Portable user profiles for smart buildings

Abstract

A method and system for using portable user profiles in multiple smart buildings is disclosed. A method includes: detecting the presence of a user at a smart building; determining that the user has not previously visited the smart building; retrieving a portable user profile for the user; and setting access control rules based on the portable user profile.

Description

Technical Field

[0001] The exemplary embodiments relate to the field of electronics. In particular, the present disclosure relates to a method and system for using portable user profiles for smart buildings. Background Art

[0002] Today's technology has enabled the integration of new technologies into buildings that provide a variety of benefits. For example, power consumption can be reduced by using smart technologies, as discussed in more detail in U.S. Patent Application Serial No. 62 / 644,836, entitled "Predicting the Impact of Flexible Energy Demand on Thermal Comfort." Smart building technology can provide optimization of energy use and improve usability for tenants, owners, employees, and other users of a building. One method of providing usability improvements is through portable user profiles. Summary of the invention

[0003] According to one embodiment, a method and system for using portable user profiles in multiple smart buildings is disclosed. A method includes: detecting the presence of a user at a smart building; determining that the user has not visited the smart building before; retrieving a portable user profile for the user; and setting access control rules based on the portable user profile.

[0004] In addition to or as an alternative to one or more of the features described above, further embodiments may include evaluating context information contained in the portable user profile and comparing the context information to context information for the intelligent building.

[0005] In addition to the above features, or as an alternative, further embodiments may include wherein the context information includes context information not related to a person and context information related to a person.

[0006] In addition to the above features, or as an alternative, further embodiments may include where the non-human related contextual information includes information about the layout of the smart building.

[0007] In addition to the above features, or as an alternative, further embodiments may include wherein the contextual information associated with a person includes information about the role and authorization zone of the user.

[0008] In addition to the above features, or as an alternative, further embodiments may include wherein setting access control rules includes determining a role of the user in the smart building.

[0009] In addition to or as an alternative to the above features, further embodiments may include wherein access rights to perform operations on objects are granted based on assigned attributes of the user contained in the portable user profile.

[0010] In addition to the above features, or as an alternative, further embodiments may include where the attributes are selected from one or more of: user attributes; resource attributes; environment attributes; session attributes; or action attributes.

[0011] In addition to or as an alternative to the above features, further embodiments may include wherein adjusting the environment includes adjusting thermal comfort based on information about the layout of the smart building.

[0012] In addition to the above features, or as an alternative, further embodiments may include wherein adjusting the environment includes adjusting lighting comfort based on information about the layout of the smart building.

[0013] In addition to the above features, or as an alternative, further embodiments may include wherein adjusting lighting comfort includes adjusting electrical lighting and adjusting window coverings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The following description should not be considered limiting in any way. Referring to the accompanying drawings, like elements are numbered similarly:

[0015] Figure 1 is a flow chart illustrating the operation of one or more embodiments;

[0016] Figure 2 is a flow chart illustrating the operation of one or more embodiments;

[0017] Figure 3 is a block diagram of a computer system capable of executing one or more embodiments;

[0018] Figure 4 is a block diagram of an exemplary computer program product;

[0019] Figure 5 is a flow chart illustrating the operation of one or more embodiments;

[0020] Figure 6 is a block diagram illustrating the operation of one or more embodiments;

[0021] Figure 7 is a flow chart illustrating the operation of one or more embodiments;

[0022] Figure 8 is a flow chart illustrating the operation of one or more embodiments; and

[0023] Fig. 9 It is a simplified diagram of group formation. DETAILED DESCRIPTION

[0024] A detailed description of one or more embodiments of the disclosed apparatus and methods is presented herein by way of illustration and not limitation with reference to the accompanying figures.

[0025] The term "about" is intended to include the degree of error associated with the measurement of the particular quantity based on the equipment available at the time the application was filed.

[0026] The terms used herein are for the purpose of describing specific embodiments only and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises and / or comprising" when used in this specification specify the presence of the features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0027] Thermal comfort in indoor locations is achieved through the use of heating, ventilation, and air conditioning (HVAC) units placed throughout the indoor location. HVAC can be very expensive, accounting for up to 65% of a building's energy consumption.

[0028] In the past, there were many different ways to control thermal comfort and thus the energy consumed to achieve a certain level of thermal comfort. A very similar way of doing this was to manually control air conditioning and heating units - turning them on and off as needed depending on whether the building's occupants were comfortable or not. Later, thermometers were added - if the temperature was sensed to be too high, the air conditioning system could be turned on to cool the room. If the temperature was sensed to be too low, the heating system could be turned on to warm the room. As the technology became more sophisticated, additional methods were added.

[0029] Advances in technology have enabled the use of machine learning methods and systems to monitor and learn the thermal comfort levels of residents. Using voting techniques, one or more embodiments can determine the comfort level of each resident in a group of residents. The thermal profile can then be updated based on the feedback received.

[0030] In addition to the thermal profile, there may be additional parameters stored in the full profile. In one or more embodiments, machine learning methods and systems may be used to monitor and learn various categories of information (also referred to as "aspects"). The full profile includes various information mined from historical data that can be used to improve building efficiency and / or increase user convenience or experience. In one or more embodiments, five or more aspects may be mined to gain insights about the user in a variety of different categories. These aspects are stored as tuples in a knowledge base.

