Insect attraction prediction method and insect attraction prediction system

Abstract

To accurately predict an insect attraction amount according to seasonal temperature change.SOLUTION: An insect attraction prediction method in an insect attraction prediction system comprises: a temperature acquisition step in which a temperature acquisition unit acquires a temperature for a date corresponding to a prediction target date from a temperature record information storage unit that stores temperature record information indicating the temperature record for each date in past years; and a prediction step in which a prediction unit predicts an insect attraction status for the prediction target date based on insect attraction information corresponding to the temperature acquired in the temperature acquisition step among insect attraction information stored in the insect attraction information storage unit and indicating the status of insects being attracted to illumination in correspondence with a temperature.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to an insect attraction prediction method and an insect attraction prediction system. [Background technology]

[0002] There is known technology that predicts the number of insects that will be attracted to lighting used during construction work during the construction period by utilizing information that indicates the number of insects that will be attracted depending on the type of environment (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2015-130812 A Summary of the Invention [Problem to be solved by the invention]

[0004] The amount of insects attracted to lighting (the amount of insects attracted) varies depending on, for example, temperature changes according to the transition of seasons throughout the year.

[0005] An object of the present invention is to enable accurate prediction of the amount of insects attracted in response to seasonal changes in temperature. [Means for solving the problem]

[0006] (1) One aspect of the present invention that solves the above-mentioned problems is an insect attraction prediction method in an insect attraction prediction system, the method including: a temperature acquisition step in which a temperature acquisition unit acquires the temperature for a date corresponding to a prediction target date from a temperature record information storage unit that stores temperature record information indicating the actual temperature for each date on a yearly basis in the past; and a prediction step in which a prediction unit predicts the insect attraction status on the prediction target date based on the insect attraction information corresponding to the temperature acquired by the temperature acquisition step among the insect attraction information stored in the insect attraction information storage unit and indicating the status of insects being attracted to lighting in response to temperature.

[0007] (2) One aspect of this embodiment is the insect attraction prediction method described in (1), wherein the insect attraction information indicates an attraction amount of insects attracted to the lighting at a corresponding temperature for each specified insect classification, and the prediction step may predict the attraction amount for each insect classification.

[0008] (3) One aspect of this embodiment is the insect attraction prediction method described in (1) or (2), wherein the prediction step may calculate an overall attraction situation by combining the predicted attraction situations for multiple predicted target dates in a certain period of time.

[0009] (4) One aspect of this embodiment is the insect attraction prediction method described in any one of (1) to (3), wherein the insect attraction information indicates, for each type of lighting, the amount of insects attracted to the lighting at a corresponding temperature, and the prediction step may predict the amount of attraction according to a specified type of lighting.

[0010] (5) One aspect of this embodiment is the insect attraction prediction method described in any one of (1) to (4), wherein the temperature corresponding to the insect attraction information and the temperature indicated by the actual temperature information may be the temperature corresponding to a specified time determined based on the time after sunset in a day.

[0011] (6) One aspect of this embodiment is an insect attraction prediction system including a temperature record information storage unit that stores temperature record information indicating the temperature record for each date on a yearly basis in the past; an insect attraction information storage unit that stores insect attraction information indicating the situation in which insects are attracted to lighting in correspondence with temperature; a temperature acquisition unit that acquires the temperature for a date corresponding to a prediction target date from the temperature record information storage unit; and a prediction unit that predicts the insect attraction situation on the prediction target date based on the insect attraction information stored in the insect attraction information storage unit that corresponds to the temperature acquired by the temperature acquisition unit. Effect of the Invention

[0012] According to the present invention, it is possible to obtain an effect of accurately predicting the amount of insects attracted in response to seasonal changes in temperature. [Brief description of the drawings]

