Sensing system and utility meter assembly

Abstract

A utility metering device includes a housing positioned to communicate with a utility distribution system. The utility sensor unit is configured to measure at least one parameter of the utility distribution system to obtain utility system data. A gas sensor unit is in communication with an external environment external to the housing and is configured to obtain gas data related to an air quality of the external environment. One or more processors are in communication with the utility sensor unit and the gas sensor unit. A memory unit is in communication with the one or more processors. The communication unit is configured to transmit and receive data over a network. Multiple gauges may be used to form a sensing system to detect environmental conditions.

Description

[0001] Related applications

[0002] This application is based upon and claims priority from provisional application serial number 63 / 433,248, filed on December 16, 2023, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure relates to sensing systems and utility meter assemblies, and more particularly to implementing gas sensors within a network of utility meters (eg, electricity meters). Background Art

[0004] Utility grids transport utilities (e.g., gas, electricity, water, etc.) from generation or distribution facilities to end users around the world. Meters are placed throughout these grids to monitor and record distribution data. Networked or smart utility meters are becoming increasingly common in utility systems. These networked meters allow data to be provided to central utility systems over the internet for processing, billing, maintenance, and more.

[0005] One example is the use of smart meters in power distribution systems. Smart meters can be connected to the grid to monitor power distribution data within the system. These meters can be powered directly from the grid, eliminating the need for an external power source. This can improve meter uptime and communication capabilities, especially in remote areas. Summary of the Invention

[0006] According to certain implementations, a utility metering device includes a housing positioned to communicate with a utility distribution system. A utility sensor unit is configured to measure at least one parameter of the utility distribution system to obtain utility system data. A gas sensor unit is in communication with an external environment outside the housing and is configured to obtain gas data related to air quality in the external environment. One or more processors are in communication with the utility sensor unit and the gas sensor unit. A memory unit is in communication with the one or more processors. A communication unit is configured to send and receive data via a network.

[0007] According to certain implementations, a utility metering device includes a housing positioned to communicate with a utility distribution system. A utility sensor unit is configured to measure at least one parameter of the utility distribution system to obtain utility system data. A gas sensor unit is in communication with an external environment outside the housing and is configured to obtain gas data related to air quality in the external environment. One or more processors are in communication with the utility sensor unit and the gas sensor unit. A memory unit is in communication with the one or more processors. A communication unit is configured to send and receive data via a network.

[0008] According to certain implementations, a sensing system includes a plurality of utility meters. Each utility meter includes a memory unit, one or more processors, a communication unit configured to communicate via a network, a utility sensor unit configured to measure at least one parameter of a utility distribution system, and a gas sensor. The gas sensor communicates with the processor and is configured to provide gas sensor data to the processor. A remote monitor communicates with the plurality of meters via the network. At least one of the plurality of utility meters is configured to transmit the gas sensor data to the remote monitor via the network. The remote monitor is configured to be accessed by a user device.

[0009] According to certain implementations, a method for determining environmental conditions from a utility meter includes positioning a utility meter along a utility distribution system. The utility meter includes a utility sensor unit configured to measure at least one parameter associated with the utility distribution system and a gas sensor unit in communication with an external environment. The gas sensor unit obtains gas sensor data associated with air quality of the external environment. The gas sensor data is transmitted to a remote monitor via a network.

[0010] According to certain implementations, a utility metering device includes a housing positioned to communicate with a utility distribution system. A utility sensor unit is configured to measure at least one parameter of the utility distribution system to obtain utility system data. A gas sensor unit is in communication with an external environment outside the housing and is configured to obtain gas data related to air quality of the external environment. One or more processors are in communication with the utility sensor unit and the gas sensor unit. The one or more processors are configured to implement a neural network to analyze the gas sensor data to determine whether an alarm condition is met. A memory unit is in communication with the one or more processors. A communication unit is configured to send and receive data via a network.

[0011] According to certain implementations, a utility metering device includes a housing positioned to communicate with a utility distribution system. A utility sensor unit is configured to measure at least one parameter of the utility distribution system to obtain utility system data. A gas sensor unit is in communication with an external environment outside the housing and is configured to obtain gas data related to air quality of the external environment. One or more processors are in communication with the utility sensor unit and the gas sensor unit. The one or more processors are configured to process the gas sensor data to reduce the dimensionality of the data. A memory unit is in communication with the one or more processors. A communication unit is configured to send and receive data over a network.

[0012] In view of the following heuristic description and drawings, the disclosure herein should become apparent to one of ordinary skill in the art. The drawings are for illustrative purposes only and, unless otherwise indicated, are not drawn to scale. The drawings are not intended to limit the scope of the present invention. The following heuristic disclosure is intended for one of ordinary skill in the art and assumes an understanding and appreciation of those aspects that are within the capabilities of one of ordinary skill. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Various aspects and advantageous features of the present disclosure will become more apparent to those of ordinary skill in the art as the detailed description of the preferred embodiments is described with reference to the accompanying drawings.