[0031] In general, a tuple may be formed from: the type of input data; the data preprocessing chain; and which computational model is used to process the input data. The smart building may access the tuple to customize the experience of each user in the smart building based on the mined data. The data is preprocessed and a machine learning algorithm is used on the data to perform predictions on the data or to perform clustering on the data (to determine similarities between users). A global score may be defined for each aspect and each user. The global scores may then be compared and clustered between multiple users based on similarities.

[0032] These aspects will now be described. Thermal comfort is described in more detail in co-pending US patent application Ser. No. 62 / 644,813, entitled "Machine-Learning Method for Conditioning Individual or Shared Areas," the contents of which are incorporated herein by reference.

[0033] To briefly explain thermal profiles, in smart buildings, a concept called thermal comfort can be used to control heating, ventilation, and air conditioning (HVAC) systems. Thermal comfort uses various measurements of a room to estimate the thermal comfort level of the room. The measurements may include temperature, humidity, wind speed, etc. A field estimating the thermal comfort level of the conditioned room may be generated. Using one of a variety of different methods, a user may indicate whether the user is comfortable in the current thermal conditions. If the user is too cold or too hot, the user's deviation from the estimate may be stored in a thermal profile. The thermal profile will indicate, for example, that a particular user generally likes his room to be warmer than most people or cooler than most people. The thermal profile may allow a smart building to sense or predict that a user has entered a particular room and adjust the thermal comfort level of the room when the user enters the room. This can even be done in advance if, for example, the user's calendar or reservation indicates that the user will be at a particular location at a particular time.

[0034] The use of such thermal profiles also has advantages in terms of building efficiency. If a user does not like a cold room, unnecessary use of the air conditioning system can be avoided. If a room is not in use, there is no need to heat or condition the room knowing that it will be at a comfortable temperature when the room is occupied.

[0035] Another aspect that can be monitored and stored in the user profile is visual (or lighting) comfort. Visual comfort can include various aspects of the room that are related to the user's vision. This can include lighting, shades, blinds, etc.

[0036] For lighting comfort, each user may have different comfort levels depending on the quantity and quality of lighting. The quantity of lighting may include the amount of lighting measured in, for example, lumens using a illuminance meter. Typically, some users may prefer a brighter environment than other users. Some users may have poor night vision and therefore prefer to have brighter lighting than other users. Other users may be sensitive to bright lighting and prefer less bright areas. The quantity of lighting may also include lighting from windows. Shades, blinds, and other window coverings may be controlled by smart buildings (e.g., by using electric window coverings) to provide the desired amount of lighting. Sensors may measure the amount of natural light in a room and adjust the brightness in the room based on natural light. The quality of lighting may include various aspects of the type of lighting. Aspects such as the color temperature of lighting may also be monitored and adjusted. For example, it may be found that a certain user prefers a natural color temperature (e.g., about 5000K) during the day and a "warmer" color temperature (e.g., about 2700K) at night. Afterwards, when the user is in the room, the color temperature of the light may be adjusted to meet his preferences.

[0037] Service interaction refers to the way in which users interact with the various services provided by a building. For example, one user may utilize the elevator four times a day, and another user may use the elevator eight times a day. One user may prefer a particular cafeteria, while different users may use different cafeterias more often.

[0038] Service interaction data can be used in conjunction with data about movement patterns. Movement patterns refer to the areas of a building that are utilized by a user. These movements can be tracked in one or more of a variety of different ways. For example, some buildings have access cards or key cards that utilize various technologies such as RFID or magnetic strips that enable the cardholder to access certain areas of the building. In addition, some buildings are now adding access technology to mobile electronic devices such as smart phones, tablets, MP3 players, e-readers, smart watches, health trackers, and any other type of device with computing capabilities. Those mobile electronic devices can then be used to gain access to various rooms. Other access granting devices can use biometric information, such as fingerprint readers, facial recognition, retinal scans, and other biometric devices that rely on human characteristics to grant access to rooms or areas of a building. Information about access to rooms or areas can be stored as movement patterns.

[0039] In addition, various sensors placed throughout the smart building can allow tracking of users as they move through the building. The sensors can be of any type. For example, a facial recognition algorithm can be used in conjunction with a camera to determine when a user enters certain areas of a smart building. The user's mobile electronic device can be used in conjunction with a wireless transmitter such as Bluetooth, WiFi, near field communication (NFC), ANT, and other wireless protocols. A signal can be sent via a wireless transmitter. When the mobile electronic device receives the signal, the mobile electronic device can transmit a response signal. The response signal can be associated with a specific mobile electronic device. Each mobile electronic device can then be associated with a user. In this way, the user's movement can be tracked to determine what areas of the building the user frequently enters and exits.

[0040] Another aspect is health status. Health status may include any type of health information that is typically tracked using a mobile electronic device. For example, heart rate and body temperature may be tracked to determine if the user is sick. If the user is sick, adjustments may be made to the room the user is in to improve the user's comfort.

[0041] These aspects can be combined with context information.Context information can be broadly categorized into context information not related to a person and context information related to a person.

[0042] Non-person-related contextual information includes information that is the same for every user within a building. Examples of non-person-related contextual information include information about the building (e.g., layout of the building, materials of the building, size of the building, orientation, etc.) and weather information (e.g., temperature, cloud cover, sunrise / sunset times, etc.).

