JP6238067B2 - Insect prediction method and attractant prediction system - Google Patents

Description

  The present invention relates to an insect attracting prediction method and an insect attracting predicting system for quantitatively predicting the kind and amount of insects attracted from a peripheral region outside the target region by a lighting fixture installed in a target region such as a planned construction site.

  Conventionally, damage to crops caused by night moss has been a problem mainly in the agricultural field such as orchards and farmland. In order to deal with such problems, a light emission evaluation method has been proposed that can easily evaluate the action suppression effect against nocturnal insects such as night owls without conducting field experiments.

For example, Patent Document 1 (Japanese Patent Application Laid-Open No. 2005-65601) discloses a light emission evaluation method for evaluating the effect of suppressing nocturnal insect behavior by light emission of a light emission unit including at least a light source, and λ is a light emission. , S (λ) is a function indicating the spectral sensitivity of the retina of the insect to be controlled, Φe (λ) is a function indicating the spectral characteristics of the radiation intensity by the light emitting part, and vis is S (λ)> 0 A method has been proposed in which the action suppression effect is higher as the evaluation index obtained by the equation E = ∫visS (λ) Φe (λ) dλ is larger when E is the evaluation index. ing.

  Insects such as night moths become a cause of damage to crops as described above, and night lighting is used to deal with this, while night lighting provided on highways etc. is insects There is also the problem of affecting the behavior and ecology of many organisms.

  For example, fireflies can no longer breed at as little as 0.2 lux. In addition, if insects gather in mercury lamps, the organisms that used the insects will lose the opportunity for supplementary food and starve. For this reason, conventionally, when performing night illumination, consideration has been given to attaching a louver or using illumination that is difficult for insects to approach.

  By the way, conventionally, it has been a problem because it is impossible to quantitatively predict how much insects gather by night illumination at a planned site such as a newly established road.

In view of this, the applicant quantitatively predicts the type and amount of insects attracted from a peripheral region outside the target region by a lighting fixture installed in the target region in Patent Document 2 (Japanese Patent Laid-Open No. 2013-215106). A method for preparing an insect attractant, a database preparation step for preparing a database in which the types and amounts of attracted insects are converted into data for each light source type and environment type per unit time, unit area, and unit light quantity; A light source type setting step for setting a light source type of a lighting fixture to be installed in the target area, a light amount setting step for setting the light quantity of the light source set in the light source type setting step, and a lighting fixture to be installed in the target area The step of setting the light leakage state for setting the area of the region irradiated with the light leaking to the surrounding region and the type of environment, and the step of preparing the database From the database, the light source type set in the light source type setting step, the light amount set in the light amount setting step, and the area and environment type set in the light leakage state setting step, Proposed a prediction step that predicts the type and amount of insects attracted from the surrounding area.
JP-A-2005-65601 JP 2013-215106 A

  In the method described in Patent Document 2, the influence on the ecosystem is quantified based on the amount of insects gathered in one day, but in reality, the construction period during lighting continues to affect the ecosystem.

  However, the conventional method described in Patent Document 2 has a problem that night lighting by a lighting fixture continues to affect the ecosystem over the construction period, but such a situation is not taken into consideration.

  Further, in the conventional method described in Patent Document 2, only insects are considered as influences affecting the ecosystem, and no consideration is given to the species that feed on the insects. More specifically, there is a problem that it is not known how much the species that prey on the insects are affected by the insects lost by the nighttime lighting.

  This invention solves the said subject, The invention which concerns on Claim 1 quantitatively quantifies the kind and quantity of the insect which the lighting fixture installed in a target area attracts from the surrounding area outside the said target area Insect prediction method for predicting worms and preparing an irritant database in which the types and amounts of attracted insects are converted into data per unit time, unit area, and unit light quantity for each type of light source and environment type A database preparation step, a temperature database preparation step for preparing a database in which annual temperature transitions in the target area are converted into data, a construction period setting step for setting a construction start date and a construction end date, and a lighting fixture to be installed in the target area A light source type setting step for setting the light source type, a light amount setting step for setting the light amount of the light source set in the light source type setting step, and the target A step for setting the light leakage status to set the area and environment type of the light leakage area irradiated with light leaking at night from the lighting equipment installed in the area, and a transfer to set the type and amount of insects to be transferred to the light leakage area during the day The amount setting step, the attracting database prepared by the attracting database preparation step, and the temperature database preparing step for each day from the construction start date to the construction end date set in the construction period setting step. Annual temperature change, light source type set in the light source type setting step, light amount set in the light amount setting step, area and environment type set in the light leakage status setting step, and the amount of transfer The type of insects attracted from the surrounding area by the luminaire from the type and amount of insects that enter the light leakage area set in the setting step The daily prediction step for predicting the amount for one day, and the type and amount of attracted insects for the day predicted in the daily prediction step were set in the construction period setting step. And a cumulative step from the construction start date to the construction end date.

