Technologies for dynamic isopleth generation and analysis

Abstract

Technologies for dynamic isopleth generation includes a system with a computing device and one or more remote devices such as mobile computing devices, personal sensor devices, and autonomous sensor devices. The computing device collects multiple samples of an environmental property such as noise level from the remote devices. The samples include a measured value of the property value, a spatial location within a monitored location, and a timestamp. The computing device interpolates environmental property data over one or more time periods based on the samples. The computing device may generate an isopleth representation or a time series of isopleth representations of the monitored location in response to interpolating the environmental property data. The computing device may determine a total exposure along a path within the monitored location over the one or more time periods in response to interpolating the environmental property data. Other embodiments are described and claimed.

Description

Docket No. 55879-431420TECHNOLOGIES FOR DYNAMIC ISOPLETH GENERATION AND ANALYSISCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application Serial No. 63 / 716,361, filed November 5, 2024, the entire disclosure of which is hereby incorporated by reference.BACKGROUND

[0002] Occupational safety and health professionals typically collect occupational noise data, which is subsequently used to form maps (e.g., isopleth or contour maps) of noise in the work environment. Isopleth maps are a convenient way to visualize parameters that vary by location within a space. Typically, noise data is collected for a work environment by manually measuring sound levels with a sound level meter (SLM) at various points located on a predetermined, two-dimensional grid of the work environment. The measured sound level data may be used to manually generate isopleth maps of the facility by splining contours among the various data points. Accordingly, such isopleths typically provide a static view of the collected noise data as a discrete snapshot in time.SUMMARY

[0003] According to one aspect of the disclosure, a system for dynamic isopleth generation includes a computing device. The computing device includes a sample manager and an interpolation engine. The sample manager is configured to collect a plurality of samples of an environmental property in a monitored location. Each sample of the plurality of samples includes a measured value of the environmental property and a spatial location within the monitored location. The interpolation engine is configured to interpolate environmental property data based on the plurality of samples. In one embodiment, the spatial location is a two-dimensional location or a three-dimensional location.

[0004] In one embodiment, the measured value of the environmental property comprises a sound intensity level. In one embodiment, the measured value of the environmental property comprises a radiation level, a dust level, a chemical exposure level, a temperature, or an electromagnetic radiation level. In one embodiment, the measured value of the environmental property comprises a multi-dimensional value indicative of a plurality of environmental properties. In one embodiment, the plurality of environmental properties comprises sound intensity level and frequency.Docket No. 55879-431420

[0005] In one embodiment, the system further includes a remote sensor device. To collect the plurality of samples of the environmental properties includes to receive data indicative of the measured value from the remote sensor device. In one embodiment, the remote sensor device comprises a mobile computing device or a personal sensor device. In one embodiment, the remote sensor device comprises an autonomous sensor device. In one embodiment, the system further includes a remote device. To collect the plurality of samples of the environmental properties comprises to receive data indicative of the spatial location from the remote device. In one embodiment, the remote device comprises the remote sensor device. In one embodiment, the remote device is different from the remote sensor device.

[0006] In one embodiment, the system further includes a first remote sensor device and a second remote sensor device. To collect the plurality of samples of the environmental properties includes to receive first data indicative of the measured value from the first remote sensor device, to receive second data indicative of the measured value from the second remote sensor device, and to combine the first data and the second data.

[0007] In one embodiment, each sample of the plurality of samples further includes a timestamp. To interpolate the environmental property data based on the plurality of samples further includes to interpolate the environmental property data over a first time period based on the plurality of samples. In one embodiment, to interpolate the environmental property data over the first time period based on the plurality of samples includes to interpolate a value of the environmental property for a spatial location between samples of the sample data. In one embodiment, to interpolate the environmental property data over the first time period based on the plurality of samples further includes to interpolate a value of the environmental property for a time between samples of the sample data. In one embodiment, to interpolate the value of the environmental property includes to interpolate with a Kriging process.

[0008] In one embodiment, the computing device further includes an analysis engine configured to generate an isopleth representation of the environmental property data over the first rime period in response to interpolation of the environmental property. In one embodiment, the analysis engine is further configured to generate a time series of isopleth representations of the environmental property data over the first time period. In one embodiment, the computing device further includes an analysis engine configured to determine an uncertainty for the environmental property data at a first spatial location in the monitored location in response to interpolation of the environmental property data. In one embodiment, the computing device further includes an analysis engine configured to determine an exposure limit at a first spatial location within theDocket No. 55879-431420 monitored location based on the environmental property data at the first spatial location over the first time period in response to interpolation of the environmental property data.

[0009] In one embodiment, the computing device further includes a path monitor and an analysis engine. The path monitor is configured to determine a first individual path through the monitored location over the first time period. The analysis engine is configured to determine a total environmental property exposure value along the first individual path over the first time period based on the environmental property data in response to interpolation of the environmental property data. In one embodiment, the first individual path comprises a measured path, an estimated path, or a typical path.

[0010] In one embodiment, the computing device further includes an analysis engine configured to identify a first region of concern within the monitored location in response to interpolation of the environmental property data. The first region of concern is associated with environmental property values that have a predetermined relationship to a predetermined threshold. The sample manager is further configured to collect the plurality of samples with higher spatial resolution or temporal resolution within the first region of concern. In one embodiment, to collect the plurality of samples with higher spatial resolution or temporal resolution includes to direct an autonomous sensor device to the first region of concern. In one embodiment, to collect the plurality of samples with higher spatial resolution or temporal resolution includes to select a remote sensor device within the first region of concern.

[0011] According to another aspect of the disclosure, a method for dynamic isopleth generation includes collecting, by a computing device, a plurality of samples of an environmental property in a monitored location, wherein each sample of the plurality of samples comprises a measured value of the environmental property and a spatial location within the monitored location; and interpolating, by the computing device, environmental property data based on the plurality of samples. In one embodiment, the spatial location is a two-dimensional location or a three-dimensional location.

[0012] In one embodiment, the measured value of the environmental property comprises a sound intensity level. In one embodiment, the measured value of the environmental property comprises a radiation level, a dust level, a chemical exposure level, a temperature, or an electromagnetic radiation level. In one embodiment, the measured value of the environmental property comprises a multi-dimensional value indicative of a plurality of environmental properties. In one embodiment, the plurality of environmental properties comprises sound intensity level and frequency.Docket No. 55879-431420

[0013] In one embodiment, collecting the plurality of samples of the environmental properties includes receiving data indicative of the measured value from a remote sensor device. In one embodiment, the remote sensor device comprises a mobile computing device or a personal sensor device. In one embodiment, the remote sensor device comprises an autonomous sensor device. In one embodiment, collecting the plurality of samples of the environmental properties includes receiving data indicative of the spatial location from a remote device. In one embodiment, the remote device comprises the remote sensor device. In one embodiment, the remote device is different from the remote sensor device.

[0014] In one embodiment, collecting the plurality of samples of the environmental properties includes receiving first data indicative of the measured value from a first remote sensor device, receiving second data indicative of the measured value from a second remote sensor device, and combining the first data and the second data.

[0015] In one embodiment, each sample of the plurality of samples further includes a timestamp, and interpolating the environmental property data based on the plurality of samples further includes interpolating the environmental property data over a first time period based on the plurality of samples. In one embodiment, interpolating the environmental property data over the first time period based on the plurality of samples includes interpolating a value of the environmental property for a spatial location between samples of the sample data. In one embodiment, interpolating the environmental property data over the first time period based on the plurality of samples further includes interpolating a value of the environmental property for a time between samples of the sample data. In one embodiment, interpolating the value of the environmental property includes interpolating with a Kriging process.

