Method for determining a fill level change in a container, and data processing device for determining a fill level change

Abstract

The invention relates to a method for determining a fill level change (ΔFE) in a container, in particular for determining an increase in the fill level of a waste container. In order to be able to cost-effectively determine a fill level change (ΔFE), the invention proposes detecting position data of mobile devices, in particular smart phones, and determining the fill level change (ΔFE) from a number (n) of mobile devices which are moved into predefined surroundings around the container within a predefined time interval, in particular are moved into surroundings which are less than 50 m away from the container. The invention also relates to a data processing device in which position data of containers, in particular waste containers, are stored and which is configured to receive position data of mobile devices, in particular smart phones.

Description

[0001]Method for determining a fill level change in a container and data processing device for determining a fill level change The invention relates to a method for determining a fill level change in a container, in particular for determining an increase in the fill level of a waste container. The invention further relates to a data processing device for determining a fill level change. Methods for determining a fill level and for determining a fill level change are well known in the art. For example, in garbage cans, waste containers and the like, it is known to install sensors in the container in order to detect a fill level and, if necessary, to make it available online in a database or the like, so that the container can be emptied not only based on time, but also based on the fill level.However, it has been shown that the prior art methods are cost-intensive. This is where the invention comes in. The object of the invention is to specify a method of the type mentioned at the outset which enables a cost-effective determination of a fill level or a change in fill level. Furthermore, a data processing device is to be specified for this purpose. The first object is achieved according to the invention by a method of the type mentioned at the outset, in which position data, in particular data relating to a location, a speed and / or a length of stay, are recorded by mobile devices, in particular smartphones, and the change in fill level is determined depending on a number of mobile devices which are moved within a predefined time interval into or within a predefined environment around the container, in particular are moved into an environment which is less than 50 m away from the container.Within the scope of the invention, it was recognized that position data from mobile devices such as smartphones, smartwatches, and the like, which can be purchased anonymously from mobile phone providers, can be used to determine a change in the fill level of a container such as a waste bin. For example, it has been shown that a high number of mobile devices passing through the waste bin is a statistically significant indication of an increase in the fill level of the waste bin. Analogously, a decrease in the fill level of a product container, such as a newspaper display, a coffee machine, a snack vending machine, and the like, also correlates with the number of mobile devices passing through the respective container or vending machine. Thus, in order to determine a change in the fill level using a method according to the invention, it is no longer mandatory to provide a sensor on each container whose fill level is to be determined.This allows the fill level change to be determined in a particularly cost-effective manner. Typically, the fill level change is determined using a correlation between a number of mobile devices that move into a predefined area around the container within a predefined time interval and the fill level change based on the number of mobile devices that move into a predefined area around the container within a predefined time interval. The correlation can be linear or non-linear. The number of mobile devices can therefore be understood, for example, as all mobile devices that move into the immediate vicinity of the container or are moved within the immediate vicinity. However, the number of mobile devices to be considered for determining the fill level change of the container can also be just a subset of these mobile devices in the immediate vicinity.For example, mobile devices that are in the immediate vicinity or are moved into the immediate vicinity but exceed a predefined speed or are only in the immediate vicinity of the container for a very short time can be automatically excluded, i.e. not taken into account when calculating the number. When calculating the number to be considered for determining the fill level change, all mobile devices can be filtered so that only those mobile devices that are highly likely to cause a fill level change are taken into account for determining the fill level change. Currently, a positioning accuracy of around 25 meters can be achieved with mobile phone data, which is why an area of ​​around 50 meters is preferably considered as the predefined environment around the container or area close to the container.However, future mobile radio generations will enable more precise positioning, so that the method can also be implemented with more precisely defined or smaller environments around the container to achieve greater accuracy, for example, an environment that is less than 25 meters, less than 15 meters, less than 10 meters, or less than 5 meters from the container. Accordingly, within the scope of the method according to the invention, an area with a distance of less than 50 meters, preferably less than 25 meters, in particular less than 10 meters, can also be considered an area close to the container in order to accommodate increasing accuracy in determining the positions of mobile devices.By recording the position data of the mobile devices, not only can the number of mobile devices that pass the container within a certain time interval be determined, but also, for example, the speeds of the mobile devices and the length of time they spend in the immediate vicinity of the container can be recorded, in particular by recording the position