System and method for detecting occupancy of room

Abstract

Systems and methods for detecting occupancy of a room are provided. The method includes receiving, from a first door sensor associated with a door of the room, a sensor reading indicative of an opening operation of the door. The method includes determining that an occupancy state of the room is a positive state based on the sensor reading of the first door sensor. The method includes receiving, from a second door sensor, a sensor reading indicative of a closing and / or locking operation of the door. The method includes determining whether the closing and / or locking operation has been performed. The method comprises verifying that the occupancy state of the room is the positive state when it is determined that the closing and / or locking operation has been performed; and triggering a supplemental detection process to detect the occupancy of the room when it is determined that the closing and / or locking operation has not been performed.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 596,973, filed on November 8, 2023, entitled “SYSTEMS AND METHODS FOR DETECTINGOCCUPANCY OF ROOMS,” the disclosure of which is incorporated herein by reference. Technical Field

[0003] The present disclosure relates generally to detection systems, and more particularly to systems and methods for detecting occupancy of a room. Background Art

[0004] Occupancy detection methods are increasingly used in private homes and in institutions that provide accommodation to customers. An occupant may spend some time in a room and then may leave the room, leaving the room unoccupied thereafter. When a room is unoccupied, facilities in the room, such as lights and heating, cooling and air conditioning (HVAC) units, may continue to operate, resulting in energy consumption. In this case, occupancy detection can help determine whether a room is occupied or unoccupied. If the room is unoccupied, the facilities of the room can be turned off, thereby reducing energy consumption.

[0005] Conventional techniques for detecting the presence of occupants in a room include motion sensors. However, motion sensors do not cover every area of ​​the room, resulting in blind spots that may cause the room to be incorrectly classified as unoccupied. In addition, motion sensors are useless in situations where the occupants are sleeping or sitting still. Therefore, detection using motion sensors may result in a high false negative rate, i.e., the room is detected as unoccupied when it is occupied.

[0006] In other conventional techniques, a single sensor based system, proximity sensor, etc. may be employed, however, such techniques result in unbalanced detection. For example, proximity sensor based detection results in a high false positive rate, i.e., a room is detected as occupied when it is not occupied. When a proximity sensor is employed, the field of view and distance are limited. In addition, a single sensor based system is not resilient because a failure of a sensor causes the entire detection system to fail.

[0007] In sensor-based technologies, data from sensors may be processed to determine occupancy in a room. The processing may be based on certain algorithms or mathematical models. Continuously employing algorithms and models to determine occupancy may result in increased processing time and cost. Furthermore, in cases where the algorithms and models are remote, network bandwidth is also required to detect occupancy. This delays detection results and increases costs.

[0008] Therefore, it would be advantageous to provide a solution that overcomes the above-mentioned problems. Summary of the invention

[0009] This summary is provided to introduce some concepts in a simplified form, which are further described in the detailed description of the present disclosure. This summary is neither intended to identify key or significant inventive concepts of the present disclosure nor to determine the scope of the present disclosure.

[0010] Disclosed herein is a system for detecting occupancy of a room, the system comprising a first door sensor and a second door sensor associated with a door of the room. The first door sensor is configured to detect an opening operation of the door, and the second door sensor is configured to detect a closing and / or locking operation of the door. The system comprises a control unit communicatively connected to the first door sensor and the second door sensor. The control unit comprises one or more processors, the one or more processors being configured to receive a sensor reading indicating an opening operation of the door from the first door sensor. The one or more processors are configured to determine that the occupancy state of the room is a positive state based on the sensor reading of the first door sensor. The one or more processors are configured to receive a sensor reading indicating a closing and / or locking operation of the door from the second door sensor. The one or more processors are configured to determine whether the closing and / or locking operation has been performed based on the sensor reading of the second door sensor. The one or more processors are configured to verify that the occupancy state of the room is a positive state when determining that the closing and / or locking operation has been performed. The one or more processors are configured to trigger a supplementary detection process to detect the occupancy of the room when determining that the closing and / or locking operation has not been performed.

[0011] In one or more embodiments, the system includes a plurality of sensors configured to measure corresponding occupancy parameters. To trigger the supplemental detection process, the one or more processors are configured to receive sensor readings indicating corresponding occupancy parameters from the plurality of sensors, wherein each of the received sensor readings includes a sequence of measurement results of the corresponding occupancy parameter within a predetermined duration. In addition, the one or more processors are configured to determine an occupancy state of the room and one or more of a confidence value associated with the determined occupancy state based on the received sensor readings. The occupancy state represents one of a positive state indicating that the room is occupied and a negative state indicating that the room is not occupied.

[0012] In one or more embodiments, the one or more processors are further configured to, in response to determining an occupancy state, trigger a control action associated with the room based on the determined occupancy state. To trigger the control action associated with the room, the one or more processors are configured to control the operation of one or more devices associated with the room. The one or more devices include a heating, ventilation, and air conditioning (HVAC) system, a thermostat, and a lighting unit.

[0013] In one or more embodiments, in order to determine one or more of the occupancy state and the confidence value, the one or more processors are configured to select a mathematical model from a plurality of mathematical models based on the types of the plurality of sensors and the corresponding occupancy parameters. In addition, the one or more processors are configured to determine one or more of the occupancy state and the confidence value based on the selected mathematical model. In addition, the one or more processors are configured to determine the first classification probability value and the second classification probability value associated with the occupancy state of the room based on the selected mathematical model and the sequence of measurement results. In addition, the one or more processors are configured to compare the first classification probability value and the second classification probability value. In addition, the one or more processors are configured to determine that the occupancy state is a positive state indicating that the room is occupied in response to determining that the first classification probability value is greater than the second classification probability value. In addition, the one or more processors are configured to determine that the occupancy state is a negative state indicating that the room is not occupied in response to determining that the second classification probability value is greater than the first classification probability value.

[0014] In one or more embodiments, the one or more processors are configured to cause information related to the occupancy status to be displayed on one or more of a user interface disposed within the room and a user interface associated with a handheld device of the user.

[0015] In one or more embodiments, the door opening operation includes opening the door from the outside of the room, and the door closing and / or locking operation includes one of closing or locking the door from the inside of the room.

[0016] A method for detecting the occupancy of a room is disclosed herein. The method includes receiving a sensor reading indicating an opening operation of the door from a first door sensor associated with the door of the room, the first door sensor being configured to detect the opening operation of the door. In addition, the method includes determining that the occupancy state of the room is a positive state based on the sensor reading of the first door sensor. In addition, the method includes receiving a sensor reading indicating a closing and / or locking operation of the door from a second door sensor associated with the door of the room, the second door sensor being configured to detect the closing and / or locking operation of the door. In addition, the method includes determining whether the closing and / or locking operation has been performed based on the sensor reading of the second door sensor. In addition, the method includes verifying that the occupancy state of the room is a positive state when it is determined that the closing and / or locking operation has been performed. In addition, the method includes triggering a supplementary detection process to detect the occupancy of the room when it is determined that the closing and / or locking operation has not been performed.

[0017] In one or more embodiments, triggering a supplemental detection process includes receiving sensor readings indicating corresponding occupancy parameters from a plurality of sensors configured to measure the corresponding occupancy parameters, wherein each of the received sensor readings includes a sequence of measurement results of the corresponding occupancy parameter within a predetermined duration. In addition, the method includes determining an occupancy state of the room based on the received sensor readings and one or more of confidence values ​​associated with the determined occupancy state, wherein the occupancy state represents one of a positive state indicating that the room is occupied and a negative state indicating that the room is not occupied.