[0016] In one embodiment, the method further includes generating, by the computing device, an isopleth representation of the environmental property data over the first time period in response to interpolating the environmental property. In one embodiment, the method further includes generating, by the computing device, a time series of isopleth representations of the environmental property data over the first time period. In one embodiment, the method further includes determining, by the computing device, an uncertainty for the environmental property data at a first spatial location in the monitored location in response to interpolating the environmental property data. In one embodiment, the method further includes determining an exposure limit at a first spatial location within the monitored location based on the environmental property data at the first spatial location over the first time period in response to interpolating the environmental property data.Docket No. 55879-431420

[0017] In one embodiment, the method further includes determining, by the computing device, a first individual path through the monitored location over the first time period; and determining, by the computing device, a total environmental property exposure value along the first individual path over the first time period based on the environmental property data in response to interpolating the environmental property data. In one embodiment, the first individual path comprises a measured path, an estimated path, or a typical path.

[0018] In one embodiment, the method further includes identifying, by the computing device, a first region of concern within the monitored location in response to interpolating the environmental property data, wherein the first region of concern is associated with environmental property values that have a predetermined relationship to a predetermined threshold; and collecting, by the computing device, the plurality of samples with higher spatial resolution or temporal resolution within the first region of concern. In one embodiment, collecting, by the computing device, the plurality of samples with higher spatial resolution or temporal resolution includes directing an autonomous sensor device to the first region of concern. In one embodiment, collecting, by the computing device, the plurality of samples with higher spatial resolution or temporal resolution includes selecting a remote sensor device within the first region of concern.BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The concepts described herein are illustrated by way of example and not by way of limitation in the accompanying figures. For simplicity and clarity of illustration, elements illustrated in the figures are not necessarily drawn to scale. Where considered appropriate, reference labels have been repeated among the figures to indicate corresponding or analogous elements.

[0020] FIG. 1 is a simplified block diagram of at least one embodiment of a system for dynamic isopleth generation and analysis in a monitored location;

[0021] FIG. 2 is a simplified block diagram of at least one embodiment of various environments that may be established by the system of FIG. 1 ;

[0022] FIG. 3 is a simplified flow diagram of at least one embodiment of a method for dynamic isopleth generation and analysis that may be executed by a computing device of the system of FIG. 1;

[0023] FIG. 4 is a simplified flow diagram of at least one embodiment of a method for environmental property data collection that may be executed in connection with the method of FIG. 3 by a computing device of the system of FIG. 1 ;Docket No. 55879-431420

[0024] FIG. 5 is a simplified flow diagram of at least one embodiment of a method for environmental property data analysis and visualization may be executed in connection with the method of FIG. 3 by a computing device of the system of FIG. 1 ;

[0025] FIG. 6 is a schematic diagram illustrating a dynamic isopleth chart that may be generated by the system of FIG. 1 according to the methods of FIGS. 3-5; and

[0026] FIG. 7 is a schematic diagram illustrating an environmental exposure limits chart that may be generated by the system of FIG. 1 according to the methods of FIGS. 3-5.DETAILED DESCRIPTION OF THE DRAWINGS

[0027] While the concepts of the present disclosure are susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will be described herein in detail. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims.

[0028] References in the specification to “one embodiment,-’ “an embodiment,” “an illustrative embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may or may not necessarily include that particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. Additionally, it should be appreciated that items included in a list in the form of “at least one A, B, and C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C). Similarly, items listed in the form of “at least one of A, B, or C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).

[0029] The disclosed embodiments may be implemented, in some cases, in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on a transitory or non-transitory machine- readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. A machine-readable storage medium may be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a machine (e.g., a volatile or non-volatile memory, a media disc, or other media device).Docket No. 55879-431420

[0030] In the drawings, some structural or method features may be shown in specific arrangements and / or orderings. However, it should be appreciated that such specific arrangements and / or orderings may not be required. Rather, in some embodiments, such features may be arranged in a different manner and / or order than shown in the illustrative figures. Additionally, the inclusion of a structural or method feature in a particular figure is not meant to imply that such feature is required in all embodiments and, in some embodiments, may not be included or may be combined with other features.

[0031] Referring now to FIG. 1, an illustrative system 100 for dynamic isopleth generation and analysis includes a computing device 102, which may be in communication with one or more mobile computing devices 104, personal sensor devices 106, and / or autonomous sensor devices 108 over a network 110. In use, as described further below, the computing device 102 collects samples of an environmental property such as sound intensity level (i.e., noise), radiation exposure, dust, chemical exposure, temperature, electromagnetic radiation level, or other variable in a monitored location. The monitored location may be an indoor or outdoor location such as a factory or other workspace. The environmental property values may be measured by sensors of the computing device 102 itself or, in some embodiments, various combinations of mobile computing devices 104, personal sensor devices 106, and / or autonomous sensor devices 108 located within the monitored location. The measured samples are associated with location data within the monitored location and timestamps. The computing device 102 interpolates environmental data over one or more time periods based on the measured samples. Using the interpolated environmental data, the computing device 102 analyzes environment variable data, for example by generating dynamic isopleth diagrams (i.e., contour maps or similar), determining environmental exposure for locations and / or time periods within the monitored location, generating exposure limit maps for the monitored location, and performing other analysis.

[0032] Therefore, by automating the collection of isopleth data, the system 100 dramatically reduces the data collection time required as compared to typical, largely manual approaches for measuring environmental properties. Accordingly, by reducing the required data collection time, the system 100 may provide a more uniform snapshot of environmental property values, especially compared to typical systems with lengthy data collection times. This may provide a more accurate measurement of environmental property values, particularly when the environmental property changes during the day. Further, by simplifying and reducing data collection time, the system 100 may perform more frequent data collection, which may improve worker safety, for example as compared to typical annual noise assessments. As an additionalDocket No. 55879-431420 advantage over existing systems, by automating data collection and interpolation, the system 100 facilitates dynamic, time-varying analysis and display of isopleths, for example displaying a time series of isopleths. The system 100 may also support additional analysis with increased accuracy for time-based environmental property values, such as determining worker exposure (time- weighted averages).

[0033] From a diagnostic perspective, the system 100 also helps occupational safety and health (OSH) professionals and engineers locate the noisiest processes and even detect when a machine may be in need of repair based on noise signatures (e.g., using noise signatures of machines to determine when tooling is worn and / or machines are out of balance). The system 100 can also be used to identify trends and measure abatement effectiveness, not just at the source of the noise (e.g., a new or repaired machine) but in the surrounding areas impacted by that noise. The system 100 can even be used to discover emerging problems such as machines failing (e.g., bad bearings, etc.). Some of these machine noise “signatures” may not be readily discernable or even audible to humans, allowing the system 100 to simultaneously monitor worker exposure, machine behavior, and other environmental changes.

