acquired by the above-mentioned information acquisition means. The sprinkler system according to claim 1, wherein the sprinkler system according to claim 1 is characterized in that, based on the reference variety information according to the provided variety information, the one having the higher degree of association is prioritized and the watering plan for the lawn is searched. The above information acquisition means acquires sensing information that detects the approach of an animal body, and obtains sensing information.
The search means refers to a combination of the ground image information for reference and the sensing information for reference that detects the approach of an animal body, and the degree of association of three or more stages with the watering plan for the lawn, and further acquires the information. The sprinkler system according to claim 1, wherein the sprinkler system according to claim 1 is characterized in that, based on the reference variety information according to the variety information acquired by the means, the one having the higher degree of association is prioritized and the watering plan for the lawn is searched. The above information acquisition means acquires weather information regarding the state of the weather and
The search means gives priority to the one with a higher degree of association based on the reference ground image information according to the ground image information acquired by the information acquisition means, and the watering plan for the lawn based on the weather information. The sprinkler system according to claim 1, wherein the sprinkler system is characterized by searching for. The above information acquisition means acquires sensing information that detects the approach of an animal body, and obtains sensing information.
The search means gives priority to the one with a higher degree of association based on the reference ground image information according to the ground image information acquired by the information acquisition means, and the watering plan for the lawn based on the sensing information. The sprinkler system according to claim 1, wherein the sprinkler system is characterized by searching for. The above information acquisition means acquires smoke detection information indicating whether or not smoke has been detected, and obtains smoke detection information.
The search means gives priority to the one with a higher degree of association based on the reference ground image information according to the ground image information acquired by the information acquisition means, and waters the lawn based on the smoke detection information. The sprinkler system according to claim 1, wherein the sprinkler system is characterized by searching for a plan. The prinkler system according to any one of claims 1 to 9, wherein the search means utilizes the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence. In the lawn condition management system that manages the condition of the planted lawn,
Information acquisition means for acquiring ground image information obtained by imaging the lawn to be sprinkled from the ground,
Referencing the degree of association between the reference ground image information obtained by imaging the lawn from the ground in the past and the categorization information categorizing the roughness of the lawn in three or more stages, and using the ground image information acquired by the above information acquisition means. An estimation means for estimating categorization information by giving priority to those having a higher degree of association based on the corresponding ground image information for reference.
A lawn condition management system including a notification means for notifying categorization information estimated by the above estimation means.

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