CN115083132B - Research and judgment method for reducing false alarm rate of fire alarm - Google Patents
Research and judgment method for reducing false alarm rate of fire alarm Download PDFInfo
- Publication number
- CN115083132B CN115083132B CN202210850814.9A CN202210850814A CN115083132B CN 115083132 B CN115083132 B CN 115083132B CN 202210850814 A CN202210850814 A CN 202210850814A CN 115083132 B CN115083132 B CN 115083132B
- Authority
- CN
- China
- Prior art keywords
- alarm
- equipment
- data
- internet
- fire
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
- G08B29/18—Prevention or correction of operating errors
- G08B29/185—Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
- H04W88/16—Gateway arrangements
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Security & Cryptography (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses a research and judgment method for reducing the false alarm rate of fire alarm, belonging to the technical field of fire alarm and comprising the following steps: capturing data of the Internet of things sensing equipment through the Internet of things gateway; the edge computing platform analyzes the captured data of the Internet of things sensing equipment to obtain original alarm data and sends the original alarm data to the fire-fighting alarm auxiliary identification system and the health state studying and judging system of the Internet of things sensing equipment; analyzing the equipment state data and the metadata by the health state studying and judging system of the internet of things equipment, sending the equipment state data and the metadata to a fire-fighting cloud platform, and manually judging the alarm information again after generating the alarm information; when the alarm information is judged manually, if the alarm is equipment fault information, fault feedback information is sent to a fire fighting equipment health state studying and judging training system, and the studying and judging method for reducing the false alarm rate of fire fighting alarm realizes the adaptability of the fire fighting alarm auxiliary recognition system and the equipment health state studying and judging system to specific scenes and specific equipment.
Description
Technical Field
The invention belongs to the technical field of fire alarm, and particularly relates to a research and judgment method for reducing the false alarm rate of fire alarm.
Background
Along with the accelerated development of domestic economy, in the burglar alarm field, the range of application of alarm is more and more extensive, become present theftproof from single theftproof function before, control, alarming function, at high-grade district, factory building garden at present, the commercial district uses more, along with the demand in market, single security protection function can not satisfy people's demand, thereby more products have evolved, especially the front-end equipment of surveying, more and more, and the mode of communication is various, although many systems are perfect at present to the analysis function of reporting to the police, but still have the following problem:
1. the diversity of the front-end equipment cannot be integrated into unified data;
2. the secondary determination can not be carried out when false alarm occurs;
3. the front-end equipment has single function and cannot perform multi-angle detection, and if a plurality of front-end equipment are arranged in a certain space, resource waste and cost increase are caused;
in order to solve the above problems, a research and judgment method for reducing the false alarm rate of fire alarm needs to be developed to solve the existing problems.
Disclosure of Invention
The invention aims to provide a research and judgment method for reducing the false alarm rate of fire alarm so as to solve the problem of more false alarms of fire alarm information.
In order to achieve the purpose, the invention provides the following technical scheme: a research and judgment method for reducing the false alarm rate of fire alarm comprises the following steps:
capturing data of the Internet of things sensing equipment through the Internet of things gateway;
the edge computing platform analyzes the captured data of the Internet of things sensing equipment to obtain original alarm data and sends the original alarm data to the fire alarm auxiliary identification system and the health state studying and judging system of the Internet of things sensing equipment;
analyzing the equipment state data and the metadata by the health state studying and judging system of the internet of things equipment, sending the equipment state data and the metadata to the fire fighting cloud platform, and manually judging the alarm condition information again after generating alarm information;
when the alarm condition information is judged manually, if the alarm condition is equipment fault information, sending the fault information to an Internet of things equipment health state studying and judging training system, and after the Internet of things equipment health state studying and judging training system optimizes equipment health state conditions, sending the equipment health state conditions to the Internet of things equipment health state studying and judging system;
when the alarm information is judged manually, if the alarm is false alarm information, sending the false alarm information to a fire alarm information recognition training system, optimizing alarm conditions and then sending the optimized alarm conditions to a fire alarm auxiliary recognition system; and the fire alarm auxiliary identification system sends the alarm information to a fire cloud platform.