[0034] Referring again to FIG. 1 , the computing device 102 may be embodied as any type of device capable of performing the functions described herein. For example, the computing device 102 may be embodied as, without limitation, a server, a rack-mounted server, a blade server, a workstation, a network appliance, a web appliance, a desktop computer, a laptop computer, a tablet computer, a smartphone, a consumer electronic device, a distributed computing system, a multiprocessor system, and / or any other computing device capable of performing the functions described herein. Additionally, in some embodiments, the computing device 102 may be embodied as a “virtual server” formed from multiple computing devices distributed across the network 110 and operating in a public or private cloud. Accordingly, although the computing device 102 is illustrated in FIG. 1 as embodied as a single computing device, it should be appreciated that the computing device 102 may be embodied as multiple devices cooperating together to facilitate the functionality described below. As shown in FIG. 1 , the illustrative computing device 102 includes a processor 120, an RO subsystem 122, memory 124, a data storage device 126, and a communication subsystem 128. Of course, the computing device 102 may include other or additional components, such as those commonly found in a server computer (e.g., various input / output devices), in other embodiments. Additionally, in some embodiments, one or more of the illustrative components may be incorporated in, or otherwise form a portion of, another component. For example, the memory 124, or portions thereof, may be incorporated in the processor 120 in some embodiments.Docket No. 55879-431420

[0035] The processor 120 may be embodied as any type of processor or compute engine capable of performing the functions described herein. For example, the processor may be embodied as a single or multi -core processor(s), digital signal processor, microcontroller, or other processor or processing / controlling circuit. Similarly, the memory 124 may be embodied as any type of volatile or non-volatile memory or data storage capable of performing the functions described herein. In operation, the memory 124 may store various data and / or software used during operation of the computing device 102 such as operating systems, applications, programs, libraries, and drivers. The memory 124 is communicatively coupled to the processor 120 via the I / O subsystem 122, which may be embodied as circuitry and / or components to facilitate input / output operations with the processor 120, the memory 124, and other components of the computing device 102. For example, the I / O subsystem 122 may be embodied as, or otherwise include, memory controller hubs, input / output control hubs, firmware devices, communication links (i.e., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.) and / or other components and subsystems to facilitate the input / output operations. In some embodiments, the I / O subsystem 122 may form a portion of a system-on-a-chip (SoC) and be incorporated, along with the processor 120, the memory 124, and other components of the computing device 102, on a single integrated circuit chip.

[0036] The data storage device 126 may be embodied as any type of device or devices configured for short-term or long-term storage of data such as, for example, memory devices and circuits, memory cards, hard disk drives, solid-state drives, or other data storage devices. The communication subsystem 128 of the computing device 102 may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications between the computing device 102, the mobile computing devices 104, the personal sensor devices 106, the autonomous sensor device 108, and / or other remote devices. The communication subsystem 128 may be configured to use any one or more communication technologies (e.g., wireless or wired communications) and associated protocols (e.g., Ethernet, Bluetooth®, Bluetooth Low Energy (BLE), Wi-Fi®, WiMAX, 3G LTE, 5G, etc.) to effect such communication.

[0037] As shown in FIG. 1, the computing device 102 may include a display 130. The display 130 may be embodied as any type of display capable of displaying digital images or other information, such as a liquid crystal display (LCD), a light emitting diode (LED), a plasma display, a cathode ray tube (CRT), or other type of display device. In some embodiments, the display 130 may be coupled to a touch screen to allow user interaction with the computing device 102.Docket No. 55879-431420

[0038] Each mobile computing device 104 may be embodied as a smartphone, a tablet computer, a laptop computer, a wearable device, a consumer electronic device, a distributed computing system, a multiprocessor system, and / or any other computing device capable of performing the functions described herein. As shown, each mobile computing device 104 includes components and devices commonly found in a smartphone or similar computing device, such as a processor 140, an I / O subsystem 142, a memory 144, a data storage device 146, a communication subsystem 148, and / or a display 150. Those individual components of the mobile computing device 104 may be similar to the corresponding components of the computing device 102, the description of which is applicable to the corresponding components of the mobile computing device 104 and is not repeated herein so as not to obscure the present disclosure.

[0039] As shown in FIG. 1, the mobile computing device 104 additionally includes one or more environmental sensors 152, which may be embodied as any electronic sensor or other component capable of measuring an environmental value within the monitored location. For example, the environmental sensors 152 may include one or more microphones capable of measuring sound intensity (e.g., sound pressure level or other measure of noise) and / or one or more cameras capable of capturing images within the monitored location. As another example, the environmental sensors 152 may include a radiation detector capable of measuring ionizing particles or other environmental radiation. As still further examples, the environmental sensors 152 may include dust sensors, portable gas monitors, chemical sensors, temperature sensors, electromagnetic radiation sensors, or any other sensor capable of measuring an environmental property within the monitored location.

[0040] The mobile computing device 104 further includes location circuitry 154. The location circuitry 154 may be embodied as any type of circuit capable of determining the precise or approximate position of the mobile computing device 104. For example, the location circuitry 154 may be embodied as a global positioning system (GPS) receiver, capable of determining the precise coordinates of the mobile computing device 104. In other embodiments, the location circuitry 154 may triangulate or trilaterate the position of the mobile computing device 104 using distances or angles to cellular network towers or other radio beacons with known positions, which may be provided by the communication subsystem 148. In other embodiments, the location circuitry 154 may determine the approximate position of the mobile computing device 104 based on association to wireless networks with known positions, using the communication subsystem 148. In some embodiments, the location circuitry 154 may be capable of determining the location of the mobile computing device 104 using a local positioning system 112 of the monitored location. The local positioning system 112 may include radio frequency beacons, visible lightDocket No. 55879-431420 beacons, visible markers, or other positioning devices installed in the monitored location. Additionally, or alternatively, in some embodiments the location circuitry 154 may include active positioning circuitry such as LIDAR, ultrawideband (UWB) radio, radar, sonar, structured light sensors, time of flight sensors, or other sensors or combinations of sensors (sensor integration) capable of detecting and / or ranging objects in the environment of the mobile computing device 104.

[0041] Each personal sensor device 106 may be embodied as a sound level meter (SLM), a dosimeter, a smartwatch, a wearable device, a consumer electronic device, a distributed computing system, a multiprocessor system, and / or any other computing device capable of performing the functions described herein. Accordingly, each personal sensor device 106 may include one or more environmental sensors 160, a communication subsystem 162 and / or location circuitry 164, as well as one or more components and devices commonly found in a wearable or similar computing device, such as a processor, an I / O subsystem, a memory, a data storage device, and / or a display. Those individual components of the personal sensor device 106 may be similar to the corresponding components of the computing device 102 and / or the mobile computing device 104, the description of which is applicable to the corresponding components of the personal sensor device 106 and is not repeated herein so as not to obscure the present disclosure.

[0042] Each autonomous sensor device 108 may be embodied as an autonomous ground vehicle, autonomous aerial vehicle, drone, multirotor aircraft, or other vehicle capable of autonomously navigating in the monitored location. Accordingly, each autonomous sensor device 108 may include one or more environmental sensors 180, a communication subsystem 182, and / or location circuitry 184, as well as one or more components and devices commonly found in an autonomous robot or similar computing device, such as a processor, an I / O subsystem, a memory, a data storage device, and / or a display. Those individual components of the autonomous sensor device 108 may be similar to the corresponding components of the computing device 102 and / or the mobile computing device 104, the description of which is applicable to the corresponding components of the autonomous sensor device 108 and is not repeated herein so as not to obscure the present disclosure.

[0043] Additionally, the autonomous sensor device includes a mobility subsystem 186, which any control system and / or motive system capable of autonomously controlling movement of the autonomous sensor device 108. In some embodiments, the mobility subsystem 186 may be capable of navigating the autonomous sensor device 108 through the monitored location (e.g., from a starting position to a destination position, along a predetermined route, along a dynamically determined route, or otherwise within the monitored location) and fully controllingDocket No. 55879-431420 acceleration, deceleration, steering, collision avoidance, and other navigation tasks. Additionally, or alternatively, in some embodiments the mobility subsystem 186 may be embodied as an autonomous flight control subsystem. In such embodiments, the mobility subsystem 186 may fully control rotor speed, altitude, attitude (e.g., pitch, roll, and yaw), ground speed, and other flying tasks of the autonomous sensor device 108.