  The invention according to claim 2 is the insect attractant prediction method according to claim 1, wherein the amount of insects transferred to the light leakage area during the day set in the transfer amount setting step decreases every day. It is characterized by doing.

  According to a third aspect of the present invention, in the insect attracting prediction method according to the first or second aspect, in the one-day prediction step, if the temperature set by the temperature database preparation step is equal to or lower than a predetermined value, illumination is performed. It is predicted that there is no insect attracted from the surrounding area by the instrument.

  Moreover, the invention which concerns on Claim 4 prepares the calorie database which converted the calorie per unit amount according to the kind of insect into data in the attractant prediction method of any one of Claim 1 thru | or 3. Based on the calorie database preparation step, the types and amounts of attracted insects accumulated in the accumulation step, and the calorie database prepared in the calorie database preparation step, the influence on the predetermined organism species is determined. It has a biological species effect determination step to predict.

  Further, the invention according to claim 5 is the power amount database in which the amount of power used per unit time is converted into data for each type of light source in the insect attractant prediction method according to any one of claims 1 to 4. A power amount database preparation step, a power amount database prepared in the power amount database preparation step, and a construction start date and a construction end date set in the construction period setting step, A power consumption calculating step for calculating power consumption;

  Further, the invention according to claim 6 is an insect attracting prediction system that predicts quantitatively the kind and amount of insects attracted from a peripheral region outside the target region by a luminaire installed in the target region, Insect database that converts the type and amount of insects attracted per unit time, unit area, and unit light intensity for each type and environment type, and a temperature database that converts annual temperature changes in the target area into data A work period setting means for setting a construction start date and a construction end date, a light source type setting means for setting a light source type of a lighting fixture installed in the target area, and a light amount of the light source set by the light source type setting means A light amount setting means for setting the light leakage state setting means for setting the area and environment type of the light leakage area irradiated to the light leaking at night from the lighting equipment installed in the target area, and the light leakage during the day A transfer amount setting means for setting the type and amount of insects to be transferred to the area; and for each day from the construction start date to the construction end date set by the construction period setting means, the insect attracting database, the temperature database, The light source type set by the light source type setting means, the light quantity set by the light quantity setting means, the area and environment type set by the light leakage state setting means, and the transfer amount setting means Based on the type and amount of insects that are transferred to the light leakage area, it is predicted by the daily prediction unit that predicts the type and amount of insects attracted from the surrounding area by a lighting fixture, and the daily prediction unit. And a cumulative means for accumulating the types and amounts of attracted insects for one day from the construction start date to the construction end date set by the construction period setting means.

  The insect attracting prediction method and the insect attracting predicting system according to the present invention include the type and amount of attracted insects for one day predicted in the one day predicting step, from the construction start date set in the construction period setting step. Since there are cumulative steps accumulated until the construction end date, according to the insect attracting prediction method and the insect attracting predicting system according to the present invention, it becomes possible to grasp the influence on the ecosystem over the construction period by the lighting equipment.

  In addition, the attracting insect prediction method according to the present invention provides a predetermined organism based on the kind and amount of insects accumulated in the accumulation step and the calorie database prepared in the calorie database preparation step. Since there is a species impact determination step for predicting the impact on the species, it is possible to grasp how much the species that are feeding on the insects are affected.

It is a figure which shows an example of the computer which can perform the insect attracting prediction method which concerns on embodiment of this invention. It is a figure which shows the example of a flowchart of a preparatory process for the insect attracting prediction method which concerns on this invention. It is a figure which shows an example of the data structure of the database used for the insect attracting prediction method which concerns on this invention. It is a figure which shows an example of the data structure of the database used for the insect attracting prediction method which concerns on this invention. It is a figure which shows an example of the data structure of the database used for the insect attracting prediction method which concerns on this invention. It is a figure which shows an example of the data structure of the database used for the insect attracting prediction method which concerns on this invention. It is a figure which shows the example of a flowchart of a setting process process of the insect attracting prediction method which concerns on this invention. It is a figure which shows the example of a flowchart for making a computer perform the attracting insect prediction method which concerns on this invention. It is a figure which shows the view of various parameter settings in the insect attracting prediction method which concerns on this invention. It is a figure which shows the view of various parameter settings in the insect attracting prediction method which concerns on this invention. It is a figure which shows the view of various parameter settings in the insect attracting prediction method which concerns on this invention. It is a figure which shows the view of various parameter settings in the insect attracting prediction method which concerns on this invention. It is a figure explaining the amount of insects increasing in a light leakage area | region by the movement of the insect during the daytime. It is a figure explaining the predation relationship of a biological species. It is a figure which shows the example of a setting of the parameter of a lighting fixture. It is a figure which shows the example of an output of the result in the attracting insect prediction method which concerns on this invention. It is a figure which shows the example of an output of the result in the attracting insect prediction method which concerns on this invention. It is a figure which shows the example of an output of the result in the attracting insect prediction method which concerns on this invention. It is a figure which shows the example of an output of the result in the attracting insect prediction method which concerns on this invention.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a diagram showing an example of a computer that constitutes the worm attracting prediction system according to the embodiment of the present invention. In FIG. 1, 10 is a system bus, 11 is a CPU (Central Processing Unit), 12 is a RAM (Random Access Memory), 13 is a ROM (Read Only Memory), 14 is a communication control unit that controls communication with an external information device, 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 composed of input devices such as a keyboard, pointing device, and mouse, 19 is an output unit such as a printing device, Reference numeral 20 denotes an external storage device such as an HDD (Hard Disk Drive), 21 denotes a graphic control unit, and 22 denotes a display device.

