Floor plan-based planning of building systems

Abstract

A method for planning a building system includes incorporating user requirements into a floor plan accessed via a mobile device, generating an installation diagram of locations of components corresponding to the user requirements, and displaying placement of components on the floor plan based on specifications of the components and the user requirements.

Description

[0001] 1. Cross-reference to related applications

[0002] This application claims the benefit of U.S. Provisional Patent Application Serial No. 62 / 137,531, filed on March 24, 2015, and entitled “FLOOR PLAN BASED PLANNING OF BUILDING SYSTEMS,” which is incorporated herein by reference in its entirety.

[0003] 2. Field of the Invention

[0004] The present disclosure relates to building systems and, more particularly, to planning and placing components, such as in a floor plan of a building.

[0005] 3. Description of Related Technology

[0006] The installation of security systems is often preceded by system planning or design, which involves a walk-through site survey by experts to determine the number, type, and location of the various system components in the building. The manual nature of system planning makes it error-prone and time-consuming. Yet traditional systems that aid planning remain narrow in focus and require a lot of manual input.

[0007] Installing security systems in buildings is a complex problem that typically involves the placement of heterogeneous sensors in order to maximize the probability of detecting an intruder entering a room, or to minimize the time required to detect an intrusion. Different performance parameters, such as coverage, sensitivity, and false alarm rates of different types of sensors (such as motion, camera, etc.) depend on the installation location and orientation ("placement") of each deployment in the field. These sensors are typically designed to be installed on specific surfaces and locations, like walls vs. ceilings or outdoor vs. indoors, and most of them support a limited number of placements.

[0008] Traditional solutions require manual input of mounting locations and orientations, or trying random locations and orientations to see which show better coverage, an approach that can result in placement in invalid locations or mounting orientations. Manually providing the required information is a time-consuming and error-prone task. Additionally, it is not always possible or desirable to use traditional automated algorithms to obtain the desired sensor performance in a building.

[0009] Such conventional methods and systems are generally considered satisfactory for their intended purposes. However, there remains a need in the art for improved methods and systems for placing components within a floor plan of a building. The present disclosure provides a solution to this need. SUMMARY OF THE INVENTION

[0010] A method for planning building systems includes incorporating user requirements into a floor plan accessed via a mobile device, generating an installation diagram of locations of components corresponding to the user requirements, and displaying placement of the components on the floor plan based on specifications of the components and the user requirements.

[0011] The display component may include an indication showing the expected safety performance of a given component on a floor plan. The expected safety performance may be displayed by an overlay provided by the component. The expected safety performance may also be displayed by a probability of detection of a hazardous event provided by the component. The expected safety performance may be displayed by the number of selected devices placed on the floor plan relative to the maximum number of locations for which protection is requested. The display component may include an indication showing the expected safety performance of a given device placement on a floor plan.

[0012] The user requirements may include specifications for the types of components available. The types and specifications of the available components may be downloaded from a remote server database. The user requirements may include system partitioning of the components or the system as a whole, system functionality, compliance with building standards and regulations, communication parameters, coverage parameters, detection performance, accuracy, battery life, cost, and false alarm probability. The user requirements may also include constraints on component placement. The components may be selected from the group consisting of PIR, motion sensor, contact sensor, glass break sensor and camera, impact sensor, panel, and security sensor.

[0013] Generating an installation diagram may include automatically placing the components based on a dynamic threat model for dangerous events. The dynamic threat model may include a statistical probability distribution of dangerous events moving throughout the floor plan. The dynamic threat model may also include a relative threat level specified by a user for each room of the floor plan. The dynamic threat model may include a relative threat level specified by processing an interior building image for each room of the floor plan. The dynamic threat model may include a relative threat level specified by a user for each entrance and exit of the floor plan. The relative threat level may be estimated using historical robbery data for the geographic site and aerial or exterior building images of the entrances and exits of the building. Generating an installation diagram may include repeatedly placing different combinations of components throughout the floor plan, and evaluating the performance of each combination using the dynamic threat model and component characteristics. The optimal combination of components is identified based on system cost and performance of a given dynamic threat model.

[0014] Generating the installation diagram may further include placing at least one component in a room of the floor plan, and evaluating the achieved performance based on parameters of the room and characteristics of the component. The steps of placing at least one component and evaluating the performance are repeated until optimal placement for each room is achieved. Generating the installation diagram may include placing the components based on connectivity between the rooms. Generating the installation diagram may include placing the components based on values ​​of content within the rooms of the floor plan specified by a user.

[0015] Generating the installation diagram may include automatically placing the components based on the wireless connection model. The wireless connection model may include a statistical probability distribution of correctly received data at each component location in the floor plan.

[0016] Generating the installation diagram may include manually placing at least one component within the installation diagram using the mobile device, such that manually placing the at least one component may include allowing a user to select a component from a predefined list and indicate a desired location. The indicated desired location may be selected from a set of valid installation locations and orientations identified based on the characteristics of the component and the characteristics of the floor plan. The valid installation locations may be displayed as a visual aid for manually placing the selected component based on the characteristics of the selected component and the floor plan.

[0017] The method may include displaying a coverage area for each component based on placement, orientation, and tilt of the component.At least one component may be a glass break sensor, wherein optimal placement is based on glass size of the window, glass break sensor specifications, and room layout.

[0018] At least one component may be a motion sensor, wherein optimal placement is based on the direction of target movement relative to the location of the motion sensor, field of view, motion sensor specifications, and room layout, and based on the size of the motion sensor coverage area and the size of the room. At least one component may be a motion sensor, wherein optimal placement avoids overlap of the coverage area with components that monitor doors and windows of the room. At least one component may be a motion sensor placed in a room with two or more perimeter windows, a room with a maximum number of interior doors, or a room with more than a certain number of interior doors and no perimeter doors or windows.

[0019] Placement can be based on associating sensor types with room types. Placement can also be based on associating sensor types with the types of room characteristics being monitored.

[0020] An apparatus for planning a security system for a building includes an interactive user interface having a visual display for specifying building information and user requirements. A processor is operably connected to the user interface having a memory, wherein the memory includes instructions recorded thereon, which when read by the processor, cause the processor to generate a preferred installation diagram of a plurality of components within the building.

[0021] These and other features of the disclosed systems and methods will become more readily apparent to those skilled in the art from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] So that those skilled in the art to which the subject disclosure pertains will readily understand how to make and use the disclosed apparatus and methods without undue experimentation, preferred embodiments thereof will be described in detail herein below with reference to certain drawings, in which:

[0023] Figure 1A is a schematic diagram of an example embodiment of a system constructed in accordance with the present disclosure, showing a web-based installation and commissioning framework (IAC) accessible by one or more devices during all phases of the IAC process;

[0024] Figure 1B It is shown that the use Figure 1A A flowchart of a method for planning and quoting a building using a system;

[0025] Figure 1C It is shown that the use Figure 1A A flowchart of a method for installing and wiring a building using a system;

[0026] Figure 1D It is shown that the use Figure 1A A flowchart of a method for commissioning and testing a building using a system;

[0027] Figure 2A is a schematic diagram of an example embodiment of a method constructed according to the present disclosure, showing steps for component placement;

[0028] Figure 2B is a schematic diagram of a system according to the present disclosure showing modules for placing components on a floor plan;

[0029] Figure 2C is a schematic diagram illustrating the calculation of the orientation of a component when a reference axis falls inside a polygon of interest;

[0030] Figure 2D is a schematic diagram illustrating the calculation of the orientation of a component when the reference axis falls outside the polygon of interest;

[0031] Figure 2E is a schematic diagram showing the calculation of orientation from a plurality of rays falling inside a polygon of interest;

[0032] Figure 2F is an example diagram showing the use of the manufacturer's specifications to determine the mounting axis and field of view of a component;

[0033] Figure 2G is a schematic diagram illustrating visual feedback during manual placement of a component;

[0034] Figure 2H is a schematic diagram showing component coverage visualization;

[0035] Fig.2I is a schematic diagram showing field of view constraints affecting placement of components;

[0036] Figure 2J is an example diagram showing the hierarchical placement of the drawings;

[0037] Figure 2K is a flow chart showing building level component placement;

[0038] Figure 2L is an example diagram illustrating a dynamic threat movement model;

[0039] Figure 2M is an example diagram showing component placement output;

[0040] Figure 2N is an example graph illustrating the reduction in threat level achieved through automatic placement of components;

[0041] Fig.2O is a schematic diagram of a completed floor plan showing components placed therein;

[0042] Figure 3A is a schematic diagram of an example embodiment of a system constructed in accordance with the present disclosure, showing a user using a mobile device to capture a 360 degree panoramic image of floor-to-wall intersection measurements and room geometry;

[0043] Figure 3B yes Figure 3A A schematic diagram of a mobile device;

[0044] Figure 3C is a schematic diagram showing the geometric relationship for calculating the distance from the capture position to the intersection of the floor and the wall and the height of the mobile device and the ceiling;

[0045] Figure 3D yes Figure 3A A schematic diagram of a system showing a user capturing a 360 degree ceiling to wall intersection measurement and an image of a room geometry;

[0046] Figure 3E yes Figure 3A A schematic diagram of a system showing the use of laser scanning to capture a 3D view of a room geometry;

[0047] Figure 3F yes Figure 3A A schematic diagram of a system showing different locations for laser scanning measurements to capture room geometry;

[0048] Figure 3G yes Figure 3A A schematic diagram of a system showing environmental measurements taken while capturing a 360 floor-to-wall intersection image;

[0049] Figure 4A is a block diagram of an example embodiment of a system constructed according to the present disclosure, illustrating system modules;

[0050] Figure 4B is a schematic diagram of the floor plan showing component locations and coverage areas;

[0051] Figure 5A is a schematic diagram of an example embodiment of a system constructed in accordance with the present disclosure, showing an overview of device registration;

[0052] Figure 5B yes Figure 5A A schematic diagram of an example device registration message exchange of a system;

[0053] Figure 5C yes Figure 5A A schematic diagram of an example device table of a system;

[0054] Figure 5D is used for Figure 5A a view of an example GUI on a mobile device for device registration in a system of FIG.

[0055] Figure 5E is a schematic diagram of an example method of device positioning, which shows a mobile device for positioning a wired distributed device;

[0056] Fig. 5F is a schematic flow chart illustrating an example method for automatic positioning of distributed devices according to the present disclosure;

[0057] Figure 5G is with Fig. 5F A schematic diagram of a predicted RF fingerprint table used in conjunction with the method;

[0058] Figure 5H is with Fig. 5F A schematic diagram of a measurement RF fingerprint table used in conjunction with the method;

[0059] Fig.5I is used for Fig. 5F Schematic diagram of the matching and association scheme of the method;

[0060] Figure 5J yes Fig.5I Schematic diagram of the results after one iteration of the scheme of , showing the correlation results and ambiguity;

[0061] Fig. 6A is a schematic diagram of an example embodiment of a system having a multi-dimensional building model for planning solar-powered lighting fixtures and their corresponding multi-dimensional building models having stored point-in-time illumination data;

[0062] Figure 6B is a schematic diagram of modules comprising the disclosed system and method for planning an energy harvesting device;

[0063] Figure 6C is used with Fig. 6AA schematic diagram of a lighting schedule for a given area for use with the system;

[0064] Fig.6D is with Fig. 6A A schematic diagram of an example process for importing historical radiation data for use with a system of FIG.

[0065] Fig. 7A illustrates a system overview and data flow in one embodiment of system operation;

[0066] Figure 7B shows a building information model generated by a Wireless Planning and Performance Analysis System (WiSPA) module;

[0067] Figure 7C A flow chart illustrating a method used by a WiSPA module;

[0068] Fig.7D A flow chart illustrating a method for minimizing prediction error in determining wall material type and thickness;

[0069] Fig. 7E showing a first connection visualization that graphically indicates a prediction of wireless system performance,

[0070] Figure 7F showing a second connection visualization that graphically indicates a level of RF connectivity at a receiver location different from an RF transmitter device disposed at a particular location;

[0071] Figure 7G A location specific device performance estimation system is shown;

[0072] Figure 7H An example power delay profile (PDP) of a multipath channel showing a signal varying according to time delay;

[0073] Fig.7I A schematic diagram showing a two-node Markov model;

[0074] Figure 7J shows large and rapid RF signal fades as perceived by a receiver device;

[0075] Fig. 8A A schematic block diagram showing an intruder threat detection system according to the present disclosure is shown;

[0076] Figure 8B showing a graphical user interface (GUI) showing a display showing a building floor plan with indications showing relative vulnerability to intruder threats;

[0077] Figure 8C A hierarchical model for modeling intrusion threats to a building and / or an exterior perimeter surrounding a building is shown;

[0078] Fig.8D An intruder movement model is shown that models the likelihood of an intruder moving between rooms in a building;

[0079] Fig. 8E showing a displayed GUI showing a room-level threat model with visual indicators showing probability distribution of intruder movement within the room; and

[0080] Figure 8F A displayed GUI is shown showing a floor-level threat model with visual indicators showing the relative threat of intruder movement of different rooms on a floor.

[0081] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0082] The illustrated embodiments are now described more fully with reference to the accompanying drawings, wherein the same reference numerals identify similar structural / functional features. The illustrated embodiments are not limited in any way to the illustrated embodiments, as the illustrated embodiments described below are merely exemplary and, as will be appreciated by those skilled in the art, may be implemented in various forms. Therefore, it should be understood that any structural and functional details disclosed herein should not be interpreted as limiting, but only as a basis for the claims, and as a representation for teaching those skilled in the art to employ the discussed embodiments differently. In addition, the terms and phrases used herein are not intended to be limiting, but rather to provide an understandable description of the illustrated embodiments.

[0083] Where a range of values ​​is provided, it is understood that each intermediate value, to one tenth of the unit of the lower limit (unless the context clearly dictates otherwise), between the upper and lower limits of the range, and any other stated or intermediate value in the stated range is encompassed within the illustrated embodiments. The upper and lower limits of these smaller ranges may be independently included in the smaller ranges and are also encompassed within the illustrated embodiments, subject to any specifically excluded limits within the stated ranges. Where the stated range includes one or two limits, ranges other than one or both of these included limits are also encompassed within the illustrated embodiments.

[0084] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those generally understood by those of ordinary skill in the art to which the present disclosure belongs. Although any methods and materials similar or equivalent to those described herein may also be used in the practice or testing of the illustrated embodiments, exemplary methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe methods and / or materials related to the publications cited.

[0085] It is noted that as used herein and in the appended claims, the singular forms (a / an / the) include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a stimulus" includes a plurality of such stimuli and reference to "a signal" includes reference to one or more signals and equivalents thereof known to those skilled in the art, and so forth.

[0086] The publications discussed herein are provided only for their disclosure prior to the filing date of the present application. Nothing herein should be construed as admitting that the illustrated embodiments are not entitled to advance such publications by virtue of prior inventions. In addition, the date of the publication provided may be different from the actual publication date that may require independent confirmation.

[0087] It should be understood that the illustrated embodiments discussed below are preferably software algorithms, programs or codes resident on a computer usable medium having control logic for execution on a machine having a computer processor. The machine typically includes a memory storage device configured to provide output from executing a computer algorithm or program.

[0088] As used herein, the term "software" is intended to be synonymous with any code or program that may be in a processor of a host computer, regardless of whether the implementation is in hardware, firmware, or as a software computer product available on a disk, memory storage device, or for downloading from a remote machine. The embodiments described herein include such software for implementing the above equations, relationships, and algorithms. Those skilled in the art should understand other features and advantages of the illustrated embodiments based on the above embodiments. Therefore, the illustrated embodiments are not limited to the embodiments specifically shown and described, except as indicated by the appended claims. All publications and references cited herein are expressly incorporated herein by reference in their entirety.

[0089] The present disclosure is described below with reference to the flow chart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure.It should be understood that each frame of the flow chart and / or block diagram and the combination of the frames in the flow chart and / or block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, so that the instruction executed via the processor of a computer or other programmable data processing device creates a mode for realizing the function / action specified in one frame or multiple frames of a flow chart and / or block diagram.

[0090] These computer program instructions may also be stored in a computer-readable medium, which can guide a computer, other programmable data processing device or other apparatus to work in a specific manner so that the instructions stored in the computer-readable medium produce an article of manufacture, including instructions for implementing the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0091] Computer program instructions may also be loaded onto a computer, other programmable data processing device or other apparatus to cause a series of operating steps to be performed on the computer, other programmable device or other apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide a process for implementing the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0092] Reference will now be made to the drawings, wherein like reference numerals identify similar structural features or aspects of the subject disclosure. For purposes of explanation and illustration, and not limitation, partial views of example embodiments of systems and methods according to the present disclosure are shown in FIG. Figure 1A , and is generally indicated by reference character 100. Figure 1B-8F Other embodiments of systems and methods according to the present disclosure, or aspects thereof, are provided in the accompanying drawings, as will be described.

[0093] It should be understood that systems 100, 300, 400, 500, 600, 700 are used interchangeably throughout the document, wherein all references refer to the same system highlighting different aspects thereof. The system 100 of the present disclosure automates key tasks and enables consistent information flow in three process stages (planning and quotation, installation and wiring, and commissioning and testing) associated with an integrated system. As shown in FIG1 , the system 100 is composed of an installation and commissioning (IAC) tool 102. The IAC tool 102 is an application that can be accessed by a mobile device or a computing device to help and guide the user throughout the three-stage process, as will be discussed in further detail below. The IAC tool 102 includes a database 104 that enables a memory 106 to be operably connected to a processor 108 and a server 114a for processing, storing, and disseminating the latest information associated with each building. The server 114a, together with the database, can take the form of a web application hosted on a network (web) server or cloud computing platform located in different locations, which can be accessed by various client entities to perform different tasks in the three processing stages. The database 102 may be accessed by one or more mobile devices 110a, 110b, 110c and / or a computing device with network capabilities. For example, a computer located in a sales representative's office and a tablet computer that may be used by an installer or customer may each access the database. The IAC tool 102 may also be accessed by an installation and wiring component 112 installed as part of a building system. In another embodiment, the entire system 100 may exist on a single platform (such as a tablet computer without a network connection or a computing device with a network connection).

