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(11) | EP 4 390 892 A1 |
| (12) | EUROPEAN PATENT APPLICATION |
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| (54) | AUTOMATIC PARAMETERSET SELECTION |
| (57) A method and an arrangement for automatically providing suitable parameter sets for
a hazard detection system (e.g. fire alarm system) to improve the detection performance
of the detection system to increase the level of safety within the building.
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FIELD OF THE INVENTION
[0001] The present invention relates to a method and an arrangement for providing parameter sets for a hazard detection system to improve the detection performance of the detection system.
BACKGROUND
[0002] Almost all households or buildings are nowadays equipped with hazard detectors, especially smoke detectors. It happens again and again that hazard detectors, especially smoke detectors set off alarms without there actually being a hazard, like a fire. In the case of a false alarm, e.g. a smoke detector triggers a fire alarm even there is no fire. If a hazard detector is too sensitive, false alarms could be caused. On the other hand, if a hazard detector is too robust, false alarms are avoided, but a lower level or fire detection safety or protection is provided. This is annoying and can be expensive in case that the fire brigade is notified. Reasons for false alarms can be faulty measurement technology of the detector, or incorrect or not optimal settings of the detector.
SUMMARY OF THE INVENTION
[0003] The object of the invention is to provide optimal parameter settings for the detector to reduce the number of false alarms of a hazard detector at maximum possible sensitivity.
[0004] A first aspect of the invention is a method for providing parameter sets for a hazard detection system, comprising a hazard control panel connected to hazard detectors, especially fire detectors, to improve the detection performance of the detection system, the method comprising:
providing measured values of at least one of the detectors representing the operation status in case no hazard is detected ("normal status") by the respective detector;
providing measured values of at least one of the detectors representing events ("different from normal status") in case a hazard ("real hazard") or a potential hazard ("false alarm", "No hazard") is detected by the respective detector;
annotating (e.g. tagging) the measured values representing an event in case of occurrence of a real hazard and in case of a potential hazard;
analyzing the measured values for the case no hazard is detected and the annotated measured values representing events in case a hazard or a potential hazard is detected to identify a parameter set for the hazard detection system to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms.
[0005] A second aspect of the invention is an arrangement for providing parameter sets for a hazard detection system to improve the detection performance of the detection system, the arrangement comprising:
a hazard control panel connected to hazard detectors;
an analysis engine for analyzing measured values of at least one of the detectors;
wherein at least one of the hazard detectors is configured to provide measured values representing the operation status ("normal status") in case no hazard is detected to the analysis engine;
wherein at least one of the hazard detectors is configured to provide measured values in case a hazard ("real hazard") or a potential hazard ("false alarm", "No hazard") is detected to the analysis engine;
means for annotating (e.g. tagging) the measured values representing an event in case of occurrence of a real hazard and in case of a potential hazard;
wherein the analysis engine is configured to analyze the measured values for the case no hazard is detected and the measured values representing events in case a real hazard or a potential hazard is detected and the annotated measured values representing events in case a hazard or a potential hazard is detected to identify a parameter set for the hazard detection system to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms.
[0006] A further aspect of the invention is a data processing system comprising instructions or means to carry out the steps of the inventive method.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The above mentioned and other concepts of the present invention will now be addressed with reference to the drawings of the preferred embodiments of the present invention. The shown embodiments are intended to illustrate, but not to limit the invention. The drawings contain the following figures, in which like numbers refer to like parts throughout the description and drawings and wherein:
- FIG 1
- illustrates a first exemplary arrangement for providing parameter sets for a hazard detection system,
- FIG 2
- illustrates a second exemplary arrangement for providing parameter sets for a hazard detection system,
- FIG 3
- illustrates a third exemplary arrangement for providing parameter sets for a hazard detection system,
- FIG 4
- illustrates an exemplary flowchart of a method for providing parameter sets for a hazard detection system,
- FIG 5
- illustrates exemplary time series of exemplary measured values, and
- FIG 6
- illustrates three exemplary types of danger signal patterns.
DETAILED DESCRIPTION
[0008] False alarms (e.g. false fire alarms) in a building are annoying for the occupants and can be expensive in case that the fire brigade is notified. Reasons for false alarms can be faulty measurement technology of the detector (e.g. improperly working sensor or detector), or incorrect or not optimal sensitivity settings of the detector.
[0009] In most of the cases when the customer is faced with a couple of false alarms the most robust parameter sets are applied but not an appropriate parameter set which could be as sensitive as possible in this environment.
[0010] Today the hazard detection systems are often delivered with a medium sensitive parameter set. Very often this is not the most sensitive parameter set which would be possible in this specific environment to provide optimal protection.
[0011] One aspect of the invention is to provide a process of automated selecting an optimal parameter set from a couple of already approved and/or certified parameter sets for hazard detectors and/or for hazard control panels.
[0012] Figure 1 illustrates a first exemplary arrangement for automatically providing parameter sets for a hazard detection system HDS1 to improve the detection performance of the detection system. The arrangement according to figure 1 comprises:
a hazard control panel HCP1 connected to hazard detectors HD1 - HD3 via a detector line DL;
an analysis engine AE for analyzing measured values MV_NS, MV_RH, MV_PH of at least one of the detectors HD1 - HD3;
wherein at least one of the hazard detectors HD1 - HD3 is configured to provide measured values MV_NS representing the operation status ("normal status") in case no hazard is detected to the analysis engine AE;
wherein at least one of the hazard detectors HD1 - HD3 is configured to provide measured values MV_RH, MV_PH in case a hazard ("real hazard") or a potential hazard ("false alarm", "No hazard") is detected to the analysis engine AE;
means TM1 for annotating or tagging the measured values MV_RH, MV_PH representing an event in case of occurrence of a real hazard and in case of a potential hazard;
wherein the analysis engine AE is configured to analyze the measured values MV_NS for the case no hazard is detected and the annotated measured values MV_RHT, MV_PHT representing events in case a hazard or a potential hazard is detected to identify a parameter set IPS for the hazard detection system to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms.
[0013] Advantageously the analysis engine AE is determining the optimal parameter setting for the detector to achieve the highest level of fire detection safety/protection (=highest sensitivity) without causing risks for false alarms or for too many false alarms.