  In FIG. 1, the CPU 11 retrieves and acquires data by communicating with an external device in accordance with a program ROM stored in the ROM 13 or a program stored in the large-capacity external storage device 20. Processing of output data in which graphics, images, characters, tables, etc. are mixed is executed, and management of a database stored in the external storage device 20 is further executed.

  Further, the CPU 11 comprehensively controls each device connected to the system bus 10. The program ROM in the ROM 13 or the external storage device 20 stores an operating system program (hereinafter referred to as OS) that is a basic program for controlling the CPU 11. The ROM 13 or the external storage device 20 stores various data used when performing output data processing or the like. The RAM 12 as a main memory functions as a main memory, work area, and the like for the CPU 11.

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

  The external storage device control unit 17 is an external storage device 20 such as an HDD (Hard Disk Drive) or a floppy disk (FD) that stores a boot program, various applications, font data, user files, editing files, printer drivers, and the like. Control access to. A system program for realizing the worm attracting prediction method of the present invention is stored in the external storage device 20 as described above. The graphic control unit 21 is configured to perform drawing processing on information to be displayed on the display device 22.

  Further, the communication control unit 14 controls communication with an external device via a network, thereby acquiring data required by the system from a database held by an external device on the Internet or an intranet, It is configured to be able to send information to an external device.

  In addition to an operating system program (hereinafter referred to as OS) that is a control program for the CPU 11, the external storage device 20 is installed with a system program for operating the insect attracting prediction system of the present invention on the CPU 11, data used in the system program, and the like.・ It is remembered.

  As data used in the system program for realizing the insect attracting prediction method of the present invention, it is basically assumed that the data is stored in the external storage device 20. It is also possible to configure to acquire from an external device on the Internet or an intranet via the control unit 14. Further, the data used in the system program for realizing the insect attracting prediction method of the present invention can be obtained from various media such as a USB memory, a CD, and a DVD.

  Next, an insect attracting prediction method according to the present invention that can be executed by the computer having the above system configuration will be described below. The insect attracting prediction method according to the present invention is a method for quantitatively predicting what kind of insects are gathered by night illumination of lighting equipment over a predetermined construction period in a planned site such as a newly established road. It is.

  Furthermore, in the insect attracting prediction method according to the present invention, it is predicted how much the species that prey on the insects are affected by the night illumination of the lighting apparatus over a predetermined construction period.

  FIG. 2 is a diagram showing an example of a flowchart of a preparation process for the insect attracting prediction method according to the present invention. In FIG. 2, step S101 is a process of preparing an insect attracting database. This database will be described with reference to FIGS. 3 and 4 are diagrams showing an example of a data structure of a database used in the worm attracting prediction method according to the present invention.

FIG. 3A shows data of the types and amounts of attracted insects per unit time, unit area, and unit light quantity for each environmental type when a mercury lamp is used as the light source of the lighting fixture. . In creating such data, for example, it can be assumed that the lighting fixture has been turned on for 12 hours at night per day. In FIG. 3A, H _{11 to} H ₅₉ indicate the amount of attracted insects in units of weight.

  In creating data, the environmental types include, for example, “paddy field”, “field”, “evergreen forest”, “deciduous forest”, “river forest”, “grassland”, “river bed”, “flowing water area”, Although classification is performed as “residential land”, the present invention is not limited to such an example.

  In addition, the types of insects are classified as “butterfly moth”, “stink bug unka”, “bee bee ant”, “fly chironomid”, “kochu”, etc. It is not limited to a specific example.

  Fig. 3 (B) shows data on the types and amounts of attracted insects per unit time, unit area, and unit light quantity for each environmental type when a sodium lamp is used as the light source of the lighting fixture. is there. The data creation method and configuration are the same as those in FIG.