[0094] I. Planning and Quotation

[0095] exist Figure 1B 120 for completing the first phase of integrating building systems. A user, such as a sales representative or customer, accesses the IAC tool 102 via a mobile device or other network-connected computing device to create a customer account. The customer account also includes a building description, such as a floor plan, address, number of occupants, typical building usage, etc. As shown in box 124, the IAC tool 102 includes a guided interface that requests building system requirements, including the type of building system (e.g., intrusion detection system, video surveillance, access control, fire safety, HVAC, elevators, etc.) and its required certification level (e.g., NFPA rating for fire safety sensors, ASHRAE standards for HVAC and UL, or EN levels for intrusion sensors). The IAC tool 102 allows for updating selected components based on their available inventory and delivery time.

[0096] Based on the building system requirements, building description and available inventory of components, a deployment plan of the building is generated by using an automatic computer program as part of the IAC tool 102 or manually, as shown in block 122. The deployment plan includes a floor plan (i.e., a geometric representation of the building) with selected components and configurations of the selected components located at their planned installation locations. The selected components may include motion sensors, fire sensors, light sensors, image sensors, video or infrared cameras, heat, flame, smoke or carbon monoxide detectors, sound detectors, shock detectors, vibration sensors, accelerometers, moisture detectors, humidity sensors, magnetic contacts, temperature sensors, photoelectric detectors, actuators (e.g., key fobs, garage door openers, sounders, emergency buttons, thermostats, lighting and appliance control modules, light switches, etc.), routers, repeaters, mobile or static displays, smart meters, smart appliances. If an automatic computer program is used to create the deployment plan, the program automatically places the selected components at the designated installation locations on the building floor plan, as described in further detail below. The building description (including address, floor plan, number of occupants, building use, etc.), system requirements, and deployment plans are stored in the database 104 as detailed building information, as shown in block 125. The building information identifies a set of building system components from a database of supported components that meet the requirements of the customer and the building. The building information also includes the location of the selected components in the building. This can be displayed graphically on a floor plan or as a list with location identifiers. Next, the building information shows the configuration of each selected component in the building. The configuration can be specific to the building type, building use, and location of the component within the building.

[0097] The customer account including detailed building information may be accessed by the system designer at a later time, as shown in block 126. The system designer may also retrieve the customer / building information using a separate device as the device for receiving and storing building information. In other words, multiple users may be able to access the stored building information from a unique device at any point in time after the deployment plan is stored under the associated customer account.

[0098] The building information is used to generate a bill of materials and pricing quote for the sale, installation, commissioning of the building systems as shown in block 128. This step may again be performed automatically or by a sales representative or customer with the assistance of a computer program provided by the IAC tool.

[0099] To provide a detailed deployment plan, the system provides an interactive user interface for specifying building information and user requirements, which includes automated algorithms for quickly placing sensors and evaluating connectivity, as well as a visual representation of an installation diagram to assist in field deployment. The system 100 allows multiple users to complete the method 201 for planning building systems, such as Figure 2Aand blocks 202, 204, 206, 208, 210, and 212. Method 201 includes the following key steps: (1) obtaining a floor plan; (2) obtaining user requirements; (3) selecting system components; (4) placing system components; (5) evaluating connectivity; and (6) generating a price quote.

[0100] A. Get a floor plan

[0101] To obtain a floor plan for a building, a user can browse through a set of model floor plans to select a model floor plan that represents their building. The user is a customer, such as a home, who wishes to install an integrated system in a building. The user can modify the model floor plan using typical drawing tools, and also provide measurement information for different rooms so that the floor plan fits the building closely. The user selects a model floor plan based on the following information: area, number of floors, number of rooms, architectural style, and year of construction.

[0102] In another embodiment, the user uses a mobile device interface to access a web-based tool for capturing 360-degree panoramic images of each room and creating a floor plan. When creating a floor plan, the user also categorizes each room (e.g., bedroom, bathroom, basement, living room, etc.).

[0103] like Figure 3A The illustrated system 300 provides a method for capturing, updating, and processing multi-dimensional building information from a site to assist in the design and deployment of building systems. The system 300 allows a user to walk around a building using a mobile device such as a tablet computer or mobile phone to capture descriptive information about the building. For example, geographic location attributes, 2D or 3D scaled floor plans, and local environment descriptors within the building. Figure 3A , showing an example of a multi-dimensional building information model. The system 300 consists of two key modules: a mapping module 302 and a positioning and measurement (LAM) module 310.

[0104] i. Mapping module

[0105] The mapping module 302 uses a mobile device 110a, 304 (eg, a mobile device, a tablet computer, glasses, etc.) equipped with at least one orientation sensor 306 and at least one motion sensor 308. The mobile device 304 is Figure 1ADetailed schematic diagram of the mobile device 110a referenced in . The direction and movement sensors 306, 308 may include a camera, an accelerometer, a gyroscope, a digital compass, a laser pointer and / or a laser meter (i.e., a distance sensor). The laser pointer or laser meter is attached to the tablet computer so that the projected laser beam is aligned with (i.e., parallel to) the optical axis of the camera. A mapping module is implemented on the mobile device 304 to allow a user to map a room within a building to scale. A user (e.g., a customer) stands at a given capture location L1 in a room (e.g., Room A) and points the camera and / or laser pointer at the floor and rotates around the capture location L1 to obtain a 360 degree panoramic image of the floor to wall intersection while ensuring that the intersection is within a visual guide overlaid on the visual display 316. As the user captures a 360 degree image of the floor to wall intersection of the room, the mapping module 304 uses the laser meter and gyroscope to record the distance and azimuth from the capture location to various points on the perimeter of the floor to wall intersection, such as Figure 3A These points are polar coordinates with the capture position as the origin (d flaser , θ) represents the distance d flaser The user holds the mobile device 304 at a height h ftablet can be converted to the distance d from the capture position L1 at floor level by using the Pythagoras theorem floor ,like Figure 3C This step provides an initial set of vertices (d floor , θ). To correct for various capture errors (such as laser meter measurement errors and user errors) in ensuring alignment of the laser with the floor-to-wall intersection, segmentation and line extraction algorithms like splitting and merging and line fitting can be applied to the initial set of room vertices to obtain an improved estimate of the 2D room geometry.

[0106] The user repeats the steps of capturing the dimensions of the 360 ​​degree image in at least one additional room of the building (e.g., Room B). The user repeats the steps of rotating 360 degrees at the capture location L2 while keeping the floor to wall intersection visible within the display of the mobile device 304. When the two images are captured, the 360 ​​degree image of the first room (Room A) and the 360 ​​degree image of the second room (Room B) are recorded within the memory 312 of the mobile device 312. The user instructs 302 how to connect the rooms to create a floor plan of the building. For example, the user may indicate which adjacent walls are shared between the first room and the second room. Alternatively, adjacent rooms may be determined by using compass readings associated with the corners of the rooms. Room corners whose readings are the same or closer may be connected to automatically assemble a floor plan from the rooms. By repeating these steps in each room of the building, the user creates a floor plan of the building. As the 360 ​​images are captured, the mapping module 302 automatically detects doorways and window frames so that the mapping module builds these features of the room / building into the floor plan.

[0107] The mapping module allows the user to create a three-dimensional (3D) floor plan based on the stored 360-degree images of the room's floor to walls. To create a 3D floor plan of a room, the user tilts the mobile device toward the ceiling and captures a 360-degree image of the intersection of the ceiling and the walls, such as Figure 3D The mapping module uses an accelerometer to measure the tablet tilt angle I as the user changes the tablet tilt from floor-to-wall facing to ceiling-to-wall facing. The mapping module also uses a laser meter to capture the distance d to the intersection of the ceiling and the wall claser .like Figure 3C As shown, by adding h ftablet &h ctablet , (from I, d claser and d flaser Export) to calculate the ceiling height h ceiling After the ceiling height is calculated, repeat the above steps to obtain the polar coordinates d of each point along the ceiling where it intersects the wall floor , θ, to obtain an estimate of the ceiling geometry (e.g., the change in ceiling height). The images of the floor-to-wall geometry and the ceiling-to-wall geometry can then be compared with the calculated ceiling height h ceilingto provide a 3D representation of the room. Those skilled in the art will readily appreciate that the steps for capturing the ceiling to wall intersection may be repeated in additional rooms throughout the building to create a 3D representation of each room of the floor plan. Alternatively, floor plans at different levels (e.g., first floor, second floor, etc.) may be aligned on top of each other by: a) allowing the user to drag and drop floor plans on top of each other and specify the ceiling height for each floor, or b) by using compass readings associated with room corners to automatically align floor plans on top of each other. The mapping module may also capture a 2D representation using only the mobile device's laser meter. The user stands at a given capture location L3 in the room to capture a 360 degree laser scan of the room wall surfaces, such as Figure 3E As shown. The user rotates around capture position L3 while continuously adjusting the tablet tilt so that the projected laser beam falls on the room wall surface. In a room filled with furniture and other objects, the user may need to check the full spectrum of tablet tilt angles, for example, all the way from floor-wall intersection to floor-ceiling intersection. In some cases, to speed up the capture process, the user may obtain laser scan measurements from the corners of the room at ceiling level or floor level or in between, depending on the visibility of the corners. In such cases, the room geometry can be directly constructed by connecting the captured corners via straight lines, such as Figure 3F As shown in Figure 1, O1 and O2 indicate two locations where a user can stand to capture visible corners. As the user captures a 360-degree laser scan, the mapping module uses a laser meter, accelerometer, and gyroscope to record the distance (d laser ), tilt angle (I), and azimuth (θ). The mapping module uses the tilt angle (I) measurement at each point to convert d laser Distance converted to d floor distance, which is equivalent to the distance from the capture location to each point on the wall on the same plane (tablet height (h ftablet )).like Figure 3C As shown, if the inclination angle I is less than 90°, then d floor =d laser × sin; and if the inclination angle I>90°, then e=I-90°, d floor =d laser ×cos e.

[0108] In the event that all parts of the room are not within the user's line of sight from any single location, the mapping module provides the option to capture the room from multiple locations. First, the user captures the portion of the room that is visible from a given location. The mapping module then guides the user to reposition to a more appropriate location within the room and capture a new set of points that were previously hidden. In addition, the user must choose to capture at least two points that were previously captured in order for the room to be automatically assembled.

[0109] Alternatively, an inertial measurement unit in the device can be used to assess the user's displacement between capture locations in the user's room. Either way, the measurements from all locations are merged into a single room using accelerometer and gyroscope measurements along with the distances obtained from the laser. This process can be used recursively to map rooms with complex geometry. The mapping module then calculates the distance d floor Combined with the corresponding azimuth to obtain a floor , θ), a polygon representing the 2D room geometry. During the process of capturing the room, the user also clicks the laser and records the location of the room details of interest (e.g., doors, windows, etc.) into the mapping module. Room details may include, but are not limited to, thermostats, HVAC ducts, light sources, smoke alarms, CO2 sensors, sprinklers, outlets, intrusion sensors. The mapping module automatically recognizes these features as room details and incorporates the room details into the floor plan. Alternatively, the location of the room details may be recorded after the entire room geometry has been captured.

[0110] ii. Positioning and measurement module

[0111] The location and measurement (LAM) module 310 takes location measurements from various sensors on the mobile device 304 to create a building information model with floor plans integrated with the location measurements. Figure 3G As shown, when a user maps a room (e.g., Room A) by rotating the mobile device 304 around the capture location origin L1, the LAM module 310 initializes the capture location to the origin of Cartesian coordinates (X=0, Y=0, Z=0). In addition, when the user rotates around the capture location L1, the LAM module 310 records measurements at certain intervals from various environmental sensors 313 of the mobile device 304. Environmental sensors may include, but are not limited to, light sensors, radio signal sensors, temperature sensors, etc. In other words, while the user is capturing a 360-degree image of the room geometry, the LAM module 310 is using the environmental sensors 313 to capture, for example, light intensity, temperature, and / or humidity at the capture location. The LAM module simultaneously records the measurements with each sensor reading (S li ) associated with the tablet computer tilt (I li ) and the rotation (θ li ). (Subscript l describes the location, l=0 is used for the coordinates X, Y, Z described by the origin. Subscript (i) describes multiple readings taken at the same location. Radio receivers like Wi-Fi, Bluetooth, cellular networks, etc. are also considered sensors by the LAM module. The LAM module is able to measure various properties of the radio signal from each connected receiver, such as: received signal strength indicator (RSS), link quality indicator, signal-to-noise ratio, noise level, channel state information and network or transmitter identifier.

[0112] The LAM module 310 measures the incident light brightness (in lux) via a light sensor and by using the camera feature of the mobile device 304 (described in further detail below). The LAM module 310 can also measure the reflected light brightness via the rear camera at various positions seen from the capture position L1. When the mapping module 302 uses a laser pointer, the LAM module 310 will measure the reflected light brightness at the laser point visible from the rear camera. The position coordinates of the laser point thus provide a position associated with the measured reflected light brightness. Also using the known color of the laser point, the reflected brightness measurement can be converted into an estimate of the incident light brightness at the laser point position.

[0113] To complete and update the building information model, the user preferably captures additional environmental information from at least one more location in the room. After capturing the location origin (0, 0, 0) to complete the mapping and measurement, the user moves to a different location in the room. The LAM module 310 uses the readings from the accelerometer and gyroscope to estimate the user's displacement and direction, and applies the dead reckoning method to obtain the coordinates (X, Y, Z) of the new location. Once the coordinates of the new location are established, the module will repeat the above steps to obtain environmental sensor measurements at the new location.

[0114] As described above, the system 600 uses the multi-dimensional building information model 602 to calibrate a site-specific light simulation model, such as Fig. 6A The light simulation model is then used to estimate the illuminance at any given location within the building. Knowing the average illuminance available at a location allows Figure 6B The Energy Harvesting System Performance Analysis (ESPA) module 604 shown determines the feasibility of powering an electronic device (e.g., a door / window contact sensor) via a photovoltaic energy harvester. The goal of this module 604 is to simplify the process for the installer and eliminate the need to perform any extensive data processing analysis regarding energy harvesting.

[0115] The ESPA module 604 uses the building information model 602, which provides illumination measurements at some locations within the building, together with the light simulation model to determine the feasibility of using photovoltaic (PV) cell powered electronics at any given location within the building. The method used by the ESPA module 604 is described below.

[0116] The system 600 uses point-in-time illuminance measurements at various locations within a building under known lighting conditions. The building information model 602 may provide two sets of illuminance measurements: one under typical gaze conditions and the other under typical indoor light conditions. To obtain point-in-time illuminance estimates at locations where measurements are not available, the ESPA module 604 uses the following approach. The ESPA module 604 defines virtual illuminators at locations with illuminance measurements. The virtual illuminators are assumed to radiate light in an omnidirectional pattern with an intensity equal to the measured illuminance at the corresponding locations. The ESPA module 604 then uses a light simulation model like ray tracing to estimate the illuminance based on the room geometry and wall material properties (given by the room geometry). Figure 6B The mapping module 606 in FIG. 6A tracks the light from the virtual luminaires to any given position in the room. Thus, an estimate of the illumination at any given position can be obtained by summing the light received at that position from all virtual luminaires.

[0117] For known indoor lighting conditions where the locations of interior lighting fixtures are available along with their photometric specifications, the ESPA module 604 can directly use the lighting simulation model 618 ( Fig.6D ) together with room geometry and material information (provided by mapping module 606) to obtain an initial estimate of the illuminance at various locations, as shown in Figure 6B The available illuminance measurements at given locations are then compared to the estimates at those locations. The room wall material properties can then be adjusted to minimize the difference between the measured illuminance values ​​and the estimated illuminance values ​​at the given locations. These adjusted material properties can then provide a more accurate point-in-time illuminance estimate at any location.

[0118] Similarly, for known daylight conditions known from door and curtain / blind locations and other building data 612, the ESPA module 604 utilizes historical sunlight data 608 available for geographic locations to obtain an initial estimate of the illuminance at various locations, such as Fig.6D 610 . The historical data may be obtained, for example, from Google Maps (e.g., available from Google Inc. of Mountain View, California). The available illuminance measurements at given locations are then compared to the estimates at those locations 610. The room wall material properties and door / curtain positions are then adjusted to minimize the difference between the measured illuminance values ​​and the estimated illuminance values ​​at a given location 610. The adjusted material properties and curtain positions can then provide a more accurate estimate of the illuminance at any location 610 at a point in time.

[0119] The size and shape of doors and windows in a room may be obtained by performing image content analysis on a panoramic image of the room captured by mapping module 606. Image content analysis may also be performed on an aerial image or exterior image of the building and / or site to obtain the location of neighboring buildings / exterior obstacles and orientation of the building. Magnetic bearing readings (e.g., from a mobile device compass) associated with points on the door / window are used to obtain the position of the door and / or window relative to the sun. All possible lighting conditions may be defined for a building or area within a building using the already associated illuminance measurement data and a machine learning algorithm to classify all new point-in-time measurements with one of the possible indoor lighting conditions.