[0014] A parameter set or a parameter setting is for instance a named collection of numbers or attributes describing the configuration of the hazard detection algorithm in the detection system. It may for instance comprise filter parameters, weights, and/or thresholds for conditional behaviors.
[0015] Advantageously also historic measured values MV_RH, MV_PH representing an event in case of occurrence of a real hazard and in case of a potential hazard will be annotated and tagged to enlarge the set of data for further analysis.
[0016] Advantageously the analysis determines settings of the hazard detector to reduce the number of false alarms of a hazard detector at maximum possible sensitivity of the detector.
[0017] In the illustration according to figure 1 the means TM1 for annotating or tagging the measured values MV_RH, MV_PH representing an event in case of occurrence of a real hazard and in case of a potential hazard are implemented by a tagging mechanism TM1 which is integrated in the hazard control panel HCP1. The hazard control panel HCP1 receives via the detector line DL the following measured values from at least one of the hazard detectors HD1 - HD3:
| MV_NS | Measured Values Normal Status |
| MV_RH | Measured Values Real Hazard |
| MV_PH | Measured Values Potential Hazard. |
[0018] Advantageously the measured values MV_NS, MV_RH, MV_PH comprise in each case information (e.g. metadata) which enables to assign these measured values to the respective hazard detector HD1 - HD3.
[0019] By using the tagging mechanism TM1 a person B (e.g. facility manager, service person) is tagging or annotating the measured values MV RH (Measured Values Real Hazard) and MV PH (Measured Values Potential Hazard) in case if a real hazard (e.g. fire, smoke) is present. The tagging or annotating the measured values MV_RH (Measured Values Real Hazard) and MV_PH (Measured Values Potential Hazard) is confirming that a real hazard is present.
[0020] Advantageously the measured values MV_NS (Measured Values Normal Status) are tagged or annotated accordingly that the normal status or situation is present, and no hazard is detected. The tagging or annotating of the measured values MV_NS (Measured Values Normal Status) can be performed manually by the person B or automatically by the tagging mechanism TM1.
[0021] The tagging (or annotating) mechanism TM1 can comprise input means (e.g. input panel, touch screen) for manual tagging. Advantageously the tagging mechanism TM1 is performing the tagging or annotating automatically. By comparing and evaluating received measured values MV_NS, MV_RH, MV_PHfrom more than one of the hazard detectors HD1 - HD3. Advantageously evaluating and tagging the received measured values MV_NS, MV_RH, MV_PH also comprises inputs from other sources like manual call points (MCP).
[0022] Via an appropriate communication connection CC1 (e.g. radio connection, Internet) the hazard control panel HCP1 is in data connection with the analytics engine AE.
[0023] The hazard control panel HCP1 is sending the following data to the analytics engine AE:
- MV_NS
- Measured Values Normal Status
- MV_NST
- Measured Values Normal Status Tagged
- MV_RHT
- Measured Values Real Hazard Tagged
- MV_PHT
- Measured Values Potential Hazard Tagged.
[0024] Advantageously the measured values MV_NS, respectively the tagged measured values MV_NST, MV_RHT, MV_PHT comprise in each case information (e.g. metadata) which enables to assign the respective measured values, respectively the tagged measured values to the respective hazard detector HD1 - HD3.
[0025] The analytics engine AE is hosted by a server S. The server S comprises appropriate communication means, processing means, I/O means, and memory means. The server S has access to a suitable database DB. The database DB comprises BIM-data (BIM: Building Information Model) of the hazard detection system and the building where the hazard detection system is installed. The database DB further comprises a set of parameter sets (e.g. configurations or configuration data) SPS for the hazard detection system. The parameter sets comprising configuration data for the hazard control panel HCP1 and for the respective detectors HD1 - HD3. Advantageously all parameter sets are certified and comply with the respective standard.
[0026] The analytics engine AE comprises appropriate simulation means and/or analytics means (e.g. machine learning mechanism, rule based reasoning mechanisms, and/or simulation mechanisms) to analyze the received data MV_NS, MV_NST, MV_RHT, MV_PHT to identify an improved parameter set IPS for the hazard detection system which reduces the number of false alarms or minimizes the detection time of a hazard situation (e.g. fire, smoke, gas) without increasing the number of false alarms. Based on the received data the analytics engine AE is selecting the most suitable parameter set from the set of parameter sets (e.g. configurations or configuration data) SPS which reduces the number of false alarms or minimizes the detection time of a hazard situation (e.g. fire, smoke, gas) without increasing the number of false alarms.
[0027] Advantageously the analytics engine AE and the Building Information Model BIM are representing a digital twin of the hazard detection system. Based on the received data the analytics engine AE can run simulations on the digital twin to identify an improved parameter set IPS.
[0028] The improved parameter set IPS is sent via the communication connection CC1 to the hazard control panel HCP1. The hazard control panel HCP1 transmits the respective improved parameter set IPS to the respective hazard detector HD1 - HD3. The improved parameter set IPS can comprise configuration data for the hazard control panel HCP1 and/or the respective hazard detector HD1 - HD3.
[0029] Advantageously the configuration for HCP1 and HD1-HD3 may also comprise multi-detector dependency, eg. HCP1 considers MV_PH received simultaneously from both HD1 and HD2 located in the same room as a real hazard.
[0030] Advantageously the analytics means of the analytics engine AE are implemented by appropriate software programs running on the server S. Advantageously the server S and the database DB are implemented or hosted in Cloud infrastructure C.
[0031] In principle the analytics engine AE and the database DB can be hosted or implemented on the hazard control panel HCP1 if the hazard control panel HCP1 comprises appropriate processing and memory power.
[0032] Exemplary advantages of the invention:
- Finding optimal parameter set to decrease the number of false alarms and to increase the level of safety in the building.
- Improvement of the detection performance of the hazard detection system: making the detectors more sensitive or less sensitive.
[0033] Advantageously identifying an improved parameter set IPS for the hazard detection system is not a one-time job. Advantageously this will be performed frequently or periodically (e.g. by continuously performing a suitable simulation).
[0034] Optionally identifying an improved parameter set IPS for the hazard detection system is performed on demand. For instance, if there are changes or modifications in the building (e.g. new rooms arrangements, open office spaces in the building, new air flows in the building).