Preferably, the method for capturing the data of the internet of things sensing equipment by the internet of things gateway includes:
the data of the Internet of things sensing equipment is sent to the user information transmission device through NB-IOT/LoRA, and the user information transmission device sends the data to the Internet of things gateway;
data of the Internet of things sensing equipment is sent to the Internet of things gateway through RS485/232 or LoRA;
and the Internet of things gateway transcodes the data of the Internet of things sensing equipment and then performs network transmission.
Preferably, the parsing of the data by the edge computing platform includes:
identifying, classifying and cleaning, wherein the method for identifying, classifying and cleaning comprises the following steps:
judging whether the original alarm data of the equipment is alarm data or not, if so, sending the alarm data to a fire alarm auxiliary identification system, and sending the alarm data to a fire cloud platform by the fire alarm auxiliary identification system;
if the data is not the alarm data, judging whether the data is equipment state data, and if the data is the equipment state data, sending the data to the health state studying and judging system of the Internet of things equipment; otherwise, judging whether the equipment metadata is the equipment metadata, if so, sending the equipment metadata to the health state studying and judging system of the internet of things equipment, and otherwise, discarding the equipment metadata.
Preferably, the fire alarm auxiliary recognition system is used for comparing original alarm data with comparison alarm data for multiple times in a set time interval, matching the comparison alarm data with historical alarm information of the equipment, calling peripheral alarm equipment for auxiliary judgment if the fire alarm is judged to be false alarm, calling peripheral video monitoring equipment simultaneously, and calling the peripheral video monitoring equipment if the fire alarm is judged to be fire alarm information, wherein the video monitoring equipment uploads the alarm data and an auxiliary recognition result.
Preferably, the health state studying and judging system of the equipment in the internet of things is used for performing data standardization analysis on state data and equipment metadata of the sensing equipment in the internet of things, judging whether the data is related to the health state of the equipment, if the data is related to the health state, analyzing whether the data is fault information of the equipment, if the data is the fault information of the equipment, analyzing the fault information, generating a fault result, and sending the fault result to the data collection;
if the fault information of the equipment is not the fault information of the equipment, judging whether corresponding metadata exists, if the corresponding metadata exists, comparing the metadata with historical information, analyzing the decline trend of the health state, generating the result data of the health state of the equipment, and uploading the data after data are collected.
Preferably, the health state studying and judging system of the equipment in the internet of things is used for performing data standardization analysis on state data and equipment metadata of the sensing equipment in the internet of things, judging whether the data is related to the health state of the equipment, if the data is related to the health state, analyzing whether the data is an equipment fault, if the data is equipment fault information, analyzing the fault information, generating a fault result, and sending the fault result to the data collection;
if the metadata is not the equipment fault information, judging whether corresponding metadata exists, if the metadata exists, comparing the metadata with historical information, analyzing the health state descending trend, generating equipment health state result data, and uploading the data after data are collected.
Preferably, the fire fighting cloud platform is used for performing data collection on alarm data and equipment state data, performing data application and management after the data collection, and sending the data to a worker for judging alarm information and equipment faults, wherein if the worker judges that the alarm information is false alarm information, the false alarm information is fed back to a fire fighting alarm information recognition training system, and if the alarm information is fire alarm information, the fire fighting alarm information is sent to a fire fighting emergency department;
if the fault information is the fault information, the fault information is fed back to the health state studying and judging training system of the fire fighting internet of things equipment, and the fault information is sent to an equipment maintenance department; the health state studying and judging training system of the Internet of things equipment is used for optimizing the health state condition of the equipment;
the data application and management stores data in a cloud.
Preferably, the fire alarm information recognition training system is used for acquiring false alarm data, labeling false alarm characteristics, classifying the false alarm characteristics, generating specific labeled content information aiming at specified equipment for non-common characteristics, and guiding the labeled content information into the AI learning system;
generating labeled content information for the common characteristics, and sending the labeled content information to an AI learning system; and the AI learning system generates a trained model file and synchronizes the model file to the fire-fighting alarm auxiliary identification system through the OTA.