  FIG. 3C shows data of the types and amounts of attracted insects per unit time, unit area, and unit amount of light for each environment type when an LED is used as the light source of the lighting fixture. . The data creation method and configuration are the same as those in FIG.

  In FIG. 2, step S102 is a step of preparing a light source database.

  FIG. 4 shows the data structure of the database based on the difference in the type of light source of the lighting fixture.

FIG. 4A shows the data per unit light amount for each type of light source of the lighting fixture. In FIG. 4A, M _{1 to} M ₃ indicate costs per unit light quantity in units of circles.

FIG. 4B is a diagram in which the amount of light per piece is converted into data for each type of light source of the lighting fixture. In FIG. 4B, Q _{1 to} Q ₃ indicate the amount of light per light source, for example, in lumen units.

FIG. 4C shows the data of the power consumption per unit time for each type of light source of the luminaire. In FIG. 4C, W _{1 to} W ₃ indicate the power consumption per unit time of one light source, for example, in watts.

  In FIG. 2, step S103 is a step of preparing a temperature database. FIG. 5 shows the data structure of the temperature database.

  The temperature database is configured to store the average nighttime average temperature in the construction target area in association with the date. Thereby, for example, it becomes possible to refer to the nighttime temperature of ** month ** days.

  For example, when the temperature falls below 10 ° C, most insects will not move. For this reason, even during the construction period, when the temperature is below 10 ° C. during the time period when lighting is used, organisms are excluded from the cumulative total of biomass because they do not gather (no effect). For this calculation, a temperature database is used in the present invention.

  Note that the threshold of 10 ° C can be varied according to the insect's ability to move in the field. (If it moves even if it is low, it can be set lower. Or can be set higher).

In FIG. 2, step S104 is a step of preparing a calorie database. FIG. 5 shows the data structure of the calorie database. Data on how many calories an insect has per unit weight. C _{1 to} C _{6 in} FIG. 5 indicate the number of calories per unit weight of “Butterfly moth”, “Stink bug unka”, “Hachi wing ant”, “Fly Yusurika”, and “Kochu”.

By using such a calorie database, it is possible to determine how much calories have been lost for the species that feed on insects by night lighting. That is, in the present invention, it is possible to indicate how many insects corresponding to food are lost by night lighting from the calories required by the organism species in one day.

  The databases shown in FIGS. 3 to 6 can be used repeatedly once created.

  Next, the setting process process in the insect attracting prediction method according to the present invention will be described. FIG. 7 is a diagram showing an example of a flowchart of the setting process step for the worm attracting prediction method according to the present invention. Various parameters are set by each step of FIG. By repeatedly performing the prediction while changing the parameters in each step of FIG. 7, it is possible to propose a night lighting scheme with a lower environmental load.

  In FIG. 7, in step S201, the start date and end date of the assumed construction are input, and the construction period is set. Corresponding to this step S201, the construction start date and end date are input to the computer on the system.

  In the next step S202, the area of the region irradiated with the light leaking from the lighting fixture installed in the target region to the peripheral region and the type of environment are set. The concept in this step will be described with reference to FIGS. 9 to 12 are views showing the concept of setting various parameters in the insect attracting prediction method according to the present invention.

  FIG. 9 shows a map around the planned construction site (target area). This map is colored for each environment. As shown in the figure, there are two types of environments, “evergreen forest” and “field” in this example.

  FIG. 10 shows a state in which a target area that is a planned construction site is set in the map. In this example, the target area is set across three types of environments, “evergreen forest” and “field”.

FIG. 11 shows a state in which lighting fixtures are set at points O _{1 to} O ₇ in the target area. FIG. 12 depicts a circle with a predetermined radius centered on the points O _{1 to} O ₇ where the respective lighting fixtures are provided. In this example, it is assumed that the light from the luminaire irradiates the inside of the circle regardless of the weather or the like.

The area of the region irradiated with the light leaking from the lighting fixtures provided at the points O _{1 to} O ₇ to the peripheral region is the area S _{1 to} S of the region indicated by the oblique lines in FIG. _It is equivalent to ₇ .

Further, the environment type in which the light leakage region irradiated with the light leaking from the lighting fixture to the surrounding region is an environment type corresponding to the area of the region. That is, in this example, the environmental type corresponding to the areas S ₁ , S ₂ , S ₃ , S ₄ , S ₅ , S _{6 of the} region irradiated with light leaking from the lighting fixture to the surrounding region is “Evergreen Forest”. The environment type corresponding to the surface area S ₇ is “field”.

  In step S202, as in the example shown in FIGS. 9 to 12 as described above, the area of the light leakage region and the type of environment irradiated to the light leaking from the luminaire installed in the target region to the peripheral region are set in the computer. input.