[0013] [Figure 1] FIG. 2 is a diagram illustrating an example of a functional configuration of the insect attraction prediction device according to the present embodiment. [Diagram 2] FIG. 2 is a diagram showing an example of construction plan information in the present embodiment. [Diagram 3] FIG. 4 is a diagram showing an example of lighting specification information in the present embodiment. [Figure 4] FIG. 4 is a diagram showing an example of past temperature information in the present embodiment. [Diagram 5] 4 is a diagram showing an example of insect calorie information in the present embodiment. FIG. [Figure 6] FIG. 4 is a diagram showing an example of insect attracting information in the present embodiment. [Figure 7] 10 is a flowchart showing an example of a processing procedure executed by the insect attraction prediction device in the present embodiment in response to prediction of an insect attraction state and prediction of an impact on a biological species based on the predicted insect attraction state. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] 1 shows an example of the functional configuration of an insect attraction prediction device 100 according to this embodiment. The insect attraction prediction device 100 according to this embodiment predicts a situation in which insects are attracted by lighting fixtures used at a construction site (insect attraction situation). The insect attraction prediction device 100 according to this embodiment also predicts the impact on a specific biological species according to the predicted insect attraction situation. The function of the insect attraction prediction device 100 in the figure is realized by causing a CPU (Central Processing Unit) provided in the insect attraction prediction device 100 as hardware to execute a program. The insect attraction prediction device 100 in the figure includes a user interface unit 101, a control unit 102, and a storage unit 103.

[0015] The user interface unit 101 is a part that realizes a user interface function by a device corresponding to a user interface. The hardware corresponding to the user interface unit 101 may include, for example, input devices such as a mouse and a keyboard, and output devices such as a display device. The hardware corresponding to the user interface unit 101 may also include a touch panel that allows operations on the display surface of a display device.

[0016] The control unit 102 executes various controls in the insect attraction prediction device 100. The control unit 102 includes a temperature acquisition unit 121 and a prediction unit 122.

[0017] The temperature acquisition unit 121 acquires, from the actual temperature information storage unit 133, the temperature that corresponds to the date to be predicted (prediction target date). The prediction unit 122 predicts the insect attraction state on the prediction target day by using the insect attraction information associated with the temperature acquired by the temperature acquisition unit 121. The prediction unit 122 acquires the insect attraction information associated with the temperature acquired by the temperature acquisition unit 121 from the insect attraction information storage unit 135. Furthermore, the prediction unit 122 estimates the impact on a particular biological species during the construction period by using the prediction result of the insect attraction state on the prediction target date.

[0018] The storage unit 103 stores various information corresponding to the insect attraction prediction device 100. The storage unit 103 includes a construction plan information storage unit 131, a lighting specification information storage unit 132, a performance temperature information storage unit 133, an insect calorie information storage unit 134, an insect attraction information storage unit 135, and a prediction result storage unit 136.

[0019] The construction plan information storage unit 131 stores information (construction plan information) relating to the plan of the construction work (target construction work) for which the insect attraction state is predicted.

[0020] An example of construction plan information is shown in Fig. 2. The construction plan information in this figure includes fields for the construction schedule and lighting usage information. The construction period field indicates the date on which the construction work will be carried out. The lighting usage information is information about the lighting fixtures used in the target construction work. The lighting usage information includes fields for the type of light source, the lighting time, and the number of uses. The light source type field stores information on the type of light source of the lighting fixtures used in the target construction (light source type: for example, mercury lamp, sodium lamp, LED, etc.). When a single light source type is used during the construction period, the light source type field may store that light source type. On the other hand, when there is a plan to use lighting fixtures with multiple light source types during the construction period, for example, information on the light source type used in association with each day or each hour of the day may be stored. The lighting duration field stores the duration for which the lights are turned on (used) during the target construction work for each day. The light source type field may store the lighting duration planned for each day, for example, if the lighting duration is planned to vary from day to day during the construction period. The field for the number of uses stores the number of lights used in the target construction work. The field for the type of light source may store the number of uses associated with each date or time period, for example, in the case of a plan in which the number of uses varies depending on the date, time period, etc. during the construction period. The construction plan information may store information indicating the location (construction site) where the target construction work will be carried out.

[0021] Returning to Fig. 1 for explanation, the lighting specification information storage unit 132 stores lighting specification information. The lighting specification information is information indicating predetermined specifications (characteristics) for each light source type of a lighting fixture.

[0022] Fig. 3 shows an example of lighting specification information. The lighting specification information in the figure stores the light intensity and power consumption for each of three light source types: "mercury lamp", "sodium lamp", and "LED". The light intensity and power consumption may each be a value corresponding to one lighting fixture.