[0120] Reference now Figure 6C , the lighting schedule 614 of a building or a specific room specifies the possible lighting conditions in the room at a specific time interval within a specific day of the week. The lighting schedule can describe the lighting conditions as either outdoor or indoor or mixed. For daylight conditions, the lighting schedule can further specify different door / window positions at different times, such as fully opening doors and windows in the morning, opening partial windows and closing doors in the evening, etc. For indoor lighting conditions, the schedule 614 can further specify different types of lights turned on at different times, such as turning on night lights at night, turning on reading lights in the evening, etc. The lighting schedule can be automatically filled by the ESPA module 604 based on user demographics, geographic location, and annual season. In an embodiment, some or all of the lighting schedule 614 can be provided by a user via a user interface at a central panel in a building, for example, or a mobile device connected to a central panel.

[0121] The ESPA module 604 generates cumulative point-in-time illuminance estimates under the different lighting conditions specified by the lighting schedule 614. The ESPA module 614 is thus able to obtain weekly (and / or daily) averages of the light energy available at the location 610. The ESPA module 604 then uses the efficiency specifications of the photovoltaic cells to estimate the average power that can be harvested at any given location from the available light illuminance at that location.

[0122] The ESPA module 604 determines the feasibility of operating a PV powered device. The ESPA module 604 compares the available average power of the PV cells at the location 610 with the power usage of the electronic device. If the available power exceeds the required average power, the ESPA module 604 recommends the deployment of a PV powered electronic device. The ESPA module 604 may also search all locations within the room to determine locations 610 where the average power that can be collected exceeds the power usage of a given electronic device. In an embodiment, the ESPA module 604 sorts the locations 610 based on the collectible power. The final installation location of the electronic device may then be determined by combining other placement criteria based on the device type (e.g., a door / window sensor must be placed on a door / window) and connection requirements (e.g., the device will require sufficient wireless signal strength from the panel to transmit its measurements to the panel). The output may also drive requirements for sensors (e.g., removable or repositionable PV panels). The method may include determining the type of collector used at the installation location (e.g., a removable or repositionable PV panel) given the sensing and connection requirements of a particular device type.

[0123] The energy harvesting system performance analysis described in the present disclosure provides the following potential advantages over conventional systems: allows installers to verify the feasibility of enabling continuous operation of photovoltaic cell powered electronic devices based on their installation location, easy to use functionality, when integrated with the mapping module also described herein, allows battery-free alternatives to be used for devices where feasible, provides a set of locations within a building that will be able to facilitate continuous operation of photovoltaic powered devices. Providing an integrated tool that takes into account indoor and daylight conditions to predict the performance of energy harvesting devices over time provides a method to calibrate indoor and daylight simulation models using point-in-time illuminance measurements from the field and correct errors in building information inputs, and eliminates the need for accurate photometric specifications for lighting fixtures to predict indoor lighting performance.

[0124] The LAM module 310 is thus able to automatically measure various environmental attributes and locate them onto a 2D and / or 3D map of the room. The module also allows the user to manually specify the location of various objects within the room (like routers, lighting fixtures, furniture, etc.). This detailed building information model is then used by other modules or external systems to assist in building operations and maintenance.

[0125] B. Obtain user requirements

[0126] Once the floor plan / building information model is completed, the user will specify the requirements. For example, the user can specify multiple partitions of the building so that each partition can be planned and controlled independently. The user can also specify the perceived threat at each entrance / exit and the level of protection for each room based on importance / value. The user can further specify the presence and protection preferences of pets when away from home or at home. The user can use the mobile device interface to select the rooms or areas belonging to each partition. The user can then select the functions of each partition, such as security, safety, home automation (lighting control, environmental monitoring and self-monitoring and control). The user can also specify the compliance of each function. For example, for security functions, the user can select the UL standard that the system must comply with. The user can also select the regulatory standards that the system should comply with. The system 100 automatically determines the applicable regulatory standards based on the location of the building. For example, in the United States, the system 100 will be able to determine the regulatory requirements for smoke and carbon dioxide sensors based on the state where the building is located.

[0127] C. Select system components

[0128] After obtaining the user's requirements, the system 100 automatically selects the components that can meet the user's requirements and are suitable for the building from the manufacturer's database. In doing so, the system 100 analyzes the size of the building floor plan and the number of rooms to determine the required system capacity. The system 100 also takes into account the types of available components, such as PIR / motion sensors, door / window / contact sensors, glass break sensors, image sensors, video or infrared cameras, heat, flame, smoke or carbon monoxide detectors, sound detectors, impact detectors, vibration sensors, accelerometers, moisture detectors, humidity sensors, magnetic contacts, temperature sensors, photoelectric detectors, actuators (such as key fobs, garage door openers, sounders, emergency buttons, thermostats, lighting and electrical control modules). For each component, the system 100 also evaluates parameters, such as coverage (radial range, angular range), detection performance (such as detection probability), accuracy, battery life, wireless connectivity, false alarm possibility, false alarm probability, etc. Before selecting a component, the constraints on component placement such as compatibility of doors and windows and possible constraints on the location will also be reviewed. Additionally, system 100 allows a user to select a desired system control panel and choose system components that are compatible with the selected panel that meet the user's requirements.

[0129] D. Place system components

[0130] i. Placement based on component type

[0131] Next, the system 100 automatically places the selected components on the floor plan by using different methods for different types of components.

[0132] For magnetic contact sensors, the system 100 analyzes the floor plan to identify all perimeter doors and windows, for example, by selecting doors and windows that do not belong to more than one room.

[0133] For motion sensors, the system 100 analyzes the floor plan and the motion sensor coverage specifications to automatically place sensors by identifying rooms suitable for motion sensor placement. This is done by analyzing the floor plan to identify rooms with specific characteristics. For example, room sizes are compared, and rooms with an area greater than a predetermined percentile are considered large rooms and are considered suitable for motion sensors. Other rooms suitable for motion sensors include: rooms with two or more perimeter windows, rooms with more than a certain number of interior doors and no windows on the perimeter doors, and rooms labeled "living room" or other similar categories. The system 100 also identifies the type of motion sensor that is most suitable for a specified room. This can be done by calculating the difference between the room area and the coverage area for a compatible motion sensor (provided in the sensor specification) and then selecting the motion sensor that provides the smallest absolute difference.

[0134] For glass break sensors, the system 100 analyzes the floor plan to identify rooms with one or more glass doors, windows, etc., and automatically places glass break sensors in each of these rooms. For a single item (e.g., a glass window) in a room, potential placement areas for the glass break sensors are calculated based on item characteristics / attributes (e.g., size and material of the glass), sensor specifications describing the placement requirements / rules for the glass break sensors in terms of maximum and minimum radial distances from the window and angular relationship to the item, and finally the room layout and item location (i.e., relative coordinates) in the building.

[0135] In an alternative embodiment, to calculate potential glassbreak sensor placement areas for protecting a single item, the room area may be sampled to obtain a distributed grid of points. Points that meet the sensor placement requirements relative to the item to be protected are determined, and the sensor is automatically placed at the optimal location.

[0136] Whenever multiple items in the same room need to be protected, the system 100 attempts to find a single mounting area from which a single glass break sensor can be placed to protect all items. When this is not feasible, the proposed method places additional glass break sensors to provide protection for all required items in the room. The mounting area of ​​a particular item is picked and intersected with the mounting area of ​​another item, and if the resulting intersection area is greater than a certain minimum size, the resulting area is considered as input and intersected with other single mounting areas until all single areas corresponding to different items have been checked, or the resulting intersection area is less than the minimum size. Whenever the intersection area is less than the minimum size, the sensor will be placed in the last valid intersection area. This is repeated for the remaining windows where multiple sensors are placed. When no more areas remain to be checked, the glass break sensor is placed in the last resulting intersection area.

[0137] In another embodiment, the installation areas for all possible window placements are intersected with each other to find which combination of intersections provides the best solution in terms of the number of sensors needed to protect all available items in the room. In yet another embodiment, the grid points are checked against the installation rules for all the different items to be protected to verify which items can be protected if a sensor is to be placed at a specific point. Once the protected items for each grid point are known, the engine will select any point that covers all items as a placement point. If no single point covers all items, several points are selected so that all items are covered.

[0138] ii. Placement based on location and orientation within the room

[0139] Once the system 100 determines the preferred location for each component based on type, the system 100 automatically determines the optimal mounting location (i.e., ceiling, wall, corner, etc.) and orientation within the room for a given component and dynamically visualizes the coverage achieved for the component on the floor plan. Valid mounting locations and orientations can also be used as input to the automatic placement algorithm. Figure 2B An overview of the system taking as input parameters a floor plan 222 obtained by the user and the manufacturer specifications 224 for each component is shown. Four key modules are used to determine the installation location and orientation: an installation location determination module 226, an automatic sensor placement module 227, a module 228 to assist in manual placement, and an overlay visualization module 229. The installation location determination module 226 analyzes the generated floor plan to identify valid installation locations and orientations for the verified components based on physical constraints derived from the component manufacturer's specifications. For a given component, the installation location determination module automatically identifies the vertices of furniture (e.g., wall-mounted cabinets) at room and ceiling heights. The identified vertex sets are analyzed and combined to generate a set of polygons describing each room within the floor plan.

[0140] For ceiling mounted sensors, the mounting type is determined by the manufacturer's specifications (extracted from the sensor specification database), and the room polygon is discretized in a grid of a specific size to obtain an exhaustive set of potential mounting locations. The appropriate grid size is determined based on the sensing range, room size, and other mounting guidelines of the manufacturer's specifications.

[0141] For wall mounted sensors, an exhaustive set of potential mounting locations can be determined by segmenting each wall section into equal parts. This can be done by identifying a set of points between two consecutive polygon vertices separated by a specified minimum distance, again determined based on the manufacturer's specifications.

[0142] For corner mounted sensors, each vertex in the room polygon is a potential mounting location. The set of valid mounting locations is then obtained by processing the exhaustive set of potential mounting locations, eliminating points that overlap with objects / furniture at ceiling height in the room. For specific points, different heights may be considered, depending on the manufacturer's mounting specifications for the sensor.

[0143] For wall and corner mounted sensors, this can be done by first estimating the primary mounting axis 235 (e.g. Figure 2F ) to determine the effective mounting orientation for each effective position, the mounting axis being the normal of the wall segment as it appears in the room in the case of a wall position, and the angle bisector of the corner as it appears in the room in the case of a corner position. To determine the mounting axis in two dimensions (2D), a ray 250 is sent toward the reference axis with a small delta radius, resulting in a new reference vector and point ( Figure 2C and Figure 2D ). For the reference point, a point-in-polygon algorithm is run to determine whether it falls within the polygon of interest. If the point is within the polygon, the orientation relative to the reference axis or vector is obtained by equations (1) and (2), as Figure 2C As shown in:

[0144]

[0145]

[0146] On the other hand, if the reference point does not fall within the polygon of interest, the orientation relative to the reference axis or vector is obtained by the following equation (3): Figure 2D ):

[0147]

[0148] In an additional embodiment, to determine the mounting axis in 2D, at a given mounting point, rays 252 are sent in all possible directions around the point for a given radius, thereby generating a new set of inspection points whose directions or orientations relative to the reference axis are known (see Figure 2E ). For each point, a point containment algorithm is applied to check if they remain within the primitive room polygon of interest. Finally, the installation axis of the points in the 2D floor plan is the average between the farthest orientations (relative to the reference axis) of the checked points that are found to be within the primitive polygon of interest.

[0149] Once the mounting axis has been determined, the manufacturer's specifications can be used to obtain a set of valid mounting orientations in the form of a set of possible mounting angles relative to the mounting axis, such as Figure 2F For each valid mounting angle, possible mounting tilts are also available, such as tilt down for pet immunity, tilt up, or flush mount for reverse mounting. Additional checks can be performed on each calculated position and orientation to eliminate position and orientation combinations that do not meet any mounting constraints or guidelines specified in the manufacturer's specifications. For example, a PIR-based motion sensor should not be placed so that its field of view is pointed toward a window, exterior door, or heat source like a heating vent.

[0150] For sensors like PIRs or cameras with tilt specifications, the tilt is specified for the determined placement based on the room height provided by the floor plan description and manufacturer specifications. In embodiments implementing an automatic sensor placement algorithm, this set of valid mounting placements can then be passed as input to the placement algorithm, which can then select the best mounting location to achieve the desired performance.

[0151] The manual component placement module 228 facilitates manual placement of sensors. The system 100 provides a user interface that shows a floor plan and allows the user to drag and drop a specific component at a specific location on the floor plan. To assist the user during manual component placement, the module 228 allows the user to select a component type from a predefined list. For example, the user can drag and drop a component icon. The module keeps track of the component icon location, obtains a subset of valid locations near the icon, and, for example, highlights these valid locations 232a-f nearby, such as Figure 2G This provides visual / audio / tactile feedback to guide the user through potential valid placement options. The module automatically places the component at the nearest valid location when the user drops the component at the location ("snaps to a valid location") and automatically orients the placed component along the wall tangent axis.

[0152] Once the component is mounted in the optimal location and oriented, the coverage visualization module 229 estimates the coverage provided by the component based on its sensing modality, sensing range, position, orientation and tilt. This can be done in many different ways. For example, in the case of a door / window contact sensor, the coverage can be simply visualized by a circle 236 around the protected door or window, such as Figure 2H In the case of a motion sensor or camera, coverage visualization can be done by first computing the visible polygon, which is the 360-degree set of all points visible from the mounting position. This takes into account the shape of the room polygon and any kind of obstructions inside the room, such as furniture, stairs, etc., as defined by the floor plan description. The field of view can then be calculated for a given mounting position, orientation, height, and tilt, which can be obtained from the manufacturer's specifications, such as Figure 2F By extracting the intersection of the visible polygon and the field of view polygon, the coverage polygon can be obtained. If the component placement is done manually, module 229 allows the coverage visualization to be done dynamically as the user drags the component across the floor plan.

[0153] iii. Arrangement based on room layout

[0154] For rooms within a floor plan that have free / open space but may be of arbitrary shape and may have obstacles that result in visibility constraints to one location from another, the system 100 uses a coverage maximization process to optimize the placement of components. The system 100 also evaluates the coverage of the component network taking into account the capabilities of each individual component as well as a probabilistic model of intruder locations and the directionality of intruder movement. Fig. 8A , shows an intruder threat detection system 840 that models, maps, and generates a GUI that visualizes the intrusion threat of a given building for display on a display device. The model displayed on the mobile device allows the user to update the intruder model to recalculate optimal component placement in real time.

[0155] The task of optimally placing components in a defined domain can be formulated as the problem of minimizing the distance between the coverage distribution (which captures how well each point in the domain is covered by sensors) and the prior distribution of intruders. Minimizing this distance results in the sensor footprint being concentrated in areas where the probability of intruder detection is high. This can be achieved by changing the position and orientation of the components until a minimum cost or distance is achieved. Stochastic gradient descent methods are particularly powerful in this application. For situations where the number of components and possible positions is low, the system 100 can enumerate all possible combinations of sensor positions and orientations to find the optimal position and orientation for each sensor. The method is independent of specific component characteristics (like detection range and detection probability).

[0156] First, the room layout is considered at creation time during the generation of the floor plan. The room layout includes the following: the geometry and coordinates of the room, the set of valid sensor installation locations and orientations within the room, the locations and sizes of other objects in the room (such as walls, furniture, windows, etc.), and the intruder prior activity map (which captures the possible locations of intruders and the possible directions of movement of intruders (discussed in further detail below)). The method also takes as input a list of sensors to be used and their respective characteristics (like detection range and detection performance) to optimally place a given sensor in the room in order to minimize the coverage metric. The coverage metric also takes into account visibility constraints due to the location of objects like walls and furniture.

[0157] The method may also take as input field of view (FOV) constraints to prevent the component from viewing certain objects in the room (like windows, radiators, or vents). By taking these FOV constraints into account, the method may reduce the false alarms generated. The FOV constraints may be passed as a set of coordinates that the component may not be aimed at, which may represent the location of light sources, heat sources, etc. To evaluate whether a selected position or orientation of the sensor does not meet the constraints, a set of rays 242 may be sent at a specific position and orientation to see if the rays intersect an object 241 (such as a Fig.2I If an intersection is found, then either the position or the orientation or both may be discarded.

[0158] For sensor placement optimization, the possible directions of intruder movement can also be considered, see Figure 2L . The system 100 includes a dynamic threat model that uses data collected from previous intruders in similar buildings to analyze and determine the expected routes that an intruder may take based on the entry point into the building. The dynamic threat model considers a static probability distribution of intruders over an entire floor plan defined over a set of grid points. The model also incorporates the relative threat levels of rooms in the building based on the likelihood of finding an intruder in the room. It is also contemplated that the relative threat level of each exterior door / window may be provided as input by the user. These may then be used to derive the threat level for a given room. In this case, a graph is constructed with nodes corresponding to (X, θ) pairs, where X is a location in the room and θ is one of a possible finite set of angles at which location X can be observed by a component. Two nodes on the graph are considered adjacent if and only if the corresponding locations are close to each other and the corresponding angles are close to each other.

[0159] Next, using the adjacent matrix A of the graph obtained in the previous step, we calculate the symmetric Laplacian matrix L = D -1 / 2 AD -1 / 2 , and calculate the eigenvector of matrix L, as shown in equation (4):

[0160] LFk =λ k f k (4)

[0161] The final step is to find the optimal position and orientation of each component that minimizes the following cost function, as shown in Equation (5):

[0162]

[0163] where μ k = <μ, f k >, c k = <C,f k > and N is the number of nodes on the graph. Here μ is a probability distribution defined on the nodes of the graph that captures the value of observing each node on the graph. And C is a coverage distribution defined on the nodes of the graph that captures how well each node on the graph is covered by sensors. The coverage distribution is computed using visibility polygons corresponding to the location of each component and its mounting orientation. The visibility polygon corresponding to location X is the set of locations that are visible from location X, given the location and size of the object that is a visibility obstruction.