[0035] Identifying an improved parameter set IPS for a hazard detection system can depend on the criticality of the building where the hazard detection system is implemented. If the building is part of critical infrastructure (e.g. airport, train station) identifying an improved parameter set IPS can be performed for instance daily, weekly or monthly. For highly critical infrastructure it makes sense to identify an improved parameter set IPS for a hazard detection system every hour.
[0036] For a new building or for a newly installed hazard detection system identifying an improved parameter set IPS for a hazard detection system should be performed in the beginning more frequently. Later in the lifetime of the building weekly, monthly or if the usage of the building has changed. E.g. after rebuilding the building, changes on the building.
[0037] Advantageously the analysis engine AE is configured to analyze the measured values to identify the best fitting parameter set IPS for the hazard detection system to reduce the number of false alarms by simulating a digital twin of the hazard detection system and/or by simulating a digital twin of the hazard detectors.
[0038] Advantageously the analysis engine AE is a rule-based analytic engine configured to run scenarios of existing parameter sets to identify the parameter set able to reduce the number of false alarms of a hazard detector at maximum possible sensitivity.
[0040] Advantageously the hazard detectors HD1 - HD3 are fire detectors or smoke detectors or gas detectors or heat detectors or presence detectors. One or more hazard detectors HD1 - HD3 can also be embodiments of a multi-sensor or a multi-criteria detector comprising a combination of means for smoke and/or gas and/or heat and/or flame detection.
[0041] Figure 2 illustrates a second exemplary arrangement for automatically providing parameter sets for a hazard detection system HDS2 to improve the detection performance of the detection system. In the hazard detection system HDS2 according to figure 2 the hazard control panel HCP2 does not communicate directly with the server S and the analytics engine AE.
[0042] In the hazard detection system HDS2 according to figure 2 the hazard control panel HCP2 communicates to the server S and the analytics engine AE via an edge gateway EG. The edge gateway EG is part of a building network. The communication of the network-nodes (e.g. devices connected by the building network) of the building network to the cloud C is routed via the edge gateway EG. The hazard control panel HCP2 is a device (node) of the building network.
[0043] In the arrangement of the hazard detection system HDS2 according to figure 2 the hazard control panel HCP2 receives via the detector line DL the following measured values from at least one of the hazard detectors HD1 - HD3:
| MV NS | Measured Values Normal Status |
| MV_RH | Measured Values Real Hazard |
| MV PH | Measured Values Potential Hazard. |
[0044] Advantageously the measured values MV_NS, MV_RH, MV_PH comprise in each case information (e.g. metadata) which enables to assign these measured values to the respective hazard detector HD1 - HD3.
[0045] By using the tagging mechanism TM2 a person B (e.g. facility manager, service person) is tagging or annotating the measured values MV_RH (Measured Values Real Hazard) and MV_PH (Measured Values Potential Hazard) in case if a real hazard (e.g. fire, smoke) is present. The tagging or annotating the measured values MV_RH (Measured Values Real Hazard) and MV_PH (Measured Values Potential Hazard) is confirming that a real hazard is present.
[0046] Advantageously the measured values MV_NS (Measured Values Normal Status) are tagged or annotated accordingly that the normal status or situation is present, and no hazard is detected. The tagging or annotating of the measured values MV_NS (Measured Values Normal Status) can be performed manually by the person B or automatically by the tagging mechanism TM2.
[0047] For instance, the tagging mechanism TM2 comprises input means (e.g. input panel, touch screen) for manual tagging and verification of the respective hazard event. Advantageously the tagging mechanism TM2 is performing the tagging or annotating automatically by comparing and evaluating received measured values MV_NS, MV_RH, MV_PH from more than one of the hazard detectors HD1 - HD3. Advantageously evaluating and tagging the received measured values MV_NS, MV_RH, MV_PH also comprises inputs from other sources like manual call points (MCP).
[0049] Via an appropriate communication connection CC2 (e.g. radio connection, Internet) the hazard control panel HCP2 is sending the following data to the edge gateway EG:
| MV_NS | Measured Values Normal Status |
| MV_NST | Measured Values Normal Status Tagged |
| MV_RHT | Measured Values Real Hazard Tagged |
| MV_PHT | Measured Values Potential Hazard Tagged. |
[0050] Advantageously the measured values MV_NS, respectively the tagged measured values MV_NST, MV_RHT, MV_PHT comprise in each case information (e.g. metadata) which enables to assign the respective measured values, respectively the tagged measured values to the respective hazard detector HD1 - HD3.
[0051] Since the hazard control panel HCP2 and the edge gateway EG are nodes or subscribers of a building network the communication connection CC2 depends on the communication protocol of the building network (e.g. BACnet, BACnet IP, IP protocol).
[0052] The edge gateway EG is sending these data via an appropriate communication connection CC3 (e.g. radio connection, Internet) to the analytics engine AE which is hosted by a server S.
[0053] The server S has access to a suitable database DB. The database DB comprises BIM-data (BIM: Building Information Model) of the hazard detection system and the building where the hazard detection system is installed. The database DB further comprises a set of parameter sets (e.g. configurations or configuration data) SPS for the hazard detection system. The parameter sets comprising configuration data for the hazard control panel HCP and for the respective detectors HD1 - HD3. Advantageously all parameter sets are certified and comply with the respective standard.
[0054] The analytics engine AE comprises appropriate simulation and/or analytics means (e.g. machine learning mechanism, rule based reasoning mechanisms, and/or simulation mechanisms) to analyze the received data MV_NS, MV_NST, MV_RHT, MV_PHT to identify an improved parameter set IPS for the hazard detection system which reduces the number of false alarms or minimizes the detection time of a hazard situation (e.g. fire, smoke, gas) without increasing the number of false alarms.
[0055] Advantageously the analytics engine AE determines settings of the hazard detector to reduce the number of false alarms of a hazard detector at maximum possible sensitivity of the detector.
[0056] Advantageously the analysis engine AE is determining the optimal parameter setting for the detector to achieve the highest level of fire detection safety/protection (=highest sensitivity) without causing risks for false alarms or for too many false alarms.
[0057] Based on the received data the analytics engine AE is selecting the most suitable parameter set from the set of parameter sets (e.g. configurations or configuration data) SPS which reduces the number of false alarms or minimizes the detection time of a hazard situation (e.g. fire, smoke, gas) without increasing the number of false alarms.