[0023] Returning to Fig. 1 for explanation, the actual temperature information storage unit 133 stores actual temperature information. The actual temperature information is information indicating a temperature (actual temperature) derived based on a history (actual results) of temperatures measured on a daily basis in the past. FIG. 4 is an example of the recorded temperature information. The recorded temperature information in the figure shows the recorded temperature for each date in one year (yearly unit). The recorded temperature in the recorded temperature information may be, for example, the temperature recorded for each date in a year corresponding to last year, or may be based on the statistics of the temperature for each date in a predetermined number of years in the past. The method for obtaining the statistics of the temperature for each date is not particularly limited and may include, for example, the average value, the mode, normal distribution, etc. Furthermore, the amount of insects attracted by lighting occurs after sunset, which is immediately after sunset when the temperature is highest after sunset. Therefore, the recorded temperature indicated in the recorded temperature information may be calculated based on temperatures measured at a time or time period determined in the past to correspond to immediately after sunset. In addition, it is preferable that the past temperature information is created based on temperatures measured at the location where the construction work to be predicted is carried out.

[0024] The insect calorie information storage unit 134 stores insect calorie information. The insect calorie information is information indicating, for each insect classification, the calories of insects in the corresponding classification.

[0025] An example of insect calorie information is shown in Figure 5. The insect calorie information in this figure stores information on calories per unit weight for five insect types: butterflies and moths, stink bugs and planthoppers, bees and winged ants, flies and midges, and crickets.

[0026] Returning to Fig. 1, the insect attracting information storage unit 135 stores the insect attracting information. The amount of insects attracted to lighting (insect attraction amount) changes according to changes in temperature throughout the year. The insect attraction information of this embodiment is information that indicates the amount of insects attracted in each temperature category that changes throughout the year. The insect attraction information of this embodiment also indicates the amount of insects attracted corresponding to each classification of a specific insect. The insect attraction information of this embodiment also indicates the amount of insects attracted corresponding to each type of light source.

[0027] Fig. 6(A), Fig. 6(B), and Fig. 6(C) are insect attracting information for each light source type stored in the insect attracting information storage unit 135. Fig. 6(A) is the insect attracting information when the light source type is a mercury lamp. Fig. 6(B) is the insect attracting information when the light source type is a sodium lamp. Fig. 6(C) is the insect attracting information when the light source type is an LED (Light Emitting Diode). Insect attracting information corresponding to one light source type divides annual temperature changes of 10°C or more into multiple (seven in the figure) temperature ranges, and indicates the amount of insects attracted by a given insect classification for each divided temperature range. In the figure, five insect classifications are shown as examples: butterflies and moths, stink bugs and planthoppers, bees and winged ants, flies and midges, and gnats. For example, in the insect attracting information for a mercury lamp in the light source type of Figure 6(A), the amount of insects attracted by each temperature range corresponding to each insect classification is shown as T11 to T17, T21 to T27, T31 to T47, T41 to T47, and T51 to T57.

[0028] The amount of insects attracted indicates the amount of insects attracted to the lighting under a combination of a given unit volume, unit time, and unit light amount. The amount of insects attracted may indicate the weight of the insects, or may indicate the number of insects.

[0029] In each of Figures 6(A), 6(B), and 6(C), the seven temperature categories correspond to one of three periods: pre-peak, peak, and post-peak. Each of the pre-peak, peak, and post-peak periods is not fixedly associated with a specific date, but for example, in the recorded temperature information (Figure 4), the pre-peak period is the period before the recorded temperature exceeds 23°C, the peak period is the period with the recorded temperature between 23°C and 25°C, and the post-peak period is the period after the peak period with the recorded temperature below 25°C.

[0030] In this case, there are temperature categories that include the same temperatures in the pre-peak and post-peak periods. Specifically, for example, the temperature category of 17°C to 23°C, which corresponds to the pre-peak period, and the temperature category of 22°C to 14°C, which corresponds to the post-peak period, share the range of 17°C to 22°C. For example, the insect attracting information may be structured to indicate the amount of insects attracted by each insect classification simply by corresponding to each temperature division, without corresponding to the season. However, the amount of insects attracted by each insect classification may differ between the period close to spring before the temperature peak and the period close to autumn after the peak, even if the temperature is the same. In other words, the amount of insects attracted by each insect classification may differ depending on the corresponding season, even if the temperature is the same. Therefore, by corresponding the temperature divisions to the pre-peak, peak, and post-peak periods, as in this embodiment, it is possible to accurately respond to the difference in the insect attracting situation according to the season, and the accuracy of the prediction result can be improved. The period for each temperature category may be set to be divided into two periods, for example, early and late.

