WO2010124875A2

WO2010124875A2 - Method and device for determining warnings in a sensor-assisted early warning system

Info

Publication number: WO2010124875A2
Application number: PCT/EP2010/002661
Authority: WO
Inventors: Joachim Post; Kai Zosseder; Stephanie Wegscheider; Matthias Mück; Ulrich Raape; Tilmann Steinmetz
Original assignee: Deutsches Zentrum für Luft- und Raumfahrt e.V.; Julius-Maximilians Universität Würzburg
Priority date: 2009-04-30
Filing date: 2010-04-30
Publication date: 2010-11-04
Also published as: DE102009019606B4; DE102009019606A1; WO2010124875A3

Abstract

The invention relates to a system and method for the computer-assisted processing and evaluation of information generated by a plurality of sensors in an early warning system for detecting natural events or technical events. The sensors of the early warning system are associated with a predetermined spatial region. One or more propagation models indicate one or more spatially and temporally differentiated effect curves depending on different predetermined information. A spatially and temporally differentiated effect curve is determined on the basis of the captured information and the propagation models, wherein the propagation models are used to calculate a corresponding likely effect curve of the event. One or more reaction models indicate a time frame depending on different predetermined effect curves or different predetermined propagations models, wherein the time frame corresponds to a previously established time that is necessary for initiating protective measures in the corresponding spatial region. A warning information signal is calculated on the basis of the calculated spatially and temporally differentiated effect curve and reaction models.

Description

Method and device for determining warnings in a sensor-based early warning system

The invention relates generally to a method and apparatus within an early warning system for calculating alerts or alert times for a geographic area based on information from a sensor system. In particular, the invention relates to an apparatus and a method for calculating decision-relevant information which are suitably provided for triggering a warning or evacuation signal for different spatial subareas of an underlying spatial area of an early warning system.

An early warning system generally serves to provide timely warning of events, and in particular natural disasters that require protection of people, goods and the environment or their evacuation. These include, for example, natural disasters of tectonic causes, such as tsunamis or earthquakes, and natural disasters caused by climatic factors, such as tornadoes or snow avalanches. The present invention can be used for early warning systems for such natural disasters. An early warning system, upon detection of such an event, should provide an effective warning to detect a potential threat at an early stage and to inform affected regions, in particular the local population, well in advance of the consequences of the event and actions and responses to protect people, goods and the environment in good time.

In the early warning, in addition to the configuration of the sensor systems, the compression of sensor information and other information sources linked to the time factor is of central importance. For this purpose, the time from the occurrence of the event to the arrival of the effect of the event is divided into time segments, so-called time components. The present invention particularly relates to the possibility of determining the reaction time, i. the time that elapses from the sending of the warning until the impact of an event.

Existing early warning systems only use to determine a warning decision

Sensor information which detects the initial occurrences of an event and transmits it to a central detection or warning decision unit. It must

Warnings that, due to the often short period of time between The first detection of the event and the arrival of the impact of the event are subject to high uncertainties or even lead to false warnings, which leads to great material and possibly even personal injury.

Currently known early warning systems use for this purpose sensor systems, which provide a plurality of sensor data from a plurality of sensors and / or different sensor types. Such an early warning system is shown by way of example in FIG. The sensors or the sensor system 120 consists of various sensors, which z. B. different buoys are near the coast to measure wave heights. The sensors can transmit measuring signals continuously or at predefined time intervals. Alternatively, the sensors may already pre-process the measured values and only send signals to a central sensor detection system 110 if a relevant measurement is present. By way of example, the sensors can classify the measured values on the basis of preset threshold values and send them to the central detection unit 110 in accordance with the classification of corresponding signals. If the sensors are buoys near the coast, z. B. a minimum wave height for each buoy are defined, from which the buoy sends a corresponding signal to the central detection unit 110. In addition, various signals may be sent to the central unit 110 according to different threshold values of the measured wave height. Such quantification of the measured values can be used in the same way for other sensor types and other early-warning systems. These sensor systems used for known early warning systems can be used in the same way for the present invention. Furthermore, a distinction can be made between evidence-giving or direct sensor systems and indicative or indirect sensors. Direct sensor systems detect the impact of an event directly (e.g., measuring the occurring wave height of a tsunami) while indirect sensor systems allow only event inference, for example, by measuring the magnitude of an earthquake at one or more locations, which may be the cause of a tsunami.

In known early warning systems, the central sensor detection unit 110 will process the received signals of the various sensors and output a warning signal 130 according to these values. This warning signal can either be sent directly to the affected region and population via a communication system, or it can be used by authorities as a basis for decision-making for the purpose of protection or protection Evacuation measures in the area concerned. In the following, a distinction can be made between the observation area and the warning area. The observation area refers to the area covered by the sensors, while the warning area refers to the area or areas for which one or more warning signals can be generated.

In such early warning systems, the problem arises that as the size of the area to be covered increases, the number of sensors increases and it is very difficult to decide from the large number of sensor information whether there is an event for which a corresponding warning must be output. Furthermore, there is the problem that, despite a large number of sensors for an observation area, there are gaps in the sensor technology detection, so that a decision as to whether a warning and / or evacuation event exists can not in principle be calculated precisely. Accordingly, the problem arises of how a natural disaster can be predicted with sufficient accuracy despite the abundance of sensor information and sensor coverage gaps and how false alarms can be prevented by an overly cautious warning of the early warning system Invention considerably reduced or avoided.

