Insect attraction prediction method and insect attraction prediction system

Abstract

This invention provides an insect attraction prediction method that can make detailed predictions that reflect the different amounts of insects attracted and the different insect species at each location, even within the same area. [Solution] An insect attraction prediction method for predicting the type and quantity of insects attracted to each predetermined location within a target area, comprising the steps of: preparing a location-specific database that digitizes the type and quantity of insects attracted per unit time, unit area, and unit light intensity for each type of light source and location; a light source type setting step that sets the type of light source of the lighting fixture to be installed at the location; a light intensity setting step that sets the light intensity of the light source set in the light source type setting step; and a prediction step that predicts the type and quantity of insects attracted based on the location-specific database prepared in the location-specific database preparation step, the type of light source set in the light source type setting step, and the light intensity set in the light intensity setting step.

Description

[Technical Field]

[0001] The present invention relates to an insect attraction prediction method and an insect attraction prediction system that predict the type and quantity of insects attracted by lighting fixtures installed at predetermined locations within a target area. [Background technology]

[0002] Many flying insects are phototactic, attracted to light leaking from indoors to outdoors or to lighting used during nighttime work. Therefore, flying insects can cause contamination in factories and their large-scale attraction raises concerns about their impact on ecosystems. Since many insect species are known to be attracted to ultraviolet light sources, using light sources with low UV emission, such as LEDs, can prevent insect attraction. However, some species, such as mayflies, are known to be attracted in large numbers to white LEDs.

[0003] Methods for predicting the amount of insects attracted outdoors are limited, as disclosed in, for example, Patent Document 1. This system can classify the surrounding environment in which lighting is installed and predict the insect species attracted to the lighting from a database for each environment.

[0004] Patent Document 1 classified the surrounding environment into categories such as "paddy field," "farmland," "evergreen forest," "deciduous forest," "riparian forest," "grassland," "riverbed," "flowing water area," and "residential area," and evaluated the amount of insects attracted and the insect species. Here, the amount of insects attracted is defined as the number of insects attracted to the light source. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2015-130812 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] However, the amount of attracted insects and the insect species vary greatly depending on the location even in the same environment. For example, even in the same "evergreen forest", the amount of attracted insects and the insect species are thought to vary greatly depending on the amount of trees and the environment of the undergrowth. Conventional prediction techniques predict the amount of attracted insects in the environment from the average value of data within the same environment and cannot cope with the changes at predetermined individual locations within the environment.

Means for Solving the Problems

[0007] This invention solves the above problems, An insect attraction prediction method for predicting the types and amounts of insects attracted by lighting fixtures installed at predetermined locations within a target area, A location-specific database preparation step of preparing a location-specific database in which the types of light sources and, for each location, the types and amounts of insects attracted per unit time, per unit area, and per unit light quantity are digitized, A light source type setting step of setting the type of the light source of the lighting fixture to be installed at the location, A light quantity setting step of setting the light quantity of the light source set in the light source type setting step, A prediction step of predicting the types and amounts of insects attracted from the location-specific database prepared in the location-specific database preparation step, the type of the light source set in the light source type setting step, and the light quantity set in the light quantity setting step, and includes.

Advantages of the Invention

[0008] The insect attraction prediction method and the insect attraction prediction system according to the present invention can make a detailed prediction reflecting the different amounts of attracted insects and insect species for each location even within the same area.

Brief Description of the Drawings

[0009] [Figure 1] It is a diagram showing an example of a computer capable of executing the insect attraction prediction method according to an embodiment of the present invention. [Figure 2]This figure shows an example flowchart of the preparation steps for the insect attraction prediction method according to the present invention. [Figure 3] This figure shows an example of the data structure of the database used in the insect attraction prediction method according to the present invention. [Figure 4] This figure shows an example of the data structure of a location-specific database used in the insect attraction prediction method according to the present invention. [Figure 5] This figure shows an example of the data structure of the database used in the insect attraction prediction method according to the present invention. [Figure 6] This figure shows an example of the data structure of the database used in the insect attraction prediction method according to the present invention. [Figure 7] This figure shows an example of the data structure of the database used in the insect attraction prediction method according to the present invention. [Figure 8] This figure shows an example flowchart of the setting process for the insect attraction prediction method according to the present invention. [Figure 9] This figure shows the concept behind setting various parameters in the insect attraction prediction method according to the present invention. [Figure 10] This figure shows the concept behind setting various parameters in the insect attraction prediction method according to the present invention. [Figure 11] This figure shows the concept behind setting various parameters in the insect attraction prediction method according to the present invention. [Figure 12] This figure shows the concept behind setting various parameters in the insect attraction prediction method according to the present invention. [Figure 13] This diagram illustrates how the number of insects in the light-leakage area increases due to insect movement during the day. [Figure 14] This figure shows an example flowchart for causing a computer to execute the insect attraction prediction method according to the present invention. [Figure 15] This is a diagram illustrating the predator-prey relationships between different species. [Figure 16] This figure shows an example of the output of the results in the insect attraction prediction method according to the present invention. [Figure 17] This figure shows an example flowchart of the database preparation step in a method for selecting an insect-repellent light source according to another embodiment of the present invention. [Figure 18] This figure shows an example of the data structure of a database used in a method for selecting an insect-repellent light source according to another embodiment of the present invention. [Figure 19] This figure shows an example flowchart for causing a computer to execute a method for selecting an insect-repelling light source according to another embodiment of the present invention. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings. Figure 1 is a diagram showing an example of a computer constituting an insect-attracting prediction system according to an embodiment of the present invention. In Figure 1, 10 is a system bus, 11 is a CPU (Central Processing Unit), 12 is RAM (Random Access Memory), 13 is ROM (Read Only Memory), 14 is a communication control unit that handles communication with external information devices, 15 is an input control unit such as a keyboard controller, 16 is an output control unit, 17 is an external storage device control unit, 18 is an input unit consisting of input devices such as a keyboard, pointing device, and mouse, 19 is an output unit such as a printing device, 20 is an external storage device such as an HDD (Hard Disk Drive), 21 is a graphics control unit, and 22 is a display device. Each means of the insect-attracting prediction system described later is composed of some functions of these computer elements.