data of the mobile devices at regular intervals, for example, a position or coordinate recording every second. Accordingly, both the number of mobile devices that are moved into the immediate vicinity of the container and the number of mobile devices that are already in the immediate vicinity of the container or in the predefined environment can be used to determine the increase in fill level. In particular, certain movement patterns within the area can be used to determine the relevant mobile devices or to determine the number.It has been shown that, for example, a decreasing speed of the mobile devices in an area around the container or a minimum stay time exceeding a predetermined duration can be an indicator of a change in the fill level, and at the same time a speed of the mobile device exceeding a predefined speed is an indication that the mobile device is being moved in a vehicle and therefore cannot be causally related to a change in the fill level.It is therefore advantageous if the fill level change is determined depending on a number of mobile devices that are moved within a predefined environment around the container with a predefined movement pattern, in particular a speed within a predefined range, for example, 1 km / h to 10 km / h, a temporary decrease in speed in an area near the container, a stay in an area near the container that exceeds a predefined minimum stay time, and / or the like. Thus, the number to be considered when determining the fill level change is filtered, for example, according to speeds or speed changes, which is why the number then only includes a subset of all mobile devices located in or entering the vicinity.Of course, mobile devices that meet multiple movement pattern criteria can also be used to determine the fill level change, for example, if they are moved at a speed within a predefined range and are simultaneously located in an area around the container for a predefined duration. Movement patterns are therefore understood here as positions, speeds, accelerations, and speed profiles, possibly in conjunction with a position relative to the container, for example, a temporary reduction in speed in an area near the container, which can be recorded by capturing the position data from mobile devices and used to determine the fill level change.This makes it easy and automated to exclude, for example, smartphones moving past a container in a vehicle at a (high) speed unattainable for pedestrians from the calculation of the number of people passing by, which is taken into account for determining the fill level change. A relationship between a fill level change and the number of mobile devices passing by the container can generally be determined in various ways, for example, by estimating it based on empirical values. For example, previous filling or emptying intervals and data regarding the position of the container within these intervals could be used.In order to enable a particularly accurate determination of the fill level change, it is particularly advantageous if a correlation of the number of mobile devices that are moved into or out of a predefined environment around the container within a predefined time interval with the fill level change within this time interval is determined by recording the actual fill level in the container at several points in time and comparing it with the position data of the mobile devices between these points in time, after which the correlation for determining the fill level change within this time interval is determined depending on the number of mobile devices that are moved into the predefined environment around the container within a predefined time interval.The predefined time interval can be, for example, one hour, one day, one week, or one month, depending on the container type and location. The time interval for which the number of new mobile devices entering an environment is recorded therefore corresponds to the time interval to which the fill level change refers. For example, by combining data relating to an actual fill level with position data from mobile devices, it can be determined that a certain number of mobile devices passing the container, for example 1,000 mobile devices passing the container at a distance of less than 50 meters, in particular less than 10 meters, lead to a fill level change of the container of, for example, 10 percent. This can create a correlation or dependency of a 10 percent fill level change per 1,000 mobile devices.Accordingly, based on position data from mobile devices and the correlation found, a statistical probability for a time when the container is completely full can be calculated and emptying can be planned accordingly. In this context, mobile devices passing the container are considered to be mobile devices that are moved into a predefined environment around the container, for example an environment that is a maximum of 50 meters away from the container. It is advantageous if the actual fill level is recorded manually, in particular by a person driving the vehicle emptying or filling the container. This person can determine the fill level with or without the aid of a device, for example with a container scale on a vehicle, with which a fill level can be determined with a high degree of accuracy, especially when emptying a waste glass container.For example, a driver of a garbage truck can use an app or similar to enter whether a recently emptied garbage bin was partially or completely full, or whether there were even objects next to the bin, in order to determine the actual fill level at a specific time before emptying. With this method, another known fill level can be recorded at the same time based on the emptying, so that a fill level before and a fill level after emptying can be determined. Based on the different actual fill levels recorded in this way, an actual change in fill level between two emptying processes can be determined. This change can be compared with the number of mobile devices passing the container in the corresponding time unit to determine a correlation. In order to then predict a fill level based on the correlation.to determine a change in the fill level in the container based on position data from mobile devices. Alternatively or in addition to manual fill level detection, the actual fill level can also be detected