Furthermore, an early-warning system should only output decision-relevant signals or decision-relevant information. Based on this information, it should be possible to decide whether and, if so, how, a protective or evacuation measure of people, goods and the environment should take place. This decision-relevant information of the early warning system is usually passed on immediately after their detection in order to have the maximum time to decide whether an evacuation is required and, where appropriate, for appropriate protection or evacuation measures. However, there is the problem that an immediate initiation of protection or evacuation measures is not desired, since such measures must be operated costly and with considerable business disadvantages and organizational effort. Therefore, in order to rule out possible false alarms, further information or signals of the early warning system are often awaited, which either confirm the natural event or allow a previous warning to be recognized as a false alarm. However, in the event of an actual natural event, valuable time will elapse for initiating protection and evacuation measures, an improvement on the known ones It requires early warning systems to provide more accurate and secure information and alerts.

It is therefore the object of the present invention to provide a method and system for an early warning system, with which more accurate and secure information and possible warning signals can be provided based on sensor information in order to overcome the aforementioned disadvantages of existing early warning systems.

This object is solved by the present invention and in particular by the subject matter of the independent claims. Further preferred embodiments of the invention are the subject of the dependent claims and will be explained in more detail with reference to the drawings.

One embodiment of the invention relates to a system for computer-aided processing and evaluation of information generated by a plurality of sensors in an early warning system for detecting natural events or technical events, the plurality of sensors of the early warning system being associated with a predetermined spatial area. The system may include a capture unit, a calculation unit, and one or more data storage units.

The detection unit is used for detecting the information of the plurality of sensors, the information being suitable for directly or indirectly indicating an event relating to at least a part of the given spatial area. For this purpose, the given spatial area can be subdivided into a multiplicity of partial areas. A data storage unit stores one or more propagation models, wherein the propagation models indicate one or more spatially and temporally differentiated effects for the plurality of subareas in response to different predetermined information of the plurality of sensors. The propagation models can be determined in advance and stored in the data storage unit.

According to one embodiment of the invention, the calculation unit calculates a spatially and temporally differentiated course of impact for the plurality of subareas based on the acquired information of the detection unit and the propagation models stored in the data storage unit. For this purpose, the calculation unit can receive data from the detection unit and the received data can be sent to the distribution unit. Apply application models in order to calculate a probable course of effect for one or more subdomains corresponding to the received data. A data storage unit stores one or more reaction models, the response models specifying at least one time period for each of the plurality of subdomains depending on various predetermined impact histories or different predetermined propagation models.

Furthermore, the time span may correspond to a predetermined time required to take protective measures in the corresponding subarea, and the response models may be determined in advance and stored in the data storage unit. In accordance with a further embodiment, the determination unit calculates a warning information signal based on the spatially and temporally differentiated course of effects calculated by the calculation unit for the multiplicity of subareas and the reaction models stored in the data storage unit. The determination unit may for this purpose receive data from the calculation unit, which display the calculated spatially and temporally differentiated impact profile, apply the received data to the reaction models to determine a probable time span for one or more subareas, and a warning information signal based on the probable time period for determine one or more subareas. The warning information signal may be transmitted to an output unit or a memory unit.

According to a further embodiment, the determination unit further calculates from the determined probable time period for each of the one or more subregions a remaining period of time after the expiry of which a warning must be taken to take protective measures in the one or more subregions.

According to a further embodiment, the warning information signal corresponds to a hint of a recommended seizure of protective measures or information regarding a detected event in the one or more subregions, and the warning information signal is transmitted to the output unit or a memory unit only after the remaining time has elapsed.

According to a further embodiment, the warning information signal corresponds to a recommended taking of protective measures in the one or more subareas and the determining unit decides according to preset parameters and / or threshold values of the reaction models for each of the one or more subbranches. whether to generate and transmit a warning information signal. For this purpose, the warning information signal can be transmitted to the output unit or memory unit only after the remaining time has elapsed, if, after the remaining time has elapsed, it is determined from the preset parameters and / or threshold values of the reaction model that a warning information signal is to be generated and transmitted.

According to a further embodiment, the calculated information of the calculation unit is fed back to the detection unit, and the detection unit uses the feedback information to acquire further sensor information.

According to a further embodiment, the calculated information of the determination unit is fed back to the calculation unit, and the calculation unit uses the returned information for further calculating the spatially and temporally differentiated impact history, wherein the propagation models take into account feedback information of the determination unit.

According to a further embodiment, the determination unit further calculates, from the determined probable time span for the affected subareas, an expected effect on the affected subareas.

According to another embodiment, the early warning system is a tsunami early warning system.

Further preferred embodiments of the invention and their details are explained in more detail with reference to the drawings.

FIG. 1 shows a block diagram of an early warning system according to the prior art

Technology.

Figure 2 shows a block diagram of an early warning system according to an embodiment of the present invention.