[0058] Advantageously the analytics engine AE and the Building Information Model BIM are representing a digital twin of the hazard detection system. Based on the received data the analytics engine AE can run simulations on the digital twin to identify an improved parameter set IPS.
[0059] The improved parameter set IPS is sent via the communication connection CC3 to the edge gateway EG. The edge gateway EG transmits the improved parameter set IPS to the hazard control panel HCP2. The hazard control panel HCP2 transmits the respective improved parameter set IPS to the respective hazard detector HD1 - HD3. The improved parameter set IPS can comprise configuration data for the hazard control panel HCP and/or the respective hazard detector HD1 - HD3.
[0060] Advantageously the analytics means of the analytics engine AE are implemented by appropriate software programs running on the server S. Advantageously the server S and the database DB are implemented or hosted in Cloud infrastructure C.
[0061] Advantageously the analysis engine AE is configured to analyze the measured values to identify the best fitting parameter set IPS for the hazard detection system to reduce the number of false alarms by simulating a digital twin of the hazard detection system and/or by simulating a digital twin of the hazard detectors.
[0062] A criterion for the best fitting parameter set can be the number of changes which have to be performed in the currently existing setting.
[0063] Advantageously the analysis engine AE is a rule-based analytic engine configured to run scenarios of existing parameter sets to identify a parameter set able to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms.
[0064] Optionally the analytics engine AE and the database DB can be hosted or implemented on the hazard control panel HCP2 if the hazard control panel HCP comprises appropriate processing and memory power.
[0065] Optionally the analytics engine AE and the database DB can be hosted or implemented on the edge gateway EG if the edge gateway EG has appropriate processing and memory power.
[0066] Figure 3 illustrates a third exemplary arrangement for automatically providing parameter sets for a hazard detection system HDS3 to improve the detection performance of the detection system. In the hazard detection system HDS3 according to figure 3 the hazard control panel HCP3 does not communicate with the server S and the analytics engine AE for automatically providing parameter sets for a hazard detection system HDS3 to improve the detection performance of the detection system.
[0067] In the exemplary hazard detection system HDS3 according to figure 3 the hazard detectors HD1 - HD3 are configured to communicate to the server S and the analytics engine AE via an edge gateway EG. The edge gateway EG is part of a building network. The communication of the network-nodes (e.g. devices connected by the building network) of the building network to the cloud C is routed via the edge gateway EG. hazard detectors HD1 - HD3 are devices (nodes) of the building network.
[0068] In the arrangement of the hazard detection system HDS3 according to figure 3 the edge gateway EG receives via the respective communication connection CC4, CC5 the following measured values from at least one of the respective hazard detectors HD1 - HD3:
| MV_NS | Measured Values Normal Status |
| MV_RH | Measured Values Real Hazard |
| MV PH | Measured Values Potential Hazard. |
[0069] Advantageously the measured values MV_NS, MV_RH, MV_PH comprise in each case information (e.g. metadata) which enables to assign these measured values to the respective hazard detector HD1 - HD3.
[0070] In the arrangement of the hazard detection system HDS3 according to figure 3 the edge gateway EG comprises a tagging mechanism TM3 for tagging or annotating the measured values MV_RH (Measured Values Real Hazard) and MV_PH (Measured Values Potential Hazard) in case if a real hazard (e.g. fire, smoke) is present. The tagging or annotating the measured values MV_RH (Measured Values Real Hazard) and MV_PH (Measured Values Potential Hazard) is confirming that a real hazard is present.
[0071] Advantageously the measured values MV_NS (Measured Values Normal Status) are tagged or annotated accordingly that the normal status or situation is present, and no hazard is detected.
[0072] Advantageously the tagging mechanism TM3 is performing the tagging or annotating automatically. By comparing and evaluating received measured values MV_NS, MV_RH, MV_PH from more than one of the hazard detectors HD1 - HD3. Advantageously evaluating and tagging the received measured values MV_NS, MV_RH, MV_PH also comprises inputs from other sources (e.g. from a building management station). Automatically evaluating and tagging the received measured values MV_NS, MV_RH, MV_PH can also be performed based on hazard event messages HEM from manual call points MCP. In the arrangement of the hazard detection system HDS3 according to figure 3 the edge gateway EG is configured to receive hazard event messages HEM from manual call points MCP via an appropriate communication connection CC7.
[0073] Via an appropriate communication connection CC6 (e.g. radio connection, Internet) the edge gateway EG is sending the following data to the analytics engine AE which is hosted by a server S:
| MV_NS | Measured Values Normal Status |
| MV_NST | Measured Values Normal Status Tagged |
| MV_RHT | Measured Values Real Hazard Tagged |
| MV_PHT | Measured Values Potential Hazard Tagged. |
[0074] Advantageously the measured values MV_NS, respectively the tagged measured values MV_NST, MV_RHT, MV_PHT comprise in each case information (e.g. metadata) which enables to assign the respective measured values, respectively the tagged measured values to the respective hazard detector HD1 - HD3.
[0075] Since the hazard detector HD1 - HD3 and the edge gateway EG are nodes or subscribers of a building network the communication connections CC4, CC5 depend on the communication protocol of the building network (e.g. BACnet, BACnet IP, IP protocol).
[0076] The server S has access to a suitable database DB. The database DB comprises BIM-data (BIM: Building Information Model) of the hazard detection system and the building where the hazard detection system is installed. The database DB further comprises a set of parameter sets (e.g. configurations or configuration data) SPS for the hazard detection system. The parameter sets comprising configuration data for the hazard control panel HCP and for the respective detectors HD1 - HD3. Advantageously all parameter sets are certified and comply with the respective standard.
[0077] The analytics engine AE comprises appropriate analytics means (e.g. machine learning mechanism, rule based reasoning mechanisms, and/or simulation mechanisms) to analyze the received data MV_NS, MV_NST, MV_RHT, MV_PHT to identify an improved parameter set IPS for the hazard detection system which reduces the number of false alarms or minimizes the detection time of a hazard situation (e.g. fire, smoke, gas) without increasing the number of false alarms. Based on the received data the analytics engine AE is selecting the most suitable parameter set from the set of parameter sets (e.g. configurations or configuration data) SPS which reduces the number of false alarms or minimizes the detection time of a hazard situation (e.g. fire, smoke, gas) without increasing the number of false alarms.