[0031] Returning to Fig. 1 for the explanation, the prediction result storage unit 136 stores the insect attraction status for each target day predicted by the prediction unit 122. The prediction result storage unit 136 may also store the overall insect attraction status corresponding to the construction period predicted by the prediction unit 122 based on the insect attraction status for each target day, prediction results regarding the impact on biological species, and the like.

[0032] An example of a processing procedure executed by the insect attraction prediction device 100 in response to prediction of an insect attraction state and prediction of an impact on a biological species based on the predicted insect attraction state will be described with reference to the flowchart in FIG.

[0033] Step S100: The temperature acquisition unit 121 acquires the construction start date by referring to the information on the construction period stored in the construction plan information. In the insect attraction prediction device 100, the temperature acquisition unit 121 sets the construction start date as a target date to be predicted during the construction period.

[0034] Step S102: The temperature acquisition unit 121 acquires, from the achieved temperature information storage unit 133, the achieved temperature associated with the date set as the target day in step S100 or in step S116 described below.

[0035] Step S104: The prediction unit 122 determines whether the actual temperature acquired in step S102 is outside the prediction target. Specifically, an actual temperature below 10° C. is outside the prediction target. That is, in this embodiment, in an environment with a temperature below 10° C., it is assumed that the lighting does not attract insects.

[0036] Step S106: If it is determined that the actual temperature acquired in step S104 is not outside the scope of the prediction, the prediction unit 122 identifies the light source type of the lighting fixture to be used on the target day in the construction work that is the subject of this prediction by referring to the light source type in the construction plan information.

[0037] Step S108: The prediction unit 122 acquires, from the insect attracting information storage unit 135, the insect attracting information associated with the light source type identified in step S106.

[0038] Step S110: The prediction unit 122 predicts the insect attraction status on the target day based on the insect attraction information acquired in step S108. Specifically, the prediction unit 122 predicts the amount of insects attracted for each insect classification ("butterflies and moths," "stink bugs and planthoppers," "bees and winged ants," "flies and midges," and "butterflies") as the insect attraction status on the target day. In this case, the prediction unit 122 may directly use the amount of insects attracted for each insect classification shown in the insect attraction information acquired in step S108 as the predicted result of the insect attraction status. Alternatively, the prediction unit 122 may specify the amount of light from the light source of the lighting used in the target construction work by referring to the lighting specification information storage unit 132. Then, the prediction unit 122 may calculate the amount of insects attracted according to the amount of light from the light source of the lighting used in the construction work by multiplying the amount of light by a coefficient based on the ratio between the specified amount of light and the unit amount of light corresponding to the insect attracting information. In addition, the prediction unit 122 may modify the amount of insects attracted for each insect classification indicated in the acquired insect attracting information, for example by multiplying it by a coefficient based on the environment of the construction site, and use the modified result as a prediction result of the insect attraction situation.

[0039] Step S112: If it is determined in step S104 that the actual temperature is outside the range of the prediction, the prediction unit 122 predicts that the amount of attracted insects is zero for all insect classifications as the insect attraction state.

[0040] Step S114: The prediction unit 122 stores the prediction result of the insect attraction state in step S110 or step S112 in the prediction result storage unit 136 in association with the date of the target day.

[0041] Step S116: The prediction unit 122 may also calculate the amount of power consumption of the lighting fixtures to be used for the construction work on the target day. Specifically, the prediction unit 122 identifies the light source type, lighting time, and number of uses of the lighting fixtures to be used on the target day, with reference to the construction plan information stored in the construction plan information storage unit 131. The prediction unit 122 identifies the power consumption corresponding to the light source type of the lighting fixture to be used, with reference to the lighting specification information stored in the lighting specification information storage unit 132. The prediction unit 122 calculates the power consumption according to the use of the lighting fixtures on the target day, using the identified lighting time, number of uses, and power consumption.

[0042] Step S118: The prediction unit 122 may store the calculation result of the power consumption amount in step S116 in the prediction result storage unit 136 in association with the target date.

[0043] Step S120: The prediction unit 122 determines whether or not the currently set target date is the construction end date.