[0014] Figure A shows a schematic diagram of an illustrative embodiment of a sensing system.

[0015] Figure 2 A schematic diagram of an illustrative embodiment of an electricity meter assembly is shown.

[0016] Figure 3 An illustrative process is shown in which a remote monitor configures an electricity meter to control or otherwise communicate with a gas sensor.

[0017] Figure 4 An illustrative process is shown in which a remote monitor retrieves gas measurement data from an electricity meter.

[0018] Figure 5 An example of a heater curve is shown.

[0019] Figure 6 An illustrative process is shown in which the dimensionality of gas sensor data is reduced.

[0020] Figure 7 An exemplary plot of gas sensor data after dimensionality reduction is shown.

[0021] Figure 8An illustrative process is shown in which a neural network is trained to analyze gas sensor data to determine an alert condition.

[0022] Figure 9 An example of an air quality index (AQI) reference table is shown.

[0023] Figure 10 and Figure 11 A partially exploded view of an illustrative embodiment of an electricity meter assembly is shown.

[0024] Figure 12 Shown without cover and hydrophobic mesh Figure 10 and Figure 11 An assembly drawing of an illustrative electrical meter assembly. DETAILED DESCRIPTION

[0025] Various embodiments relate to systems, methods, and apparatus for providing gas monitoring services using utility meters. Air quality monitoring has become a critical task. It can be used to determine pollutants in the air and also to identify hazardous conditions such as fires or gas leaks. Utilizing utility meters with gas monitoring sensors offers numerous advantages over deploying specialized, dedicated sensor networks to collect air quality data.

[0026] In certain embodiments, a power meter may be provided and may facilitate communication with one or more monitoring systems and user devices (such as personal computers and / or mobile devices). For example, the power meter may transmit data, and the data may be viewed in a specific format on the user device. The data may be provided to the user device via a web portal or other application. Additionally, various user commands associated with the operation of the power meter and / or gas monitoring sensor may be transmitted to the meter to adjust or control the operation of the device.

[0027] Data from the meters can be processed to determine the occurrence of an event, and alerts or notifications can be provided to the user as needed. The user can also be presented with one or more graphical representations of real-time or historical data obtained from one or more meters. Utilizing such a system can help monitor air quality, detect gas leaks, detect fires, and help determine the starting point or origin of a dangerous condition, such as a fire.

[0028] Figure 1An illustrative embodiment of a sensing system 100 is shown that includes a plurality of utility meters, such as electricity meters 110, configured to communicate over a network N. Each electricity meter 110 may include or communicate with a gas sensor 120. Each meter 110 may be configured to communicate over the network N, or some meters 110 may be configured to communicate with meters 110 connected to the network. In some configurations, the meters 110 may have a communication link to a gateway 130 and / or other device having communication capabilities for accessing the network N. These communications may include various wired or wireless communications and utilize any type of communication protocol understood by one of ordinary skill in the art. Although an electricity meter 110 is shown, the systems and components described herein may be used with other utility meters, including gas, water, etc.

[0029] In certain configurations, the system may include a remote monitor 140 connected to a network to receive data, including gas monitoring data, from the meter 110. The remote monitor 140 may include a server having a communication interface, a memory, and one or more processors. The remote monitor 140 may receive and store data from the meter 110, process the data, and provide an output.

[0030] A user device 150 may be connected to the network N. The user device 150 may be a personal computer, mobile device, tablet computer, or other computing device configured to access the network N and transmit and receive data to and from the remote monitor 140 and / or meter 110 via the network N. In some implementations, the user device 150 may be directly or locally connected to the remote monitor 140 such that communication over the network is not required. Various combinations of user devices may be used.

[0031] One or more servers 160 may also be connected to the network N for storing data from the meter 110 and / or the remote monitor 140. The server 160 may also be configured to handle communications and data packet exchanges between the devices. One of ordinary skill in the art will appreciate that fewer components may be used, or additional components may be incorporated into the system 100 as desired.

[0032] Each electricity meter 110 may be connected to an electrical grid and configured to measure data from the grid and receive data from the gas sensor 120. The received data may be processed by the meter 110 and transmitted over the network N, or raw data may be transmitted from the meter 110 over the network N. The data may be transmitted to any combination of a remote monitor 140, a user device 150, and a server 160.

[0033] Gas sensor 120 may include, but is not limited to, a microelectromechanical system (MEMS) gas sensor. Potential advantages of this configuration may include a small footprint, low cost, and low power consumption. Examples of suitable gas sensors 120 may include, but are not limited to, BME688 (manufactured by Bosch commercial) or ZMOD4510 (by Commercialization). Gas sensor 120 can be implemented as a gas sensor on a printed circuit board (PCB). The BME688 is a gas sensor that utilizes a metal oxide (MOX) surface. The resistance of the MOX surface is partially affected by the composition of the gas surrounding the surface and the surface temperature.