[0043] Contextual information related to people is information specific to a particular user. For example, the range of a user's visit can be part of the contextual information. Although the above use anticipates a single user, where thermal and lighting comfort is prioritized for a single user, there is often a situation where there are multiple users in a room. In this case, thermal and lighting comfort is set so that a larger number of users are within a specific comfort level. When determining comfort levels for a user group, certain users can be prioritized so that their preferences gain greater weight. For example, a hotel may choose to prioritize the comfort of guests over the comfort of employees. Therefore, the status of a user as a guest or employee can be considered as part of the contextual information related to people. For a particular user, the status as a guest or employee can be changed based on context. For example, a user may be an employee at a hotel, but may be a guest at the same hotel chain at a different location (e.g., on vacation). This status can also travel to different businesses. For example, a user's profile at an office building can be shared with a hotel. Therefore, when a user goes on vacation (or business trip) at a hotel, the user's preferences for thermal comfort and lighting can be retrieved, and these preferences can be used to make the user's stay at the hotel more pleasant.

[0044] The various aspects described above can be combined with context information and used together, so as to provide a better experience for the user, and improve the efficiency of the building. For example, based on the mobile mode tracked, the building can predict that a specific user will wake up at 6 o'clock in the morning. The thermal condition and lighting condition of the room can be optimized for that user. Afterwards, the area where the user eats breakfast can be prepared in advance for the user based on the predicted user position. This can also be done in an office environment, where the conference room is prepared for the user even before the user enters the conference room. In this way, the comfort of the user and the user's preference can be found and expected. In addition, the same profile can be shared between multiple buildings and used as the digital signature of the user between buildings. For example, a hotel chain may have a user's profile. When a user enters another hotel of the hotel chain, even if the user has never been to that specific hotel before, the user's preference for thermal comfort, lighting comfort, etc. can be set for him, thereby providing him with a consistent user experience. The sharing among the building will be discussed in more detail below.

[0045] In some embodiments, any of the features listed above may be turned off for privacy reasons. While some users may appreciate the features of customizing the user experience in a smart building, other users may value their privacy. In some embodiments, a user may turn off one or more of the tracking features at any time. For embodiments where profiles are shared among multiple smart buildings, a user may have the option of turning on tracking in some buildings (e.g., the user's own home) and turning it off in public buildings (e.g., a hotel).

[0046] against Figure 1 , a method 100 illustrating the operation of one or more embodiments is presented. The method 100 is merely exemplary and is not limited to the embodiments presented herein. The method 100 may be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the procedures, processes, and / or activities of the method 100 may be performed in the order presented. In other embodiments, one or more of the procedures, processes, and / or activities of the method 100 may be combined, skipped, or performed in a different order. In some embodiments, the method 100 may be performed by the system 300.

[0047] Method 100 illustrates a process for creating a profile for a smart building. A user is sensed (block 102). The sensing may be performed using one of a variety of different methods. For example, a variety of different sensors may detect the presence of a user at a certain location. The sensor may include a camera, an audio sensor, a card reader, a wireless transducer that detects the presence of a mobile electronic device, etc.

[0048] Thereafter, the user's actions are monitored (block 104). As described above, monitoring may include the user's movements and the user's use of the building's facilities (e.g., rooms, elevators, restaurants, vending machines, etc.). In some embodiments, monitoring may include integration with the user's electronic calendar. For example, the user may use an electronic calendar system to maintain their calendar. One or more embodiments may link to the user's calendar (with the user's permission) in order to determine where the user will be meeting or having a conference, such as in a particular conference room or office within a building complex.

[0049] In addition to monitoring, there may also be a set of access control rules associated with a profile. Access control rules may be configured to grant or deny a user access to a certain set of resources. Resources may include actuators, controls, sensors, or commands. Resources may also include floors of a building or rooms within a building. Thus, a profile may indicate that certain people (e.g., those doing maintenance work) have access to areas that are otherwise restricted to the general public. Similarly, an access control rule may indicate that a user whose apartment is located on the twelfth floor of a building has access only to the twelfth floor of the building (and any public areas of the building).

[0050] For employees, access control rules may indicate that a user may have the ability to change certain parameters that typical tenants do not have access to. For example, security personnel may have access to elevator controls that general users of the building do not have.

[0051] Access control rules may also include contextual information related to the user, such as the user's role or scope of access, as well as contextual information not related to the user, such as information related to the physical layout of the building.

[0052] In some embodiments, access control rules may be applied in a given environment even if there are no environment-related instructions located in the user's profile. In such cases, alternative applicable rules may be identified by using available context information. For example, if the user's context is a guest, the user may be set to always be able to control the lighting and HVAC parameters of a guest room.

[0053] Monitoring may also include user feedback (block 106). User feedback may take the form of a human machine interface (HMI) by which a user interacts with the smart building. An exemplary human machine interface may include a mobile electronic device or a terminal located on a wall. An exemplary use of an HMI is that a user may utilize his mobile electronic device to indicate that he is too hot. The smart building will record the adjustments made relative to the current thermal comfort level of the area in which the user is located. Similarly, the user may make similar notifications regarding lighting preferences.

[0054] A profile is constructed using the collected data and user feedback (block 108). The profile may include information about each of the aspects described above, as well as any other aspects that may be useful for a smart building.

[0055] The profile may be shared with other buildings (block 110). This may include other buildings within the same location (e.g., other buildings in an office park or university campus), related buildings (e.g., buildings operated by the same entity), or subscribers to the profile service.