FIG. 3 shows a representation to illustrate a spatial distribution of a

Warning area in various areas of an early warning system according to an embodiment of the present invention. FIG. 4 shows a flow chart for illustrating exemplary steps for determining warning times according to an embodiment of the present invention.

FIG. 5 shows a schematic representation of an example for determining a potentially endangered area.

Figure 6 shows an example of a spatial location of access points adjacent to the potentially affected area.

FIG. 7 shows the spatial exemplary representation of the evacuation rate distribution with regard to the population density.

FIG. 8 shows a flowchart for calculating the evacuation time according to an embodiment of the invention.

FIG. 9 shows an exemplary representation of a calculated inverse velocity distribution.

FIG. 10 shows by way of example for t = 60 minutes areas which have a longer evacuation time (dark) and areas which have a shorter evacuation time (light).

FIG. 11 shows an example diagram for determining the number of people involved as a function of the time available for the evacuation action.

FIG. 12 illustrates an exemplary implementation of an embodiment of the invention as a screen copy of an embedding of the response-dependent warning times in the GITEWS decision support system.

FIG. 13 shows a schematic representation with time components in a warning chain according to an embodiment of the invention.

FIG. 2 shows an early warning system 200 according to an embodiment of the present invention. The early warning system uses a sensor system 120 which provides information from various sensors. This sensor system can, as in the previously described be formed usual early warning systems. One aspect of the present invention is based on the finding that a mere increase in the number of sensors can not avoid the aforementioned disadvantages of known early warning systems. Increasing the number of sensors simultaneously increases the number of pieces of information that the early warning system must process in real time or in a very short time without, however, providing full and complete coverage of the measured characteristics, such as: B. wave height, vibration or movement to provide. This leads to a greatly increasing complexity of the calculation, which can be realized in real time or in a very short time. Furthermore, for cost reasons, the smallest possible number of sensors is desirable.

The present invention combines the sensor systems with various previously determined models for spatially and temporally differentiated calculation of warning decisions. Thus, the area to be observed is divided into different areas, and according to the present invention, a predicted effect of the sensor information preprocessed by the sensors 120 and the sensor detection system 210 is calculated for each warning area. For this purpose, according to an embodiment of the invention, different propagation models 220 can be stored in a data memory. On the basis of these propagation models and the sensor data of the sensor detection system 210, a propagation scenario is determined by an impact determination system or calculation unit 230. Due to the topographic location and nature of the observation area, the observation area can be subdivided into different areas.

FIG. 3 shows an exemplary representation in which the warning area of the early warning system 310 is subdivided into eight areas 320 to 327. Also shown are the various sensors 120, which in the case of a tsunami early warning system can be buoys in front of the coastal section 310. According to the subdivision of the area into different zones or subareas, one or more propagation models 220 are determined beforehand in order to provide an expected effect for each of these subareas based on the information of the sensors 120. In addition, according to one embodiment of the invention, the propagation models 220 provide a temporal history of the effects, such as a tsunami, on the shore 310 for each of the areas 320-327. This can be done by calculations and simulations based on the nature and nature of the area 310 and its area, such as area 310. For example, the topography and optionally the land- land cover (wooded area, urban development, agricultural fields, swamps, etc.). Since the location of the sensors with respect to the subdivided area 310 is known in advance and determined, such propagation models can also be created in advance and stored in a corresponding storage medium. Using these propagation models, an impact detection system 230 of the early warning system can use the sensor data to determine a spatially and temporally differentiated propagation curve of the event detected by the sensors.

According to another embodiment, the sensor data obtained is processed by the sensor detection system 210 so that the impact detection system 230 can match this sensor data to the various stored propagation models. For this purpose, various logical operations and filter functions can be used, which in turn already describe a propagation model 220 that takes into account local conditions and relationships between the positions of the sensors 120 and the corresponding affected areas 320 to 327. If some or all of the sensors provide signals indicative of a natural event, the most likely propagation scenario can be determined from the propagation models. This indicates, for each of the regions 320 through 327, how the event will propagate in the areas accordingly. In the example of a tsunami early warning system, this corresponds to a temporal course with regard to the size and possibly the direction of a wave in the respective regions of the coast 310.

Based on the spatially and temporally differentiated propagation scenario determined by the impact determination system 230, it can be determined for each of the regions 320 to 327 whether the region is affected by the predicted event or whether it is a so-called "safe area" Furthermore, the spatially and temporally differentiated propagation scenario of the impact assessment system allows a calculation or prediction of when and, if appropriate, in what order the regions of the event (eg, a tsunami) can be affected. Thus, the time remaining until the event occurs in each sub-area can be calculated, and due to these times, remaining for each area until the occurrence of the event, a ranking list or a priority list of the respective regions can be created For example, the regions that are first affected by the event receive a higher priority than those regions which will be affected later or not at all by the event.

According to a further embodiment, the ranking list or the priority of the individual regions can also be determined on the basis of the remaining time components up to the impact of the predicted event. For example, for a remaining time of more than three hours before the occurrence of the event in area G (326), a low priority level may be assigned, whereas area B (321) is assigned a higher priority if the impact of the predicted event is already within one Hour reached this area.

