• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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    • 专利摘要:
    A lighting device is controlled to illuminate at least one scene (151-158) in an area (150) with a first light characteristic. Measurement data are received which are indicative of a behavior of a flow of people (180) in the area (150) when illuminating the at least one scene (151-158) with the first light characteristic. A second light characteristic is then determined based on the measurement data. The lighting device is activated to illuminate the at least one scene (151-158) with the second light characteristic.
    公开号:AT17251U1
    申请号:TGM109/2017U
    申请日:2017-05-15
    公开日:2021-10-15
    发明作者:
    申请人:Zumtobel Lighting Gmbh；
    IPC主号:
    专利说明:

    description
    INTELLIGENT CONTROL OF LIGHT CHARACTERISTICS
    TECHNICAL AREA
    Various examples of the invention generally relate to techniques for controlling a light characteristic of a lighting device for illuminating at least one scene in an area. Various examples of the invention relate in particular to determining the light characteristic based on measurement data which are indicative of a behavior of a flow of people in the area.
    BACKGROUND
    The directing of flows of people can be desirable in various applications. Examples include, in particular, buildings or rooms with a comparatively small area per participant in the flow of people. Further examples include buildings or rooms with a high speed of the flow of people, i.e. many participants per time passing through a certain area. In train stations or airports, for example, it may be necessary to control the flow of people in order to avoid the flow of people becoming compressed or delayed.
    It is often possible that the behavior of a flow of people in a building or room varies as a function of time. For example, the flow of people can behave differently in the morning than in the afternoon, etc. Then it may be necessary to actively control the flow of people by acting on the participants in the flow of people in a time-variable manner. Reference implementations use variable signage, for example, to control the flow of people over time. Other reference implementations for directing flows of people use movable guide elements, for example controllable doors or barriers, etc.
    Such reference implementations for directing flows of people can, however, have certain disadvantages and limitations. For example, due to boundary conditions, it may sometimes not be possible or only possible to a limited extent to take active measures to control the flow of people. For example, depending on the installation space, it may not be possible, or only to a limited extent, to provide controllable doors etc. In addition, in some examples, active addressing of participants in a flow of people may not be desired or inefficient.
    BRIEF SUMMARY OF THE INVENTION
    Therefore, there is a need for improved pedestrian flow control techniques. In particular, there is a need for such techniques that overcome at least some of the disadvantages or limitations noted above.
    This object is achieved by the features of the independent patent claims. The features of the dependent claims define embodiments.
    A method comprises the control of a lighting device for illuminating at least one scene in an area with a first light characteristic. The method also includes receiving measurement data that are indicative of a behavior of a flow of people in the area when illuminating the at least one scene with the first light characteristic. The method comprises the determination of a second light characteristic based on the measurement data, as well as the activation of the lighting device for illuminating the at least one scene with the second light characteristic.
    For example, a scene can designate a sub-area of the area. The area can therefore contain several scenes. For example, the area could be a building or a room
    his or some other defined area. For example, the area could have one or more entrances for the flow of people; as well as one or more exits for the flow of people. The trajectory of the flow of people between the one or more entrances and the one or more exits can run along one or more scenes of the area. The trajectory of the flow of people can have branches.
    The flow of people can describe an ensemble of participants. The flow of people can be described, for example, by different behavior, such as the speed of the flow of people, condensation of the flow of people, widening of the flow of people, a branching factor of the flow of people or an attractiveness factor of scenes for the flow of people. Based on such macroscopic properties of the flow of people, the behavior of individual participants can also be predicted. For example, the speed of the flow of people could be indicative of the length of time that individual participants in the flow of people spend on different scenes in the area. Densification of the flow of people can result from the fact that several participants in the flow of people spend a particularly long time in one or more specific scenes in the area. Depending on the geometry of the area, it may be possible that the flow of people has ramifications, so that the branching factor of the flow of people assumes a high value. This means that individual participants in the tower of persons can have the opportunity to choose different trajectories through the area, i.e. to visit different scenes. This can correlate with the attractiveness factor, which can describe the fraction of all participants in the flow of people visiting a particular scene.
    In the various examples described herein, it would be possible that the measurement data are indicative of such characteristics of the flow of people, such as its speed, a compression or a branching factor of one or more of the scenes of the area.
    By determining the second light characteristic based on the measurement data, the light characteristic can be adapted to the measured behavior of the flow of people. This enables the flow of people to be appropriately directed. By monitoring the actual behavior, a particularly precise control of the flow of people can be made possible. Such techniques are based on the knowledge that there can be a connection between the light characteristic used for the at least one scene in the area and the behavior of the flow of people. For example, some light characteristics can appear particularly attractive or unattractive for participants in the flow of people, so that a corresponding control of the flow of people is possible.