[0078] Advantageously the analytics engine AE and the Building Information Model BIM are representing a digital twin of the hazard detection system. Based on the received data the analytics engine AE can run simulations on the digital twin to identify an improved parameter set IPS.
[0079] The improved parameter set IPS is sent via the communication connection CC6 to the edge gateway EG. The edge gateway EG transmits the respective improved parameter set IPS to the respective hazard detectors HD1 - HD3. In case an improved parameter set IPS for the hazard control panel HCP3 is provided by the analytics engine AE, said improved parameter set IPS will be transmitted via the detector line DL to the hazard control panel HCP3.
[0080] Advantageously the analytics means of the analytics engine AE are implemented by appropriate software programs running on the server S. Advantageously the server S and the database DB are implemented or hosted in Cloud infrastructure C.
[0081] Advantageously the analysis engine AE is configured to analyze the measured values to identify the best fitting parameter set IPS for the hazard detection system to reduce the number of false alarms by simulating a digital twin of the hazard detection system and/or by simulating a digital twin of the hazard detectors.
[0082] Advantageously the analysis engine AE is a rule-based analytic engine configured to run scenarios of existing parameter sets to identify the parameter set able to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms.
[0083] Optionally the analytics engine AE and the database DB can be hosted or implemented on the edge gateway EG if the edge gateway EG has appropriate processing and memory power.
[0084] Figure 4 illustrates an exemplary flowchart of a method for providing parameter sets for a hazard detection system (HDS1 - HDS3), comprising a hazard control panel connected to hazard detectors, especially fire detectors, to improve the detection performance of the detection system.
[0085] The method comprising:
(S1) providing measured values (MV_NS) of at least one of the detectors representing the operation status in case no hazard is detected ("normal status") by the respective detector;
(S2) providing measured values (MV_RH, MV_PH) of at least one of the detectors representing events ("different from normal status") in case a hazard ("real hazard") or a potential hazard ("false alarm", "No hazard") is detected by the respective detector;
(S3) annotating (e.g. tagging) the measured values (MV_RH, MV_PH) representing an event in case of occurrence of a real hazard and in case of a potential hazard;
(S4) analyzing the measured values (MV_NS) for the case no hazard is detected and the annotated measured values (MV_RHT, MV_PHT) representing events in case a hazard or a potential hazard is detected to identify a parameter set for the hazard detection system to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms.
[0086] Annotating or tagging increases the robustness of the hazard detectors and of the hazard detection system in the long term. The ability to build up a database of tagged or annotated measured values (MV_NST, MV_RHT, MV_PHT) for continuous development and application of improved parameter sets based on high quality field data provides new opportunities for identifying and tackling important field challenges as well as for monitor success of implementation. Advantageously the database of tagged or annotated measured values (MV_NST, MV_RHT, MV_PHT) is filled automatically by the server S.
[0087] An aspect of the invention is to identify and to select a parameter set from a set of existing parameters to determine a new parameter set which is better than others. The use and integration of better parameter sets means faster and more reliable detection of hazard situations.
[0088] Advantageous embodiments are that also the measured values (MV_NS) representing the operation status in case no hazard is detected ("normal status") are annotated to indicate the case no hazard is detected or the case a real hazard is detected; and that said annotated measured values (MV_NST) are used in the analyzing step to identify a parameter set for the hazard detection system to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms.
[0089] Advantageously analyzing the measured values to identify a parameter set for the hazard detection system to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms is performed by simulating a digital twin of hazard detection system and/or by simulating a digital twin of the hazard detectors.
[0090] Advantageously identifying the parameter set to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms is performed by the respective digital twin by evaluating existing parameter sets to select the best suitable parameter set.
[0091] Advantageously identifying the parameter set to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms is performed by a rule-based analytic engine running scenarios of existing parameter sets.
[0092] The hazard detectors can be fire detectors or smoke detectors or gas detectors or presence detectors.
[0093] Advantageously the measured values (MV_RH, MV_PH) representing events ("different from normal status") in case of a real hazard ("real hazard") or a potential hazard ("false alarm", "No hazard") are provided based on threshold triggering.
[0094] Advantageously annotating or tagging the measured values (MV_RH, MV_PH) representing an event in case of occurrence of a real hazard and in case of a potential hazard is performed by a user by entering a respective acknowledgment on the hazard control panel.
[0095] Optionally annotating (e.g. tagging) the measured values (MV_RH) representing an event in case of occurrence of a real hazard is performed by automatic approval of a second hazard detection system and/or by analyzing hazard events (HE) from manual call points (MCP).
[0096] Optionally annotating (e.g. tagging) the measured values (MV_RH) representing an event in case of occurrence of a real hazard is performed when a defined number of further hazard detectors are also providing measured values (MV_RH) representing an event in case of occurrence of a real hazard.
[0097] The method can be realized by appropriately configured components (hazard detectors, detector line, panel, server, communication means), equipped with appropriate computing power, memory space, and software. Advantageously the server is implemented in a cloud infrastructure.
[0098] Roughly explained, an event is a defined status of the detector, which is different from "normal status". For example, in an average office environment, the fluctuations of raw data are in the range of plus / minus 1 digit and so more or less boring and without value. Events are basically periods of signals related to an upcoming false alarm or real alarm or a status, which is slightly different from "normal status". Implementing such a procedure makes it obsolete to transmit huge amounts of data without any value and to focus only on the interesting periods of detector status.
[0099] Figure 5 illustrates exemplary time series of exemplary measured values or signals. The exemplary measured values or signals (e.g. life time long signals) are illustrated in a time series chart TSC. The X-axis of the time series chart TSC represents the time t. The Y-axis of the time series chart TSC represents the danger counts DC. In figure 5 the embedded small chart TSNS represents a period of time for a time series of signals representing "normal status". The embedded small chart TSDL3E represents a period of time collecting "events" (e.g. danger level 3 events), in this case exemplary danger level 3 events DL3E1 to DL3E4. The right side of the time series chart TSC represents a legend for the chart TSC for further information illustrated in the chart TSC:
- Danger Min to Max (DMintoMax),
- Danger 5% to 95% percentile (D5to95),
- Danger Median (DM)
- Danger Level 3 (DL3).