[0164] The described optimization essentially minimizes the distance between the intruder prior distribution μ and the coverage distribution C by performing stochastic gradient descent or exhaustive search over all valid sensor positions and orientations. Stochastic gradient descent is an iterative method that starts with an initial guess of the sensor positions and orientations and proceeds by randomly changing the component positions and orientations in such a way that the cost φ is reduced. s , until no further improvement is possible. The optimization method can be modified to take into account field of view (FOV) constraints in order to prevent components from viewing certain objects in the room (like windows, radiators, or vents). If the FOV constraint cannot be met, the method will make valid component recommendations to ensure that the constraint is met. In addition, the optimization can be accelerated by truncating the number of eigenvectors used in the sum to calculate the described cost function.

[0165] The output of the method is the optimal component position and orientation for intruder detection. The quality of the solution given by the optimization method can be evaluated by calculating the detection probability of an intruder (using Monte Carlo simulation) or by calculating the fraction of free space in the room covered (or observed) by the sensor. The quality of the solution can also be evaluated by calculating the false alarm rate caused by the sensor position / orientation.

[0166] The intruder threat detection system 840 includes an identification input module 842 that identifies input parameters specific to the building being modeled in order to model building specific threats. The inputs are selected from building and location information 844, which includes crime statistics for the building, satellite maps, and building floor layouts. Example input parameters identified by the identification input module 842 include:

[0167] ○ Neighborhood crime rates - In an embodiment, the identification input module 842 uses the building's geographic location information (e.g., GPS coordinates or zip code) to obtain current and historical crime rates for the building's neighborhood from a website or locally stored crime statistics database. In certain embodiments, a user can manually input crime rates using a user interface;

[0168] o Criminal Population Ratio - In an embodiment, the identification input module 842 uses the geographic location information (e.g., GPS coordinates) of the building to obtain the ratio of the criminal population relative to the total population within a given radius (e.g., 2 miles) of the building;

[0169] o Criminal Activity - In an embodiment, the identification input module 842 uses the geographic location information (e.g., GPS coordinates) of the building to gather estimates of recent or historical criminal activity near the building, such as from information obtained from a local newspaper or law enforcement agency;

[0170] o Building Information Model - In an embodiment, the recognition input module 842 can obtain the 2D or 3D geometric layout of the building from an electronic file (such as in Figure 8B , which shows a layout diagram 870);

[0171] o Type of building - In an embodiment, the identification input module 842 uses the geographic location information (e.g., address information) of the building to identify the building as a residential or commercial property by querying a website or a locally stored database. More information about the use of the building (e.g., office space, factory, vacation home, etc.) can be provided by the user via the user interface;

[0172] ○ Visibility, Monitorability, and Accessibility - In an embodiment, the identification input module 842 uses the geographic location information of the building to obtain a satellite view of the building and its vicinity, and analyzes the satellite view (e.g., image) by using image processing techniques (such as edge detection, object detection, and object distance calculation) to identify, for example, roads adjacent to the building, the amount of traffic on the adjacent roads, the proximity of the building to neighboring buildings, and other characteristics determined:

[0173] o Monitorability of building location - Identification input module 842 uses, for example, traffic volume information available from public and / or private sources, and image processing results of satellite imagery to understand whether the building is located in a remote area or near a suburb, in which case the monitorability of the building will be rated low because there are few neighbors or passersby who would notice or deter an intruder from scoping the area and planning the intrusion;

[0174] ○ Visibility of building entry points - The identification input module 842 uses the image processing results and the determined traffic volume on the adjacent roads to determine the visibility of various entry points (e.g., doors, windows, etc.) from adjacent roads and adjacent buildings. The higher the visibility of the entry points from the traveling adjacent roads, the less likely it is that an intruder will break into the building from these entry points;

[0175] o Accessibility of building entry points - The identification input module 842 determines the accessibility of various entry points based on the presence of detected alleys or roads in front of the entry points. The easier an entry point is to access from an alley or road, the more likely an intruder is to attempt a break-in. Accessibility may also be derived from: size / type of door / window (e.g., size, slide, hinge, handle), locks used, material of door / window, height of door / window from the ground, and structures (e.g., fire escapes, crosspieces, vines, drain pipes) present on the exterior of the building near or in the vicinity of the door / window (which may make it easier for an intruder to access);

[0176] ○ Vulnerability level of an entry point - In an embodiment, the identification input module 842 uses the accessibility and visibility metrics identified above for an entry point to determine its vulnerability to intrusion relative to other entry points in the building. Alternatively, a user may be able to manually enter this vulnerability information for each entry door, window, or side of a building via the GUI by clicking on the building and selecting a vulnerability level of high, low, medium, or none. Figure 8B ). The vulnerability of the break-in can be determined based on the level (for example, the vulnerability level of the bottom floor is higher than that of the first floor, and the vulnerability level of the first floor is higher than that of the upper floors);

[0177] ○ The vulnerability of breaking in can be determined based on the type of windows, doors or walls provided at the entry point of a building or room. The types of windows may include, for example, tempered glass windows, conventional glass windows, front side windows, rear side windows, fixed windows, hanging windows, sliding windows, bay windows, etc. The types of doors may include, for example, front doors, rear doors, security doors, garage doors, French doors, metal doors, wooden doors, etc. The types of walls may include, for example, brick walls, wooden walls, stone walls, armored walls, etc.;

[0178] ○ Protection level of building areas - In an embodiment, the identification input module 842 determines the protection level of an area based on the likelihood of having valuables or occupants in a given area in a building. For example, in a residential building, a bedroom may be set at a high protection level because it is likely to have both occupants and valuables.

[0179] In an embodiment, a building floor plan may be semantically rich, which in this article refers to providing details that can be used to estimate vulnerabilities for breaking in, such as the location, type, and size of a structure. The floor plan may list different elements describing a building, such as walls, doors, windows, ceilings, floors, areas, and internal objects and their specific relationships, attributes, and properties, such as height, width, material, shape, armor, the presence of locks, associated values ​​or qualities, names, etc. In an embodiment, the type of window, wall, and / or door set at a building entry point or room entry point may be determined by extracting and processing the information provided by the semantically rich building floor plan. In an embodiment, the type of window, wall, and / or door set at a building entry point or room entry point may be determined by extracting and processing the information provided in a captured image of a building. For example, the characteristics of a semantically rich building floor plan and / or a captured image may be mapped to a computer database to determine information about a building, such as the type of window, wall, and / or door.

[0180] In certain embodiments, a user may be able to manually input protection levels for different zones via a GUI, such as Figure 8B In certain embodiments, the above input parameters may be input via a user interface. For example, a user may be able to select and input a rating value for a parameter, such as high, medium, low, or none.

[0181] Intruder threat detection system 840 also includes a threat modeling module 846 . Figure 8C A hierarchical model 860 is shown that is used by the threat modeling module 846 to model intrusion threats to a building.

[0182] At the highest hierarchical level, the neighborhood safety rating model 862 provides structured data that uses neighborhood crime rates, offender-to-population ratios, and a measure of recent criminal activity to provide an estimated measure of neighborhood safety based on equation (6):

[0183] Neighborhood safety level = 1 / (neighborhood crime rate × crime population ratio) (6)

[0184] The higher the neighborhood security level, the lower the average intrusion threat to neighborhood buildings.

[0185] At the next highest hierarchical level, the building risk level model 864 provides structured data that provides an estimated measure of the risk level of a given building based on risk equation (7):

[0186] Building risk level = probability of intrusion × impact of intrusion (7)

[0187] The probability of intrusion depends on the neighborhood security level, the type of building (residential properties have a higher risk level than commercial properties), the monitorability of the building's location, the visibility of the building's entry points, and the accessibility of the building's entry points. The probability of intrusion can be calculated using equation (8):

[0188] Probability of break-in = Monitorability of building location + Visibility of building entry points - Accessibility of building entry points (8)

[0189] The impact of a break-in depends on the occupancy of the building or room (occupancy factor) and the value or sensitivity of the assets in the room or building (asset factor).

[0190] The possibility of intrusion associated with a room can also be determined based on the importance of the room. Importance indicates a certain degree of protection that should be provided to the room relative to other rooms, and / or the possibility that an intruder may attempt to access the room. The importance of a room can be obtained by comparing the semantic information available in the floor plan with the available intrusion statistics, and can be changed by the user. The possibility of intrusion statistics indicating intrusion into different types of rooms. Burglary studies indicate that intruders first go to the master bedroom to find jewelry and / or cash that is easy to carry, followed by entering the kitchen or living room to find mobile phones, electronic products, etc., and then go to the bathroom to find narcotics. (See: 2011, FBI UCR's Crime in US (American Crime); U.S. Department of Justice, DL Weisel's Burglary of Single Family Houses (Single Family House Burglary); 2012, K. Raposo's Burglar Reveals 15 Trade Secrets (The thief revealed 15 trade secrets); JN Gilbert's Criminal Investigation (Criminal Investigation), 7th edition, 1007). These statistics can be used to automatically assign importance levels to rooms based on the names or descriptions of the rooms provided by the floor plan (eg, master bedroom is high importance, kitchen and living room are medium importance, and bathroom is low importance).

[0191] In an embodiment, the importance of a room may be specified by user input. For example, a user may specify a higher level of importance to a baby's bedroom than to a master bedroom.

[0192] Regarding the intrusion effect, the rooms occupied during the afternoon hours will have more intrusion effect than the rooms not occupied. The intrusion effect can be calculated using equation (9):

[0193] Intrusion impact = residence factor + asset factor (9)

[0194] The higher the building risk level, the higher the likelihood of attempts to break into the building and the higher the recommended protection of the building.

[0195] At the next hierarchical level, building threat model 866 provides structured data that models each perimeter door, window, and perimeter wall segment as a potential entry point for a break-in. The relative likelihood of a break-in using a given entry point is derived from the vulnerability level assigned by the identification input module 842 described above. In an embodiment, the sum of the relative likelihoods of all entry points within a building is 100%. If λ 1 , 2 , 3 ...is the relative intrusion probability of the listed entry points, then the normalized probability is obtained according to equation (10):

[0196]

[0197] The building threat module 866 provides structured data that provides an estimated measure of the relative vulnerability of different areas, floors, or rooms in a building, which is a function directly connected to the entry points of a given area and the likelihood of a break-in for a room according to equation (11), such as Figure 8B As shown:

[0198]

[0199] Where the probability of moving between rooms is the probability of moving from a connected room to a given room, which can be a function of the room labels describing the purpose of the room (e.g., bathroom, bedroom, living room, office, warehouse, computer server room).

[0200] The building threat module 866 may also provide structured data that provides an estimated measure of relative vulnerability along the perimeter of the building. Vulnerability along the perimeter may be determined based on exits at the building's entry points and environmental characteristics outside the building or exterior of the building, such as lighting, fire exits, and whether the area is visible from nearby streets.

[0201] At the next hierarchical level, the room threat model 868 provides structured data that models the perimeter of the building, each perimeter door, window, and perimeter wall segment in a room as a potential entry point, and each interior door or opening as an exit point. Assuming a break-in is attempted via a room, the relative room-level likelihood of a break-in using a given room entry point is derived from the building-level likelihood value for the entry point. The module then uses probabilistic modeling such as a Markov decision process to track the movement of the intruder from entry point to exit point, assuming the following:

[0202] ○ The intruder moves along the shortest path with a high given probability P

[0203] ○ The invader explores the room with probability 1-P

[0204] ○ The intruder avoids obstacles in the room (like furniture)

[0205] ○ Intruders can escape the room without reaching a specific exit

[0206] ○ Intruder detection should occur when approaching the entry and exit points

[0207] Probabilistic modeling can generate a probability distribution of intruder movement within a room. It should be noted that in alternative embodiments, other subject-based simulation techniques can be used to obtain a probability distribution of intruder movement within a room based on the above factors. In other embodiments, the probability distribution of intruder movement in a room is derived by aggregating intruder distributions obtained from several simulation runs, where each individual run assumes a given opening in the room as an entry point, and all other openings as potential exits with a certain probability, and then obtains an intruder distribution, and then repeats the simulation with another opening as an entry point and all other openings as exits, until all openings in the room are considered entry points at least once.

[0208] The intruder threat detection system 840 also includes a threat visualization module 848. The threat visualization module 848 uses the output from the threat modeling module 846 to generate a displayable visualization of threats on a display of a building floor plan (which can be provided as a GUI displayed on a display device). The displayable visualization allows a user to visualize threats at the building level, floor level, and room level. Such visualizations can depict the relationship between intrusion threat probabilities and locations on a building floor plan.

[0209] Intruder threat detection system 840, particularly recognition input module 842, threat modeling module 846, and threat visualization module 848 may be used Fig. 7A The example computing system environment 700 is implemented as shown in FIG.

[0210] about Fig.8D, shows an intruder movement model 872 that models the building's entry and exit points (perimeter openings) and connectivity between rooms. Rooms are modeled as network nodes. Physical room connectivity identifies possible node transitions. An intruder is assumed to move from a given node to all connected nodes with some probability. A pair of rooms is considered connected when an intruder can move between them (without entering a third room). The movement probability may be a function of the importance of the connected rooms and the past history of the rooms visited. Model 872 may also indicate connectivity between floors, such as via stairs or elevators. About Fig. 8E , a room-level threat visualization is shown as a room-level threat heat map 880. The threat modeling module 846 uses the probability distribution of intruder movement generated by the room threat model module 868 to generate a display of the calculated threat probability distribution, such as the heat map 880. The heat map 880 uses visual indicators such as color coding to display different probabilities of threats and their locations relative to the building floor plan.

[0211] about Figure 8F , the floor-level threat visualization is shown as a floor-level threat heat map 890. In an embodiment, to generate a display of threat probabilities at the floor level, the threat visualization module 848 adjusts the room-level threat heat map 880 for each room using the relative vulnerability level of that room based on the relative vulnerability derived by the building threat model 866 and its location relative to the building floor plan. Figure 8F A visualization of an aggregated room-level threat heat map 880 arranged at the floor level may also be depicted. In another embodiment, a floor-level threat heat map 890 may include a heat map that visually indicates the relative vulnerability level of the room for each room, for example using a color code.

[0212] In an embodiment, a building-level threat visualization may be generated for display on a display device, wherein the floor-level threat heatmaps 890 are stacked according to the floor arrangement in the building. The building-level threat visualization may indicate the relative threat levels of different floors of the building. In other embodiments, the relative vulnerability levels of all rooms and / or areas on a given floor are aggregated to obtain the relative vulnerability level of the floor. The building-level threat visualization may visually indicate the relative vulnerability level of the floor, for example, using a color code for each floor.

[0213] In an embodiment, a building-level threat visualization may indicate relative vulnerability along a perimeter. A visualization of relative vulnerability along a perimeter of a building may be overlaid on a satellite image of the building.

[0214] Return to reference Fig. 8A In certain embodiments, the output from the threat modeling module 846 and the heat maps 880 and 890 may be used for the following threat modeling applications:

[0215] ○ Safety and security system placement 850 - Given a visual heat map depicting intrusion threat probabilities, such as a room-level threat heat map 880 or a floor-level threat heat map 890, a user may manually specify the placement of safety and / or security devices to combat threats. In an embodiment, the visual heat map may include a graphical depiction of the coverage areas of devices such as cameras, thermal detectors, or motion sensors. The user may determine locations for placing devices in rooms and / or areas of a building or on the exterior of the building such that the displayed coverage areas of the sensors overlap with threats mapped within the room, area, or along the perimeter of the building; in an embodiment, a computer program may automatically determine locations for placing devices in rooms, areas, or along the perimeter of the building such that the calculated coverage areas of the devices overlap with the mapped threats;

[0216] o Safety and security management contracts 852 - the visual heat maps 880 and 890 and / or determination of device placement may be used to manage safety and security provisions, such as developing a tender proposal or security management contract for a security provider to protect a building;

[0217] o Security Procedure Determination 854 - Visual heat maps 880 and 890 and / or determination of device placement may be used by a safety and / or security consultant to determine appropriate security procedures for a building.

[0218] In accordance with the above, in certain embodiments of the present disclosure, safety or security threats to a building may be identified, modeled, and / or visualized for display, providing useful information to the user regardless of the user's technical background or knowledge of security. For example, a user may specify a priority level (e.g., high, medium, low, or none) for protecting an area and / or room based on the value or importance associated with the area and / or room. In another example, the threat level of a building may be estimated based on the geographic location and type of the building. In yet another example, the threat distribution within a building may be estimated, and a visualization of the estimated threat distribution may be generated for display. In another example, intruder movement within and throughout a room may be estimated, and a visualization of the estimated intruder movement may be generated for display. The planning time and error of deploying security devices may be reduced to mitigate security threats to a building by reducing the skills required for such planning. For example, a detailed probabilistic model of the threat distribution within a building may be generated, which may be used to automatically determine device placement to maximize threat detection.

[0219] iv. Placement based on hierarchical level

[0220] Components are placed first at the room level and then at the building level to determine the best placement and achieve the highest performance. Different types of components are placed in a given room to obtain the best detection performance in the room from the component or combination of components. The output of this room-level placement is a table describing the cost, detection performance, and false alarm performance achieved by the component or combination of components for the room, such as Figure 2J As shown. In other embodiments, it can also provide the precise location and angular orientation of the component in the room to achieve estimated performance. This placement can be done in several different ways. In a preferred embodiment, an automated method for placing a glass break detector, motion sensor or camera in a room can be implemented to find the best location for the component in the room to maximize coverage or detection in the room. The false alarm rate for a given component position is also evaluated based on the presence of various factors in the room (which cause false alarms for a given sensor type). For example, in the case of a motion sensor, the presence of south-facing windows, roads, stoves, light radiation and other heat sources within the coverage of the component will result in a higher false alarm rate.