automatically using a sensor. The sensor can be located on or in the container, or in or on a vehicle used to fill or empty the container, for example, a sensor that detects the force required to lift a waste container for emptying. In this context, it is advantageous if the fill level is detected using a sensor, in particular an infrared sensor, laser sensor, radar sensor, or ultrasonic sensor.The sensor can, for example, be arranged on the lid of a waste container and measure a distance from the lid to an object in the waste container continuously or at predetermined times, for example every 15 minutes, in order to determine the fill level, either absolutely, for example in liters, and / or relative to the container size as a percentage. Alternatively or additionally, the sensor can be arranged on or in a vehicle used to fill or empty the container. It has proven useful to connect the sensor to a data processing device via a data link, in which the correlation is calculated from position data from the mobile devices and data relating to the fill level recorded by the sensor.It is advantageous if the determination of a fill level change takes place in several containers, wherein in at least one container the actual fill level is recorded manually and / or with a sensor and based on the actually recorded fill level a correlation between the actual fill level change and the number of mobile devices is determined, after which the increase in the fill level of this additional container is determined based on this determined correlation and a number of mobile devices which are moved within a predefined time interval into a predefined environment around at least one additional container, at which additional container the actual fill level is not recorded.Thus, by combining a single sensor with position data from mobile devices, the fill levels of a wide variety of containers can be determined simultaneously. This can result in considerable cost savings compared to prior art methods, which require a sensor for each container. For example, a fill level change of 10 percent for 1,000 mobile devices passing the container at a distance of less than 10 meters, which is detected with a sensor for one container, can be used to infer a corresponding correlation for another, similar container. So for the other container, it is sufficient to compare the position data of the mobile devices with the position of the container in order to be able to determine a fill level change in this other container as well, without a sensor. Accordingly, with known correlations orDue to the correlation between the number of mobile devices and changes in fill level, a physical sensor on the containers is no longer required; instead, the fill level of a large number of containers can be determined using the method purely based on position data from mobile devices. It has been shown that, depending on various parameters, different filling and emptying characteristics can be observed for containers. Such parameters include, for example, a position or installation location, in particular near a market, a parking lot, a bar, or a football stadium, container type, container volume, previous collection interval, access to the container (always, depending on the time of day, or depending on the season), population density around the container, weather, and the like. It is therefore expedient to treat similar containers similarly, including with regard to determining the fill level or change in fill level using position data from mobile devices.Particularly preferably, groups of similar containers are formed, and each group of containers has at least one sensor, so that different correlations are determined for the individual groups. The fill level changes of the containers are then determined according to the groups based on the position data of the mobile devices and the respective correlations. This allows a model to be created that can subsequently be used completely sensor-free yet with high accuracy to determine the fill level changes in the individual containers.For example, it has been shown that waste bins at sports stadiums have a stronger correlation with position data from mobile devices, especially at the end of a football match, than waste bins located near bus stops, especially since many people passing a bus stop are usually on a passing bus and are therefore unable to fill the bin. Accordingly, the creation of groups has proven effective, taking into account differences arising, for example, from locations or times of day. To determine the individual groups, it can be provided, for example, that actually recorded data regarding the fill level of different bins is compared with position data from mobile devices in order to identify groups of bins in which the fill level changes are similarly dependent on the number of mobile devices passing the bin.Similar containers are then grouped together and determined by fill level changes depending on mobile devices entering the vicinity with a similar relationship within the group. The position data of the mobile devices can be recorded and / or measured in a variety of ways. Preferably, the position data of the mobile devices is determined using base stations or transmitting stations and / or satellites, in particular by means of propagation time measurements and / or signal strength measurements. Methods for determining positions by propagation time measurements are well known both in mobile radio networks, where propagation times of radio signals between the base station and the mobile device are measured, and in satellite-based positioning, in particular positioning using GPS.It is also known to determine the position of mobile devices using cell phone positioning. This can be done by triangulating three signals with their respective signal strengths relative to the cell phone. The mobile phone connects to a cell phone and determines the signal strengths for two other cells in the immediate vicinity. From these three pieces of information (three signal strengths, each relative to a cell phone) and the knowledge of the precise GNSS / location position of the respective cell phone or base station, the current position of the mobile phone can then be determined.This method can be further improved if Wi-Fi sniffing is permitted on mobile phones. This