In the subsequent step S203, the type of the light source of the luminaire to be installed at the points O _{1 to} O ₇ in the target area is set. In the example shown in FIGS. 9 to 12, a mercury lamp (H) is assumed as a lighting fixture installed at points O _{1 to} O ₇ . Corresponding to step S203, the type of the light source of the luminaire to be installed in the target area is input to the computer.

In the next step S204, the light quantity of the light source of each luminaire installed at the points O _{1 to} O ₇ in the target area is set. Corresponding to step S204, the number of light sources of each lighting fixture is input on the system. For example, in this example, the input is made assuming that a total of A ₁ light sources are used for all lighting fixtures installed at points O _{1 to} O ₇ .

  In the next step S205, the lighting time of each lighting fixture is set. Based on this lighting time, it becomes possible to calculate the power consumption of each lighting fixture from the data on the amount of power used in the light source database. In response to this step S205, the lighting time is input on the system.

  Next, in step S206, the type and amount of insects that enter the light leakage area during the day are set. In the insect attracting prediction method according to the present invention, it is assumed that all insects in the light leakage area are invited on the day after night illumination, and that the number and type of insects in the light leakage area are zero.

On the other hand, during the day of the next day, it is assumed that the number and type of insects that have become zero increase in the light leakage area due to the movement of insects from the surrounding area. FIG. 13 is a diagram illustrating the increase in the amount of insects in the light leakage region S _{1 due to} the movement of insects during the day in the case of the vicinity of the point O _{1 of the} target region, and S ′ around the light leakage region of area S ₁ . from the area indicated by _1, by the movement of insects is a diagram obtained by images that insects gather light leakage area of the area S _1.

  In step S206, the type and amount of insects that enter the light leakage area during the day may be expressed as a decreasing function, with the date elapsed from the construction start date as a variable, or from the construction start date. The type and amount of insects transferred on the first day, the type and amount of insects transferred on the second day,..., And such a table may be used. In addition, when the function is used as a function of decreasing the date that has elapsed from the construction start date as a variable, a function that does not become a certain value or less may be used no matter how many days pass. In addition, when creating a function or table as described above, it is preferable to appropriately model the movement of insects during the day.

  Hereinafter, an example of a concept for modeling will be described.

  The amount of insects collected the day after night lighting is affected by the amount reduced the previous day. Here, it is assumed that all living things gather in the range of 10 m from the light source that is the light leakage area, and the amount of insects in the morning of the next day becomes 0, but the inflow of living organisms occurs from the vicinity and compensates.

  It is assumed that the source of insects to be compensated for in the light leakage area is within a range of 10 to 20 m from the light source, and there is an amount of 100% insects. When compensated, the distance and the amount of insects are proportional. If the amount of insects at the light source position is 0 and the amount of insects 20 meters ahead is 100, 25% of the total is in the range from 0 to 10 m. The remaining 75% is included in the range of 10 m to 20 m. This value can be changed to a more realistic value through the local conditions, future surveys, experiments, and implementation results.

  The range to be compensated is 100 m from the target location, and the amount of insects is not reduced at the 100 m point (100% is compensated). This range is determined from the distance that the main organism can move overnight, but this value can also be changed to a more realistic value based on the local conditions, future surveys, experiments, and implementation results. It shall be possible.

From the night of the next day, 25% of the range from 0 to 10m will be reduced, but the next 10
Since the range of ˜20 m has been reduced to 75% on the previous day, 25% can be compensated for the range of 0 to 10 m = 75% = 19%. Further, the range of 10 to 20 m is reduced by 25% from the first day of lighting, but compensation is received from the adjacent range of 20 to 30 m. This amount is about 6% because 25% of 25%.

  In this way, as the number of days passes, the resources to be compensated will expand to the next, but considering the distance of insect movement, it will not increase beyond 100 m and will be a fixed amount. At that time, on the 10th day, the amount of reduction per day is about 5%. According to this calculation, when the construction period is less than 10 days, 100% on the first day, 125% on the second day, 144% on the third day, 159% on the fourth day, 172% on the fifth day, 6% The cumulative reduction amount is 183% on the day, 192% on the 7th day, 199% on the 8th day, and 205% on the 9th day.

  The 10th day is 210%, and when the number of days is more than that, the number of days × 5% is added. These values can also be changed to more realistic values through local conditions, future surveys, experiments, and implementation results.

  However, when the temperature falls below 10 ° C, most insects will not move. Therefore, even during the construction period, when the temperature falls below 10 ° C during the time period when the lighting is used, the organism is excluded from the cumulative amount of insects because it does not gather (no effect). This threshold value of 10 ° C. can be varied according to the insect's ability to move in the field (set to a lower value when moving even if it is low, or even higher if it does not move even if it is high). And can be set).