[0100] If the relevant data records from one or more detectors are available to be analyzed (e.g. in a cloud server), these data records can be used to improve the detector performance in two directions (increase the sensitivity or decrease the sensitivity) to adapt the detectors sensitivity by changing the parameter set.
Case 1: Increase the sensitivity
[0101] Increase the sensitivity in the sense of "minimizing the time until a real or potential hazard is identified". Such an improvement needs several data records from "normal status". Such data records should be recorded at certain times (e.g. specific times during the day or during the night, when the room is populated or without any person inside or to be done with the customer based on specific situations). Also, the length of these normal status records needs to be defined. Some statistical insights or experience could be helpful to define the point of time and the length for the record. Proposed here is one minute to one hour, preferably five minutes. Limitation to five minutes or less mainly because of the required space in the cloud.
[0102] Here we propose to record such normal status events during an observation period over a timeframe of 1 to 12 months preferably 12 months to cover all seasons to have such records available with a certain reliability for further improvement of the parameter sets. These times need to be implemented in the procedures for improvement for collecting these records.
[0103] Advantageously for the method "recording by threshold" it should be defined how many events we need for an improvement, e.g. 10 recorded data sets, better more, maybe 100 might be sufficient. In addition, the number of such events could be defined also by the timeframe as described for normal status.
[0104] In fact, more important than the number of events collected, is collection of "normal status signals" which are representative for the site, and which cover all operation modes of the site. Therefore, a combination of both methods might be beneficial which could be trying to get records by threshold over a time as described between 1 to 12 months. If not sufficient events are detected then use the method to get records on predefined times as described above. Sufficient is again a number of records 10 and 100 but could be defined also specifically.
[0105] Normally the detectors are installed with a parameter set at medium or even minimal sensitivity. By using the data records recorded with the described methods above, we could increase the sensitivity by applying more sensitive parameter sets until a certain level of pre-alarm is achieved.
[0106] In this improvement procedure a simulator for detection behavior is used. This is basically a digital twin of the detector. All records need to run in this digital twin for all parameter sets which are more sensitive than the currently applied parameter set (but we should not limit this only to the more sensitive parameter sets because sensitivity might not be the only property of a parameter set.).
[0107] The sensitivity could be increased by selecting a certain parameter set as far as the simulation results will not exceed a certain limit.
[0108] The simplest way would be to use the limit of danger level 1 (DL 1). A certain limit of signal counts, for any sensor raw signal or danger signal, would also be possible. If we keep the limit significantly below danger level 3 (DL 3) the simulated parameter set might be good enough to provide sufficient false alarm sensitivity. Sensitivity could be increased as far as all simulations of recorded data run below danger level 1 (or danger level 2 or 3) depending on the risk of false alarms the customer is willing to accept. The more precise and more general procedure could be achieved by keeping the results of simulations below a certain level of counts. This level could be agreed with the customer.
[0109] The result of a possible improvement with higher sensitivity, respectively shorter time to alarm, could be documented in a report and agreed with the customer.
Case 2: Decrease the sensitivity
[0110] One prerequisite to decrease the sensitivity is the method of recording events by threshold triggering. Recording of data for a longer timeframe with hope to catch critical events (analogue to the procedure described in Case 1 seems not beneficial as it is less reliable to catch the critical time periods with events close to DL 3.
[0111] But the general method is similar:
We need to run simulations at least for a certain number of data records with events or for all events related to one detector in the digital twin for all parameter sets. All events need to be tagged. This means that we need to know if the event is related to a real fire or to a false alarm. Only tagged events should be used for simulations to provide a parameter set selection. The target of such simulations is to get the lowest number of false alarms within a certain timeframe or the lowest level for the pre-alarm danger signal.
[0112] Simulations must run of all collected events tagged with "false alarm" for all parameter sets preferably for all parameter sets with lower sensitivity than the currently applied parameter set (but we should not limit this only to the less sensitive parameter sets because sensitivity might not be the only property of a parameter set.). Then, you can select that parameter set, which results in the lowest number of events exceeding DL3 (or lower threshold e.g. DL2 or DL1 or a defined number of danger signal counts) to decrease the sensitivity by applying a more robust or appropriate parameter set.
[0113] The result of a possible improvement with lower or more appropriate sensitivity could be documented in a report and agreed with the customer.
[0114] The operator could define or propose the maximum acceptable number of false alarms and the algorithm could propose an appropriate parameter set (if this is possible at all). Normally such an optimization will reduce the sensitivity in general within the allowed limits as all parameter sets are approved.
[0115] Of course, the optimization algorithm should propose not the most robust parameter set but the parameter set, which will generate only a few accepted false alarms or the parameter set where all events of this detector won't create any false alarm.
[0116] It is basically not necessary to run simulations with all events tagged with alarm to prove that with the new setting the detector is still sensitive enough to detect all fires. All parameter sets are approved and should work properly. Nevertheless, we can do such simulation runs to show the customer, if a(n) (allowed) delay in fire detection will occur when the new setting is applied. This can be accomplished in case you have some events from real fires for an optimized detector, especially when test fires were performed after setting the fire detection system was set into operation. If these events are correctly tagged, then we can run a simulation with the optimized parameter. With a report showing the simulation results before and after the optimization we can prove, that the alarm will still take place within allowed limits or to show the customer a possible allowed additional delay in alarming time compared to the originally applied parameter set.
[0117] Figure 6 illustrates three exemplary types of recorded danger signal patterns or danger signals for alternative detector configurations. On the left side of figure 6 the signals for three different situations (or types) DPST1 to DPST3 as recorded are shown. On the right side of figure 6 the possible optimization of the respective situation is shown: ODPST1 to ODPST3.
- Type 1 (situation shown at the top of figure 6): several parameter sets are applied but none of them could suppress the first peak which lead to a danger level 3 (e.g. alarm situation). Nevertheless, the time until DL3 will be achieved is prolonged.
- Type 2 (situation shown in the middle of figure 6): several optimization options are shown on the right side to suppress a danger level. Time is also delayed.