involves using the Wi-Fi module in the mobile device to check which Wi-Fi network, with which SSID and signal strength, is near the mobile phone. Knowing the GNSS / location of the Wi-Fi signal can be used to determine the position of the mobile phone. Since there are generally considerably more radio cells in cities than in rural areas, cell site location in cities often works with significantly greater accuracy, for example, with an accuracy of 25 meters. By using base stations or satellites for position determination, separate devices for detecting mobile devices on the containers can be completely eliminated, which means that the method can be easily implemented even with a large number of containers. The positions or position data of the mobile devices are particularly preferably determined or transmitted via mobile data or a mobile network.measured, for example via GSM positioning, cell-of-origin, timing advance (TA), uplink time difference of arrival (UTDOA), enhanced Observed Time Difference (E-OTD). Alternatively or additionally, the position can also be determined using satellites, for example the Global Navigation Satellite System (GNSS) or a GPS sensor arranged in the mobile device, with data from the mobile device being transmitted via a cellular network, for example. In this way, position data can be determined at regular intervals and the speeds of the mobile devices can also be recorded. Determining the position data via the cellular network or via GPS using a sensor arranged in the mobile device has the particular advantage that no special devices for detecting mobile devices need to be arranged on the container(s), which means that the method can be implemented with particularly little effort.In a method for determining the fill level of a container, in particular for determining the fill level of a waste container, wherein an absolute value of the fill level is determined and a current fill level is calculated based on a determined fill level change, it is advantageous if the fill level change is determined using a method according to the invention. An absolute value of the fill level can be recorded, for example, during manual filling or emptied of the container, in particular with 100 percent fill level when filling, for example, a product container and 0 percent fill level when emptying a waste container. Starting from this actual fill level, a fill level or a current fill level can then be determined using the value of the fill level change determined using position data from mobile devices or a temporal progression of this fill level change.A temporal progression of the fill level can be determined, based on which determined fill level, the refilling of the container with products or the emptying of a waste container can then be planned. To determine a temporal progression of the fill level change, it is therefore sufficient to know a temporal progression of the number of mobile devices that pass the respective container, for example, pass the container within a predefined time interval of 15 minutes or within such a time interval into an environment around the container or are moved within the environment with a certain movement pattern such as a reduction in speed or are close to the container for a certain time.From the temporal progression of the fill level change determined in this way, a temporal progression of the fill level can then be deduced by summing up over time for known fill levels at specific points in time. It is particularly preferred that the absolute value of the fill level be determined when the container is emptied or when the container is filled. This can be done manually or automatically, in particular by comparing position data of a vehicle that is filling or emptying the container with the position of the container, especially since this vehicle is generally only approached for filling or emptying. The method is preferably carried out as a computer-implemented method on a data processing device such as a computer.The method can be used not only to determine a current fill level value, but also to predict a future fill level in order to be able to plan refilling or emptying. For example, historical data on mobile devices passing the container can be used to predict a time at which the container is completely full or empty and to plan refilling or emptying accordingly in good time. It is therefore advantageous in a method for predicting a fill level of a container, in particular for predicting a complete fill level of a waste container, wherein an absolute value of the fill level is determined and a future fill level is predicted based on a determined fill level change, if the fill level change is determined using a method according to the invention.In a method for filling or emptying a container, in particular for emptying a waste container, wherein the container is filled or emptied by a vehicle, which vehicle carries out the filling or emptying depending on the fill level of the container, it is advantageous if the fill level is determined or predicted in a method according to the invention. The vehicle can be moved manually or automatically to the container. This makes it easy to prevent, using a waste container as an example, a partially filled waste container from being approached, thus avoiding unnecessary logistics costs, or a full waste container from not being emptied, as is often the case with fixed emptying intervals.The further object is achieved according to the invention by a data processing device in which position data of containers, in particular waste containers, is stored and which is configured to receive position data from mobile devices, in particular smartphones. The data processing device is configured to determine a fill level change based on received position data from mobile devices and position data of the containers, in particular in a method according to the invention. The data processing device can, for example, be connected to a mobile communications provider in order to receive position data from mobile devices and subsequently determine fill level changes and fill levels of the containers based on determined relationships between position data from mobile devices and fill level changes.It is advantageous if the data processing device is configured to receive absolute values ​​of