JP2023143539A - information processing system - Google Patents

Abstract

To provide an information processing system for supporting the effective use of observation means of an event.SOLUTION: An information processing system comprises a trigger information generation server 10, a selector management system server 20, and a user terminal 30. A control section 21 of the selector management system server 20 includes a trigger information acquisition section 211, a constraint information acquisition section 212, and an index calculation section 213. The trigger information acquisition section 211 acquires trigger information indicating a region and period in which an event requiring observation occurs. The constraint information acquisition section 212 acquires constraint information indicating at least geographical and temporal constraints in the observation of observation means capable of observing an event. The index calculation section 213 calculates an index indicating evaluation of a candidate of an actual measurement range to be used for actual observation among observable ranges in a geographical range and a temporal range, under constraints indicated by the acquired constraint information on the basis of the acquired trigger information.SELECTED DRAWING: Figure 4

Description

The present invention relates to an information processing system.

Patent Document 1 discloses that, for each of a plurality of users, a user with a higher priority for using an artificial satellite has a larger usage allowance, and a request amount indicating the amount of resources that each of the plurality of users requests to use is calculated. A technique is disclosed in which a value corresponding to the difference between the requested amount and the allowed usage amount of a first user whose requested amount is below the allowed amount is added to the allowed usage amount of a second user whose requested amount exceeds the allowed usage amount. ing.

Patent No. 6362529

Observation means for observing disaster-related events, such as observational satellites, may be limited in their observable periods and regions, so it is desirable to use them effectively.

In view of the above circumstances, the present invention supports the effective use of event observation means.

(1) According to one aspect of the present invention, an information processing system is provided. This information processing system includes a first acquisition section, a second acquisition section, and a calculation section. The first acquisition unit acquires trigger information indicating the region and time when an event requiring observation occurs. The second acquisition unit acquires constraint information indicating observational constraints of an observation means capable of observing an event, and the constraint information indicates at least geographical and temporal constraints. Based on the acquired trigger information, the calculation unit calculates an index indicating the evaluation of a candidate for an actual measurement range to be used in actual observation among the observable ranges under the constraints indicated by the acquired constraint information, and The range is a geographical range and a temporal range.

According to this aspect, it is possible to support effective use of the event observation means.

1 is a diagram showing the overall configuration of an event observation system 1. FIG. 1 is a diagram showing a hardware configuration of a trigger information generation server 10. FIG. 3 is a diagram showing a hardware configuration of a user terminal 30. FIG. FIG. 3 is a diagram showing the functional configuration of a control section of each device. FIG. 3 is an activity diagram showing an example of observation request processing. It is a figure showing an example of generated trigger information. It is a figure showing an example of acquired constraint information. It is a figure showing an example of a point table.

Embodiments of the present invention will be described below with reference to the drawings. Various features shown in the embodiments described below can be combined with each other.

By the way, the program for implementing the software appearing in this embodiment may be provided as a non-transitory computer-readable recording medium, or may be downloaded from an external server. The program may be provided in a manner that allows the program to be started on an external computer and the function thereof is realized on the client terminal (so-called cloud computing).

Furthermore, in this embodiment, the term "unit" may include, for example, a combination of hardware resources implemented by circuits in a broad sense and software information processing that can be concretely implemented by these hardware resources. . In addition, various types of information are handled in this embodiment, and these information include, for example, the physical value of a signal value representing voltage and current, and the signal value as a binary bit collection consisting of 0 or 1. It is expressed by high and low levels or quantum superposition (so-called quantum bits), and communication and calculations can be performed on circuits in a broad sense.

Further, a circuit in a broad sense is a circuit realized by at least appropriately combining a circuit, a circuit, a processor, a memory, and the like. That is, application specific integrated circuits (ASIC), programmable logic devices (for example, simple programmable logic devices (SPLD), complex programmable logic devices (COMPLEX), etc. Programmable Logic Device (CPLD), and field This includes a field programmable gate array (FPGA) and the like.

1. Hardware Configuration This section describes the hardware configuration of the event observation system according to this embodiment.

FIG. 1 is a diagram showing the overall configuration of an event observation system 1. As shown in FIG. The event observation system 1 is a system that provides a function of observing disaster-related events, and is an example of an "information processing system." The event observation system 1 includes a communication line 2, a plurality of observation satellites 3 (only observation satellites 3-1 and 3-2 are shown), and a plurality of satellite control systems 4 (satellite control systems 4-1 and 4-). 2 is shown), a trigger information generation server 10, a selector management system server 20, and a user terminal 30.

The communication line 2 includes the Internet, etc., and mediates the exchange of data between devices connected to the communication line. A trigger information generation server 10 , a selector management system server 20 , a user terminal 30 , and a satellite control system 4 are connected to the communication line 2 . The observation satellite 3 is an artificial satellite equipped with a sensor for observing a predetermined event. The predetermined event is, for example, a typhoon, heavy rain, landslide, flood, river water rise, eruption, ash fall, earthquake, tsunami, or the like. The satellite control system 4 is a system that controls the operation of the observation satellite 3 and receives observation data from the observation satellite 3.

The trigger information generation server 10 acquires observation information B1 and prediction information C1 and generates trigger information. Trigger information is, for example, information indicating the region and time when an event requiring observation occurs. Observation information B1 and prediction information C1 are provided by an organization that has a function of observing predetermined events. Such observation organizations include, for example, the Japan Meteorological Agency, the Ministry of Land, Infrastructure, Transport and Tourism, and the National Research Institute for Earth Science and Disaster Prevention. Note that the observation institution is not limited to a public institution, and may be a private institution. The trigger information generation server 10 supplies the generated trigger information to the selector management system server 20.

The selector management system server 20 executes processing for selecting the observation satellite 3 to be used for observation based on the trigger information supplied from the trigger information generation server 10 and the constraint information D1. The constraint information D1 is information indicating observation constraints of an observation means that can observe a predetermined event. Observational constraints are, for example, geographical and temporal constraints. The user terminal 30 is a terminal operated by a user who uses the event observation system 1.

FIG. 2 is a diagram showing the hardware configuration of the trigger information generation server 10. The trigger information generation server 10 includes a control section 11 , a storage section 12 , a communication section 13 , and a bus 14 . The bus 14 electrically connects each part of the trigger information generation server 10.

(Control unit 11)
The control unit 11 is, for example, a central processing unit (CPU) not shown. The control unit 11 implements various functions related to the event observation system 1 by reading predetermined programs stored in the storage unit 12. That is, information processing by software stored in the storage unit 12 is specifically implemented by the control unit 11, which is an example of hardware, so that it can be executed as each functional unit included in the control unit 11. These will be explained in more detail in the next section. Note that the control section 11 is not limited to a single control section, and may be implemented so as to have a plurality of control sections 11 for each function. It may also be a combination thereof.

