Swarming management method and swarming management system

Abstract

To provide a technique for managing swarming of honeybee.SOLUTION: A swarming management method for honeybee includes a step of managing swarming of the honeybee on the basis of a temporal change of at least one of a carbon dioxide concentration and an oxygen concentration within a bee hive of the honeybee.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a swarm management method and a swarm management system.

Background Art

[0002] Honeybees have the habit of swarming, in which the queen bee leads the colony to split the hive for their own growth. Since the honey production decreases in the hive where swarming occurs, beekeepers manage the frequency and timing of swarming. To manage swarming, it is necessary to appropriately detect the occurrence and signs of swarming. Therefore, beekeepers need to frequently inspect all hives internally, but the problem is that the labor cost is high. Thus, a technique for detecting the occurrence of swarming without inspecting the hive internally is required.

[0003] Patent Document 1 describes a technique for detecting the occurrence of swarming by detecting changes in temperature and sound inside a honeybee hive. Patent Document 2 describes a technique for detecting the occurrence of swarming based on the formation of honeybee clustering detected by temperature changes inside the hive.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] According to the techniques described in Patent Documents 1 and 2, it is possible to detect the occurrence of swarming without inspecting the hive internally, but a technique that can manage swarming with higher accuracy is required.

[0006] One aspect of the present invention aims to provide a technique for managing swarming.

Means for Solving the Problem

[0007] In order to solve the above problems, a honeybee swarming management method according to one aspect of the present invention includes a step of managing the swarming of the honeybees based on the change over time of at least one of the carbon dioxide concentration and the oxygen concentration in the beehive of the honeybees.

[0008] A honeybee swarming management system according to one aspect of the present invention includes a measuring device that measures at least one of the carbon dioxide concentration and the oxygen concentration in the beehive of the honeybees, and a swarming management device that analyzes the change over time of the carbon dioxide concentration and the oxygen concentration measured by the measuring device.

Effect of the Invention

[0009] According to one aspect of the present invention, it is possible to provide a technique for predicting and detecting the occurrence of swarming.

Brief Description of the Drawings

[0010]

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Embodiments for Carrying Out the Invention

[0011] 〔Swarming Management Method〕 One aspect of the present invention provides a honeybee swarming management method (hereinafter simply referred to as the swarming management method) for managing the swarming of honeybees. The swarming management method manages swarming by predicting and detecting swarming in which a honeybee colony divides its nest. By predicting and detecting swarming by the swarming management method, it is possible to manage the occurrence frequency, occurrence time, etc. of swarming.

[0012] The swarming management method includes a step of managing the swarming of the honeybees based on at least one of the temporal changes in the carbon dioxide concentration and the oxygen concentration in the honeybee hive. The present inventors have found that the carbon dioxide concentration and the oxygen concentration in the hive change before and during the swarming of honeybees, and have thus completed the present invention. The present inventors provide a technique for managing honeybee swarming by predicting and detecting honeybee swarming based on the temporal changes in the carbon dioxide concentration and the oxygen concentration in the hive.

[0013] In the swarming management method, the carbon dioxide concentration and the oxygen concentration in the honeybee hive can be, for example, values measured by a carbon dioxide concentration sensor and an oxygen concentration sensor installed in the hive. In the swarming management method, the carbon dioxide concentration and the oxygen concentration are continuously measured at a predetermined time interval for a predetermined period and accumulated in time series, thereby representing the temporal changes in the carbon dioxide concentration and the oxygen concentration in the hive.

[0014] The change over time in the carbon dioxide concentration and oxygen concentration in the hive is information obtained by performing time series analysis on the measured values of at least one of the carbon dioxide concentration and oxygen concentration measured over a predetermined period. The change over time in the carbon dioxide concentration and oxygen concentration used in the swarm management method is obtained by performing time series analysis on the data obtained by accumulating the measured values of the carbon dioxide concentration and oxygen concentration measured over a predetermined period in time series. By time series analysis, the measured values accumulated in time series can be decomposed into three components: "long-term trend", "periodic change", and "other noise". Time series analysis can be performed using a conventionally known analysis algorithm. An example of the analysis algorithm is STL (Seasonal Trend Decomposition using LOESS) analysis.