[0221] Next, building level placement determines the rooms and / or perimeter doors and / or windows (where components should be placed to achieve the desired system cost or performance for the entire building) taking into account the following: (a) the relative threat level of each perimeter door, window or room, (b) the connectivity between rooms (c) the contents of the room (valuables) or the desired level of protection to develop a dynamic threat model. This dynamic threat model is then used with information about the type of door / window to estimate the detection performance of a given combination of components placed in the building, such as Figure 2K and Figure 2L The output of this process is the selection of components and their locations that provide the greatest probability of detecting an intruder.

[0222] The method has the ability to de-conflict when there are multiple options for placement of a device. Secondary metrics such as false alarm rate, detection latency, and user preferences are invoked to interactively make decisions. For example, in a given room with two windows, it may be preferable to place contact sensors on both windows rather than a single motion sensor, thereby providing lower detection latency. Furthermore, if a higher probability of detection is desired, the room may also be measured with an additional motion sensor. Figure 2M Describes the typical component placement (3 D / W sensors and 1 PIR) obtained from the tool for a given floor plan. The 'Open' area was chosen for the placement of the PIR as it is connected to most rooms. Therefore, placing the sensor there would maximize the likelihood of detecting an intruder.

[0223] The optimal component placement is obtained based on a trade-off between bill of materials / cost and detection performance. In addition, the tool provides vulnerability reduction at the entry point based on the type and number of optimally placed sensors. Figure 2N shows one such result.

[0224] The tool has the capability to obtain the performance of a set of sensors placed in a given location and known configuration. Performance metrics may include:

[0225] ○ Detection probability (out-of-station): the security level provided by all deployed sensors;

[0226] ○ Detection Probability (Armed Stay / Night Mode): Security level provided by armed perimeter door / window sensors and glass break / impact sensors only;

[0227] ○ Detection latency: the distribution (and average) of the time it takes to detect an intrusion;

[0228] o False Alarm Rate: The probability of generating a false alarm based on the type, configuration and number of sensors. Vary the false alarm rate based on the geometry of the room, the presence of windows, paths, stoves, light radiation and other obstructions; and

[0229] ○ Coverage: The coverage of the volumetric space in the house. It is defined as shown in equations (12), (13), and (14):

[0230]

[0231]

[0232] cover 总数 = Cover 周边 +(1-cover 周边 )×cover 内部 (14)

[0233] After the components are manually and / or automatically placed in optimal locations and orientations as described above, the completed floor plan (e.g. Fig.2O The system 100 allows the user to reposition, add or remove components via the user interface while ensuring that the user cannot add incompatible sensors.

[0234] E. Evaluate connectivity

[0235] Once the user is satisfied with the selection and placement of components on the floor plan, the system will evaluate the connectivity of the sensors to the panel. For wired sensors, the system calculates the distance from all wired components to the panel to estimate the amount and type of wiring required to support panel-to-component communication and power requirements. This also allows the system to estimate the cost of wiring for subsequent steps. For wireless components, the system estimates the connectivity from all wireless devices to the panel by using an empirical range estimation formula based on the type of building. The system highlights devices that do not have adequate connectivity and then recommends that the user add a wireless repeater or mobile device.

[0236] Figure 4A 4. A system 400 for automatically obtaining parameter values ​​and component pairing of sensor components in a building is shown in FIG. The system 400 consists of the following key modules: component placement 410; coverage estimation 420; application reasoning 430; component pairing 440; and parameter configuration 450.

[0237] i. Component placement

[0238] Component placement module 410 provides a mobile device-based user interface to facilitate placement of components on a floor plan. Components may include sensors such as motion detection sensors, door / window contact sensors, light detection sensors, glass break sensors, image sensors, video or infrared cameras, heat, flame, smoke or carbon monoxide detectors, sound detectors, shock detectors, vibration sensors, accelerometers, water or moisture detectors, humidity sensors, magnetic contacts, temperature sensors, photoelectric detectors, actuators (e.g., key fobs, garage door openers, sounders, emergency buttons, thermostats, lighting and appliance control modules, light switches, actuators, control panels, etc. The placement of components may be generated by an automated tool that is part of the system or accessible from a mobile device. It is also contemplated that the user may manually complete the placement of sensors. The user may include a customer and / or a sales representative. Module 410 presents a machine-readable floor plan on a visual display of the mobile device and allows the user to place a specific component from a library of available components at a specific location on the floor plan. For example, a user may drag a component from a library of components and drop the component icon as required on the floor plan. As Figure 4B Shown is an example floor plan 412 having components W1, W2, W3, M1, M2, D1, S1 placed therein. The module 410 also associates descriptive location tags, location IDs, and location coordinates 414 with the components within the floor plan.

[0239] ii. Coverage estimation

[0240] Once the components have been placed at different locations on the floor plan, the coverage estimation module 420 estimates the coverage area of ​​any given component based on its sensing modality and sensing range. Depending on the type of component, estimating the coverage area can be done in several different ways. For example, for a door / window contact sensor, the coverage area is estimated by a circle around the protected door or window, such as Figure 4B shown.

[0241] For motion sensors M1, M2 and cameras, the coverage area is estimated by: i) computing the visibility polygon, which is the set of all points visible from the mounting position; ii) computing the field of view range polygon for a given mounting position and orientation, which can be obtained from the manufacturer's specifications; and iii) obtaining the coverage area by taking the intersection of the visibility polygon and the field of view range polygon. For example, Figure 4B Describes the coverage area of ​​the motion sensor.

[0242] For glass break sensors and smoke sensors S1, based on Figure 4B Coverage pattern / range as specified in the manufacturer's specifications shown, coverage is estimated by calculating the range polygon for a given mounting location and orientation.

[0243] For lighting fixtures and luminaires L1, L2, the following is estimated: Figure 4B The coverage shown: i) computes a visibility polygon, which is the set of all points visible from the installation location; ii) computes a range polygon for illumination above a certain threshold (e.g. 50 lux or 10% of maximum lux) in a typical indoor environment, which can be obtained from the manufacturer's photometric specifications; and iii) obtains the coverage area by taking the intersection of the visibility polygon and the range polygon.

[0244] iii. Applied Reasoning

[0245] The application reasoning module analyzes the sensor type and the location or coverage of a given sensor on the floor plan to classify the sensors into the following categories: i) External protection: sensors with 100% coverage area on the outside of the building perimeter; ii) Internal protection: sensors with 100% coverage area on the inside of the building perimeter; iii) Perimeter protection: door / window contact sensors installed on perimeter doors or windows, e.g. Figure 4B W1, W2, W3, D1 shown, and glass break sensors with perimeter walls, doors or windows within their coverage area; and iv) Entry Protection: perimeter sensors mounted on doors, such as D1, including external sensors and internal sensors, with perimeter doors within their coverage area.

[0246] iv. Component pairing

[0247] The component pairing module 440 analyzes the relationship between the footprints of components on the floor plan to automatically pair components with each other based on component type so that an event triggering one of the paired components will actuate the other component to perform a specific action or correlate with an event generated by the other component.

[0248] To pair a given component with other components on the floor plan, module 440 calculates the overlap between the given component's footprint and the other components on the floor plan.

[0249] Module 440 may pair components under the following conditions:

[0250] o There is overlap between coverage areas. Module 440 pairs components, such as L1 and L2 or L1 and M2, if the overlap (i.e., intersection) exceeds a predefined threshold (e.g., 25% of the coverage area of ​​one component); and

[0251] o There is no overlap between coverage areas. If the distance between two coverage areas is less than a predefined threshold and there is a high likelihood of an intruder or occupant path between the two coverage areas, module 440 pairs components, such as M1 and M2. A probability distribution of intruder movement in an area may be estimated using a Markov decision process or the like, which allows the most likely intruder path or trajectory within a building to be generated. In an embodiment, other simulation techniques may be used to obtain a probability distribution of intruder or occupant movement.

[0252] Common pairing scenarios based on coverage area may include:

[0253] ○ Camera pairing - Module 440 pairs the camera with door / window contacts, motion sensors, glass break sensors, and smoke sensors such that an event detected by any of these paired components will cause the camera to record a video clip of a predefined duration with both a pre-trigger capture and a post-trigger capture of the scene. In order to pair the camera with the above components, module 440 calculates the overlap between the coverage areas of the camera and other sensors on the floor plan. If the overlap (i.e., intersection) with the coverage area of ​​a given sensor exceeds a predefined threshold, module 440 pairs the sensor with the camera, otherwise the presence of the high likelihood paths described above may be used to decide on the pairing. If the camera has a pan-tilt feature, module 440 may analyze the coverage overlap of all lens orientations;

[0254] o Light fixture pairing - Module 440 pairs light fixtures with door / window contacts, motion sensors, glass break sensors, so that an event detected by one of these paired components will cause the light fixture to turn on or off. To pair light fixtures with the above components, module 440 uses the above methods for camera pairing. To resolve situations where more than one possible trigger or actuator may exist, the distances between components derived from the floor plan description may be used to resolve conflicts, i.e. the closest suitable sensor is paired with the closest suitable light fixture;

[0255] ○ Motion Sensor Pairing - Module 440 pairs the motion sensor with a door / window contact, motion sensor, glass break sensor so that events detected by any of these paired components will be associated with events from the motion sensor to generate an alert or ignore the event. For example, if a smoke sensor trigger event is followed or preceded by an event from a paired motion sensor, the system can provide a local notification instead of generating an alert to the alarm monitoring center. To pair the motion sensor with the above components, module 440 uses the above method for camera pairing.

[0256] Module 440 may also identify the best matching component if the coverage area of ​​a given component overlaps with more than one other compatible component. Figure 4B As shown, in the case of pairing between motion sensors M1, M2 and luminaires L1, L2, module 440 uses only one motion sensor (whose coverage area has the highest overlap) to be paired with a given luminaire (eg, M1 and L2).

[0257] In an alternative embodiment, components may be grouped and paired regardless of coverage and based on location within a floor plan. In this embodiment, module 440 automatically groups components of the same type in the same room, components of the same type in the same floor, and components of the same type in a building so that grouping information for a primary control device (e.g., control panel C1) may be automatically populated. This allows the control panel to collectively control and respond to devices within the group. Sensors may be grouped in several different ways, including but not limited to all external sensors and actuators, all internal sensors and actuators, all sensors belonging to a given room, all sensors belonging to a given floor, all sensors of a given type belonging to a room, all actuators of a given type belonging to a room, all external actuators of a given type. In this embodiment, the binding and pairing of components is established based on their type, location ID or tag, and location separation.

[0258] Binding components can be based on:

[0259] ○ A given actuator is bound only to compatible sensors, as in the following two examples:

[0260] ■Light fixtures - compatible with events generated by wall switches, motion sensors, light sensors, door / window contacts, etc.;

[0261] ■ Video or image cameras - compatible with events generated by motion sensors, door / window contacts, smoke sensors, etc.;

[0262] ○ A given actuator is bound to a compatible sensor with the same position tag or ID;

[0263] ○ When multiple types of compatible sensors have the same location ID, but only a single actuator has the same location ID, if the pairing is centralized (i.e. the panel acts as an intermediary), then all compatible sensors are bound to the single actuator by default. In this case, when several sets of sensors can control a single device, any one of them may be able to turn the device on, but all of them need to agree to turn the device off. If the pairing is peer-to-peer, then only one device may be able to actuate on another;

[0264] o When multiple compatible sensor-actuator pairs have the same location ID, a given actuator is paired with the closest compatible sensor.

[0265] The above guidelines can be used to automatically pair sensors and actuators. For example, where there is a single sensor (e.g., wall switch) and actuator (e.g., light switch) in a room, one is bound to the other. If different switches and sensors are available, they are bound based on their characteristics or type. If there are several sensors and switches of the same type in the same room, they are separated in groups proportional to their number and bound to each other based on the distance they are separated, etc.

[0266] Regardless of whether the binding / pairing is based on coverage areas or location tags, module 440 automatically creates scenes based on the type of device. For example, all lights located outside turn on at 6 pm. Another example, a motion sensor triggered in the living room can turn on all lamps in the living room and start video recording with a camera in the living room, or trigger a motion sensor located outside to turn on all paired lamps.

[0267] Once the preferred groupings, bindings, and scenes are determined, they are displayed to the user to accept or edit as desired.

[0268] v.Parameter configuration module

[0269] The component parameter configuration (PC) module 450 analyzes the component types and coverage areas on the floor plan to automatically configure the components based on predefined characteristics. For entry delay, the module determines the time an authorized user must disarm the system after triggering the entry component without causing an alarm. For each component classified as perimeter protection but not as entry protection, the entry delay can be set to 0. For each sensor classified as entry protection in the system, the PC module 450 can first determine the distance between the entry component on the floor plan and the system disarming unit (usually located in the system control panel). The PC module 450 then calculates the entry delay based on the "distance to disarming unit" (d) and the "mode of entry" (which is indicated by the location tag associated with the entry component). For example, a door / window sensor installed in a garage will indicate a mode of car and foot entry. Therefore, the sensor will have a higher entry delay because it needs to provide enough time for the user to drive in, lock the car and then walk to the disarming unit.

[0270] For the departure delay, the module determines the time that an authorized user must leave the premises after arming the system without causing an alarm. For each sensor classified as perimeter protection but not as entry protection, the departure delay can be set to 0. For each sensor classified as "entry protection" in the system, the PC module 450 first determines the distance between the entry / exit component on the floor plan and the system arming unit (usually located in the system control panel). The PC module 450 then calculates the departure delay based on the "distance to the departure component" and the "exit mode" (which is indicated by the location tag associated with the departure component). For example, a door / window sensor installed in a garage will indicate a pattern of both foot and car entry. Therefore, the sensor will have a higher departure delay because it needs to provide the user with enough time to walk to the car, unlock the car, and drive away.

[0271] The PC module 450 also determines the appropriate sensing sensitivity of the component based on an analysis of their coverage areas on the floor plan for the motion sensor and the thermal sensor. For the motion sensor, the PC module 450 can adjust the sensitivity to match the coverage area with the visible area. More specifically, the PC module adjusts the sensitivity of the component to match the field of view range polygon size with the visibility polygon size from a given installation location. If the visibility polygon is smaller than the field of view range polygon, this will reduce the sensitivity, and if vice versa, it will increase the sensitivity. If a heating source (such as an HVAC vent or stove) is present in the coverage area of ​​the sensor, the PC module 450 will reduce the sensitivity of the motion sensor. For the smoke sensor, if any heating source (such as a stove, hot shower, etc.) is present in the coverage area of ​​the sensor (such as S1), the PC module 450 can adjust the sensitivity of the smoke sensor.

[0272] In addition to the above methods for sensitivity adjustment, in an alternative embodiment, the PC module 450 adjusts sensitivity based on location tags associated with the sensors. Instead of analyzing the coverage area where heating sources exist, the module simply uses the descriptive location tags for each different area in the building to infer objects that may fall within the coverage area of ​​the sensors installed in those areas. For example, a kitchen or synonym location tag indicates that a stove, microwave, refrigerator, blender / grinder, dishwasher, etc. are present in the area. A bathroom or synonym location tag indicates that a hot shower, etc. are present in the area. A furnace room or synonym location tag indicates that a furnace and other heating equipment are present in the area.

[0273] Those skilled in the art will appreciate that all of the above methods occur before the actual system is installed. In other words, the placement of components, estimation of coverage areas, and associating components are done virtually. Only when the system is installed and the actual sensors are associated with the virtual sensors, will all of the configuration details be moved from the virtual setup to the real building.

[0274] vi. Determine performance

[0275] After virtually placing the components using the IAC tool 102, Fig. 7A , which illustrates a processing system 700 that generally includes at least one processor 702 or processing unit or multiple processors, a memory 704, at least one input device 706, and at least one output device 708, which are coupled together via a bus or a set of buses 710. In a particular embodiment, the input device 706 and the output device 708 can be the same device. An interface 712 can also be provided for coupling the processing system 700 to one or more peripheral devices, for example, the interface 712 can be a PCI card or a PC card. At least one storage device 714 that houses at least one database 716 can also be provided. The memory 704 can be any form of memory device, such as volatile or non-volatile memory, solid-state storage devices, magnetic devices, etc.

[0276] The processor 702 may include one or more different processing devices, for example, to handle different functions within the processing system 700. The input device 706 receives input data 718 and may include, for example, a keyboard; a pointing device, such as a pen-like device or a mouse; an audio receiving device for voice control activation, such as a microphone; a data receiver device or antenna, such as a modem or a wireless data adapter, a data acquisition card, etc.

[0277] Input data 718 may come from different sources, such as keyboard instructions in conjunction with data received via a network. Reference to "accessing" data by processor 702 includes generating data, receiving data via transmission to processor 702 or via input from input device 706, or retrieving data by processor 702, such as from memory 104 or an external memory device or by requesting data from a software module or another processing device. Output device 708 produces or generates output data 720, and may include, for example, a display device or monitor, in which case the output data 720 is visual; a printer, in which case the output data 720 is printed; a port, such as a USB port; a peripheral component adapter; a data transmitter or antenna, such as a modem or a wireless network adapter, etc. Output data 720 may be different and come from different output devices, such as a visual display on a monitor in conjunction with data sent to a network. A user may view the data output or an interpretation of the data output on, for example, a monitor or using a printer. Storage device 714 may be any form of data or information storage device, such as volatile or non-volatile memory, a solid-state storage device, a magnetic device, etc.

[0278] In use, the processing system 700 is adapted to allow data or information to be stored in and / or retrieved from at least one database 716 via wired or wireless communication means. The interface 712 may allow wired and / or wireless communication between the processing unit 702 and peripheral components that may be used for specialized purposes. Preferably, the processor 702 receives instructions as input data 718 via an input device 706, and may display processing results or other outputs to a user by utilizing an output device 708. More than one input device 706 and / or output device 708 may be provided. It should be understood that the processing system 700 may be any form of terminal, server, dedicated hardware, etc.