Preferably, the health state studying and judging system of the fire fighting internet of things equipment is used for acquiring equipment state data, judging the state data or fault data, if the state data is the state data, comparing the state data with the equipment state data, labeling the health state characteristics of the equipment, and generating labeled content information;
and if the fault data is subjected to fault information characteristic marking, generating marking content information, importing the marking content into an AI learning system, generating a trained model file, and synchronizing the OTA to the fire alarm auxiliary identification system.
Preferably, thing allies oneself with sensing equipment includes flame active detection terminal, flame active detection terminal includes unable adjustment base, support column, cruise driving motor, cruise gear, antenna, circuit board, casing, infrared sensor, photodiode, unable adjustment base is the cuboid structure, and its up end is fixed with the columniform support column, central point puts and is provided with cruise driving motor in the support column, just the support column with cruise driving motor is coaxial to be distributed, cruise driving motor's output rod stretches out the support column and is located the casing, casing swing joint in the support column, the inner wall of casing is provided with the gear, be provided with cruise gear on the output rod, cruise gear and the inner wall gear phase-match of casing, when the output rod is rotatory, drive cruise gear rotates, drive the casing rotates, be provided with the circuit board in the casing, cruise driving motor is connected with the circuit board, and the circuit board sends driving signal for cruise driving motor, just the circuit board still is connected with infrared sensor, photodiode, infrared sensor, photodiode inlay the front end of casing for detect flame signal, just the up end of casing is provided with the antenna, the antenna with the circuit board is connected, the circuit board is provided with communication microprocessor and communication module.
Preferably, the cross section of the shell is rectangular, the length of the upper end face of the shell is larger than that of the lower end face of the shell, the distance between the infrared sensor and the photodiode is 1/3 of the height of the shell, the distance between the infrared sensor and the inner wall of the upper end face is 1/3 of the height of the shell, and the distance between the photodiode and the inner wall of the lower end face is 1/3 of the height of the shell.
The invention has the technical effects and advantages that: the research and judgment method for reducing the false alarm rate of the fire alarm enables a fire alarm information recognition training system and a fire fighting internet of things equipment health state research and judgment training system to be linked with a fire fighting cloud platform and fire fighting first-line workers, so that the self-learning, self-optimization and self-upgrading capabilities of a fire alarm auxiliary recognition system and an internet of things equipment health state research and judgment system are formed, and the adaptive capacity of the fire alarm auxiliary recognition system and the internet of things equipment health state research and judgment system to specific scenes and specific equipment is realized; compared with the existing common technology, the method can effectively reduce the false alarm rate of fire alarm, realize the accurate trend estimation of the health state of the fire-fighting internet of things equipment, and simultaneously have the capability of providing decision assistance for a fire-fighting manager; simultaneously, the active detection range maximization in a unit range is realized through the active flame detection terminal, the problems that the traditional detection equipment can only detect the fire source in a specific direction and cannot realize the maximization processing of equipment utilization are solved, the detection direction is actively adjusted if the situation that the alarm information is judged by people is false alarm, the auxiliary identification is rapidly realized, and the identification accuracy is improved.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a schematic diagram of electrical connection components of the active flame detection terminal according to the present invention;
FIG. 3 is a schematic structural diagram of the active flame detection terminal according to the present invention;
fig. 4 is a schematic diagram illustrating steps of a method for capturing data of an internet of things sensing device by an internet of things gateway according to the present invention;
FIG. 5 is a schematic diagram illustrating the steps of the method for identifying, classifying and cleaning data by the edge computing platform according to the present invention;
FIG. 6 is a flowchart illustrating the operation of the fire alarm assisted identification system of the present invention;
FIG. 7 is a flowchart illustrating the operation of the health status evaluation system of the Internet of things device according to the present invention;
FIG. 8 is a flow chart of the fire cloud platform operation of the present invention;
FIG. 9 is a flowchart of the fire alarm information recognition training system of the present invention;
FIG. 10 is a flowchart of the health status evaluation system for the fire fighting Internet of things.