[0018] In one or more embodiments, the method includes, in response to determining an occupancy state, triggering a control action associated with the room based on the determined occupancy state, wherein triggering the control action includes controlling operation of one or more devices associated with the room, wherein the one or more devices include a heating, ventilation, and air conditioning (HVAC) system, a thermostat, and a lighting unit.

[0019] In one or more embodiments, determining the occupancy state and the confidence value includes selecting a mathematical model from a plurality of mathematical models based on the types of the plurality of sensors and the corresponding occupancy parameters. In addition, the method includes determining one or more of the occupancy state and the confidence value based on the selected mathematical model. In addition, the method includes determining a first classification probability value and a second classification probability value associated with the occupancy state of the room based on the selected mathematical model and the sequence of measurement results. In addition, the method includes comparing the first classification probability value and the second classification probability value. In addition, the method includes, in response to determining that the first classification probability value is greater than the second classification probability value, determining that the occupancy state is a positive state indicating that the room is occupied. In addition, the method includes, in response to determining that the second classification probability value is greater than the first classification probability value, determining that the occupancy state is a negative state indicating that the room is not occupied.

[0020] In one or more embodiments, the method includes causing information related to the occupancy status to be displayed on one or more of a user interface disposed within the room and a user interface associated with a handheld device of the user.

[0021] In one or more embodiments, the door opening operation includes opening the door from the outside of the room, and the door closing and / or locking operation includes one of closing or locking the door from the inside of the room.

[0022] The present invention also discloses a system for detecting the occupancy of a room. The system includes a CO2 sensor and a noise sensor located in the room, each of the CO2 sensor and the noise sensor being configured to measure a corresponding occupancy parameter. The system includes a control unit that is communicatively connected to the CO2 sensor and the noise sensor, the control unit including one or more processors, the one or more processors being configured to receive sensor readings indicating corresponding occupancy parameters from the CO2 sensor and the noise sensor. In addition, the one or more processors are configured to determine, based on the received sensor readings, that the occupancy state of the room is one of a positive state indicating that the room is occupied and a negative state indicating that the room is not occupied. In addition, the one or more processors are configured to trigger a control action associated with the room based on the determined occupancy state in response to determining the occupancy state.

[0023] In one or more embodiments, each of the received sensor readings includes a sequence of measurements of a corresponding occupancy parameter over a predetermined duration. The occupancy parameter measured by the CO2 sensor includes the CO2 level in the room. The occupancy parameter measured by the noise sensor includes the sound frequency in the room.

[0024] In one or more embodiments, to determine the occupancy state, the one or more processors are configured to determine one of the CO2 level or the rate of change of the CO2 level in the room based on the sensor readings received from the CO2 sensor. In addition, the one or more processors are configured to determine the sound frequency in the room based on the sensor readings received from the noise sensor. In addition, the one or more processors are configured to determine the occupancy state based on one or more trained mathematical models, the determined sound frequency, and the determined CO2 level or the rate of change of the CO2 level.

[0025] In one or more embodiments, to trigger a control action associated with a room, the one or more processors are configured to control the operation of one or more devices associated with the room, wherein the one or more devices include a heating, ventilation, and air conditioning (HVAC) system, a thermostat, and a lighting unit.

[0026] In one or more embodiments, in order to trigger a control action associated with the room, the one or more processors are configured to cause information related to the occupancy status to be displayed on one or more of a user interface arranged in the room and a user interface associated with the user's handheld device.

[0027] A method for detecting occupancy of a room is also disclosed herein. The method includes receiving sensor readings indicating corresponding occupancy parameters from a CO2 sensor and a noise sensor located in the room, wherein each of the CO2 sensor and the noise sensor is configured to measure the corresponding occupancy parameter. In addition, the method includes determining, based on the received sensor readings, that an occupancy state of the room is one of a positive state indicating that the room is occupied and a negative state indicating that the room is not occupied. In addition, the method includes, in response to determining the occupancy state, triggering a control action associated with the room based on the determined occupancy state.

[0028] In one or more embodiments, each of the received sensor readings includes a sequence of measurements of a corresponding occupancy parameter over a predetermined duration. The occupancy parameter measured by the CO2 sensor includes the CO2 level in the room. The occupancy parameter measured by the noise sensor includes the sound frequency in the room.

[0029] In one or more embodiments, determining the occupancy state includes determining one of a CO2 level or a rate of change of the CO2 level in the room based on a sensor reading received from a CO2 sensor. In addition, the method includes determining a sound frequency in the room based on a sensor reading received from a noise sensor. In addition, the method includes determining the occupancy state based on one or more trained mathematical models, the determined sound frequency, and one of a determined CO2 level or a rate of change of the CO2 level.

[0030] In one or more embodiments, triggering a control action associated with a room includes controlling operation of one or more devices associated with the room, wherein the one or more devices include a heating, ventilation, and air conditioning (HVAC) system, a thermostat, and a lighting unit.

[0031] In one or more embodiments, triggering a control action associated with the room includes causing information related to the occupancy status to be displayed on one or more of a user interface disposed within the room and a user interface associated with a user's handheld device.

[0032] In order to further illustrate the advantages and features of the method, system and device, a more specific description of the method, system and device will be presented by reference to its specific embodiments illustrated in the accompanying drawings. It should be understood that these drawings only depict typical embodiments of the present disclosure and should not be considered as limiting the scope thereof. The present disclosure will be described and explained with additional specificity and details in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] These and other features, aspects and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout the several views, wherein:

[0034] Figure 1 A system environment for detecting occupancy of a room according to one or more embodiments of the present disclosure is illustrated;

[0035] Figure 2 A schematic block diagram of a system for detecting occupancy of a room according to one or more embodiments of the present disclosure is illustrated;

[0036] Figure 3 Another system environment for detecting occupancy of a room according to one or more embodiments of the present disclosure is illustrated;

[0037] FIG. 4A to FIG. 4B illustrating graphical representations of sensor readings from a CO2 sensor and a noise sensor, respectively, according to one or more embodiments of the present disclosure;

[0038] Figure 5 illustrates an operational process flow for detecting occupancy of a room according to one or more embodiments of the present disclosure; and

[0039] Figure 6 to Figure 7 A process flow diagram depicting a method for detecting occupancy of a room according to one or more embodiments of the present disclosure is illustrated.

[0040] Furthermore, the skilled artisan will appreciate that the elements in the drawings are illustrated for simplicity and may not necessarily be drawn to scale. For example, a flow chart illustrates a method in terms of the most important steps involved to help improve understanding of various aspects of the present disclosure. Furthermore, with respect to the construction of the device, one or more components of the device may have been represented in the drawings with conventional symbols, and the drawings may show only those specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that would be readily apparent to one of ordinary skill in the art having the benefit of the description herein. DETAILED DESCRIPTION

[0041] In order to promote an understanding of the principles of the present disclosure, reference will now be made to various embodiments and specific language will be used to describe these embodiments. However, it should be understood that the scope of the present disclosure is not thereby limited, and such changes and further modifications to the illustrated system, as well as further applications of the principles of the illustrated disclosure, are all generally considered by those skilled in the art of the relevant technical field of the present disclosure.

[0042] Those skilled in the art will understand that the foregoing general description and the following detailed description are illustrative of the present disclosure and are not intended to limit the present disclosure.

[0043] References throughout this specification to "aspect," "another aspect," or similar language are intended to indicate that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases "in one embodiment," "in another embodiment," "some embodiments," "one or more embodiments," and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0044] The terms "comprises", "includes" or any other variations thereof are intended to cover a non-exclusive inclusion such that a process or method that includes a list of steps may not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or subsystems or elements or structures or parts beginning with "comprises..." do not, without more constraints, exclude the presence of other devices or other subsystems or other elements or other structures or other parts or additional devices or additional subsystems or additional elements or additional structures or additional parts.