- Type 3 (situation shown at the bottom of figure 6): The peak which achieves DL3 cannot be suppressed by optimization. Nevertheless, the picture on the right side shows, that the amplitude of the peak after optimization is lower.
[0118] Optimizing a hazard detector can also be accomplished by improved handling of alarms and by optimal parameter set selection at first day of operation.
Case 3: Improve handling of alarms
[0119] In some cases, a parameter set for suppressing a false alarm may not exist. Then, alternative measures can be defined for parameter set selection to improve the situation.
[0120] Increase time between danger level DL1 (or danger level DL2) and danger level DL3. In some regions the fire panel triggers a pre-alarm allowing for local inspections before an automatic alarm is transmitted to the fire brigade. Typically, such inspection procedure is triggered at danger level DL3.
[0121] Similarly, it may be desirable to issue very early local warnings eg. at DL1 or DL2, to achieve a longer time for local inspections, eg. if distance to inspection area is very long. Therefore, selecting a parameter set triggering danger level DL1 or danger level DL2 faster may be beneficial.
[0122] Propose parameter set considering multi-detector dependencies If more than one detector is available in the area of interest (room, space, corridor, ...), a fire panel can be configured to evaluate those detectors as a group. Such groupings for multi-detector dependencies, meaning that more than one detector would need to exceed a defined danger signal threshold, may also be proposed as a result of multiple detector simulations. Therefore, collecting the field data including its semantic tags is particularly valuable. The semantic information describing relationships between detectors (e.g. located in room x, is-neighbor) can be derived from the P2-topology, existing engineering tools but may also need to be enriched from e.g. BIM data as walls within a fire zone need to be considered.
[0123] Advantageously the selection or configuration of parameter sets for hazard control panels and for hazard detectors may also comprise multi-detector dependency. For instance, a panel considers measured values received simultaneously from other detectors located in the same room as a real hazard.
Case 4: Select optimal parameter set at first day of operation
[0124] Over time, the cloud-based approach for configuration of fire detection systems and collection of detector raw data provides a knowledge base from previous installations and their performances. Such experiences to some extend exist today, but are distributed across locations, or specific to individual experts and therefore transferring such knowledge is more effort. Less transparent is the performance / false alarm rate of sites.
[0125] Each site may have its own characteristics, but after data from plenty of sites has been collected, each site can be categorized into groups of sites which are sharing similarities, eg. kindergarden, shopping mall, office, hotel room, kitchen, restaurant, smoking area, various types of industrial sites, ... or also geographic regions where people may have specific habits or different types of constructions. Preferably, the sites are tagged appropriately, or BIM models (Building Information Model) provided information on the type of building and rooms. State of the art signal clustering methods may assist. The groups are validated with respect to its similarities using also state of the art data analysis methods.
[0126] The system can propose for a new site a typical parameter set being most used / having least false alarm rates, in case the site matches existing groups based on tags. This could then be applied by a service technician or automatically by the system. The initial settings will then be validated during / at the end of the observation period.
[0127] A further aspect of the invention is annotating or tagging events (e.g. alarms) but also signals representing the "normal status".
[0128] The basic idea is as follows: The record only receives the full information content when it has been evaluated. This is particularly important if the alarm threshold has been exceeded. Then it would be especially important to know whether it was a real fire or a false alarm. This can best be done by evaluating by a human expert. In order for the evaluation of a human to take place, the following implementations on the higher-level system are conceivable:
- 1. At the end of the recording of a record, the superordinate system compulsorily requires an evaluation of the event. For this purpose, a call is made in a specific menu to evaluate the event.
- 2. An alarm must be acknowledged today at the panel. This acknowledgement takes place at a standardized panel. It would be conceivable to extend this acknowledgement, e.g. by a second button. However, it would have to be clarified whether this is permissible.
- 3. An automatic evaluation could also take place by the fact that with additional activation of one or more manual call points the one alarm is classified as a correct alarm.
- 4. An automatic evaluation could also be made (but with even greater uncertainty than variant 3) if several automatic fire detectors are additionally activated. Then it can be assumed with higher probability that it is a real alarm and not a false alarm.
- 5. All records are transferred to a maintenance center (e.g. a server configured to accomplish this task). This investigates in detail and evaluates the records.
[0129] The inventive optimization procedures could run periodically, e.g. daily, weekly, monthly, or even every hour. If the optimization procedure finds detectors with parameter sets clearly below the optimal sensitivity (which means the parameter sets are more robust than necessary) the operator or the system could propose the customer to improve the protection of the system. This could be sold as a service.
[0130] If the customer has an appropriate service contract the findings on parameter sets could be applied automatically or the customer will get an offering for improvement.
[0131] In case of false alarms, we could run such an analysis as described above. The customer will get an offer to improve the false alarm robustness, if a more robust parameter set is available, which would provide at least a suppression of a part of false alarms.
Exemplary advantages of the invention
[0132]
- Hazard protection will be improved.
- False alarm suppression will be improved without going onsite.
- Finding optimal parameter set to decrease the number of false alarms and to increase the level of safety in the building.
- Improvement of the detection performance of the hazard detection system: making the detectors more sensitive or less sensitive.
[0133] A method and an arrangement for automatically providing suitable parameter sets for a hazard detection system (e.g. fire alarm system) to improve the detection performance of the detection system to increase the level of safety within the building.