[0011] In Figure 1, the CPU 11 performs calculations such as searching for and retrieving data by communicating with external devices according to programs stored in the program ROM in ROM 13 or in the large-capacity external storage device 20, processing output data containing a mixture of graphics, images, characters, tables, etc., and managing the database stored in the external storage device 20.

[0012] Furthermore, the CPU 11 comprehensively controls each device connected to the system bus 10. The program ROM in ROM 13 or the external storage device 20 stores the operating system program (hereinafter referred to as OS), which is the basic program for controlling the CPU 11. In addition, the ROM 13 or the external storage device 20 stores various data used when performing output data processing, etc. The RAM 12, which is the main memory, functions as the main memory and work area of ​​the CPU 11.

[0013] The input control unit 15 controls the input unit 18 from a keyboard or a pointing device (not shown). The output control unit 16 controls the output of an output unit 19, such as a printer.

[0014] The external storage device control unit 17 stores boot programs, various applications, font data, user files, editing files, printer drivers, etc., on external storage devices such as HDDs (Hard Disk Drives) or floppy disks (FDs). Access to 0 is controlled. The system program that realizes the insect attraction prediction method of the present invention is stored in the external storage device 20 as described above. The graphics control unit 21 is configured to process the drawing of information to be displayed on the display device 22.

[0015] Furthermore, the communication control unit 14 controls communication with external devices via the network, enabling the system to obtain data it needs from databases held by external devices on the internet or intranet, or to transmit information to external devices.

[0016] In addition to the operating system program (hereinafter referred to as OS), which is the control program for the CPU 11, the external storage device 20 may also have a system program that runs the insect-attracting prediction system of the present invention on the CPU 11, and data used by this system program installed and stored on it.

[0017] The data used in the system program for implementing the insect attraction prediction method of the present invention is basically assumed to be stored in the external storage device 20, but in some cases, it is also possible to configure the system to acquire this data from external devices on the internet or intranet via the communication control unit 14. Furthermore, the data used in the system program for implementing the insect attraction prediction method of the present invention can also be configured to be acquired from various media such as USB memory, CDs, and DVDs.

[0018] Next, the insect attraction prediction method according to the present invention, which can be executed by a computer with the system configuration described above, will be explained below. The insect attraction prediction method according to the present invention makes it possible to quantitatively predict what kinds of insects will gather and to what extent at a planned site such as a newly constructed road, over a predetermined construction period, due to nighttime lighting from lighting fixtures.

[0019] Furthermore, the insect attraction prediction method according to the present invention makes it possible to predict to what extent insect-preying species will be affected by the nighttime illumination of lighting fixtures over a predetermined construction period.

[0020] Figure 2 is a diagram showing an example flowchart of the preparation steps for the insect attraction prediction method according to the present invention. In Figure 2, step S101 is the step of preparing the insect attraction database. This database will be described with reference to Figures 3 to 7. Figures 3 to 7 are diagrams showing an example of the data structure of the database used in the insect attraction prediction method according to the present invention.

[0021] Figure 3 shows data on the types and quantities of insects attracted per unit time, per unit area, and per unit light intensity for each type of environment when various light sources are used as lighting fixtures. To create such data, it can be assumed, for example, that the lighting fixtures are turned on for 12 hours at night per day. In Figure 3, L 11 ~L 59 This indicates the amount of insects attracted, in units of weight.

[0022] When creating the data, environmental types are classified as, for example, "paddy fields," "farmland," "evergreen forests," "deciduous forests," "riparian forests," "grasslands," "riverbanks," "flowing water areas," and "residential areas," but the present invention is not limited to such examples.

[0023] Furthermore, while insects are classified into categories such as "butterflies and moths," "stink bugs and planthoppers," "bees and winged ants," "flies and midges," and "beetles," the present invention is not limited to these examples.

[0024] Figure 4 is a location-specific database that records the types and quantities of insects attracted per unit time, unit area, and unit light intensity for each location when various light sources are used as lighting fixtures. In creating such data, it can be assumed, for example, that the lighting fixtures are turned on for 12 hours at night per day. In Figure 4, L 181 ~L 58n This indicates the amount of insects attracted, in units of weight.

[0025] When creating data, locations are classified as, for example, "1st," "2nd," ... "n-1st," "nth," etc., but the present invention is not limited to such examples.

[0026] For example, survey locations may be freely set according to local conditions and the habitats of the target organisms. An example of survey location setting is shown below. However, the scope of the present invention is not limited to this example.