[0044] As discussed in more detail below, the computing device 102, the mobile computing devices 104, the personal sensor devices 106, and the autonomous sensor devices 108 may be configured to transmit and receive data with each other and / or other devices of the system 100 over the network 110. The network 110 may be embodied as any number of various wired and / or wireless networks. For example, the network 1 10 may be embodied as, or otherwise include, a wired or wireless local area network (LAN), a wired or wireless wide area network (WAN), a cellular network, and / or a publicly-accessible, global network such as the Internet. As such, the network 110 may include any number of additional devices, such as additional computers, routers, stations, and switches, to facilitate communications among the devices of the system 100.

[0045] Referring now to FIG. 2, in the illustrative embodiment, the computing device 102 establishes an environment 200 during operation. The illustrative environment 200 includes a sample manager 202, an interpolation engine 204, a sensor manager 206, an analysis engine 208, a spatial locator 210, and a path monitor 212. The various components of the environment 200 may be embodied as hardware, firmware, software, or a combination thereof. As such, in some embodiments, one or more of the components of the environment 200 may be embodied as circuitry or a collection of electrical devices (e.g., sample manager circuitry 202, interpolation engine circuitry 204, sensor manager circuitry 206, analysis engine circuitry 208, spatial locator circuitry 210, and / or path monitor circuitry 212). It should be appreciated that, in such embodiments, one or more of those components may form a portion of the processor 120, the I / O subsystem 122, and / or other components of the computing device 102.

[0046] The sample manager 202 is configured to collect samples of an environmental property in a monitored location. Each sample includes a measured value of the environmental property and a spatial location within the monitored location. In some embodiments, each sample also includes or is otherwise associated with a timestamp. Of course, in some embodiments, the samples may include location and measured value without time to create a traditional spatial isopleth. The samples may be stored in sample data 214 managed or otherwise accessible by the computing device 102. The spatial location may be embodied as, for example, a two-dimensional location or a three-dimensional location. The measured value of the environmental property mayDocket No. 55879-431420 be embodied as a sound intensity level, a radiation level, a dust level, a chemical exposure level, a temperature, and / or an electromagnetic radiation level. In some embodiments, the measured value of the environmental property may be embodied as a multi-dimensional value for multiple environmental properties, for example sound intensity level and frequency.

[0047] In some embodiments, the samples of the environmental properties may be collected by receiving data indicative of the measured value from one or more remote sensor devices, such as a mobile computing device 104, a personal sensor device 106, and / or an autonomous sensor device 108. Similarly, collecting the samples may include receiving data indicative of the spatial location from a remote device, which may be the same device as the remote sensor device, or another device. In some embodiments, collecting the samples may include receiving data from multiple remote sensor devices and combining the received data.

[0048] In some embodiments, the sample manager 202 is further configured to collect samples with a higher spatial resolution or a higher temporal resolution within a specified region of concern within the monitored location, determined as described further below. Collecting the samples with higher spatial or temporal resolution may include directing an autonomous sensor device 108 to the region of concern and / or selecting a remote sensor device positioned within the region of concern.

[0049] The sensor manager 206 is configured to measure the measured value of a sample with an environmental sensor of the computing device 102. The spatial locator 210 may be configured to determine the spatial location of a sample with the local positioning system 112 of the monitored location or to determine the spatial location of a sample with a position sensor of the computing device 102 (e.g., using the location circuitry or another position sensor).

[0050] The interpolation engine 204 is configured to interpolate environmental property data based on the collected samples. In some embodiments, the environmental property data may be interpolated over one or more specified time periods based on the collected samples. Interpolating the environmental property data over a time period may include interpolating a value of the environmental property for a spatial location between samples or for a time between samples of the sample data 214. The value of the environmental property may be interpolated with a Kriging process.

[0051] The analysis engine 208 is configured to generate an isopleth representation of the environmental property data over the specified time periods in response to interpolation of the environmental property. In some embodiments, the analysis engine 208 is configured to generate a time series of isopleth representations of the environmental property data over the specified time period. In some embodiments, the analysis engine 208 is configured to determine anDocket No. 55879-431420 uncertainty for the environmental property data at spatial locations in the monitored location. In some embodiments, the analysis engine 208 is configured to determine an exposure limit at a spatial location within the monitored location based on the environmental property data at that spatial location over a specified time period. In some embodiments, the analysis engine 208 is configured to determine a total environmental property exposure value along a path through the monitored location over a specified time period based on the environmental property data. In some embodiments, the analysis engine 208 is configured to identify a region of concern within the monitored location. The region of concern is associated with environmental property values that have a predetermined relationship to a predetermined threshold (e.g., greater than the threshold, greater than or equal to the threshold, lower than the threshold, lower than or equal to the threshold, etc.).

[0052] The path monitor 212 is configured to determine an individual path through the monitored location over a specified time period. The determined individual path may include a measured path, an estimated path, or an average path. For example, as described further below, the path monitor 212 may monitor the motion of mobile computing devices 104 and / or personal sensor devices 106 over time through the monitored location, which may correspond to the motion of individuals. The path monitor 212 may determine the individual path based on monitored data from an individual device and / or aggregate monitored data (e.g., average data, typical data, etc.).

[0053] Still referring to FIG. 2, in the illustrative embodiment, the mobile computing device 104 establishes an environment 220 during operation. The illustrative environment 220 includes a sensor manager 222, a spatial locator 224, and a path monitor 226. The various components of the environment 220 may be embodied as hardware, firmware, software, or a combination thereof. As such, in some embodiments, one or more of the components of the environment 220 may be embodied as circuitry or a collection of electrical devices (e.g., sensor manager circuitry 222, spatial locator circuitry 224, and / or path monitor circuitry 226). It should be appreciated that, in such embodiments, one or more of those components may form a portion of the processor 140, the VO subsystem 142, and / or other components of the mobile computing device 104.

[0054] The sensor manager 222 is configured to measure the measured value of a sample with an environmental sensor 152 of the mobile computing device 104. The sensor manager 222 may be further configured to send the measured value to the computing device 102.

[0055] The spatial locator 224 is configured to determine the spatial location of a sample and may be further configured to send the spatial location to the computing device 102. In someDocket No. 55879-431420 embodiments, the spatial locator 224 is configured to determine the spatial location of the sample using the local positioning system 112 of the monitored location. In some embodiments, the spatial locator 224 is configured to determine the spatial location of a sample using a position sensor of the mobile computing device 104 (e.g., using the location circuitry 154 or another position sensor).

[0056] The path monitor 226 is configured to determine an individual path through the monitored location over a specified time period. For example, the path monitor 226 may monitor the motion of the mobile computing device 104 over time through the monitored location, which may correspond to the motion of an individual.

[0057] Still referring to FIG. 2, in the illustrative embodiment, the personal sensor device 106 establishes an environment 240 during operation. The illustrative environment 240 includes a sensor manager 242 and a spatial locator 244. The various components of the environment 240 may be embodied as hardware, firmware, software, or a combination thereof. As such, in some embodiments, one or more of the components of the environment 240 may be embodied as circuitry or a collection of electrical devices (e.g., sensor manager circuitry 242 and / or spatial locator circuitry 244). It should be appreciated that, in such embodiments, one or more of those components may form a portion of the environmental sensors 160, the communication subsystem 162, the location circuitry 164, and / or other components of the personal sensor device 106.