[0015] The time series analysis of the carbon dioxide concentration and oxygen concentration will be described using FIGS. 1 and 2 as examples. FIG. 1 shows the components obtained by decomposing the data obtained by accumulating the carbon dioxide concentration in the hive in time series by time series analysis, and FIG. 2 shows the components obtained by decomposing the data obtained by accumulating the oxygen concentration in the hive in time series by time series analysis. In FIGS. 1 and 2, "original data" is the data itself obtained by accumulating the measured carbon dioxide concentration or oxygen concentration in time series, "trend" is the component representing the long-term trend obtained by performing time series analysis on the original data, "daily variation" is the component representing the periodic change obtained by performing time series analysis on the original data, and "residual" is the component representing the noise other than the trend and daily variation obtained by performing time series analysis on the original data.

[0016] (Swarm prediction) In the swarm management method, in the management process, the trend of at least one of the carbon dioxide concentration and oxygen concentration within a predetermined period obtained by time series analysis is compared with the trend of at least one of the carbon dioxide concentration and oxygen concentration during the normal period when the bees are not swarming, to predict the swarming of the bees. In the swarm management method, the trend component representing the trend of at least one of the carbon dioxide concentration and oxygen concentration within a predetermined period obtained by time series analysis is used to predict the swarming of the bees.

[0017] Referring to FIG. 1, the prediction of honeybee swarming using the trend component of carbon dioxide concentration will be described. In FIG. 1, it has been confirmed that swarming occurred at point A when the carbon dioxide concentration in the original data increased. The trend component shown in FIG. 1 shows a downward trend that gradually decreases in the period before point A. Thus, when the trend component of the carbon dioxide concentration shows a downward trend compared to the trend component of the carbon dioxide concentration during the normal period when the honeybees are not swarming, it can be predicted that swarming will occur soon.

[0018] Next, referring to FIG. 2, the prediction of honeybee swarming using the trend component of oxygen concentration will be described. In FIG. 2, it has been confirmed that swarming occurred at point a when the oxygen concentration in the original data increased. The trend component shown in FIG. 2 shows an upward trend that gradually increases in the period before point a. Thus, when the trend component of the oxygen concentration shows an upward trend compared to the trend component of the oxygen concentration during the normal period when the honeybees are not swarming, it can be predicted that swarming will occur soon.

[0019] In the swarming management method, for the prediction of honeybee swarming, at least one of the carbon dioxide concentration and the oxygen concentration in the hive can be used. However, by using both, swarming can be predicted more accurately.

[0020] Here, that the trend component shows a downward trend or an upward trend can be determined, for example, by detecting that it has been gradually decreasing or increasing continuously for a predetermined period or more. The predetermined period can be, for example, 1 day or more, 2 days or more, 3 days or more, etc., and can be set based on the relationship between the past occurrence of swarming and the trend component at that time.

[0021] In addition, whether the trend component shows a downward trend or an upward trend can also be determined using the moving average of past carbon dioxide concentrations or oxygen concentrations. That is, in the management step, the beekeeping swarm separation management method can predict the swarm separation of honeybees by comparing the trend of at least one of the carbon dioxide concentration and the oxygen concentration obtained by time series analysis within a predetermined period with the moving average of at least one of the past carbon dioxide concentration and the oxygen concentration.

[0022] A method for determining that the trend component of the carbon dioxide concentration shows a downward trend using a moving average will be described with reference to FIG. 3. FIG. 3 is a graph showing the trend component of the carbon dioxide concentration and the moving average line of the past carbon dioxide concentration. In FIG. 3, "Trend" indicates the trend component obtained by performing STL analysis on the measured values of the carbon dioxide concentration accumulated at 15-minute intervals. In FIG. 3, "SMA100" indicates a moving average line composed of the past 100 measured data of the carbon dioxide concentration (15-minute intervals × 100), and "SMA200" indicates a moving average line composed of the past 200 measured data of the carbon dioxide concentration (15-minute intervals × 200).