fill levels and to determine and output a fill level based on the determined fill level change and the received absolute values. The fill level determined by the data processing device can then be used, for example, to trigger filling or emptying of the container. The data processing device is typically configured to continuously compare the position data of the mobile devices with the position data of the containers or to determine the distances of the mobile devices to the individual containers. In this way, the number of mobile devices newly entering the vicinity of the individual containers within a predefined time interval can be easily determined automatically. This number can then be used to calculate the fill level changes of these containers.Within the scope of the method according to the invention, a mobile communications provider can, for example, supply data relating to the coordinates of mobile devices in conjunction with time information, after which speeds, accelerations, and positions, or paths and speed profiles, are calculated in the data processing device from the location and time information. Accordingly, it is preferred that the data processing device be configured to calculate speeds, accelerations, the duration of stay of the mobile devices in an area near the container, and the like from the location and time information. Alternatively, a mobile communications operator can, of course, also supply data relating to speeds, accelerations, duration of stay, and the like.In addition to the positions of the mobile devices, speeds, accelerations, and / or the length of time the mobile devices are in the vicinity of the containers can also be used to determine the change in fill level. In this context, it is preferred that the data processing device is configured to output a signal when a current or predicted fill level exceeds or falls below a predefined threshold in order to initiate filling or emptying of the container depending on this signal, in particular by means of a vehicle. For example, the signal from the data processing device can be incorporated into the route planning of a garbage truck, so that a waste container is emptied exactly when it is completely full. Further features, advantages, and effects of the invention will become apparent from the exemplary embodiment presented below.In the drawing, to which reference is made: Fig. 1 shows a flow diagram of a method according to the invention; Fig. 2 shows groupings of waste containers; Fig. 2 is a diagram showing a temporal progression of a determined fill level and a number of mobile devices. Fig. 1 shows a flow diagram of a method according to the invention for determining a fill level using the example of a waste container. In a first method step 1, groups of similar containers are formed, i.e. groups of containers in which a filling behavior depends in a similar way on the number of mobile devices passing through the container. For example, containers located at bus stops can be assigned to the same group and containers located in front of supermarkets can be assigned to a different group in order to divide the containers whose fill level FE is to be determined into groups of containers that are as similar as possible.The first process step thus produces groups G1…Gn, each containing one or more containers or waste bins C11…Cnm, i.e., 1 to m (1…m). Here, FE always refers to determined, predicted, or calculated fill levels, and FT refers to an actually measured fill level, for example, with a sensor in the waste bin C11…Cnm or by manual input, especially during emptying. Accordingly, ∆F refers to the same. E Determined or calculated level changes and ∆FT actually recorded level changes. It is understood that with a sufficiently high accuracy of the method, which can be determined by precisely determining the relationships or correlations and determining the mobile devices passing through the container as precisely as possible, the actual level F T always the determined fill level F EIn a second process step 2, in each group of containers, at least one representative container R1…R n selected and equipped with a sensor with which the actual level F T of the container is measurable. This can be, for example, an infrared sensor or an ultrasonic sensor, which detects a distance between a container lid and objects located in the container in order to determine an actual fill level FT absolutely and / or relatively. The second method step thus provides the identification of containers R1…Rn representative of the individual groups or their position data, whereby these containers are equipped with sensors at the end of method step 2. In a third method step 3, correlations or relationships Z1…Z nbetween a number n1…nn of mobile devices passing through the respective containers and fill levels FT1…FTn or fill level changes ∆FT1…∆FTn at the representative containers R1…R n of the individual groups G1…G n , determined. For this purpose, position data from mobile devices such as position data from mobile phones, smartwatches and the like are obtained, usually from a mobile phone provider, and numbers of the individual representative containers R1…R n mobile devices passing or entering the vicinity, with actual data on fill levels F T1 …F Tn which are compared with the sensors in these waste containers R1…R n This correlation or the determined relationship Z1…Z n is subsequently used to determine the determined level changes ∆F E11 …∆F Enmor to determine the measured fill levels ∆FT11…∆FTnm on the other containers in this group. When determining the number n 1… n n In the sense of a filter, mobile devices can also be automatically excluded that pass the container at a speed exceeding a predefined speed, for example, more than 30 km / h, especially since this is a strong indication of a mobile device being moved in a vehicle, which mobile device does not cause a change in the fill level. Alternatively or additionally, the number n to be considered for determining the change in fill level can be 1… n nThe number of mobile devices can be restricted by applying filter parameters, so that, for example, only mobile devices are considered for which a temporary decrease in speed in an area close to the container or a stay in the immediate vicinity of the container that exceeds a certain minimum stay time are observed. The number of mobile devices to be considered for determining the fill level change can therefore include all mobile devices in the immediate vicinity or be a subset of these mobile devices, whereby filtering can be performed, for example, depending on speed, acceleration, or stay time.For example, with regard to group G1 of waste bins C11…C1m located at bus stops, this can result in a correlation Z1 of 10 percent or 5 litres fill level increase for every 1000 mobile devices passing a bin of this group at a predefined maximum distance, for example mobile devices passing the bin at a distance of less than 10 metres, and with regard to the waste bins C located at supermarkets. 