  Next, the prediction process performed after the setting process in which various parameters are set as described above will be described. FIG. 8 is a diagram showing an example of a flowchart for causing a computer to execute the insect attracting prediction method according to the present invention.

  In FIG. 8, when the prediction process is started in step S300, (start date) is set to the variable MMDD in the subsequent step S301.

  Subsequently, in step S302, the temperature database of the corresponding day is referred to, and in step S303, it is determined whether (temperature of the corresponding day) ≧ To ° C. Here, the temperature To ° C. is a threshold temperature at which the insect activity stops, and can be set to 10 ° C., for example.

  When determination in step S303 is NO, it progresses to step S311 and the insect attracted from a peripheral region with a lighting fixture is estimated to be 0.

  On the other hand, when the determination in step S303 is YES, the process proceeds to step S304, and the type and amount of insects attracted from the surrounding area by the lighting fixture are predicted.

In step S105, the amount and type of insects attracted from the surrounding area by the lighting fixture are predicted. Here, a specific description will be given based on the examples shown in FIGS.
● The amount and type of insects to attract the peripheral area by installing the lighting fixture to the point O ₁ is FIG. 3 (A)
By referring to the data relating to the mercury lamp shown in Fig. 1, it can be obtained as follows.
(Insect type) = (Insect amount)
(Butterfly moth) = H ₁₃ × S ₁
(Stink bug unka) = H ₂₃ × S ₁
(Bee, feather ant) = H ₃₃ × S ₁
(Fly, chironomid) = H ₄₃ × S ₁
(Kochu) = H ₅₃ × S ₁
● The amount and type of insects to attract the peripheral area by installing the lighting fixtures to the point O _2, as shown in FIG. 3 (A)
By referring to the data relating to the mercury lamp shown in Fig. 1, it can be obtained as follows.
(Insect type) = (Insect amount)
(Butterfly moth) = H ₁₃ × S ₂
(Stink bug unka) = H ₂₃ × S ₂
(Bee, feather ant) = H ₃₃ × S ₂
(Fly, chironomid) = H ₄₃ × S ₂
(Kochu) ＝ H ₅₃ × S ₂
● The amount and type of insects to attract the peripheral area by installing the lighting fixtures to the point O ₃ is FIG. 3 (A)
By referring to the data relating to the mercury lamp shown in Fig. 1, it can be obtained as follows.
(Insect type) = (Insect amount)
(Butterfly moth) = H ₁₃ × S ₃
(Stink bug unka) = H ₂₃ × S ₃
(Bee, feather ant) = H ₃₃ × S ₃
(Fly, chironomid) = H ₄₃ × S ₃
(Kochu) ＝ H ₅₃ × S ₃
● The amount and type of insects to attract the peripheral area by installing the lighting fixtures to the point O ₄ is FIG 3 (A)
By referring to the data relating to the mercury lamp shown in Fig. 1, it can be obtained as follows.
(Insect type) = (Insect amount)
(Butterfly moth) = H ₁₃ × S ₄
(Stink bug unka) ＝ H ₂₃ × S ₄
(Bee, feather ant) = H ₃₃ × S ₄
(Fly, chironomid) = H ₄₃ × S ₄
(Kochu) ＝ H ₅₃ × S ₄
● The amount and type of insects to attract the peripheral area by installing the lighting fixtures to the point O ₅ is FIG 3 (A)
By referring to the data relating to the mercury lamp shown in Fig. 1, it can be obtained as follows.
(Insect type) = (Insect amount)
(Butterfly moth) = H ₁₃ × S ₅
(Stink bug unka) = H ₂₃ × S ₅
(Bee, feather ant) = H ₃₃ × S ₅
(Fly, chironomid) = H ₄₃ × S ₅
(Kochu) ＝ H ₅₃ × S ₅
● The amount and type of insects to attract the peripheral area by installing the lighting fixtures to the point O _6, as shown in FIG. 3 (A)
By referring to the data relating to the mercury lamp shown in Fig. 1, it can be obtained as follows.
(Insect type) = (Insect amount)
(Chou-ga) = H ₁₃ × S ₆
(Stink bugs) = H ₂₃ × S ₆
(Bee, feather ant) = H ₃₃ × S ₆
(Fly, chironomid) = H ₄₃ × S ₆
(Kochu) = H ₅₃ × S ₆
● The amount and type of insects to attract the peripheral area by installing the lighting fixtures to the point O _7, as shown in FIG. 3 (A)
By referring to the data relating to the mercury lamp shown in Fig. 1, it can be obtained as follows.
(Insect type) = (Insect amount)
(Butterfly moth) = H ₁₂ × S ₇
(Stink bug unka) = H ₂₂ × S ₇
(Bee, feather ant) = H ₃₂ × S ₇
(Fly, chironomid) = H ₄₂ × S ₇
(Kochu) = H ₅₂ × S ₇
● By summing the amount of insects attracted from the surrounding area by each of the lighting fixtures installed at points O _{1 to} O ₇ as described above, the insects attracted from the surrounding area by the lighting fixtures installed in the target area The quantity and type are as follows.
(Insect type) = (Insect amount)
(Butterflies and _{gas) = H 13 × (S 1} + S 2 + S 3 + S 4 + S 5 + S 6) + H 12 × S 7
_{(Hemiptera-planthopper) = H 23 × (S 1} + S 2 + S 3 + S 4 + S 5 + S 6) + H 22 × S 7
(Bee, feather ant) = H ₃₃ × (S ₁ + S ₂ + S ₃ + S ₄ + S ₅ + S ₆ ) + H ₃₂ × S _{7
(Fly-midges) = H 43 × (S 1} + S 2 + S 3 + S 4 + S 5 + S 6) + H 42 × S 7
(Kochu) = H ₅₃ × (S ₁ + S ₂ + S ₃ + S ₄ + S ₅ + S ₆ ) + H ₅₂ × S ₇
As described above, in step S105, the amount and type of insects attracted from the surrounding area by the lighting device can be predicted based on the database shown in FIG. 3 and the parameters set in the step of FIG. .