[0034] Providing gas sensing capabilities at the electrical meter 110 can offer numerous advantages. For example, leveraging a network of already deployed and powered electrical meters 110 can reduce cost, manpower, time, and complexity compared to implementing independent gas sensors across a wide geographic area. Connecting the gas sensors 120 at the meter 110 to the power grid can also help avoid the need for batteries or alternative power sources. This continuous power supply can enable the gas sensors 120 to operate for longer periods of time and transmit data over greater distances than conventional standalone devices, which are limited by the need to conserve power.

[0035] Furthermore, using the meter 110 to perform gas sensing over a wide geographic area can provide accurate detection of hazardous events such as smog or wildfires, and can help identify the source of such events quickly and in greater detail than previously possible. Detailed air quality maps can also be generated, for example, in real time. When used with a programmable gas detector, the sensing system 100 can further help detect gasoline fumes, natural gas leaks, methane sources, electrical fires, propane leaks, sewer gas, carbon monoxide leaks, and other hazardous events.

[0036] Providing gas sensing capabilities at the electricity meter 110 can provide additional synergistic benefits. For example, the electricity meters 110 are generally more densely distributed in densely populated areas. This is particularly advantageous when detecting events that are more likely to occur in densely populated areas, such as certain types of fires or certain types of gas leaks.

[0037] Remote monitor 140 may be a remote device bidirectionally connected to electricity meter 110 via network N. Network N communications may be operable to transmit data packets over network N. Network N may include any combination of devices and wired and / or wireless communication links. Feeding network N is electricity meter 110, which may be a residential electricity meter, a commercial electricity meter, or a combination thereof. The remote monitor may be a utility or another entity.

[0038] The network N may be an advanced metering infrastructure (AMI) network. In some embodiments, the network may be a cellular network. In certain configurations, the network N may employ radio data transmission. For example, lower frequency transmissions (e.g., approximately 450 MHz) may be utilized for communication. Compared to higher frequency signals (e.g., 900 MHz), lower frequency signals can better penetrate obstacles and propagate more efficiently, and can travel significantly further using the same power level in the transmitter. This is particularly advantageous when the meter is located in a meter pit or basement, or behind other obstructions (such as dense trees).

[0039] Network N also provides distributed data collection with redundant coverage and leverages existing power infrastructure, for example, by employing data collection units (DCUs) mounted on existing utility poles and buildings. This allows each meter to be read by multiple DCUs, resulting in ideal redundancy. Furthermore, utilizing existing power infrastructure reduces power and operating costs and facilitates management and maintenance efforts compared to conventional systems that might utilize separate communications for gas sensors.

[0040] The electricity meter 110 may include a communication component (e.g., a transceiver) configured to communicate over the network N. The communication component may be operable to transmit data packets using a variety of protocols over different communication media (such as wired and / or wireless communications). Examples of communication technologies and / or protocols may include LoRaWAN, WiFi-ah (802.11ah), 3G, 4G, LTE, 5G, Sigfox, Ingenu, Digimesh, Synergize RF or TWAC PLC, Bluetooth Low Energy, Bluetooth Mesh, Near Field Communication, Thread, TLS (Transport Layer Security), Wi-Fi (e.g., IEEE, 802.11), Wi-Fi Direct (for peer-to-peer communication), Z-Wave, Zigbee, HaLow, cellular communication, LTE, low power wide area network (Sigfox, Lora, Ingenu), VSAT, Ethernet, MoCA (Multimedia over Coax Alliance), PLC (Power Line Communication), DLT (Digital Line Transport), etc. Other suitable communication technologies and / or protocols may be used without departing from the scope of this disclosure. In certain implementations, LPWAN protocols may provide increased communication range. HaLow may provide lower energy requirements and higher data rates. Other protocols may be used without departing from the scope of this disclosure.

[0041] In some embodiments, network N may include a local area network and / or a wide area network. In some embodiments, network N may include one or more of the following: a secure network, a Wi-Fi network, an AMI network, a mesh network, the Internet, a cellular network or other wide area network, one or more peer-to-peer communication links, and / or some combination thereof, and may include any number of wired or wireless links. Communication over network N may be achieved, for example, via a communication interface using any type of protocol, protection scheme, encoding, format, encapsulation, etc.

[0042] The computing systems and devices discussed herein may include one or more processors and one or more memory devices. The computing systems and devices may be distributed so that their components may be located in different geographical areas. The technology discussed herein refers to computer-based systems and the operations performed by computer-based systems and the information sent to and from computer-based systems. Those of ordinary skill in the art will recognize that the inherent flexibility of computer-based systems allows for various possible configurations, combinations, and division of tasks and functionality between components. For example, the processes discussed herein may be implemented using a single computing device or multiple computing devices working in concert. Databases, memories, instructions, and applications may be implemented on a single system or distributed across multiple systems. Distributed components may operate sequentially or in parallel.

[0043] Figure 2 An exemplary configuration of an electricity meter 110 is shown. The electricity meter 110 can be configured to measure power consumption and report various power quality measurements. The electricity meter 110 can include a power supply 210, one or more memory units 220, one or more processors 230, a clock unit 240, a communication unit 250, and an electrical sensor unit 260.

