Sensor system

Abstract

An advance warning monitoring system for monitoring a spontaneous combustion risk in a hay storage facility comprises a monitoring sensor device positioned within the facility to monitor internal ambient conditions in the facility. The monitoring sensor device is configured to be positioned at an apex of a ceiling of the facility. The system comprises a control sensor device positioned external to the facility to monitor external ambient conditions external to the facility. The system comprises a processor in data communication with the monitoring sensor device and the control sensor device, and the processor is configured to execute machine readable instructions to receive monitoring sensor data representing conditions in the facility, receive control sensor data representing conditions external to the facility, and based on the received data, determine at least one ambient condition indicator. The indicator comprises one or more of an internal ambient condition trend, an external ambient condition trend, an internal-to-external ambient condition difference; and an internal-to-external ambient condition difference trend. The processor is also configured to perform a comparison between the condition indicator with a warning threshold to determine a spontaneous combustion risk, and output a warning signal based on the based on the spontaneous combustion risk. The processor may also effect a reduction in internal temperature of the facility based on the determined spontaneous combustion risk.

Description

Sensor SystemTechnical Field

[0001] The present disclosure broadly relates to ambient condition sensor and alarm systems and, more particularly, to advance warning monitoring systems for monitoring ambient conditions in a facility, and to methods of monitoring ambient conditions in a facility, in order to pre-empt fire risk by monitoring temperature changes inside the facility. The systems and methods are particularly suited to fire hazard advance warning, for example to pre-empt and avoid spontaneous combustion in hay.Background

[0002] Spontaneous combustion in hay is a phenomenon triggered by the heating of moist hay, resulting from a blend of microbial and chemical reactions inherent in its composition. When sufficient oxygen is available, the heightened temperature within the moist hay can lead to ignition, leading to the propagation of fire throughout the stack or shed. Factors influencing hay heating include moisture content, ambient air temperature, humidity, bale size, density, stack arrangement, and crop type, which impacts the levels of water-soluble carbohydrates, such as simple sugars.

[0003] The primary cause of spontaneous combustion in hay is typically attributed to uneven curing of the hay prior to stacking. Hay with a moisture content below 25% generally does not spontaneously ignite; however, the presence of small pockets of wet material within a bale poses a greater fire hazard. It is necessary to ensure thorough curing and drying of all hay before stacking, with particular vigilance warranted for hay sourced from areas of lush growth or low-lying ground, especially following rainfall.

[0004] The buildup of heat in high-moisture hay stacks can trigger chemical reactions. Due to its insulating properties, larger haystacks experience less cooling, exacerbating the heat buildup. When the internal temperature of hay surpasses 130 degrees Fahrenheit (55 degrees Celsius), a chemical reaction occurs, producingflammable gases that can ignite under appropriate conditions. Hay fires typically happen within six weeks of baling, with heating occurring in all hay exceeding 15% moisture, peaking at 50 and 55 degrees Celsius within three to seven days, followed by a gradual decline to safe levels over the subsequent 15 to 60 days.

[0005] To mitigate the risk of hay fires, it is recommended that small, rectangular bales maintain moisture levels below 18 to 22%, while large round or rectangular bales should not exceed 16 to 18% moisture for safe storage. Regular monitoring of hay is advised, with attention drawn to signs such as a caramel odour or musty smell, indicative of heating. In cases of suspected heating, a probe can be inserted into the haystack to monitor temperature. Additionally, an iron rod or crowbar can be thrust into the stack at intervals to gauge internal heat levels by touch.

[0006] Should temperatures exceed about 55 degrees Celsius, precautionary measures should be taken to prevent ignition, including dismantling the stack and ensuring adequate firefighting equipment is available. Hay with moisture levels exceeding 16- 18% is susceptible to heating or mould formation, impacting the nutritional quality of feed. Moisture levels exceeding 20% elevate the risk of heating, while bales with moisture levels between 25-35% are prone to spontaneous combustion. Factors such as bale size, density, storage methods, and crop type influence the risk of spontaneous combustion, necessitating careful management and monitoring to mitigate potential hazards.