[0058] The sensor manager 242 is configured to measure the measured value of a sample with an environmental sensor 160 of the personal sensor device 106. The sensor manager 242 may be further configured to send the measured value to the computing device 102.

[0059] The spatial locator 244 is configured to determine the spatial location of a sample and may be further configured to send the spatial location to the computing device 102. In some embodiments, the spatial locator 244 is configured to determine the spatial location of the sample using the local positioning system 112 of the monitored location. In some embodiments, the spatial locator 244 is configured to determine the spatial location of a sample using a position sensor of the personal sensor device 106 (e.g., using the location circuitry 164 or another position sensor).

[0060] Still referring to FIG. 2, in the illustrative embodiment, the autonomous sensor device 108 establishes an environment 260 during operation. The illustrative environment 260 includes a sensor manager 262, a spatial locator 264, and autonomy logic 266. The various components of the environment 260 may be embodied as hardware, firmware, software, or a combination thereof. As such, in some embodiments, one or more of the components of the environment 260 may be embodied as circuitry or a collection of electrical devices (e.g., sensorDocket No. 55879-431420 manager circuitry 262, spatial locator circuitry 264, and / or autonomy logic circuitry 266). It should be appreciated that, in such embodiments, one or more of those components may form a portion of the environmental sensors 180, the communication subsystem 182, the location circuitry 184, the mobility subsystem 186, and / or other components of the autonomous sensor device 108.

[0061] The sensor manager 262 is configured to measure the measured value of a sample with an environmental sensor 180 of the autonomous sensor device 108. The sensor manager 262 may be further configured to send the measured value to the computing device 102.

[0062] The spatial locator 264 is configured to determine the spatial location of a sample and may be further configured to send the spatial location to the computing device 102. In some embodiments, the spatial locator 264 is configured to determine the spatial location of the sample using the local positioning system 112 of the monitored location. In some embodiments, the spatial locator 264 is configured to determine the spatial location of a sample using a position sensor of the autonomous sensor device 108 (e.g., using the location circuitry 184 or another position sensor).

[0063] The autonomy logic 266 is configured to control autonomous movement of the autonomous sensor device 108, for example using the mobility subsystem 186. In some embodiments, the autonomy logic 266 is configured to direct the autonomous sensor device 108 to sample within a region of concern, which may be specified by the computing device 102.

[0064] Although illustrated in FIG. 2 as establishing various environments with certain components, it should be understood that in other embodiments the entities of the system 100 may establish environments with different numbers and / or arrangements of components, which work together to perform the operations described herein. For example, in an embodiment a personal sensor device 106 may establish the sensor manager 242 but not the spatial locator 244, and the mobile computing device 104 may establish the spatial locator 224 but not the sensor manager 222. Continuing that example, the sample manager 202 of the computing device 102 may combine measured environmental property samples from the personal sensor device 106 with corresponding spatial information samples from the mobile computing device 104. Of course, different arrangements and / or combinations are possible in other embodiments.

[0065] Referring now to FIG. 3, in use, the computing device 102 may execute a method 300 for dynamic isopleth generation and analysis. It should be appreciated that, in some embodiments, the operations of the method 300 may be performed by one or more components of the environment 200 of the computing device 102 as shown in FIG. 2. The method 300 begins with block 302, in which the computing device 102 collects environmental property data in aDocket No. 55879-431420 monitored location over time and interpolates isopleth data. As described above, the environmental property data may include noise data, radiation data, dust level data, chemical exposure data, temperature data, electromagnetic radiation data, or any other data indicative of an environmental condition within the monitored location. The environmental property data may be collected by one or more remote devices within the monitored location, such as one or more mobile computing devices 104, personal sensor devices 106, and / or autonomous sensor devices 108. The isopleth data may include data indicative of a value (measured and / or interpolated) for the environmental property at each point, region, or other subdivision within the monitored location. The isopleth data may also include contour lines or other indications of locations within the monitored location having equal value of the environmental property. One potential embodiment of a method for collecting sample data and interpolating isopleth data is described further below in connection with FIG. 4.

[0066] In block 304, the computing device 102 determines and visualizes environmental property data over time with the interpolated isopleth data. The computing device 102 may, for example, generate and / or display time series of isopleth diagrams, allowing for dynamic analysis of environmental property data. The computing device 102 may receive user input specifying time period, location, and / or other parameters and may determine and / or visualize the environmental property data based on those supplied parameters. One potential embodiment of a method for determining and visualizing environmental property data over time is described further below in connection with FIG. 5.

[0067] After determining and visualizing the environmental property data, the method 300 loops back to block 302 to continue collecting and interpolating sample data. Accordingly, the system 100 may continually collect data, allowing for improving environmental property analysis and / or tracking trends or other changes in environmental property data over time.

[0068] Referring now to FIG. 4, in use, the system 100 may execute a method 400 for environmental property data collection. The method 400 may be executed in connection with block 302 of the method of FIG. 3, as described above. It should be appreciated that, in some embodiments, the operations of the method 400 may be performed by one or more components of the environment 200 of the computing device 102 as shown in FIG. 2. The method 400 begins in block 402, in which the computing device 102 samples an environmental property in a monitored location over time. As described above, the environmental property may include any physical property or other attribute of the monitored location, such as noise data, radiation data, dust level data, chemical exposure data, temperature data, electromagnetic radiation data, or any other data indicative of an environmental condition within the monitored location. TheDocket No. 55879-431420 environmental property may be sampled using one or more sensors of the computing device 102 or, as in the illustrative embodiment, using sensors included in one or more remote devices such as the mobile computing device 104, the personal sensor devices 106, and / or the autonomous sensor devices 108.

[0069] In some embodiments, in block 404 the computing device 102 collects an environmental property measurement, location data, and a timestamp for each sample of the environmental property value. The measurement data may be received from one or more remote devices, such as the mobile computing devices 104, the personal sensor devices 106, and / or the autonomous sensor devices 108. As an illustrative example, sound level measurements may be made using a calibrated microphone included in a smartphone, a tablet, or other remote device. Typical microphones included in smartphones can be calibrated to provide good estimates (e.g., within (±0.5 dBA) of noise levels. Accordingly, environmental property value measurements may be obtained using a smartphone executing an appropriate data collection application (e.g., a native application, a web application, a background script, or other data collection process executed by the smartphone). The measured value data may be sent to the computing device 102 for aggregation (e.g. via WiFi, BLE or other technique) or may be saved on the individual device for later aggregation.

[0070] Location of the remote device may be determined using the local positioning system 112, using a global positioning system, or other location system. By tracking the location of the remote device, the remote devices can be moved throughout the monitored location continuously. Measurements can thus be maintained throughout a work shift or other period (facilitated in some embodiments by autonomous sensor devices 108 circulating in the work areas, or by workers circulating throughout their workspace carrying the mobile computing devices 104 and / or personal sensor devices 106) to generate a dynamic isopleth map as a function of time. Moving measurements may also minimize errors associated with interpolation between static points as collected in traditional 2D isopleth map construction.