[0044] The power supply 210 is configured to provide power to the components of the meter 110. The power supply 210 may also provide power to the gas sensor 120 of the same electrical meter assembly. In some configurations, the power supply 210 may include a connection to the power grid. For example, the power supply 210 may include a transformer having a primary winding coupled to the incoming power distribution line and having windings that provide a nominal voltage (e.g., 5 VDC, +12 VDC, and -12 VDC) at its secondary winding. In other embodiments, power may be supplied to the power supply 210 from an independent power source. For example, power may be supplied from a different circuit or an uninterruptible power supply (UPS). In some embodiments, the power supply 210 may include an energy storage device, such as a battery and / or a capacitor. In some embodiments, the power supply 210 may include a power collection system configured to charge the energy storage device and / or store energy in the energy storage device for use in powering the electrical meter 110 and the gas sensor 120. The power collection system may be configured to utilize one or more of, for example, solar energy, wind energy, piezoelectric energy, electromagnetic energy associated with power lines hung from utility poles, or radio frequency energy.

[0045] The electricity meter 110 may be equipped with one or more memory units 220. The memory unit 220 may be configured to store configuration data and log data, which may include real-time and historical data from the gas sensor 120 and the electrical sensor unit 260. The one or more memory units may include any combination of volatile memory and non-volatile memory. Volatile memory may be internal memory, such as random access memory. Non-volatile memory may include removable memory, such as solid-state memory, for example, a CompactFlash card, a Memory Stick, a SmartMedia card, a MultiMediaCard (MMC), or an SD (Secure Digital) memory. The one or more processors 230 may be configured to write data to the memory 220.

[0046] The one or more processors 230 may include, for example, a microcontroller, a microprocessor, a logic circuit, an application specific integrated circuit, etc. The processor 230 may be configured to perform calculations on received data and control the overall operation of the meter 110 and the gas sensor 120 .

[0047] The one or more memory units 220 may store computer-readable instructions that, when executed by the one or more processors 230, cause the one or more processors to provide functionality according to example aspects of the present disclosure. For example, the memory 220 may store computer-readable instructions that, when executed by the one or more processors 230, cause the one or more processors 230 to implement any of the data processing techniques and / or communication techniques disclosed herein. Furthermore, the data processing techniques and / or communication techniques disclosed herein may be implemented by one or more processors, such as locally by one or more processors at the electricity meter 110, at a remote device (e.g., a remote monitor 140, a user device 150, or a cloud server 160), or shared across multiple devices (e.g., multiple electricity meters 110, remote monitors 140, user devices 150, cloud servers 160, etc.). A person of ordinary skill in the art, using the disclosure provided herein, will understand that the various steps of any method provided herein, including those not shown, may be adapted, modified, rearranged, performed concurrently, or expanded in various ways without departing from the scope of the present disclosure.

[0048] In some embodiments, the electricity meter 110 may include a clock 240, such as a real-time clock. The clock 240 may be used, for example, to associate time-stamped data with data obtained by (or derived from the output of) various sensors associated with the electricity meter 110, including, but not limited to, the electrical sensor 260 and / or the gas sensor 120. The time-stamped data may be used, for example, to perform historical processing, identify trends, identify times associated with the occurrence of events, compare to other utility poles, and for other purposes.

[0049] The clock 240 may be set during installation of the electricity meter 110. Various methods may be used to address clock drift (e.g., the offset of the time provided by the clock 240 relative to true time). For example, the clock 240 may periodically synchronize with time data from a remote device when sending and / or receiving communications.

[0050] The communication unit 250 supports communication between the meter 110 and external devices (such as another electricity meter or other computer device) over remote and / or local networks. The communication unit 250 can be a modem, a network interface card (NIC), a wireless transceiver, etc. The communication unit 250 performs its functionality through wired and / or wireless connectivity. Hard-wired connections can include, but are not limited to, hard-wired cables, such as parallel or serial cables, RS232, RS485, USB cables, FireWire (1394 connectivity) cables, Ethernet, and appropriate communication port configurations. Wireless connections will operate under any of a variety of wireless protocols, including but not limited to Bluetooth. TMInterconnectivity, infrared connectivity, radio transmission connectivity (including computer digital signal broadcast and reception commonly referred to as Wi-Fi or 802.11.X (where x indicates the transmission type)), satellite transmission or any other type of communication protocol, communication architecture, or system currently existing or to be developed for wireless transmission of data, including spread spectrum 900MHz or other frequencies, Zigbee, WiFi or any mesh supporting wireless communication.

[0051] The electrical sensor unit 260 may include one or more sensors to sense electrical parameters of the power grid, such as the voltage and current on the incoming power lines. The sensor unit 260 may also include one or more A / D converters. For example, the sensors may include current transformers and voltage transformers, with one current transformer and one voltage transformer coupled to each phase of the incoming power lines. The primary winding of each transformer will be coupled to the incoming power lines, and the secondary winding of each transformer will output a voltage representative of the sensed voltage and current. The output of each transformer may be coupled to an A / D converter configured to convert the analog output voltage or current into a digital signal, which may be processed by the one or more processors 230 and stored in the memory 220.

[0052] Figure 3 An example of a process 300 is shown in which an electricity meter 110 is configured to control a gas sensor 120 located at the electricity meter 110. The gas sensor 120 can be programmed or reprogrammed to operate under certain parameters to detect one or more external conditions. This can be triggered by an external device such as a remote monitor 140 or a user device 150. The configuration sub-processes described below can be performed in various orders.

[0053] In exemplary step 310 , the device transmits one or more gas detection modules (GDMs) to the electrical meter 110 via the network N. The GDMs may include configuration settings for detecting a specific gas or combination of gases. The GDMs may be stored in the memory 220 .

[0054] In certain configurations, the GDM may include a heater curve for the gas sensor associated with the optimized configuration to detect specific environmental conditions. For example, different gas sensors 120 may detect gases using surface resistance measurements, such as the MOX resistance described above. Since the temperature of the MOX surface may affect the measurement, different temperature curves may more accurately detect different gases. Therefore, the heater curve may include instructions for heating the MOX surface to one or more specific temperatures within a set time period. For example, the surface may be heated to 300 degrees Celsius for one second, then the temperature may be reduced to 100 degrees Celsius for six seconds, then increased to 200 degrees Celsius for two seconds, and then increased to 300 degrees Celsius for two seconds. Different temperature and time combinations may be used for specific heater curves to provide optimal sensing conditions for different gases.

[0055] In exemplary step 320, the device may selectively activate and / or deactivate the GDM. This selection may be determined by a user who desires to detect a particular gas or combination of gases. Alternatively, this selection may be determined by an algorithm by a computer program that has information regarding the likelihood that a particular gas or combination of gases is present at the location of the electrical meter 110. For example, if the computer program determines that this likelihood is above a predetermined threshold, the computer program may be configured to activate one or more GDMs corresponding to the detection of that gas or combination of gases. Machine learning techniques or other processing techniques may also be used to identify the threshold. When a threshold crossing occurs, the occurrence of such an event may be identified. The magnitude of the threshold crossing may indicate the amount by which the data exceeded the threshold and / or dropped below the threshold.

[0056] In exemplary step 330, the electricity meter 110 can be configured to provide a notification or alert when a predetermined alarm condition is met. For example, if the gas sensor 120 detects the presence of a gas or gas combination exceeding a predetermined amount or concentration, an alert or notification can be sent via the network N. The predetermined amount or concentration of the gas or gas combination can vary based on a historical baseline (such as an average of historical gas or gas combination amount or concentration values ​​detected at the electricity meter 110) or can vary based on gas or gas combination amount or concentration values ​​detected at nearby electricity meters 110. Machine learning techniques or other processing techniques can also be used to identify the predetermined amount or concentration. When a threshold crossing occurs, the occurrence of such an event can be identified. The magnitude of the threshold crossing can indicate that the data exceeded the threshold and / or dropped below the threshold.

[0057] In some embodiments, the alert may be provided by the remote monitor 140 in a graphical user interface presented on a display screen associated with the remote monitor 140 or on a remote device associated with the remote monitor, such as the user device 150, or may be provided in other output forms (e.g., audio, tactile, etc.). The graphical user interface may present other information associated with the data collected by the one or more electricity meters 110, such as reports, comparisons, charts, analyses, etc.

[0058] In exemplary step 340, the electricity meter 110 may be configured to log alarm data to an alarm log stored in the memory 220 via the network N. For example, the alarm log may include alarm events associated with the satisfaction of predetermined alarm conditions. For example, the alarm event may include a timestamp and a description of the satisfaction of the condition.

[0059] In exemplary step 350, the electricity meter 110 may be configured to log gas detection events to an event log stored in the memory 220 via the network N. For example, a gas detection event may include measurements performed at predetermined times (such as at regular intervals or at preset dates / times), and / or may include measurements that produce a gas amount or concentration exceeding a predetermined value, or a gas classification that is considered abnormal or dangerous. For example, the event log may include a timestamp and the results of the measurements performed.

[0060] Figure 4 An example of a process 400 is shown in which a device such as remote monitor 140, user device 150, or server 160 can retrieve gas measurement data from electrical meter 110 and present the retrieved data to a user. The retrieval sub-processes described below can be performed in various orders, sequentially or simultaneously.

[0061] In exemplary step 410, data is requested from the electricity meter 110. The data may include event logs, alarms, logs, real-time data, or other data. In step 420, the requested data is retrieved from memory. If necessary, the data may be processed into a user-readable format. This processing may be performed by one or more processors 230 in the meter 110 or by an external device. In certain configurations, the data may be processed into a user-readable format before being stored in memory. In step 440, the data may be transmitted to the external device for presentation to the user and / or further processed by the device in step 450. For example, further processing of the data may include providing a graphical representation of current and past data to the user. This graphical representation may include a graph showing air quality data for a given area and a specific time or time range. The information may be color-coded to provide a visual representation to the user.

[0062] In some configurations, the electrical meter 110 may be configured to process the gas sensor data before storage or transmission to reduce the amount of data that needs to be stored or transmitted. For example, the sensor data may be obtained at different steps through a single heater curve. Figure 5 An example of a heater curve is shown, where the points represent the steps at which the sensor collects data. Each step is a single dimension of data obtained, resulting in ten-dimensional raw data.

[0063] Figure 6 An example of a process 600 performed on data to reduce the amount of data for storage and transmission while maintaining accurate results is shown. In step 610, data is obtained from a gas sensor. The data may represent data of different dimensions obtained from the gas sensor. After obtaining the data, the dimensionality of the data is reduced in step 620. The dimensionality of the data can be reduced by applying one or more algorithms to the data to process the data into fewer dimensions. In an exemplary configuration, linear discriminant analysis (LDA) can be used to process the data. LDA can be used to find linear combinations of features that separate two or more classes of data and form the data into clusters. Figure 7 Resulting data that may be obtained after processing the gas sensor data is shown. After processing the data, the processed data may be transmitted in step 630. Further actions may then be taken on the processed data, including transmission to a memory for storage, compilation into a log, addition to an alert, or transmission to a device such as a remote monitor or other server. In various implementations, processing the data 600 to reduce dimensionality is performed by one or more processors 230 in the electricity meter 110.

[0064] In certain configurations, the electricity meter 110 can be configured to utilize a neural network to determine whether a predetermined alarm condition has been met. The neural network can be trained to analyze sensor data to detect specific conditions indicative of an alarm condition, such as the presence of a sufficient amount of smoke within a certain period of time to indicate a fire. The neural network can be trained in a manner similar to that described in U.S. Published Application No. 2023 / 0341477, the disclosure of which is incorporated by reference in its entirety.

[0065] For example, the neural network may be a multilayer perceptron neural network that utilizes multiple hidden nodes to analyze input data from a gas sensor and classify the data to determine whether an alarm condition is met. Figure 8An exemplary process 700 for training a neural network and applying it to gas sensor data obtained from an electricity meter 110 is shown. Test data for training the neural network is obtained in step 710. The test data can be obtained, for example, by conducting laboratory tests under controlled conditions using an associated gas sensor unit 120 operating according to a specific heater curve. The test data is applied to the neural network in step 720, and the output from the network is scored and feedback is provided in step 730. Steps 710-730 can be iterated as needed to achieve a satisfactory model confidence, as shown in step 740. In some implementations, several gas detectors can be operated simultaneously to detect different gases, and the results can be compiled to provide different packages to multiple meters in the field.

[0066] In some implementations, the trained neural network can then be loaded into the meter and used to analyze data obtained from the gas sensor 120. In some implementations, the neural network data can be updated in real time over the network and loaded into the field. The analysis can be performed on the raw data or the processed data. In certain configurations, the neural network is loaded into the meter's memory, and one or more processors are configured to implement the neural network to analyze the gas sensor data to determine whether an alarm condition is met.

[0067] In operation, the electricity meter 110 may configure the gas sensor 120 based on configuration data stored in the memory 220 of the electricity meter 110. The configuration data may include data from or generated using the GDM, such as, for example, but not limited to, configuration data that sets a frequency of gas sensor measurements, or configuration data that sets a sensing curve for the gas sensor 120. The sensing curve may include, for example, a pre-programmed temperature curve configured for making air quality measurements based on, for example, temperature, as well as air pressure, humidity, and / or any other parameter known to affect sensor measurements.

[0068] The gas sensor 120 may periodically (eg, at predetermined intervals) send gas measurements to the electrical meter 110. These gas measurements may be interpreted and / or stored in the memory 220.

[0069] The above data related to gas measurements can be transmitted via the network N along with data related to electricity measurements obtained by the electricity meter 110 (such as, but not limited to, electricity consumption data). Collecting and / or transmitting electrical data along with the gas measurement data can advantageously provide valuable data, for example, in detecting electrical fires. For example, the gas measurement data can be used to detect gases or gas components associated with electrical fires, while the electrical data can be used to determine electrical events (e.g., electrical short circuits) associated with electrical fires. Thus, these data can advantageously help confirm the determination that an electrical fire has occurred. In this case, a remote disconnect function can be utilized to remotely shut off the circuit connected to the electricity meter.

[0070] Data transmission over the network N can be performed by sending one or more data packets from the electricity meter 110 to the remote monitor 140. The data packets may include, for example, a header, a payload, and an authentication portion. The payload may include an identifier for the electricity meter 110, such as, for example, a serial number and / or other identifier associated with the specific electricity meter 110. The identifier of the electricity meter 110 can assist a remote device (such as the remote monitor 140) in determining the location where a particular event occurred (e.g., by reconciling the identifier of the electricity meter 110 with a location stored in a database). The payload may include data associated with the condition of the electricity meter 110, such as data derived from measurements performed by the electrical sensor unit 260 or the gas sensor 120. The data may be transmitted after being processed, conditioned, and / or filtered by one or more processors 230 of the electricity meter 110. The data packets transmitted from the remote monitor 140 to the electricity meter 110 may also include an identifier for the electricity meter 110.

[0071] In some embodiments, the output by the sensing system 100 may be in the form of data indicating the air quality. For example, a graphical user interface (e.g., at the remote monitor 140 or the user device 150) may display a color indicating the air quality, or any other information, such as a reference to the air quality. Figure 9 A reference chart of air quality indexes (AQIs) is shown. Furthermore, sensing system 100 can create a detailed map (e.g., a real-time map) of the air quality in a particular geographic area. This is particularly advantageous as various regulatory agencies (e.g., the U.S. Environmental Protection Agency) have recently shown particular interest in improving air quality sensing.

[0072] In some embodiments, the output of the sensing system 100 may indicate smoke or wildfire detection, or greenhouse gas detection. In some embodiments, the output of the gas sensor 120, and therefore the output of the sensing system 100, may indicate measurement of any of the following: temperature, air pressure, humidity, ozone and nitrogen dioxide, carbon dioxide, particulate matter (e.g., MP2.5 and PM10, which conventionally require complex and large sensors), or a custom gas combination. Detection of custom gas combinations can be programmed, and machine learning can assist in identifying the desired gas combination.

[0073] In some configurations, the gas sensor 120 may include a metal oxide (MOX) sensor. A MOX sensor can detect gases through oxidation / reduction on its sensitive layer. Metal oxides change resistance due to chemical reactions (where reduction corresponds to lower resistance and oxidation corresponds to higher resistance) and react to most volatile compounds and pollutant gases. Sensor temperature affects oxidation and reduction. Therefore, the heater temperature and duration can be programmed, with the target temperature generally between 200°C and 400°C. By performing multiple resistance measurements at different temperatures and durations, specific gas compositions can be identified. Machine learning can be used to identify unique compositions as discussed herein.

[0074] In some configurations, the gas sensor 120 may include multiple sensors that are installed as a module into the meter 110. The sensors may include MOX sensors, MEM sensors, particulate matter sensors, and other sensors.

[0075] In some embodiments, the sensing system 100 can utilize programmable sensor heating curves to perform AQI measurements or identify gaseous constituents. For example, Herrmann et al., Air Quality Measurement Based on Advanced PM2.5 and VOC Sensor Technologies, Sensors & Transducers, Vol. 243, No. 4, August 2020, pp. 1-5 (https: / / sensorsportal.com / sensors_and_transducers.html) (incorporated herein by reference in its entirety). Figure 5 It illustrates how comparing environmental sensing data with reference data can enable detection of different gas compositions.

[0076] Figures 10 to 12An illustrative embodiment of an electricity meter assembly 800 is shown that includes an electricity meter 810 and a gas sensor 820. The gas sensor 820 can be attached to the electricity meter 810. For example, the gas sensor 820 can be mounted to or with the electricity meter 810.

[0077] In some embodiments, a removable or fixed cover 840 of the electricity meter assembly 800 (which forms part of the housing of the electricity meter assembly) protects the components of the electricity meter assembly 800 from inclement weather. The cover 840 can include an opening 842 that provides communication between the gas sensor 820 and the external environment. In some embodiments, the opening 842 is located on a portion of the housing of the electricity meter assembly 800 other than the cover 840 of the electricity meter assembly.

[0078] One technical challenge in implementing gas sensor 820 in electricity meter 810 is that, on the one hand, gas sensor 820 must be exposed to inclement weather to allow sufficient airflow to detect gas, but on the other hand, for safety reasons, electricity meter 810 must be protected from the elements. According to illustrative embodiments of the present disclosure, this technical challenge can be addressed in the following manner. For example, electricity meter assembly 800 may further include a hydrophobic mesh 830 positioned between opening 842 and gas sensor 820 to protect gas sensor 820 from inclement weather. Furthermore, cover plate 840 may include an overhang 844 that protects opening 842 from the elements.

[0079] One of ordinary skill will understand that exact dimensions and materials are not critical to the present disclosure and that all suitable variations, if deemed suitable for carrying out the purposes of the present disclosure, are considered within the scope of the present disclosure.

[0080] Those skilled in the art will also readily appreciate that modifying one or more of the components to implement various embodiments of the present disclosure is well within the capabilities of those skilled in the art. Once armed with this specification, routine experimentation is all that is required to determine the adjustments and modifications that will implement the present disclosure.

[0081] The above embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure or the applicability of the features described herein to, for example, a specific electrical meter assembly or gas sensor. Those skilled in the art will also appreciate that various adaptations and modifications of the above-described preferred embodiments may be configured without departing from the scope and spirit of the present disclosure. Therefore, it should be understood that within the scope of the appended claims, the present invention may be practiced otherwise than as specifically described.

Claims

1. A utility metering device comprising: a housing positioned to communicate with a utility distribution system; a utility sensor unit configured to measure at least one parameter of the utility distribution system to obtain utility system data; a gas sensor unit in communication with an external environment outside the housing and configured to obtain gas data related to air quality of the external environment; one or more processors in communication with the utility sensor unit and the gas sensor unit; a memory unit in communication with the one or more processors; as well as A communication unit configured to send and receive data over a network. 2 . The utility meter device of claim 1 , further comprising a power supply configured to supply power to the gas sensor.

3. The utility meter device of claim 2, wherein the power supply receives power from the utility distribution system. 4 . The utility meter device of claim 1 , wherein the one or more processors are configured to execute instructions to change the temperature of the sensing surface of the gas sensor within a time interval. 5 . The utility meter device of claim 1 , wherein the one or more processors are configured to process the gas data to reduce a dimensionality of the data. 6 . The utility meter device of claim 1 , wherein the one or more processors are configured to implement a neural network to analyze the gas data to determine whether an alarm condition is met.

7. The utility meter device of claim 1, wherein the housing includes an opening providing communication between the gas sensor and the external environment.

8. The utility meter device of claim 7, wherein a hydrophobic mesh is positioned between the gas sensor and the opening.

9. The utility meter device of claim 7, wherein the housing includes an overhang extending outwardly above the opening.

10. The utility meter device of claim 1, wherein the utility distribution system is an electric distribution system and the utility sensor unit is an electrical sensor unit.

11. A sensing system comprising: a plurality of utility meters, each utility meter comprising a memory unit, one or more processors, a communication unit configured to communicate over a network, a utility sensor unit configured to measure at least one parameter of a utility distribution system, and a gas sensor, wherein the gas sensor is in communication with the processor and configured to provide gas sensor data to the processor; as well as a remote monitor in communication with the plurality of meters over the network, wherein at least one utility meter of the plurality of utility meters is configured to transmit the gas sensor data to the remote monitor over the network, Wherein the remote monitor is configured to be accessed by a user device.

12. The sensing system of claim 11, wherein at least one utility meter of the plurality of utility meters includes a power supply configured to provide power to the associated one or more processors and the gas sensor.

13. The sensing system of claim 11, wherein the one or more processors of at least one utility meter of the plurality of utility meters are configured to process the gas sensor data to reduce a dimensionality of the data.

14. The sensing system of claim 11, wherein the remote monitor is configured to transmit one or more gas detection modules to one or more utility meters of the plurality of utility meters via the network.

15. The sensing system of claim 14, wherein the gas detection module includes a heater curve.

16. The sensing system of claim 11, wherein the remote monitor is configured to selectively activate and / or deactivate a gas detection module stored in one or more utility meters of the plurality of utility meters.

17. The sensing system of claim 11, wherein one or more utility meters of the plurality of utility meters are configured to alert the remote monitor when a predetermined alarm condition is met.

18. The sensing system of claim 17, wherein the one or more processors in the plurality of utility meters are configured to implement a neural network to analyze the gas sensor data to determine whether an alarm condition is met.

19. The sensing system of claim 11, wherein one or more utility meters of the plurality of utility meters are configured to log a gas detection event to an event log stored in the memory of the utility meter.

20. The sensing system of claim 11, wherein the remote monitor is configured to retrieve the gas sensor data from the plurality of utility meters.

21. A method of determining an environmental condition from a utility meter, comprising: providing a utility meter along a utility distribution system, the utility meter having a utility sensor unit configured to measure at least one parameter associated with the utility distribution system and a gas sensor unit in communication with an external environment; obtaining, via the gas sensor unit, gas sensor data associated with air quality of the external environment; as well as The gas sensor data is transmitted to a remote monitor via a network.

22. A utility metering device comprising: a housing positioned to communicate with a utility distribution system; a utility sensor unit configured to measure at least one parameter of the utility distribution system to obtain utility system data; a gas sensor unit in communication with an external environment outside the housing and configured to obtain gas data related to air quality of the external environment; one or more processors in communication with the utility sensor unit and the gas sensor unit, wherein the one or more processors are configured to implement a neural network to analyze the gas sensor data to determine whether an alarm condition is met; a memory unit in communication with the one or more processors; as well as A communication unit configured to send and receive data over a network.

23. A utility metering device comprising: a housing positioned to communicate with a utility distribution system; a utility sensor unit configured to measure at least one parameter of the utility distribution system to obtain utility system data; a gas sensor unit in communication with an external environment outside the housing and configured to obtain gas sensor data related to air quality of the external environment; one or more processors in communication with the utility sensor unit and the gas sensor unit, wherein the one or more processors are configured to process the gas sensor data to reduce the dimensionality of the data; a memory unit in communication with the one or more processors; as well as A communication unit configured to send and receive data over a network.

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