[0045] In addition to overcoming challenges associated with detecting the presence of occupants in a room, the present disclosure provides a system that enables accurate occupancy detection in an efficient manner. Furthermore, energy and cost savings associated with appliances such as HVAC systems and lighting units in the room can be achieved. Personnel efficiency can also be improved based on accurate detection of the presence of occupants in the room.

[0046] Hereinafter, embodiments of the present disclosure are described in detail with reference to the accompanying drawings.

[0047] Figure 1 A system environment 100 for detecting occupancy of a room is illustrated. The system environment 100 may include an indoor area / environment 110, which includes a plurality of rooms 120a-120n (hereinafter interchangeably referred to as "120"). In one embodiment, the indoor area 110 may be associated with an institution that provides accommodation to users, such as, but not limited to, a hotel, a homestay, a guesthouse, etc. The plurality of rooms 120a-120n may include rooms that have been assigned to users and rooms that are available for assignment to users looking for accommodation. In another embodiment, the indoor area 110 may be associated with a private residence, etc., and the plurality of rooms 120a-120n may include various rooms, such as a bedroom, a guest room, a living room, etc.

[0048] Each room 120 may be associated with a first door sensor 121a and a second door sensor 121b. The first door sensor 121a and the second door sensor 121b may be positioned adjacent to or integrated within the corresponding door of the room 120. In some embodiments, the first door sensor 121a and the second door sensor 121b may be integrated into a single sensing device. The first door sensor 121a may be configured to detect whether the corresponding door has been opened from outside the room 120, such as when an occupant has opened the door to enter the room 120. The first door sensor 121a may therefore be configured to detect an opening operation of the door from outside the room 120. The opening operation of the door may include, for example, opening the door with a key, with a smart card, etc.

[0049] The second door sensor 121 b can be configured to detect whether the corresponding door has been operated from the inside of the room 120, such as when an occupant has closed or locked the door from the inside of the room 120. For example, an occupant may enter the room 120 and lock the door with a bolt and / or other locking mechanism. The second door sensor 121 b can therefore be configured to detect the closing and / or locking operation of the door from the inside of the room 120.

[0050] In addition, the room 120 may be associated with a plurality of environmental sensors 122a-122n (hereinafter referred to as "plurality of sensors 122a-122n"). The plurality of sensors 122a-122n may be located within the room 120. The plurality of sensors 122a-122n may be configured to measure corresponding occupancy parameters associated with the room 120. In some embodiments, each of the plurality of sensors 122a-122n may be configured to measure a corresponding occupancy parameter, which indicates the occupancy of the room 120. Specifically, based on the corresponding occupancy parameters sensed by the plurality of sensors, the occupancy of the room 120 may be determined, as will be described in further detail below. Those skilled in the art will appreciate that the details explained with respect to one room 120 may equally apply to each of the plurality of rooms 120a-120n without departing from the scope of the present disclosure.

[0051] In some embodiments, the corresponding occupancy parameters include multiple parameters such as, but not limited to, motion, temperature, humidity, pressure, proximity, noise, particulate matter (PM) 2.5 (for particles 2.5 microns in diameter or less), PM 10 (for particles 10 microns in diameter or less), volatile organic compounds (VOCs), carbon dioxide (CO2), light intensity, lock operation, and plug power consumption. The plurality of sensors 122a-122n may include sensors configured to sense one or more of the plurality of occupancy parameters and generate sensor readings indicative of the sensed occupancy parameters.

[0052] In some embodiments, the plurality of sensors 122a-122n may include a passive infrared (PIR) sensor for detecting the motion of any occupant in the room 120. In some embodiments, the plurality of sensors 122a-122n may include a temperature sensor, a humidity sensor, and a pressure sensor for measuring the temperature, humidity, and pressure within the room 120, respectively. In some embodiments, the plurality of sensors 122a-122n may include a proximity sensor for sensing the proximity of any occupant relative to the proximity sensor. In some embodiments, the plurality of sensors 122a-122n may include an indoor air quality (IAQ) sensor for measuring a plurality of parameters, such as, but not limited to, PM 2.5, PM 10, VOC, and CO2. In some embodiments, the plurality of sensors 122a-122n may include a noise sensor and a light sensor for measuring the amount of noise and the amount of light intensity within the room 120, respectively. In some embodiments, the plurality of sensors 122a-122n may include a current transformer (CT) sensor for measuring power plug consumption. In some embodiments, the plurality of sensors 122a - 122n may include motion sensors and / or radio frequency identification (RFID) tags for detecting locking (and unlocking) operations of doors associated with the room 120 .

[0053] In some embodiments, one or more of the plurality of sensors 122a-122n may be sensors independently arranged at corresponding positions within the room 120. In some embodiments, one or more of the plurality of sensors 122a-122n may be integrated with a detection unit arranged within the room 120. In some embodiments, the temperature sensor, the humidity sensor, and the proximity sensor may be integrated with a thermostat arranged within the room 120. In some embodiments, the temperature sensor, the humidity sensor, the PIR sensor, the noise sensor, and the IAQ sensor may be integrated with a smoke detector arranged within the room 120. In some embodiments, the IAQ sensor may be integrated with an IAQ monitoring unit arranged within the room 120.

[0054] In some embodiments, each room 120 of the plurality of rooms 120a-120n may be associated with a heating, ventilation, and air conditioning (HVAC) system 124, a thermostat 126, and a lighting unit 128. The HVAC system 124 may be configured to facilitate circulation of fresh air in the room, and further facilitate heating and cooling of the room 120. The thermostat 126 may be configured to provide instructions to the HVAC system 124 regarding desired heating and cooling set points in the room 120. The lighting unit 128 may include various electrical components, such as electrical outlets, light sources, lamps, and other electrical devices within the room 120.

[0055] The system environment also includes a control device 130 that is communicatively coupled to the first door sensor 121a, the second door sensor 121b, and the plurality of sensors 122a-122n via a communication network 140, as discussed below throughout the present disclosure. In some embodiments, the HVAC system 124, the thermostat 126, and the lighting unit 128 may be communicatively coupled to the control device 130 via the communication network 140. The control device 130 may be configured to detect the operation of a door of the room 120 based on readings from the first door sensor 121a and the second door sensor 121b. The control device 130 may also be configured to detect the occupancy of the room 120 based on occupancy parameters sensed by the plurality of sensors 122a-122n associated with the room 120. The control device 130, the first door sensor 121a, the second door sensor 121b, and the plurality of sensors 122a-122n may form a system 150 for detecting the occupancy of the room 120. In some embodiments, the functionality of the system 150 including the control device 130, the first door sensor 121a, the second door sensor 121b, and the plurality of sensors 122a-122n may be provided by a single device.

[0056] In some embodiments, the control device 130 may be a server-based device remote from the indoor area 110. In some embodiments, the control device 130 may be a cloud-based device. In some embodiments, the control device 130 may be provided within the indoor area 110. For example, the control device 130 may be integrated with the thermostat 126, and the functionality of the control device 130 may be provided by the thermostat 126, and thus in such embodiments, the thermostat 126 may form part of the system 150. In some embodiments, one or more components of the control device 130 may be provided on the cloud, and one or more components of the control device 130 may be provided on the server.

[0057] Figure 2 A schematic block diagram of a system 150 for detecting occupancy of a plurality of rooms 120a - 120n in an indoor area 110 is illustrated.