[0007] The iron rod method offers a general indication of internal heat levels but lacks precision. Conversely, inserting probes into the hay introduces the risk of contamination and oxygen into the material, potentially compromising feed quality and increasing the risk of ignition. Depending on the design and construction of the probe, there is a risk of damaging the hay during insertion, leading to potential spoilage or loss of feed quality. Moreover, inserting probes into the hay can be a time-consuming process, especially in large or densely packed stacks, which may not be practical for regular monitoring. If probes are not properly sterilised or maintained between uses, there is a risk of introducing pathogens or contaminants into the hay, furtherjeopardising its suitability for animal consumption. The accuracy of temperature readings obtained from probes may be influenced by factors such as probe placement, depth of insertion, and calibration errors, potentially leading to inaccurate assessments of hay stack conditions. In addition, the use of probes may not be feasible in certain storage configurations or environments, such as tightly packed stacks or areas with limited accessibility, further limiting their applicability in temperature monitoring efforts.

[0008] In summary, hay season on a large scare involved many moving parts. It only takes a break down of one these parts to cause a situation where hay can spontaneously combust in a storage facility. In the event of spontaneous combustion, by the time there is smoke, it is already too late to save the shed asset and / or salvage any unaffected hay. Current devices on the market rely on point contact, and to insert a probe in the precise bale that is going to cause an issue is the equivalent of finding a needle in a haystack.

[0009] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each claim of this application.Summary

[0010] Described herein are novel systems and methods for tracking environmental conditions associated with spontaneous combustion of stored hay. To monitor temperature changes, measurements are taken inside and at the apex of the shed, where rising hot air accumulates and where the internal temperature of a storage shed is typically at its highest. Regular readings are recorded, starting at or before daybreak and then at intervals throughout the day, allowing for tracking of temperature and / or humidity fluctuations. These changes are compared to ambient temperatures measured outside the shed to assess any abnormal increases indicative of combustion risk. Probe devices are utilised to measure and record temperature and / or humidity inside andoutside the shed. By analysing fluctuations in these data sets over time, the system can identify heightened combustion risk.

[0011] In one aspect there is provided an advance warning monitoring system for monitoring a spontaneous combustion risk in a hay storage facility, the monitoring system comprising: a monitoring sensor device positioned within the facility to monitor internal ambient conditions in the facility, wherein the monitoring sensor device is configured to be positioned at an apex of a ceiling of the facility; a control sensor device positioned external to the facility to monitor external ambient conditions external to the facility; and a processor in data communication with the monitoring sensor device and the control sensor device, the processor configured to execute machine readable instructions to: a. receive monitoring sensor data representing conditions in the facility; b. receive control sensor data representing conditions external to the facility; c. based on the received data, determine at least one ambient condition indicator comprising one or more of: i. an internal ambient condition trend; ii. an external ambient condition trend; iii. an internal -to-extemal ambient condition difference; and iv. an intemal-to-extemal ambient condition difference trend; d. perform a comparison between the condition indicator with a warning threshold to determine a spontaneous combustion risk; e. output a warning signal based on the based on the spontaneous combustion risk; and f. effect a reduction in internal temperature of the facility based on the determined spontaneous combustion risk.

[0012] In another aspect there is provided an advance warning monitoring system for monitoring ambient conditions in a facility, the monitoring system comprising: a monitoring sensor device positioned within the facility to monitor internal ambient conditions in the facility; a control sensor device positioned external to the facility to monitor external ambient conditions external to the facility; and a processor in data communication with the monitoring sensor device and the control sensor device, the processor configured to execute machine readable instructions to: a. receive monitoring sensor data representing conditions in the facility; b. receive control sensor data representing conditions external to the facility; c. based on the received data, determine at least one ambient condition indicator; d. perform a comparison between the condition indicator with a warning threshold to determine a safety risk; and e. output a warning signal based on the safety risk.

[0013] The monitoring sensor device may be configured to be positioned at a ceiling of the facility. The monitoring sensor device may be configured to be positioned at an apex of a ceiling of the facility.

[0014] The monitored ambient conditions may comprise at least one of temperature and humidity.

[0015] The control sensor device may comprise the processor and a communication interface in communication with the monitoring sensor device for receiving the internal ambient condition data.

[0016] The system may comprise a user device in communication with the processor via a communication network, and the warning signal may be output to the user device.