    In the various examples described herein, different types of light characteristics can be taken into account. For example, the light characteristic used could be selected from the following group: light intensity; Light contrast; Collotype; and light color. One or more of the aforementioned light characteristics can also be set in combination.
    In the various examples described herein, different target variables for directing the flow of people can be taken into account when determining the second light characteristic. For example, targeted steering of the flow of people along certain trajectories through the area could be desired. The second light characteristic can then be further determined based on such a target variable. Further examples of target variables include: avoidance of compaction; Avoidance of certain scenes; Guidance to specific scenes; Increase or decrease the speed of the flow of people in relation to certain scenes.
    The observed relationship between the behavior of the flow of people and the light characteristics used can be specifically exploited in some examples by a control loop. In some examples it would be possible to implement a control loop that sets a target behavior of the flow of people - for example based on one or more
    reren target variables is determined - has as a reference variable, the measured data as a control variable and the second light characteristic of the lighting device as a control variable. This can therefore mean that the second light characteristic of the lighting device is iteratively changed several times until the measurement data indicate a behavior of the flow of people that corresponds to the target behavior. For example, incremental changes in the second light characteristic could be used to map corresponding changes in the behavior of the flow of people using the measurement data until the flow of people has finally reached the target behavior. When using such a control loop, it may be unnecessary to use a priori knowledge about the behavior of the flow of people as a function of the light characteristics used, for example in the context of a predetermined model. In addition, the flow of people can be directed in a particularly robust manner, i.e. taking into account various time-variable disturbance variables, etc.
    Sometimes, however, there may also be a priori knowledge about the behavior of the flow of people as a function of the light characteristics used. This a priori knowledge can then be taken into account when determining the second light characteristic. An example for taking the a priori knowledge into account uses a model: for example, it would also be possible for the second light characteristic to be determined based on a model of the behavior of the flow of people as a function of the second light characteristic.
    The model can be predetermined. The model can provide an assumption as to how a certain change in the light characteristic used will influence the behavior of the flow of people. The model can therefore anticipate an influence of the second light characteristic on the behavior of the flow of people. By means of such techniques it can be possible to achieve a desired behavior of the flow of people particularly quickly through targeted implementation of the second light characteristic. This enables the flow of people to be directed particularly efficiently.
    In some examples, the model could be trained empirically. Machine learning techniques such as deep learning, etc., can be used for this purpose. Weights of neural networks can be adjusted.
    For such training it would be possible to monitor the measurement data over a period of time, for example over at least 10 minutes, optionally at least 30 minutes, further optionally at least 120 minutes. It has been observed that significant statistics on the behavior of the flow of people can be collected during such periods of time, while at the same time the behavior of the flow of people cannot yet be subject to any changes, or no significant changes. The model could then be trained based on the monitoring of the measurement data. For example, certain basic assumptions that are mapped by the model can be checked on the basis of the monitoring of the measurement data and, if necessary, adjusted if a discrepancy is determined. By training the model, it can be achieved that a particularly precise steering of the flow of people is made possible and the possibly occurring time drift depending on the behavior of the flow of people on the light characteristics used can be adaptively taken into account by adapting the model. In addition, certain local peculiarities of the behavior of the flow of people, which are specific for the area under consideration, for example, can be mapped in the model through the training. This can be particularly advantageous if reference measurement data are also taken into account for training the model, which can be taken into account for other areas - which may have different peculiarities in the behavior of the flow of people.
    In some examples, a larger database could be used for training the model than merely the measurement data that are indicative of the behavior of the flow of people in the respective area. For example, it would be possible for reference measurement data to be received which are indicative of the behavior of at least one further flow of people in at least one further area. Then the model could be trained based on the reference measurement data. In other words, the reference measurement data can be empirical
    Observations concern a connection between the light characteristics used and the behavior of pedestrian flows in areas other than the area to which the model is subsequently applied.
    This can be possible, for example, if certain similarities exist, such as the lighting characteristics used, the types of scenes in the areas or the geometric relationship of the scenes in the areas. Taking into account a particularly large database can make it possible to design the model particularly comprehensively and precisely. In particular, it can thereby be achieved that the model describes the behavior of the flow of people particularly precisely as a function of the light characteristics used. In order to suitably map local peculiarities of the area under consideration in the model, it would be possible, for example, to take more into account measurement data that were recorded for the area under consideration when training the model than reference measurement data that are indicative of the behavior of other flows of people in other areas are.
    The model can describe the dependence of the behavior of the flow of people on the light characteristics used, taking into account one or more parameters. An exemplary parameter would be the geometric arrangement of the at least one scene in the area. It would therefore be possible for the model to describe different behavior of the flow of people as a function of the second light characteristic for different geometric arrangements in the at least one scene in the area. For example, it would be possible for the model to predict a different behavior of the flow of people for a scene that is positioned close to the entrance or exit of the area, depending on the lighting characteristics used, than for a scene that is further away from the entrance or exit of the Area is positioned. Accordingly, it would be possible, for example, that the model for a scene that has to be searched for by participants in the flow of people with no alternative or with few alternatives when moving from the entrance to the exit, predicts a different behavior of the flow of people from the lighting characteristics used than for a scene, for which many alternative scenes are available for participants in the flow of people. By taking into account the geometric arrangement of the at least one scene in the area, the model can predict the behavior of the flow of people particularly precisely as a function of the light characteristics used.
    Another parameter that can be taken into account by the model are, for example, different types of the scenes under consideration. As an alternative or in addition, different types of the area can also be taken into account as parameters. For example, the model could predict a different behavior of the flow of people depending on the lighting characteristics used for a scene that corresponds to a passage area of the area without opportunities to stay for the flow of people than for a scene in which actions by participants of the flow of people are possible or required and thus there are opportunities to stay for the participants in the flow of people. Examples of types of areas include, for example: airports; Railway stations; Office building; Shopping centers; Self-service shops; Hotels; Seminar building; etc. Examples of types of scenes relate to, for example, airports or train stations: entrance area; Waiting room; Security control; Gate area; Retail area; Restaurant area; Transfer area; Customs area; Luggage area; etc. Examples of types of scenes for retail stores include: entrance area; Checkout area; Sales area; Sales area for certain products such as the frozen food department, fresh produce counter, vegetables and fruit, etc .; Restaurant area; Etc.
    Another parameter that can be taken into account by the model relates to one or more environmental parameters. It would therefore be possible, for example, for the model to describe different behavior of the flow of people as a function of the second light characteristic for different values of at least one environmental parameter of the at least one scene and / or the area. Environmental parameters can, for example, be parameters that are defined extrinsically, i.e. not directly dependent on the flow of people. Examples of such environmental parameters can include, for example: time of day; Weather; Where-
    chentag; Daylight brightness; Outside temperature; and internal temperature. For example, the model could periodically predict the same behavior of the flow of people depending on the lighting characteristics used for the same time of day or the same day of the week. Such techniques are based on the knowledge that the participants in the flow of people can vary depending on such environmental parameters. A more precise prediction of the behavior of the flow of people can therefore be made as a function of the light characteristic used if the model takes one or more such environmental parameters into account as parameters. In general, it may be possible for corresponding control data to be received that are indicative of at least one environmental parameter of the at least one scene and / or of the area. For example, such control data can be received by corresponding measuring devices. Other data, such as the day of the week or the time of day, can be received from a central point. The determination of the second light characteristic can then continue to be based on the control data.
    In some examples it would be possible that the second light characteristic is further determined based on a predetermined rate of change starting from the first light characteristic. For example, the rate of change could describe a maximum change in the light characteristic per time. This ensures that the light characteristics are not changed abruptly or abruptly, which could have negative effects on the flow of people. In the context of the control loop described above, such a rate of change could be taken into account as the inertia of the control loop or as a maximum incremental change between control cycles. For example, a change in the color temperature could be implemented over a period of several minutes, for example not less than 5 minutes or not less than 15 minutes. A change in the brightness which, for example, exceeds a relative strength of 5% compared to the maximum brightness, could also be implemented over a period of several minutes, for example not less than 5 minutes or not less than 15 minutes. In the various examples described herein, it would be possible for the rate of change in the light characteristic to correspond to a period of time which is greater than a typical dwell time of participants in the flow of people in the at least one scene. In this way it can be avoided that individual participants in the flow of people are unsettled or negatively influenced by the change in the light characteristics. This can prevent uncontrolled behavior of the flow of people.
    In some examples, it may be possible for one or more measuring devices to be activated for acquiring the measurement data. A wide variety of types and types of measuring devices can be used in the various examples described herein. Depending on the type of measuring device used, different observables are measured. Examples of such measured observables include: instantaneous speed of the flow of people at one or more scenes; average speed of the flow of people in one or more scenes; and position of participants in the flow of people in relation to one or more scenes.
    For example, the instantaneous speed of the flow of people could designate the speed of the flow of people defined over a particularly short time interval, for example integrated over one or a few seconds. In contrast, the averaged speed of the flow of people could have a greater time average of the speed, which can in particular be greater than the typical rate of change in the speed of the flow of people. For example, the average speed of the flow of people could be averaged over a period of minutes or more. Such an averaged speed - or in general averaged observables in relation to the flow of people - can be obtained, for example, by integrated measurements, for example by measuring devices that are placed at at least one entrance and / or at least one exit of the area. For example, a differential measurement between two points along the flow of people could provide such an average speed. Correspondingly, the averaged speed could also relate to an averaging in relation to several scenes that are sequential
    be traversed by the flow of people. In other examples, it would also be possible to record individual participants in the flow of people, for example their position or speed, and to draw conclusions from this about the behavior of the ensemble of participants, i.e. the behavior of the flow of people.
    In order to measure such observables or other observables, different measuring devices can be used. An exemplary measuring device could include a video camera, by means of which individual participants in the flow of people are mapped onto video data. The behavior of the various participants in the flow of people could then be analyzed by means of object recognition and conclusions can be drawn from this about the behavior of the flow of people. Other examples can concern, for example, passage counting, for example at the entrance or at the exit of an area. For example, with respect to retail stores, passage counting could be done by looking at checkout data. In particular, the number of items purchased or the sales made by individual participants in the flow of people could enable a statement to be made regarding the average speed of the flow of people. By assigning the types of the purchased items to different scenes in the area, information about the averaged speed of the flow of people at the corresponding scenes can also be obtained therefrom. By means of such information, which is already available, an accurate determination of the behavior of the flow of people in the different scenes within the area can be made. This reduces the technical complexity, since often no additional measuring devices are required.
    A controller comprises at least one processor. The at least one processor is set up to carry out the following steps: controlling a lighting device for lighting at least one scene in an area with a first light characteristic; Receiving measurement data which are indicative of a behavior of a flow of people in the area when illuminating the at least one scene with the first light characteristic; Determining a second light characteristic based on the measurement data; and controlling the lighting device for illuminating the at least one scene with the second light characteristic.
    A computer program product comprises program code that can be executed by at least one processor. Execution of the program code causes the at least one processor to carry out the following steps: controlling a lighting device for lighting at least one scene in an area with a first light characteristic; Receiving measurement data which are indicative of a behavior of a flow of people in the area when illuminating the at least one scene with the first light characteristic; Determining a second light characteristic based on the measurement data; and controlling the lighting device to illuminate the at least one scene with the second light characteristic.
    A computer program comprises program code that can be executed by at least one processor. Execution of the program code causes the at least one processor to carry out the following steps: controlling a lighting device for lighting at least one scene in an area with a first light characteristic; Receiving measurement data which are indicative of a behavior of a flow of people in the area when illuminating the at least one scene with the first light characteristic; Determining a second light characteristic based on the measurement data; and controlling the lighting device to illuminate the at least one scene with the second light characteristic.
    A method comprises the control of a lighting device for illuminating at least one scene in an area with a first light characteristic. The method also includes receiving measurement data that are indicative of a behavior of at least one person in the area when illuminating the at least one scene with the first light characteristic. The method comprises the determination of a second light characteristic based on the measurement data, as well as the activation of the lighting device for illuminating the at least one scene with the second light characteristic.
    A use of a lighting device for illuminating at least one scene in an area with a light characteristic for directing a flow of people is disclosed.
    The features set out above and features which are described below can be used not only in the corresponding combinations explicitly set out, but also in further combinations or in isolation, without departing from the scope of protection of the present invention.
    BRIEF DESCRIPTION OF THE FIGURES
    FIG. 1 schematically illustrates a system comprising a controller, lights, and measuring devices, the system being set up according to various examples for directing a flow of people through an area with a plurality of scenes illuminated by the lights.
    FIG. 2 schematically illustrates an association between scenes of the area and lights and measuring devices according to various examples.
    FIG. 3 schematically illustrates a geometric arrangement of the scenes in the area according to various examples.
    FIG. 4 is a flow diagram of a method according to various examples. FIG. 5 is a flow diagram of a method according to various examples.
    FIG. 6 schematically illustrates a model for a speed of a flow of people as a function of the light intensity used to illuminate scenes according to various examples, FIG. 6 illustrates a parameterization of the model for different types of scenes.
    FIG. 7 schematically illustrates a model for a speed of a flow of people as a function of the light intensity used to illuminate scenes according to various examples, FIG. 7 illustrates a parameterization of the model for different times of the day.
    DETAILED DESCRIPTION OF EMBODIMENTS
    The properties, features and advantages of this invention described above and the manner in which they are achieved will become clearer and more clearly understood in connection with the following description of the exemplary embodiments, which are explained in more detail in connection with the drawings.
    The present invention is explained in more detail below on the basis of preferred embodiments with reference to the drawings. In the figures, the same reference symbols denote the same or similar elements. The figures are schematic representations of various embodiments of the invention. Elements shown in the figures are not necessarily shown to scale. Rather, the various elements shown in the figures are reproduced in such a way that their function and general purpose can be understood by a person skilled in the art. Connections and couplings shown in the figures between functional units and elements can also be implemented as indirect connections or couplings. A connection or coupling can be implemented in a wired or wireless manner. Functional units can be implemented as hardware, software, or a combination of hardware and software.
    Various techniques relating to directing people flows through areas are described below. The techniques described herein can be used, for example, for directing flows of people through airports, train stations, shops, shopping centers, etc. In general, the techniques described here can therefore be used flexibly wherever flows of people with many participants through areas are desirable. Such techniques make it possible for the often restricted
    To use space efficiently, especially in city centers, and to avoid delays or obstructions for individual participants in the flow of people. This also generally promotes the safety in the operation of facilities. Depending on the type of control desired, different target values can be achieved with regard to the behavior of the flow of people.
    Various techniques described herein are based on the knowledge that it is possible to direct flows of people through an area with several scenes by illuminating one or more of the scenes in the area with a suitably selected light characteristic. Examples of such light characteristics include light intensity, light contrast, light pressure and light color. Influencing participants in a flow of people in this way can result in a statistically significant control of the flow of people as an ensemble of several participants. For example, it has been observed that certain light colors have a particularly attractive or unattractive effect on participants in a flow of people, so that when the corresponding light color is used, the attractiveness of the corresponding scene for the flow of people can be increased or decreased in a targeted manner. As a result, it can be achieved, for example, that - depending on the desired target variable for steering the flow of people - more or fewer participants in the flow of people visit the corresponding scene.
    Corresponding relationships can also be observed for other light characteristics.
    In order to ensure the most precise and targeted guidance of the flow of people, measurement data indicative of the behavior of the flow of people in the area when illuminating at least one scene of the area with a first light characteristic are received in various examples. A second light characteristic can then be determined based on the measurement data and a lighting device for illuminating the at least one scene with the second light characteristic can be controlled. Such techniques make it possible to adapt the light characteristics used to the observed behavior of the flow of people.
    FIG. 1 illustrates aspects relating to a system 100 configured to direct people flows in accordance with various examples described herein. The system 100 includes several lights 111-113.
    Examples of lights include: light-emitting diodes, light-emitting diode arrays, halogen lamps, gas discharge lamps, incandescent lamps, etc. For this purpose, the lights 111-113 can be controlled by a controller 101. The lights 111-113 are set up to implement different light characteristics. For example, control data which are transmitted from the controller 101 to the luminaires 111-113 can indicate the desired light characteristic. The communication link between the controller 101 in the luminaires 111-113 can be wireless or wired, for example, via phase control modulation or a control bus system such as DALI. Further examples of the communication link include, for example, Bluetooth, IEEE 802.15.3 Wireless Personal Area Networks or Internet Protocol (IP).
    The controller 101 - implemented for example by a computer, FPGA or ASIC - is also connected to measuring devices 121, 122.
    The measuring devices 121, 122 are set up to acquire measurement data. The measurement data are indicative of the behavior of a flow of people in an area which is illuminated by the various lights 111-113. Examples of measuring devices 121, 122 include, for example: cameras with object / image recognition; Passage counter; Pay machines with an evaluation according to sold articles; Near-field positioning of individual participants in the flow of people; etc. Different types of measuring devices 121, 122 can be used complementarily in the system 100. Depending on the type of measuring device 121, 122 used, different observables can be measured. Examples of observables relating to the flow of people include: instantaneous velocity of the flow of people; average speed of the flow of people; and position of participants in the flow of people. At-
    For example, a pay machine could be used to evaluate which articles are in particular demand from participants in the flow of people. As a result, it can be concluded that the instantaneous speed of the flow of people in the area of the sales area is comparatively low for articles that are particularly in demand. A corresponding statement could also be made by evaluating video data from a camera using object recognition, for example. It can then be determined, for example, that people spend a long time in the area of the sales area, so that the speed of the flow of people there is comparatively low. A corresponding statement could also be made by arranging a motion detector or a passage counter in the area of the corresponding scene.
    The controller 101 is also connected to a database 102. For example, information relating to a model of the behavior of a flow of people as a function of the light characteristics used can be stored in the database 102. The database 102 is optional.
    It is possible that the controller 101 and / or the database 102 are at least partially decentralized with respect to the lights 111-113 and the measuring devices 121, 122. For example, remote access can take place via the Internet. Cloud implementations are conceivable.
    FIG. 2 illustrates aspects relating to an assignment between scenes 151-154 of an area 150 to luminaires 111-114 and measuring devices 121-124. From the example of FIG. 2 it can be seen that the area 150 comprises several scenes 151-154. For example, the different scenes 151-154 can be associated with different partial areas of the area 150. The scenes 151-154 can also be structured thematically. For example, the area 150 could correspond to an office building and the different scenes 151-154 could for example correspond to an entrance area, a waiting area, an elevator area, a stairwell area, a work area, a communal kitchen, etc. For example, the area 150 could correspond to a shop and the various scenes 151-154 could correspond to an entrance area, an exit area, a checkout area, different sales areas, depending on the product offered, etc.
    From FIG. 2 it can be seen that a lamp 111-114 is assigned as a lighting device in different scenes 151-154, it generally also being possible for more than one lamp 111-114 to be provided per scene 151-154. The respective lamp 111-114 is set up to illuminate the corresponding scene 151-154 with a suitable light characteristic.
    A local measuring device 121, 122 is also provided in each of the scenes 151, 154, which is set up to record measurement data that are indicative of the behavior of a flow of people in the respective scene 151, 154. In addition, measuring devices 123, 124 are provided that are not assigned to any specific scene 151-154. For example, these measuring devices 123, 124 could be positioned at a central location in the area 150. The measuring devices 123, 124 can be set up to acquire measurement data which are indicative of a behavior of the flow of people integrated over the multiple scenes 151-154. In further examples, however, it would also be possible for one of the measuring devices 123, 194 to be set up to provide measurement data which is indicative of the behavior of the flow of people broken down into one or more of the scenes 151-154. An example of this would be, for example, a cash machine in a shop which, on the basis of the registered articles, can make a statement about the behavior of the flow of people in the different areas 151-154 associated with the articles.
    FIG. 3 illustrates aspects relating to a geometric arrangement of the various scenes 151-158 of a region 150 with respect to one another. FIG. 3 illustrates the geometric arrangement of the various scenes 151-158 as a graph. The flow of people 180 enters the area 150 through an entrance 171; in general there could be more than one input. The input 171 is connected to a scene 151. In FIG. 3, a speed 162 of the flow of people in the area of the scene 151 is illustrated schematically: in the example of FIG. 3 is the
    The speed 162 in the area of the scene 151 is comparatively low (illustrated schematically in FIG. 3 by the arrow at the lower end of the scale), which is why undesired condensation of the flow of people 180 can occur in the area of the scene 151. Such compression in the area of the scene 151 can be avoided by appropriately directing the flow of people.
    The entire flow of people 180 then leaves the area of the scene 151 and enters the area of the scene 152 (shown in FIG. 3 by the full portion 161 of the flow of people 180). There is a branching of the flow of people and a certain portion 161 of the flow of people 180 then enters the area of the scene 154, while the complementary portion 161 of the flow of people 180 enters the area of the scene 153. From FIG. 3 it can be seen that the attractiveness factor of the scene 153 is significantly greater than the attractiveness factor of the scene 154, which is why a larger proportion 161 of the flow of people 180 visits the scene 153. In the example of FIG. 3, compression can occur in particular in the area of scenes 151 and 156, because many participants in the flow of people 180 run up there with a large velocity gradient. The techniques described below can be used to achieve suitable control of the flow of people based on the use of suitable light characteristics, for example in the area of these scenes 151, 156 and / or in the area of the adjacent scenes 155, 157, 152. In this way, the flow of people 180 can generally be directed not only in relation to the target variable for avoiding condensation, but also in relation to other target variables.
    FIG. 4 is a flow diagram of an exemplary method. The method according to the example of FIG. 4 enables the flow of people to be controlled. First, in step 1001, a lighting device for illuminating a scene with a first light characteristic is controlled. The lighting device can comprise one or more lights. The light characteristic can relate, for example, to a light intensity, a light contrast, a light pressure and / or a light color. Further examples of the light characteristics include the local light distribution. For example, the local light distribution can be set based on movable lights or matrix elements.
    Then, in step 1002, measurement data is received. The measurement data are indicative of the behavior of a flow of people when illuminating the scene with the first light characteristic. For example, these measurement data could be indicative of the speed of the flow of people and / or a compression of the flow of people and / or a branching factor of the flow of people and / or an attractiveness factor of one or more scenes of the corresponding area for the flow of people.
    Then in step 1003 a second light characteristic for the lighting device is determined and in step 1004 the lighting device is controlled for illuminating the scene with the second light characteristic. In step 1003, the second light characteristic can be determined based on the measurement data from step 1002. This enables targeted control of the flow of people based on the currently measured behavior. For example, in order to avoid an abrupt change in the light characteristic, it would also be possible in step 1003 to determine the second light characteristic based on the first light characteristic, for example by taking into account a predetermined rate of change based on the first light characteristic. In step 1003, for example, a specific target variable for directing the flow of people could be taken into account. Examples of such target variables include: avoidance of compaction;
    Increasing the attractiveness of certain scenes; Etc.
    In addition, in step 1003, different techniques can basically be used for determining the second light characteristic. In an exemplary technique, a control loop is used to reliably implement a target behavior of the flow of people as a reference variable of the corresponding control loop.
    FIG. 5 is a flow diagram of an exemplary method. FIG. 5 illustrates aspects relating to the determination of the second light characteristic. In particular, FIG. 5 aspects
    with regard to determining the second light characteristic by implementing a control loop. Such techniques have the advantage that it can be dispensable to have a priori knowledge, for example in the form of a model, of the dependence of the behavior of the flow of people on the light characteristics used.
    First, in step 1011, a current light characteristic is determined. For example, the current light characteristic could be determined based on a specific rate of change compared to the previously used light characteristic. In other words, incremental changes in the light characteristics can be used. In this case, for example, a certain latency can be taken into account in order not to exceed a predetermined maximum rate of change of the light characteristic used.
    Subsequently, in step 1012, the lighting device for illuminating the scene with the current light characteristic from step 1011 is activated. In step 1013, measurement data are again received which are indicative of the behavior of the flow of people when illuminating the scene with the current light characteristic. In step 1014 it is then checked on the basis of the measurement data from step 1013 whether the currently observed behavior of the flow of people already corresponds to the target behavior of the flow of people. If this is not the case, steps 1011-1014 are carried out again. Otherwise the current light characteristic will continue to be used.
    From FIG. 5 it can therefore be seen that it may be possible to implement a control loop with a target behavior of the flow of people as a reference variable, the measured data as a controlled variable and the light characteristics of the lighting device as a control variable.
    As an alternative or in addition to such a determination of the light characteristic based on the implementation of the control loop, it may also be desirable in some examples to take into account additional information about a dependence of the flow of people on the light characteristic used. For example, it can thereby be possible to use a-priori knowledge about such a dependency for the targeted and rapid control of the flow of people.
    FIG. 6 illustrates aspects relating to a model of the behavior of the flow of people as a function of the light characteristic used. The model can be taken into account when determining the light characteristic to be used. In particular, the example of FIG. 6 shows a model 250 which illustrates a relationship between the speed of the flow of people (vertical axis in FIG. 6) and the light intensity used (horizontal axis in FIG. 6). The qualitative and quantitative dependency in FIG. 6 purely by way of example.
    From the example of FIG. 6 it can be seen that the model 250 describes different speeds of the flow of people depending on the variable light intensity for different scenes 151-153. The parameterization in relation to the observed scene 151153 could be the case, for example, due to the different geometric arrangements of the scenes 151-153 in the area 150 and / or due to different types of the scenes 151-153.
    In FIG. 6, a first light intensity 201 and a second light intensity 202 are highlighted. For example, when the scene 151 is illuminated with the first light intensity 201, the measurement data could indicate that the speed of the flow of people 180 is too low. The second light intensity 202 could then be determined as being suitable for directing the flow of people 180. In this way it may be possible to determine the second light intensity 202 based on the model 250 of the behavior of the flow of people, here in particular the speed, as a function of the light characteristic used, here in particular the light intensity.
    In principle, different techniques are possible in order to maintain or train the model 250. For example, the model could be determined by machine learning techniques. For this purpose, light characteristics used over a longer period of time can be correlated with the observed behavior of the flow of people. For example, the measurement data could be over a period of at least 10 minutes, optionally at least 30 minutes
    Minutes, further optionally at least 120 minutes can be monitored and then the model 250 can be trained based on the monitoring of the measurement data. In principle, it is also possible to take reference measurement data into account that were recorded for other areas or other scenes.
    In addition to such dependencies of the model 250 on the geometry or the types of the scenes 151-153, as shown in FIG. 6, the model can also take other environmental parameters into account.
    FIG. 7 illustrates aspects relating to a model of the behavior of a flow of people as a function of a light characteristic used. The example of FIG. 7 basically the example of FIG. 6. In FIG. 7, however, a parameterization of the model 250 is also shown in relation to environmental parameters, here in particular the time of day. In general, other environmental parameters can also be taken into account, such as weather, weekday, daylight brightness, outside temperature or inside temperature. For example, corresponding control data can be received which are indicative of one or more environmental parameters and the light characteristic to be used, here the light intensity 202, can be determined based on the control data.
    In summary, techniques have been described above in order to carry out a suitable control of a flow of people. The flow of people is directed by a change in the light characteristic that is used to illuminate one or more scenes in an area. The flow of people can generally be controlled with regard to different target variables.
    Of course, the features of the embodiments and aspects of the invention described above can be combined with one another. In particular, the features can be used not only in the combinations described, but also in other combinations or on their own, without departing from the field of the invention.
    权利要求:
    Claims (10)
    [1]
    1. Method, characterized in that
    that the procedure includes:
    - controlling a lighting device (111-113) for illuminating at least one scene (151-158) in an area (150) with a first light characteristic (201, 202),
    - Receiving measurement data indicative of a behavior of a flow of people (180) in the area (150) when illuminating the at least one scene (151-158) with the first light characteristic (201, 202),
    - determining a second light characteristic (201, 202) based on the measurement data, and
    - Controlling the lighting device (111-113) to illuminate the at least one scene (151-158) with the second light characteristic (201, 202).
    [2]
    2. The method according to claim 1, characterized in that the method further comprises: - Implementation of a control loop with a target behavior of the flow of people (180) as a reference variable, the measured data as a controlled variable, and the second light characteristic (201, 202) of the lighting device (111- 113) as a control variable.
    [3]
    3. The method according to claim 1 or 2, characterized in that the second light characteristic (201, 202) is further determined based on a model (250) of the behavior of the flow of people (180) as a function of the second light characteristic (201, 202).
    [4]
    4. The method according to claim 3, characterized in that the method further comprises: - monitoring the measurement data over a period of at least 10 minutes, optionally at least 30 minutes, further optionally at least 120 minutes, and - training the model (250) based on the Monitoring the measurement data.
    [5]
    5. The method according to any one of claims 3 or 4, characterized in that the method further comprises: receiving reference measurement data indicative of the behavior of at least one further flow of people (180) in at least one further area (150), and - Training the model (250) based on the reference measurement data.
    [6]
    6. The method according to any one of claims 3 to 5, characterized in that
    that the model (250) depends on different behavior of the flow of people (180)
    the second light characteristic (201, 202)
    - for different geometric arrangements of the at least one scene (151158) in the area (150) and / or
    - for different types of at least one scene (151-158) and / or
    - describes for different values of at least one environmental parameter of the at least one scene (151-158) and / or of the area (150).
    [7]
    7. The method according to any one of the preceding claims, characterized in that the method further comprises: receiving control data indicative of at least one environmental parameter of the at least one scene (151-158) and / or of the area (150), wherein the Determining the second light characteristic (201, 202) is still based on the control data.
    [8]
    8. The method according to any one of the preceding claims, characterized in that the second light characteristic (201, 202) is further determined based on a predetermined rate of change starting from the first light characteristic (201, 202), the rate of change optionally corresponding to a period of time which is greater than an average dwell time of participants in the flow of people (180) in the at least one scene (151-158).
    [9]
    9. The method according to any one of the preceding claims, characterized in that
    that the procedure further comprises:
    - Controlling at least one measuring device (121) for acquiring the measurement data, the at least one measuring device (121) measuring one or more of the following observables: instantaneous speed of the flow of people (180) at the at least one scene (151-158); average speed of the flow of people (180) at the at least one scene (151-158); and position of participants in the flow of people (180) in relation to the at least one scene (151-158).
    [10]
    10. Control with at least one processor, characterized in that the at least one processor is set up to carry out the following steps:
    - controlling a lighting device (111-113) for illuminating at least one scene (151-158) in an area (150) with a first light characteristic (201, 202),
    - Receiving measurement data indicative of a behavior of a flow of people (180) in the area (150) when illuminating the at least one scene (151-158) with the first light characteristic (201, 202),
    - determining a second light characteristic (201, 202) based on the measurement data, and
    - Controlling the lighting device (111-113) to illuminate the at least one scene (151-158) with the second light characteristic (201, 202).
    In addition 7 sheets of drawings
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    法律状态:
    优先权:
    申请号 | 申请日 | 专利标题
    DE102017204429.2A|DE102017204429A1|2017-03-16|2017-03-16|Intelligent control of the light characteristic|
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