21 …C 2ma second group G2, a relationship Z2 of 30 percent or 15 liters of fill level increase per 1000 mobile devices that pass the container within a predefined maximum distance or enter a 10-meter area around the container. It is understood that when determining the relationship Z1…Zn, reference can also be made to containers of different sizes, so that, for example, the determined relationship Z2 for the waste container C2 located at the bus stop also refers to a fill level increase ∆F. E11… ∆F Enm at another waste container at a bus stop, which has a different volume. This can be done, for example, by a size factor that is linearly incorporated into the calculation of the fill level change ∆F E11… ∆F EnmThe third method step 3 thus includes the number n1…nn of mobile devices n1…nn passing the representative waste containers R1…Rn of the individual groups G1…Gn, possibly after applying a filter that takes speed, length of stay, and the like, and data from the sensors on the actual fill levels FT1…FTn in these waste containers C1…Cn, in order to determine relationships Z1…Zn for the individual groups G1…Gn. It is understood that the third method step 3 can also include only position data from the mobile devices, and the number n1…nn of mobile devices passing the individual waste containers C1…Cn could be determined in the data processing device by comparing the position data of the mobile devices with the (usually unchangeable) position data of the waste containers C1…Cn.The third process step 3 thus provides corresponding relationships Z1…Zn between the mobile devices passing the containers and the determined fill level changes ∆F. T1… ∆F Tn for the individual groups G 1… G n For example, a relationship Z1…Zn between a fill level change ∆FT1…∆FTn and the number n1…nn of mobile devices passing through the waste containers C11…Cnm for the first group G1 can be determined simply by dividing the actual fill level change of the representative waste container of the first group by the number of mobile devices passing through the first container in the same time interval, for example within one hour, i.e. Z1 = ∆F T1 / n1where ∆FT1 is the difference between the fill levels F determined by the sensor on the representative waste container R1 of the first group G1 T1at the end and at the beginning of the time interval. In a fourth process step 4, the determined relationships Z1…Zn between the numbers n11…nnm of mobile devices passing the respective waste containers C11…Cnm and the actual fill level changes ∆F T1… ∆F TnBased on position data not only relating to the waste containers R1…Rn, which are equipped with sensors, but also position data relating to all waste containers C11…Cnm, conclusions are drawn about the fill level changes ∆FE11…∆FEnm in all containers C11…Cnm. Therefore, the fourth method step 4 includes the numbers n11…nnm of mobile devices relating to all containers C11…Cnm or the temporal progression of the numbers of mobile devices passing through the containers, as well as the relationships or correlations Z1…Zn determined in the third method step 3 with regard to the mobile devices passing through the containers C11…Cnm, separately according to groups G1…Gn, and the fill level changes ∆FT1…∆FTn.Here too, of course, it can be provided analogously to the third method step 3 that the numbers n11…nnm of mobile devices are restricted by applying one or more filters to mobile devices which have an increased probability of causing a fill level change ∆FT1…∆FTn, whereby here too the speed of mobile devices and the length of stay can be filter criteria in order to be able to exclude, for example, mobile devices moving at high speed in a vehicle which pass the container C11…Cnm at a short distance but at a speed which is too high for a fill level change ∆FT1…∆FTn.For example, it has also been shown that a stay of more than one minute in an area near a container is highly likely to indicate a change in the fill level, especially if the mobile device is stationary within the container's vicinity and there are no other objects in the area that could make such a stay plausible. It goes without saying that the same filter criteria are generally applied in process steps 3 and 4. In the case of a linear relationship between the number of waste containers C. 11… C nm passing mobile devices and the level change ∆F T1… ∆F Tn or increase in level can thus increase the level or change in level ∆F E11… ∆F Enm in the individual containers C 11… C nm by simply multiplying the number n 11 …n nm which the respective container C 11… Cnm passing mobile devices with the respective contexts Z1…Z n be determined so that the determined level changes ∆FE11…∆FEnm at the individual waste containers C11…Cnm of groups 1 to n are as follows: Group G1: ∆F E11 = n 11 x Z1 to ∆F E1m = n 1m x Z1… … Group Gn: ∆FEn1 = nn1 x Zn to ∆FEnm = nnm x Zn or generally ∆F Enm = n nm xZ n where ∆F E11 up to ∆F Enm in turn the determined fill level changes on the individual containers in the individual groups G1…G n The result of the fourth process step 4 is the determined fill level changes ∆FE11 to ∆FEnm in the individual waste containers C11…Cnm of the groups G1…Gn. In a fifth process step 5, the fill level changes ∆F determined in the fourth process step 4 are E11 … ∆F Enmor fill level increases are combined with absolute values ​​in relation to actual fill levels FT11…FTnm, in the case of waste containers C11…Cnm typically emptyings, so that starting with known actual fill levels FT11…FTnm at emptyings i.e. fill levels FT11…FTnm of zero percent, and an integration over time or summation of the fill level changes ∆FE11 … ∆FEnm determined in the fourth process step, which here consist exclusively of the numbers n 11 …n nm which the containers C 11… C nm passing mobile devices, to current (calculated) fill levels F E11 … F Enm or a temporal progression of the same can be concluded. Thus, in the fifth step 5, point-by-point data F T11(tp) …F Tnm(tp)in relation to absolute values ​​of the fill levels of the individual containers in connection with the points in time at which these absolute values ​​existed. From these point-by-point data F T11(tp) …F Tnm(tp) is thus combined with the data determined in the fourth process step 4 regarding the level changes ∆F E11 … ∆F Enm of the individual containers C 11… C nm on a temporal progression of fill levels F E11 … F Enm in the individual containers C 11… C nmclosed, so that in the fifth method step 5 the current fill levels FE11…FEnm or their temporal progressions are obtained. With appropriately timely transmission of the number of mobile devices passing through the containers, online monitoring of the fill levels FE11…FEnm of the individual containers results, without the containers having to be equipped with sensors for this purpose. In a sixth method step, not shown, a future fill level FE11…FEnm in the individual containers C11…Cnm could be predicted based on historical data on fill levels or positions of mobile devices. The fill levels FE11…FEnm or their temporal progressions obtained in the method according to the invention can subsequently be used for level-dependent emptying of the waste containers.The process is typically implemented in a data processing device that, on the one hand, is connected to a mobile operator to receive position data from them, and, on the other hand, initially receives data from sensors in the waste containers and, starting with process step five, data on actual fill levels or completed emptying. Figure 2 shows groups of containers C grouped according to filling behavior. 11 to C nm , where representative containers R1 to R n in the individual groups G1 to G n which are used to determine the relationships Z1 to Z n are equipped with sensors as described above. As can be seen, the containers are divided into n groups G1 to G n grouped, three of which are shown, with containers C 11 to C nm each group G1 to G nexhibit similar behavior in terms of filling depending on the mobile devices passing through the container. Containers C 11 to C nm of the first group G1, for example, waste bins at bus stops, the bins C 21 to C 2m the second group G2, for example, containers at supermarkets and containers C n1 to C nm the nth group G ncan be, for example, waste bins at sports stadiums. Fig. 3 schematically shows two diagrams, with the upper diagram showing a temporal progression of the number n23 of mobile phones passing in the vicinity of a waste bin within a time interval, for example, within 15 minutes, and the lower diagram showing a temporal progression of a determined fill level FE23 of this waste bin C23. In the example, the number n23 of mobile phones passing through the waste bin C23 and the fill level F23 are shown in relation to the third waste bin of group 2, which is therefore designated C 23 Accordingly, the upper diagram, which shows the progression of the number of mobile phones passing through the waste bin, is labelled t [h] on the abscissa and n on the ordinate. 23The lower diagram is labeled t [h] on the abscissa and F on the ordinate. E23 As can be seen, with increasing number n 23 of mobile phones in close proximity to waste container C 23 , for example in a close range of less than 10 meters around the waste container C 23 , a determined fill level F E23 of the waste container increases more, so that a gradient of the fill level F E23 for example, a course of an integral of the number n 23 which fills the waste container C 23passing mobile phones. The fill level change ∆FE23 thus increases approximately linearly with the number n23 of mobile phones passing through the vicinity. It is understood that the number n23 of mobile phones corresponds to a number n23 of new mobile phones in the vicinity in a predefined time interval, especially since it can be assumed that people who are in the vicinity for an extended period of time do not repeatedly change the fill level F T23 of the container. In Fig.3, an emptying time t E The time at which the waste container is emptied is shown and is also entered into the data processing system for level determination. As shown, this changes the fill level F E23 or the level is set to zero, after which a further change in level ∆F E23 again linear with the number n 23of mobile phones in the immediate vicinity of the waste container. Analyses have shown that the fill levels determined using the method according to the invention correspond to the actual fill levels of the individual containers with a high degree of probability and high accuracy. Consequently, the lower diagram in Fig. 3 can also be viewed as a temporal progression of the actual fill level of this container. Thus, as shown, intervals with high mobile phone traffic correspond to a higher fill level increase, which is why the detection of mobile phones passing a waste container in the immediate vicinity indicates a fill level increase ∆F E23, i.e., the derivative of the fill level with respect to time, and in conjunction with the known emptying times tE, the fill level FE23 can be determined. Historical data on mobile phones in the area around the waste bin can thus be used to determine future mobile phone usage around the waste bin, making it possible to predict the future fill level of the waste bin. This allows the waste bin to be emptied exactly when it is completely full.Although the method according to the invention has been described here in connection with a waste container that fills as mobile phones pass by, it is understood that the method can generally be implemented to determine a change in the fill level of containers depending on mobile devices entering the vicinity of the containers, for example, to determine the fill level of a coffee machine in a publicly accessible location and to be able to refill it based on the fill level. The fill level determination using the method according to the invention is thus particularly cost-effective and, at the same time, highly accurate.