[0279] It should be understood that processing system 700 can be part of a network communication system. Processing system 700 can be connected to a network, such as the Internet or a WAN. Input data 718 and output data 720 can be transmitted to other devices via the network. Transmission of information and / or data over a network can be accomplished using a wired communication device or a wireless communication device. A server can facilitate data transmission between a network and one or more databases. The server and one or more databases provide examples of information sources.

[0280] therefore, Fig. 7A The processing computing system environment 700 shown in the figure can be operated in a networked environment using logical connections to one or more remote computers. The remote computer can be a personal computer, a tablet device, a smart phone device, a server, a router, a network PC, a peer device or other public network node, and typically includes many or all of the above elements.

[0281] It should also be understood that Fig. 7A The logical connections depicted in the 700 include local area networks (LANs) and wide area networks (WANs), but may also include other networks, such as personal area networks (PANs). Such network environments are common in offices, enterprise-wide computer networks, intranets, and the Internet. For example, when used in a LAN network environment, the computing system environment 700 is connected to the LAN through a network interface or adapter. When used in a WAN network environment, the computing system environment typically includes a modem or other device for establishing communications over the WAN (such as the Internet). The modem, which may be located internally or externally, may be connected to the system bus via a user input interface or via another appropriate mechanism. In a networking environment, program modules or portions thereof depicted relative to the computing system environment 700 may be stored in a remote memory storage device. It should be understood that Fig. 7A The network connections shown are exemplary and other means of establishing a communications link between the computers may be used.

[0282] Fig. 7A It is intended to provide a brief general description of an illustration and / or a suitable example environment in which the embodiments of the present disclosure described below may be implemented. Fig. 7A The examples of suitable environments are not intended to suggest any limitation on the structure, scope of use or functionality of the embodiments of the present disclosure. A specific environment should not be interpreted as having any dependency or requirement related to any one component or combination of components shown in the example operating environment. For example, in certain cases, one or more elements of an environment may be considered unnecessary and omitted. In other cases, one or more other elements may be considered necessary and added.

[0283] In the following description, reference may be made to a computer system that is operated by one or more computing devices such as Fig. 7A Specific embodiments are described by symbolic representations of actions and operations performed by a computing system environment 700 of the computer system. Therefore, it should be understood that such actions and operations sometimes referred to as computer execution include the manipulation of electrical signals representing data in a structured form by the processor of the computer. This manipulation converts the data or keeps it in a position in the memory system of the computer, which reconfigures or otherwise changes the operation of the computer in a manner understood by those skilled in the art. The data structure in which the data is kept is the physical location of the memory with a specific property defined by the data format. However, although the embodiments are described in the above case, it is not meant to be limiting, as those skilled in the art will understand that the actions and operations described below can also be implemented in hardware.

[0284] It should also be understood that the embodiment can be implemented with many other general or special computing devices and computing system environments or configurations. Examples of well-known computing systems, environments and configurations applicable to the embodiment include, but are not limited to, personal computers, handheld or notebook devices, personal digital assistants, tablet devices, smart phone devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, networks, minicomputers, server computers, game server computers, web server computers, mainframe computers and distributed computing environments including any of the above systems or devices. The embodiment can be described in the general context of computer executable instructions (such as program modules) executed by a computer. Typically, a program module includes routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The embodiment can also be practiced in a distributed computing environment (wherein, tasks are performed by a remote processing device linked by a communication network). In a distributed computing environment, a program module can be located in local and remote computer storage media (including memory storage devices).

[0285] Figure 7B is shown as being provided by a wireless (eg, radio frequency (RF)) planning and performance analysis system (WiSPA) 724 (which is generally shown and discussed above). Fig. 7A Schematic diagram of a multi-dimensional building information model 722 generated and / or calibrated using an example computing system environment 700 implementation of a computer system (such as a computer system). A building information model 724 is a site-specific model of a particular building. The building information model 724 may be further used to predict the performance (e.g., communication range) of a wireless RF device based on the location of the wireless device within the building. The device may include sensors such as motion detectors, heat detectors, door / window contact sensors, heat, flame, smoke or carbon monoxide detectors, image sensors, video or infrared cameras, sound detectors, impact detectors, moisture detectors, humidity sensors, magnetic contacts, temperature sensors, photoelectric detectors, actuators such as key fobs, sounders, emergency buttons, thermostats, lighting and appliance control modules (communication devices with wireless communication capabilities or without sensing or actuation interfaces, such as system panels, repeaters, and mobile displays).

[0286] Figure 7CA flow chart illustrating a method for analyzing a multi-dimensional building information model and predicting the performance of a wireless RF system within a building by the WiSPA module 722. Operation 732 of the method includes accessing a building information model 724. At operation 734, the building information model 724 is used to identify wall types. Operation 734 includes analyzing, by the WiSPA module 722, a geometric representation of the building to identify different walls and ceilings within the building. Each wall is classified as an interior wall, a partition wall, or a perimeter wall. The walls are further associated with specific room types (e.g., bathrooms, bedrooms, kitchens, etc.). Identifying wall types may include modeling large objects as collections of walls. Such large objects may include, for example, stairs and structures with high ceilings.

[0287] Operation 736 includes determining (e.g., such as upon initialization or update following a construction project) values ​​of building material properties, such as wall material type and wall thickness, using the following information from the building information model 724:

[0288] ○ Wall Type - The materials used for walls of the same wall type (e.g., perimeter) are typically the same, while the materials for walls of different types (e.g., partition vs. perimeter) typically vary. Therefore, understanding the wall type is used to reduce the number of wall and material type combinations within a building;

[0289] ○ Geographic location of the building - Different parts of the world use different construction styles and materials. However, within a given location, the types of construction materials used are typically a small subset of materials used globally. Obtaining the geographic location thus helps to reduce the set of possible material options for a given building;

[0290] ○ Building Type - The building address can be searched in a third-party database to determine whether the building is a commercial space or a residential space. The building type information can indicate the construction materials and style based on whether the building is a residential building or a commercial building. The building type information helps select a more accurate material type for each wall in the building during initialization of the WiSPA module;

[0291] ○ Year of Construction - The types of construction materials used in buildings evolve over time. The initial material type can be identified based on the year of construction or major renovation operations.

[0292] At operation 738, wall properties are accessed and / or confirmed. The WiSPA module 722 provides a user interface via which a user can confirm or change wall properties, for example, to confirm or change automatically determined building material properties, such as wall material and thickness values. The user interface of the WiSPA module allows the user to modify the properties of each wall segment individually, or modify the properties of a group of walls simultaneously. The grouping can be based on, for example, a user selection of a wall type, such as perimeter wall, floor, partition, etc.

[0293] At operation 740, the performance and RF signal strength of the RF device are predicted in the building. This may include obtaining transmitter and receiver characteristics of the RF device. At operation 742, hardware specification characteristics of the RF device are accessed (e.g., input). The radio hardware specifications may be used to predict the performance of the RF device in the building performed at operation 740. The hardware specification characteristics include, for example:

[0294] ○Transmitter characteristics:

[0295] Transmit output power

[0296] Transmitter frequency

[0297] Antenna pattern

[0298] Internal circuit loss

[0299] ○Receiver characteristics:

[0300] Receiver sensitivity

[0301] Receiver frequency

[0302] Antenna pattern

[0303] Internal circuit loss

[0304] Analysis of transmitter and receiver characteristics may include analyzing transmit signal paths and installation directivity, and determining antenna directivity and gain direction based on antenna patterns and device installation.

[0305] The hardware specification characteristics may be accessed by the WiSPA module 722 at operation 742, such as by looking up a given RF device part number in an external specification database. Alternatively, the hardware specification characteristics may be manually entered or read and sent directly from an optical code (e.g., a QR code) or RF interface attached to the device.

[0306] Operation 740 may also include predicting received signal strength, which may include accessing estimates of RF signals received at various locations from a given RF transmitter device located at a given location within a building. For example, the transmitter device may be provided with a panel for the RF system. The panel may be located, for example, at a location such as near a door that is conveniently accessible to a user during entry and exit of the building. In an embodiment, the panel may be located at a control area (such as a control room) for a building or a group of buildings. The WiSPA module 722 may apply an RF propagation model to obtain estimates of RF signals received at various locations from a given RF transmitter device located at a given location within the building. (See Schaubach et al., "A raytracing method for predicting path loss and delay spread in microcellular environments," Proceedings of the IEEE Vehicular Technology Conference, 1992, and COST Action 231, Digital mobile radio towards future generation systems, Final Report, European Commission, Brussels, 1999, both of which are incorporated herein by reference in their entirety.) Listed below are the inputs accessible through the RF propagation model:

[0307] ○ 2D, 2.5D or 3D geometric representation of the building;

[0308] ○The material type and thickness of the building’s walls and floors;

[0309] ○Hardware characteristics of RF transmitter and receiver;

[0310] o RF transmitter device location, provided by the user or another design module (e.g., a Location and Measurement (LAM) module that takes location measurements from various sensors on the mobile device), and;

[0311] o RF receiver device locations, provided by the user, another design module (eg, a LAM module), or an assumed uniform distribution of receiver device locations across a building.

[0312] The RF propagation model uses these inputs to determine the path loss (PL) of the RF signal from the transmitter device to the various receiver device locations. dB The path loss value is used to calculate the received signal strength (RSS) at each receiver device location according to equation (15).dB ):

[0313] RSS dB =Gt dB -Lt dB +Pt dB -PL dB +Gr dB -Lr dB , among which (15)

[0314] (Gt dB , Gr dB ) characterizes the transmitter and receiver gains, where the antenna gain depends on the vertical and horizontal patterns defined for the antenna if a ray tracing model is used, and (Lt dB , Lr dB ) represents the internal circuit loss.

[0315] At operation 744, the wall properties are calibrated. To improve the prediction accuracy of step 740 of the received signal strength estimate provided by the building information model 724, the WiSPA module 722 obtains field signals, such as local RF signal measurements collected by the LAM module from the transmitter device location(s) (e.g., Wi-Fi routers), at operation 746. The WiSPA 722 may calculate a prediction error, which may include comparing the RF signal measurements obtained at various locations with the signal strength values ​​estimated for the same locations.

[0316] Reference now Fig.7D , a flow chart is shown that illustrates various example implementations that may be implemented at operation 744 to minimize prediction error when determining wall material type and thickness. It should be noted that it is not necessary Fig.7D The order of steps shown, therefore, in principle, the various steps may not be performed in the order shown. Certain steps may also be skipped, different steps may be added or replaced, or selected steps or groups of steps may be performed in a separate application following the embodiments described herein.

[0317] At operation 760, the wall material type and / or wall thickness may be input and changed, such as by a user operating a user interface provided by the WiSPA module 722. At operation 762, a new prediction estimate is obtained using the RF propagation model. At operation 764, a prediction error is calculated. At operation 766, a determination is made as to whether the current prediction error value is less than the previously calculated error value calculated at operation 764. If the determination at operation 766 is yes, then at operation 768, the current wall material type and wall thickness values ​​are saved. If the determination at operation 764 is no, then the method returns to operation 760.

[0318] The WiSPA module 722 uses the construction-specific building information obtained at operation 736 to minimize the search space for wall material type / thickness calibration. Thus, the calibration performed at operation 744 may include using an optimization method that compares measurements to predicted results from multiple simulation runs until the simulation model parameters are optimized to have highly correlated results with the measured data. A frequently used RF characteristic for optimization may be average total power. Other RF characteristics such as channel impulse response peak power or delay spread may be used to improve accuracy.

[0319] Once the wall material properties have been calibrated to minimize the RF received signal strength prediction error, the prediction error for each room can be calculated to estimate the prediction confidence level. Therefore, the prediction confidence level can be improved by calibrating the RF wall properties. The predicted signal strength for each room can be calculated according to operation 744 and compared with the actual measurement value. A higher prediction error for a room results in a lower prediction confidence level for that room. In other embodiments, the prediction confidence levels can be grouped into levels finer than rooms.

[0320] At operation 748, the WiSPA module 722 calculates recommended link margin values ​​for wireless devices deployed in the room using the predicted confidence level determined for each room. The recommended link margin value for the RF receiver in a particular room may be determined by analyzing the recommended link margin for the particular room and the installation location of the wireless device in the particular room. When a sufficient amount of sampled measurements are available, the time-varying variance from the collected sample measurements may be employed to fit a probability distribution that may be used to improve the accuracy of the confidence value. A lookup table may be employed to recommend link margin values. A higher link margin value may be recommended for devices in a room having a lower predicted confidence level than recommended for devices having a higher predicted level.

[0321] At operation 748, the RF connectivity level is calculated and a visualization (e.g., graphical depiction), graphical depiction is generated for display via a graphical user interface (GUI). The WiSPA module 722 calculates the multi-hop connectivity c_(i, k, j)(x, y, z) from a transmitter device i to a receiver device j at a location with coordinates (x, y, z) using a radio repeater k by employing the system of equations (16):

[0322]

[0323] in Know

[0324] RSS k is the predicted received signal strength of receiver device k as described with reference to operation 740,

[0325] S kis the receiver sensitivity of receiver device k as defined in the RF hardware datasheet, and

[0326] LM ik is the recommended margin value for links i and k.

[0327] The RF connectivity level can be determined by analyzing the predicted strength and link margin of RF signals and RF receiver properties of RF receivers located in different corresponding areas of the floor plan, wherein the RF connectivity level indicates the possibility of successful packet reception by a particular RF receiver on the floor plan from an RF transmitter device located at a particular location on the floor plan.

[0328] The connectivity levels of RF transmitter devices and RF receiver devices located at different locations on the floor plan can be determined by retrieving packet error rate thresholds from, for example, a computer database. The packet error rate thresholds can be adjusted using information related to the determined building type and installation locations where the RF transmitter and receiver devices are located.

[0329] Fig. 7E A first connectivity visualization 770 is shown that graphically indicates (eg, using color coding) a prediction of wireless system performance. Figure 7F 7 shows a second connectivity visualization 780 that can be generated by the WiSPA module 722 for display via the GUI, wherein the RF signal strength received at different locations from the RF transmitter devices located at one or more RF transmitter device locations is graphically indicated (e.g., using color coding). Even without technical knowledge, the user can use Fig. 7E and / or Figure 7F to select a location for placing the new receiver unit in the floor area.

[0330] Connectivity visualizations 770 and 780 include visual indications for a floor plan of a building, such as hashing or color coding, to indicate areas with good, poor, and non-existent connections for receiving signals from a transmitter device. In addition, wireless devices are shown, where the color coding indicates the quality of the connection between each wireless device and a wireless gateway in the building. In addition, links between different wireless devices are shown, where the color coding indicates the quality of the connection between the various devices. For example, the indication of connectivity may include a color-coded line from a wireless transmitter device to each RF receiver in a group of RF receivers. In another example, the indication of connectivity may include an indication of connectivity between a particular wireless transmitter device and each RF receiver in a group (e.g., all) of RF receivers.

[0331] The color-coded lines can also provide an indication of the parent-child relationship between transmitters and receivers by presenting lines connecting each receiver to only the transmitter that provides the highest level of connectivity. This feature can be used at commissioning to reduce wireless interference by configuring the repeater to only retransmit signals from the transmitter that provides the highest RF connectivity level and is present in the same area.

[0332] According to the above, a seamless method is provided for improving prediction performance using local RF signal strength measurements obtained in the field. Calibration of building materials is accelerated by utilizing geographic location information associated with the building. A user may provide input specifying the material of a given type of wall segment. By specifying the wall segment type, all wall segments of the specified type may be updated with the input material. Link margin recommendations may be provided for devices based on prediction confidence levels. An intuitive and non-technical visualization of connection quality between different wireless devices may be provided. By providing a GUI for display on a display device, peer-to-peer link quality between wireless devices may be visualized (e.g., presented as a graphical depiction) to indicate weak links.

[0333] refer to Figure 7G , shows a location-specific device performance estimation system 790 that uses quasi-static modeling, time-varying modeling, and device performance estimation to provide location-specific wireless device performance estimates for indoor building safety and security systems. In an embodiment, the performance estimates are used with system and customer requirements to provide automated climate control, safety, and security building system designs.

[0334] Devices included in system 790 may include motion detectors, heat detectors, smoke or carbon monoxide detectors, image sensors, video or infrared cameras, sound detectors, impact detectors, moisture detectors, humidity sensors, magnetic contacts, glass break detectors, temperature sensors, photoelectric detectors, key fobs, sounders, emergency buttons, thermostats, lighting and appliance control modules, system panels, repeaters, and mobile displays.

[0335] The sensor device may include, for example, an infrared sensor, a temperature sensor, a visible light sensor, a sound sensor, a smoke or carbon monoxide sensor, a magnetic field sensor, a vibration sensor, and / or a humidity sensor to sense at least one characteristic of the sensor's environment. The device also includes a transmitter, such as an RF transmitter, to transmit information, such as information related to the sensed characteristic. The device may also include an actuator, such as a power switch, a light bulb, a water valve, a fan, or a sprinkler. Device performance is related to the ability of the device to transmit information.

[0336] The quasi-static modeling module 791 (also referred to as the baseline modeling module 791) models the performance of each device given a quasi-static building environment (e.g., a vacant building with zero occupancy). The quasi-static modeling module 791 uses received device radio and battery specification information 792, floor plan layout information 793, and device location information 794 describing the device location within the building to perform modeling. The quasi-static modeling module 791 predicts the range of rapid RF signal fluctuations caused by interference between two or more versions of the same transmitted signal (which arrive at the receiver device through different paths due to the building geometry). These waves, called multipath waves, combine at the receiver antenna of the receiver device to give a composite signal (which may vary in amplitude and phase depending on the intensity distribution and relative propagation time of the waves). Even if the building is empty and the receiver device is stationary, the multipath signal amplitude and phase will vary over time due to slight environmental changes in the radio channel.

[0337] These rapid fluctuations in received signal strength due to multipath signal variations can be predicted from the channel's Power Delay Profile (PDP). Some well-known wave propagation models that can predict the PDP are ray tracing and dominant path models. Figure 7H An example graph 782 of a PDP is shown indicating the power strength of each signal received through a multipath channel as a function of time delay. A Ricean probability distribution can be empirically fitted from the PDP to estimate the multipath variance according to equation (17):

[0338]

[0339] Where parameter A represents the peak amplitude of the main signal, parameter σ represents the variance of the multipath, and I 0 is a modified Bessel function of the first kind and zeroth order.

[0340] The quasi-static battery model also predicts the power consumption of the device during the zero occupancy period due to periodic monitoring by the building system panel of the indoor building safety and security system to ensure that the system is operating at the desired performance level. This monitoring involves functions such as continuous sensing of the environment (e.g., for detection events) and periodic radio communication of monitoring alarms. The energy consumed in the quasi-static state, E, can therefore be calculated according to equation (18): qs :

[0341]

[0342] Among them, Po i is the power required by board component i (e.g., microcontroller, sensor, or radio), and t iis the amount of time such a component is required to perform for monitoring purposes per the manufacturer's data sheet (e.g., monitoring alarm frequency).

[0343] The time-varying modeling module 795 models the performance of each device in a time-varying environment due to the movement of people within the building. To perform the modeling, the time-varying modeling module 795 uses building occupancy pattern information 796 about occupant movement patterns in the building in addition to device radio and battery specification information 792, building floor plan layout information 793, and device location information 794. In an embodiment, statistics about the building occupancy pattern information 796 are automatically generated from previous device deployment data in similar buildings, floor plan room labels, and census data about building occupancy, and are used by the time-varying modeling module 795.

[0344] The time-varying modeling module 795 predicts the range of fluctuations in average signal strength caused by shadows of people in the radio path. Building system panels and devices (e.g., sensors, actuators) are often placed on ceilings, walls, windows, and doors where event detection is performed. From a radio propagation perspective, these locations can cause signals between building system panels and devices to cross over people in the building, which can cause slow changes in average signal strength.

[0345] In one embodiment, the slow signal changes are obtained offline by setting up the device at a typical device placement inside the building and measuring the human density during offline operation. These measurements are then used to fit a probability distribution function parameterized by the human density ρ and the distance d from the building system panel. As described by Klepal et al., "Influence of people shadowing on optimal deployment of WLAN access points", Proceedings of the 60th IEEE Vehicular Technology Conference, 1004, according to equation (19), the probability distribution P tv (σ, μ) can be used to obtain the signal shadow caused by human movement:

[0346] σ=log 7 (55ρ+1)0.5 (19)

[0347] μ(d,ρ)=(3dρ) 0.7

[0348] The time-varying modeling module 795 can also predict the power consumption of the device due to event detection operations related to the flow of people in the building. Each time a building sensor (such as a motion detector) detects a person in the environment, the building sensor detects, processes, and sends a radio signal packet to the panel, thus consuming more energy than in the quasi-static case. The sensor energy consumption in the event detection state can be calculated according to equation (20):

[0349]

[0350] where P is the probability of an event when the sensor detects the presence of people at density ρ and distance d.

[0351] The device performance estimation module 797 obtains the device performance information from the quasi-static modeling module 791 and the time-varying modeling module 795. A probability model (such as a Markov model) is implemented by the device performance estimation module 797.

[0352] Available Fig. 7A The example computing system environment 700 implements the device performance estimation system 790, specifically the quasi-static modeling module 791, the time-varying modeling module 795, and the device performance estimation module 797.

[0353] refer to Fig.7I , showing a schematic diagram 784 of a two-node Markov model. The two-node Markov model can use state probabilities S and (1-S) to model the performance of each device. For battery performance estimation, the state probability represents the amount of time that people cross the sensing area and trigger an alarm. On the other hand, for radio performance estimation, the state probability represents the amount of time that people cross the entire radio path. In an embodiment, the state probabilities are calculated offline using offline measurements obtained during offline measurement operations. As disclosed in Klepal et al., "Influence of people shadowing on optimal deployment of WLAN access points" (IEEE 60th Vehicular Technology Conference Proceedings, 1004), which is incorporated herein by reference in its entirety, the channel switching parameter value S(d, ρ) can be calculated at each device location based on people movement and link length using empirical equation (21):

[0354] S(d,ρ)=(1-ρ) 0.2d (twenty one)

[0355] The resulting location-specific device performance estimates γ for the radio link margin output 798 and the battery life output 799 may be obtained as a linear combination of the static and time-varying models using the location-specific handover values ​​according to equation (22):

[0356] γ(d,ρ)=S·P qs (σ, A)+(1-S)·P tv (σ,μ) (22) In an example method according to the present disclosure, the WiSPA module 722 receives specification information of a wireless RF device to be positioned in a building, floor plan layout information for the building, location information of the wireless RF device positioned on the floor plan, and building occupancy pattern information. The WiSPA module 722 uses the received information to provide switching parameters for a probability model. The probability model includes two states, including a quasi-static state corresponding to an idle state of the building, and a time-varying state corresponding to an occupied state of the building. The two states are used to model the effects of fluctuations in occupancy of people in the building. The switching parameters are used to indicate the amount of time that the probability model spends in each of the two states. The WiSPA module 722 uses the probability model to estimate the performance of the wireless RF device.

[0357] WiSPA 722 models the range of signal strength fluctuations associated with wireless RF devices in a quasi-static state and predicts the range of signal fluctuations attributable to interference between two or more versions of the same transmit signal arriving at one of the wireless RF devices via different transmit paths.

[0358] The WiSPA module 722 can predict the range of signal strength fluctuations due to shadowing of building occupants in the radio path under time-varying conditions.Signal shadowing associated with example building sensor placement and building occupant density can be obtained from offline signal measurement operations.

[0359] In a certain embodiment, WiSPA 722 may receive specification information of a battery-powered device to be located in a building, floor plan layout information for the building, location information on the floor plan where the battery-powered device is located, and building occupancy pattern information. WiSPA 722 may utilize the received information to provide a probabilistic model. WiSPA 722 may use the probabilistic model to estimate the remaining power level of the battery-powered device.

[0360] In an embodiment, when a quasi-static state is modeled, power consumption may be attributed to periodic radio communications of monitoring alarms. Further, in an embodiment, when a time-varying state is modeled, power consumption may be attributed to expected building occupant mobility according to building occupancy patterns, and may also be attributed to offline power signal measurement operations based on example building sensor placement and building occupant density.

[0361] In certain embodiments, WiSPA 722 may provide a GUI on a computer display that indicates a visualization of the remaining power level of the battery powered device. The visualization may include a color-coded indication of the area of ​​the floor plan where the corresponding battery powered device is located. The color code associated with the area may indicate the remaining power level based on the initial battery capacity of the battery of the associated battery powered device and the power consumed during the indicated time operation.

[0362] F. Generate a Quote

[0363] After all system components (including wired and wireless repeaters, where necessary) have been placed on the floor plan, the system 100 generates a quote detailing the cost of the components, wiring, and installation labor.

[0364] II. Installation and wiring

[0365] After the sale is closed, the IAC tool 102 schedules the installation work for the site associated with the customer account. Figure 1C 100. When the installer arrives at the site, the mobile device used by the installer and linked to the web-hosted IAC tool 102 will automatically retrieve the site's deployment plan based on its GPS location, address, or customer account details, as shown in block 132. The installer may include any third-party individual with access to the IAC tool 102. As shown in block 134, the installer is able to install and wire the building system components according to the layout plan and also track the status of the tasks involved.

[0366] As the installer installs, wires, and powers up the various components, the installer uses the mobile device to capture and verify the physical installation locations in the building by using a combination of an in-building positioning system (which locates the mobile device coordinates within the building) and the mobile device's camera (which captures pictures of the installed components), as shown in block 136. This allows the mobile device to tag pictures of installed components with their locations on the floor plan, compare them to the planned locations, and, if necessary, update the planned locations with the actual installation locations.

[0367] The installer also verifies the wiring and power-up of the components by capturing the LED activity on the installed components via a camera, as shown in block 138. This LED activity can be automatically analyzed and verified by using a simple video analysis algorithm. The installer also updates the web-hosted IAC tool 102 with the verification status of the installed and wired components.

[0368] III. Debugging and Testing

[0369] When the installation and wiring of the building system components are complete, the controller 114(s) of the building system 112 automatically searches for and requests the association of the selected components with the IAC tool 102 (described in further detail below). Figure 1D . This can be done by using a web service discovery protocol or by pre-configuring the controller 114 with a web address of a remote repository and a processing engine. Upon receiving an association request from the controller 114, as shown in boxes 142 and 143, the IAC tool 102 links the controller to the corresponding customer account and site. Linking the controller to the corresponding customer account can be based on a controller identifier, a site identifier received as part of the association request, by manually entering or by using a scanning tool to associate the controller serial number with the customer account. As shown in box 144, the IAC tool 102 will then be able to download the installation location and associated configuration information saved during the planning and quotation phase to the controller. In an alternative embodiment, the mobile device retrieves the configuration information and the controller address from the remote repository, and then directly connects to the controller at the site to download the configuration from the tablet computer to the controller. The IAC tool 102 also allows a commissioning technician, who may be a separate individual from the installer, to use a mobile device to add, modify and update configuration information while commissioning the system on site, as shown in box 146. The technician then uses the mobile device to test and verify the functionality of different components, as shown in box 148. This can be accomplished by sending test commands and receiving test messages from the mobile device to the controller, either directly or via the IAC tool 102. The mobile device also keeps track of the status of tasks involved in the debugging and testing phases of the process.

[0370] After completing the three stages of the process, the controllers installed within the building system continue to remain associated with the IAC tool 102 and send periodic diagnostic information, such as the quality of the local communication link between components, battery status, etc., to assist in the maintenance of the system. The IAC tool 102 also allows maintenance technicians to remotely view the complete system and deployment layout, configuration, and changes to the configuration when they need to troubleshoot any issues related to the system.

[0371] When subsequent installations are performed at the same site, sales and installation technicians can reuse the building information by logging into the IAC tool 102 and retrieving information associated with the customer account. The IAC tool 102 allows new components to be added to the existing system design, configurations to be added to the new components, the configurations of the existing components to be updated (if necessary), and the remaining stages of the IAC process to be performed for the new components as described above. The IAC tool 102 also allows sales personnel from a dealer or VAR organization to anonymize the building information for their customers and use it to solicit quotations from other third-party organizations for building system products that the organization has not sold. Sales personnel from other third-party organizations can use mobile devices connected to the IAC tool 102 to review and bid for the requested operations. The IAC tool 102 also allows searching for specific attributes (such as location, building area, number of rooms, presence of garages or basements, etc.) in the building information database for buildings and / or customers.

[0372] As mentioned above, in Figure 5A The system 500 and method 502 schematically illustrated in FIG. 5 provide tools using a floor plan-based user interface on a mobile device 504 (such as a tablet computer) to discover, locate, authorize, authenticate, and register distributed devices 506 using a system control panel 508. Example devices 506 include motion sensors, cameras, locks, etc. The tools disclosed herein allow for complete flexibility in the order in which the devices 506 are installed and powered on, as well as the order in which the steps of the method 502 are performed. Figure 5A The order of operations in method 502 shown is only an example. Those skilled in the art will readily appreciate that any other suitable order may be used. Wi-Fi, ZigBee, Z-Wave, or any other suitable dedicated or non-dedicated wired or wireless protocol may be used for communication between device 506 and panel 508, such as Figure 5A As indicated in .

[0373] The method 502 includes discovering a plurality of devices 506 at a central panel or server 508, as indicated in block 510. This allows the system control panel 508 to discover the distributed devices 506 in the field. In the case where the distributed devices 506 are installed and powered on before the system control panel 508, the devices may periodically broadcast join requests, which include a unique identifier (ID) of the device and a description of its operating situation (such as device type, application situation type, application input, application output, allowed content types, and query format, etc.), until a response is provided. Any battery-powered device in the devices 506 may conserve battery power by sleeping between periodic requests. The periodic request rate may be adjusted by the device 506 based on the available battery capacity and the number of registration attempts.

[0374] like Figure 5BAs shown, the system control panel 508 enters the listening mode immediately after it is installed and powered on. Upon receiving a join request from a device 506, the panel 508 displays a list of devices in its device table 520 (an example of which is shown in FIG. Figure 5C 506 is checked. If the device 506 is authorized, the panel responds to the device with a message to proceed with joining or authorization permission, which includes the panel's unique identifier (ID). If the device is not found in the device table, the panel 508 creates an entry for the device with a cleared authorization flag. If the device 506 is not authorized, the panel responds to the device with a wait for authorization message or a join rejection message.

[0375] Upon receiving the Wait for Authorization message, the device 506 may continue to broadcast join requests periodically to see if other panels respond, or begin sending unicast registration messages to the panel 508 while waiting for authorization. In some embodiments, joining the device 506 may not require sending a special join request. Any message (with a device unique ID) received for the first time from a new device 506 may be treated as a join request by the panel 508. If the device 506 is not authorized, the panel 508 simply ignores all subsequent messages from the device 506 until it is authorized. In another embodiment, when the device 506 receives the Wait for Authorization message, the device 506 enters a low power mode and waits for an external trigger (e.g., a button press, power re-apply) to restart the registration process.

[0376] In another aspect, the method 500 may include comparing the device identifier in each join request to the panel's device table 520, and sending a continue join signal only to the devices 506 marked as authorized in the device table 520. The table 520 with device identifiers may be created by the panel 508 when it begins receiving join requests, or may be pre-downloaded to the panel 508, for example, via the mobile device 504 or a server. It should be noted that in some cases a continue join message may not be sent, or may not need to be sent, because some devices are one-way.

[0377] The method 502 also includes locating the devices 506 as indicated by block 512. To locate the distributed devices 506 relative to each other within the building, the application in the mobile device 504 uses the building floor plan of the building as a basis. Figure 5A The floor plan 524 shown in FIG. 5 can be presented to scale on a tablet computer touch screen. This allows the coordinates of any given point on the floor plan 524 to be determined. The floor plan 524 can be divided into different physical areas (such as rooms or open areas) with unique location labels for each area (e.g., master bedroom, dining room, living room, etc.). Each area can be further divided into, for example, eight octants, which are composed of Figure 5DThe application on mobile device 504 allows the installer to plan the system installation by adding icons for different types of devices (e.g., identified by their stock keeping units (SKUs)) on floor plan 524 and then dragging and dropping devices 506 onto the locations on floor plan 524 where they should be installed within the building. Any other suitable input technique may also be used.

[0378] By way of example, the installer may walk to each location where the device 506 is to be installed; identify the location on the floor plan 524 on the mobile device application; and scan the barcode, QR code, RFID, etc. on the device 506 by clicking on the device's icon on the screen of the mobile device 504 while pointing the camera of the mobile device 504 at the barcode on the device 506, as shown in FIG. Figure 5E 506 . Manual entry of the code is also contemplated. This allows the application to simultaneously learn the device identifier and locate the device 506 via a single click on the floor plan 524. The barcode or the like provides the unique device identifier included by the device 506 in the join request message. Optionally, the code may also provide an encoded default encryption key (decoded by the mobile device application to authenticate the message from the device 506), the device type, and the device specification.

[0379] The mobile device application identifies the location on the floor plan 524 where the installer clicked to determine the corresponding location coordinates on the floor plan 524, and also identifies the area and octant surrounding the coordinates. The mobile device application then associates the above location information with the device identifier and other optional device information read from the code. The device location can be represented by the coordinates of the indicated location on the floor plan 524. If the device type of the scanned code does not match the device type of the clicked icon on the floor plan, the mobile device application can cause an error. The application also creates a unique name for the scanned device by concatenating the following information fields: <Area Location Tag><Closest Direction><Device Type>, for example, Kitchen NE Motion. The application then creates an entry for the newly scanned and located device 506 to enter the device table 520 stored on the mobile device 504, where the relevant fields are populated, such as Figure 5C shown.

[0380] In another embodiment, the application may allow the installer to scan multiple devices 506 with QR codes, etc., in succession or simultaneously. The application then sorts the device information in ascending or descending order of the device identifier field and automatically assigns the device information to the planned device icons on the floor plan 524 in a clockwise direction, such as starting from the entry door, while also matching the device type from the scanned code with the device type of the icon on the floor plan 524. This allows the installer to learn the devices off-site (e.g., backstage) before arriving at the site. For example, if the living room has two door / window contacts and a motion sensor, all three sensors can be scanned simultaneously by clicking on the living room displayed on the mobile device 504. The application may then start with the device icon closest to the entrance of the living room (e.g., door contact sensor) and assign it the smallest device ID, where the device type is a door / window contact. The application may then move in a clockwise direction, such as finding the next closest device icon in the living room, such as a motion sensor, and assign it the smallest device ID, where the device type is a motion sensor. The application can continue to move clockwise and assign the scanned device IDs to locations on the floor plan 524 until all the simultaneously scanned device IDs have been mapped. The application can display the device IDs below the corresponding icons to allow the installer to properly install the devices on site. Optionally, the application can connect to a printer to print the assigned device names on labels that can be attached to the devices with the corresponding device IDs.

[0381] The installer may start with an unplanned floor plan 524 without any device icons. When installing the device 506 on site, the installer may position the floor plan 524 appropriately and may then scan a QR code or the like on the device 506 by clicking on the floor plan location where the device is being installed. The application may then determine the device type (SKU) based on the scanned device information and place the corresponding device icon on the floor plan 524 at the clicked location. The application may then identify the location information, create a new entry in the device table 520 as described above, and populate the relevant fields with the location and device information of the scanned device 506, and may set the relevant authorization flag to yes. For example, the site address / location may be obtained and saved from a GPS receiver in the mobile device 504.