[0056] Thereafter, each time the user enters a location that has a profile for the user, the profile may be retrieved (block 112) and adjustments may be made to the user's environment based on the information in the profile.

[0057] In a group setting (ie, a user is in a room or area with multiple users), a profile for each user may be retrieved and analyzed as above. Adjustments may be made to the score assigned to each user using machine learning techniques based on consideration of similarities between users (block 114).

[0058] against Figure 2 , a method 200 illustrating the operation of one or more embodiments is presented. The method 200 is merely exemplary and is not limited to the embodiments presented herein. The method 200 may be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the procedures, processes, and / or activities of the method 200 may be performed in the order presented. In other embodiments, one or more of the procedures, processes, and / or activities of the method 200 may be combined, skipped, or performed in a different order. In some embodiments, the method 200 may be performed by the system 300.

[0059] In addition to the above, the user's habits can also be mined from historical data and then used to improve the capabilities of the physical access control system. Mining the user's historical usage data from historical data generated during user / building interactions (as described below for Figure 6 Detailed description in ). Usage data can be mined to determine contextual information for use with access control systems. In this case, the focus of profiling is to identify patterns in user habits. These patterns may include rooms visited, elevators used, facilities used, and services used.

[0060] The method may include an integration platform. The integration platform may be used to obtain a history of building-occupant interactions from heterogeneous building systems. Exemplary heterogeneous building systems include, but are not limited to, access control systems, cameras, occupancy sensors, indoor location beacons, agenda information, and structural plans and models of buildings. The learned patterns are used to analyze access events (e.g., use of a card reader or other access granting device).

[0061] Method 200 details an algorithm that can be used to determine potential security threats or other abnormal behavior when analyzing access events. Method 200 assumes that a profile already exists. The user's location is continuously monitored using a combination of sensors, access granting devices, etc. (box 202). As described above, this may include monitoring the user's mobile electronic device and the user's key card or other access granting device. The user's activities may be compared to previously stored activities in a knowledge base (box 204). The user's activities may include the user's habits, including rooms visited, elevators used, facilities and services utilized, and other tracking aspects described above. If an anomaly is detected, a potential security threat is indicated (box 206). Thereafter, further investigation of the user's movements and actions may be performed (box 208).

[0062] If the user's credentials are used on an area that the user does not normally go to or that the user does not have access to, this may indicate that a bad actor has the credentials. For example, in the case of an office building, if the user only goes to the fifth and eighth floors, those trends may be stored in the user's profile within the knowledge base. If the user is visiting the tenth floor, this may be marked as unusual. This may not be an immediate alarm because the user may have a good reason to be on the tenth floor when he does not normally go there. For example, a once-a-year meeting may be held on the tenth floor. Or, the user may be delivering a package that he received in error.

[0063] Similarly, some profiles include access grant privileges. A user may be allowed to access certain rooms that are restricted by key cards or mobile electronic device readers. However, the user may not be allowed to access other rooms that are restricted by key cards or mobile electronic device readers. If a user attempts to access a room or area that he is not allowed to access, an exception may be triggered.

[0064] However, using the user's credentials in an atypical manner may indicate that the user has lost his key card or mobile electronic device. The investigation may be conducted in one of a variety of different ways. For example, the user's calendar (if the user previously granted access) may be compared to the user's movements. If the annual meeting on the tenth floor is found in the calendar, the anomaly is explained and no further investigation is required.

[0065] In some instances, if the anomaly cannot be explained, the user's credentials may be further reviewed (block 210). In this manner, each action of the user may be more closely monitored to ensure that the user is not a dangerous person.

[0066] against Figure 5 , a method 500 illustrating the operation of one or more embodiments is presented. The method 500 is merely exemplary and is not limited to the embodiments presented herein. The method 500 may be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the procedures, processes, and / or activities of the method 500 may be performed in the order presented. In other embodiments, one or more procedures, processes, and / or activities of the method 500 may be combined, skipped, or performed in a different order. In some embodiments, the method 500 may be performed by the system 300.

[0067] Method 500 is a method for automatically measuring user satisfaction with smart buildings. Users can indicate comfort or discomfort for each aspect of the measurement. The user's comfort level can be measured to find and rank uncomfortable conditions. This can be useful for building management to repair or improve their buildings. Method 500 determines the user's satisfaction during a visit and can be configured to determine the satisfaction of each visit. After the user profile has been established, method 500 is used. Observe the user's actions within a specific time period (box 502). The length of the time period can be set to any convenient time length. In some embodiments, the time length is about one week. Based on the user's profile, a set of user expectations are generated using machine learning techniques (box 504). The current environmental state of the building is obtained (box 506). The environmental state includes lighting comfort and thermal comfort. The environmental state may also include data about the building, such as the presence of a fault and the current population of the building (how many people are currently occupying the building).

[0068] The user's satisfaction is measured (block 508). This can be done using a human machine interface (HMI). An exemplary HMI is a mobile electronic device. A software application (also referred to as an "app") can be executed on the mobile electronic device (such as a smart phone, tablet, or smart watch). On the app, the user can record his satisfaction level. In some embodiments, the user can record details of why he feels his current satisfaction level.