[0071] In some embodiments, in block 406, the computing device 102 determines a three- dimensional location for a sample. In such embodiments, isopleth maps can be generated in a “volumetric” fashion, having a height measure in the vertical axis in addition to the standard 2D maps. In the example of noise monitoring, this is typically performed at an assumed average ear height. This is accomplished by capturing the height as well as the (x, y) cartesian coordinates where the measurement was taken. This information is readily available from some LiDAR generated positioning, which may already include this 3D data. Having a volumetric map of measured phenomena may allow more precise exposure calculations for workers of differentDocket No. 55879-431420 heights and those working at different elevations throughout the workspace. Additionally, or alternatively, in other embodiments, rather than height, the third dimension may be another quantity such as time, or may be used to relay some other related information metric. For example, in some embodiments, the isopleth map may be generated for multi-dimensional values, which include values for multiple environmental properties such as sound intensity level and frequency. In those embodiments, one or more of the multiple dimensions may be represented as three-dimensional data. Additionally, or alternatively, in some embodiments other 3D graphs or visualizations may be used to display multi-dimensional values, such as waterfall graphs for noise data that can be analyzed by frequency exposure.

[0072] In some embodiments, in block 408 the computing device 102 combines measurements from multiple sample devices. For example, the computing device 102 may receive sample measurements from multiple mobile computing devices 104 and / or personal sensor devices 106 within the monitored location. In some embodiments, in block 410 the computing device 102 combines measured values and location data from separate devices. For example, the computing device 102 may combine measured values received from a personal sensor device 106 with corresponding location data received from a mobile computing device 104. It should be noted that the position sensing mechanism and the physical measurement device do not need to be integrated into a single device as long as they are positioned in proximity to each other. For example, an existing dosimeter may be modified to collect position and time data along with cumulative noise dose output. Additionally, or alternatively, in some embodiments, the dosimeter does not need to be modified to include location circuitry. Continuing that example, the location data may be provided by another system, such as LiDAR cameras mounted in the workplace. These cameras can track multiple workers wearing dosimeters at the same time capturing location information that could be synced to the data that it is collected by the dosimeter. This syncing is straightforward, requiring only that the time stamps for data collection be mapped to the time stamps from the worker location data.

[0073] In an illustrative embodiment, workers fitted with position sensors (e.g., coupled with their smartphone or some other device) can have their full shift noise exposure estimated while they perform their jobs and move about the workspace; these data obtained by the workers can be continuously collected and used to continuously update the isopleth. In this way, dosimetry can be collected or estimated every day rather than annually. Dynamic isopleths can be completed quickly and easily as plant conditions change (e.g., new equipment, new materials, changes in production levels, etc.).Docket No. 55879-431420

[0074] In some embodiments, in block 412 the computing device 102 concentrates or otherwise prioritizes sample collection in one or more regions of higher concern. A region of higher concern may be a part of the monitored location with measured values with a predetermined relationship to a threshold value (e.g., above or below the threshold value as appropriate). For example, in an embodiment, the region of higher concern may include parts of the monitored location with measured noise level values that exceed a predetermined threshold such as 80 dBA. Sample collection may be concentrated, for example, by increasing the number and / or frequency of samples collected from mobile computing devices 104, personal sensor devices 106, and / or other remote devices that are positioned within the region of concern. For nonautomated methods, a user mapping the monitored location can be directed in the priority directions by real-time measurement feedback. This differential sampling may help to more quickly identify the areas of highest concern without having to equally map the entire space. This also allows for prioritizing the areas that are known to have workers over those that do not have workers in them (e.g., robotic work cells that are shut down when workers enter the area). In some embodiments, in block 414 the computing device 102 may direct or otherwise control an autonomous sensor device 108. For example, the computing device 102 may instruct the autonomous sensor device 108 to collect sample data from within one or more regions of concern. Continuing that example, a robotic measuring device can differentially sample the space to better map the highest concentration areas of measured phenomena. Additionally, or alternatively, in some embodiments, the monitored location may include multiple fixed sensor devices (e.g., SLMs similar to the personal sensor devices 106) located at predetenuined locations throughout the monitored location, and capable of continually monitoring the environmental property to provide continuous real-time sampling data.

[0075] After sampling the environmental property data, in block 416 the computing device 102 interpolates values for the environmental property based on the sampled values. The computing device 102 performs a spatial interpolation process (e.g. Kriging) to estimate environmental property values. In some embodiments, in block 418 the computing device 102 interpolates values for locations between samples. In some embodiments, in block 420 the computing device 102 interpolates values for times between samples. By performing automated, mathematical interpolation of the environmental property, the computing device 102 may improve accuracy as compared to manual splining or other estimation approaches that may be used in prior art systems, particularly for logarithmically-represented data such as sound levels in decibels (dB).Docket No. 55879-431420

[0076] Additionally, or alternatively, in some embodiments, the collected samples, including measured environmental property values, location data, and timestamps, may be used to train one or more machine learning prediction models. Such machine learning models may include artificial neural networks (ANNs), support vector machines (SVMs), decision trees, or any other regression or classification model. Training a machine learning model on the collected sample may capture complex and subtle nonlinear behavior, complicated patterns, and other behavior captured in the sample data.

[0077] After performing interpolation of environmental property values, the method 400 is completed. As described above in connection with FIG. 3 and below in connection with FIG. 5, after performing interpolation the system 100 may perform analysis and / or visualization of the environmental property in the monitored location. Additionally, or alternatively, the system 100 may continue to execute the method 400 to continue collecting environmental property values.

[0078] Referring now to FIG. 5, in use, the system 100 may execute a method 500 for environmental property data analysis and visualization. The method 500 may be executed in connection with block 304 of the method of FIG. 3, as described above. It should be appreciated that, in some embodiments, the operations of the method 500 may be performed by one or more components of the environment 200 of the computing device 102 as shown in FIG. 2. The method 500 begins in block 502, in which the computing device 102 determines and displays an isopleth map for one or more requested times, intervals, schedules, or other time periods. As described above, an isopleth diagram may be embodied as a contour map of the monitored location, with contour lines or other indications of locations within the monitored location having equal value of the environmental location. The isopleth diagram may also include color coding or other indications of the particular value (interpolated or sampled) associated with each portion of the monitored location (e.g., each point, pixel, voxel, or other unit of the diagram). In some embodiments, the isopleth diagram may be presented as an interactive diagram, for example allowing a user to zoom, scroll, or otherwise investigate the environmental property data presented in the isopleth diagram. Any manner of post processing can be done on the captured data to determine trends or capture unexpected sources of noise that occur infrequently and may be often missed by one-time measurements of noise traditionally carried out on perhaps an annual schedule. An illustration of one potential embodiment of an isopleth diagram is described below in connection with FIG. 6. In some embodiments, in block 504 the computing device 102 displays a time series of isopleth maps. In some embodiments, in block 506 the computing device 102 displays uncertainty visually with the isopleth map.Docket No. 55879-431420

[0079] In block 508, the computing device 102 may determine and display exposure time limits for one or more locations within the monitored location. The computing device 102 may, for example, determine total exposure at each location based on the interpolated isopleth data and compare that exposure to a set of exposure limits. The computing device 102 may support multiple different sets of exposure limits, and a user may specify which exposure limits are applicable. An illustration of one potential embodiment of a diagram of exposure limits is described below in connection with FIG. 7.

[0080] In block 510, the computing device 102 may determine a specific individual path exposure based on the interpolated isopleth data. The individual path exposure represents total exposure along a particular path through the monitored location at a particular time or times associated with the path. In some embodiments, in block 512 the computing device 102 determines exposure for an individual path through the monitored location combined with particular timing, such as time of day, duration of path, number of repetitions of path, or other measurements of timing. As described further below, the particular path may represent the measured or estimated path of individuals (e.g., workers in a workplace) and / or may represent various potential scenarios (e.g., potential changes to schedules or workplace layouts).