  Next, in step S305, the amount of power used by the lighting fixture is calculated based on the power database shown in FIG. 4C and the lighting duration set in step S205 in FIG.

  In step S306, it is determined whether or not the variable MMDD has reached (end date). Here, if the determination in step S306 is NO, since it is necessary to continue the calculation, the process proceeds to step S312, the variable MMDD is advanced by one day, and in step S313, based on the setting of step S206 in FIG. Determine the type and amount of insects that will enter the light leakage area. At this time, as described above, an appropriate function or table can be used for the type and amount of insects to be transferred to the light leakage region.

  After step S313, the process proceeds to step S302 and loops. On the other hand, if the determination in step S306 is YES, the calculation for the entire construction period will be completed. Next, in step S307, the amount of power for each day determined in step S305 used by the lighting equipment. To calculate the total power consumption during the construction period. According to such a step, by appropriately changing the light source type parameter, the total power consumption used during the construction period can be obtained for each light source type.

  Next, in step S308, the type and amount of attracted insects during the construction period are calculated by accumulating the types and amounts of attracted insects for each day during the construction period.

  In step S309, the type and total amount of insects calculated in step S308 and the calorie database shown in FIG. 6 are referred to, and the total calories lost due to night lighting during the construction period are calculated.

  In step S310, it is a step of determining how much the predetermined species is affected by the total calories lost by night illumination. For example, a hawk will be described as an example of the predetermined species. FIG. 14 is a diagram for explaining the predation relationship of biological species.

  Insects are preyed on by moths and reptiles, and when moths and reptiles are supplemented by hawks as a model, the total energy required by one hawk per day is converted by insects. Assuming Co, the number of affected hawk wings N can be calculated by dividing the total calories calculated in step S309 by Co × (construction period).

  As described above, in the worm attracting prediction method according to the present invention, it is possible to indicate how many insects corresponding to a specific biological species are lost by night illumination.

  In step S314, the prediction process ends.

  In the insect attracting prediction method according to the present invention, the type and amount of attracted insects for one day predicted in the one-day predicting step are determined from the construction start date set in the construction period setting step to the construction end date. Therefore, according to the insect attracting prediction method according to the present invention, it is possible to grasp the influence on the ecosystem over the construction period by the lighting equipment.

  In addition, the attracting insect prediction method according to the present invention provides a predetermined organism based on the kind and amount of insects accumulated in the accumulation step and the calorie database prepared in the calorie database preparation step. Since there is a species impact determination step for predicting the impact on the species, it is possible to grasp how much the species that are feeding on the insects are affected.

  Several output modes of prediction / calculation results by the worm attracting prediction method according to the present invention as described above are shown. 16 to 19 are diagrams showing output examples of results in the worm attracting prediction method according to the present invention. The following example shows a case in which prediction and calculation of the three plans shown in FIG. 15 are performed over the construction period of 10 days by the insect attracting prediction method according to the present invention.

  FIG. 16 shows an output example of the relative amount of attracted insects when the plan A is set to 1, and the cost of each plan. Further, as shown in the legend, the amount of insects is displayed based on the “mercury lamp”, the amount of insects based on the “sodium lamp”, and the amount of insects based on the “LED”. In addition, FIG. 16 shows the power consumption of each plan.

  FIG. 17 is an example in which the weight of the insect attracted per unit time is output for each plan.