[0027] Paddy fields and cultivated fields: These are farmlands excluding those where grains are grown or orchards. Since different biomes may be formed depending on the type, variety, and growth stage of the crops being cultivated, survey points will be established for each plot. Evergreen forests, deciduous forests: These are forests mainly composed of evergreen or deciduous trees. Since different tree species may result in different biotas, survey points should be set up for each tree species community. Riverside forests and riverbeds: These are woodlands near rivers and lakes, or flat areas near rivers where water does not normally flow. Since different organisms are thought to inhabit these areas at different elevations, survey points will be established every 5 meters of elevation. Furthermore, even within the same elevation zone, if the nearby rivers are different, or if the tree communities within a 5-meter radius are different, the zone will be divided and additional survey points will be added. Grasslands: These are plant habitats other than cultivated land and forests. If the grassland is demarcated by trees or other structures, survey points should be established at each location where the dominant species constituting the undergrowth differ. Even if the grassland is not demarcated by trees or other structures, survey points should be established if the constituent species are clearly different or if the grass heights differ. Flowing water area: This refers to an area with a clear flow of water, such as a mountain stream or river. Residential areas: Settlements such as rural areas and residential areas, with survey points set at predetermined distances (e.g., 50m). If the prediction target area includes green spaces such as parks, additional survey points will be set up because there is a possibility that richer biodiversity is nurtured there compared to other areas. If the entire area affected by the lighting falls into the same category, survey points will be established at predetermined distances (e.g., 50m).

[0028] The following documents may be used as reference when setting up the survey locations. National Institute for Land and Infrastructure Management, Terrestrial Insect Survey Section https: / / www.nilim.go.jp / lab / fbg / ksnkankyo / mizukokudam / system / download / H28D_Chousamanual_dam / H28D_10rikukon.pdf Ministry of the Environment, Prediction and Evaluation Methods, 6.1 Selection of Items for Environmental Impact Assessment https: / / www.env.go.jp / policy / assess / 3-2search / tosholist / 15 / erimo_06.pdf

[0029] Furthermore, while insects are classified into categories such as "butterflies and moths," "stink bugs and planthoppers," "bees and winged ants," "flies and midges," and "beetles," the present invention is not limited to these examples.

[0030] The data shown in Figures 3 and 4 represent actual measurements of insect attraction and insect species using various light sources. However, the amount of insect attraction and insect species may also be measured using at least one of the following light sources: black light, mercury lamp, sodium lamp, or LED. Alternatively, the amount of insect attraction and insect species may be calculated by converting the measured amount of insect attraction from one light source to the values ​​for other light sources.

[0031] In Figure 2, step S102 is the process of preparing the light source database.

[0032] Figure 5 shows the data structure of the database based on differences in the type of light source of lighting fixtures.

[0033] Figure 5(A) shows the cost per unit of luminous energy for each type of light source in lighting fixtures. In Figure 5(A), M1 to M4 represent the cost per unit of luminous energy in yen.

[0034] Figure 5(B) shows the luminous output per light source for each type of light source in a lighting fixture. In Figure 5(B), Q1 to Q4 represent the luminous output per light source, for example, in lumens.

[0035] Figure 5(C) shows the power consumption per unit time for each type of light source in a lighting fixture. In Figure 5(C), W1 to W4 represent the power consumption per unit time of a single light source, for example, in watts.

[0036] In Figure 2, step S103 is the process of preparing the temperature database. Figure 6 shows the data structure of the temperature database.

[0037] The temperature database is configured to store the annual average nighttime temperature for the construction area, associated with the date. This makes it possible to refer to, for example, the nighttime temperature on a specific date (e.g., Month X, Day Y).

[0038] For example, when the temperature drops below 10°C, most insects cease to move. Therefore, even during construction periods, if the temperature falls below 10°C during the hours when lighting is in use, organisms are not expected to gather (there is no impact), and these periods are excluded from the cumulative biomass calculation. In this invention, a temperature database is used for such calculations.

[0039] Furthermore, the 10°C threshold can be adjusted to match the actual mobility of insects in the field (it can be set even lower if they move even at low temperatures, or even higher if they stop moving even at high temperatures).

[0040] In Figure 2, step S104 is the process of preparing the calorie database. Figure 7 shows the data structure of the calorie database. It contains data on how many calories insects have per unit weight. C1 to C6 in Figure 7 show the number of calories per unit weight for "butterflies and moths," "stink bugs and planthoppers," "bees and winged ants," "flies and midges," and "beetles."

[0041] Using such a calorie database, it becomes possible to determine how many calories are lost for insect-preying species due to nighttime lighting. In other words, this invention makes it possible to show how many insects, equivalent to the amount of food a given species needs per day, are lost due to nighttime lighting.

[0042] The databases shown in Figures 3 to 7 can be reused repeatedly once they have been created.

[0043] Next, the setting process in the insect attraction prediction method according to the present invention will be described. Figure 8 is a flowchart example of the setting process in the insect attraction prediction method according to the present invention. Various parameters are set in each step of Figure 8. By repeatedly performing predictions while changing the parameters in each step of Figure 8, it becomes possible to propose nighttime lighting plans with lower environmental impact.

[0044] In Figure 8, step S201 involves entering the expected start and end dates for the construction work and setting the construction period. In response to step S201, the system inputs the start and end dates of the construction work into the computer.