(Storage unit 12)
The storage unit 12 stores various information defined by the above description. This may be used, for example, as a storage device such as a solid state drive (SSD) that stores various programs related to the event observation system 1 executed by the control unit 11, or as a temporary storage device related to program calculations. It can be implemented as a memory such as a random access memory (RAM) that stores information (arguments, arrays, etc.) necessary for the process. The storage unit 12 stores various programs, variables, etc. related to the event observation system 1 executed by the control unit 11.

(Communication Department 13)
The communication unit 13 is configured to be able to transmit various electrical signals from the trigger information generation server 10 to external components. Furthermore, the communication unit 13 is configured to be able to receive various electrical signals from external components to the trigger information generation server 10. More preferably, the communication unit 13 has a network communication function, so that various information can be communicated between the trigger information generation server 10 and external devices via the communication line 2.

The selector management system server 20 has the same hardware configuration as the trigger information generation server 10. Note that the control unit of the selector management system server 20 will be described with the reference numeral ``control unit 21'' in order to distinguish it from the control unit 11 of the trigger information generation server 10.

FIG. 3 is a diagram showing the hardware configuration of the user terminal 30. The user terminal 30 includes a control section 31 , a storage section 32 , a communication section 33 , an input section 34 , an output section 35 , and a bus 36 . The bus 36 electrically connects each part of the user terminal 30.

(Input section 34)
The input unit 34 includes keys, buttons, a touch screen, a mouse, etc., and accepts input from the user.
(Output section 35)
The output unit 35 includes a display, a speaker, and the like, displays images on a display surface, and outputs sounds including audio.

2. Functional Configuration This section describes the functional configuration of this embodiment. As described above, the information processing by the software stored in the storage section of each device is specifically implemented by the control section, which is an example of hardware, so that each functional section included in the control section can be executed.

FIG. 4 is a diagram showing the functional configuration of the control section of each device. The control unit 11 of the trigger information generation server 10 includes an observation information acquisition unit 111, a prediction information acquisition unit 112, a trigger information generation unit 113, and a trigger information output unit 114. The control unit 21 of the selector management system server 20 includes a trigger information acquisition unit 211, a constraint information acquisition unit 212, an index calculation unit 213, an index output unit 214, an observation means determination unit 215, and an observation request unit 216. Be prepared. The control unit 31 of the user terminal 30 includes an index display unit 311, a decision reception unit 312, and a result notification unit 313.

The observation information acquisition unit 111 acquires observation information that is information that has already been observed regarding a predetermined event. The observation information includes, for example, precipitation (unit time, cumulative), wind speed (maximum, average), river water level, seismic intensity, and the like. The prediction information acquisition unit 112 acquires prediction information that is information predicted by an external organization or system regarding a predetermined event. Forecast information includes, for example, a precipitation forecast, a wind speed forecast, a typhoon course forecast, a probability that the typhoon will enter a stormy region, a river flow forecast, a flood forecast, and a tsunami forecast.

The trigger information generation unit 113 generates the above-mentioned trigger information, that is, information indicating the region and time when an event requiring observation occurs, based on the acquired observation information B1 and prediction information C1. Details of the trigger information generation method will be explained later. The trigger information output unit 114 outputs the generated trigger information to the selector management system server 20.

The trigger information acquisition unit 211 acquires trigger information output from the trigger information generation server 10. The trigger information acquisition section 211 is an example of the "first acquisition section" of the present invention. The constraint information acquisition unit 212 acquires constraint information D1. The constraint information D1 indicates at least geographical and temporal constraints on observation by the observation satellite 3. The constraint information acquisition section 212 is an example of the "second acquisition section" of the present invention. Details of the constraint information D1 will be explained later.

The index calculation unit 213 calculates an index (hereinafter referred to as "evaluation index") indicating the evaluation of a candidate for an actual measurement range used in actual observation among the ranges observable by the observation satellite 3. The observable range here is a geographical range and a temporal range. Specifically, based on the trigger information acquired by the trigger information acquisition unit 211, the index calculation unit 213 calculates the evaluation index in the observable range under the constraint indicated by the constraint information D1 acquired by the constraint information acquisition unit 212. calculate. The index calculation unit 213 is an example of the “calculation unit” of the present invention. Details of the method for calculating the evaluation index will be explained later.

The index output unit 214 outputs the calculated evaluation index. The index output unit 214 outputs the evaluation index to a first output destination or a second output destination. The first output destination is the observation means determination unit 215. The observation means determination unit 215 determines the observation means (the observation satellite 3 in this embodiment) to be used for observation based on the output evaluation index. The observation means determining unit 215 notifies the observation requesting unit 216 of the determined observation means. The observation request unit 216 requests the notified observation means to perform observation. The observation request unit 216 requests observation by transmitting request data indicating the observation request to the satellite control system 4.

The second output destination is the user terminal 30. The index display unit 311 of the user terminal 30 displays the output evaluation index. The decision accepting unit 312 accepts an operation by a user who has viewed the displayed evaluation index to determine the observation means to be used for observation. The result notification unit 313 transmits result data indicating the determination result of the observation means by the user to the selector management system server 20. The observation request unit 216 requests the observation means indicated by the transmitted result data to perform observation. As described above, in the event observation system 1, the event observation system 1 itself can determine the observation means, or the user can determine the observation means.

3. Information Processing In this section, information processing of this embodiment will be explained. The event observation system 1 executes an observation request process of requesting observation to an observation means.

FIG. 5 is an activity diagram showing an example of observation request processing. The observation request process is repeatedly executed at predetermined time intervals, for example. The predetermined time interval is, for example, every day, every half day, every few hours, or any other interval that allows the request to be changed in response to changes in the situation of the event to be observed. determined.

First, in A11, the trigger information generation server 10 acquires observation information B1 through the observation information acquisition section 111, and acquires prediction information C1 through the prediction information acquisition section 112. Next, in A12, the trigger information generation server 10 causes the trigger information generation unit 113 to generate trigger information based on the acquired observation information B1 and prediction information C1. In this embodiment, the trigger information generation unit 113 generates, as trigger information, information indicating the region and time when an event requiring observation occurs, as well as the priority of observation of the event.

FIG. 6 is a diagram showing an example of generated trigger information. In FIG. 6, trigger information E101 is shown when the event requiring observation is a flood caused by river flooding. In recent years, methods for predicting river water levels, flood areas, etc. have been developed. The prediction information acquisition unit 112 acquires prediction information C1 indicating river water level, flooded area, etc. from a business operator that performs such prediction. Based on the acquired prediction information C1, the trigger information generation unit 113 determines the area where an event requiring observation is predicted to occur (hereinafter referred to as a "predicted area") and the time when the event is predicted to occur. Information indicating a time period (hereinafter referred to as "predicted time period"; in the example of FIG. 6, predicted time period T01) is generated as trigger information E101.