  FIG. 18 shows an example in which the weight of the insect attracted per unit time is output for each plan for each environment type.

  FIG. 19 is an example in which the weight of the insect attracted per unit time is output for each plan for each type of insect.

  According to the output examples of the results shown in FIGS. 16 to 19 as described above, it is possible to visually grasp the results of the prediction / calculation of each plan, which helps to plan an environment-friendly construction plan. can do.

10 ... System bus 11 ... CPU (Central Processing Unit)
12 ... RAM (Random Access Memory)
13 ... ROM (Read Only Memory)
DESCRIPTION OF SYMBOLS 14 ... Communication control part 15 ... Input control part 16 ... Output control part 17 ... External storage device control part 18 ... Input part 19 ... Output part 20 ... External storage device 21 ... Interface unit 21 ... Graphic control unit 22 ... Display device

Claims (6)

A luminaire installed in a target area is a method for predicting insects that quantitatively predicts the type and amount of insects attracted from a peripheral area outside the target area,
Insect database preparation step of preparing an attractant database in which the types and amounts of attracted insects are converted into data per unit time, unit area, and unit light amount for each type of light source and environment,
A temperature database preparation step of preparing a database in which annual temperature transition in the target region is converted into data;
Construction period setting step for setting construction start date and construction end date,
A light source type setting step for setting a type of a light source of a luminaire to be installed in the target area;
A light amount setting step for setting the light amount of the light source set in the light source type setting step;
A light leakage state setting step for setting the area of the light leakage region irradiated to the light leaking at night from the lighting equipment installed in the target region and the type of environment;
A transfer amount setting step for setting the type and amount of insects transferred to the light leakage area during the day;
For each day from the construction start date set in the construction period setting step to the construction end date, the insect attracting database prepared by the insect attracting database preparing step, and the annual temperature change set by the temperature database preparing step The light source type set in the light source type setting step, the light amount set in the light amount setting step, the area and environment type set in the light leakage state setting step, and the transfer amount setting step From the type and amount of insects transferred to the light leakage area, a daily prediction step for predicting one day of the type and amount of insects attracted from the surrounding area by a lighting device;
The cumulative step of accumulating from the construction start date to the construction end date set in the construction period setting step, the type and amount of attracted insects predicted in the one-day prediction step,
A method for predicting worms, comprising: The insect attractant prediction method according to claim 1, wherein the amount of insects that are transferred to the light leakage area during the day, which is set in the transfer amount setting step, decreases every day. The said one-day prediction step predicts that there is no insect attracted from the surrounding area by the lighting device when the temperature set by the temperature database preparation step is a predetermined value or less. The attracting insect prediction method according to claim 2. A calorie database preparation step of preparing a calorie database in which calories per unit amount according to the type of insect are converted into data;
In the accumulation step, the type and amount of attracted insects accumulated,
Based on the calorie database prepared by the calorie database preparation step,
The insect attractant prediction method according to any one of claims 1 to 3, further comprising a biological species effect determination step that predicts an influence on a predetermined biological species. For each type of light source, an electric energy database preparation step for preparing an electric energy database in which electric energy used per unit time is converted into data, and
An electric energy database prepared in the electric energy database preparation step;
5. The power consumption calculation step for calculating the power consumption during the construction period based on the construction start date and the construction end date set in the construction period setting step. 6. The method for predicting insect attracting described in 1. A luminaire installed in a target area is an insect attracting prediction system that quantitatively predicts the type and amount of insects attracted from a peripheral area outside the target area,
For each type of light source and environment type, an insect attracting database in which the types and amounts of attracted insects are converted into data per unit time, unit area, and unit light quantity;
A temperature database in which annual temperature changes in the target area are converted into data;
Construction period setting means for setting the construction start date and construction end date,
A light source type setting means for setting a type of a light source of a luminaire to be installed in the target area;
A light amount setting means for setting the light amount of the light source set by the light source type setting means;
Light leakage status setting means for setting the area of the light leakage region and the type of environment irradiated to the light leaking at night from the lighting equipment installed in the target region,
A transfer amount setting means for setting the type and amount of insects transferred to the light leakage area during the day;
Every day from the construction start date set by the construction period setting means to the construction end date,
The insect database, the temperature database, the type of light source set by the light source type setting means, the light quantity set by the light quantity setting means, the area and environment type set by the light leakage status setting means, 1 day prediction means for predicting the amount of insects attracted from the surrounding area by the lighting equipment and the amount of the insects for one day from the kind and amount of the insects transferred to the light leakage area set by the transfer amount setting means; A cumulative means for accumulating the type and amount of attracted insects for one day predicted by the one-day prediction means from the construction start date to the construction end date set by the construction period setting means; ,
Insect prediction system characterized by comprising

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