In the figure: 11. a fixed base; 12. a support pillar; 13. a cruise drive motor; 14. a cruise gear; 15. an antenna; 16. a circuit board; 17. a housing; 18. an infrared sensor; 19. a photodiode.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The invention provides a research and judgment method for reducing the false alarm rate of fire alarm as shown in figure 1, which comprises the following steps:
step 1, capturing data of an Internet of things sensing device through an Internet of things gateway; as shown in fig. 4, the method for capturing the data of the internet of things sensing device by the internet of things gateway includes:
the data of the Internet of things sensing equipment is sent to the user information transmission device through NB-IOT/LoRA, and the user information transmission device sends the data to the Internet of things gateway;
data of the Internet of things sensing equipment is sent to the Internet of things gateway through RS485/232 or LoRA;
the internet of things gateway transcodes the data of the internet of things sensing equipment and then performs network transmission;
in the embodiment, the internet of things gateway is a heterogeneous internet of things gateway, bears various common information transmission modes in the fire protection industry, and realizes data transcoding on the heterogeneous internet of things gateway; in this embodiment, the fire-fighting cloud platform is a fire-fighting digital ecological cloud platform; the fire alarm auxiliary identification system, the Internet of things equipment health state studying and judging training system, the fire alarm information identification training system and the fire alarm auxiliary identification system are all constructed based on the existing systems;
step 2, the edge computing platform analyzes the captured data of the Internet of things sensing equipment to obtain original alarm data and sends the original alarm data to a fire alarm auxiliary identification system and an Internet of things equipment health state studying and judging system; as shown in fig. 5, the parsing of the data by the edge computing platform includes:
identifying, classifying and cleaning, wherein the identifying, classifying and cleaning method comprises the following steps:
judging whether the original alarm data of the equipment is alarm data or not, if so, sending the alarm data to a fire alarm auxiliary identification system, and sending the alarm data to a fire cloud platform by the fire alarm auxiliary identification system;
if the data is not the alarm data, judging whether the data is equipment state data, and if the data is the equipment state data, sending the data to the health state studying and judging system of the Internet of things equipment; otherwise, judging whether the equipment metadata is equipment metadata, if so, sending the equipment metadata to the health state studying and judging system of the internet of things equipment, and otherwise, discarding the equipment metadata;
the health state studying and judging system of the Internet of things equipment judges whether the equipment state data and the metadata are equipment state data or not and sends the equipment state data and the metadata to the fire-fighting cloud platform; the fire-fighting internet of things equipment health state studying and judging system is used for acquiring equipment state data, judging the state data or fault data, if the state data is the state data, comparing the state data with the equipment state data, marking equipment health state characteristics and generating marked content information;
and if the fault information characteristic label is generated for the fault data, label content information is generated, label content is imported into an AI learning system, a trained model file is generated, and the OTA is synchronized to the fire-fighting alarm auxiliary identification system.
As shown in fig. 6, the fire alarm auxiliary recognition system is configured to compare original alarm data with alarm data for multiple times within a set time interval, in this embodiment, the original alarm data is compared for multiple times at a short interval and is matched with historical alarm information of the device, if a fire alarm is determined to be a false alarm, peripheral alarm devices are called for auxiliary judgment, peripheral video monitoring devices are called for the same time, and if the fire alarm is determined to be fire alarm information, the peripheral video monitoring devices are called, and the video monitoring devices upload the alarm data and the auxiliary recognition result.
As shown in fig. 7, step 3, the health state studying and judging system of the internet of things device analyzes the device state data and the metadata and sends the device state data and the metadata to the fire-fighting cloud platform, and after alarm information is generated, the alarm information is manually judged again; the system for studying and judging the health state of the equipment in the internet of things is used for carrying out data standardization analysis on state data and equipment metadata of the sensing equipment in the internet of things, judging whether the data is related to the health state of the equipment, if the data is related to the health state, analyzing whether the data is an equipment fault, and if the data is the fault information of the equipment, analyzing the fault information to generate a fault result and sending the fault result to a data collection set;
if the metadata is not the equipment fault information, judging whether corresponding metadata exists, if the metadata exists, comparing the metadata with historical information, analyzing the health state descending trend, generating equipment health state result data, and uploading the data after data are collected.