[0069] The satisfaction level and the deviation from the user's expectations and the current state of the environment are recorded and stored in a knowledge base (box 510). Once such data is obtained for multiple users, the data is analyzed and the generation of expectations is optimized (box 512). The analysis may include assigning weights to each of the aspects and other tracked information. For example, although congestion may not be an aspect, congestion may affect thermal comfort. Therefore, congestion can be assigned a weight to predict how congestion affects the user's satisfaction level. A feature ranking method can be used to rank each aspect. In this way, using data from each user, the aspects of maximum information, such as the aspects that need the most improvement, can be determined. Exemplary algorithms that can be used include F-tests and mutual information algorithms.

[0070] Machine learning techniques can be used to optimize the solution for determining the weights assigned to each condition. In some embodiments, a support vector machine can be used as a classifier. Once the system has performed the classification, corrective actions can be used to resolve the problems found. In some embodiments, a learning model (Modulo) theory (LMT) method can be used as an optimization solver for determining weights.

[0071] Once the weights are determined, the method 500 may be re-iterated to further refine the weights. The weights may be used to determine how the smart building should best respond to certain conditions. For example, certain levels of crowding may affect users in unforeseen ways, meaning that the smart building should respond differently to certain environmental conditions than when the building is less crowded.

[0072] Cluster analysis can be used to find users with similar feelings. These clusters can be used to find statistically significant discomfort conditions. This can involve statistical tests such as the Kolmogorov-Smirnov test to compare clusters and highlight statistically relevant differences.

[0073] In this manner, an automated system for detecting and weighting uncomfortable conditions during user occupancy of a building is disclosed. Smart buildings can enable corrective actions for improving the user experience based on measures of satisfaction.

[0074] Figure 6A block diagram of a system 600 is depicted for the purpose of mining and deploying user profiles for smart buildings. First, a profile collection phase is performed. A user 602 interacts with the smart building through various interfaces 610, 612, and 614. Although Figure 6 Only three interfaces are shown, but it should be understood that a greater or lesser number of interfaces may be used. Interfaces 610, 612, and 614 represent any way that a user 602 can interact with a smart building. These may include a user's own mobile electronic device, a key card or other access card, an elevator call button, an access control device, a light switch, other traditional control systems (e.g., a thermostat), etc. Each of interfaces 610, 612, and 614 may interact with a building service integration platform 616. The building service integration platform 616 acts as a link between each of interfaces 610, 612, and 614 and the actual services provided by the smart building. Services may include access control 620 (such as door locks and other entrance control devices), elevators 622, HVAC 624, and lighting 626. It should be understood that lighting 626 may include not only control of lamps, but also control of window coverings (e.g., shades, blinds, etc.). Each interaction of a user 602 with a smart building through a building service integration platform 616 is handled by an aspect manager 630. Aspect manager 630 may receive additional information (such as contextual information) from knowledge base 640. As more and more data is collected for each user, the information processed by aspect manager 630 is stored in distributed profile repository 632.

[0075] The aspect manager 630 can collect event information and mine the data through each aspect. Afterwards, the ideal machine learning algorithm can be determined for processing the data. This can be done using an iterative process, where a new machine learning algorithm is used for each iteration to determine which algorithm produces the best model. The selected aspect model is then stored in the profile repository 632.

[0076] The second phase that may be performed is the "adaptive formulation" phase. This phase applies the profile to various aspects. This phase uses the system 600, which will combine Figure 7 700 is discussed.

[0077] An event occurs (block 702). The occurrence may be an input by the user 602 using one of the interfaces 610, 612, or 614. Alternatively, it may be a sensor acting as one of the interfaces detecting the user 602. The events are filtered to determine if there are any related events (block 704). There may be instances where multiple interfaces detect the same event or related events. For example, going to a floor to enter a room may be considered a related event. Information is then retrieved from the profile repository 632 (block 706) to determine which model to use based on the aspects and the user, and information is retrieved from the knowledge base 640 (block 708) to gather context information.

[0078] The aspect manager 630 then selects a model and executes the selected model (block 710) to recommend a course of action (block 712). The recommended course of action is then executed by the building services integration platform 616. For example, the course of action may be to change lighting, call an elevator, change HVAC settings, etc.

[0079] Another aspect of the above system is attribute-based access control. User authorization profiles can be stored in a distributed profile repository or in a user's mobile electronic device. The profile can include credentials as well as a collection of activity history logs, user-related attributes, and operation / resource request templates. The profile can encode abstract actions on building resources that a given human-machine interface can use in the authentication profile of the requesting user. In some embodiments, there can be multiple types of profiles - authorization profiles, which grant access to various areas of a smart building and various aspects of a building, and "comfort profiles," which can be used to control preferences regarding lighting and comfort. In some embodiments, comfort profiles and authorization profiles can be combined into a single user profile.

[0080] There are various attributes that can be used. User attributes can include attributes that describe the user, such as age, name, office number, job role, security clearance, address, trust level, etc. User attributes can also include dynamic context information, such as the location of the user and the scope of the user's visit. Resource attributes include a description of the resource that is the subject of the action performed by the user. This can include metadata such as type (set point, command point, sensor point), the device associated with the resource (e.g., thermostat or card reader), associated physical or condition characteristics (e.g., temperature or fault condition), the user's location, etc.