As shown in FIG. 6, the trigger information generation unit 113 generates, as trigger information E101, information indicating on a map the geographical range of each predicted area and the observation priority of each predicted area. In the trigger information E101, the first priority area F1 (encircled by a solid line in FIG. 6) is the predicted area with the highest observation priority, and the predicted area with the second highest observation priority. The second priority area F2 (encircled by a broken line in Figure 6) and the third priority area F3 (encircled by a two-dot chain line in Figure 6), which is the predicted area with the third highest observation priority. It is shown. Hereinafter, if these are not distinguished, they will be referred to as "observation priority area F."

The first priority area F1 includes first priority areas F11, F12, F13, F14, F15, and F16. The second priority area F2 includes second priority areas F21, F22, and F23. The third priority area F3 includes third priority areas F31, F32, F33, and F34. The first priority area F1 is an area scattered from northern Kanto to the southern part of the Tohoku region. The second priority area F2 is an area that extends across Gunma, Saitama, Yamanashi, and Shizuoka. The third priority area F3 is an area that extends to the Hokushinetsu region, Iwate, and Akita.

Each priority area is a generally rectangular-shaped area bounded by a border line along the latitude lines and a border line along the longitude lines. Specifically, each priority area is an area in which roughly square-shaped areas surrounded by boundaries along the latitude lines and boundaries along the longitude lines are arranged vertically and horizontally. This square area will be referred to as a "unit area" below. In the event observation system 1, the location and identification information of unit areas existing in the area to be observed (for example, Japan) are determined in association with each other, and based on this unit area, the positional relationship between areas and the area are determined. Such judgments are made.

The trigger information generation unit 113 identifies an area with a high risk of flooding in the predicted time period T01, for example, based on the river water level indicated by the observation information B1 and the flood forecast indicated by the forecast information C1. For example, the trigger information generation unit 113 divides a river flowing through an area to be observed (all over Japan in this embodiment) into one or more areas. Then, the trigger information generation unit 113 generates, for each divided area, a value obtained by multiplying the ratio of the current river water level to the normal river water level and the risk of flood occurrence indicated by the flood forecast by respective coefficients. The total value is calculated as flood risk.

For example, the trigger information generation unit 113 identifies a unit area including a river basin where the calculated flood risk is greater than or equal to a threshold Th1 and less than a threshold Th2, and designates a rectangular area circumscribing the identified unit area as a third priority area. Specify as F3. Similarly, the trigger information generation unit 113 identifies an area where the flood risk is equal to or higher than the threshold Th2 and less than the threshold Th3 as the second priority area F2, and identifies the area where the flood risk is the highest, equal to or higher than the threshold Th3, as the first priority area F1. Specify as.

The trigger information generation unit 113 identifies the observation priority area F described above for a plurality of predicted time periods included in the period from the occurrence of the event to be observed until its convergence. The trigger information generation unit 113 generates, as trigger information, information that associates the observation priority area F identified in this manner with the predicted time period in which the observation priority area F was identified. Trigger information E101 shown in FIG. 6 is information associated with the predicted time period T01. The trigger information generation unit 113 generates trigger information E1 including trigger information E101 and trigger information associated with other observation time periods.

In the above example, the event that requires observation is a flood, an event that is predicted to occur. Then, the trigger information generation unit 113 generates, as trigger information, information indicating that the higher the probability of occurrence of an event, the higher the priority. For example, the probability of flood occurrence in the first priority area F1 is higher than the probability of flood occurrence in the second priority area F2, and the probability of flood occurrence in the second priority area F2 is higher than the probability of flood occurrence in the third priority area F3. , the trigger information sets the priority of the first priority area F1 as the first priority, the priority of the second priority area F2 as the second priority, and the priority of the third priority area F3 as the third priority. generated.

At A13, the trigger information generation server 10 outputs the generated trigger information E1 to the selector management system server 20 by the trigger information output unit 114. At A21, the selector management system server 20 acquires the output trigger information E1 by the trigger information acquisition unit 211. In this way, the trigger information acquisition unit 211 acquires information indicating the priority of observation of an event as trigger information. More specifically, as described above, the trigger information acquisition unit 211 acquires, as trigger information, information indicating that the higher the probability of occurrence of an event, the higher the priority.

Next, in A22, the selector management system server 20 uses the constraint information acquisition unit 212 to acquire the above-mentioned constraint information D1.

FIG. 7 is a diagram illustrating an example of acquired constraint information. In Figure 7, multiple observable areas G001 to G122 (hereinafter referred to as "observable areas G" if not distinguished from each other) that can be observed by observation satellite 3-1 shown in Figure 1 are superimposed on Japan map J1. ) is shown. Each of the observable areas G is represented as a generally square-shaped area in which the same number of unit areas described above are arranged vertically and horizontally.

The observation satellite 3-1 observes while moving toward the northwest over the movement route K1 indicated by the arrow in the figure. Therefore, the farther south observable area G is, the earlier the observation is possible. In the example of FIG. 7, the observation satellite 3-1 performs observation from time t01 to time t18. It is assumed that all times from time t01 to time t18 are included in the predicted time period T01. For example, the southernmost observable areas G118 to G122 are the areas that can be observed by the observation satellite 3-1 at time t01. Note that it is assumed that there is only one observable area G that can be observed at the same time. For example, the observable area at time t01 is any one of observable areas G118 to G122.

Furthermore, in the example of FIG. 7, observable areas G083 to G092 are observable areas at time t05. Observable areas G050 to G053 are observable areas at time t10. Observable areas G008 to G015 are observable areas at time t15. The observable area G001 or G002 located furthest north is the observable area at time t18. In this way, the constraint information D1 indicates the time constraints that occur because the observation satellite 3 performs observation while orbiting around the earth.

Furthermore, the sensor included in the observation satellite 3-1 is limited in its measurable direction and distance, and in the example of FIG. 7, it can observe the observable area G1 and the observable area G2. The observation satellite 3-1 moves above the movement route K1, but cannot observe the area H1 that exists beyond a certain vertical downward angle. Furthermore, the observation satellite 3-1 cannot observe the area H2 that is more than a certain distance away. In this way, the constraint information D1 indicates geographical constraints that occur because the observation satellite 3 performs observation while orbiting around the earth.

The constraint information D1 shown in FIG. 7 indicates the observable area when the observation satellite 3-1 passes over Japan once, and the observable time and area will change the next time it passes. Therefore, temporal and geographical constraints are indicated by another constraint information D1. In this way, for the observation satellite 3-1, temporal and geographical constraints on observation are indicated by the plurality of constraint information D1.