Claims

Patent claims 1. Method for determining a fill level change (∆FE) in a container, in particular for determining an increase in the fill level (FE) of a waste container (C), characterized in that position data from mobile devices, in particular smartphones, are recorded, and the fill level change (∆FE) is determined depending on a number (n) of mobile devices that are moved within a predefined time interval into or within a predefined environment around the container, in particular into an environment that is less than 50 m away from the container. 2.Method according to claim 1 or 2, characterized in that the fill level change (∆FE) is determined as a function of a number (n) of mobile devices which are moved within a predefined environment around the container (C) with a predefined movement pattern, in particular a speed within a predefined range, for example 1 km / h to 10 km / h, a temporary reduction in speed in an area near the container, a length of stay in an area near the container which exceeds a predefined minimum length of stay, for example one minute, or the like.

3. Method according to claim 1 or 2, characterized in that a correlation (Z) of the number (n) of mobile devices which are moved within a predefined time interval into or within a predefined environment around the container with the fill level change (∆F. E) within this time interval by recording an actual fill level (FT) in the container at several points in time and comparing it with the position data of the mobile devices between these points in time, after which the correlation (Z) is used to determine the change in fill level (∆F E ) is determined depending on the number (n) of mobile devices that are moved into the predefined environment around the container or within this environment within a predefined time interval.

4. Method according to claim 3, characterized in that the actual fill level (FT) is recorded manually, in particular by a person driving a vehicle for emptying or filling the container.

5. Method according to claim 3 or 4, characterized in that the detection of the actual fill level (FT) is carried out with a sensor, in particular with an infrared sensor, laser sensor, radar sensor or ultrasonic sensor, which is arranged in or on the container.

6. Method according to claim 5, characterized in that the sensor is connected via a data connection to a data processing device in which position data of the mobile devices are received and in which the correlation (Z) is calculated from position data of the mobile devices and data detected by the sensor with regard to the actual fill level (FT).

7. Method according to one of claims 1 to 6, characterized in that the determination of a fill level change (∆FE) is carried out for several containers, wherein for at least one container a detection of the actual fill level (F T ) manually and / or with a sensor and based on the actually recorded fill level (FT ) a correlation between the actual level change (∆F T ) and the number (n) of mobile devices is determined, after which, based on this determined correlation and a number (n) of mobile devices which are moved within a predefined time interval into a predefined environment around at least one further container or in this environment, at which further container no recording of the actual fill level (F T ), the increase in the filling level (F E ) of this further container is determined.

8. Method according to claim 7, characterized in that groups (G) of similar containers are formed and each group of containers has at least one sensor, so that different correlations are determined for the individual groups (G), according to which the fill level changes (∆F E) the containers are determined according to the groups (G) based on the position data of the mobile devices and the respective correlations.

9. Method according to one of claims 1 to 8, characterized in that the position data of the mobile devices are determined using base stations and / or satellites, in particular by means of propagation time measurement and / or signal strength measurement.

10. A method for determining a fill level (FE) of a container, in particular for determining a fill level (FE) of a waste container (C), wherein an absolute value of the actual fill level (FT) is determined and a current fill level (FE) is calculated based on a determined fill level change (∆FE), characterized in that the fill level change (∆FE) is determined in a method according to one of claims 1 to 9.

11. A method according to claim 10, characterized in that the absolute value of the fill level (FT) is determined when the container is emptied.

12. A method for predicting a fill level (FE) of a container, in particular for predicting a complete filling of a waste container (C), wherein an absolute value of the fill level (FT) is determined and a current fill level (FE) is calculated based on a determined fill level change (∆F E ) a future fill level (F E ), characterized in that the change in level (∆FE ) is determined in a method according to one of claims 1 to 9.

13. Method for filling or emptying a container, in particular for emptying a waste container (C), wherein the container is filled or emptied by means of a vehicle, which vehicle carries out the filling or emptying depending on a fill level (F E ) of the container, characterized in that the filling level (F E ) is determined in a method according to one of claims 10 to 12.

14. Data processing device in which position data of containers, in particular of waste containers (C), are stored and which is set up to receive position data from mobile devices, in particular smartphones, characterized in that the data processing device is set up to determine a change in fill level (∆F E), in particular in a method according to one of claims 1 to 9.

15. Data processing device according to claim 14, characterized in that the data processing device is set up to receive absolute values ​​of fill levels and to determine and output a fill level (FE) based on the determined fill level change (∆FE) and obtained absolute values ​​of fill levels (FT).

16. Data processing device according to claim 14 or 15, characterized in that the data processing device is configured to output a signal when a current or predicted fill level (FE) exceeds or falls below a predefined threshold value in order to initiate filling or emptying of the container depending on this signal, in particular by means of a vehicle.

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