[0045] In the next step, S202, the area of ​​the region illuminated by light leaking from the lighting fixture installed in the target area and the type of environment are set. The concept in this step will be explained with reference to Figures 9 to 12. Figures 9 to 12 are diagrams showing the concept of setting various parameters in the insect attraction prediction method according to the present invention.

[0046] Figure 9 shows a map of the area surrounding the planned construction site (target area). This map is color-coded according to the environment, and as shown in the illustration, in this example there are two types of environments: "flowing water area" and "grassland".

[0047] Figure 10 shows the map with the target area, which is the planned construction site, defined. In this example, the target area spans two types of environments: "water flow area" and "grassland."

[0048] Figure 11 shows the configuration in which lighting fixtures are installed at each point O1 to O7 within the target area. Figure 12 shows circles with a predetermined radius drawn, centered on points O1 to O7 where each lighting fixture is installed. In this example, it is assumed that regardless of weather conditions, the light from the lighting fixtures illuminates the area within the aforementioned circles.

[0049] The area of ​​the region illuminated by light leaking from the lighting fixtures installed at each point O1 to O7 corresponds to the area S1 to S7 of the shaded region in Figure 12, with the above-mentioned circle as the boundary.

[0050] Furthermore, the type of environment over which the light-leaking area illuminated by light leaking from the lighting fixture into the surrounding area overlaps is the same as the type of environment corresponding to the area of ​​that region. In other words, in this example, the type of environment corresponding to the areas S1, S2, S3, S4, S5, and S6 of the region illuminated by light leaking from the lighting fixture into the surrounding area is "flowing water area," and the type of environment corresponding to area S7 is "grassland."

[0051] In step S202, as shown in the examples in Figures 9 to 12 above, the area of ​​the light-leakage region illuminated by light leaking from the lighting fixture installed in the target area into the surrounding area, and the type of environment are input into the computer.

[0052] Furthermore, in this embodiment, insect attraction prediction data for each point O1 to O7 within the target area, where lighting fixtures are installed, is input into the computer. For example, in conventional insect attraction prediction methods, uniform insect attraction prediction data was used for all parts of the same environment. In contrast, in this embodiment, by using insect attraction prediction data for each point O1 to O7 within the target area, where lighting fixtures are installed, as shown in Figure 4, it is possible to make more detailed insect attraction predictions and select more appropriate lighting.

[0053] Next, in step S203, the computer sets the type of light source for the lighting fixtures to be installed at each point O1 to O7 in the target area, using the light source type setting means. In the examples shown in Figures 9 to 12, black lights (L) are assumed to be the lighting fixtures to be installed at each point O1 to O7. In accordance with step S203, the type of light source for the lighting fixtures to be installed in the target area is input to the computer.

[0054] In the next step, S204, the computer sets the light intensity of the light sources for each lighting fixture installed at points O1 to O7 in the target area, using light intensity setting means. In response to step S204, the system inputs the number of light sources for each lighting fixture. For example, in this example, it is input that a total of A1 light sources are used for all lighting fixtures installed at points O1 to O7.

[0055] In the next step, S205, the on-time for each lighting fixture is set. Based on this on-time, it becomes possible to calculate the power consumption of each lighting fixture from the power consumption data in the light source database. In response to step S205, the on-time is entered into the system.

[0056] Next, in step S206, the type and quantity of insects that will enter the light-leak area during the daytime are set. In the insect attraction prediction method according to the present invention, it is assumed that all insects in the light-leak area will be attracted the day after nighttime lighting, and that the type and number of insects in the light-leak area will be 0.

[0057] On the other hand, during the daytime on the following day, it is assumed that the number and types of insects in the light-leakage region, which were zero, will increase due to the movement of insects from the surrounding region. Figure 13 is a diagram illustrating how the amount of insects in the light-leakage region increases due to insect movement during the day, using the case of the vicinity of the first point O1 in the target region. It is a diagram that visualizes how insects gather in the light-leakage region of area S1 due to the movement of insects from the region indicated by S'1 surrounding the light-leakage region of area S1.

[0058] In step S206, the types and quantities of insects that enter the light-leakage area during the daytime can be expressed as a decreasing function with the number of days elapsed since the start of construction as a variable, or a table can be created listing the types and quantities of insects that enter on day 1, day 2, etc., and such a table can be used. If a decreasing function is used with the number of days elapsed since the start of construction as a variable, a function can be used that will never fall below a certain value, no matter how many days have passed. When creating such functions or tables, it is advisable to appropriately model the movement of insects during the daytime.

[0059] The following is an example of a modeling approach.

[0060] The number of insects that gather the day after nighttime lighting is influenced by the amount that decreased the previous day. Here, we assume that all organisms gather within a 10m radius of the light source (the light leakage area), resulting in zero insects the following morning, but that this is replenished by an influx of organisms from the surrounding area.

[0061] The source of insects to fill the light-leakage area is assumed to be within a range of 10m to 20m from the light source, where 100% of the insects are located. When filling the area, the amount of insects is proportional to the distance. If the amount of insects at the light source is 0 and the amount at 20m is 100, then 25% of the total will be contained within the range of 0 to 10m, and the remaining 75% will be contained within the range of 10m to 20m. These figures can be adjusted to more realistic values ​​based on local conditions and future surveys, experiments, and implementation results.