[0081] Environmental attributes may be derived from the conditions at the time the request was made. Changing conditions may require other attributes to be evaluated at the time of the request. Environmental attributes may also include temperature, ambient noise, or other contextual information relevant to access control. Session attributes may include characteristics related to past sessions or the user's current session, such as physical characteristics of the human-machine interface, rate or type or history of access requests, etc. Action attributes may be used to specify actions to be performed on a resource.

[0082] In operation, the access control system can verify resource access requests submitted via the human-machine interface. The access control system can also verify the user's credentials. The access control system can interact with the knowledge base and with the smart building to add context and session attributes to resource access requests.

[0083] Submitted resource access requests are evaluated against a set of access policies available for a particular site. Access policies can be created using a policy language and restrict access to certain resources or objects. This can be based on the results of Boolean statements comparing attributes. This allows for flexible implementation of real-world policies while only requiring knowledge of a certain subset of attributes about a given user, as compared to knowing the user's identity and manually assigning roles or permissions to the user.

[0084] If at least one access policy grants authorization for the request, and no policy blocks it, the token can be sent back to the HMI. The HMI passes the token to the smart building. The smart building validates the token through the access control system. If validated, the requested access is performed. The output of the action is returned via the HMI. The results are presented to the user, and the action is recorded.

[0085] During various interactions between users and smart buildings, resource access requests can be derived from request templates, profile information, and context information. Requests can be defined in terms of attributes (describing users, resources, environments, and actions) that are linked to shared descriptions provided by a knowledge base for seamless application across heterogeneous environments.

[0086] Another feature of the above system is the ability to share configuration files across multiple sites. The user configuration files described above can be portable across different sites. This can include not only buildings owned or operated by the same entity (such as a university campus or office park or hotel chain), but also buildings owned or operated by different entities. In other words, a hotel chain can share configuration information with office buildings, shopping malls or apartment buildings owned or operated by different entities.

[0087] As described above, user profiles are created by fusing and coordinating data from one or more building system interfaces (e.g., interfaces 610, 612, and 614). The profile for each user is stored in a distributed profile repository 632. Each system and associated building is described in a knowledge base 640 according to a shared conceptual structure. Each profile in the profile repository 632 is periodically updated according to the user's interactions with the systems operating in each visited building and is linked to a shared description (e.g., contextual information) stored in the knowledge base 640. In this way, each user's profile can be seamlessly applied across numerous environments.

[0088] Later, when the user enters an unvisited environment, the user's profile can be retrieved. The profile may include credentials, a user's activity history log, user-related attributes, a collection of action / resource request templates, etc. The unvisited environment can be compared with the environment and the context of the environment that the user has visited before. Later, the environment of the unvisited location can be adjusted to the estimated comfort level based on the user's profile and the context of the place he has visited before. For example, if the user has visited a hotel as a guest before, different hotels can use that information to determine the appropriate environment for the user to stay in a hotel that has not been visited before as a guest. Information about the user's preferences as an employee can be given less weight because those locations do not share the same context. The user's profile can be regarded as a "digital signature" that can implement an advanced interface and provide an overall, cohesive and personalized experience across different buildings and the systems therein.

[0089] In addition, access control policies may be issued by different authorities. Access control policies are used to express permission or prohibition of access in a declarative manner. Access control policies may also include contextual information related to the user and contextual information not related to the user.

[0090] The configuration file may include user authorization information detailing access control rules that are portable across multiple buildings. Contextual information associated with the user (e.g., whether the user is an employee or a guest) may be used to determine the access rights the user has to the system. Additionally, contextual information independent of the user (e.g., the size and shape of the building, the purpose of the various rooms, etc.) may be used to determine thermal and lighting comfort.

[0091] There may be alternative applicable access control rules identified by available context information. For example, a user may always be allowed access to lighting and HVAC parameters of hotel rooms where the user is a registered guest.

[0092] In some embodiments, it may be desirable to detect user intent for various advanced building applications. Exemplary applications may include exit, occupancy-based building control, and destination management systems. It may be desirable to model the behavior of a group in order to learn the user's intent while protecting the user's privacy and detecting abnormal behavior.

[0093] exist Figure 8 Flowchart 800 illustrating such a use case is shown in FIG. An environment is monitored using one or more sensors (block 802). The monitored environment may be a room within a building or a public area near a building, etc. The environment may include several rooms. The sensors used may be any one sensor or a combination of sensors. Exemplary sensors may include cameras, presence detection sensors, wireless transceivers, etc.

[0094] People are detected within the monitored environment (block 804). A group of people may be sensed in one of a variety of different ways. A group may be broadly understood as a social unit comprising a number of members having identities and relationships with one another. The types of groups analyzed may include a separate conversation group (FCG). An FCG is a collection of co-present people participating in an ad-hoc encounter. They may be considered a centralized encounter. Exemplary FCGs may include a party, a renovation session, or an office meeting.

[0095] FCG can be detected by calculating the facing foramtion (also known as F-formation) from the spatial position and orientation of the residents. F-formation is a combination of three social spaces (such as Fig. 9 910). O-space 910 is a convex empty space surrounded by people (902) participating in the social interaction, where each participant faces inwardly into O-space 910. There are no outsiders in O-space 910. P-space 920 is a ring surrounding O-space 910. People in the FCG are located in P-space 920. R-space 930 is a space surrounding P-space 920 and is also monitored by FCG participants.

[0096] Reference Figure 8 , using the user's orientation and location to determine the presence of one or more FCGs within the user group. The orientation and location of each user is tracked to find the O-space (block 806).