[0044] Step S122: If it is determined in step S120 that the target date is not the construction end date, the prediction unit 122 sets the date of the next working day for the target date, and returns the process to step S102.

[0045] Step S124: As a process when it is determined in step S120 that the currently set target date is the construction end date, the prediction unit 122 may calculate the total power consumption corresponding to the construction period of the target construction. In this case, the prediction unit 122 may calculate the total power consumption by accumulating the power consumption for each day in the construction period stored in the prediction result storage unit 136 in step S116. In addition, the prediction unit 122 may store the calculated total power consumption in the prediction result storage unit 136.

[0046] Step S126: The prediction unit 122 may also predict a comprehensive insect attraction situation (comprehensive insect attraction situation) corresponding to the construction period of the target construction work. In this case, the prediction unit 122 may output the prediction result of the comprehensive insect attraction situation by performing a process of integrating the prediction results of the insect attraction situation for each day during the construction period stored in the prediction result storage unit 136. The prediction result of the comprehensive insect attraction situation may be the amount of insects attracted by each type during the construction period.

[0047] Step S128: The prediction unit 122 may also predict the impact on a specific biological species caused by the insects being attracted by the lighting fixtures used in the target construction work, using the prediction result of the overall insect attraction status obtained in step S126. For this purpose, the prediction unit 122 calculates the calorie loss. In this embodiment, insects attracted to the light of the lighting fixture are regarded as being excluded from the prey of higher-ranking predators in the food chain that includes insects. The lost calories indicate calories lost by predators due to insects being attracted to the light of the lighting fixture during the construction period. The prediction unit 122 may calculate the lost calories using the total amount of insects attracted by each type of insect shown in the total insect attraction status and the calories of the corresponding type of insect.

[0048] Step S130: The prediction unit 122 predicts the impact on a specific biological species caused by insects being attracted to the light of the lighting fixture during the construction period of the target construction based on the lost calories calculated in step S128. The specific biological species to be predicted is not particularly limited as long as it is a biological species that is higher than insects in the food chain. As an example, the prediction unit 122 calculates the total amount of attracted insects during the construction period by adding up the amount of attracted insects for each insect type shown in the total insect attraction status. The prediction unit 122 multiplies the calories required per day for each specific biological species by the number of construction days in the construction period to calculate the calories required by the specific biological species corresponding to the construction period (required calories). The prediction unit 122 may predict the value obtained by dividing the total amount of attracted insects by the required calories as the number of specific biological species affected during the construction period. The prediction unit 122 may store the prediction result by step S130 in the prediction result storage unit 136.

[0049] The prediction results obtained in step S126 or step S130 may be output in the form of display, printing, or the like on the user interface unit 101 under the control of the control unit 102. Such output of the prediction results may be performed in response to a user operation. 7 may be started in response to, for example, a user operating the user interface unit 101 to input construction plan information (FIG. 2).

[0050] As can be understood from the above description, the insect attraction prediction device 100 of this embodiment is configured to predict the insect attraction state using the insect attraction amount information indicating the amount of insects attracted according to the temperature with the change of the season of the year. With such a configuration, this embodiment can accurately predict the amount of insects attracted taking into account the change of the temperature with the transition of the seasons.

[0051] In addition, depending on the surrounding environment of the target construction site, the amount of insects attracted by each insect type may differ even under the same light source type, light amount, etc. Therefore, for example, the insect attracting information may further reflect the amount of insects attracted according to the type of surrounding environment. Then, the prediction unit 122 may obtain the insect attracting information corresponding to the combination of the light source type and the surrounding environment of the target construction site, and predict the insect attracting situation in step S110.

[0052] In the above example, the case where construction work is the target is taken as an example, but the configuration in the above embodiment may also be applied to a case where lighting equipment is used for an outdoor event, for example.

[0053] The function of the insect attraction prediction device 100 may be configured as an insect attraction prediction system distributed among a plurality of devices or servers. For example, the insect attraction prediction device 100 may be distributed among a user terminal having a user interface unit 101 and a server having a control unit 102 and a storage unit 103. In this case, the function may be distributed among a server having the function of the control unit 102 and a server having the function of the storage unit 103.