In this embodiment, it is assumed that a plurality of pieces of constraint information D1 indicating constraints on observation of each observation satellite 3 are stored in advance in the storage means of the selector management system server 20, for example. Note that the constraint information D1 is not unchanging information, and is updated by the operator of the event observation system 1 when an observation satellite 3 is added, excluded, or changed in operation. Note that the constraint information D1 may be automatically updated. In that case, for example, the constraint information acquisition unit 212 periodically calculates the orbit information of the observation satellite 3, updates the constraint information, and acquires the updated constraint information. The constraint information acquisition unit 212 reads out and acquires the latest constraint information D1 of each observation satellite 3 from the storage means of its own device.

Next, in A23, the selector management system server 20 uses the index calculation unit 213 to calculate the above-mentioned evaluation index. As described above, the evaluation index is an index that indicates the evaluation of a candidate for an actual measurement range to be used in actual observation among the ranges observable by the observation satellite 3. The evaluation index is expressed, for example, as a numerical value, and the larger the numerical value, the higher the possibility that useful observation results will be obtained for the purpose of observation. The purpose of observation is, for example, to understand the damage situation over a wide area, which is necessary for initial disaster response.

The index calculation unit 213 calculates an evaluation index for each combination of the observation satellite 3 and the observation priority area F and predicted time period indicated by the trigger information E1. For example, the index calculation unit 213 calculates an evaluation index when the observation satellite 3 observes the first priority area F11 in the predicted time period T01. For example, there are N1 prediction time periods, the average number of observation priority areas F in each prediction time period is N2, and the average number of observation satellites 3 that can observe observation priority area F during the prediction time period is N3. In some cases, approximately N1*N2*N3 evaluation indicators are calculated.

In this embodiment, the index calculation unit 213 evaluates the observable range of high priority events higher than the observable range of low priority events among the ranges observable by the observation satellite 3. Calculate the index as an evaluation index. In addition, the index calculation unit 213 selects a range that can be observed by the observation satellite 3 using an observation means that has elements that should be prioritized in observing an event (hereinafter referred to as "priority elements"). The evaluation index is calculated as an index that is evaluated more highly than one using observation means that do not have the same characteristics. The priority factor is, for example, the high coverage rate that indicates the observable range of the area requiring observation.

For example, the index calculation unit 213 indicates, for each combination of the observation priority area F and the observation satellite 3, a priority point indicating the observation priority of the observation priority area F and a coverage rate of the observation satellite 3 for the observation priority area F. Coverage points are calculated, and the sum of priority points and coverage points is calculated as an evaluation index. The priority point is, for example, a value obtained by multiplying the above-mentioned flood risk by a predetermined coefficient. Note that the priority points may be fixed values for each of the first priority area F1, the second priority area F2, and the third priority area F3.

The coverage point is, for example, a value obtained by multiplying the ratio of the number of unit areas included in the observation priority area F that can be observed by the observation satellite 3 during the predicted time period by a predetermined coefficient. In other words, the coverage point is a value corresponding to the ratio of the area of the observation priority area F that can be observed by the observation satellite 3 during the predicted time period. The index calculation unit 213 calculates an evaluation index according to the priority and coverage as described above by summing the priority points and coverage points thus calculated.

Subsequently, in A24, the selector management system server 20 outputs the calculated evaluation index using the index output unit 214. The index output unit 214 outputs the evaluation index to the observation means determination unit 215, which is the first output destination described above, for example. In that case, in A25, the selector management system server 20 causes the observation means determination unit 215 to determine the observation means to be used for observation based on the output evaluation index.

The observation means determination unit 215 determines an observation means for each observation time period. For example, the observation means determining unit 215 selects, among the observation satellites 3, the one with the largest sum of evaluation indicators in the prediction time period T01 calculated for each observation priority area F as the first observation satellite to be used for observation in the prediction time period T01. Decided as a means of observation. The observation means determination unit 215 calculates the observation priority area F for each observation priority area F except for the observation priority area F observed by the first observation means when the first observation means alone cannot observe all the observation priority areas F. The one with the largest total evaluation index in the prediction time period T01 is determined as the second observation means to be used for observation.

Similarly, the observation means determining unit 215 selects the Nth observation means (N is a natural number) until all observation priority areas F are observed or there are no observation satellites 3 capable of observing the remaining observation priority areas F. ) to determine. The observation means determining section 215 supplies the observation requesting section 216 with determination data indicating the observation priority area F and the observation means determined as the observation means for observing the observation priority area F for each predicted time period.

Furthermore, the index output unit 214 may output the evaluation index to the user terminal 30, which is the second output destination described above. In that case, in A31, the user terminal 30 displays the output evaluation index for each predicted time period using the index display unit 311. Next, in A32, the user terminal 30 receives an operation by the user to determine the observation means for observing each observation priority area F for each predicted time period through the determination reception unit 312. Then, in A33, the user terminal 30 sends determination data indicating the observation priority area F and the observation means determined by the user as the observation means for observing the observation priority area F for each predicted time period by the result notification unit 313. is sent to the selector management system server 20. The selector management system server 20 supplies the received decision data to the observation request unit 216.

After A25 or A33, in A41, the selector management system server 20 causes the observation request unit 216 to generate observation request data for the observation satellite 3 indicated by the supplied decision data. Specifically, the observation request unit 216 sends request data requesting observation of the observation priority area F indicated by the similarly supplied decision data, that is, the observation priority area F that the observation satellite 3 should observe during the predicted time period. generate. The observation request unit 216 stores in advance the satellite control system 4 that manages each observation satellite 3, and transmits the request data to the satellite control system 4 that manages the observation satellite 3 indicated by the request data.

Upon receiving the request data, the satellite control system 4 performs an instruction process in A42 to instruct the observation satellite 3 indicated by the received request data to observe the observation priority area F during the predicted time period indicated by the received request data. Execute. The observation satellite 3, which is instructed to observe by the satellite control system 4, observes the specified observation priority area F at a time when the observation priority area F can be observed in the specified predicted time period. In this way, observation data indicating the results observed by the observation satellite 3 is collected via the satellite control system 4 and utilized for disaster prevention and the like.

As described above, the observable range of an event with a high observation priority is highly evaluated, so that the event with a high priority is preferentially observed. As a result, compared to the case where observation priorities are not taken into account, even if the number of observation means is insufficient to observe all observation priority areas F, for example, events with high observation priorities will be It is possible to make it difficult for a situation in which the observation is not made to occur. Furthermore, by giving a higher priority to events with a higher probability of occurrence, it is possible to more easily observe events that will occur in the future (in this example, events with a higher probability of occurrence) than when priorities are not considered.