Step 4, when the alarm information is judged manually, if the alarm is equipment fault information, sending the fault information to a health state studying and judging training system of the fire-fighting equipment in the Internet of things, and after optimizing the health state condition of the equipment, sending the equipment health state studying and judging training system of the fire-fighting equipment in the Internet of things to a health state studying and judging system of the equipment in the Internet of things;
step 5, when the alarm information is judged manually, if the alarm is false alarm information, sending the false alarm information to a fire alarm information recognition training system, and optimizing an alarm condition and then sending the alarm condition to a fire alarm auxiliary recognition system; the fire alarm auxiliary identification system sends alarm information to a fire cloud platform;
as shown in fig. 8, the fire fighting cloud platform is configured to perform data aggregation on alarm data and equipment status data, perform data application and management after the data aggregation, and send the data to a human to determine alarm information and determine an equipment fault, where if the alarm information is determined by the human as false alarm information, the false alarm information is fed back to a fire fighting alarm information recognition training system, and if the alarm information is determined by the human as fire alarm information, the false alarm information is sent to a fire fighting emergency department;
if the fault information is the fault information, the fault information is fed back to the health state studying and judging training system of the fire fighting internet of things equipment, and fault data is sent to an equipment maintenance department;
and the data application and management stores the data in a cloud mode.
The fire alarm information recognition training system is used for collecting false alarm data, labeling false alarm characteristics, classifying the false alarm characteristics, generating specific labeled content information aiming at specified equipment for non-common characteristics, and sending the labeled content information to the labeled content import AI learning system;
the AI learning system is an Artificial Intelligence (Artificial Intelligence) learning engine based on advanced algorithms such as Bayesian (Bayesian), neural network algorithm (neural network algorithm), particle Swarm algorithm (Particle Swarm Optimization) and the like;
generating labeled content information for the common characteristics, and sending the labeled content information to an AI learning system; and the AI learning system generates a trained model file and synchronizes the model file to the fire-fighting alarm auxiliary identification system through the OTA.
Specifically, in this embodiment, the edge computing platform expands native container orchestration and scheduling capabilities to the edge, and provides infrastructure support for edge application deployment, metadata synchronization between the cloud and the edge, edge device management, and the like;
in the embodiment, the health state studying and judging training system of the fire fighting internet of things equipment is based on a deep learning system, and the health state condition of the equipment is optimized through the learning capacity of a neural network;
as shown in fig. 2 and 3, the internet of things sensing device includes a flame active detection terminal, the flame active detection terminal includes a fixed base 11, a support column 12, a cruise drive motor 13, a cruise gear 14, an antenna 15, a circuit board 16, a housing 17, an infrared sensor 18 and a photodiode 19, the fixed base 11 is a cuboid structure, the upper end surface of the fixed base is fixed with the cylindrical support column 12, the cruise drive motor 13 is arranged at the center position in the support column 12, the support column 12 and the cruise drive motor 13 are coaxially distributed, an output rod of the cruise drive motor 13 extends out of the support column 12 and is located in the housing 17, the housing 17 is movably connected to the support column 12, a gear is arranged on the inner wall of the housing 17, the cruise gear 14 is arranged on the output rod, the cruise gear 14 is matched with the gear on the inner wall of the housing 17, the cruise gear 14 is driven to rotate when the output rod rotates, the housing 17 is driven to rotate, a circuit board 16 is arranged in the housing 17, the cruise drive motor 13 is connected with the circuit board 16, the circuit board 16 sends a drive signal to the cruise drive motor 13, the circuit board 16 is further connected with the infrared sensor 18, the photodiode 19 is connected to a front communication module, and a communication module is arranged on the communication module 15 and connected with the microprocessor 15. The cross section of the shell 17 is rectangular, the length of the upper end face of the shell is larger than that of the lower end face of the shell, the distance between the infrared sensor 18 and the photodiode 19 is 1/3 of the height of the shell 17, the height from the infrared sensor 18 to the inner wall of the upper end face is 1/3 of the height of the shell 17, and the height from the photodiode 19 to the inner wall of the lower end face is 1/3 of the height of the shell 17.