[0062] The area to be compensated will be up to 100m from the target location, and the amount of insects will not decrease at this 100m point (100% compensation will be provided). This range is determined based on the distance that the main organisms are believed to be able to travel overnight, but this value may be adjusted to a more realistic value based on local conditions and future surveys, experiments, and implementation results.

[0063] From the following night, the area from 0 to 10m will decrease by the 25% amount that was compensated, but the adjacent area from 10 to 20m has decreased to 75% of its original amount from the previous day, so the amount that can be compensated for in the 0 to 10m area is 25% of 75% = 19%. Also, although the area from 10 to 20m has decreased by 25% from the first day of illumination, it will receive compensation from the adjacent area from 20 to 30m. This amount is 25% of 2 Since it's 5%, it becomes approximately 6%.

[0064] Thus, as the days pass, the source of funds to be replenished expands to the neighboring area, but considering the distance insects travel, it will not expand beyond 100m and will remain at a constant amount. At that point, it will be the 10th day, and the daily decrease will be approximately 5%. Calculating in this way, if the construction period is less than 10 days, the cumulative decrease will be 100% on day 1, 125% on day 2, 144% on day 3, 159% on day 4, 172% on day 5, 183% on day 6, 192% on day 7, 199% on day 8, and 205% on day 9.

[0065] Ten days will be set at 210%, and for longer periods, an additional 5% will be added for each additional day. These figures can be adjusted to more realistic values ​​based on local conditions and future surveys, experiments, and implementation results.

[0066] However, when the temperature drops below 10°C, most insects cease to move. Therefore, even during construction periods, if the temperature falls below 10°C during the hours when the lights are in use, the insects will not gather (and will not be affected), and these periods will be excluded from the cumulative insect count. This 10°C threshold can be adjusted to match the actual mobility of insects at the site (it can be set even lower if they move even at low temperatures, or even higher if they stop moving even at high temperatures).

[0067] Next, we will explain the prediction process that takes place after the setting process in which various parameters are set as described above. Figure 14 is a flowchart example showing how to cause a computer to execute the insect attraction prediction method according to the present invention.

[0068] In FIG. 14, when the prediction process is started in step S300, in the subsequent step S301, the variable MMDD is set to (start date).

[0069] Subsequently, in step S302, the temperature database for the day is referred to. In step S303, the insect attraction amount prediction data for each location shown in FIG. 4 is referred to.

[0070] Next, in step S304, it is determined whether (the temperature on that day) ≥ To °C. Here, the temperature To °C is the threshold temperature at which the activity of insects stops, and for example, it can be set to 10 °C.

[0071] If the determination in step S304 is NO, the process proceeds to step S311, and it is predicted that the number of insects attracted from the surrounding area by the lighting fixture is 0.

[0072] On the other hand, if the determination in step S304 is YES, the process proceeds to step S305, and a computer as a prediction means predicts the type and amount of insects attracted from the surrounding area by the lighting fixture.

[0073] In step S305, the amount and type of insects attracted from the surrounding area by the lighting fixture are predicted. Here, based on the examples shown in FIGS. 9 to 12, a specific explanation will be given.

[0074] The amount and type of insects attracted from the surrounding area by the lighting fixture installed at the first location O1 in the flowing water area can be obtained as follows by referring to the data related to the black light shown in FIG. 4. (Type of insect) = (Amount of insect) (Butterfly) = L 181 × S1 (Stink bug) = L 281 × S1 (Bee) = L 381 × S1 (Fly) = L 481 × S1 (Beetle) = L 581 × S1

[0075] The quantity and types of insects attracted from the surrounding area by the lighting fixture installed at point O2, the second location in the flowing water area, can be determined as follows by referring to the data related to blacklights shown in Figure 4. (Type of insect) = (Quantity of insects) (Butterfly / moth) = L 182 ×S2 (Stink bugs / planthoppers) =L 282 ×S2 (Bee / Winged Ant) = L 382 ×S2 (Flies and midges) = L 482 ×S2 (Beetle) = L 582 ×S2

[0076] In this way, the quantity and types of insects attracted from the surrounding area by lighting fixtures installed at points O1 to O6 in the flowing water area can be determined by referring to the data related to blacklights shown in Figure 4. In this embodiment, the data for each point determined in this way is referred to.

[0077] Furthermore, the quantity and types of insects attracted from the surrounding area by the lighting fixture installed at point O7, the seventh location in the grassland area, can be determined as follows by referring to the data related to blacklights shown in Figure 4. (Type of insect) = (Quantity of insects) (Butterfly / moth) = L 167 × S7 (Stink bugs / planthoppers) =L 267 × S7 (Bee / Winged Ant) = L 367 × S7 (Flies and midges) = L 467 × S7 (Beetle) = L 567 × S7

[0078] Furthermore, as described above, by totaling the amount of insects attracted from the surrounding area by each lighting fixture installed at points O1 to O7, it is also possible to determine the amount and type of insects attracted from the surrounding area by the lighting fixtures installed in the target area. (Type of insect) = (Quantity of insects) (Butterfly / moth) = L 18 ×(S1+S2+S3+S4+S5+S6)+L 16 × S7 (Stink bugs / planthoppers) =L 28 ×(S1+S2+S3+S4+S5+S6)+L 26 × S7 (Bee / Winged Ant) = L 38 ×(S1+S2+S3+S4+S5+S6)+L 36 × S7 (Flies and midges) = L 48 ×(S1+S2+S3+S4+S5+S6)+L 46 × S7 (Beetle) = L 58 ×(S1+S2+S3+S4+S5+S6)+L 56 × S7

[0079] As described above, in step S305, the quantity and type of insects attracted from the surrounding area by the lighting fixture can be predicted based on the database shown in Figures 3 and 4 and the parameters set in step 8.