[0058] like Figure 2As shown, the system 150 includes at least a control device 130, a first door sensor 121a, a second door sensor 121b, and a plurality of sensors 122a-122n associated with the room 120. It should be understood that the control device 130 can communicate with the first door sensor 121a, the second door sensor 121b, and the plurality of sensors 122a-122n associated with all of the plurality of rooms 120a-120n, and that the details provided with respect to the first door sensor 121a, the second door sensor 121b, and the plurality of sensors 122a-122n of the room 120 are also applicable to the first door sensors 121a, the second door sensor 121b, and the plurality of sensors 122a-122n of the other rooms.

[0059] In one or more embodiments, the control device 130 may include one or more processors 202 , a memory 204 , one or more modules 206 , and a communication interface 208 .

[0060] The one or more processors 202 may be configured to communicate with the memory 204 to store sensor readings generated by the first door sensor 121a, the second door sensor 121b, and the plurality of sensors 122a-122n. In some embodiments, the memory 204 may include a sensor data unit 204a for storing sensor readings generated by the first door sensor 121a, the second door sensor 121b, and the plurality of sensors 122a-122n. The sensor readings may include real-time sensor readings and historical sensor readings. In one or more embodiments, the one or more processors 202 may be one or more microprocessors or microcontrollers. The one or more processors 202 may include one or more processors, which may include one or more general-purpose processors such as a central processing unit (CPU), an application processor (AP), etc., a graphics processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an artificial intelligence (AI) dedicated processor such as a neural processing unit (NPU).

[0061] In some embodiments, the memory 204 may store data and instructions that can be executed by the processor 202 to perform the method steps for detecting the occupancy of the room 120, as discussed herein throughout the present disclosure. The memory 204 may also include, but is not limited to, non-transitory computer-readable storage media, such as various types of volatile and non-volatile storage media, including, but not limited to, random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, tape or disk, optical media, etc. In addition, the non-transitory computer-readable storage medium of the memory 204 may include executable instructions in the form of modules 206 and a database for storing data. The module 206 may include a set of instructions that can be executed to cause one or more processors 202 to perform any one or more of the methods for detecting the occupancy of multiple rooms 120a-120n based on sensor readings, as disclosed herein throughout the present disclosure. Specifically, one or more modules 206 may be configured to use the data stored in the database of the memory 204 to perform the steps of the present disclosure to detect the occupancy of multiple rooms 120a-120n based on sensor readings. In another embodiment, module 206 may be one or more hardware units that may be external to memory 204. In one embodiment, memory 204 may communicate via a bus within processor 202.

[0062] In one or more embodiments, the communication interface 208 may include a transmitter and a receiver configured to communicate with the first door sensor 121a, the second door sensor 121b, and the plurality of sensors 122a-122n via the communication network 140. In some embodiments, the communication interface 208 may be configured to communicate with one or more of the HVAC system 124, the thermostat 126, and the lighting unit 128 via the communication network 140. The communication via the communication network 140 may be based on a wireless communication protocol. The communication interface 208 may be configured to communicate internally between internal hardware components and to communicate with external devices (e.g., the first door sensor 121a, the second door sensor 121b, and the plurality of sensors 122a-122n) via one or more networks (e.g., radio technologies). The communication interface 208 may include electronic circuitry specific to a standard that enables wireless communication.

[0063] refer to Figure 1 and Figure 2, the one or more processors 202 may be configured to receive a sensor reading from a first door sensor 121a associated with the room 120. The sensor reading may indicate an opening operation of the room 120. The one or more processors 202 may be configured to determine an occupancy state of the room 120 based on the sensor reading received from the first door sensor 121a. Specifically, the one or more processors 202 may be configured to determine the occupancy state of the room 120 to be occupied when the first door sensor 121a detects an operation of the door performed from outside the room 120. The operation of the door may include opening the door with a key or a card.

[0064] The one or more processors 202 may be configured to receive a sensor reading from a second door sensor 121b associated with the room 120. The sensor reading may indicate a closing and / or locking operation of the door. The one or more processors 202 may be configured to verify the occupancy state of the room 120 based on the sensor reading received from the second door sensor 121b. Specifically, the one or more processors 202 may be configured to verify the occupancy state of the room 120 to be occupied when the second door sensor 121b detects the operation of the door performed from the inside of the room 120. The operation of the door performed from the inside of the room 120 may include closing and / or locking the door with a key or card.

[0065] In some embodiments, the one or more processors 202 may be configured to receive a sensor reading from the second door sensor 121b after a predefined time period from determining the occupancy state of the room 120 based on the sensor reading received from the first door sensor 121a. For example, the first door sensor 121a may detect the opening of the door at a first moment. After the predefined time period, for example at a second moment, the second door sensor 121b may detect the locking of the door. Thus, the one or more processors 202 may determine and verify the occupancy state of the room 120 to be occupied. In a non-limiting example, the predefined time period may be 1 minute, 2 minutes, 5 minutes, 10 minutes, etc.

[0066] In some embodiments, the one or more processors 202 may be configured to receive sensor readings from a second door sensor 121b associated with the room 120 and determine that there is no closing and / or locking operation of the door. This may indicate that the door has not been locked or closed from the interior of the room 120. In response to determining that there is no closing and / or locking operation of the door, the one or more processors 202 may be configured to trigger a supplemental occupancy detection process based on the plurality of sensors 122a-122n.

[0067] In some embodiments, the occupancy state of the room 120 may indicate one of a positive state or a negative state. A positive state may indicate that the room 120 is occupied, while a negative state may indicate that the room 120 is not occupied.

[0068] During the supplemental occupancy detection process, the one or more processors 202 may be configured to receive sensor readings from a plurality of sensors 122a-122n associated with the room 120. The sensor readings may indicate corresponding occupancy parameters associated with the room 120. In some embodiments, the one or more processors 202 may be configured to receive sensor readings from at least two sensors of the plurality of sensors 122a-122n. In some embodiments, the one or more processors 202 may be configured to receive sensor readings from a combination of sensors in the plurality of sensors 122a-122n. For example, the one or more processors 202 may be configured to receive sensor readings from a combination of an IAQ sensor and a PIR sensor, a combination of an IAQ sensor and a noise sensor, a combination of an IAQ sensor, a PIR sensor and a noise sensor, a combination of an IAQ sensor, a PIR sensor and a proximity sensor, a combination of an IAQ sensor, a noise sensor and a proximity sensor, and the like.

[0069] In some embodiments, the sensor readings from each of the plurality of sensors 122a-122n may include a sequence of measurements of the corresponding occupancy parameter, the sequence of measurements being sensed over a predetermined duration. The sequence of measurements from each of the plurality of sensors 122a-122n may include real-time measurements of the corresponding occupancy parameter and historical measurements of the corresponding occupancy parameter. In some embodiments, the predetermined duration may include any amount of duration, such as, but not limited to, 30 minutes, 45 minutes, 1 hour, 2 hours, etc. In some embodiments, the sequence of measurements may be in the form of time series data indicating measurements of the corresponding occupancy parameter over the predetermined duration.

[0070] The one or more processors 202 may be configured to receive sensor readings from the plurality of sensors 122a-122n and determine, based on the received sensor readings, an occupancy state of the room 120 and / or a confidence value associated with the occupancy state of the room 120. The occupancy state of the room 120 indicates whether the room 120 is occupied (a positive state) or unoccupied (a negative state).

[0071] Upon detecting that the occupancy state of the room 120 is a negative state or unoccupied, the one or more processors 202 may wait for further sensor readings from the first door sensor 121a to detect whether the door has been operated and whether the room 120 is occupied.