As shown in fig. 9 and 10, the studying and judging method for reducing the false alarm rate of fire alarm enables the fire alarm information recognition training system and the fire fighting internet of things equipment health state studying and judging training system to be linked with the fire fighting cloud platform and the fire fighting first-line worker, so that the self-learning, self-optimization and self-upgrading capabilities of the fire alarm auxiliary recognition system and the fire fighting internet of things equipment health state studying and judging system are formed, and the adaptive capacity of the fire alarm auxiliary recognition system and the fire fighting internet of things equipment health state studying and judging system to specific scenes and specific equipment is realized; compared with the existing common technology, the method can effectively reduce the false alarm rate of fire alarm, realize the accurate trend estimation of the health state of the fire-fighting internet of things equipment, and simultaneously have the capability of providing decision assistance for a fire-fighting manager; simultaneously, the active detection range maximization in a unit range is realized through the active flame detection terminal, the problems that the traditional detection equipment can only detect the fire source in a specific direction and cannot realize the maximization processing of equipment utilization are solved, the detection direction is actively adjusted if the situation that the alarm information is judged by people is false alarm, the auxiliary identification is rapidly realized, and the identification accuracy is improved.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (9)
1. A research and judgment method for reducing the false alarm rate of fire alarm is characterized in that: the method comprises the following steps:
capturing data of the Internet of things sensing equipment through the Internet of things gateway;
the edge computing platform analyzes the captured data of the Internet of things sensing equipment to obtain original alarm data and sends the original alarm data to the fire-fighting alarm auxiliary identification system and the health state studying and judging system of the Internet of things sensing equipment;
the health state studying and judging system of the internet of things equipment analyzes equipment state data and metadata and sends the equipment state data and the metadata to the fire-fighting cloud platform, and after alarm information is generated, the alarm information is manually judged again;
when the alarm information is judged manually, if the alarm is equipment fault information, sending the fault information to an equipment health state studying and judging training system of the Internet of things, and after the equipment health state condition is optimized, sending the equipment health state studying and judging training system of the Internet of things to a health state studying and judging system of the Internet of things;
when the alarm information is judged manually, if the alarm is false alarm information, sending the false alarm information to a fire alarm information recognition training system, optimizing alarm conditions and then sending the optimized alarm conditions to a fire alarm auxiliary recognition system; the fire alarm auxiliary identification system sends alarm information to a fire cloud platform; the analysis of the data by the edge computing platform comprises the following steps:
identifying, classifying and cleaning, wherein the method for identifying, classifying and cleaning comprises the following steps:
judging whether the original alarm data of the equipment is alarm data or not, if so, sending the alarm data to a fire alarm auxiliary identification system, and sending the alarm data to a fire cloud platform by the fire alarm auxiliary identification system;
if the data is not the alarm data, judging whether the data is equipment state data, and if the data is the equipment state data, sending the data to the health state studying and judging system of the Internet of things equipment; otherwise, judging whether the equipment metadata is the equipment metadata, if so, sending the equipment metadata to the health state studying and judging system of the internet of things equipment, and otherwise, discarding the equipment metadata.
2. The studying and judging method for reducing the false alarm rate of fire fighting alarm according to claim 1, characterized in that: the method for capturing the data of the Internet of things sensing equipment by the Internet of things gateway comprises the following steps:
the data of the Internet of things sensing equipment is sent to a user information transmission device through NB-IOT/LoRA, and the user information transmission device sends the data to the Internet of things gateway;
data of the Internet of things sensing equipment is sent to the Internet of things gateway through RS485/232 or LoRA;
and the Internet of things gateway transcodes the data of the Internet of things sensing equipment and then performs network transmission.
3. The studying and judging method for reducing the false alarm rate of fire fighting alarm according to claim 1, characterized in that: the fire alarm auxiliary recognition system is used for comparing original alarm data with alarm data for multiple times in a set time interval and matching with historical alarm information of equipment, calling peripheral alarm equipment for auxiliary judgment if a fire alarm is judged to be a false alarm, calling peripheral video monitoring equipment simultaneously, calling peripheral video monitoring equipment if the fire alarm is judged to be fire alarm information, and uploading the alarm data and an auxiliary recognition result by the video monitoring equipment.