[0080] In step S306, it is determined whether the variable MMDD has reached the (end date). If the determination in step S306 is NO, the calculation must continue, so the process proceeds to step S312, where the variable MMDD is advanced by one day. In step S313, the type and quantity of insects that move into the light leakage area are determined based on the settings in step S206 in Figure 8. At this time, the type and quantity of insects that move into the light leakage area can be determined using an appropriate function or table, as explained earlier.

[0081] After step S313, the process proceeds to step S304 and loops. On the other hand, if the determination in step S306 is YES, the calculation for the entire construction period is completed, and then in step S307, the total power consumption for the construction period is calculated by accumulating the amount of power used each day by the lighting fixtures. By following these steps and appropriately changing the parameters of the type of light source, it is possible to determine the total power consumption used during the construction period for each type of light source.

[0082] Next, in step S308, the types and total number of insects attracted during the construction period are calculated by accumulating the types and quantities of insects attracted each day during the construction period.

[0083] In step S309, the total number of insect species and their quantities calculated in step S308, along with the calorie database shown in Figure 7, are used to calculate the total calories lost due to nighttime lighting during the construction period.

[0084] Step S310 is a step in which it is determined to what extent a given species will be affected by the total calories lost due to nighttime lighting. For example, a hawk is used as an example of the given species. Figure 15 is a diagram illustrating the predator-prey relationships of the species.

[0085] If we consider a model in which insects are preyed upon by frogs and reptiles, and frogs and reptiles are preyed upon by hawks, then if we assume that the total energy required by one hawk per day is Co in terms of insects, then by dividing the total calories calculated in step S309 by Co × (construction period), we can calculate the number of hawks N that will be affected.

[0086] Thus, the insect attraction prediction method according to the present invention can indicate how many insects equivalent to the food source of a specific species will be lost due to nighttime lighting.

[0087] In step S314, the prediction process is terminated.

[0088] The output configuration of the prediction and calculation results using the insect attraction prediction method according to the present invention, as described above, is shown. Figure 16 is a diagram showing an example of the output of results in the insect attraction prediction method according to the present invention. In Figure 16, the results of predicting the amount of insects attracted by LEDs at the first and second locations, based on the measurement results of the amount of insects attracted and the insect species at the first and second locations using black lights are shown.

[0089] With conventional prediction methods, since the first and second locations are in the same environmental area, the measurement results of insect attraction by black light and insect species at both locations are averaged, resulting in the same prediction for insect attraction by LED at both locations.

[0090] As shown in Figure 16, the insect attraction prediction method according to the present invention allows for detailed predictions that reflect different insect attraction amounts and insect species at each location, even within the same area. Furthermore, it becomes possible to visually grasp the results of the insect attraction amount prediction at each location, which can be helpful in formulating more detailed construction plans.

[0091] Next, we will describe the insect-repellent light source selection method according to the present invention, which selects a light source to reduce insect attraction. The insect-repellent light source selection method according to the present invention is a method for selecting a light source that can prevent insect attraction as much as possible when used in lighting fixtures installed in apartment building entrances and on roads. In order to implement such an insect-repellent light source selection method, various databases are prepared. The databases used in the insect-repellent light source selection method according to the present invention will be described below.

[0092] Figure 17 is a flowchart showing an example of the database preparation step in the insect-attracting light source selection method according to an embodiment of the present invention. In Figure 17, step S401 is the step of preparing a location-specific database. The location-specific database may be a database in which the amount of insects attracted was actually measured using a black light, similar to that in Figure 4. Alternatively, a database in which the amount of insects attracted was measured using a light source such as a mercury lamp, sodium lamp, or LED may be used.

[0093] In Figure 17, step S402 is the process of preparing a wavelength range database. This database will be explained with reference to Figure 18(A).

[0094] Figure 18(A) is a database that stores the relationship between the wavelength range of the light source of a lighting fixture and the types and quantities of insects attracted by it. This database is referred to as the "wavelength range database" in this specification.

[0095] In the wavelength range database, light sources can be classified by wavelength range, for example, as "400-500nm," "500-600nm," "600-700nm," and "700-800nm." On the other hand, insect species can be classified as "butterflies and moths," "stink bugs and planthoppers," "bees and winged ants," "flies and midges," and "beetles." It should be noted that the classification method for light source wavelength ranges and insect species is not limited to the classification method of this embodiment.

[0096] In the wavelength range database shown in Figure 18(A), for example, in the wavelength range of "400-500 nm", "butterflies and moths" are listed as insect species. 11 The weight units attracted were "stink bugs and planthoppers" as insect species. 21 It is recorded that insects are attracted to certain weight units. Similarly, the wavelength range of a light source is associated with the type and weight of the insect. Such a wavelength range database can be constructed based on previous research findings.

[0097] In Figure 17, step S403 is the process of preparing the frequency database. This database will be explained with reference to Figure 18(B).