[0023] As shown in FIG. 3, the point at which Trend crosses SMA100 or SMA200 from top to bottom (the position surrounded by a broken line in FIG. 3) is the point at which the trend of the carbon dioxide concentration has started to decline compared to the normal period when the past honeybees did not swarm, and it can be determined that a downward trend has occurred.

[0024] A method for determining that the trend component of the oxygen concentration shows an upward trend using a moving average will be described with reference to FIG. 4. FIG. 4 is a graph showing the trend component of the oxygen concentration and the moving average line of the past oxygen concentration. In FIG. 4, "Trend" indicates the trend component obtained by performing STL analysis on the measured values of the oxygen concentration accumulated at 15-minute intervals. In FIG. 4, "SMA100" indicates a moving average line composed of the past 100 measured data of the oxygen concentration (15-minute intervals × 100), and "SMA200" indicates a moving average line composed of the past 200 measured data of the oxygen concentration (15-minute intervals × 200).

[0025] As shown in FIG. 4, the point in time when Trend crosses SMA100 or SMA200 from bottom to top (the position surrounded by the dashed line in FIG. 4) is the point in time when the oxygen concentration begins to rise above the trend of the normal period when the past bees did not swarm, and it can be determined that an upward trend has occurred.

[0026] In the swarming management method, it can be predicted that swarming will occur within a predetermined period after at least one of the downward trend of the carbon dioxide concentration and the upward trend of the oxygen concentration occurs. As an example, it is predicted that swarming will occur within 7 days, 6 days, 5 days, 3 days, etc. after at least one of the downward trend of the carbon dioxide concentration and the upward trend of the oxygen concentration occurs.

[0027] In the swarming management method, since swarming can be predicted before the predetermined period when swarming occurs, it is possible to execute a process of aborting or delaying swarming within that period, and swarming can be appropriately managed.

[0028] (Swarming detection) In the process of management, the swarming management method detects the swarming of bees by comparing the amount of change per unit time of at least one of the carbon dioxide concentration and the oxygen concentration with the amount of change in the normal period when the bees do not swarm. In the swarming management method, the swarming of bees is detected using the original data obtained by accumulating the measured values of the carbon dioxide concentration and the oxygen concentration measured over a predetermined period in time series, or the residual component obtained by time series analysis of the original data.

[0029] In FIG. 1, at the swarming time point A shown in the original data, the original data and the residual component rise sharply and vary greatly from the value before swarming. At the time of swarming, the carbon dioxide concentration rises more than before swarming and immediately drops to the same carbon dioxide concentration as before swarming. Also, in FIG. 2, at the swarming time point a shown in the original data, the original data and the residual component drop sharply and vary greatly from the value before swarming. At the time of swarming, the oxygen concentration drops more than before swarming and immediately rises to the same oxygen concentration as before swarming.

[0030] Thus, during swarming, the carbon dioxide concentration and the oxygen concentration vary greatly in a short period of time. As shown in FIGS. 1 and 2, even during normal times when swarming does not occur, fluctuations in the carbon dioxide concentration and the oxygen concentration can be observed, but large fluctuations in a short period of time like during swarming are not observed. Therefore, in the management process, the amount of change per unit time of at least one of the carbon dioxide concentration and the oxygen concentration is compared with the amount of change during normal times when the bees are not swarming, and when the amount of change is greater than that during normal times, it can be detected that swarming has occurred.

[0031] In the swarming management method, for detecting the swarming of bees, at least one of the carbon dioxide concentration and the oxygen concentration inside the hive box can be used, but by using both, swarming can be detected more accurately.

[0032] Here, as an example, the amount of change per unit time serving as a criterion for detecting swarming is such that the amount of change within a unit time such as within 1 hour, within 45 minutes, within 30 minutes, within 15 minutes, etc. is 0.25% (2500 ppm) or more, 0.5% (5000 ppm) or more, 1% (10000 ppm) or more, etc., and it can be set based on the relationship between the occurrence of past swarming and the amount of change at that time.

[0033] Also, a rapid increase in the carbon dioxide concentration or a rapid decrease in the oxygen concentration can be determined using the standard deviation calculated from past amounts of change. That is, the swarming management method can detect the swarming of bees by comparing, in the management process, the residual component of at least one of the carbon dioxide concentration and the oxygen concentration obtained by time series analysis within a predetermined period with the standard deviation calculated from the residual components of at least one of the past carbon dioxide concentration and the oxygen concentration.

[0034] A method for determining that the carbon dioxide concentration has risen sharply using the standard deviation will be described with reference to FIG. 5. FIG. 5 is a graph showing the residual component of the carbon dioxide concentration and six times the standard deviation calculated from the past carbon dioxide concentrations. In FIG. 5, "residual" indicates the residual component obtained by performing STL analysis on the measured values of the carbon dioxide concentration accumulated at 15-minute intervals, and "six times the standard deviation" indicates a value six times the standard deviation calculated from the past 200 measurement data of the carbon dioxide concentration (15-minute intervals × 200).

[0035] As shown in FIG. 5, the point in time when the residual exceeds the standard deviation (the position surrounded by the dashed line in FIG. 5) is a point in time when the amount of variation in the carbon dioxide concentration is larger than in the normal period when the past bees did not swarm, and it can be determined that this is the point in time when the carbon dioxide concentration has risen sharply.

[0036] A method for determining that the oxygen concentration has dropped sharply using the standard deviation will be described with reference to FIG. 6. FIG. 6 is a graph showing the residual component of the oxygen concentration and six times the standard deviation calculated from the past oxygen concentrations. In FIG. 6, "residual" indicates the residual component obtained by performing STL analysis on the measured values of the oxygen concentration accumulated at 15-minute intervals, and "six times the standard deviation" indicates a value six times the standard deviation calculated from the past 200 measurement data of the oxygen concentration (15-minute intervals × 200).

[0037] As shown in FIG. 6, the point in time when the residual exceeds the standard deviation (the position surrounded by the dashed line in FIG. 6) is a point in time when the amount of variation in the oxygen concentration is larger than in the normal period when the past bees did not swarm, and it can be determined that this is the point in time when the oxygen concentration has dropped sharply.

[0038] In the swarming management method, it is possible to detect that swarming has occurred when at least one of a sharp increase in the carbon dioxide concentration and a sharp decrease in the oxygen concentration occurs. Thus, in the swarming management method, since it is possible to detect the occurrence of swarming for each hive body, it is possible to identify the hive body in which swarming has occurred. Therefore, it is possible to manage the occurrence frequency and occurrence time of swarming for each hive body, and it is possible to appropriately manage swarming.

[0039] [Swarming management system 100] The swarming management system 100 according to one aspect of the present invention will be described with reference to FIGS. 7 to 9. FIG. 7 is a block diagram showing the main configuration of the swarming management system 100 according to one aspect of the present invention. FIG. 8 is a flowchart for explaining an example of swarming prediction processing executed by the swarming management device 10 of the swarming management system 100 according to one aspect of the present invention. FIG. 9 is a flowchart for explaining an example of swarming detection processing executed by the swarming management device 10 of the swarming management system 100 according to one aspect of the present invention.

[0040] The swarming management system 100 includes a measuring device 20 that measures at least one of the carbon dioxide concentration and the oxygen concentration in the honeybee hive, and a swarming management device 10 that analyzes the temporal changes in the carbon dioxide concentration and the oxygen concentration measured by the measuring device 20.

[0041] The measuring device 20 measures at least one of the carbon dioxide concentration and the oxygen concentration in the honeybee hive. The measuring device 20 is installed, for example, in the vicinity of the bee colony inside the hive. The measuring device 20 may be able to accumulate the measurement results and retrieve the accumulated data, or may be able to transfer the measurement results to the swarming management device 10 or the storage device 30 by wireless communication or the like. The measuring device 20 measures at least one of the carbon dioxide concentration and the oxygen concentration at a predetermined interval such as every 15 minutes.

[0042] The storage device 30 stores the programs and data used by the swarming management device 10. The storage device 30 stores the carbon dioxide concentration and the oxygen concentration measured by the measuring device 20. As an example, the storage device 30 stores the original data in which the carbon dioxide concentration and the oxygen concentration measured by the measuring device 20 are stored in time series. The storage device 30 may have a database for storing the carbon dioxide concentration and the oxygen concentration on the cloud or a server.

[0043] The output device 40 outputs information regarding the prediction result and detection result of the swarm management device 10. The mode of output by the output device 40 is not particularly limited. The output device 40 may be, for example, a display device that displays the information as an image, a printing device that prints the information, or an alarm device that outputs the information as sound.

[0044] The swarm management device 10 includes a control unit 11. The control unit 11 comprehensively controls each part of the swarm management device 10 and is realized by, for example, a processor and a memory. In this example, the processor accesses a storage (not shown), loads a program (not shown) stored in the storage into the memory, and executes a series of instructions included in the program. Thereby, each part of the control unit 11 is configured. As each of these parts, the control unit 11 includes a data acquisition unit 12, an analysis unit 13, a prediction unit 14, and a detection unit 15.

[0045] The data acquisition unit 12 acquires the carbon dioxide concentration and oxygen concentration measured by the measuring device 20. The data acquisition unit 12 acquires the carbon dioxide concentration and oxygen concentration based on an input signal indicating the start instruction of the prediction or detection of swarming from an input device (not shown). As an example, the data acquisition unit 12 reads the original data from the storage device 30. The data acquisition unit 12 outputs the acquired original data to the analysis unit 13.

[0046] The analysis unit 13 acquires the original data and performs time series analysis. For the details of the time series analysis of the original data, the description above regarding the swarm management method is incorporated. The analysis unit 13 decomposes the original data into a trend component, a daily variation component, and a residual component by time series analysis. The analysis unit 13 outputs each component data obtained by time series analysis to the prediction unit 14 and the detection unit 15.

[0047] The prediction unit 14 acquires the trend component and predicts the swarming of honeybees. For the details of the prediction of honeybee swarming using the trend component, the description in the swarming prediction of the swarm management method is incorporated. The prediction unit 14 outputs the prediction result to the output device 40.

[0048] The detection unit 15 acquires the original data or the residual component and detects the swarming of honeybees. For the details of honeybee swarming detection using the original data or the residual component, the description in the swarming detection of the swarming management method is incorporated. The detection unit 15 outputs the detection result to the output device 40.

[0049] (Flow of swarming prediction process) FIG. 8 is a flowchart for explaining an example of the swarming prediction process executed by the swarming management system 100 according to an aspect of the present invention.

[0050] First, the data acquisition unit 12 acquires the time-series data of the carbon dioxide concentration or the oxygen concentration in the hive measured by the measuring device 20 (step S11). Next, the analysis unit 13 performs time-series analysis on the time-series data and extracts the trend component (step S12). Then, the prediction unit 14 compares the trend component with the trend component of the past carbon dioxide concentration or oxygen concentration to predict swarming (step S13), and ends the swarming prediction process.

[0051] (Flow of swarming detection process) FIG. 9 is a flowchart for explaining an example of the swarming detection process executed by the swarming management system according to an aspect of the present invention.

[0052] First, the data acquisition unit 12 acquires the time-series data of the carbon dioxide concentration or the oxygen concentration in the hive measured by the measuring device 20 (step S21). Next, the analysis unit 13 performs time-series analysis on the time-series data and extracts the residual component (step S22). Then, the detection unit 15 compares the residual component with the residual component of the past carbon dioxide concentration or oxygen concentration to detect swarming (step S23), and ends the swarming prediction process.