4. The studying and judging method for reducing the false alarm rate of fire fighting alarm according to claim 1, characterized in that: the system for studying and judging the health state of the equipment in the Internet of things is used for carrying out data standardization analysis on state data and equipment metadata of the sensing equipment in the Internet of things, judging whether the data is related to the health state of the equipment or not, if the data is related to the health state, analyzing whether the data is fault information of the equipment or not, and if the data is the fault information of the equipment, analyzing the fault information, generating a fault result and sending the fault result to a data aggregation;
if the failure information of the equipment is not the failure information of the equipment, judging whether corresponding metadata exists, if the metadata exists, comparing the metadata with historical information, analyzing the health state descending trend, generating equipment health state result data, and uploading the data after data collection.
5. The studying and judging method for reducing the false alarm rate of fire fighting alarm according to claim 1, characterized in that: the fire fighting cloud platform is used for performing data collection on alarm data and equipment state data, performing data application and management after the data collection, and sending the data to a worker for judging alarm information and equipment faults, wherein the worker judges that the alarm information is false alarm information, the false alarm information is fed back to a fire fighting alarm information recognition training system, and if the alarm information is fire alarm information, the false alarm information is sent to a fire fighting emergency department;
if the fault information is the fault information, the fault information is fed back to the health state studying and judging training system of the fire fighting internet of things equipment, and the fault information is sent to an equipment maintenance department; the health state studying and judging training system of the Internet of things equipment is used for optimizing the health state condition of the equipment;
the data application and management stores data in a cloud.
6. The studying and judging method for reducing the false alarm rate of fire fighting alarm according to claim 1, characterized in that: the fire alarm information recognition training system is used for collecting false alarm data, labeling false alarm characteristics, classifying the false alarm characteristics, generating specific labeled content information aiming at specified equipment for non-common characteristics, and guiding the labeled content information into an AI learning system;
generating labeled content information for the common characteristics, and sending the labeled content information to an AI learning system; and the AI learning system generates a trained model file and synchronizes the model file to the fire-fighting alarm auxiliary identification system through the OTA.
7. The studying and judging method for reducing the false alarm rate of fire fighting alarm according to claim 1, characterized in that: the health state studying and judging system of the internet of things equipment is used for acquiring equipment state data, judging the state data or fault data, comparing the state data with the equipment state data if the state data is the state data, marking the health state characteristics of the equipment and generating marked content information;
and if the fault information characteristic label is generated for the fault data, label content information is generated, label content is imported into an AI learning system, a trained model file is generated, and the OTA is synchronized to the fire-fighting alarm auxiliary identification system.
8. The studying and judging method for reducing the false alarm rate of fire fighting alarm according to claim 1, characterized in that: the thing allies oneself with sensing equipment includes flame active detection terminal, flame active detection terminal includes unable adjustment base (11), support column (12), drive motor (13) cruises, cruises gear (14), antenna (15), circuit board (16), casing (17), infrared sensor (18), photodiode (19), unable adjustment base (11) is the cuboid structure, and its up end is fixed with columniform support column (12), central point puts and is provided with cruises drive motor (13) in support column (12), just support column (12) with cruises drive motor (13) coaxial distribution, cruises the output pole of drive motor (13) and stretches out support column (12) and be located casing (17), casing (17) swing joint in support column (12), the inner wall of casing (17) is provided with the gear, be provided with cruises gear (14) on the output pole, cruises gear (14) and casing (17)'s inner wall gear phase-match, when the output pole is rotatory, drive cruises gear (14) and rotates, drives casing (17) and rotates, be provided with drive motor (16) in casing (17), cruises drive circuit board (13) and cruise drive motor (16) is connected with cruises signal transmission circuit board (13), and circuit board (16) still is connected with infrared sensor (18), photodiode (19), infrared sensor (18), photodiode (19) are inlayed and are located the front end of casing (17) for survey flame signal, just the up end of casing (17) is provided with antenna (15), antenna (15) with circuit board (16) are connected, circuit board (16) are provided with microprocessor and communication module, communication module is connected with antenna (15).