[0382] Positioning device 506 may include accepting input to mobile devices 504 specifying a respective location on floor plan 524 for each device 506. For example, positioning device 506 may include first locating mobile device 504 within floor plan 524, and then locating device 506 relative to mobile device 504, such as by processing the relative strength of its RF signal received by mobile device 504. Positioning device 506 may include displaying floor plan 524 on mobile device 504 and simply accepting input from a user (e.g., an installer) indicating the location of device 506 on floor plan 524.

[0383] Method 502 includes authorization of device 506 by mobile device 504 in communication with central panel 508 or server, such as Figure 5A and Figure 5B Device 506 may be authorized to register with panel 508 via a mobile device application. Figure 5A As shown, the application first discovers the panel 508 and logs into the panel 508 using a secure connection. The application then sends a copy of its device table 520 to the panel 508, which then authorizes all devices 506 for which the device ID field in the device table 520 is populated. Figure 5B As shown, the panel 508 also synchronizes its own local device table with the copy received from the mobile device application. Once the mobile device application logs into the panel 508, it can immediately authorize the devices 506 by sending the device IDs read from the barcode / QR code, etc. to the panel 508, for example, one at a time as they are scanned. It is also contemplated that a user (e.g., an installer) can authorize a device 506 by manually accepting a discovered device 506 via the panel user interface. Once a device 506 has been authorized on the panel, the panel 508 sends an authorization-granted message in response to any subsequent registration message or join request from the authorized device 506.

[0384] Method 502 may include verifying the link quality with each device 506 (as indicated in block 516) by comparing the signal quality between each device 506 and central panel 508 to a predefined threshold level before registering the device with central panel 508 (as indicated in block 518). Upon receiving an authorization-granted message from panel 508, as indicated in block 519, Figure 5B As shown, device 506 may initiate a link margin verification sequence to verify that the signal quality of messages exchanged between device 506 and panel 508 exceeds a predefined threshold level. If the link quality of device 506 is below the predefined level, panel 508 may deny device registration.

[0385] The method 502 also includes registering the device 506 with the central panel 508, as indicated in block 514. Once the link quality verification is successfully completed, as shown in FIG. Figure 5B As shown in , the panel 508 can complete the registration of the device 506 by sending a device registration message to the device 506. Thereafter, the process for establishing a key to ensure the communication between the panel 508 and the device 506 will begin.

[0386] Reference now Fig. 5F , locating the devices 506 can be automated. The process 600 for automating the locating of the devices 506 includes predicting a signal fingerprint of each device 506 based on the location of each respective device 506 on the floor plan 524, as indicated in block 602. The process 600 also includes measuring the signal fingerprint of each device, as indicated in block 604, and determining the location of each device 506 in the actual building based on a comparison of the predicted signal fingerprint and the measured signal fingerprint, as indicated in block 606.

[0387] This method of automatically mapping device IDs to their corresponding physical locations on the floor plan 524 occurs once the installer has installed the devices 506 on site and powered them up according to the planned deployment diagram. Hereinafter, fingerprints refer to the characteristics of the signals exchanged between one device 506 and the remaining devices 506 in the system plan. For example, the fingerprint of a given device 506 may be a vector containing the signal strengths received from all other devices 506 at that device at a particular time. Fig. 5F As shown, the system 100 can perform self-localization of the device 506 on-site through the following three steps: fingerprint prediction 602, fingerprint measurement 604, and matching and associating 606 the predicted fingerprint with the measured fingerprint.

[0388] System 500 can be used Fig. 5F The planned device deployment map 608 shown is used to generate a predicted RF fingerprint (in dBm) for each device location, such as Figure 5G 610, where the fingerprint of each device listed in the left column is represented by the RF signal strength value (in dBm) in the corresponding row, where the minimum signal strength value is -100 dBm and the maximum is no greater than 0 dBm. The fingerprint prediction module can use a site-specific RF propagation model, such as ray tracing, to predict the RF signal strength at a given device 506 location (e.g., LOC1, LOC2, etc.) received from all other devices 506 deployed at different locations depicted in the deployment diagram 608. For example, in Figure 5GThe row labeled "LOC0" in FIG508 shows the predicted RF signal strength seen by device 506 location LOC0 as received from all other devices 506 on deployment map 508 (i.e., at LOC1 through LOC8). The site-specific RF model uses floor plan 524, building material information, device RF output power, and antenna patterns to generate a high-fidelity estimate of the predicted fingerprint. In an embodiment, fingerprints collected by mobile devices 504 during system planning are employed to calibrate the site-specific RF propagation model that predicts fingerprints at planned device locations.

[0389] Once the installer has installed and turned on the devices 506 at their respective locations on site, the devices 506 can begin exchanging RF packets with each other. This allows the devices 506 to measure the received signal strength of packets from all neighboring devices 506 and create a measured RF fingerprint for its location. For example, Figure 5H Line 1 in the diagram shows that the device Dev 0 The measured signal strength from all other devices 506. Device 506 can periodically update and report its measured fingerprints to the gateway (or panel 508), which collects these measured fingerprint data and stores them in table 612, such as Figure 5H As shown, the minimum signal strength value is -100 dBm and the maximum value is no greater than 0 dBm. If system 100 includes battery-powered devices, these devices can save battery power by measuring the received signal strength from the reference packet used for clock synchronization (i.e., as a beacon) and entering a sleep power state during the remaining time.

[0390] Once the measurement fingerprint table 612 is created at the gateway or panel 508, the matching and association module calculates a one-to-one association between the device 506 and the location on the plan (e.g., LOC1, LOC2, etc.). This can be done by having the matching and association module maximize the predicted fingerprint (e.g., Figure 5G as shown) and measuring fingerprints (as Figure 5H In an embodiment, a cost matrix is ​​generated, which records the similarity of each predicted fingerprint to all measured fingerprints (or vice versa). The matrix columns are device ID Dev 0 To Dev n , the rows are locations (LOC0, LOC1, LOC2, etc.), and each entry in the matrix is ​​the similarity between the predicted fingerprint associated with the location relative to the row and the fingerprint of the device relative to the column. In one embodiment, this similarity is expressed as the Euclidian distance between the two received signal strength vectors relative to the predicted fingerprint and the measured fingerprint. Any existing correlation method can be used to measure the global correlation (one-to-one correlation between the device and the planned location), such as Fig.5IIteration operation shown. In an embodiment, dynamic programming (DP) is used to find the global association using the Hungarian Algorithm. It is necessary to iterate over all possible orders of the devices in the cost matrix. Fig.5I At each step of the iterative process depicted, a sequence of visit points is selected and compared with Figure 5H The relative values ​​of the matrix in are copied to the prediction (hollow) matrix ( Fig.5I The measurement (hollow) matrix ( Fig.5I The values ​​of the bottom matrix on the right are copied only once at the beginning of the process. Figure 5J The total cost of the best association is selected in the table 614 shown. The configuration of the device 506 (order in table 614) and the association corresponding to the lowest cost from all iterations can be selected as the global association. Figure 6 shows an example of a measured and predicted signal table taken from an actual deployment and a distance matrix after a single iteration.

[0391] If two locations or two devices 506 have similar fingerprints, the matching and association process 606 may not return the best association. This ambiguity can be mitigated or prevented by analyzing the predicted fingerprints at the time of planning (before deployment). This can be done by calculating a cross-similarity matrix (similar to the cost matrix used for association, but the columns and rows correspond to the predicted fingerprints). If at least two entries in any column / row are similar (fuzzy) and cannot be resolved by global allocation, the ambiguity will be detected. For example, the locations on the map 608 can be rearranged manually or automatically to make the fingerprints more recognizable. In another technique, one device 506 that causes ambiguity (in the case of two devices with similar fingerprints) can be ignored in the first association stage and added later when the remaining nodes are associated. This process can be generalized to any number of nodes that cause ambiguity.

[0392] Once the location of the devices 506 in the field has been identified, this information can be used to ensure that all planned devices have been installed in the field. The matching and association scheme also allows detection of whether any devices have been incorrectly swapped during installation. This can be done by reporting device type information along with the measured RF fingerprint data. The planned deployment diagram may have captured the device type of each planned device. If the measured fingerprint data reported from a device of type A matches the predicted fingerprint data at the location LOC1 where device type B is planned, this indicates that the device has been installed in the wrong location.

[0393] The location information can also be used to authorize a device 506 request to join the network. The measured fingerprint reported by the device can also be used to grant or deny its request to join the network. If the measured fingerprint reported by the device does not match any predicted fingerprint, this indicates that the device may be a rogue or from a neighboring deployment and does not belong to the network.

[0394] The location information can be used to remotely configure the device. Once the location of the device is established, the configuration of the device (which can be highly dependent on the location and device type) can be transmitted remotely from the system software to the device via the gateway.

[0395] Another use of location information is to locate non-wireless devices 506. If the deployed building system also includes wired devices 506 in addition to or instead of wireless devices 506, the self-location method described above can be adapted to include a mobile device 504, such as a handheld wireless device, like a smart phone or tablet computer, which exchanges messages with other wireless positioning devices temporarily installed in the building to facilitate positioning. After installation and powering up on site, the installer can scan the device 506, for example from a tag on the device 506. The mobile device 504 can then exchange packets with the temporary positioning wireless device installed in the building to measure the RF fingerprint at the installation location and append it to the ID scanned from the device, which serves as the MAC ID of the device. This allows the system to automatically map the scanned installation device to its location on the floor plan, rather than the installer doing it manually.

[0396] Location information can also be used to identify faulty devices and misconfigurations. If a building system 100 reports a fault linked to a specific device ID after commissioning, the location information allows the facility manager to identify the location of such device 506 within the building floor plan 524.

[0397] Another use of device location information is to enhance decision support systems. Once a device identifier is associated with its physical location, a geographic information system can be created. This system can be used, for example, to design decision support systems through big data analysis.

[0398] Yet another use of location information is alarm and event reporting that is more intuitive than conventional systems. Mapping device identifiers to physical locations can be used to display device data (such as alarms) on a floor plan-based graphical user interface (e.g., floor plan 524 on mobile device 504). This type of GUI can be more intuitive and interactive than conventional text-based reports, aiding the work of system operators.

[0399] The systems and methods described herein for registering distributed devices provide the following potential advantages over conventional systems: complete flexibility in the order in which devices are installed and powered up in the field, flexibility in the order in which device discovery, device location, and device authorization are performed, single-touch authorization and location of devices, eliminating the need to run back and forth between the panel and the device during registration, authorizing the device simultaneously to facilitate authorization and location of the device in the background, no registration required for devices with poor link quality ensuring fewer call-backs for installers from the field, and reducing time and errors in the device learning process.

[0400] A significant difference between the systems and methods for device location described herein and conventional systems is that they do not rely solely on measured received signal strength for directly calculating the device location (i.e., through triangulation techniques). Instead, a site-specific RF model is employed to generate a predicted RF fingerprint that is matched to the measured RF fingerprint by using a robust n-to-n matching and association scheme to determine the location of the device within the building. This reduces the location inaccuracy of existing received signal strength techniques because the problem is framed as an association problem rather than a pure location problem.

[0401] The systems and methods for positioning described herein provide the following potential advantages over conventional systems: automatically mapping wireless devices installed in a building to their physical locations on the building floor plan, thereby saving time and eliminating errors, supporting automatic positioning of non-wireless devices (wired or otherwise) through the use of handheld wireless devices and temporary positioning nodes, providing flexibility in how and when the system is installed and configured (for example, an installer may install, wire and power on a device and leave, or a commissioning technician may remotely commission a device at a later point in time), ensuring that devices are installed as planned, and ensuring that only valid devices join the network.

[0402] For some of the above-mentioned embodiments shown, it should be understood that the various non-limiting embodiments described herein can be used alone, in combination or selectively combined for specific applications. In addition, some of the various features of the above-mentioned non-limiting embodiments can be used without corresponding use of other described features. Therefore, the above description should be considered as merely illustrating the principles, teachings and exemplary embodiments of the present invention, rather than its limitation.

[0403] It should be understood that the above-described arrangements are merely illustrative of the application of the principles of the illustrated embodiments. Numerous modifications and alternative arrangements may be devised by those skilled in the art without departing from the scope of the illustrated embodiments, and the appended claims are intended to cover such modifications and arrangements.

Claims

1. A method for planning a building system, comprising: incorporating user requirements into floor plans accessed via mobile devices; generating an installation diagram corresponding to the locations of the components required by the user; as well as displaying, on the mobile device, placement of the component on the floor plan based on the specification of the component and the user request; The user requirements include specification of available component types, building system partitioning of the components or the building system as a whole, building system functionality, compliance with building standards and regulations, communication parameters, coverage parameters, detection performance, accuracy, battery life, cost and false alarm probability.

2. The method according to claim 1, wherein: The display includes an indication showing expected safety performance for a given placement of the component on the floor plan.

3. The method according to claim 2, wherein: The expected safety performance is shown based on the coverage provided by the components.

4. The method according to claim 2, wherein: The expected safety performance is shown based on the probability of detection of hazardous events provided by the components.

5. The method according to claim 2, wherein: The expected security performance is shown as the number of selected devices placed on the floor plan relative to the maximum number of locations requested for protection.

6. The method according to claim 1, wherein: The display includes instructions showing placement of the components on the floor plan with optimal component locations and orientations for intruder detection.

7. The method of claim 1, wherein the types and specifications of the available components are downloaded from a remote server database.

8. The method according to claim 1, wherein: The user requirements include constraints on the placement of the components.

9. The method according to claim 1, wherein: The component is selected from the group consisting of a PIR, a motion sensor, a contact sensor, a glass break sensor and a camera, an impact sensor, a panel, and a security sensor.

10. The method according to claim 1, wherein: Generating the installation diagram includes automatically placing the components based on a dynamic threat model for hazardous events, and the dynamic threat model includes a statistical probability distribution of the hazardous events moving throughout the floor plan.

11. The method according to claim 1, wherein: Generating the installation diagram includes automatically placing the components based on a dynamic threat model of hazardous events, and the dynamic threat model includes a relative threat level specified by the user for each room of the floor plan.

12. The method according to claim 1, wherein: Generating the installation diagram includes automatically placing the components based on a dynamic threat model of hazardous events, and wherein the dynamic threat model includes a relative threat level specified by the user for each entrance and exit of the floor plan.

13. The method according to claim 1, wherein: Generating the installation diagram includes automatically placing the components based on a dynamic threat model of dangerous events, wherein generating the installation diagram includes: repeatedly placing different combinations of said components throughout said floor plan; evaluating the performance of each combination using the dynamic threat model and component characteristics; and An optimal combination of the components is identified based on system cost and performance given a dynamic threat model.

14. The method according to claim 1, wherein: The generation further includes: placing at least one of the components in a room of the floor plan; evaluating the achieved performance based on the room's parameters and component characteristics; and The steps of placement and evaluation are repeated until the optimal placement for each room is achieved.

15. The method of claim 1, wherein: Generating the installation diagram includes placing the components based on connectivity between rooms.

16. The method of claim 1, wherein: Generating the installation diagram includes placing the components based on values ​​of content within rooms of the floor plan specified by the user.

17. The method of claim 1, wherein: Generating the installation diagram includes automatically placing the components based on a wireless connection model.

18. The method of claim 17, wherein: The wireless connection model includes a statistical probability distribution of correctly received data at each component location in the floor plan.

19. The method of claim 1, wherein: Generating the installation diagram further includes manually placing at least one of the components within the installation diagram using the mobile device.

20. The method of claim 19, wherein: Manual placement includes allowing the user to select the component from a predefined list and indicate the desired location.

21. The method of claim 20, wherein: The indicated desired location is selected from a set of valid installation locations and orientations identified based on component characteristics and floor plan characteristics.

22. The method of claim 21, wherein: Valid installation locations are displayed as a visual aid for manually placing the selected components based on the selected components and characteristics of the floor plan.

23. The method of claim 1, further comprising displaying a footprint of each of the components based on the placement, orientation, and tilt of the components.

24. The method of claim 1, wherein: At least one of the components is a glass break sensor, wherein optimal placement is based on the glass size of the window, the glass break sensor specifications, and the room layout.

25. The method of claim 1, wherein: At least one of the components is a motion sensor, wherein optimal placement is based on a direction of target movement relative to a location of the motion sensor, field of view, motion sensor specifications, and room layout.

26. The method of claim 1, wherein: At least one of the components is a motion sensor, wherein optimal placement is based on a size of an area covered by the motion sensor and a size of the room.

27. The method of claim 1, wherein: At least one of the components is a motion sensor, wherein optimal placement avoids overlap of the coverage area with components monitoring doors and windows of a room.

28. The method of claim 1, wherein: Optimal placement is based on associating sensor type with room type.

29. The method of claim 1, wherein: Optimal placement is based on correlating the sensor type with the type of room characteristics being monitored.

30. The method of claim 1, wherein: At least one of the components is a motion sensor placed in a room with two or more perimeter windows, a room with a maximum number of interior doors, or a room with more than a certain number of interior doors and no perimeter doors or windows.

31. An apparatus for planning a building system, comprising: Interactive user interface means for specifying building information and user requirements with visual displays; as well as a processor operably connected to the user interface device, the processor having a memory, wherein the memory includes instructions recorded thereon, which when executed by the processor cause the processor to perform the following operations: generating an installation diagram corresponding to the locations of the components required by the user; and displaying a visual display placement of the component on a floor plan based on the specification of the component and the user requirements, The user requirements include specification of available component types, building system partitioning of the components or the building system as a whole, building system functionality, compliance with building standards and regulations, communication parameters, coverage parameters, detection performance, accuracy, battery life, cost and false alarm probability.

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