[0053] According to the swarm management system 100, since it is possible to predict swarming a predetermined period before swarming occurs, it is possible to execute a process of stopping or delaying swarming within that period, and swarm can be appropriately managed. Further, according to the swarm management system 100, since the occurrence of swarming can be detected for each hive body, it is possible to identify the hive body in which swarming has occurred, and swarm can be appropriately managed.

[0054] 〔Example of implementation by software〕 The functions of the swarm management device 10 (hereinafter referred to as "device") can be realized by a program for causing a computer to function as the device, and by a program for causing a computer to function as each control block (particularly each part included in the control unit 11) of the device.

[0055] In this case, the above device includes a computer having at least one control device (for example, a processor) and at least one storage device (for example, a memory) as hardware for executing the above program. By executing the above program by this control device and storage device, each function described in each of the above embodiments is realized.

[0056] The above program may be recorded on one or more computer-readable recording media, not temporarily. This recording medium may or may not be provided in the above device. In the latter case, the above program may be supplied to the above device via any wired or wireless transmission medium.

[0057] Also, part or all of the functions of each of the above control blocks can also be realized by a logic circuit. For example, an integrated circuit in which a logic circuit functioning as each of the above control blocks is formed is also included in the scope of the present invention. In addition to this, for example, it is also possible to realize the functions of each of the above control blocks by a microcontroller or a quantum computer.

[0058] In addition, each process described in the above embodiments may be executed by AI (Artificial Intelligence). In this case, the AI may operate in the above control device, or may operate in other devices (for example, an edge computer or a cloud server, etc.).

[0059] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope shown in the claims. Embodiments obtained by appropriately combining technical means disclosed in different embodiments are also included in the technical scope of the present invention.

Description of Reference Numerals

[0060] 10 Swarm management device 20 Measuring device 100 Swarm management system

Claims

1. A method for managing honeybee swarming, comprising a step of managing honeybee swarming based on a change over time in at least one of the carbon dioxide concentration and the oxygen concentration in a honeybee hive. A method for managing honeybee swarming.

2. The change over time is obtained by performing time series analysis on measurement values obtained by measuring at least one of the carbon dioxide concentration and the oxygen concentration for a predetermined period. The method for managing honeybee swarming according to Claim 1.

3. In the step of managing, the tendency of at least one of the carbon dioxide concentration and the oxygen concentration within a predetermined period obtained by the time series analysis is compared with the tendency of at least one of the carbon dioxide concentration and the oxygen concentration during a normal period when the honeybees are not swarming, to predict honeybee swarming. The method for managing honeybee swarming according to Claim 2.

4. In the step of managing, the tendency of at least one of the carbon dioxide concentration and the oxygen concentration within a predetermined period obtained by the time series analysis is compared with the moving average of at least one of the past carbon dioxide concentration and the oxygen concentration, to predict honeybee swarming. The method for managing honeybee swarming according to Claim 2.

5. In the step of managing, the amount of change per unit time of at least one of the carbon dioxide concentration and the oxygen concentration is compared with the amount of change during a normal period when the honeybees are not swarming, to detect honeybee swarming. The method for managing honeybee swarming according to Claim 1 or 2.

6. In the step of managing, the residual component of at least one of the carbon dioxide concentration and the oxygen concentration within a predetermined period obtained by the time series analysis is compared with the standard deviation calculated from the residual components of at least one of the past carbon dioxide concentration and the oxygen concentration, to detect honeybee swarming. The method for managing honeybee swarming according to Claim 2.

7. A swarming management system comprising a measuring device for measuring at least one of the carbon dioxide concentration and the oxygen concentration in a honeybee hive, and a swarming management device for analyzing the change over time of the carbon dioxide concentration and the oxygen concentration measured by the measuring device. A swarming management system.

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