[0098] Figure 18(B) is a database that stores the relationship between the flashing frequency of the light source of a lighting fixture and the type and quantity of insects attracted by it. This database is referred to as the "frequency database" in this specification.

[0099] In the frequency database, the flashing frequencies of light sources can be classified as, for example, "1Hz," "2Hz," "5Hz," "10Hz," "20Hz," and "50Hz." On the other hand, insect species can be classified as, for example, "butterflies and moths," "stink bugs and planthoppers," "bees and winged ants," "flies and midges," and "beetles." It should be noted that the classification methods for light source frequencies and insect species are not limited to those of this embodiment.

[0100] In the frequency database shown in Figure 18(B), for example, at a frequency of "1 Hz", the insect species "butterfly / moth" is F 11 The weight units attracted, and the insect species was "stink bugs and planthoppers". 21 It is recorded that certain weight units are attracted to certain insects. Similarly, the frequency of the light source is associated with the type and weight of the insect. Such a frequency database can be constructed based on previous research findings.

[0101] Incidentally, in the frequency database, white light is used as the flashing light source to collect data. Ideally, it is preferable to construct the frequency database by acquiring data on the relationship between the frequency of the flashing light source according to the wavelength range and the type and quantity of insects, but even without acquiring such data, it is possible to select a light source that is effective in preventing insect attraction using a frequency database based on data acquired with white light alone.

[0102] Next, we will describe an algorithm that, based on the databases prepared as described above, selects light sources that can prevent insect attraction as much as possible when used in lighting fixtures installed in apartment building entrances and on roads.

[0103] Figure 19 is a diagram showing an example flowchart for causing a computer to execute an insect-repelling light source selection method according to an embodiment of the present invention. This embodiment selects an appropriate light source by referring to a location-specific database, a wavelength range database, and a frequency database.

[0104] In Figure 19, when processing starts in step S500, step S501 prompts the user to set the sunset temperature for the period at the location where the lighting fixture will be installed. In response to this step, the user inputs the target location and the sunset temperature from the input unit 18 (location setting step).

[0105] In the following step S502, the location set in step S501 is referenced to the location-specific database shown in Figure 4 (location-specific database reference step). Next, in step S503, the insect species that takes the maximum value is selected. For example, if the first location is entered, in step S503, the insect that takes the maximum value at the first location is selected (insect species selection step). In this example, L in Figure 4 281 Let's assume that this was the maximum value. Then, the insect species to be considered when selecting the light source will be "stink bugs and planthoppers". In other words, in this example, the insect species selected in the insect species selection step S503 is "stink bugs and planthoppers".

[0106] In the following step S504, the insect species selected in step S503 is referenced against the wavelength range database (wavelength range database reference step). Subsequently, in step S505, the computer, acting as a wavelength range selection means, selects the wavelength range of the light source that minimizes the weight (wavelength range selection step). In other words, in this example, in the wavelength range selection step of step S505, min{WL 21 WL 22 WL 23 WL 24} is processed. For example, in this example, min{WL 21 WL 22 WL 23 WL 24}=W 21 If that were the case, the wavelength range of the light source would be 400-500 nm.

[0107] Following step S505, the process proceeds to step S506, in which the computer, acting as an insect species selection means, refers to the insect species selected in the insect species selection step (step S503) and the frequency database (frequency reference step). Subsequently, in step S507 (frequency selection step), the computer, acting as a frequency selection means, selects the frequency of the light source that minimizes the weight. In other words, in this example, in the frequency selection step of step S507, min{F 21 ,F 22 ,F 23 ,F 24 ,F 25 ,F 26} is processed. For example, in this example, min{F 21 ,F 22 ,F 23 ,F 24 ,F 25 ,F 26}=F 24 If that is the case, then 10Hz is selected as the frequency of the light source.

[0108] In the subsequent step S508 (output step), the selected insect type (in this example, "stink bug / planthopper"), the selected wavelength range (in this example, "400nm~500nm"), and the selected light source frequency (in this example, "10Hz") are output to the output unit 19 and the display device 22 as output means, informing the user of the insect type to be considered and the wavelength range of the light source to be selected, and the process ends in step S509. In this embodiment, the wavelength of the light source is selected in the wavelength range selection step (step S505), and then the frequency of the light source is selected in the frequency selection step (step S507). However, the order of these steps may be reversed. Furthermore, only one of the steps, either the wavelength range selection step (step S505) or the frequency selection step (step S507), may be performed.

[0109] As described above, the insect attraction prediction method of this embodiment is an insect attraction prediction method that predicts the type and quantity of insects attracted by lighting fixtures installed at predetermined locations within a target area, and comprises: a location-specific database preparation step of preparing a location-specific database that digitizes the type of light source and the type and quantity of insects attracted per unit time, unit area, and unit light intensity for each location; a light source type setting step of setting the type of light source of the lighting fixtures to be installed at the locations; a light intensity setting step of setting the light intensity of the light source set in the light source type setting step; and a prediction step of predicting the type and quantity of insects attracted from the location-specific database prepared in the location-specific database preparation step, the type of light source set in the light source type setting step, and the light intensity set in the light intensity setting step.

[0110] Therefore, according to the insect attraction prediction method of this embodiment, even within the same area, it is possible to make detailed predictions that reflect the different amounts of insects attracted and the different insect species at each location.

[0111] Furthermore, in the insect attraction prediction method of this embodiment, the light source type setting step includes a wavelength range database reference step that refers to a wavelength range database that stores the relationship between the wavelength range of the light source and the type and weight of the insects that are attracted; an insect type selection step that selects the insect type whose weight is the maximum value based on a location-specific database; a wavelength range selection step that selects the wavelength range of the light source whose weight is the minimum value based on the insect type selected in the insect type selection step and the wavelength range of the light source selected in the wavelength range selection step; and an output step that outputs the insect type selected in the insect type selection step and the wavelength range of the light source selected in the wavelength range selection step.

[0112] Therefore, according to the insect attraction prediction method of this embodiment, even within the same area, it is possible to make detailed predictions that reflect the different amounts of insects attracted and the different insect species at each location, and to select a light source that prevents insect attraction as much as possible.

[0113] Furthermore, in the insect attraction prediction method of this embodiment, the light source type setting step includes a frequency database reference step that refers to a frequency database that stores the relationship between the frequency of the light source and the type and weight of the insects that are attracted; an insect type selection step that selects the insect type whose weight is the maximum value based on a location-specific database; a frequency selection step that selects the frequency of the light source whose weight is the minimum value based on the insect type selected in the insect type selection step and the frequency database; and an output step that outputs the insect type selected in the insect type selection step and the frequency of the light source selected in the frequency selection step.

[0114] Therefore, according to the insect attraction prediction method of this embodiment, even within the same area, it is possible to make detailed predictions that reflect the different amounts of insects attracted and the different insect species at each location, and to select a light source that prevents insect attraction as much as possible. [Explanation of Symbols]

[0115] 10. System bath 11...CPU(Central Processing Unit) 12...RAM(Random Access Memory) 13. ROM (Read Only Memory) 14. Communications Control Unit 15. Input Control Unit 16.. Output control unit 17. External Storage Unit 18.. Input section 19. Output section 20...External storage device 21. Interface section 21. Graphics Control Unit 22. Display device

Claims

1. A method for predicting the type and quantity of insects attracted by lighting fixtures installed at predetermined points within a target area, A site-specific database preparation step involves preparing a site-specific database that compiles data on the type of light source and, for each location, the types and quantities of insects attracted per unit time, unit area, and unit light intensity. A light source type setting step for setting the type of light source for lighting fixtures to be installed at the aforementioned location, A light intensity setting step in which the light intensity of the light source set in the light source type setting step is set, A prediction step that predicts the amount of insects attracted and the insect species from the location-specific database prepared in the location-specific database preparation step, the type of light source set in the light source type setting step, and the light intensity set in the light intensity setting step. Having, Methods for predicting insect attraction.

2. The aforementioned light source type setting step is: A wavelength range database referencing step involves referring to a wavelength range database that stores the relationship between the wavelength range of a light source and the type and weight of insects it attracts. A step of selecting an insect species that has the maximum weight based on the aforementioned location-specific database, A wavelength range selection step in which, based on the insect species selected in the insect species selection step and the wavelength range database, a wavelength range of the light source that results in the minimum weight is selected, An output step which outputs the insect type selected in the insect type selection step and the wavelength range of the light source selected in the wavelength range selection step, Having, The insect attraction prediction method according to claim 1.

3. The aforementioned light source type setting step is: A frequency database referencing step involves referring to a frequency database that stores the relationship between the frequency of a light source and the type and weight of insects it attracts. A step of selecting an insect species that has the maximum weight based on the aforementioned location-specific database, A frequency selection step in which, based on the insect species selected in the insect species selection step and the frequency database, a light source frequency is selected that results in the minimum weight. An output step which outputs the insect type selected in the insect type selection step and the frequency of the light source selected in the frequency selection step, Having, The insect attraction prediction method according to claim 1.

4. An insect attraction prediction system that predicts the type and quantity of insects attracted by lighting fixtures installed at predetermined locations within a target area, A location-specific database containing data on the type of light source and, for each location, the types and quantities of insects attracted per unit time, unit area, and unit light intensity, A light source type setting means for setting the type of light source of a lighting fixture to be installed at the aforementioned location, A light intensity setting means for setting the light intensity of a light source set by the light source type setting means, A prediction means for predicting the amount of insects attracted and the insect species from the aforementioned location-specific database, the type of light source set by the light source type setting means, and the light intensity set by the light intensity setting means, Equipped with, Insect attraction prediction system.

5. The aforementioned light source type setting means is A wavelength range database that stores the relationship between the wavelength range of a light source and the type and weight of insects it attracts, An insect species selection means that selects the insect species with the maximum weight based on the aforementioned location-specific database, A wavelength range selection means selects the wavelength range of a light source that minimizes weight based on the insect species selected by the insect species selection means and the wavelength range database. An output means that outputs the insect species selected by the insect species selection means and the wavelength range of the light source selected by the wavelength range selection means, Having, The insect attraction prediction system according to claim 4.

6. The aforementioned light source type setting means is A frequency database that stores the relationship between the frequency of a light source and the type and weight of insects it attracts, An insect species selection means that selects the insect species with the maximum weight based on the aforementioned location-specific database, A frequency selection means selects the frequency of the light source that results in the minimum weight based on the insect species selected by the insect species selection means and the frequency database. An output means that outputs the insect type selected by the insect type selection means and the frequency of the light source selected by the frequency selection means, Having, The insect attraction prediction system according to claim 4.

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