[0054] In addition, a program for realizing the function of the insect attracting prediction device 100 described above may be recorded in a computer-readable recording medium, and the program recorded in the recording medium may be read into a computer system and executed to perform the processing of the insect attracting prediction device 100 described above. Here, "reading a program recorded in a recording medium into a computer system and executing it" includes installing the program into a computer system. The "computer system" here includes hardware such as an OS and peripheral devices. In addition, the "computer system" may include a plurality of computer devices connected via a network including the Internet, a WAN, a LAN, a dedicated line, and other communication lines. In addition, the "computer-readable recording medium" refers to a portable medium such as a flexible disk, an optical magnetic disk, a ROM, a CD-ROM, and a storage device such as a hard disk built into a computer system. In this way, the recording medium storing the program may be a non-transient recording medium such as a CD-ROM. In addition, the recording medium also includes a recording medium provided inside or outside the computer system that can be accessed from a distribution server in order to distribute the program. The code of the program stored in the recording medium of the distribution server may be different from the code of the program in a format executable by a terminal device. That is, as long as it can be downloaded from a distribution server and installed in a form executable by a terminal device, the format in which it is stored in the distribution server does not matter. The program may be divided into multiple parts, downloaded at different times, and then combined on the terminal device, or each of the divided programs may be distributed by a different distribution server. Furthermore, the "computer-readable recording medium" includes a program that holds a program for a certain period of time, such as a volatile memory (RAM) inside a computer system that becomes a server or a client when a program is transmitted via a network. The program may also be a program for realizing part of the above-mentioned functions. Furthermore, the program may be a so-called difference file (difference program) that can realize the above-mentioned functions in combination with a program already recorded in the computer system.

[0055] The "Sustainable Development Goals (SDGs)" are 17 international goals that were adopted at the United Nations Summit in September 2015. The insect attraction prediction device and insect attraction prediction system according to the present embodiment can contribute to the achievement of the 17 SDGs, for example, goal 15, "Protect and preserve the richness of land." [Explanation of symbols]

[0056] 100 Insect attraction prediction device, 101 User interface unit, 102 Control unit, 103 Memory unit, 121 Temperature acquisition unit, 122 Prediction unit, 131 Construction plan information memory unit, 132 Lighting specification information memory unit, 133 Actual temperature information memory unit, 134 Insect calorie information memory unit, 135 Insect attraction information memory unit, 136 Prediction result memory unit

Claims

1. An insect attraction prediction method in an insect attraction prediction system, comprising: a temperature acquisition step in which a temperature acquisition unit acquires the temperature on a date corresponding to the prediction target date from a temperature record information storage unit that stores temperature record information indicating the temperature record for each date on a yearly basis in the past; a prediction step of predicting an insect attraction state on the prediction target day based on the insect attraction information corresponding to the air temperature acquired in the air temperature acquisition step among the insect attraction information stored in the insect attraction information storage unit and indicating the state in which insects are attracted to the lighting in correspondence with air temperature; The method includes:

2. The insect attraction information indicates an amount of insects attracted to the lighting at a corresponding temperature for each predetermined insect classification, The prediction step predicts the amount of attraction for each insect classification. The method for predicting insect attraction according to claim 1.

3. The prediction step calculates a total attraction situation by integrating the attraction situations predicted for each of a plurality of predicted target dates in a certain period. The method for predicting insect attraction according to claim 1 or 2.

4. The insect attraction information indicates, for each type of lighting, the amount of insects attracted to the lighting at a corresponding temperature; The prediction step predicts an attraction amount according to a specified type of lighting. The method for predicting insect attraction according to claim 1 or 2.

5. The temperature corresponding to the insect attracting information and the temperature indicated by the temperature record information are temperatures corresponding to a predetermined time determined based on the time after sunset in a day. The method for predicting insect attraction according to claim 1 or 2.

6. a temperature record information storage unit that stores temperature record information indicating the temperature record for each date on a yearly basis in the past; an insect attracting information storage unit that stores insect attracting information that indicates the situation in which insects are attracted to the lighting in correspondence with air temperature; a temperature acquisition unit that acquires the temperature on a date corresponding to the prediction target date from a temperature record information storage unit; a prediction unit that predicts an insect attraction state on the prediction target day based on the insect attraction information corresponding to the temperature acquired by the temperature acquisition unit among the insect attraction information stored in the insect attraction information storage unit; An insect attraction prediction system.

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