Reference Signs
[0134]
- HDS1 - HDS3
- Hazard Detection System
- HCP1 - HCP3
- Hazard Control Panel
- S
- Server
- DB
- Database
- AE
- Analytic Engine
- BIM
- Building Information Model
- SPS
- Set of Parameter Sets
- CC1 - CC7
- Communication Connection
- HD1 - HD3
- Hazard Detector
- DL
- Detector Line
- TM1 - TM4
- Tagging Mechanism
- MV_NS
- Measured Values Normal Status
- MV_RH
- Measured Values Real Hazard
- MV_PH
- Measured Values Potential Hazard
- MV_NST
- Measured Values Normal Status Tagged
- MV_RHT
- Measured Values Real Hazard Tagged
- MV_PHT
- Measured Values Potential Hazard Tagged
- IPS
- Improved Parameter Set
- B
- User
- EG
- Edge Gateway
- MCP
- Manual Call Point
- HEM
- Hazard Event Message
- TSC
- Time Series Chart
- DC
- Danger Counts
- DM
- Danger Median
- Leg
- Legend
- DLLT1
- Danger Level less than 1
- DL1
- Danger Level 1
- DL2
- Danger Level 2
- DL3
- Danger Level 3
- D5to95
- Danger 5% to 95% Percentile
- DMintoMax
- Danger Min to Max
- TSNS
- Timeserie Normal Status
- TSDL3E
- Timeserie DL3 Events
- DL3E1 - DL3E4
- DL3 Events
- t
- Time
- DSPT1 - DSPT3
- Danger Signal Pattern Type
- ODSPT1 - ODSPT3
- Optimized Danger Signal Pattern Type
- S1 - S4
- Step
1. A method for providing parameter sets for a hazard detection system (HDS1 - HDS3),
comprising a hazard control panel connected to hazard detectors, especially fire detectors,
to improve the detection performance of the detection system, the method comprising:
(S1) providing measured values (MV_NS) of at least one of the detectors representing the operation status in case no hazard is detected ("normal status") by the respective detector;
(S2) providing measured values (MV_RH, MV_PH) of at least one of the detectors representing events ("different from normal status") in case a hazard ("real hazard") or a potential hazard ("false alarm", "No hazard") is detected by the respective detector;
(S3) annotating the measured values (MV_RH, MV_PH) representing an event in case of occurrence of a real hazard and in case of a potential hazard;
(S4) analyzing the measured values (MV_NS) for the case no hazard is detected and the annotated measured values (MV_RHT, MV_PHT) representing events in case a hazard or a potential hazard is detected to identify a parameter set for the hazard detection system to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms.
2. The method according to claim 1,
wherein also the measured values (MV_NS) representing the operation status in case no hazard is detected ("normal status") are annotated to indicate the case no hazard is detected or the case a real hazard is detected; and
wherein said annotated measured values (MV_NST) are used in the analyzing step to identify a parameter set for the hazard detection system to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms.
3. The method according to one of the previous claims, wherein analyzing the measured
values to identify a parameter set for the hazard detection system to reduce the number
of false alarms and/or to minimize the detection time without increasing the number
of false alarms is performed by simulating a digital twin of hazard detection system
and/or by simulating a digital twin of the hazard detectors.
4. The method according to claim 3, wherein identifying the parameter set to reduce the
number of false alarms and/or to minimize the detection time without increasing the
number of false alarms is performed by the respective digital twin by evaluating existing
parameter sets to select the best suitable parameter set.
5. The method according to one of the previous claims, wherein identifying the parameter
set to reduce the number of false alarms and/or to minimize the detection time without
increasing the number of false alarms is performed by a rule-based analytic engine
running scenarios of existing parameter sets.
6. The method according to one of the previous claims, wherein the hazard detectors are
fire detectors or smoke detectors or gas detectors or presence detectors.
7. The method according to one of the previous claims, wherein the measured values (MV_RH,
MV_PH) representing events ("different from normal status") in case of a real hazard
("real hazard") or a potential hazard ("false alarm", "No hazard") are provided based
on threshold triggering.
8. The method according to one of the previous claims, wherein annotating (tagging) the
measured values (MV_RH, MV_PH) representing an event in case of occurrence of a real
hazard and in case of a potential hazard is performed by a user by entering a respective
acknowledgment on the hazard control panel.
9. The method according to one of the previous claims, wherein annotating (tagging) the
measured values (MV_RH) representing an event in case of occurrence of a real hazard
is performed by automatic approval of a second hazard detection system and/or by analyzing
hazard events (HE) from manual call points (MCP).
10. The method according to one of the previous claims, wherein annotating (tagging) the
measured values (MV_RH) representing an event in case of occurrence of a real hazard
is performed when a defined number of further hazard detectors are also providing
measured values (MV_RH) representing an event in case of occurrence of a real hazard.
11. An arrangement for providing parameter sets for a hazard detection system to improve
the detection performance of the detection system, the arrangement comprising:
a hazard control panel connected to hazard detectors;
an analysis engine for analyzing measured values of at least one of the detectors;
wherein at least one of the hazard detectors is configured to provide measured values (MV_NS) representing the operation status ("normal status") in case no hazard is detected to the analysis engine;
wherein at least one of the hazard detectors is configured to provide measured values (MV_RH, MV_PH) in case a hazard ("real hazard") or a potential hazard ("false alarm", "No hazard") is detected to the analysis engine;
means for annotating (tagging) the measured values (MV_RH, MV_PH) representing an event in case of occurrence of a real hazard and in case of a potential hazard;
wherein the analysis engine is configured to analyze the measured values (MV_NS) for the case no hazard is detected and the measured values (MV_RH, MV_PH) representing events in case a real hazard or a potential hazard is detected and the annotated measured values (MV_RHT, MV_PHT) representing events in case a hazard or a potential hazard is detected to identify a parameter set for the hazard detection system to reduce the number of false alarms and/or to minimize the detection time without increasing the number of false alarms.
12. The arrangement according to claim 11,
wherein the analysis engine is configured to analyze the measured values to identify the parameter set for the hazard detection system to reduce the number of false alarms by simulating a digital twin of the hazard detection system and/or by simulating a digital twin of the hazard detectors.
wherein the analysis engine is configured to analyze the measured values to identify the parameter set for the hazard detection system to reduce the number of false alarms by simulating a digital twin of the hazard detection system and/or by simulating a digital twin of the hazard detectors.
13. The arrangement according to claim 11 or claim 12,
wherein the analysis engine is a rule-based analytic engine configured to run scenarios of existing parameter sets to identify the parameter set able to reduce the number of false alarms.
wherein the analysis engine is a rule-based analytic engine configured to run scenarios of existing parameter sets to identify the parameter set able to reduce the number of false alarms.
14. The arrangement according to one of the claims 11 to 13,
wherein the analysis engine is implemented in a cloud infrastructure.
wherein the analysis engine is implemented in a cloud infrastructure.
15. The arrangement according to one of the claims 11 to 14, wherein the hazard detectors
are fire detectors or smoke detectors or gas detectors or presence detectors.
16. A data processing system comprising instructions or means to carry out a method according
to one of the claims 1 to 10.