According to a further embodiment of the invention, these two aspects are combined. Thus, both the expected remaining time to occurrence of the event and the order in which the various regions are affected by the predicted event are considered for prioritizing the various areas. For example, a temporal quantization can take place in hourly intervals. If the propagation models determine, based on the current sensor information, the arrival of the event in areas A, B, C and F within two hours and in areas E, G and D within three hours, areas A, B, C and F receive a higher priority than the areas E, G, D, which in turn is higher than the priority of the area H, which will not be affected by the predicted event. Furthermore, if the order in which the effect of the event hits the affected areas is taken into account, so that, for example, first region A, then region B, then region F and then region C will be affected, this order could lead to a further distinction in the classification or prioritization regions are used, so region A has the highest priority, region B the second highest priority, followed by region F and C ₁ and regions E, G, D and region H.

According to a further embodiment of the invention, in addition to the propagation models, further reaction models or attitude condition and evacuation models can be determined in advance for the different regions of the area 310 and stored in a data memory 240. These additional models can be used by a warning signal determination system or unit 250 for calculating a warning information signal to further process the spatially and temporally differentiated propagation scenarios determined by the impact determination system 230. to provide further improved and optimized warning signals and As described above, the propagation models 220 can be used to create a spatially and temporally differentiated propagation prediction. For this purpose, pre-stored models are applied to the sensor information of the sensor system 120 in order to determine the most probable propagation scenario. As a result, the accuracy of the warning signal is already improved because not the pure sensor data are used, but this sensor information is evaluated and processed by means of previously determined, theoretically possible propagation scenarios for various possible events. However, this calculation of possible propagation scenarios can be further improved if further information on the location and nature of each region is used. These parameters may include, but are not limited to, the number of population to be evacuated, the number of critical facilities such as hospitals and schools or airports, the number and nature of roads and paths available for evacuation, and the impact of neighboring regions. Other parameters may include the age and gender distribution of the population as well as the spatial location of possible evacuation sites and evacuation routes.

While with the propagation models 220 an improved prediction of the effects of the predicted events detected by the sensor information is achieved, with the further location, condition and evacuation models 240 the measures for the protection and evacuation of individual regions in the determination of warning signals can already be taken into account become. Thus, the spatially and temporally differentiated determined spread of the forecasted event in the individual regions, in particular the remaining time until the occurrence of the event, can be linked to the time required for possible protection and evacuation measures. This is comparable to a cost function or a weighting of the spatially and temporally differentiated impact calculation determined by the unit 230 by the early warning system. For example, if the system 230 detects an event occurring in regions A, B, and F in 2 hours, and in regions E, C, and G in 3 hours, then as previously explained, classification or prioritization of the various regions already results. The additional location, condition and evacuation models 240 can further enhance this prioritization and time warning determination for the regions. For example, in Region B there is an increased number of critical facilities, such as: Schools, airports or hospitals or a high population density the required evacuation time correspondingly high. If, on the other hand, regions A and F are sparsely populated, a much shorter evacuation time can be assumed. Based on the time of arrival of the effect of the event in two hours in the regions A, B and F calculated by the impact determination unit 230, the models 240 can be used to change the time to warning for each region based on the required evacuation time. Thus, in the previous example for Region B, despite the currently expected arrival of the event in two hours, only 50 minutes could be left before a corresponding warning must be issued as the evacuation time for this area is 1 hour 10 minutes. For regions A and F, the time available until a warning must be given may be much longer, corresponding to the lower evacuation time. As another example, the impact determination system 230 may determine a predicted time of arrival of the event in region G at three hours. However, if the models 240 say that a very long evacuation time is needed here, as e.g. For example, if there are no suitable roads for evacuation, the remaining time to which a warning for region G must be issued may be less than in areas A and B where the event will arrive within two hours.

Another advantage of the propagation models 220 and the evacuation models 240 is that a time can be determined to which a corresponding alert must be issued for each area. This has the advantage that a warning does not have to be issued immediately and further data of the sensor system 120 can be awaited. If the early warning system has predicted an event for a region, it can simultaneously determine that a warning is not yet required. Therefore, additional sensor data can be awaited in order to be able to recognize the predicted event as a false alarm. The early warning system can therefore decide whether a warning for a detected event must already be issued or whether further sensor data can be awaited.

This dynamic processing of the sensor systems based on the models 220 and / or 240, according to a further embodiment of the invention, allows the dynamic calculation of spatially and temporally differentiated warnings taking into account previously determined data. This is exemplified by the feedback of the information 270 and 280 in FIG. Thus, the propagation models 220 and the location, Procurement and evacuation models 240 may be configured to take into account temporal changes in sensor information. This is possible because the warning signal detection system 250 can independently determine whether a warning must already be made, or whether further temporally subsequent sensor data can be taken into account before a warning signal must be output.

The information calculated by the alerting system and the impact detection system may be communicated to a display or warning system 260 to display according to a user or to send them directly to the corresponding areas. This allows the user to differentiate the affected areas, and the different areas can already be prioritized by the system and the underlying models so that the remaining time in which a warning must be given already by the early warning system can be spent with. This reduces the number of false alarms, improves the accuracy of the predictions, and at the same time indicates which protection or evacuation measures should be taken when and in which area.