[0097] Groups can change over time. Two groups of four people can become groups of five and three, a single group of eight, or any of a variety of different sized groups. Additional people can join or leave groups when entering or leaving the monitored area. Groups can be expanded into subgroups, can merge, can disappear, and so on. Graphs and topological methods can be used to determine these behaviors. Exemplary methods of calculating entropy include using the VonNeumann entropy equation to measure groupness over time and using persistent entropy to detect group fusion in circular patterns.

[0098] Each interaction within and between groups can be represented as a time-dependent network (box 808). This can be achieved using an undirected graph. A graph is a mathematical structure for modeling pairwise relationships between objects. A graph is an ordered pair G = (V, E) comprising a set of V vertices (or nodes or points) together with a set of E edges associated with two vertices. Using the Von Neumann entropy equation, entropy can be found because it is related to the graph. Using persistent homology techniques, shapes can be clustered and higher-dimensional correlations that cannot be otherwise identified using classical statistical methods can be found. Therefore, the graph can be transformed into a topological object (box 810). Using a persistent homology algorithm, the connectivity between vertices can be determined, thereby tracking groups over time by detecting temporary and persistent groups in circular patterns (box 812). Once the model has been deployed, it can be used for real-time analysis (box 814). In this way, abnormal behavior can be detected for groups and individuals (including but not limited to for Figure 2 In other words, the intentions of individual groups and the intentions between groups can be mined. The mined information can be used to discover abnormal behaviors within the group. In addition, the mined information can be used to develop emergency response plans, such as ideal escape routes and potential problems on existing routes (box 816).

[0099] Figure 3 A high-level block diagram of a computer system 300 that can be used to implement one or more embodiments is depicted. More particularly, the computer system 300 can be used to implement the hardware components of a system capable of performing the methods described herein. Although one exemplary computer system 300 is shown, the computer system 300 includes: a communication path 326 that connects the computer system 300 to additional systems (not depicted); and may include one or more wide area networks (WANs) and / or local area networks (LANs), such as the Internet, (one or more) intranets, and / or (one or more) wireless communication networks. The computer system 300 and the additional systems communicate via the communication path 326, for example, to pass data therebetween.

[0100] The computer system 300 includes one or more processors, such as processor 302. Processor 302 is connected to a communication infrastructure 304 (e.g., a communication bus, a cross-over bar, or a network). The computer system 300 may include a display interface 306 that forwards graphics, text content, and other data from the communication infrastructure 304 (or from a frame buffer not shown) for display on a display unit 308. The computer system 300 also includes a main memory 310 (preferably a random access memory (RAM)), and may also include an auxiliary memory 312. The auxiliary memory 312 may include, for example, a hard drive 314 and / or a removable storage drive 316, which represents, for example, a floppy disk drive, a tape drive, or an optical drive. The hard drive 314 may take the form of a solid-state drive (SSD), a traditional magnetic disk drive, or a hybrid of the two. There may also be more than one hard drive 314 contained within the auxiliary memory 312. The removable storage drive 316 reads from and / or writes to a removable storage unit 318 in a manner well known to those skilled in the art. Removable storage unit 318 represents, for example, a floppy disk, compact disk, magnetic tape, or optical disk, etc., which is read and written by removable storage drive 316. As will be appreciated, removable storage unit 318 includes a computer-readable medium having stored therein computer software and / or data.

[0101] In alternative embodiments, the secondary memory 312 may include other similar components for allowing computer programs or other instructions to be loaded into the computer system. Such components may include, for example, a removable storage unit 320 and an interface 322. Examples of such components may include: a package and a package interface (such as a package and a package interface found in a video game device); a removable memory chip (such as an EPROM, a secure digital card (SD card), a compact flash card (CF card), a universal serial bus (USB) memory, or a PROM) and an associated socket; and other removable storage units 320 and interfaces 322 that allow software and data to be transferred from the removable storage unit 320 to the computer system 300.

[0102] The computer system 300 may also include a communication interface 324. The communication interface 324 allows software and data to be transferred between the computer system and external devices. Examples of the communication interface 324 may include a modem, a network interface (such as an Ethernet card), a communication port or PC card slot and card, a universal serial bus port (USB), etc. The software and data transferred via the communication interface 324 take the form of signals, which may be, for example, electronic signals, electromagnetic signals, optical signals, or other signals that can be received by the communication interface 324. These signals are provided to the communication interface 324 via a communication path (i.e., channel) 326. The communication path 326 carries the signals and may be implemented using wires or cables, optical fibers, telephone lines, mobile phone links, RF links, and / or other communication channels.

[0103] In this description, the terms "computer program medium", "computer usable medium" and "computer readable medium" are used to refer to media such as main memory 310 and secondary memory 312, removable storage drive 316, and hard disk installed in hard disk drive 314. Computer programs (also known as computer control logic) are stored in main memory 310 and / or secondary memory 312. Computer programs can also be received via communication interface 324. Such computer programs, when run, enable the computer system to perform the features discussed herein. In particular, the computer programs, when run, enable the processor 302 to perform the features of the computer system. Therefore, such computer programs represent controllers of the computer system. Therefore, it can be seen from the above detailed description that one or more embodiments provide technical benefits and advantages.

[0104] Reference now Figure 4 , generally illustrates a computer program product 400 including a computer-readable storage medium 402 and program instructions 404 according to an embodiment.