In addition, by highly evaluating the observable range by observation means that have a high coverage rate of the area that requires observation as described above, it is possible to conduct observations that cover more of the area that requires observation than when the coverage rate is not considered. It is possible to support the selection of more appropriate observation means. Furthermore, the evaluation index calculated as described above can quantitatively indicate which observation means are more useful for observation, and can support the effective use of event observation means.

<Other embodiments>

(1) Priority Elements (1-1) Observable Range Priority elements (elements that should be prioritized in observing events) are not limited to the coverage rate described above. The priority factor may be, for example, the width of the observable range by the observation satellite 3. For example, as shown in FIG. 7, the width of the observable range of the observation satellite 3 is represented by the total area of the observable area G (observable area G001 to observable area G122) indicated by the constraint information. In this case, the index calculating unit 213 calculates, for example, observation range points instead of the above-mentioned coverage points.

The index calculation unit 213 calculates, for example, a value obtained by multiplying the total area of the observable region G indicated by the constraint information by a predetermined coefficient as the observation range point. Then, the index calculation unit 213 calculates the sum of the above-mentioned priority points and observation range points as an evaluation index. By calculating the evaluation index in this way, the observation satellite 3 with a wider observable range is more likely to be determined as an observation means to be used for observation.

Furthermore, the index calculation unit 213 may calculate the evaluation index by assigning weights to priority elements as the range of events that require observation is wider. In the example of FIG. 6, the first priority area F11 calculates the evaluation index by multiplying the observation range points by using, for example, the number of unit areas included in the observation priority area F=weighting coefficient. As a result, the wider the observation priority area F, the higher the evaluation index for the observation satellite 3 whose observable range is wider. According to this aspect, it is possible to efficiently observe events whose occurrence locations are spread over a wide range using as few observation satellites 3 as possible.

(1-2) Observation Cost Furthermore, the priority factor may be the low cost required for observation of the actual measurement range candidate by the observation satellite 3. When observing using the observation satellite 3, usage fees paid to the operating organization that manages and operates the observation satellite 3 may be incurred as a cost. The fee for using the observation satellite 3 is generated based on a usage contract concluded in advance, for example, in the form of a pay-as-you-go fee or a fixed amount depending on the observation performance. In this case, the index calculation unit 213 calculates, for example, cost points instead of the coverage points described above.

For example, the index calculation unit 213 determines a reference point, and calculates a value obtained by dividing the reference point by the cost that would be generated when observation by the observation satellite 3 is performed, as a cost point. The cost points calculated in this way become larger as the observation satellite 3 requires a lower cost for observation. Then, the index calculation unit 213 calculates the sum of the above-mentioned priority points and cost points as an evaluation index. By calculating the evaluation index in this way, it is possible to support observations with lower costs than when the cost required for observation is not considered.

(1-3) Observation lead time Trigger information E101 shown in FIG. 6 indicates the area where a flood, which is an event requiring observation, is predicted to occur. That is, in the embodiment, the event that requires observation is an event that is predicted to occur in the future. Furthermore, as described above, the observation satellite 3 is operated by an appropriate operating organization. In that case, the priority factor may be the length of lead time required from the time when a request for observation is issued to the operating organization until the event is observed by the observation satellite 3 operated by the operating organization.

The lead time required for observation varies, for example, depending on the interval at which the operating organization receives observation requests. For example, the lead time of an operating organization that can immediately accept observation requests is likely to be shorter than the lead time of an operating organization that accepts observation requests periodically (for example, at a fixed time every day). Furthermore, the shorter the time required for the approval process from receiving a request to obtaining observation permission, the shorter the lead time will likely be.

The index calculating unit 213 calculates, for example, lead time points instead of the above-mentioned coverage points. The index calculating unit 213, for example, obtains the lead time performance value in actual observation by the operating organization of the observation satellite 3, and calculates a value obtained by dividing the reference point by the average value of the obtained performance value as the lead time point. In addition, the index calculation unit 213 acquires information indicating the mode of acceptance of observation requests by the operating organization of the observation satellite 3 (immediate or periodic) and information indicating the approval process (for example, the number of approvers), and obtains each information The lead time point may be calculated by dividing the reference point by the sum of the values corresponding to the lead time point. These are both first calculation methods in which the shorter the lead time, the larger the lead time point.

Then, the index calculation unit 213 calculates the sum of the above-mentioned priority points and lead time points as an evaluation index. By calculating the evaluation index using the first calculation method described above, the observation satellite 3 with a shorter lead time from an observation request to actual observation is more likely to be determined as an observation means to be used for observation. According to this aspect, compared to the case where the lead time required for observation is not considered, it is possible to make it easier to find an observation satellite 3 that can meet the observation request in time even if there is a change in the prediction of the event that requires observation.

Note that the index calculation unit 213 may use a second calculation method in which the longer the lead time, the larger the lead time point. In that case, the observation satellite 3 with a longer lead time from observation request to actual observation is more likely to be determined as the observation means to be used for observation. The longer the lead time, the earlier the observation request has to be made than the actual observation, but it is more likely that the observation satellite 3's observation schedule is free. Therefore, by using the second calculation method, it is possible to find observation satellites 3 that are free for observation more easily than when the lead time required for observation is not considered. When using either the first calculation method or the second calculation method, it is possible to easily find an observation satellite 3 that meets observation needs.

(1-4) Sensor types The sensors that the observation satellite 3 has include a visible light sensor that detects visible light and near-infrared rays, an infrared sensor that detects thermal infrared rays, and a microwave sensor (also called radar sensor) that detects microwaves. There are various types such as Visible light sensors output observation results that make it easy to identify substances on the earth's surface. The infrared sensor can be observed even at night, and outputs observation results that indicate the temperature of the earth's surface. Microwave sensors can perform observations not only at night but also on rainy days because they are less affected by clouds. Although any sensor can obtain observation results according to its characteristics, the type of sensor that is more likely to obtain more useful observation results differs depending on the type of event.

For example, when the type of event is a flood, since the event usually occurs in rainy weather, observation results using a microwave sensor that can be observed without being obstructed by clouds are more useful. In addition, if the type of event is a landslide, we want to identify whether the ground is covered with plants or exposed, so observation results using visible light sensors that can easily identify materials on the ground are more useful. be. Furthermore, when the type of event is a wildfire, observation results using an infrared sensor that indicate the temperature of the ground surface are more useful.

In view of the above, a specific type of sensor may be used as a priority element, depending on the type of event that requires observation. In that case, the index calculation unit 213 calculates, for example, sensor type points instead of the above-mentioned coverage points. The index calculation unit 213 stores, for example, a point table in which event types, sensor types, and sensor type points are associated with each other.