[0072] In some embodiments, the plurality of sensors 122a-122n may include a CO2 sensor (eg, 122b) and a noise sensor (eg, 122c). The CO2 sensor 122b and the noise sensor 122c may be disposed within the room 120 and may communicate with the control device 130. Figure 3Another system environment 300 for detecting occupancy of a room according to one embodiment of the present disclosure is illustrated. The environment 300 may include a room 120 having a CO2 sensor 122b, a noise sensor 122c, an HVAC system 124, a thermostat 126, and a lighting unit 128. The CO2 sensor 122b and the noise sensor 122c may communicate with a control device 130, together forming a system 150 for detecting occupancy within the room 120. It should be understood that although in Figure 3 A single room 120 is depicted in FIG. 1 , but the details provided herein apply to Figure 1 Each of the multiple rooms 120a-120n shown. Figure 1 to Figure 2 The various details provided in the control device 130, HVAC system 124, thermostat 126, and lighting unit 128 in FIG. 1 are also applicable to Figure 3 , and these details are not repeated for the sake of brevity.

[0073] One or more processors 202 of the control device 130 (in Figure 2 120) can be configured to receive sensor readings from CO2 sensor 122b and noise sensor 122c. The sensor reading from CO2 sensor 122b can indicate the CO2 level within room 120. It should be understood by those skilled in the art that the level of CO2 may change when the room is occupied compared to when the room is not occupied. The sensor reading from noise sensor 122c can indicate various types of sounds within room 120.

[0074] The one or more processors 202 may be configured to determine the occupancy state of the room 120 based on the sensor readings from the CO2 sensor 122b and the noise sensor 122c. In some embodiments, the one or more processors 202 may be configured to determine the occupancy state based on one or more trained mathematical models, as described in further detail below. In some embodiments, the mathematical model may be stored in the model unit 204b of the memory 204.

[0075] The occupancy state represents one of a positive state indicating that the room is occupied and a negative state indicating that the room is not occupied. In some embodiments, to determine the occupancy state, the one or more processors 202 may be configured to process sensor readings received from the CO2 sensor 122b and the noise sensor 122c.

[0076] In some embodiments, the sensor readings from the CO2 sensor 122b may include a sequence of measurements over a predetermined duration. Figure 4A , which illustrates an exemplary graphical representation 400 of sensor readings from the CO2 sensor 122b. Figure 4AAs shown, the CO2 level at a particular time point (e.g., on May 4) is greater than the CO2 level at other time points (e.g., on May 6 or May 7), indicating that room 120 was occupied on May 4 but not occupied on May 6 or May 7.

[0077] In one embodiment, the one or more processors 202 may be configured to determine a rate of change of the CO2 level in the room over a predetermined duration based on the sensor readings. The one or more processors 202 may be configured to compare the rate of change to a first threshold. The one or more processors 202 may determine that the rate of change is greater than the first threshold, and in response to such a determination, may determine that the occupancy state is a positive state. In the event that the one or more processors 202 determine that the rate of change of the CO2 level is less than the first threshold, the occupancy state may be determined to be a negative state.

[0078] In another embodiment, the one or more processors 202 may be configured to determine the CO2 level in the room based on the sensor reading. The one or more processors 202 may be configured to compare the CO2 level to a second threshold. The one or more processors 202 may determine that the CO2 level in the room 120 is greater than the first threshold, and in response to such a determination, may determine that the occupancy state is a positive state. In the event that the one or more processors 202 determine that the CO2 level is less than the first threshold, the occupancy state may be determined to be a negative state.

[0079] In some embodiments, the sensor readings from the noise sensor 122c may include a sequence of measurements over a predetermined duration. Figure 4B , which illustrates an exemplary graphical representation 410 of sensor readings from the noise sensor 122c. Figure 4B As shown, the noise sensor 122c can measure the sound frequency within a predetermined duration. The one or more processors 202 can be configured to process the sound frequency via the AI / ML model and determine whether the detected sound is related to the sound from the occupant or the sound from other devices in the room 120, such as a television. In the case where the one or more processors 202 determine that the detected sound is related to the occupant, the one or more processors 202 can determine that the occupancy state is a positive state. In the case where the one or more processors 202 determine that the detected sound is related to other devices in the room 120, the occupancy state can be determined to be a negative state.

[0080] In some embodiments, the one or more processors 202 may determine a confidence value associated with the detected occupancy state. After determining the occupancy state of the room 120, the one or more processors may be configured to trigger a control action associated with the room based on the determined occupancy state, as described in further detail below.

[0081] In some embodiments, one or more processors 202 may be configured to use multiple mathematical models to detect occupancy states and / or confidence values. The multiple mathematical models may include trained neural network (NN) models, machine learning (ML) models, or artificial intelligence (AI) models that process sensor readings to detect occupancy states and / or confidence values. In some embodiments, the multiple mathematical models may include supervised classification networks, such as, but not limited to, pattern recognition networks (PRNs), long short-term memories (LSTMs), and the like.

[0082] In some embodiments, a plurality of mathematical models may be stored in the model unit 204b of the memory 204. Figure 3 In some embodiments described, a mathematical model may be associated with a combination of a CO2 sensor and a noise sensor. Figure 1 In some embodiments described, each of the plurality of mathematical models may be associated with a corresponding sensor combination of the plurality of sensors 122a-122n. Specifically, each mathematical model may be associated with the type and corresponding occupancy parameters of the plurality of sensors 122a-122n, because each mathematical model may be trained to detect the occupancy state and confidence value and the corresponding occupancy parameter of the relevant type and combination of the plurality of sensors 122a-122n. For example, a first mathematical model may be associated with a combination of an IAQ sensor and a PIR sensor, and the first mathematical model is configured to detect the occupancy state and confidence value based on the sensor readings from the IAQ sensor and the PIR sensor. For another example, a second mathematical model may be associated with a combination of an IAQ sensor, a PIR sensor, a noise sensor, and a proximity sensor, and the second mathematical model is configured to detect the occupancy state and confidence value based on the sensor readings from the IAQ sensor, the PIR sensor, the noise sensor, and the proximity sensor. Therefore, each of the plurality of mathematical models may be associated with a specific type and combination of the plurality of sensors 122a-122n and the corresponding occupancy parameter.

[0083] refer to Figures 1 to 3In some embodiments, the one or more processors 202 may be configured to select a mathematical model from a plurality of mathematical models based on the type or combination of the plurality of sensors 122a-122n that transmit sensor readings of the corresponding occupancy parameters. The one or more processors 202 may be configured to determine the occupancy state and the confidence value of the room 120 based on the selected mathematical model. In some embodiments, the selected mathematical model may be an optimal mathematical model for detecting the occupancy state of the room 120, which is predetermined during the training of the plurality of mathematical models. Therefore, the relevant mathematical model (e.g., the optimal mathematical model) may be used to accurately determine the occupancy state of the room based on the plurality of sensors used in the room and providing sensor readings of the corresponding occupancy parameters associated with the room. In addition, in the event that a particular sensor in the room fails, a different mathematical model may be used to accurately determine the occupancy state based on other working sensors in the room. In addition, in the event that the sequence of measurements received from the combination of sensors associated with the optimal mathematical model includes missing data, a different mathematical model may be selected to determine the occupancy state. For example, the optimal mathematical model may be a first mathematical model associated with a combination of a CO2 sensor and a noise sensor. However, for a predetermined duration, the data sequence from the noise sensor may be missing. Therefore, a second mathematical model associated only with the CO2 sensor may be selected to detect the occupancy status of the room.