[0105] Embodiments may be systems, methods, and / or computer program products. A computer program product may include (one or more) computer-readable storage media having computer-readable program instructions thereon for causing a processor to perform aspects of embodiments of the invention.

[0106] A computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above devices. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device (such as a punch card or a raised structure in a groove on which instructions are recorded) and any suitable combination of the above. As used herein, a computer-readable storage medium is not understood to be a temporary signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable) or an electrical signal transmitted through a line.

[0107] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device, or downloaded to an external computer or external storage device via a network such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the corresponding computing / processing device.

[0108] The computer-readable program instructions for executing the embodiment may include assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages ​​including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) and conventional process programming languages ​​(such as "C" programming language or similar programming languages). The computer-readable program instructions can be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer by any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider through the Internet). In some embodiments, the electronic circuit including, for example, a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA) can be executed by using the state information of the computer-readable program instructions to personalize the electronic circuit to execute the computer-readable program instructions, so as to perform embodiments of the present invention.

[0109] Embodiments may be implemented using one or more technologies. In some embodiments, a device or system may include one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the device or system to perform one or more method actions as described herein. In some embodiments, various mechanical components known to those skilled in the art may be used.

[0110] Embodiments may be implemented as one or more devices, systems, and / or methods. In some embodiments, instructions may be stored on one or more computer program products or computer-readable media such as temporary and / or non-temporary computer-readable media. The instructions, when executed, may cause an entity (e.g., a processor, device, or system) to perform one or more method actions as described herein.

[0111] Although the present disclosure has been described with reference to one or more exemplary embodiments, it will be appreciated by those skilled in the art that various changes may be made and elements thereof may be replaced with equivalents without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt specific circumstances or materials to the teachings of the present disclosure without departing from its basic scope. Therefore, it is contemplated that the present disclosure is not limited to the specific embodiments disclosed as the best mode contemplated for carrying out the present disclosure, but that the present disclosure will include all embodiments falling within the scope of the claims.

Claims

1. A computer-implemented method for using portable user profiles in a plurality of smart buildings, comprising: detecting the presence of a user at a smart building among the plurality of smart buildings; determining that the user has not visited the smart building before; retrieving a portable user profile for the user, the portable user profile being portable across different sites associated with the plurality of smart buildings; as well as setting access control rules based on the portable user profile, wherein the portable user profile includes user authorization information detailing access control rules that are portable among the plurality of smart buildings, and context information associated with the user is used to determine that the user has access rights to systems associated with the smart buildings.

2. The computer-implemented method of claim 1 , further comprising: Contextual information contained in the portable user profile is evaluated and compared with contextual information for the smart building.

3. The computer-implemented method of claim 2, wherein: The context information includes context information unrelated to a person and context information related to a person.

4. The computer-implemented method of claim 3, wherein: The human-independent contextual information includes information about the layout of the smart building.

5. The computer-implemented method of claim 3, wherein: The context information associated with a person includes information about the role and authorization area of ​​the user.

6. The computer-implemented method of claim 4, wherein: Setting access control rules further includes determining a role of the user in the smart building.

7. The computer-implemented method of claim 6, further comprising: Access rights to perform operations on objects are granted based on assigned attributes of the user contained in the portable user profile.

8. The computer-implemented method of claim 7, wherein: The attributes are selected from one or more of: user attributes; resource attributes; environment attributes; session attributes; or action attributes.

9. The computer-implemented method of claim 1 , further comprising: The environment is adjusted by adjusting thermal comfort based on information about the layout of the smart building.

10. The computer-implemented method of claim 1, further comprising: Adjust lighting comfort by adjusting electric lighting and adjusting window coverings.

11. A computer system for using a portable user profile in a plurality of smart buildings, comprising: processor; Memory; Computer program instructions configured to cause the processor to perform the following method: detecting the presence of a user at a smart building among the plurality of smart buildings; determining that the user has not visited the smart building before; retrieving a portable user profile for the user, the portable user profile being portable across different sites associated with the plurality of smart buildings; as well as setting access control rules based on the portable user profile, wherein the portable user profile includes user authorization information detailing access control rules that are portable among the plurality of smart buildings, and context information associated with the user is used to determine that the user has access rights to systems associated with the smart buildings.

12. The computer system of claim 11, wherein: The method further comprises: Contextual information contained in the portable user profile is evaluated and compared with contextual information for the smart building.

13. The computer system of claim 12, wherein: The context information includes context information unrelated to a person and context information related to a person.

14. The computer system of claim 13, wherein: The non-human related contextual information includes information about the layout of the smart building.

15. The computer system of claim 13, wherein: The context information associated with a person includes information about the role and authorization area of ​​the user.

16. The computer system of claim 11, wherein the method further comprises: Access control rules are set for the user based on the portable user profile.

17. The computer system of claim 16, wherein: Setting access control rules further includes determining the role and authorized area of ​​the user in the smart building.

18. The computer system of claim 11, wherein the method further comprises: The environment is adjusted by adjusting thermal comfort based on information about the layout of the smart building.

19. The computer system of claim 11, wherein: Setting access control rules further includes determining a role of the user in the smart building.

20. The computer system of claim 11, wherein the method further comprises: Adjust lighting comfort by adjusting electric lighting and adjusting window coverings.

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