FIG. 8 is a diagram showing an example of a point table. In the example of FIG. 8, when the event type is "flood damage", the sensor types are "5", "5", and "10" for three types of sensors: visible light sensor, infrared sensor, and microwave sensor. Points are associated with each other. Furthermore, when the type of event is "landslide", sensor type points "10", "5", and "5" are associated with the same three types of sensors, respectively. Further, when the event type is "tsunami", sensor type points "5", "10", and "5" are respectively associated with the same three types of sensors.

The trigger information shall indicate the type of event that requires observation. For example, in the example of FIG. 6, the trigger information E101 indicates that the type of event requiring observation is a flood. The index calculation unit 213 calculates the evaluation index for the observation satellite 3 using the sensor type points that are associated in the point table with the type of event requiring observation indicated by the trigger information and the type of sensor possessed by the observation satellite 3. Calculate.

For example, when the type of event is "flood damage," the index calculation unit 213 calculates the sensor type point as "5" for the observation satellite 3 that has a "visible light sensor" and an "infrared sensor," and For the observation satellite 3 having a "sensor", the sensor type points are calculated as "10". In addition, when the type of event is "landslide", the index calculation unit 213 calculates the sensor type points as "5" for the observation satellite 3 having "infrared sensor" and "microwave sensor", and For the observation satellite 3 having a "light sensor", the sensor type points are calculated as "10".

The sensor type points calculated in this way are for the observation satellite 3 that has a specific type of sensor (microwave sensor in the case of flood damage) determined according to the type of event that requires observation (for example, flood damage in the example of Fig. 6). , it becomes larger compared to the case of other observation satellites 3. Then, the index calculation unit 213 calculates the sum of the above-mentioned priority points and sensor type points as an evaluation index. By calculating the evaluation index in this way, it is possible to obtain more useful observation results than when the type of sensor possessed by the observation satellite 3 is not taken into account, so it is possible to obtain more useful observation results than when the type of sensor possessed by the observation satellite 3 is not considered. Accuracy can be improved.

(1-5) Observation angle The accuracy of observation results is affected by the observation angle. The observation angle is the angle between the horizontal plane at the observation point and the line connecting the observation satellite 3 and the observation point. The accuracy of observation results does not necessarily improve the closer the observation angle is to vertical; in some cases, the accuracy of observation results becomes higher when the observation angle is tilted by a certain angle rather than vertical. The observation angle at which the accuracy of observation results is likely to be the highest, that is, the observation angle that is optimal for observation, changes depending on various conditions.

The various conditions include, for example, the type of event. For example, if the type of event is a flood, the optimal observation angle for observation will be close to vertical, and if the type of event is a landslide, the optimal observation angle for observation will be an angle that is more inclined than vertical. easy to become.

The observation satellite 3 has observation sensors, and ordinary sensors other than omnidirectional sensors observe a certain range centered on a specific direction (hereinafter referred to as "observation direction"). For example, in the case of a visible light sensor, the observation direction is the direction of the optical axis, and in the case of a microwave sensor, it is the direction in which microwaves are irradiated. The observation direction changes depending on the attitude of the observation satellite 3, and the attitude of the observation satellite 3 is controlled so that the observation direction is directed in a fixed direction during normal flight.

In consideration of the above, the priority factor may be the proximity between the observation direction of the observation satellite 3 and the optimal observation angle determined according to the type of event requiring observation. In this case, the index calculation unit 213 calculates the optimal observation angle depending on the type of event. Then, the index calculating unit 213 calculates, for example, an observation angle point instead of the above-mentioned coverage point.

For example, the index calculation unit 213 determines a reference point and calculates a value obtained by dividing the reference point by the angle formed by the observation direction of the sensor and the optimal observation angle as the observation angle point. The observation angle point calculated in this way becomes larger as the observation direction approaches the optimal observation angle for observation. Then, the index calculation unit 213 calculates the sum of the above-mentioned priority points and observation angle points as an evaluation index. By calculating the evaluation index in this way, it is possible to more accurately understand the situation of the event based on the observation results, compared to the case where the observation angle is not considered.

(2) Priority and priority elements In the embodiment, the index calculation unit 213 calculates the evaluation index based on both the priority and the priority element, but it may also calculate the evaluation index based on only one of them. . In addition, the index calculation unit 213 prioritizes the coverage rate, the width of the observable range, the low cost of observation, the length of lead time required for observation, the specific type of sensor, and the optimal observation. Although any one of the height of the ability to observe by angle is used, two or more of these may be used as a priority element.

(3) Elapsed time Some phenomena that require observation change in status over time. For example, if there is heavy rain, the ground may loosen over time, creating a situation where there is a high risk of landslides or flooding. As the situation changes in this way, the degree to which priority elements of observation should be emphasized may also change in order to achieve the purpose of observing the event (for example, disaster prevention).

For example, when it starts to rain, it is important to observe a wider area and understand the situation in the entire area, so the breadth of the observable area is prioritized. Additionally, as rain accumulates, it is important to observe specific areas where disasters are a concern in more detail, so coverage is a priority factor. In such a case, the index calculation unit 213 may calculate the evaluation index by assigning a weight to the priority element according to the elapsed time from the occurrence of the event requiring observation.

In the above example, when using the width of the observable range as a priority element, the index calculation unit 213 assigns a weight that decreases as the time elapsed from the occurrence of the event increases to the priority element, and uses it as an evaluation index. Calculate. Furthermore, when using the coverage rate as a priority element, the index calculation unit 213 calculates the evaluation index by assigning a weight that increases as the elapsed time from the event occurrence increases to the priority element.

In addition to the above examples, priority factors may include low cost for observation, long lead time required for observation, and high ability to observe with a specific type of sensor and optimal observation angle. Even in cases where the observation objective is achieved, weights may be given according to the elapsed time if necessary to achieve the observation purpose. By assigning weights as described above, it is possible to more easily understand necessary situations in response to changes in events than when elapsed time is not considered.

(4) Trigger information (Part 1)
(4-1) Time until event occurrence In the embodiment, the trigger information is information indicating that the higher the probability of occurrence of an event, the higher the priority, but the trigger information is not limited to this. The trigger information may be, for example, information indicating that when the time of occurrence of an event is predicted, the shorter the time until occurrence of the event, the higher the priority. In that case, the trigger information generation unit 113 generates a predicted flood from the current time based on prediction information C1 (information indicating the date and time when a flood is predicted to occur for each river basin), for example, as in the embodiment. An area that satisfies the condition that the time until the occurrence date and time is less than the threshold Th11 (a collection of unit areas that satisfy the condition) is specified as the first priority area F1.

Further, the trigger information generation unit 113 identifies an area that satisfies the condition that the time from the current time to the predicted flood occurrence date and time is greater than or equal to the threshold Th11 and less than Th12 as the second priority area F2, and the area that the time is greater than or equal to the threshold Th12 is specified as the second priority area F2. The area that satisfies the conditions is identified as the third priority area F3. The trigger information generation unit 113 generates, as trigger information, information that associates the observation priority area F identified in this manner with the predicted time period in which the observation priority area F was identified.