FIG. 4 shows an example method according to an embodiment of the present invention for calculating a warning time in an early warning system. The method begins with collecting sensor data from various sensors of a sensor system (step 410). In step 420, the sensor information is matched with the propagation models 220 to compute a spatially and temporally differentiated impact history for the affected subdomains that is most likely to correspond to sensor data sensed sensor data.

In step 430, one or more warning information is calculated based on the calculated spatially and temporally differentiated impact history for the affected subdomains and the stored response models. In a further step 440, these warning times can be displayed together with the temporal and spatial course of the predicted event or transmitted to a user interface, an output device 250 or a memory unit. The various propagation models and the evacuation models are determined in advance and can be stored in the form of time-dependent cost functions or in the form of look-up tables as well as in the form of bases, from which the corresponding cost curve can be reconstructed and queried. According to another embodiment of the present invention, the step 430 of calculating warning information comprises at least one of the following three optional steps. In step 450 affected regions of an expected event can be determined. Further, at step 460, a spatial and temporal impact of a predicted event may be determined for different regions of the area, as described above. In a further step 470, it is possible to determine a latest warning time point for each affected region on the basis of the location, quality and evacuation models (or also reaction models) 240 and the determined temporal and spatial effects on the various regions 320 to 327.

The above-described aspects of the invention allow both "backward use" by determining the latest alert decision timing, eg, by securing or evacuating a predetermined minimum number of people and goods from a particular area, as well as "forward usage", by calculating how many people or goods from a given area can be secured or evacuated at a certain time.

The various systems 210, 230, 250 and 260 shown in FIG. 2 may also be realized by means of one or more systems or system components according to further embodiments of the invention. In addition, the propagation models 220 and the evacuation models 240 may be modeled using a model. The above-described functional features of the early warning system 200 can also be implemented as computer-implemented steps or as computer-readable instructions of a computer-readable medium.

According to another embodiment of the invention, the propagation models and the reaction models may be formed as a single model and stored in a single data storage unit.

In the following, the determination of the spatially and temporally differentiated costs will be described in more detail according to further embodiments of the invention. For this purpose, a tsunami warning system is described by way of example. However, those skilled in the art will recognize that the embodiments described below may be applied to other natural events or technical events of a sensor-based early warning system. Based on previously calculated precalculated scenarios or models, for any acceptable event determines the land area affected by the event that is to be evacuated when the event actually occurs. Then the location of safe areas is determined, which can be used in the event of an evacuation as a refuge for the affected land area. In the simplest case, the safe areas border on the affected land areas, ie the retreat area results from the unaffected land area.

FIG. 5 uses the example of the possible propagation scenarios to show the use of a so-called tsunami scenario database 510. In a possible query, the hazard area can be determined 560 for a specific warning level (referred to in the figure as a "warning level") Scenarios 1, 4 and 5 are determined 570 from scenario database 510, the affected landing points are calculated 580 and a hazard area 590 shown here as a map is determined another warning level (referred to in the figure as "Major Warning Level") determines 520, which in the example chosen applies to a wave height on the coast greater than 3 meters. Scenarios 2 and 3 are determined 530 from scenario database 510, the affected landing points are calculated 540, and a hazard area 550 represented here as a map is determined. The scenario database corresponds to the data store for storing the propagation models 220 and / or the models 240. Those skilled in the art will recognize that the cartographic representation chosen in the figure can be made in an actual implementation of the invention by means of a digital map format.

FIG. 6 shows in a cartographic representation 600 a hazard area 630 with possible access or entry points 620 into the hazard area.

However, according to one embodiment of the present invention, further criteria are advantageously taken into account, which are implemented by spatial analyzes with the aid of algorithms in Geographic Information Systems (GIS). For example, the following criteria could be considered: suitable land use (eg no dense forest or no water), suitable topography (eg slope not greater than 20 °), minimum dimensions of an area (eg greater than 10 000 m ² ), accessibility via paved roads or paths or spatial proximity to the affected land area. In addition, so-called entry points from the danger zone can be defined in a safe area, which arise as intersections of roads and paths with the boundary between affected land area and safe areas.

In addition, the temporal accessibility of a safe area can be taken into account, which essentially results from the distance of a place to the next safe area as well as the potential evacuation speed. From this, the time needed for a person to reach safety is calculated for each location.

The following factors can influence the evacuation speed:

Density of critical facilities (such as schools and hospitals), population density, age and gender distribution, topography and land cover. The topography, land cover and the density of critical facilities have a reducing effect on the evacuation speed. Population density as well as age and gender distribution are assigned characteristic speeds from empirical data for respective classes. The respective spatial data in the corresponding classifications are then assigned the respective evacuation rates or reduction factors (see Tables 1 to 4) and are included in the further calculations.

Location and characteristics of critical institutions:

Critical devices such as e.g. Schools and hospitals have specific evacuation behavior. Areas with a high density of critical facilities are therefore limited in their evacuation behavior. Depending on the density (number of devices per hectare) of equipment, the respective potential evacuation rate can be reduced (e.g., by a factor between 0 and 100 percent).