[0084] In some embodiments, the one or more processors 202 may be configured to process each of the sensor readings received from the active (working) sensors of the plurality of sensors within the room 120. The one or more processors 202 may be configured to receive a sequence of measurements sensed within a predetermined duration from the active sensors at a moment when occupancy of the room 120 is detected. The sequence of measurements includes a real-time sensor reading of the corresponding occupancy parameter at the moment and a historical sensor reading of the corresponding occupancy parameter sensed within the predetermined duration.

[0085] In some embodiments, one or more processors 202 may be configured to pre-process the received measurement sequence before providing the measurement sequence as input to the selected mathematical model. In some embodiments, the pre-processing of the received measurement sequence includes normalizing the measurement sequence, resampling and filtering at least one of the measurement sequence. In some embodiments, one or more processors 202 may be configured to resample each of the received measurement sequence based on a resolution parameter (i.e., based on a desired resolution) to generate a resampled measurement sequence. In some embodiments, the resampled measurement sequence may include a uniformly spaced data sequence associated with the received measurement sequence. In some embodiments, the sensor readings from the IAQ sensor and the noise sensor may be resampled based on an interpolation technique. In some embodiments, the sensor readings from the PIR sensor may be resampled using a zero-order hold technique. In some embodiments, the resolution parameter may be any desired resolution, such as but not limited to 5 seconds, 30 seconds, 1 minute, etc.

[0086] In some embodiments, one or more processors 202 may be configured to filter the resampled measurement sequence to generate a filtered measurement sequence. The filtered measurement sequence may correspond to a preprocessed measurement sequence determined by resampling and filtering the measurement sequence. In some embodiments, one or more processors 202 may be configured to remove noise from the resampled measurement sequence and / or remove outliers from the resampled measurement sequence. In some embodiments, one or more processors 202 may be configured to smooth the resampled measurement sequence based on one or more filtering techniques (such as but not limited to Gaussian filtering, Savitzky-Golay filtering, etc.). In some embodiments, the measurement sequence may be received at a desired resolution, and resampling may not be required. In such embodiments, one or more processors 202 may be configured to filter the measurement sequence to generate a filtered measurement sequence that may correspond to the preprocessed measurement sequence. In some embodiments, one or more processors 202 may be configured to normalize each of the received measurement sequences to generate a normalized measurement sequence that may correspond to the preprocessed measurement sequence.

[0087] In some embodiments, one or more processors 202 may be configured to pass the filtered data sequence to the selected mathematical model, and determine the occupancy state and the confidence value based on the selected mathematical model and the filtered measurement result sequence. One or more processors 202 may be configured to determine a first classification probability value and a second classification probability value associated with the occupancy state of the room 120 based on the selected mathematical model and the filtered measurement result sequence. In some embodiments, one or more processors 202 may be configured to determine a characteristic sequence of the measurement result sequence, such as a sequence of standard deviations, derivatives, moving averages, etc. The determined characteristic sequence may be provided to the selected mathematical model, and a first classification probability value and a second classification probability value associated with the occupancy state of the room 120 may be determined based on the selected mathematical model and the determined characteristic sequence. One or more processors 202 may be configured to compare the first classification probability value and the second classification probability value.

[0088] Based on the comparison of the first classification probability value and the second classification probability value, one or more processors 202 may be configured to determine an occupancy state and a confidence value for the determined occupancy state. In some embodiments, one or more processors 202 may be configured to determine that the occupancy state is a positive state indicating that the room 120 is occupied in response to determining that the first classification probability value is greater than the second classification probability value. In some embodiments, the first classification probability value may be a confidence value associated with the determined occupancy state (positive state). In some embodiments, one or more processors 202 may be configured to determine that the occupancy state is a negative state indicating that the room 120 is not occupied in response to determining that the second classification probability value is greater than the first classification probability value. In some embodiments, the second classification probability value may be a confidence value associated with the determined occupancy state (negative state).

[0089] In some embodiments, the one or more processors 202 may be configured to store the occupancy state and the confidence value of the determined occupancy state in the memory 204, such as in the sensor data unit 204a of the memory 204. In some embodiments, the one or more processors 202 may be configured to determine the occupancy state and the confidence value of the determined occupancy state at a predetermined frequency. The predetermined frequency may be any desired frequency, such as but not limited to 30 seconds, 1 minute, 2 minutes, etc. In some embodiments, the one or more processors 202 may be configured to receive a user input indicating a request to determine the occupancy state, and in response to receiving the user input, the one or more processors 202 may be configured to determine the occupancy state and the confidence value of the determined occupancy state. In some embodiments, the user input may be provided by an employee via a user device in communication with the control device 130. In some embodiments, the user input is provided via a web-based application or a mobile application on the user device.

[0090] In some embodiments, the one or more processors 202 may be configured to receive sensor readings from different combinations of sensors in the plurality of sensors 122a-122n based on one or more external parameters, such as a time of day. For example, the one or more processors 202 may be configured to receive sensor readings from a first combination of sensors during morning hours and to receive sensor readings from a second combination of sensors during evening hours.

[0091] In some embodiments, the one or more processors 202 may be configured to train multiple mathematical models based on the type or combination of multiple sensors 122a-122n that send sensor readings corresponding to occupancy parameters. Each of the multiple mathematical models may be associated with a combination of multiple sensors 122a-122n, so that in the event of a failure of one or more sensors in the room 120, the relevant mathematical model may be selected based on the active sensors within the room 120 to determine the occupancy state of the room 120. Based on the training, an optimal mathematical network may be initially selected from the multiple mathematical models to detect the occupancy state of the room 120. In some cases, a different mathematical model may be selected from the multiple mathematical models as the optimal mathematical network, such as when there is a sensor failure and / or missing information associated with one or more sensors. For example, initially, a mathematical model based on a combination of a noise sensor, a PIR sensor, and an IAQ sensor may be used as an optimal mathematical model for determining the occupancy state of the room 120. In the event of a failure of the noise sensor or in the event that the noise sensor may provide an incomplete reading, the PIR sensor and the IAQ sensor may still provide corresponding sensor readings. Another mathematical model trained to consider the sensor readings from the combination of the PIR sensor and the IAQ sensor may then be selected as the optimal mathematical model for determining the occupancy state. Thus, a reliable system for determining the occupancy state of the room 120 may be achieved regardless of whether one or more sensors fail.

[0092] In some embodiments, one or more processors 202 may be configured to train multiple mathematical models for corresponding sensor types and combinations. In some embodiments, multiple mathematical models may be trained based on supervised learning using historical sensor readings from a lab-based setting. In some embodiments, multiple mathematical models may be trained based on supervised learning using historical sensor readings and actual occupancy data associated with room 120 and / or other rooms. Thus, multiple mathematical models may be trained based on at least one of sensor readings from a lab-based setting or sensor readings from one or more rooms. In some embodiments, multiple mathematical models may be initially trained based on sensor readings from a lab-based setting, and once deployed in a room (such as room 120), may be further trained based on sensor readings from the room and / or other different rooms.

[0093] For each mathematical model, the one or more processors 202 may be configured to obtain historical sensor readings for the corresponding sensor type and combination. In some embodiments, the historical sensor readings may be retrieved from the sensor data unit 204a of the memory 204. The historical sensor readings may be for a specified time period. In some embodiments, the historical sensor readings may include a sequence of measurements for a specified time period.

[0094] In some embodiments, the one or more processors 202 may be configured to obtain actual occupancy data for a specified time period, the actual occupancy data indicating the actual occupancy state of the room 120 at each moment in the specified time period. The actual occupancy data may be used to construct a training output of a mathematical model to be trained. In some embodiments, the mathematical model to be trained may have two output labels, a label [1,0] indicating that the room is occupied and a label [0,1] indicating that the room is not occupied.