Similarly to the embodiment, the index calculation unit 213 of the selector management system server 20 sets the observable range of high-priority events to the observable range of low-priority events out of the observable range by the observation satellite 3. An index that is evaluated highly compared to the range is calculated as an evaluation index. For events that take a short time to occur, if observation means are not prioritized, the initial situation may not be observed. Therefore, by setting the priority higher in this way, it is possible to more reliably observe an event that will occur in the future (in this example, an event that will take a short time to occur) than when the priority is not considered.

(4-2) Event Occurrence Period The trigger information may be information indicating that the shorter the event occurrence period, the higher the priority. In that case, the trigger information generation unit 113 specifies, for example, an area satisfying the condition that the time from occurrence to convergence of a flood is less than a threshold value Th21, as the first priority area F1, based on the prediction information C1. Furthermore, the trigger information generation unit 113 identifies areas that satisfy the condition that the time from the occurrence of the flood to convergence is greater than or equal to the threshold Th21 and less than the threshold Th22 as the second priority area F2, and identifies areas that satisfy the condition that the time from occurrence to convergence of the flood is greater than or equal to the threshold Th22. Identified as the third priority area F3.

Events with a short period of occurrence cannot be observed if the timing is missed, so by setting the priority high in this way, events that will occur in the future (in this example, short-duration events) can be observed more reliably.

(5) Trigger information (Part 2)
(5-1) Scale of event The trigger information may be information indicating that the larger the scale of the event, the higher the priority. In that case, the trigger information generation unit 113 generates an area that satisfies the condition that the area of the area where the flood has occurred or the area of the area where the flood is predicted to occur is less than the threshold value Th31, for example, based on the observation information B1 or the prediction information C1. is identified as the third priority area F3. Furthermore, the trigger information generation unit 113 specifies areas that satisfy the condition that the area is greater than or equal to the threshold Th31 and less than the threshold Th32 as the second priority area F2, and the area that satisfies the condition that the area is greater than or equal to the threshold Th32 is given the first priority. Specify as area F1.

(5-2) Event Occurrence Point The trigger information may be information indicating that an event whose occurrence point is closer to a predetermined area has a higher priority. Examples of designated areas include urban areas where houses and commercial facilities are densely packed, stations and airports that are important transportation points, power facilities, water facilities, railway facilities, and road facilities that are important infrastructure points. Areas that have a significant impact on people's lives are determined. These predetermined areas are determined by the operator of the event observation system 1, and information indicating a unit area including the predetermined areas on the map is stored in advance in the trigger information generation server 10.

Then, the trigger information generation unit 113 determines that the distance between the area where the flood has occurred or the area where the flood is predicted to occur and the unit area including the predetermined area is less than the threshold Th41, based on the observation information B1 or the prediction information C1. The area that satisfies the conditions is identified as the first priority area F1. The trigger information generation unit 113 also specifies an area that satisfies the condition that the distance between them is greater than or equal to the threshold Th41 and less than the threshold Th42 as the second priority area F2, and an area that satisfies the condition that the distance between them is greater than or equal to the threshold Th42 as the third priority area. Specify as area F3.

(5-3) Difference from average value The trigger information may be information indicating that an event having a larger difference from an average observed value in the past has a higher priority. For example, when a heavy rain event is observed, the trigger information generation unit 113 stores in advance the monthly average precipitation for each region. Then, the trigger information generation unit 113 selects an area where the amount of precipitation that has fallen in the month including the current time or the amount of precipitation that is predicted for that month is less than the threshold value Th51 as a third priority area based on the observation information B1 or the prediction information C1. Specify as F3. Further, the trigger information generation unit 113 identifies areas where the amount of precipitation is greater than or equal to the threshold Th51 and less than Th52 as the second priority area F2, and areas where the amount of precipitation is greater than or equal to the threshold Th52 as the first priority area F1. Specify as.

In the above example, events that are likely to have an impact on the social environment are observed: events that are large in scale, events that occur close to a predetermined area, and events that have a large difference from past average observed values. . By giving these events a higher priority, we can ensure that these events are observed more reliably and improve the status of events that are more likely to impact residents' lives than if no priority was used. can be understood in more detail.

(6) Events that require observation In the embodiment, the index calculation unit 213 calculates evaluation indicators for events that are predicted to occur in the future, in other words, events that have not yet occurred, as events that require observation. However, it is not limited to this. The index calculating unit 213 may calculate the evaluation index for events that have already occurred at the time of calculating the evaluation index.

In that case, the trigger information generation unit 113 may detect an event (such as a flood) that has already occurred in the predicted time period instead of the predicted area shown in FIG. 6 (area where an event requiring observation is predicted to occur). Trigger information is generated that indicates, as a predicted area, an area where it is predicted to continue or an area where the same event as an event that has already occurred (such as a landslide) is predicted to continue to occur. Similarly to the embodiment, the trigger information in this case is information indicating the region and time when an event requiring observation occurs.

The index calculation unit 213 calculates an evaluation index based on the trigger information generated in this way, in the same manner as in each of the embodiments described above. Even if the event has already occurred, the situation of the event changes over time, so by calculating the evaluation index for the situation predicted in the future, the effectiveness of the means of observing the event can be improved as in the embodiment. We can support its use.

(7) Observation means In the embodiment, an observation satellite is used as an observation means, but the present invention is not limited to this. For example, an aircraft or a drone equipped with an observation sensor may be used as the observation means. All of these observation methods observe from a flying object, and therefore tend to be subject to time constraints on the observation range. The event observation system 1 can also support the effective use of such observation means.

(8) Variations in configuration The overall configuration shown in FIG. 1 is an example, and is not limited to this. For example, the trigger information generation server 10 and the selector management system server 20 may be integrated into one server device. In that case, the trigger information acquisition unit 211 can acquire the trigger information generated by the trigger information generation unit 113 through internal processing of the device. Further, the trigger information generation server 10 and the selector management system server 20 may each be distributed to two or more devices, or may be replaced by a cloud computing system.

The functional configuration shown in FIG. 4 is also an example, and is not limited to this. For example, each function of the trigger information generation server 10 and the selector management system server 20 may be realized by being distributed among two or more devices. Further, for example, two or more functions such as the observation information acquisition section 111, the prediction information acquisition section 112, the index calculation section 213, and the index output section 214 may be each integrated into one function. Further, the operations performed by the index calculation unit 213 (calculation of priority points, calculation of points related to priority elements, calculation of evaluation index) may be performed in a distributed manner by two or more functions. In short, as long as the operations performed by each function shown in FIG. 4 are realized in the entire event observation system 1, the functions and devices that perform those operations may have any configuration.

(9) Category of invention The aspect of the embodiment described above may be an information processing method. The information processing method includes each part of an information processing system such as the event observation system 1. Moreover, the aspect of the embodiment described above may be a program. The program causes the computer to perform the steps of a similar information handling system.

<Additional notes>
Furthermore, each aspect described below may be provided.

(2) In the information processing system, the first acquisition unit acquires information indicating the priority of observation of the event as the trigger information, and the calculation unit selects the priority among the observable ranges. The information processing system calculates the index that evaluates the observable range of a high-priority event higher than the observable range of a low-priority event.

According to this aspect, it is possible to more reliably observe an event with a high observation priority.

(3) In the information processing system, the first acquisition unit acquires the following information: An information processing system that acquires information indicating a high priority as the trigger information.

According to this aspect, it is possible to more reliably observe events that are likely to affect residents' lives.

(4) In the information processing system, the event that requires observation is an event that is predicted to occur, and the first acquisition unit determines whether the event requires observation as the event takes a shorter time to occur or the event occurs for a shorter period of time. Alternatively, an information processing system that acquires information indicating a higher priority as the event has a higher probability of occurrence as the trigger information.

According to this aspect, events that will occur in the future can be observed more reliably.

(5) In the information processing system, the calculation unit selects, from among the observable ranges, observation means that have elements that should be prioritized in observation of the event, and those that use observation means that do not have the elements. An information processing system that calculates an index that is highly evaluated compared to the one used.

According to such an aspect, a more appropriate observation means can be selected.

(6) The information processing system, wherein the element is a high coverage rate indicating an observable range of an area requiring observation.

According to such an aspect, omission of observation can be suppressed.

(7) In the information processing system, the element is the width of an observable range, and the calculation unit calculates the index by giving weight to the element as the range of the event requiring observation is wider. Information processing system.

According to this aspect, it is possible to efficiently observe events whose occurrence locations are spread over a wide range.

(8) In the information processing system, the element is low cost required for observing candidates in the actual measurement range.

According to such an aspect, it is possible to support observation with reduced costs.

(9) In the information processing system, the event requiring observation is an event predicted to occur in the future, the observation means is operated by an operating organization, and the element is an event that is predicted to occur in the future. An information processing system, which is the length of lead time required from the time a request is issued to the observation of an event by the observation means operated by the operating organization.

According to this aspect, it is possible to easily find observation means that meet observation needs.

(10) In the information processing system, the calculation unit calculates the index by assigning weights to the elements according to the elapsed time from the occurrence of the event requiring observation.

According to this aspect, it is possible to grasp the necessary situation according to changes in events.

(11) The information processing system, wherein the element is a specific type of sensor determined according to the type of the event requiring observation.

According to such an aspect, the situation of an event can be grasped with higher accuracy.

(12) The information processing system, wherein the element is the proximity of the observation direction of the observation means to the optimal observation angle determined according to the type of the event requiring observation.

According to such an aspect, the situation of an event can be grasped more accurately.
Of course, this is not the case.
Furthermore, the embodiments and modifications described above may be implemented in any combination.

Finally, although various embodiments according to the present invention have been described, these are presented as examples and are not intended to limit the scope of the invention. The new embodiment can be implemented in various other forms, and various omissions, substitutions, and changes can be made without departing from the gist of the invention. The embodiments and their modifications are included within the scope and gist of the invention, as well as within the scope of the invention described in the claims and its equivalents.

1: Event observation system 2: Communication line 3: Observation satellite 4: Satellite control system 10: Trigger information generation server 20: Selector management system server 30: User terminal 111: Observation information acquisition unit 112: Prediction information acquisition unit 113: Trigger information Generation unit 114: Trigger information output unit 211: Trigger information acquisition unit 212: Constraint information acquisition unit 213: Index calculation unit 214: Index output unit 215: Observation means determination unit 216: Observation request unit 311: Index display unit 312: Decision reception Section 313: Result notification section

Claims (12)

An information processing system,
Comprising a first acquisition unit, a second acquisition unit, and a calculation unit,
The first acquisition unit acquires trigger information indicating an area and time when an event requiring observation occurs,
The second acquisition unit acquires constraint information indicating observational constraints of an observation means capable of observing the event, the constraint information indicating at least geographical and temporal constraints,
The calculation unit calculates, based on the acquired trigger information, an index indicating an evaluation of a candidate for an actual measurement range used in actual observation among the ranges that can be observed under the constraints indicated by the acquired constraint information, The information processing system, wherein the observable range is a geographical range and a temporal range. The information processing system according to claim 1,
The first acquisition unit acquires information indicating a priority of observation of the event as the trigger information,
The calculation unit calculates the index that evaluates the observable range of the high-priority event among the observable ranges higher than the observable range of the low-priority event. , Information Processing Systems. The information processing system according to claim 2,
The first acquisition unit triggers the information indicating a higher priority for an event that is larger in scale, an event whose occurrence point is closer to a predetermined area, or an event that has a larger difference from past average observed values. An information processing system that obtains information. The information processing system according to claim 2 or 3,
The event requiring observation is an event that is predicted to occur,
The first acquisition unit acquires, as the trigger information, information indicating a higher priority for an event that takes a shorter time to occur, an event that has a shorter occurrence period, or an event that has a higher probability of occurrence. In the information processing system according to any one of claims 1 to 4,
Among the observable ranges, the calculation unit evaluates those using an observation means that has an element that should be prioritized in observing the event higher than one that uses an observation means that does not have the element. An information processing system that calculates indicators. The information processing system according to claim 5,
An information processing system in which the element is a high coverage rate indicating an observable range of an area requiring observation. The information processing system according to claim 5 or 6,
The element is the breadth of the observable range,
The calculation unit calculates the index by giving weight to the element as the range of the event requiring observation is wider. In the information processing system according to any one of claims 5 to 7,
The information processing system, wherein the element is low cost required for observation of the candidates in the actual measurement range. In the information processing system according to any one of claims 5 to 8,
The event requiring observation is an event predicted to occur in the future,
The observation means are operated by an operating organization,
An information processing system, wherein the element is a length of lead time required from the time when a request is issued to the operating organization until the observation of an event by an observation means operated by the operating organization. In the information processing system according to any one of claims 5 to 9,
The calculation unit is an information processing system that calculates the index by assigning a weight to the element according to an elapsed time from the occurrence of the event requiring observation. In the information processing system according to any one of claims 5 to 10,
The information processing system, wherein the element is a specific type of sensor determined according to the type of the event requiring observation. In the information processing system according to any one of claims 5 to 11,
The information processing system is characterized in that the element is the proximity between the optimal observation angle determined according to the type of the event requiring observation and the observation direction of the observation means.