[0081] In some embodiments, in block 514 the computing device 102 tracks a specific individual path through the monitored location. For example, the computing device 102 may track the location of a particular mobile computing device 104 and / or personal sensor device 106 carried by an individual throughout that individual’s workday. Accordingly, the system 100 allows for the determination of that individual’s exposure based on his or her unique “path” through the workplace. In some embodiments, in block 516 the computing device 102 estimates or averages an individual path through the monitored location. This estimation may be based on aggregate location data, occupancy data, or other determinations of likely paths of individuals. Accordingly, individual exposure may be estimated without requiring every individual to be tracked within the monitored location or otherwise requiring every individual to carry a dosimetry device.

[0082] In block 518, the computing device 102 may adjust sample collection based on interpolated isopleth values. In some embodiments, in block 520 the computing device 102 reroutes or otherwise directs an autonomous sensor device 108 to one or more regions of higher concern within the monitored location. In some embodiments, in block 522 the computing device 102 selects one or more distributed sensor devices (e.g., mobile computing devices 104, personal sensor devices 106, and / or autonomous sensor devices 108) within the regions of higher concern.Docket No. 55879-431420

[0083] After adjusting sample collection, the method 500 is completed. As described above in connection with FIGS. 3 and 4, after performing data analysis and / or visualization, the system 100 may perform additional environmental property data collection and interpolation. Additionally, or alternatively, the system 100 may continue to execute the method 500 to continue performing data analysis and / or visualization.

[0084] Referring now to FIG. 6, diagram 600 illustrates one potential embodiment of a sample isopleth diagram that may be generated and displayed by the system 100. The x- and y- axes of the diagram 600 represent two-dimensional geographic locations within a monitored location. Although the illustrative monitored location is a plain rectangle, in other embodiments the monitored location may be represented by a map, a floor plan, or other indication of the geography and / or internal configuration of the monitored location. The illustrative diagram 600 is an isopleth diagram of noise level (i.e., sound pressure levels), measured in decibels (dB). The measured or interpolated noise value in dB for each position within the monitored location is represented by color coding as indicated by legend 602. Additionally, the diagram includes contour lines along interpolated regions of equal sound pressure, represented as lighter lines 604. As an example, in the illustrative embodiment the diagram 600 represents environmental property values measured and / or interpolated at a particular time, which may be specified by a user. As another example, the diagram 600 may represent average or other aggregative environmental property values over a time period or specified schedule, which may be specified by the user. Continuing that example, the diagram 600 may represent environmental property values for a specified time of day (e.g., morning, afternoon, between specific times, etc.), and those values may be measured from a particular day and / or averaged over multiple days. As another example, the diagram 600 may represent a frame or other part of a time series of isopleth graphs, which may be animated over the specified time period. Accordingly, in the illustrative example, a safety professional or other user may use the diagram 600 to investigate varying noise levels in different locations within the monitored location over a typical workday. For example, the user may observe changes in noise level in the work environment as machines are activated or deactivated throughout the workday. As another example, the user may observe changes in noise level in the work environment at different times of the day, days of the week, or other schedules.

[0085] The diagram 600 further illustrates an individual path through monitored location, represented by dashed line 606. As described above, the path 606 may represent a measured path related to a particular individual or may be an estimated or average path. In addition to the spatial location of each point along the path 606, the path also includes a timing element. In some embodiments, the path 606 may include a measured timestamp for each location along the pathDocket No. 55879-431420606. Additionally, or alternatively, in some embodiments the timing may be estimated and / or aggregated. For example, the path 606 may be associated with an average speed or other value that may be used to determine the duration associated with the path 606. The computing device 102 may calculate exposure for the path 606 by determining the environmental property value for each point on the path 606 at the particular time associated with that point on the path 606. For example, a first-shift worker who typically works along the path 606 may have a different noise exposure than a second-shift worker who works along the same path 606, based on different noise levels measured at those different times. Also, such time-varying maps can be used to estimate worker exposures (time-weighted averages) over a typical workday without having to instrument every worker with a dosimeter.

[0086] Although illustrated in FIG. 6 as a 2-dimensional plot, as described above, in some embodiments the computing device 102 may generate and display a three-dimensional volumetric rendering of isopleth data. Volumetric 3D isopleths may provide a better source for estimating workers’ actual exposure when working at different levels in a workspace than current 2D isopleth maps.

[0087] Additionally, or alternatively, the disclosed system 100 focuses on the measurement of noise as an example, the system 100 can measure multiple phenomena simultaneously, therefore providing the inputs necessary for generating various isopleth exposure maps for the same time frame. By monitoring multiple phenomena simultaneously, certain emergent features (conditions that could not be illuminated by single measures alone) may be revealed. For example, multiple environmental conditions such as temperature, humidity, and pressure lead to the possibility for condensation which is problematic in many work environments.

[0088] Referring now to FIG. 7, diagram 700 illustrates one potential embodiment of a sample exposure limits diagram that may be generated and displayed by the system 100. The illustrative diagram 700 is generated based on the illustrative environmental property data of FIG. 6. Accordingly, the x- and y-axes of the diagram 700 represent the same monitored location as in FIG. 6. As described above, an exposure limit may be determined for each position within the monitored location based on the environmental property data and an appropriate exposure standard, such as an occupational safety noise exposure limit. Illustratively, the determined exposure limit for each position within the monitored location is represented by color coding as indicated by legend 702. The color coding indicates the maximum allowable time that an individual can spend at the particular location without exceeding noise limits. As shown, areas with high levels of noise intensity are marked as “no entry” areas, such as area 704. For example,Docket No. 55879-431420 noise in such areas may be at such a high level that there is no safe exposure time. Areas with low levels of noise intensity are marked as “unrestricted” areas, such as area 706. For example, such unrestricted areas may have no associated exposure time limit. Areas with intermediate levels of noise intensity are marked as “limited” areas, such as area 708, and may have an associated duration or other exposure limit. As described above, in some embodiments, areas marked as “no entry” (e.g., the area 704 and similar areas) may be identified as regions of concern, and the system 100 may perform further sampling in those regions of concern at a higher spatial and / or temporal resolution.

Claims

Docket No. 55879-431420WHAT IS CLAIMED IS:

1. A system for dynamic isopleth generation, the system comprising a computing device that includes: a sample manager configured to collect a plurality of samples of an environmental property in a monitored location, wherein each sample of the plurality of samples comprises a measured value of the environmental property and a spatial location within the monitored location; and an interpolation engine configured to interpolate environmental property data based on the plurality of samples.

2. The system of claim 1, wherein the spatial location comprises a two-dimensional location or a three-dimensional location.

3. The system of claim 1, wherein the measured value of the environmental property comprises a sound intensity level.

4. The system of claim 1 , wherein the measured value of the environmental property comprises a radiation level, a dust level, a chemical exposure level, a temperature, or an electromagnetic radiation level.

5. The system of claim 1, wherein the measured value of the environmental property comprises a multi-dimensional value indicative of a plurality of environmental properties.

6. The system of claim 5, wherein the plurality of environmental properties comprises sound intensity level and frequency.

7. The system of claim 1, further comprising a remote sensor device, wherein to collect the plurality of samples of the environmental properties comprises to receive data indicative of the measured value from the remote sensor device.Docket No. 55879-4314208. The system of claim 7, wherein the remote sensor device comprises a mobile computing device or a personal sensor device.

9. The system of claim 7, wherein the remote sensor device comprises an autonomous sensor device.

10. The system of claim 7, further comprising a remote device, wherein to collect the plurality of samples of the environmental properties comprises to receive data indicative of the spatial location from the remote device.

11. The system of claim 10, wherein the remote device comprises the remote sensor device.

12. The system of claim 10, wherein the remote device is different from the remote sensor device.

13. The system of claim 1, further comprising a first remote sensor device and a second remote sensor device, wherein to collect the plurality of samples of the environmental properties comprises to: receive first data indicative of the measured value from the first remote sensor device, receive second data indicative of the measured value from the second remote sensor device, and combine the first data and the second data.

14. The system of claim 1, wherein: each sample of the plurality of samples further includes a timestamp; and to interpolate the environmental property data based on the plurality of samples further comprises to interpolate the environmental property data over a first time period based on the plurality of samples.Docket No. 55879-43142015. The system of claim 14, wherein to interpolate the environmental property data over the first time period based on the plurality of samples comprises to interpolate a value of the environmental property for a spatial location between samples of the sample data.

16. The system of claim 15, wherein to interpolate the environmental property data over the first time period based on the plurality of samples further comprises to interpolate a value of the environmental property for a time between samples of the sample data.

17. The system of claim 15, wherein to interpolate the value of the environmental property comprises to interpolate with a Kriging process.

18. The system of claim 14, wherein the computing device further comprises an analysis engine configured to generate an isopleth representation of the environmental property data over the first time period in response to interpolation of the environmental property.

19. The system of claim 18, wherein the analysis engine is further configured to generate a time series of isopleth representations of the environmental property data over the first time period.

20. The system of claim 14, wherein the computing device further comprises an analysis engine configured to determine an uncertainty for the environmental property data at a first spatial location in the monitored location in response to interpolation of the environmental property data.

21. The system of claim 14, wherein the computing device further comprises an analysis engine configured to determine an exposure limit at a first spatial location within the monitored location based on the environmental property data at the first spatial location over the first time period in response to interpolation of the environmental property data.Docket No. 55879-43142022. The system of claim 14, wherein the computing device further comprises: a path monitor configured to determine a first individual path through the monitored location over the first time period; and an analysis engine configured to determine a total environmental property exposure value along the first individual path over the first time period based on the environmental property data in response to interpolation of the environmental property data.

23. The system of claim 22, wherein the first individual path comprises a measured path, an estimated path, or a typical path.

24. The system of claim 1, wherein the computing device further comprises: an analysis engine configured to identify a first region of concern within the monitored location in response to interpolation of the environmental property data, wherein the first region of concern is associated with environmental property values that have a predetermined relationship to a predetermined threshold; and wherein the sample manager is further configured to collect the plurality of samples with higher spatial resolution or temporal resolution within the first region of concern.

25. The system of claim 24, wherein to collect the plurality of samples with higher spatial resolution or temporal resolution comprises to direct an autonomous sensor device to the first region of concern.

26. The system of claim 24, wherein to collect the plurality of samples with higher spatial resolution or temporal resolution comprises to select a remote sensor device within the first region of concern.

27. A method for dynamic isopleth generation, the method comprising:Docket No. 55879-431420 collecting, by a computing device, a plurality of samples of an environmental property in a monitored location, wherein each sample of the plurality of samples comprises a measured value of the environmental property and a spatial location within the monitored location; and interpolating, by the computing device, environmental property data based on the plurality of samples.

28. The method of claim 27, wherein the spatial location comprises a two-dimensional location or a three-dimensional location.

29. The method of claim 27, wherein the measured value of the environmental property comprises a sound intensity level.

30. The method of claim 27, wherein the measured value of the environmental property comprises a radiation level, a dust level, a chemical exposure level, a temperature, or an electromagnetic radiation level.

31. The method of claim 27, wherein the measured value of the environmental property comprises a multi-dimensional value indicative of a plurality of environmental properties.

32. The method of claim 31, wherein the plurality of environmental properties comprises sound intensity level and frequency.

33. The method of claim 27, wherein collecting the plurality of samples of the environmental properties comprises receiving data indicative of the measured value from a remote sensor device.

34. The method of claim 33, wherein the remote sensor device comprises a mobile computing device or a personal sensor device.Docket No. 55879-43142035. The method of claim 33, wherein the remote sensor device comprises an autonomous sensor device.

36. The method of claim 33, wherein collecting the plurality of samples of the environmental properties comprises receiving data indicative of the spatial location from a remote device.

37. The method of claim 36, wherein the remote device comprises the remote sensor device.

38. The method of claim 36, wherein the remote device is different from the remote sensor device.

39. The method of claim 27, wherein collecting the plurality of samples of the environmental properties comprises receiving first data indicative of the measured value from a first remote sensor device, receiving second data indicative of the measured value from a second remote sensor device, and combining the first data and the second data.

40. The method of claim 27, wherein: each sample of the plurality of samples further includes a timestamp; and interpolating the environmental property data based on the plurality of samples further comprises interpolating the environmental property data over a first time period based on the plurality of samples.

41. The method of claim 40, wherein interpolating the environmental property data over the first time period based on the plurality of samples comprises interpolating a value of the environmental property for a spatial location between samples of the sample data.Docket No. 55879-43142042. The method of claim 41 , wherein interpolating the environmental property data over the first time period based on the plurality of samples further comprises interpolating a value of the environmental property for a time between samples of the sample data.

43. The method of claim 41, wherein interpolating the value of the environmental property comprises interpolating with a Kriging process.

44. The method of claim 40, further comprising generating, by the computing device, an isopleth representation of the environmental property data over the first time period in response to interpolating the environmental property.

45. The method of claim 44, further comprising generating, by the computing device, a time series of isopleth representations of the environmental property data over the first time period.

46. The method of claim 40, further comprising determining, by the computing device, an uncertainty for the environmental property data at a first spatial location in the monitored location in response to interpolating the environmental property data.

47. The method of claim 40, further comprising determining an exposure limit at a first spatial location within the monitored location based on the environmental property data at the first spatial location over the first time period in response to interpolating the environmental property data.

48. The method of claim 40, further comprising: determining, by the computing device, a first individual path through the monitored location over the first time period; andDocket No. 55879-431420 determining, by the computing device, a total environmental property exposure value along the first individual path over the first time period based on the environmental property data in response to interpolating the environmental property data.

49. The method of claim 48, wherein the first individual path comprises a measured path, an estimated path, or a typical path.

50. The method of claim 27, further comprising: identifying, by the computing device, a first region of concern within the monitored location in response to interpolating the environmental property data, wherein the first region of concern is associated with environmental property values that have a predetermined relationship to a predetermined threshold; and collecting, by the computing device, the plurality of samples with higher spatial resolution or temporal resolution within the first region of concern.

51. The method of claim 50, wherein collecting, by the computing device, the plurality of samples with higher spatial resolution or temporal resolution comprises directing an autonomous sensor device to the first region of concern.

52. The method of claim 50, wherein collecting, by the computing device, the plurality of samples with higher spatial resolution or temporal resolution comprises selecting a remote sensor device within the first region of concern.

53. A computing device comprising: a processor; and a memory having stored therein a plurality of instructions that when executed by the processor cause the computing device to perform the method of any of claims 27-52.Docket No. 55879-43142054. One or more machine readable storage media comprising a plurality of instructions stored thereon that in response to being executed result in a computing device performing the method of any of claims 27-52.

55. A computing device comprising means for performing the method of any of claims 27-52.

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