Table 1 shows a parameterization example for the influence of the density of critical equipment on the evacuation speed. Here, "PS" means school and "KiGa" means children. The reduction factor depends on whether hospitals are in one area or not. With a density of hospitals per hectare equal to zero, the reduction factor results after the middle column ("no hospital"), with a hospital density greater than zero after the right column ("density> 0 hospitals. / Ha").

Table 1

Population distribution and demographic factors:

From available population distribution data specific density classes (people per ha) can be derived. These can be based on empirical studies characteristic evacuation rates. Table 2 shows a parameterization example.

Table 2

In FIG. 7, a velocity distribution with respect to the population density is shown spatially by way of example.

In addition to the population density, the age and gender distribution can influence the potential evacuation rate. Based on empirical studies as well as spatially resolved data on age and gender distribution, specific evacuation rates can be assigned to specific age and gender distribution classes.

Topography:

It can be derived a clear relation between slope (topography) and evacuation speed. The stronger the slope, the lower the evacuation speed. This can be taken into account in the analysis by a reduction factor. Table 3 shows a parameterization example for the influence of the slope as a reduction factor (cost) on the evacuation speed.

Table 3

Land use:

The land use / land cover can also inhibit the evacuation rate. Thus, an evacuation on a road can be done faster than through dense forest. This reduction effect of land cover can be taken into account by reduction factors. Table 4 shows a parameterization example of the influence of land cover as a reduction factor (cost) on the evacuation rate.

Table 4 Based on the spatially resolved reduction factors, the velocity distributions and the position of the entry points, the inverse velocity can now be calculated, as shown by way of example in FIG. This can be used to obtain a spatially resolved quantification of the time required from any location in the danger zone to a safe area via an "inverse distance weighting" approach using, for example, a geographic information system This includes, for example, land use, population and topography data as well as data on critical facilities and an age and gender distribution 810. Reclassification parameters 820 are used to obtain the allocated costs 830.

The aforementioned distance-weighting approach can be expressed mathematically, for example, as follows:

_ Costs and Cover Costs _ Topographical Costs _ Critics _ Furnishings v ' ^{men =} iöö ^* iöö ^* iöö

(Costs _ Population density + Costs _ Age_ gender λ

Λ 2 y

A cost-distance algorithm 850 can calculate 860 from each point the best possible way to an entry point. About this distance and a data set indicating the inverse speed, the time required can be calculated 870. Figure 9 shows an example of an inverse Velocity distribution for the exemplary region of FIG. 7.

Accepted arrival time of an event:

From a precalculated set of scenarios, for each alert segment (in terms of a minimum warning unit for a warning process), the average and minimum time of arrival of an event's impact may be determined. For example, this time in minutes can represent the maximum time available for evacuation of people or goods, the so-called reaction time. Since you have calculated the necessary evacuation time for each landing point, you can now for certain time slices indicate, until the mean expected time of arrival, the area in which the evacuation time is insufficient.

FIG. 10 shows an example of this. The dark area indicates the area in which the local population has no opportunity to reach a safe area within the time available. A bright area indicates where this is potentially possible.

Calculation of decision-relevant costs:

With the help of population data, the number of people in the respective area can now be calculated. For a given spatial element as well as different time slices, the temporal course of the increase in the number of persons can be represented, which can no longer reach a safe area and are therefore directly affected by the effect of an event. This is illustrated by way of example in FIG. 11.

Technical implementation using the example of a tsunami early warning system:

The quantification of the increase of affected people as a function of the response time represents an important decision-making factor in the early warning process. This is included as a cost factor in the decision support process.

FIG. 12 shows by way of example how risk information is embedded in the "Decision Perspective" (the display in which all information and action proposals relevant to the decision-making process are summarized) of the tsunami decision support system (GITEWS DSS) technical determination of optimal decision proposals as well as in the representation of the same can be made usable.

The recording of costs then provides an important basis for decision-making in the early warning process in the tradeoff estimate "wait for next observation" versus "warn earlier", which can be done individually even at the warning segment level and takes into account the heterogeneity of the warning segments.

A decision support system, such as the GITEWS DSS, may utilize the above-described methods of the present invention to individually determine, in a given location, each alert segment when the latest alert time which ensures a minimum evacuation rate from a specific or general evacuation area (reverse use). The threshold values to be defined here can be specified on an application-specific basis (eg at least X% of the population, max X% or X non-evacuated, etc.).

The GITEWS DSS is thus able to send a warning message for faster evacuable warning segments later, and thus based on further or better sensor or position information, which also reduces the false alarm rate and thus the false alarm costs (damages, injuries or deaths caused by the Evacuation per se).

In forward usage, a DSS can use the new method to estimate the response for a given situation image (and estimated hazard arrival times in the warning segments) and given (or assumed) warning times (how does the response look like, how many people can and from what Area to be evacuated etc.).

As described above, the cost function can be related to a warning segment, which is regarded as the smallest addressable / warning unit in the context of the warning process. However, the method also allows the cost function to be determined for any other spatial units, if necessary.