[0017] The monitoring sensor device may comprise a data logger and log the internal ambient condition data.

[0018] The control sensor device may comprise a data logger and log the external ambient condition data.

[0019] The at least one ambient condition indicator may comprise one or more of: a. an internal ambient condition trend; b. external ambient condition trend; c. an internal-to-external ambient condition difference; and d. an internal-to-external ambient condition difference trend.

[0020] The processor may be configured to effect a reduction in internal temperature of the facility based on the determined safety risk.

[0021] In another aspect there is provided a method of monitoring ambient conditions in a facility, the method comprising: at a processor: receiving monitoring sensor data representing conditions in the facility; receiving control sensor data representing conditions external to the facility; and comparing the monitoring sensor data and the control sensor data over a time interval to monitor for an anomaly.

[0022] Comparing the monitoring sensor data and the control sensor data may comprise: determining at least one ambient condition indicator comprising one or more of: an internal ambient condition trend; an external ambient condition trend; an intemal-to-extemal ambient condition difference; and an internal -to-extemal ambient condition difference trend; and performing a comparison between the condition indicator with a warning threshold.

[0023] The method may comprise outputting a warning signal based on the comparison.

[0024] The method may comprise: measuring, by a monitoring sensor positioned relative to a ceiling of the facility, monitoring sensor data comprising an internal temperature at an apex of the ceiling of the facility; and measuring, by a control sensor positioned outside the facility, control sensor data comprising an external temperature representative of a general ambient temperature outside the facility.

[0025] Comparing the monitoring sensor data and the control sensor data may comprise comparing a difference between the monitoring sensor data and the control sensor data with a threshold value associated with a fire or combustion risk.

[0026] The method may comprise, by the processor: responsive to the comparing, communicating a warning signal if a risk threshold is exceeded.

[0027] The method may comprise, by the processor: responsive to the comparing, effecting a reduction in internal temperature of the facility. The effecting may comprise sending a control signal to a temperature control system to reduce the internal temperature.

[0028] Throughout this specification the word “comprise” or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.Brief Description of Drawings

[0029] Embodiments of the disclosure are now described by way of example with reference to the accompanying drawings in which:

[0030] Figure 1 is a diagram of an embodiment of a monitoring system.

[0031] Figure 2 is a diagram of another embodiment of a monitoring system.

[0032] Figure 3 is a diagram of another embodiment of a monitoring system.

[0033] Figure 4 is a diagram of another embodiment of a monitoring system.

[0034] Figure 5 is a diagram of an embodiment of a method of monitoring.

[0035] Figure 6 shows an embodiment of a screen display of a user interface of a monitoring platform.

[0036] Figure 7 shows another screen display of the user interface of Figure 6.

[0037] Figure 8 shows another screen display of the user interface of Figure 6.

[0038] Figure 9 shows another screen display of the user interface of Figure 6.

[0039] Figure 10 shows another screen display of the user interface of Figure 6.

[0040] Figure 11 shows another screen display of the user interface of Figure 6.

[0041] Figure 12 shows another screen display of the user interface of Figure 6.

[0042] In the drawings, like reference numerals designate similar parts.Detailed Description

[0043] Advance warning monitoring systems for monitoring ambient conditions at facilities (such as hay sheds), where there may be a risk of spontaneous combustion due to the contents of the sheds, are described herein. These systems use sensors that are located relative to the facility for the purpose of collecting data to ascertain when an increased risk occurs, for example an increased fire risk. In some embodiments, for example, a first sensor may be positioned inside a facility (where the ambient conditions may be caused by and / or may affect the fire risk) and a second sensor may be positioned outside the facility to provide a control signal. The two signals are compared to determine if there are any indicators of an increased risk to the facility.

[0044] Embodiments of the systems described herein provide Internet of Things (loT) solutions for agriculture, for example for hay and fodder. Data recording may be facilitated, for example for insurance and / or quality assurance purposes.Advantageously, the asset monitoring systems described herein enable forward warning of potential disasters or damage.System overview

[0045] Figure 1 of the drawings shows an advance warning monitoring system 100 for monitoring ambient conditions in a facility. The monitoring system 100 comprises a monitoring sensor device 102 positioned relative to the facility in a location suitable for monitoring internal ambient conditions in the facility. The monitoring system 100 also comprises a control sensor device 104 positioned relative to the facility in a location suitable for monitoring external ambient conditions external to the facility. The monitoring system comprises a processor 106 in data communication with the monitoring sensor device 102 and the control sensor device 104, and the processor is configured to cause the system 100 to monitor ambient conditions in the facility by comparing the sensed conditions in the facility and the sensed conditions external to the facility over a time interval. In some embodiments, the processor may be configured to track any changes over time of the ratio between internal and external conditions, such as the internal and external temperature. In some embodiments the processor may track a speed of change. In some embodiments the processor may track an acceleration of change.

[0046] In some embodiments, the control sensor device includes the processor, and also includes a warning or alert means, such as an alarm or a communication system (for example a networked messaging module). If the processor determines that the internal conditions compared to the external conditions exceed a safety threshold, the processor then causes the alert means to communicate an alert, such as sending a message to a user or sounding an alarm.

[0047] As illustrated in Figure 5 of the drawings, in some embodiments, the processor may be configured to execute machine readable instructions to receive, at502, internal ambient condition data from one or more monitoring sensor devices, receive. At 504, external ambient condition data from the control sensor device, and at 506 determine at least one ambient condition indicator. The indicator may comprise one or more of an internal ambient condition trend, an external ambient condition trend, an internal-to-external ambient condition difference, and an internal-to-external ambient condition difference trend. The processor may further be configured to execute instructions to compare, at 508, the condition indicator with a warning threshold to determine a safety risk (for example a fire risk), and to then output, at 510, a warning signal based on the comparison and / or the determined safety risk.

[0048] The comparison may be based on an automatic, default, and / or user input setpoint that functions as a warning threshold. The setpoint may, for example, be between 2 and 10 degrees Celsius, for example 5 degrees Celsius. The processor then performs the following algorithm to determine whether a warning alarm is generated: (Sensor measure - Control measure\ > Setpoint = alarm)

[0049] In some embodiments, the system may include one or more control outputs when a fire risk situation is identified (i.e., when the warning threshold is reached). In some embodiments the processor of the system may be configured to effect a reduction in internal temperature of the facility based on the determined safety risk, for example by sending a control signal to a temperature system. The temperature system may include one or more of: a fan, an air conditioner, a door and / or window opening mechanism.

[0050] Typically, in use, one or more monitoring devices are suspended from the shed roof, with the control device positioned inside or outside (but away from the monitoring devices) to monitor ambient temperature. The monitoring sensor device 102 may be adapted to be positioned in an upper region of the facility, for example the device may include an attachment means such as a hook, loop, screw, etc. to aid in positioning the device at, adjacent or close to a ceiling of the facility. Because hot air rises, the upper region inside the facility often tends to be the hottest region within a building. In use, the monitoring sensor device is placed close to or within the hottestregion of the facility, for example the monitoring sensor device is positioned at an apex of a ceiling of the facility.

[0051] The monitored ambient conditions may comprise temperature and / or humidity. The sensor device may include sensors to monitor other conditions, depending on the requirements for the implementation.

[0052] As illustrated in the embodiment of a monitoring system 200 shown in Figure 2 of the drawings, the control sensor device 204, positioned outside the facility or somewhere relative to the facility where the conditions are not affected by the conditions inside the facility, may include a processor 206, and has a communication interface 208 in communication with the monitoring sensor device 202 for receiving the internal ambient condition data. The communication may be via any suitable communication network or protocol, for example via a wireless communication link 210 using BLE or Wi-Fi, etc.

[0053] Figure 3 of the drawings illustrates another embodiment of a monitoring system 300. The system 300 includes a user device 312, for example a desktop computer, laptop, tablet, mobile device, etc., in communication with the processor via a communication network 314. In this type of configuration the processor may be part of an onsite standalone processing module, it may be part of the control sensor device 204, or the processing functionality may be provided by a server 316 that received the sensor data from the devices 302, 304 via the network 314. The server may be a cloudbased server. The server 316 may be associated with a database 318 for storing sensor data, processed data, and / or other data relevant to the operation of the system 300. In embodiments that include a user device, any generated warning signals may be output to the user device. One or more monitoring devices may be used, and data from each monitoring device is compared to the control device reading. The user device includes or displays a user interface, allowing for visualization and comparison of data across sheds and bays.

[0054] Some embodiments utilise an “Internet of Things” (loT) logger to record the sensor data, facilitating monitoring via either NB-IoT or WAN networks. The monitoring sensor device may comprise a data logger and log the internal ambient condition data. The control sensor device may comprise a data logger and log the external ambient condition data.

[0055] Figure 4 of the drawings illustrates another embodiment of an advance warning monitoring system 400. The monitoring system 400 is configured to monitor ambient conditions in a facility 401 such as a hay shed or other type of storage facility. The system 400 includes one or more monitoring sensor device 402 positioned within the facility, for example in an upper region of the building such as close to the apex of the roof where the devices 402 monitor the internal ambient conditions in the facility 401.

[0056] The monitoring device may be specifically configured to be position close to the apex of the ceiling, for example in shape, size, weight, and attachment means (such as having a hook, bolt, screw or other appropriate attachment extending from the body of the device in such a way that the device is attachable to a metal, wood, concrete etc. ceiling as is common for storage sheds).

[0057] The system 400 also includes a control sensor device 404 positioned external to the facility 401 to monitor external ambient conditions external to the facility. The monitoring sensor devices 402 and the control sensor device 404 are in communication with a monitoring platform 420 via a communication network 414 that may include a LoRaWAN Network.

[0058] The monitoring platform 420 may be provided by a cloud computing platform, and in the illustrated embodiment is supported by the Amazon Web Services (AWS) cloud computing platform. The monitoring platform 420 comprises one or more of the following building blocks:421 : A managed cloud service that lets connected devices easily and securely interact with cloud applications and other devices in the form of AWS loT Core;422: A web application, in this example facilitated by NestJS and / or AWS Lambda; 423 : An loT LoRaWAN network server supported by Actility;424: A database, in the exemplary embodiment provided by Amazon Relational Database Service (RDS); and425: A web application, implemented in this example by the Angular framework.Anomaly Detection

[0059] Anomaly detection is used to identify rare events, outliers, or deviations in data that significantly differ from standard behaviours or patterns. Typical techniques that may be used for anomaly detection include statistical methods, machine learning algorithms, time-series analysis, and probabilistic models. These methods involve establishing normal behaviour patterns, setting thresholds or models to detect deviations, and leveraging domain-specific knowledge to enhance accuracy. Real-time monitoring is used for promptly identifying anomalies as they occur. By combining these approaches and adapting them to the specific characteristics of the sensor data and environmental conditions, effective anomaly detection can be achieved, contributing to improved decision-making and proactive maintenance in various applications.

[0060] In some embodiments, machine learning is used for anomaly detection. Density-based methods are used to identify data points in dense regions as normal, and those in low-density regions as anomalies. Clustering methods group data points into clusters based on their similarity or proximity and assign labels to each cluster. Data points that belong to small or sparse clusters, or that are far away from their assigned cluster, can be considered anomalies. Support Vector Machines (SVM) are a supervised learning algorithm used for classification and regression. In anomaly detection, SVMs act as “one-class machines” to identify normal patterns and detect anomalies.Sensor Device

[0061] The advance warning sensor devices described herein offer a novel and resilient solution for measuring ambient temperature and humidity. In variousembodiments, the sensor device is configured to operate on NB-IoT (Narrowband Internet of Things), CAT -Ml (Category Ml), and / or LoRaWAN (Long Range Wide Area Network) supported networks.

[0062] In various embodiments, the sensor device is configured to ensure secure remote configuration through COTA (Configurable Over-The-Air) and to facilitate secure remote firmware upgrades through FOTA (Firmware Over-The-Air).

[0063] In various embodiments, security measures are bolstered by DTLS (Datagram Transport Layer Security) with support for PSK (Pre-Shared Key), x.508, and x.509 protocols.

[0064] The sensor device includes a power source, for example in the form of a replaceable and / or rechargeable battery.

[0065] In various embodiments, the device supports multiple protocols, including MQTT (a lightweight messaging protocol typically used in loT applications) and LoRa (used in radio communication).

[0066] The sensor device may include one or more status indicators, for example in the form of LEDs, facilitating ease of monitoring.

[0067] Operating within a temperature range of 0 to 65 degrees Celsius, the device is configured to provide ±2% RH (Relative Humidity) accuracy tolerance and ±0.2 degrees Celsius temperature accuracy tolerance.Temperature and Humidity Sensors

[0068] Several types of temperature sensors can be used for monitoring hay shed temperatures. Some examples suitable for the monitoring systems described herein include: a. Thermocouples: Thermocouples consist of two different metal wires joined at one end. They generate a voltage proportional to thetemperature difference between the ends. They are durable, have a wide temperature range, and are relatively inexpensive. However, they may require calibration and have lower accuracy compared to other sensors. b. Resistance Temperature Detectors (RTDs): RTDs are temperature sensors made from pure metals or metal alloys whose electrical resistance changes with temperature. They offer high accuracy, stability, and repeatability, especially in a narrow temperature range. However, they can be more expensive and less durable than thermocouples. c. Thermistors: Thermistors are temperature-sensitive resistors made from semiconductor materials. They offer high sensitivity and accuracy, especially at lower temperatures, and are relatively inexpensive. However, they have a limited temperature range and may require calibration. d. Infrared (IR) Sensors: IR sensors measure temperature by detecting infrared radiation emitted by objects. They are non-contact sensors, making them suitable for remote monitoring and inaccessible locations. They offer fast response times and can measure temperature without disturbing the environment. However, they may be affected by factors such as humidity and surface emissivity, and they typically have a limited measurement range. e. Digital Temperature Sensors: These sensors use integrated circuit technology to measure temperature and convert it into a digital signal. They offer high accuracy, resolution, and stability, along with low power consumption. They are also relatively easy to interface with microcontrollers and data acquisition systems. However, they may be more expensive than other types of sensors and may have limited measurement ranges.

[0069] The choice of temperature sensor depends on factors such as the desired accuracy, measurement range, cost, environmental conditions, and application requirements.

[0070] Several types of sensors can be used to monitor humidity levels in hay sheds Some examples suitable for the monitoring systems described herein include: a. Capacitive Humidity Sensors: These sensors measure relative humidity by detecting changes in capacitance caused by moisture absorption or desorption on a porous dielectric material. They offer good accuracy and response time, along with low power consumption. Capacitive humidity sensors are commonly used in various applications due to their reliability and cost-effectiveness. b. Resistive Humidity Sensors: Resistive humidity sensors work by measuring changes in electrical resistance caused by moisture absorption or desorption on a hygroscopic material. They are simple and inexpensive, with fast response times. However, they may require calibration and are sensitive to contamination and drift over time. c. Thermal Conductivity Humidity Sensors: These sensors measure relative humidity by detecting changes in the thermal conductivity of air caused by moisture content. They typically consist of two temperature-sensitive elements, one exposed to air and the other covered with a moisture- permeable membrane. Thermal conductivity humidity sensors offer good accuracy and stability, especially at higher humidity levels. d. Gravimetric Humidity Sensors: Gravimetric humidity sensors measure humidity by directly measuring the mass of water vapor absorbed or desorbed by a material. They offer high accuracy and precision but are less commonly used due to their complexity, high cost, and slow response times. e. Optical Humidity Sensors: Optical humidity sensors use light absorption or scattering properties of materials to measure relative humidity. Theyoffer fast response times and are less affected by contamination or drift compared to other sensor types. However, they may be more expensive and less accurate, especially in extreme environmental conditions.

[0071] The choice of humidity sensor depends on factors such as accuracy requirements, response time, environmental conditions, cost, and application-specific constraints.User Interface

[0072] The user device may be an end user device, a service provider user device, an intermediate user device, or any other user of the system. The monitoring platform provides access to data to the user via a user interface displayed on the user device. The data that is accessible and displayed may be user configured according to the specific implementation.

[0073] Referring to Figure 6, for example, a monitoring service provider may have access to data associated with various monitored facilities 610 via one exemplary embodiment of the user interface 600. If a first facility 620 (“Hay Australia”) is selected by the user, then the user interface displays monitoring information associated with that facility that relate to the DEVICES 612, the SITE 614 and a MAP 616. As shown in Figure 7, the information related to the devices include the last measured temperature 702, humidity 704, and battery charge 706 are displayed for each of a control device 710 and three monitoring devices 712, 714, 716.

[0074] Figure 8 shows an example of a user interface displaying SITE information 800 that shows an overview of data relating to the particular site. As illustrated, this may include one or more of: weather bureau (bureau of meteorology, BOM) temperature and humidity, and control device and monitoring device measurements (e.g. maximum measurements over a specified time period), together with the date that those measurements were taken. Figure 9 shows an example of MAP information 900 that displays the location of the facility.

[0075] The user interface of the monitoring platform may include a navigation panel, for example panel 1000 as illustrated in Figure 10 of the drawings. By selecting the “Map” option, the user is able to see a mapped display 1010 of the various facilities shown in the “Overview” display illustrated in Figure 6. From here the user is also able to navigate to particular facilities by selecting the facilities displayed on the map, thereby displaying information as described with reference to Figures 7 to 9. Also shown in Figure 10 is a warning 1020 associated with a particular facility, the warning indicating the relevant facility and displaying relating information (e.g. facility name, a monitoring device identifier, and the date of the alarm).

[0076] By selecting the “Trends” option in the panel 1000, the user is able to navigate to a trend display 1100 as illustrated in Figure 11. In this example, the temperature, humidity and battery measurements of the control and / or monitoring devices can be plotted on a graph 1110, and the latest numerical readings 1120 are also displayed together with a temperature variance calculation 1130.

[0077] For each facility, the user is able to configure the settings 1200 to be suitable for the specific circumstances, as shown in Figure 12 of the drawings. The monitoring and / or control devices are configurable with respect to a warning temperature variance 1210, an alarm temperature variance 1220, a reporting frequency 1230 and / or a start time 1240 (typically selected to be a cooler time during the night thus providing a cool baseline measurement).Advantages

[0078] The shed monitoring systems described herein are able to utilise advanced technology including NB-IoT, Lora-WAN technology, cloud hosting, etc., together with a user-friendly mobile platform, to alert a user of changes in temperature and humidity in exact locations of a storage facility, and allowing easy viewing and monitoring of single or multiple facilities.

[0079] Advantageously, the systems and methods described herein provide an early warning system by providing an alert or warning signal before combustion occurs, asenvironmental conditions approach a combustion danger threshold. The systems and methods may be used for a variety of commodities at risk of spontaneous combustion, such as grain, cotton seed, etc., and also waste and / or recycling plants. For example, the systems may be used for oaten hay, as the higher the grade of oaten hay, the higher the risk is of the hay spontaneously combusting if there are any high moisture bales or wet slugs captured in a bale.

[0080] By having a control sensor outside the facility or building (e.g., a hay shed), and a monitoring sensor inside the facility and / or closer to the environment or contents of the facility that are being monitored (e.g., at an apex of a hay shed apex), the variance in the measurements can be monitored for the purpose of providing information regarding potential abnormal events, such as combustion of the contents of the facility. This is significantly more effective than existing point contacts that are applied to, adjacent, or within bales, resulting in a very small observation area.

[0081] Key benefits of the sensor devices, systems and methods described herein include being cost-efficient, network agnosticism, diverse deployment capabilities, and flexible support options.

[0082] The system described herein is a significantly more cost-effective solution than monitoring systems that require a sensor per hay bale, where sensors are inserted into the hay bales to monitor its internal temperature.

[0083] The systems described herein allow for action to be taken to pre-emptively remove surrounding hay from a storage are or building that is as risk, and also allow for the readiness of fire prevention equipment onsite.

[0084] It will be understood to persons skilled in the art of the invention that many modifications may be made without departing from the spirit and scope of the invention.

Claims

CLAIMS:

1. An advance warning monitoring system for monitoring a spontaneous combustion risk in a hay storage facility, the monitoring system comprising: a monitoring sensor device positioned within the facility to monitor internal ambient conditions in the facility, wherein the monitoring sensor device is configured to be positioned at an apex of a ceiling of the facility; a control sensor device positioned external to the facility to monitor external ambient conditions external to the facility; and a processor in data communication with the monitoring sensor device and the control sensor device, the processor configured to execute machine readable instructions to: receive monitoring sensor data representing conditions in the facility; receive control sensor data representing conditions external to the facility; based on the received data, determine at least one ambient condition indicator comprising one or more of: an internal ambient condition trend; an external ambient condition trend; an intemal-to-external ambient condition difference; and an intemal-to-external ambient condition difference trend; perform a comparison between the condition indicator with a warning threshold to determine a spontaneous combustion risk; output a warning signal based on the based on the spontaneous combustion risk; and effect a reduction in internal temperature of the facility based on the determined spontaneous combustion risk.

2. An advance warning monitoring system for monitoring ambient conditions in a facility, the monitoring system comprising: a monitoring sensor device positioned within the facility to monitor internal ambient conditions in the facility; a control sensor device positioned external to the facility to monitor external ambient conditions external to the facility; and a processor in data communication with the monitoring sensor device and the control sensor device, the processor configured to execute machine readable instructions to: receive monitoring sensor data representing conditions in the facility; receive control sensor data representing conditions external to the facility; based on the received data, determine at least one ambient condition indicator; perform a comparison between the condition indicator with a warning threshold to determine a safety risk; and output a warning signal based on the safety risk.

3. The system of claim 2, wherein the monitoring sensor device is configured to be positioned at a ceiling of the facility.

4. The system of claim 2 or claim 3, wherein the monitoring sensor device is configured to be positioned at an apex of a ceiling of the facility.

5. The system of any one of the preceding claims, wherein the monitored ambient conditions comprise at least one of temperature and humidity.

6. The system of any one of the preceding claims, wherein the control sensor device comprises the processor and comprises a communication interface incommunication with the monitoring sensor device for receiving the internal ambient condition data.

7. The system of any one of the preceding claims, further comprising a user device in communication with the processor via a communication network, and wherein the warning signal is output to the user device.

8. The system of any one of the preceding claims, wherein the monitoring sensor device comprises a data logger and logs the internal ambient condition data.

9. The system of any one of the preceding claims, wherein the control sensor device comprises a data logger and logs the external ambient condition data.

10. The system of any one of claims 2 to 9, wherein the at least one ambient condition indicator comprises one or more of: an internal ambient condition trend; an external ambient condition trend; an intemal-to-external ambient condition difference; and an intemal-to-external ambient condition difference trend.

11. The system of any one of claims 2 to 10, wherein the processor is configured to effect a reduction in internal temperature of the facility based on the determined safety risk.

12. A method of monitoring ambient conditions in a facility, the method comprising: at a processor: receiving monitoring sensor data representing conditions in the facility;receiving control sensor data representing conditions external to the facility; and comparing the monitoring sensor data and the control sensor data over a time interval to monitor for an anomaly.

13. The method of claim 12, wherein comparing the monitoring sensor data and the control sensor data comprises: determining at least one ambient condition indicator comprising one or more of: an internal ambient condition trend; an external ambient condition trend; an intemal-to-external ambient condition difference; and an intemal-to-external ambient condition difference trend; and performing a comparison between the condition indicator with a warning threshold.

14. The method of claim 13 further comprising outputting a warning signal based on the comparison.

15. The method of any one of claims 12 to 14, further comprising: measuring, by a monitoring sensor positioned relative to a ceiling of the facility, monitoring sensor data comprising an internal temperature at an apex of the ceiling of the facility; andmeasuring, by a control sensor positioned outside the facility, control sensor data comprising an external temperature representative of a general ambient temperature outside the facility.

16. The method of any one of claims 12 to 15, wherein comparing the monitoring sensor data and the control sensor data comprises comparing a difference between the monitoring sensor data and the control sensor data with a threshold value associated with a fire or combustion risk.

17. The method of any one of claims 12 to 16, further comprising: by the processor: responsive to the comparing, communicating a warning signal if a risk threshold is exceeded.

18. The method of any one of claims 12 to 17, further comprising: by the processor: responsive to the comparing, effecting a reduction in internal temperature of the facility.

19. The method of claim 18, wherein the effecting comprises sending a control signal to a temperature control system to reduce the internal temperature.

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