[0095] In some embodiments, one or more processors 202 may be configured to provide historical sensor readings as training inputs to the mathematical model to be trained. In some embodiments, one or more processors 202 may be configured to resample, smooth and standardize the historical sensor readings before providing the historical sensor readings as training inputs to the mathematical model being trained. In some embodiments, one or more processors 202 may be configured to transform the historical sensor readings into a set of sequences based on a window technique. For example, a window size in minutes and a step size in minutes may be determined. In some embodiments, when the mathematical model being trained is deployed for real-time use, the window size may have the same value as the predetermined duration of the received measurement result sequence. In some embodiments, the resolution of the historical sensor readings provided as the input of the mathematical model being trained may have the same value as the resolution parameter associated with the measurement result sequence to be received when the mathematical model being trained is deployed for real-time use.

[0096] In some embodiments, one or more processors 202 may be configured to train mathematical models based on training inputs (historical sensor readings) and training outputs (actual occupancy data). Similarly, each of the plurality of mathematical models may be trained based on historical sensor readings from corresponding types and combinations of sensors. Thus, a set of fail-safe mathematical models may be trained and stored in model unit 204 b of memory 204, such that in real-time use, readings from any combination of activity sensors within room 120 may be used to determine the occupancy of room 120.

[0097] In some embodiments, based on the training, the optimal mathematical model of the room 120 may be determined by the one or more processors 202, and the optimal mathematical model for detecting the occupancy state of the room 120 may be selected by the one or more processors 202. The optimal mathematical model may refer to a mathematical model that provides the most accurate detection of the occupancy state of the room 120 based on the training. In the event that one or more sensors from the sensor combination associated with the optimal mathematical model fail, another mathematical model may be selected by the one or more processors 202 to detect the occupancy state of the room 120. In some embodiments, the training of the plurality of mathematical models may include receiving feedback from one or more employees associated with the room 120, the feedback being related to the occupancy state of the room 120. Based on the feedback, the optimal mathematical model may be updated from the plurality of mathematical models. For example, initially a first mathematical model may be selected as the optimal mathematical model for detecting the occupancy state, however, based on the feedback, it may be determined that a second mathematical model provides better accuracy, and thus the second mathematical model may be determined as the optimal mathematical model for detecting the occupancy state of the room 120.

[0098] In some embodiments, the one or more processors 202 may be configured to, in response to determining an occupancy state, trigger a control action associated with the room 120 based on the determined occupancy state. In some embodiments, the one or more processors 202 may be configured to control the operation of the HVAC system 124, the thermostat 126, and the lighting unit 128 associated with the room 120 based on the determined occupancy state of the room 120. In some embodiments, upon determining that the occupancy state of the room is a negative state (i.e., the room 120 is not occupied), the one or more processors 202 may be configured to send a control signal to the thermostat 126 and / or the HVAC system 124 to adjust the temperature set point of the room 120, such as to back off the temperature set point of the room 120. Thus, effective temperature backoff control is achieved because the temperature can be backed off for the room 120 when the room 120 is not occupied, thereby saving energy for the room 120. In some embodiments, the one or more processors 202 may be configured to send control signals to the lighting units 128, either directly to the lighting units 128 or through a lighting control unit within the room 120, to adjust the operation of the lighting units 128. For example, when the room 120 is not occupied, the lighting units 128 may be turned off, thereby further conserving energy for the room 120. In some embodiments, if the room includes one or more smart plugs or smart appliances (e.g., a television), then in response to determining that the room 120 is not occupied, a control signal may be sent to the room by the processor to turn off the smart plugs and / or smart appliances.

[0099] In some embodiments, the one or more processors 202 may be configured to cause information related to the determined occupancy state and confidence value to be displayed on a user interface of one or more user devices. In some embodiments, information related to the determined occupancy state and confidence value may be displayed on a user interface disposed within the room 120 (such as a user interface associated with the thermostat 126). In some embodiments, information related to the determined occupancy state and confidence value may be displayed on a user interface associated with a user's handheld device (such as a handheld device of one or more employees). Thus, employees can monitor the occupancy state of the room 120, thereby facilitating the employees to plan housekeeping and maintenance tasks for the room 120.

[0100] Figure 5 An operational process flow 500 for detecting occupancy within a room 120 according to one embodiment of the present disclosure is illustrated. At block 502, the door of the room 120 may be operated by, for example, an occupant to enter the room 120. The occupant may open the door 120 with a key or smart card and enter the room 120. The first door sensor 121a may detect the door opening operation from the outside of the room 120, and the one or more processors 202 may determine that the occupancy state of the room 120 is occupied (affirmative state) based on the reading from the first door sensor 121a. At block 504, the occupancy state of the room 120 may be set to occupied (affirmative state).

[0101] At box 506, after detecting that the occupancy state of the room 120 is occupied, a predefined time period has passed. After the predefined time period, at box 508, the occupant may or may not lock the door 120 from the inside of the room 120. The second door sensor 121b can detect whether a closing and / or locking operation of the door is performed from the inside of the room 120. Specifically, the second door sensor 121b can be configured to determine one of the closing and / or locking operation being performed or the absence of the closing and / or locking operation. Upon determining that the closing and / or locking operation has been performed based on the reading from the second door sensor 121b, the one or more processors 202 can verify that the occupancy state of the room 120 is occupied (affirmative state). Therefore, the one or more processors 202 can verify that the occupancy state is occupied.

[0102] At block 510, upon determining that there is no closing and / or locking operation based on the reading from the second door sensor 121b, the one or more processors 202 may be configured to trigger a supplemental detection based on the plurality of sensors 122a-122n. At block 512, as described above, the one or more processors 202 may determine whether the room is occupied or unoccupied because the one or more processors 202 may determine whether the occupancy state of the room 120 is a positive state or a negative state based on the plurality of sensors 122a-122n.

[0103] When the occupancy state of the room 120 is determined to be a positive state, the occupancy state of the room 120 may remain occupied. At block 514, when the occupancy state of the room 120 is determined to be a negative state, the occupancy state of the room 120 may be updated to unoccupied. At block 516, the one or more processors 202 may wait for further readings from the first door sensor 121a.

[0104] Thus, an effective occupancy detection technique is achieved, wherein occupancy of a room can be initially detected based on the first door sensor 121a and the second door sensor 121b. Thus, computational processing using multiple sensors 122a-122n can be avoided, where the first door sensor 121a and the second door sensor 121b can determine and verify occupancy of the room. Computational processing can be used as a supplementary detection technique to determine occupancy of a room. This saves computational time and cost.

[0105] Figure 6 A process flow depicting a method 600 for detecting occupancy of a room is illustrated. At step 602, the method 600 includes receiving a sensor reading indicating an opening operation of the door from a first door sensor associated with a door of the room, the first door sensor being configured to detect the opening operation of the door. At step 604, the method 600 includes determining that an occupancy state of the room is a positive state based on the sensor reading of the first door sensor.

[0106] At step 606, method 600 includes receiving a sensor reading indicating a closing and / or locking operation of the door from a second door sensor associated with the door of the room, the second door sensor being configured to detect the closing and / or locking operation of the door. At step 608, method 600 includes determining whether the closing and / or locking operation has been performed based on the sensor reading of the second door sensor.

[0107] At step 610, method 600 includes verifying that the occupancy status of the room is positive when it is determined that the closing and / or locking operation has been performed. At step 612, method 600 includes triggering a supplemental detection process to detect occupancy of the room when it is determined that the closing and / or locking operation has not been performed.

[0108] Figure 7 A process flow depicting another method 700 for detecting occupancy of a room is illustrated. At step 702, the method 700 includes receiving sensor readings indicating corresponding occupancy parameters from a CO2 sensor 122b and a noise sensor 122c located within the room. Each of the CO2 sensor 122b and the noise sensor 122c is configured to measure the corresponding occupancy parameter.

[0109] At step 704, method 700 includes determining, based on the received sensor readings, that an occupancy state of the room is one of a positive state indicating that the room is occupied and a negative state indicating that the room is not occupied. At step 706, method 700 includes, in response to determining the occupancy state, triggering a control action associated with the room based on the determined occupancy state.

[0110] Although Figure 6 to Figure 7 The above steps of are shown and described in a particular order, but according to various embodiments of the present disclosure, these steps may occur in variations of this order. Figure 6 to Figure 7 The detailed description of each step is given in Figures 1 to 5 The relevant description is involved and therefore omitted here for the sake of brevity.

[0111] In some embodiments, the one or more processors 202 may be configured to execute instructions included in a computer program product. The computer program product may be embodied in a non-transitory computer readable medium. The computer program product may include instructions that, when executed by the one or more processors 202, cause the one or more processors 202 to perform a reference Figure 6 to Figure 7 Instructions describing the method steps in detail.

[0112] The present disclosure provides methods and systems for accurate, high-confidence occupancy detection in a room. Detection based on environmental sensors such as CO2 sensors and noise sensors can improve detection accuracy over a wider area. Combining sensor data further improves accuracy and performance. Accurate occupancy detection enables automatic control of appliances in the room, saving energy and cost. In addition, indications from door sensors can be used to detect and verify occupancy, and computing resources are not required every time. This increases response time and reduces computing costs.

[0113] It will be apparent to those skilled in the art that various working modifications may be made to the methods disclosed herein in order to implement the inventive concepts taught herein.

[0114] In addition, the actions of any flow chart do not have to be implemented in the order shown; not all actions must be performed. In addition, those actions that do not depend on other actions can be performed in parallel with other actions.

[0115] The accompanying drawings and the preceding description provide examples of embodiments. It will be appreciated by those skilled in the art that one or more of the elements may be well combined into a single functional element. Optionally, some elements may be divided into a plurality of functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and are not limited to the methods described herein.

[0116] Benefits, other advantages, and solutions to problems have been described above with respect to specific embodiments. However, the benefits, advantages, solutions to problems, and any components that may cause any benefit, advantage, or solution to appear or become more significant should not be construed as key, required, or essential features or components of any or all claims.

[0117] Although specific language has been used to describe this theme, it is not intended to be subject to any limitation. It will be apparent to those skilled in the art that various work modifications may be made to the method to implement the inventive concept taught herein. The accompanying drawings and the foregoing description provide examples of embodiments. It will be appreciated by those skilled in the art that one or more of the elements may be well combined into a single functional element. Optionally, some elements may be divided into a plurality of functional elements. Elements from one embodiment may be added to another embodiment.

Claims

1. A system for detecting occupancy of a room, the system comprising: a first door sensor and a second door sensor, the first door sensor and the second door sensor being associated with a door of the room, the first door sensor being configured to detect an opening operation of the door, and the second door sensor being configured to detect a closing and / or locking operation of the door; as well as a control unit, the control unit being in communication connection with the first door sensor and the second door sensor; The control unit comprising one or more processors is configured to: receiving a sensor reading from the first door sensor indicative of the opening operation of the door; determining that the occupancy status of the room is a positive status based on the sensor reading of the first door sensor; receiving a sensor reading from the second door sensor indicative of the closing and / or locking operation of the door; determining whether the closing and / or locking operation has been performed based on the sensor reading of the second door sensor; When it is determined that the closing and / or locking operation has been performed, verifying that the occupancy state of the room is the affirmative state; as well as When it is determined that the closing and / or locking operation has not been performed, a supplementary detection process is triggered to detect the occupancy of the room.

2. The system of claim 1, further comprising a plurality of sensors configured to measure corresponding occupancy parameters, wherein to perform the supplemental detection process, the one or more processors are configured to: receiving sensor readings indicative of the corresponding occupancy parameter from the plurality of sensors, wherein each of the received sensor readings comprises a sequence of measurements of the corresponding occupancy parameter over a predetermined time duration; and The occupancy state of the room and one or more of a confidence value associated with the determined occupancy state are determined based on the received sensor readings, wherein the occupancy state represents one of a positive state indicating that the room is occupied and a negative state indicating that the room is not occupied.

3. A system according to claim 2, wherein the one or more processors are further configured to, in response to determining the occupancy state, trigger a control action associated with the room based on the determined occupancy state, the control action modifying the operation of one or more of a heating, ventilation, and air conditioning (HVAC) system, a thermostat, and a lighting unit associated with the room.

4. The system of claim 2, wherein to determine one or more of the occupancy state and the confidence value, the one or more processors are configured to: selecting a mathematical model from a plurality of mathematical models based on the types of the plurality of sensors and the corresponding occupancy parameters; as well as determining the one or more of the occupancy state and the confidence value based on the selected mathematical model; determining a first classification probability value and a second classification probability value associated with the occupancy state of the room based on the selected mathematical model and the sequence of measurements; comparing the first classification probability value and the second classification probability value; In response to determining that the first classification probability value is greater than the second classification probability value, determining that the occupancy state is the affirmative state indicating that the room is occupied; as well as In response to determining that the second classification probability value is greater than the first classification probability value, determining that the occupancy state is the negative state indicating that the room is unoccupied.

5. A system according to any one of claims 1 to 4, wherein the one or more processors are configured to cause information related to the occupancy status to be displayed on one or more of a user interface arranged in the room and a user interface associated with a user's handheld device.

6. A system according to any one of claims 1 to 4, wherein the opening operation of the door includes opening the door from the outside of the room, and wherein the closing and / or locking operation of the door includes one of closing or locking the door from the inside of the room.

7. A system according to any one of claims 1 to 4, wherein the first sensor is configured to detect an opening operation of the door performed from outside the room, and the second sensor is configured to detect a closing and / or locking operation of the door performed from inside the room.

8. A method for detecting occupancy of a room, the method comprising: receiving a sensor reading from a first door sensor associated with a door of the room indicating an opening operation of the door, the first door sensor being configured to detect the opening operation of the door; determining that the occupancy status of the room is a positive status based on the sensor reading of the first door sensor; receiving a sensor reading indicative of a closing and / or locking operation of the door from a second door sensor associated with the door of the room, the second door sensor being configured to detect the closing and / or locking operation of the door; determining whether the closing and / or locking operation has been performed based on the sensor reading of the second door sensor; When it is determined that the closing and / or locking operation has been performed, verifying that the occupancy state of the room is the affirmative state; as well as When it is determined that the closing and / or locking operation has not been performed, a supplementary detection process is triggered to detect the occupancy of the room.

9. The method of claim 8, wherein triggering the supplemental detection process comprises: receiving sensor readings indicative of corresponding occupancy parameters from a plurality of sensors configured to measure the corresponding occupancy parameters, wherein each of the received sensor readings comprises a sequence of measurements of the corresponding occupancy parameter over a predetermined time duration; as well as The occupancy state of the room and one or more of confidence values ​​associated with the determined occupancy state are determined based on the received sensor readings, wherein the occupancy state represents one of the positive state indicating that the room is occupied and the negative state indicating that the room is not occupied.

10. The method according to claim 8, wherein the first sensor is configured to detect an opening operation of the door performed from the outside of the room, and the second sensor is configured to detect a closing and / or locking operation of the door performed from the inside of the room.

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