The described methods according to the present invention can also be used to dynamically determine the hazard area for a given assessment area and given evacuation time and possibly for a given number of people at risk. This can be used to solve the technical problem without deriving corresponding real-time information from the vulnerable areas of a hazard estimate at a particular time (e.g., real time).

Figure 13 shows time components in a warning string and their representation on a time axis 1310. An Institutional Decision Time (IDT) determines the time of the sensor detection of the event until "pressing the warning button." INT (Institutional Notification Time) is the time that the Signal needs to be notified to the public by the word dissemination devices While the IDT is not further subdivided, the INT can be differentiated spatially and depending on the used sound technologies (sirens, speakers, radio, SMS, TV, etc.). because it depends on sociological and other characteristics, which are difficult to quantify The above methods differ only between good, medium, and poor response times, but can be modeled more accurately in the presence of more detailed data.

In the specific case of tsunami early warning in Indonesia, 5 minutes are currently expected for IDT and 3 minutes for INT; the current assumption RT = 0 minutes (all people respond directly and correctly) is still to be adjusted.

If the ETA and the warning time are given, the response time depends only on the reaction time of the people. Assuming a direct response (RT = 0 minutes), one can narrow the area 1320 where the pre-calculated evacuation time (ET) is> RsT. This area can be determined dynamically by the method described above. Thus, at any possible warning time, one would also have the description of area 1330 where the population remains and can not evacuate. The summation of the population in this area yields numbers that can be represented in absolute or relative terms.

The calculation to be performed on the basis of the phase model may be used to get from the warning triggering time or the warning triggering time to the start of the evacuation time, or vice versa.

The present invention has been explained with reference to the preferred embodiment with reference to the figures. However, it will be apparent to those skilled in the art that various modifications, variations and improvements of the present invention are possible in light of the above teachings and within the scope of the appended claims without departing from the spirit and intended scope of the invention. Further, those areas believed to be familiar to those skilled in the art have not been described herein in order to not unnecessarily obscure the invention described herein. Therefore, the invention should not be considered as limited by the specific illustrative embodiments, but only by the scope of the appended claims.

Claims

claims

A system for computer-aided processing and evaluation of information generated by a plurality of sensors (120) in an early warning system for detecting natural events or technical events, the plurality of sensors of the early warning system being associated with a predetermined spatial area (310), and wherein the system comprises:

A detection unit (210) for acquiring the information of the plurality of sensors, the information being suitable for directly or indirectly indicating an event concerning at least a part of the given spatial area, the predetermined spatial area being divided into a plurality of subregions (320-4).

327) is divided;

A data storage unit for storing one or more propagation models (220), the propagation models specifying one or more spatially and temporally differentiated effects histories for the plurality of subdomains in dependence on various predetermined information of the plurality of sensors, wherein the propagation models are pre-determined and stored in the data storage unit become;

A calculation unit (230) for calculating a spatially and temporally differentiated impact pattern for the plurality of subareas based on the detected information of the detection unit and the propagation models stored in the data storage unit, wherein the calculation unit is adapted to receive data from the detection unit (210) and the received ones Apply data to the propagation models to compute a likely impact course corresponding to the received data for one or more subregions;

A data storage unit for storing one or more reaction models (240), the response models specifying at least one time period for each of the plurality of subdomains in dependence on different predetermined effects or different predetermined propagation models, wherein the time period corresponds to a predetermined time required to take protective measures in the corresponding subarea, and the response models are determined in advance and stored in the data storage unit; A determination unit (250) for calculating a warning information signal based on the spatially and temporally differentiated impact course calculated by the calculation unit for the plurality of subareas and the reaction models stored in the data storage unit, wherein the determination unit is adapted to: receive data from the calculation unit (230) for displaying the calculated spatially and temporally differentiated course of impact to apply the received data to the reaction models in order to determine for a probable time span for one or more subregions,

Generating a warning information signal based on the probable time period for one or more subareas, and

Transmitting the Waminformationssignals to an output unit (260) or a memory unit.

2. The system of claim 1, wherein the determining unit is further adapted to calculate a remaining period of time from the determined probable time period for each of the one or more subareas after which expires a warning to take protective measures in the one or several sub-areas.

3. The system according to claim 2, wherein the warning information signal corresponds to an indication of a recommended seizure of protective measures or information relating to a detected event in the one or more subregions, the warning information signal only after the remaining time has elapsed to the output unit or a memory unit is transmitted.

4. A system according to claim 2 or 3, wherein the warning information signal indicative of a recommended seizure of protective measures or information relating to a detected event in the one or more sub-areas and the determining unit (250) decides according to preset parameters and / or threshold values of the reaction models for each of the one or more subdomains, whether to generate and transmit a warning information signal; wherein the warning information signal is transmitted to the output unit (260) or memory unit only after the remaining time has elapsed, if, after the remaining time has elapsed, it is determined from the preset parameters and / or threshold values of the reaction models that a warning information signal is to be generated and transmitted.

5. The system according to one of claims 1 to 4, wherein the calculated information of the calculation unit (230) is fed back to the detection unit (210) and the

Detection unit is adapted to use the returned information for acquiring further sensor information.

6. The system according to claim 1, wherein the calculated information of the determination unit is returned to the calculation unit and the calculation unit is adapted to use the feedback information to further calculate the spatially and temporally differentiated impact history the propagation models take account of information returned by the investigative unit.