9. The studying and judging method for reducing the false alarm rate of fire fighting alarm according to claim 8, characterized in that: the cross section of casing (17) is the rectangle, and the length of its up end is greater than down terminal surface length, infrared sensor (18), photodiode (19) interval are the 1/3 of casing (17) height, just infrared sensor (18) is 1/3 of casing (17) height apart from the height of up end inner wall, photodiode (19) is 1/3 of casing (17) height apart from the height of terminal surface inner wall down.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210850814.9A CN115083132B (en) | 2022-07-20 | 2022-07-20 | Research and judgment method for reducing false alarm rate of fire alarm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210850814.9A CN115083132B (en) | 2022-07-20 | 2022-07-20 | Research and judgment method for reducing false alarm rate of fire alarm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115083132A CN115083132A (en) | 2022-09-20 |
| CN115083132B true CN115083132B (en) | 2022-11-01 |
Family
ID=83260640
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210850814.9A Active CN115083132B (en) | 2022-07-20 | 2022-07-20 | Research and judgment method for reducing false alarm rate of fire alarm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115083132B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115909640B (en) * | 2022-10-19 | 2024-01-05 | 慧之安信息技术股份有限公司 | Intelligent convenience store danger identification system based on edge intelligence |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011104032A (en) * | 2009-11-16 | 2011-06-02 | Hochiki Corp | Hydrant apparatus monitoring system |
| CN110996285A (en) * | 2019-11-15 | 2020-04-10 | 中南大学 | College intelligent fire service system based on 6LoWPAN and design method |
| CN112767632B (en) * | 2020-04-29 | 2022-10-11 | 天津市中力神盾电子科技有限公司 | Electric fire information optimization method and system |
| CN112215452A (en) * | 2020-07-28 | 2021-01-12 | 智维云图(上海)智能科技有限公司 | Intelligent fire-fighting remote monitoring method and system and safety assessment method |
| CN114170753B (en) * | 2021-12-03 | 2024-04-19 | 天泽智联科技股份公司 | Information acquisition processing system and method for building fire protection system |
-
2022
- 2022-07-20 CN CN202210850814.9A patent/CN115083132B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN115083132A (en) | 2022-09-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104537788B (en) | High voltage electric transmission line external damage-proof intelligent image pre-warning system and method | |
| US11587417B2 (en) | Object tracking using disparate monitoring systems | |
| CN105100724A (en) | Remote and safe intelligent household monitoring method and device based on visual analysis | |
| WO2012142797A1 (en) | Video monitoring system and method | |
| CN110155842A (en) | A kind of lift running safety detection and early warning identifying system based on Intelligent hardware | |
| CN205581602U (en) | Computer lab environmental monitoring system | |
| US11972352B2 (en) | Motion-based human video detection | |
| CN115083132B (en) | Research and judgment method for reducing false alarm rate of fire alarm | |
| CN111047824B (en) | Indoor child nursing linkage control early warning method and system | |
| US11200786B1 (en) | Canine assisted home monitoring | |
| CN109544870A (en) | Alarm decision method and intelligent monitor system for intelligent monitor system | |
| US12014271B2 (en) | Training image classifiers | |
| US11409989B2 (en) | Video object detection with co-occurrence | |
| CN113269034A (en) | Power distribution station scene linkage system and method based on content identification | |
| US11257355B2 (en) | System and method for preventing false alarms due to display images | |
| CN112418194A (en) | Video monitoring system and method for intelligent community | |
| CN215231835U (en) | Intelligent simple spraying system based on Internet of things technology | |
| CN105448054B (en) | Intelligent safety and defence system for Modern Community | |
| US20220335725A1 (en) | Monitoring presence or absence of an object using local region matching | |
| US20210274133A1 (en) | Pre-generating video event notifications | |
| US11900706B1 (en) | Object of interest detector distance-based multi-thresholding | |
| CN206743270U (en) | A kind of monitoring device monitored in real time for monitoring point | |
| US20210295054A1 (en) | Ground plane filtering of video events | |
| CN111866449B (en) | Intelligent video acquisition system and method | |
| CN118114855A (en) | Intelligent control method and system for community digital information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |