EP4394734B1 - System und verfahren zur erkennung und vorhersage von waldbränden - Google Patents

System und verfahren zur erkennung und vorhersage von waldbränden

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Publication number
EP4394734B1
EP4394734B1 EP22461659.9A EP22461659A EP4394734B1 EP 4394734 B1 EP4394734 B1 EP 4394734B1 EP 22461659 A EP22461659 A EP 22461659A EP 4394734 B1 EP4394734 B1 EP 4394734B1
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EP
European Patent Office
Prior art keywords
fire
detection
alarm
devices
detection device
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EP22461659.9A
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English (en)
French (fr)
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EP4394734C0 (de
EP4394734A1 (de
Inventor
Konrad Zuchniak
Piotr Jaglarz
Eryk Kosk
Piotr Szelag
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Neuralbit Technologies Sp Z O O
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Neuralbit Technologies Sp Z O O
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Priority to EP22461659.9A priority Critical patent/EP4394734B1/de
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Publication of EP4394734C0 publication Critical patent/EP4394734C0/de
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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B17/00—Fire alarms; Alarms responsive to explosion
    • G08B17/005—Fire alarms; Alarms responsive to explosion for forest fires, e.g. detecting fires spread over a large or outdoors area
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Definitions

  • the present invention relates to a system and method for wildfire detection and prediction.
  • Such a known device, system and method for wildfire detecting is disclosed in the patent US10762758 .
  • a fire detection device and notification system configured for generating alerts based on detected environmental conditions (e.g., temperature, humidity, presence of flame or smoke or combustion gas).
  • the disclosed fire detection device employs various sensor devices (e.g., temperature, humidity, flame, smoke, gas, and the like) to collect environmental data and determine whether the detected environmental conditions indicate the presence of or the increased possibility of a fire.
  • the invention further comprises a notification system for automatically generating and transmitting alerts to one or more computing devices (e.g., responder dispatch systems) based on the detection of hazardous conditions.
  • US2021350691 A1 discloses a fire detection system (FDS) device and methods for operating an FDS device to detect a potential fire and communicate information regarding fire detection events to a central fire detection system via a wireless communication network.
  • Various embodiments include receiving information from one or more sensors configured to detect an indication of a possible fire, determining whether information received from the one or more sensors satisfy one or more threshold criteria indicative of a fire event, generating a fire warning message comprising a fire alarm object in response to determining that the information received from the one or more sensors satisfy one or more threshold criteria indicative of a fire event, and sending the generated fire warning message to a remote server via a communication network.
  • US2017169683 A1 discloses a method for detecting a fire, the method comprising the following steps: during a first time period: detecting an increase in ambient light intensity and detecting an increase in ambient humidity; responsive to the increase in ambient light intensity and the increase in ambient humidity, detecting a fire event; during a second time period: correlating a decrease in ambient light intensity with an increase in visual obscuration; detecting an increase in ambient air temperature; in response to a magnitude of the increase in visual obscuration remaining below a high obscuration threshold and a magnitude of the increase in ambient temperature remaining below a high temperature threshold, classifying the fire as an incipient fire; and, in response to the magnitude of the increase in visual obscuration exceeding the high obscuration threshold and the magnitude of the increase in ambient temperature exceeding the high temperature threshold, classifying the fire as a developed fire.
  • the fire probability and fire localization is predicted with additional use of a trained prediction model to be combined with said heuristic prediction model.
  • the predicted fire probability is a percentage probability of the occurrence of a fire calculated as an average of the output probability of the trained model and the heuristic model, while the predicted fire location is the fire location predicted by the trained model if the difference between the fire locations returned by the heuristic prediction model and the trained prediction module divided by the distance between the two most distant detection devices is less than a predefined maximal error.
  • the received alarm signal is generated upon calculating an alarm function value which exceeds a threshold value.
  • the alarm function is calculated based at least on measurements of concentration of volatile compounds.
  • the working mode management module of the detection device is configured to set frequency of measurements and frequency of sending out data at least depending on the low, medium or high fire risk determined based on environmental parameter measurements.
  • a computer-readable storage medium having instructions encoded thereon which when executed by a processor, cause the processor to perform the method according to the invention.
  • the system according to the invention is designed for the preventive detection of wildfires (with a focus on forest environments) and uses a dense network of sensors that constantly monitor environmental parameters (humidity, temperature, air particulate content, etc.) to detect even trace amounts of smoke. This allows for the detection of fires at a very early stage, which is crucial for successful firefighting operations.
  • the detection devices according to the invention sensors constantly monitor air parameters in search of anomalies. In case of even trace amounts of smoke detected, a single detection device sends the information to the central server and triggers nearby detection devices to send measurements more frequently.
  • advanced machine learning algorithms combine and analyze the data received from the sensor network in real time to eliminate false alarms and determine the exact location and direction of the fire's development. In case of danger, the local fire brigade is alerted automatically.
  • the system sends information about the location of the fire, its predicted size, and additional forecasts regarding the direction and speed of the fire's spread. Thanks to the acquisition of precise data from the sensor network, foresters and firefighters can better adapt to the existing threat.
  • the system according to the invention updates fire development data in real time, which allows for more efficient management of the firefighting operation.
  • detection method is enhanced by hybrid, combined machine learning and heuristic algorithm.
  • the method gain more robustness but also more stability.
  • Specific deep learning neural network structure allows processing data from many devices, where the algorithm can process data from a different number of sensors.
  • the method can better eliminate false positives and enables a more accurate estimation of the location of the fire and predicts its spread which enhances the usefulness of the solution during the firefighting operation by providing valuable data and predictions in real time.
  • the method/system according to the invention has better efficiency also because of the use of controllable operating modes and an intelligent battery power supply system, the entire solution is much more energy-efficient, the capacity of the power source can be much smaller, which reduces the costs of implementing the invention.
  • results of the method according to the invention are outputted in a complex manner, namely by providing appropriate emergency services with such information like the location of the fire, its predicted size, and additional forecasts regarding the direction and speed of the fire's spread.
  • the system and the method for fire detecting can be useful for ultra-early wildfire identifying in the forest.
  • the system and the method according to the invention can be of practical meaning also for wildfires in other non-urbanized areas, such as agricultural wastelands, grasses or bushes.
  • Coupled means a direct or indirect connection, such as a direct electrical, mechanical, or magnetic connection between the things that are connected or an indirect connection, through one or more passive or active intermediary devices.
  • module or “logic” or “unit” may refer to one or more passive and/or active components that are arranged to cooperate with one another to provide a desired function.
  • signal may refer to at least one current signal, voltage signal, magnetic signal, or data/clock signal.
  • the meaning of "a,” “an,” and “the” include plural references.
  • the meaning of “in” includes “in” and “on.”
  • the terms “substantially,” “close,” “approximately,” “near,” and “about,” generally refer to being within +/-10 percent of a target value.
  • real-time here generally refers to systems that respond under real-time or live constraints and generates a result within a time frame (e.g., in few or less microseconds).
  • the system 1 for wildfire detection according to the invention can be divided into three main components: a) a set of single detection devices 100 b) a cloud based fire prediction software embedded in the central unit 200 c) plurality of user stations 300.
  • the set of single detection devices is placed locally in the forest or other area of interest.
  • the main part of the system namely at least a managing module 202 and a fire predicting module 203 are embedded in the cloud based software 200 in the central unit 200.
  • the last part of the system are user stations 300 located stationary or being mobiles with the use of which fire alarms or monitoring information are delivered to the end users like fireman, forest inspectors, forest manager or other employee dealing with forest management and fire protection.
  • a single detecting device 100 according to the invention can be shown in Fig.2 .
  • it can be a waterproof box/case (not shown) with an appropriate sealing system, the outer box being made of plastic or metal. in which all electrical components are housed.
  • attaching means configured to be complementary to attaching means appropriate for particular terrain type and allowing secure attachment in the predetermined location.
  • attaching means configured to be complementary to attaching means appropriate for particular terrain type and allowing secure attachment in the predetermined location.
  • it can be a strap made of plastic with an appropriate harness for mounting the sensor on a selected tree.
  • the detection device100 according to the invention is a small device designed for long-term operation in outdoor environments.
  • the detection device 100 has several components coupled to each other. It comprises a power generating module 101, power module 102, a processing unit 103, sensor module 104 and a transmission module 105.
  • the power generating module 101 can be a built-in small photovoltaic panel 101 (4.5 cm x 4.5cm), which allows to maintain continuous power supply. In other embodiments the power generating module 101 can be any other type of green energy converter appropriate for such use.
  • the power module 102 of the detection device 100 supplies power to all electrical components of the detection device 100.
  • the detection device 100 is advantageously powered by a small battery 102.
  • the power module 102 can be a set of supercapacitors.
  • the power module 102 is recharged by the power generating module 101, for example solar energy from the photovoltaic panel 101.
  • a special dynamic inverter (not shown) is used, which selects the charging voltage in such a way as to maximize the energy that can be obtained under given lighting conditions. Thanks to this approach it is possible to efficiently power the detection device 100 also in forest conditions where there is mostly shade and the access to direct light is rare.
  • the primary sensor 106a that is used is the commercially available BOSCH BME680 which is an air quality sensor. However, it works in such a way that it is a volatile compounds' sensor 106a that detects the concentration of volatile compounds.
  • the volatile compounds sensor 106a accurately measures the resistance of the surrounding gases. The worse the air quality, understood by the concentration of volatile compounds, the higher the resistance. It turns out that this parameter is a very good indicator of fire, because the smoke from burning forest material, mulch, etc. contains a high concentration of volatile compounds.
  • the sensor module 104 comprises, in addition, for calibration purposes, an independent temperature and humidity sensor 106b that monitors air temperature and humidity.
  • Other sensors that can be present in the sensor module 103 of the detection device 100 according to the invention are atmospheric pressure sensor, radiance sensor, flame sensor, insolation sensor.
  • the temperature and humidity sensor as well as an atmospheric pressure sensor are used, but they mainly perform correction functions.
  • the main indicator of fire is the resistance of gases, which is read as raw data and already processed by the microcontroller of the already mentioned commercially available sensor itself as a parameter informing about air quality. When there are free volatile compounds in the air, the gas resistance parameter decreases and the air quality parameter (a non-linear inversely proportional function to the gas resistance) increases.
  • the detection device 100 also comprises a processing unit 103 that is responsible for several tasks and thus comprises several software implemented modules like: power managing module 103a, collected data pre-processing module 103b, working mode management module 103c, a data sending and receiving module 103d.
  • a processing unit 103 that is responsible for several tasks and thus comprises several software implemented modules like: power managing module 103a, collected data pre-processing module 103b, working mode management module 103c, a data sending and receiving module 103d.
  • power managing module 103a the detection device 100 also comprises a processing unit 103 that is responsible for several tasks and thus comprises several software implemented modules like: power managing module 103a, collected data pre-processing module 103b, working mode management module 103c, a data sending and receiving module 103d.
  • power managing module 103a collected data pre-processing module 103b
  • working mode management module 103c working mode management module
  • a data sending and receiving module 103d Detailed information on operations performed by the local processing unit 103 is given
  • the power managing module 103a is responsible for selecting the appropriate working point, giving optimal efficiency of the photovoltaic panel at the available insolation. Said module is also involved in optimization in terms of power consumption.
  • the power managing module 103a controls the operation of the detection device 100 using several deep sleep modes. This allows to remotely control the detection devices 100 and operate in different modes as needed, in particular energy-saving ones. In general, modes that measure more often use more energy, and energy-saving variants measure less often.
  • the data sending and receiving module 103d is responsible for aggregating data into packets, ordering their sending and handling data received from the server, which are then used to change the operating mode, the frequency of measurements or the frequency of data sending.
  • the collected data pre-processing module 103b is responsible for processing data collected by sensors 106. Operations that are performed over collected data are in particular averaging and calculating the value of the pre-alarm function in order to check if the initial fire detection condition is met. Pre-processing of the collected data by the local processing unit 103, namely by making processing data parallel, allows to monitor potential fires in real-time. In particular as a consequence of initial data pre-processing and filtering by the pre-processing module 103b not all readings collected by the detection device 100 are always sent. Advantageously, averaged readings are only sent.
  • the working mode management module 103c deals with setting the appropriate frequency of measurements depending on the available battery charge level and the current level of fire risk.
  • the detection device 100 comprises also a communication module 105.
  • the communication module 105 allows to communicate in the LPWAN band (low-power wide-area network). Specifically, it can be the NB-loT band which allows the detection devices 100 to communicate directly with telecommunications operators' BTS stations as shown in Fig.2 .
  • the communication module 105 allows to operate on the basis of LORA-WAN technology.
  • the gateway has to be run by a third party, in particular the operator of the detection devices 100. In said embodiment the gateway communicates with the detection devices 100 and then passes the signal on as shown in Fig.3 .
  • single detection devices 100 are disposed in different locations so as to cover the area of interest to be monitored.
  • the detection devices 100 are not directly connected to each other (it is not a mesh grid connection) however they can influence each other's operation as it will be further described in reference to Fig.6a and 6b.
  • the detection devices 100 communicate directly with the central unit which can be a computing server 200.
  • the central unit 200 communicates with at least one user station 300.
  • the user station 300 can be a laptop or a mobile phone provided with an appropriate application or alternatively the access to the system according to the invention can be supplied via a browser run on said user station 300.
  • the method for wildfire detection according to the invention can be divided into two main components: a) the operation of an individual detection device 100, b) aggregation and processing data from detection devices 100 by a cloud-based software embedded in the central unit 200. Both components of the method are run on different hardware entities.
  • the method according to the invention comprises the following steps: a) setting an initial first working mode; b) acquiring data from at least volatile compounds' sensor 106a then iteratively c) pre-processing collected data and checking a fire detection condition or checking the input data for request to change the working mode; d) if the fire detection condition is met then sending out a pre-alarm signal containing measurement values recorded by sensors and changing the working mode into the second working mode, or if the fire detection condition is not met but if the request to change the initial working mode is received then changing the working mode into the second working mode; then iteratively e) checking the input data for request to change the working mode, and f) if the request to change the initial working mode is not received, then pre-processing collected data and sending out pre-processed data collected from at least one sensor 106a according to the second working mode, or if the request to change the initial working mode is received changing the working mode to the first initial working mode.
  • a broadcasting mode is set.
  • the detection devices operates in the power saving mode, namely measurements are performed at fixed time, namely, depending on the amount of available energy and the level of fire risk, the frequency of taking and sending measurements is selected, moreover, pre-processed data sent to the central unit 200 with a selected frequency only.
  • the frequency of taking and sending measurements is selected, moreover, pre-processed data sent to the central unit 200 with a selected frequency only.
  • every period of time the data collected by the detection device 100 is sent regardless of the value of the alarm function calculated in another step.
  • the algorithm of setting measurement frequency comprises the following steps: first, information about the current state of charge of the detection device 100, the current level of fire risk and a short-term weather forecast are acquired. Short-term weather forecast is used to determine how much energy the solar panel will generate in the next few days. If the fire risk is low, the measurement frequency is selected to keep the battery at full charge. In the case of medium and high fire risk, measurements are made more frequently, ensuring that the device does not discharge completely. Moreover, the weather forecast is used to estimate how much the battery can be discharged, taking into account the charging intensity in future days, so as not to discharge the detection device 100. The greater the risk of fire, the battery level and the expected value of future charging, the more frequent the measurements. A detailed description of the algorithm for selecting the frequency of measurements and sending data is presented in the block diagram in Fig. 7b . The above-mentioned operations that result in outputting signals to other modules of the detection device 100 are performed by the working mode management module 103c.
  • sensors 106 measure at least parameters like volatile compounds concentration, which is measured by checking value of resistance of the gas surrounding the sensor and advantageously in addition parameters like temperature, humidity, atmospheric pressure or insolation.
  • step of pre-processing data collected from sensors 106 is averaged by using a moving average, with a time window of 30-300s if the current mode is the first working mode, namely broadcasting mode. If the current mode is the second working mode, namely an alarm mode then data collected from sensors 106 is averaged by using a moving average, with a time window of 10-30s.
  • the collected data pre-procesing module 103b uses an algorithm that monitors the relative growth of the monitored environment parameter values over time. Absolute values would be a bad indicator of the appearance of anomalies because the sensors 106 may become calibrated, as well as the volatile compounds sensor 106a can register a steady increase in the concentration of particulate matter and volatile compounds being a result of smog which may occur in forest areas adjacent to urban areas. To the contrary, monitoring relative increases makes it possible to become independent of the above factors.
  • the details of the calculation of the alarm function in the pre-processing module 103b are as follows: the maximum value of the alarm function from the analyzed time window is calculated. The alarm function depends on the readings recorded by the device.
  • the detection device 100 further checks the input data for the request to change the working mode into the second working mode, namely an alarm mode. However, if there is no such a request the detection device 100 returns to the step of acquiring data in the first working mode as described earlier.
  • the detection device 100 considers such a condition as the occurrence of a suspected alarm and sends notifications to the central server 200, which then wakes up neighboring devices 100. In parallel said detection device 100 itself changes the working mode into the second working mode, namely the alarm mode. Then the detection device 100 collects and preprocess data in the second working mode and iteratively checks input data for the request to change the mode into the first working mode. Such a request is sent back to all detection devices 100 after a while if the alarm is negative and the fire detection risk is not confirmed at the computing server 200 level.
  • the central unit 200 receives the information that the first detection device 100 of the plurality of detection devices 100 detected some anomalies in the environment parameter values over time allowing to determine that there was a fire, the central unit 200 sends to at least a second detection device 100 a request to change the working mode into the second working mode, namely the alarm working mode. Said at least one second detection device 100 is located in the predefined distance from the first detection device 100.
  • the processing unit 103 in response to a request for changing the working mode into the second working mode, the processing unit 103, namely the working mode management module 103c sends a setting signal to the sensors 106 in the sensor module 104 in order to set appropriate measurement which means that from now data are sent with a higher frequency as well as sends a setting signal to the data sending and receiving module 103d in order to set appropriate data sending frequencies.
  • the step of sending pre-processed data collected from at least one sensor 106a according to the alert working mode all available pre-processed data are sent to the central unit 200.
  • the input data is checked for the request to change the current working mode (namely the alarm mode) into the first initial working mode if the risk of fire has not been confirmed.
  • a special cascade triggering routine is provided for the plurality of detection devices 100 according to the invention.
  • the fire is detected by one of the plurality of detection devices 100 (see Fig. 5a )
  • other adjacent detection devices 100 are remotely forced by the central unit 200 to enter the same alert mode as the first detection device 100 (see Fig. 5b ).
  • the number of neighbouring detection devices 100 that has to be triggered in such a case depends on the number of devices that are in the vicinity, the vicinity being defined by a threshold vicinity distance, usually it is a distance of 500-1000m.
  • a mechanism to excite neighboring devices for faster alarm verification and fire location estimation is provided.
  • one detection device 100 makes an initial detection of an anomaly, it sends this information to the central unit 200 (computing server 200) in order to forward the alarm to another detection device(s) 100 and request the change of their working modes.
  • said first detection devices 100 in parallel, itself goes into the alarm mode of taking measurements at a higher frequency and sending these measurements more frequently.
  • the computing server 200 when the computing server 200 receives information about the initial alarm detection (information sent by a single first detection device 100) it remotely changes the operation mode of neighbouring detection devices 100, waking them up from sleep modes.
  • the neighbouring detection devices 100 of an alarming detection device 100 When the neighbouring detection devices 100 of an alarming detection device 100 receive a signal from the computing server 200 about the mode change, they wake up (see Fig. 5b ) and increase the frequency of measurements and the frequency of sending the collected data.
  • the computing server 200 receives denser data from multiple sensors 106 of the plurality of awaken detection devices 100. The use of this data leads to verification of an alarm detected by a single detection device 100.
  • the detailed process run in the central unit 200 is presented further in the description in reference to Fig.8 and Fig.9 .
  • the detection devices 100 remain in the alarm mode for some time after which they return to their default mode of operation, namely the broadcasting mode.
  • the detection devices 100 are requested by the central unit 200 to change their working mode to the initial one, namely to the broadcasting mode.
  • the central unit 200 receives from a first detection device 100 an alarm signal along with a set of data collected by its sensor module 104.
  • the central unit 200 sends request to other selected detection devices 100 in the predefined vicinity to change their working mode into the alarm mode
  • the method passes to a step of fire verification and prediction.
  • this step of fire verification and prediction dense data from a plurality of detection devices 100 are received and processed.
  • the step of fire verification and prediction comprises several substeps.
  • the alarm signal received from one single detection device 100 is only indicative of fire detection risk and the central unit 200 has to verify fire detection risk by checking further appropriate conditions.
  • This concept is related to the fact that it is not desirable to combine pre-alarm from unrelated events.
  • An alarm from a single sensor 106a may be false, e.g. caused by a car passing nearby.
  • the smoke spreads through the forest, and activates several sensors 106a it is much more sure that a real fire has been detected. Since the smoke spreads relatively slowly, it cannot be expected that several detection devices 100 will register a pre-alarm at exactly the same time, hence a common analysis time window was introduced with a selectable parameter, for example fixed at 15 minutes.
  • the central unit 200 sends back a request to change the working mode into the first working mode, namely broadcasting mode.
  • the detection devices 100 exit the alarm mode and switch to the standard, more energy-saving mode.lf at least two neighbouring detection devices 100 send pre-alarms within said time window then it means that the fire detection risk has been confirmed and the method pass to another substep of fire prediction.
  • the computing server 200 has to predict the fire localization and fire development advantageously with the use of combination of an heuristic prediction model and a trained prediction model.
  • the fire prediction is performed only with the use of heuristic prediction model 201b.
  • the mentioned heuristic prediction model used for prediction is shown in Fig. 8 . This algorithm can be described as follows.
  • the alarm function depends on the readings recorded by the detection devices 100. These may include volatile compound concentrations, temperature, humidity, and other monitored values.
  • the alarm function can by any function taking into account said input data in such a way that changes of input data in time are considered.
  • a point that minimizes the sum over all alarming devices is found. It can be an increasing function f(r,a) whose arguments r are the distance of the point from the detection device 100, and a is the value of the device's alarm function.
  • the heuristic algorithm finds the source of the emission in such a way that the higher the reading of a particular sensor 106a , the closer the source of the fire will be located to that sensor 106.
  • the heuristic algorithm returns the estimated location and probability of fire.
  • a trained fire verification and prediction model 201 is used as well as heuristic prediction model 202 in the substep of fire prediction.
  • the trained fire prediction model 201 is a deep neural network (DNN) and was trained in a typical manner with the use of input data in the form of time series representing the values measured by each detection device 100, along with appropriate labels, namely assigned locations of the detection devices 100.
  • DNN deep neural network
  • the deep neural network used in the trained prediction model 201 is of particular structure in which several inputs are grouped together.
  • data from individual devices are analysed independently (however, parameter sharing is used, so data from all devices are processed through layers with identical weight values).
  • Data from devices that are processed by the deep learning model are sensor indications in the form of a time series. The following values are used: concentration of volatile compounds, temperature, humidity, however additional parameters monitored by the device may be used, such as, for example, atmospheric pressure or insolation.
  • concentration of volatile compounds concentration of volatile compounds
  • temperature temperature
  • humidity a time series
  • additional parameters monitored by the device may be used, such as, for example, atmospheric pressure or insolation.
  • the representations obtained in the first phase are combined, the meteorological data vector is added and the feature vector combined in this way is subject to further processing, which results in the output in the form of fire location estimation and the probability of its occurrence.
  • the parameters of the trained prediction model 201 can be tuned so that the prediction of the situation better reflects the situation in the real environment.
  • the input data for the trained model 201 is also a time series representing the values measured by sensors 106 of each detection device 100, in particular from at least volatile compounds sensors 106a, along with the assigned locations of the devices. At least data from the same three selected detection devices 100 are sent to the trained fire prediction model 201 and in parallel to the heuristic prediction model 202.
  • heuristic model 201b which are information about the current weather condition (wind speed and direction, atmospheric pressure, cloud cover, sunshine, air temperature and humidity) to better analyze the state of the atmosphere are sent to the trained model 201.
  • the output of the trained prediction model 201a is also a probability of fire detection as well as its location. At the end both predictions are combined.
  • the deep learning model often returns more accurate predictions, but sometimes they are burdened with a very large error.
  • the heuristic model is characterized by a lower tendency to generate very large errors.
  • the central server 200 determines the displacement vector of the fire location using location estimations, calculated by an appropriate algorithm for different moments. For this purpose, a simple linear regression is used to estimate the location of the fire source in the future. This mechanism is shown in Fig. 10
  • the output of the substep of fire prediction in the second embodiment is also the probability of detecting a fire and its estimated location.
  • the two variants of the algorithm (a less accurate but predictable heuristics, and a more accurate machine learning algorithm, which sometimes returns large errors) combined together give a solution more convenient and accurate than their components.
  • the method passes to the next step of sending notifications.
  • Said notifications are immediately sent by the central unit 200 to the relevant services via end user stations 300.
  • the alarm can be in the form of an alarm on the web application or its mobile version, SMS alarm, alarm by automated telephone connection to the indicated list of numbers, alarm on the indicated list of e-mails, alarm on the indicated contact list using the indicated chat platform (e.g. telegram, whatsapp, messenger, etc.), sending information to a device (for example dedicated device) located at the headquarters of the forest inspectorate, forest guard and/or fire brigade, which produces a sound signal after receiving the alarm signal.
  • Notification solutions can be freely combined. Exemplary layouts available via the user stations 300 are presented in Fig. 11 and Fig.12 .
  • non-transitory computer readable medium comprising machine-executable code that, upon execution by one or more computer processors, implements a method for wildfire detecting run on the detection device 100 and/or on the central unit 200.
  • the code can be pre-compiled and configured for use with a machine having a processor adapted to execute the code, or it can be compiled during runtime.
  • the code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.
  • a machine readable medium such as computer-executable code
  • a tangible storage medium such as computer-executable code
  • Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings.
  • Volatile storage media include dynamic memory, such as the main memory of such a computer platform.
  • Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system.
  • Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications.
  • RF radio frequency
  • IR infrared
  • Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data.
  • Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

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  • Fire Alarms (AREA)

Claims (13)

  1. System zum Erkennen und Vorhersagen von Bränden, umfassend einen Satz lokaler Erkennungsvorrichtungen (100) und eine Zentraleinheit (200), wobei die Zentraleinheit (200) konfiguriert ist, um Umgebungsparametermessungen von dem Satz lokaler Erkennungsvorrichtungen (100) zu empfangen und zu analysieren, wobei die Zentraleinheit (200) ferner konfiguriert ist zum
    - Empfangen eines Alarmsignals, das auf ein Branderkennungsrisiko hinweist, von zumindest einer Erkennungsvorrichtung (100), und
    - Verifizieren des Branderkennungsrisikos durch Überprüfen des Vorhandenseins einer vordefinierten Anzahl von Erkennungsvorrichtungen (100), die das Alarmsignal innerhalb eines gemeinsamen Analysezeitfensters senden
    - und, wenn das Branderkennungsrisiko bestätigt wird, Vorhersagen der Brandwahrscheinlichkeit und der Brandlokalisierung unter Verwendung eines heuristischen Vorhersagemodells (201b);
    dadurch gekennzeichnet, dass das heuristische Vorhersagemodell (201b) konfiguriert ist zum
    - Berechnen, für zumindest zwei Erkennungsvorrichtungen (100), des Maximalwerts einer Alarmfunktion, wobei die Alarmfunktion zumindest eine Konzentration flüchtiger Verbindungen als Eingabe erhält
    - Umwandeln der berechneten Maximalwerte der Alarmfunktion in die Brandwahrscheinlichkeit unter Verwendung der Sigmoidfunktion gemäß der folgenden Formel: S x = 1 1 − e − x x = ∏ a Vorrichtungen a N tr a − tr b wobei a der Voralarmwert einer einzelnen Erkennungsvorrichtung (100) ist, tra und trb einstellbare Schwellenparameter sind und N die Anzahl der Erkennungsvorrichtungen (100) ist, die ebenfalls einen Alarm im gemeinsamen Analysezeitfenster registriert haben,
    - Finden eines Punktes, der die Summe über genannte alle der zumindest zwei Erkennungsvorrichtungen (100) gemäß der folgenden Formel minimiert: f r a = ∑ a Vorrichtungen r a ∗ a um so die Quelle der emittierten Konzentration flüchtiger Verbindungen zu finden, und zwar in der Weise, dass die Brandquelle umso näher an dieser Erkennungsvorrichtung (100) liegt, je höher der Messwert einer bestimmten Erkennungsvorrichtung (100) ist.
  2. System nach Anspruch 1, wobei die Brandwahrscheinlichkeit und die Brandlokalisierung unter zusätzlicher Verwendung eines trainierten Vorhersagemodells (201a) vorhergesagt werden, das mit dem heuristischen Vorhersagemodell (201b) zu kombinieren ist.
  3. System nach Anspruch 2, wobei die vorhergesagte Brandwahrscheinlichkeit eine prozentuale Wahrscheinlichkeit für das Auftreten eines Brandes ist, die als Durchschnitt der Ausgabewahrscheinlichkeit des trainierten Modells (201a) und des heuristischen Modells (201b) berechnet wird, während der vorhergesagte Brandort der vom trainierten Modell (201a) vorhergesagte Brandort ist, wenn die Differenz zwischen den vom heuristischen Vorhersagemodell und dem trainierten Vorhersagemodul zurückgegebenen Brandorten, geteilt durch den Abstand zwischen den beiden am weitesten voneinander entfernten Erkennungsvorrichtungen, kleiner als ein vordefinierter maximaler Fehler ist.
  4. System nach irgendeinem der vorhergehenden Ansprüche, wobei das empfangene Alarmsignal beim Berechnen eines Alarmfunktionswertes erzeugt wird, der einen Schwellenwert überschreitet.
  5. System nach irgendeinem der vorhergehenden Ansprüche, wobei die Alarmfunktion zumindest basierend auf Messungen der Konzentration flüchtiger Verbindungen berechnet wird.
  6. System nach Anspruch 2, wobei das Betriebsmodus-Verwaltungsmodul (103c) der Erkennungsvorrichtung (100) konfiguriert ist, um die Häufigkeit der Messungen und die Häufigkeit des Aussendens von Daten zumindest in Abhängigkeit von dem niedrigen, mittleren oder hohen Brandrisiko festzulegen, das basierend auf Umgebungsparametermessungen bestimmt wird.
  7. Verfahren zum Erkennen und Vorhersagen von Bränden, das von einer Zentraleinheit (200) durchgeführt wird, wobei das Verfahren das Empfangen und Analysieren von Umgebungsparametermessungen von einem Satz lokaler Erkennungsvorrichtungen (100) umfasst, dadurch gekennzeichnet, dass es ferner die folgenden Schritte umfasst:
    - Empfangen eines Alarmsignals, das auf ein Branderkennungsrisiko hinweist, von zumindest einer Erkennungsvorrichtung (100), und
    - Verifizieren des Branderkennungsrisikos durch Überprüfen des Vorhandenseins einer vordefinierten Anzahl von Erkennungsvorrichtungen (100), die das Alarmsignal innerhalb eines gemeinsamen Analysezeitfensters senden
    und, wenn das Branderkennungsrisiko bestätigt wird,
    - Vorhersagen der Brandwahrscheinlichkeit und der Brandlokalisierung unter Verwendung eines heuristischen Vorhersagemodells (201b);
    wobei das heuristische Vorhersagemodell (201b) konfiguriert ist zum
    - Berechnen, für zumindest zwei Erkennungsvorrichtungen (100), des Maximalwerts einer Alarmfunktion, wobei die Alarmfunktion zumindest eine Konzentration flüchtiger Verbindungen als Eingabe erhält
    - Umwandeln der berechneten Maximalwerte der Alarmfunktion in die Brandwahrscheinlichkeit unter Verwendung der Sigmoidfunktion gemäß der folgenden Formel: S x = 1 1 − e − x x = ∏ a Vorrichtungen a N tr a − tr b wobei a der Voralarmwert einer einzelnen Erkennungsvorrichtung (100) ist, tra und trb einstellbare Schwellenparameter sind und N die Anzahl der Erkennungsvorrichtungen (100) ist, die ebenfalls einen Alarm im gemeinsamen Analysezeitfenster registriert haben,
    - Finden eines Punktes, der die Summe über genannte alle der zumindest zwei Erkennungsvorrichtungen (100) gemäß der folgenden Formel minimiert: f r a = ∑ a Vorrichtungen r a ∗ a um so die Quelle der emittierten Konzentration flüchtiger Verbindungen zu finden, und zwar in der Weise, dass die Brandquelle umso näher an dieser Erkennungsvorrichtung (100) liegt, je höher der Messwert einer bestimmten Erkennungsvorrichtung (100) ist.
  8. Verfahren nach Anspruch 7, wobei die Vorhersage der Brandwahrscheinlichkeit und die Brandlokalisierung unter zusätzlicher Verwendung eines trainierten Vorhersagemodells (201a) durchgeführt wird, das mit dem heuristischen Vorhersagemodell (201b) zu kombinieren ist.
  9. Verfahren nach Anspruch 8, wobei die vorhergesagte Brandwahrscheinlichkeit eine prozentuale Wahrscheinlichkeit für das Auftreten eines Brandes ist, die als Durchschnitt der Ausgabewahrscheinlichkeit des trainierten Modells (201a) und des heuristischen Modells (201b) berechnet wird, während der vorhergesagte Brandort der vom trainierten Modell vorhergesagte Brandort ist, wenn die Differenz zwischen den vom heuristischen Vorhersagemodell und dem trainierten Vorhersagemodul zurückgegebenen Brandorten, geteilt durch den Abstand zwischen den beiden am weitesten voneinander entfernten Erkennungsvorrichtungen, kleiner als ein vordefinierter maximaler Fehler ist.
  10. Verfahren nach irgendeinem der vorhergehenden Ansprüche, wobei das empfangene Alarmsignal beim Berechnen eines Alarmfunktionswertes erzeugt wird, der einen Schwellenwert überschreitet.
  11. Verfahren nach irgendeinem der vorhergehenden Ansprüche, wobei die Alarmfunktion zumindest basierend auf Messungen der Konzentration flüchtiger Verbindungen berechnet wird.
  12. Verfahren nach Anspruch 8, wobei das trainierte Vorhersagemodell (201a) ein neuronales Deep-Learning-Netzwerk ist.
  13. Computerlesbares Speichermedium mit darauf kodierten Anweisungen, die, wenn sie von einem Prozessor ausgeführt werden, den Prozessor veranlassen, das Verfahren nach Anspruch 7 bis 12 durchzuführen.
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