7. System according to one of claims 1 to 6, wherein the propagation models is determined taking into account at least one of the following parameters:

Topography of the subareas; and land cover of the subareas;

8. System according to one of claims 1 to 7, wherein the propagation models is determined taking into account at least one of the following parameters: affected land area of the subregions;

Location of safe sub-areas or sub-regions not affected by the spread of the event;

The location and / or spread of critical facilities in the subregions; Characteristics of possible evacuation routes within and / or between the subregions;

Population density and / or population distribution of the subregions; expected evacuation speed within and / or between the sub-areas; assumed expected time of arrival of the event at the subareas;

Age and / or gender distribution of the population within and / or between the subregions;

Topography of the subareas; and land cover of the subareas.

9. System according to one of claims 1 to 8, wherein the determination unit (250) is further adapted to calculate from the determined probable time period for the affected subregions an expected effect on the affected subareas.

10. The system of claim 1, wherein the early warning system is a tsunami early warning system.

11. A method for computer-aided processing and evaluation of information generated by a plurality of sensors (120) in an early warning system for detecting natural events or technical events, wherein the plurality of sensors of the early warning system occupy a predetermined spatial area

(310), and wherein the method comprises:

Detecting (410) the information of the plurality of sensors, the information being adapted to indicate an event relating to at least a portion of the predetermined spatial area, wherein the predetermined spatial area is divided into a plurality of subareas (320-327);

Calculating (420) a spatially and temporally differentiated impact pattern for the plurality of subareas based on the acquired information of the detection unit and stored propagation models, by a probable impactor curve corresponding to the acquired information for one or more of the subdomains, wherein the propagation models indicate one or more spatially and temporally differentiated effects for the plurality of subareas in response to different predetermined information of the plurality of sensors; Calculating (430) a warning information signal based on the calculated spatially and temporally differentiated impact pattern for the plurality of sub-regions and stored reaction models, the reaction models being applied to the computed spatially and temporally differentiated impact history to assign a probable time span for one or more sub-regions determine the time period of a predetermined time required to take protective measures in the corresponding sub-area, and the response models indicate at least one time period for each of the plurality of sub-areas depending on different predetermined effects or different predetermined propagation models; and

Transmitting (440) the warning information signal to an output unit (260) or a memory unit.

12. The method of claim 12, wherein the step of calculating (430) a warning information signal comprises at least one of the following steps:

Determining (450) subareas affected by the event;

Determining (460) a temporal impact of the event in the affected subareas; and

Determine a latest warning time for each affected sub-area.

13. A method according to claim 11 or 12, wherein from the determined probable time period for each of the one or more subregions a remaining period of time becomes legitimate, after the expiration of which a warning must be taken to take protective measures in the one or more subareas.

14. The method according to claim 13, wherein the warning information signal corresponds to an indication of a recommended seizure of protective measures or information relating to a detected event in the one or more subareas, the warning information signal only after the remaining time has elapsed

Output unit (260) or storage unit is transmitted.

15. The method of claim 11, wherein the warning information signal corresponds to an indication of a recommended seizure of protective measures or information regarding a detected event in the one or more subregions and according to preset parameters and / or threshold values

Deciding whether to generate and transmit a warning information signal for each of the one or more subdomains; wherein the warning information signal is transmitted to the output unit (260) or memory unit only after the remaining time has elapsed, if at the end of the remaining period of time on the basis of the preset parameters and / or

Thresholds of the reaction models is determined that a warning information signal is to be generated and transmitted.

16. The method according to any one of claims 11 to 15, wherein the calculated information of a spatially and temporally differentiated course of effect is used for detecting further sensor information.

17. The method as claimed in claim 11, wherein the calculated information of a warning information signal is fed back and used to further calculate the spatially and temporally differentiated course of impact, wherein the propagation models take into account feedback information of the determination unit.

18. The method according to any one of claims 11 to 17, wherein the propagation models is determined taking into account at least one of the following parameters:

Topography of the subareas; and land cover of the subareas;

19. The method according to any one of claims 11 to 18, wherein the propagation models is determined taking into account at least one of the following parameters: affected land area of the subregions;

The location and / or spread of critical facilities in the subregions;

Characteristics of possible evacuation routes within and / or between the subregions;

Population density and / or population distribution of the subregions; Expects evacuation speed within and / or between subregions; assumed expected time of arrival of the event at the subareas;

Age and / or gender distribution of the population within and / or between the subregions; Topography of the subareas; and

Land cover of the subareas.

20. The method according to any one of claims 11 to 19, wherein from the determined probable time period for the affected sub-areas an expected effect on the affected sub-areas is calculated.

A method according to any of claims 11 to 20, wherein the early warning system is a tsunami early warning system.

22. The method according to any one of claims 11 to 21, wherein the propagation models and the reaction models are realized in a single model.

A computer-readable medium comprising computer-readable instructions that, when executed by a computer, comprise a method for computer-aided processing and evaluation of information from a plurality of sensors (120) in an early warning system for detecting natural events or technical events according to any of claims 11 to 22.