AIOT-based fire-fighting facility health situation detection and analysis system

Abstract

The invention relates to the technical field of fire fighting, in particular to an AIOT-based fire-fighting facility health situation detection and analysis system. Comprising an acquisition layer, a transmission layer, a data layer, a support layer and an application layer. The acquisition layer uses a detection integration device to detect fire-fighting equipment and sends detection data to the transmission layer, the transmission layer transmits the data to the data layer, the data layer transmits the data to the supporting layer, the supporting layer transmits the data to the application layer, and finally the application layer generates an evaluation report. The acquisition layer consists of a smoke / temperature monitoring sensor, a wind pressure monitoring sensor, an illumination / noise monitoring sensor, a distance / slope measurement sensor or detector and an edge computing gateway, and a detection integrated device with a replaceable acquisition head is used for acquiring the data; the system working platform is divided into six layers; the potential safety hazard rate of a single fire-fighting facility and the potential safety hazard rate of the fire-fighting facilities of a social unit (having n fire-fighting facilities) can be calculated through related formulas according to detection data, and then evaluation is carried out.

Description

Technical Field

[0001] The present invention relates to the field of fire protection technology, and in particular to an AIOT-based fire protection facility health status detection and analysis system. Background Art

[0003] One of the tasks of "prevention first" in fire protection work is to ensure that fire protection facilities are intact and effective. Only by regularly inspecting building fire protection facilities within the prescribed time and timely discovering and eliminating hidden dangers of fire protection facility failures can building fire protection equipment play the role of "prevention first".

[0006] At present, the professional quality of fire detection technicians in my country is uneven, and the collection heads of fire equipment generally have shortcomings such as low detection efficiency, poor accuracy, and low sensitivity. Therefore, improving the professional quality of fire detection technicians and developing fire facility collection heads and devices with high detection accuracy, strong sensitivity, and high detection efficiency is one of the driving forces for promoting the rapid development of fire detection.

[0007] With the help of the Internet of Things, AI artificial intelligence and big data technology, the present invention has developed a fire protection facility health status detection and analysis system based on AIOT. It is based on the Internet of Things monitoring, with efficient and accurate detection as the core, standardized detection process as the norm, and supported by the detection of fire protection facility data. It constructs a "fire protection facility health status analysis" software system with fault detection, hidden danger analysis, intelligent judgment of fire collection heads and fire safety status assessment, which comprehensively improves the regional fire protection detection level and is the primary prerequisite for realizing the fire protection work policy of "prevention first". Summary of the invention

[0008] The main purpose of the present invention is to provide a fire protection facility health status detection and analysis system so as to effectively solve the problems raised in the background technology.

[0009] To achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a fire protection facility health status detection and analysis system based on AIOT, including an acquisition layer, a transmission layer, a data layer, a support layer and an application layer; the acquisition layer uses a detection integrated device to detect the fire protection facilities, and sends the detection data to the transmission layer, the transmission layer transmits the data to the data layer, the data layer transmits the data to the support layer, the support layer transmits the data to the application layer, and finally the application layer generates an evaluation report.

[0010] Preferably, the collection layer is composed of smoke / temperature monitoring, wind pressure monitoring, illumination / noise monitoring, distance / slope measurement sensors or detectors and an edge computing gateway; the collection layer uses a detection integrated device with a replaceable collection head to collect the above data, wherein the smoke / temperature simulator collection head simulates the rated temperature of the smoke, and detects whether the smoke detector and temperature alarm have an alarm within a certain period of time. If an alarm occurs, it will be deemed qualified, and if no alarm occurs, it will be deemed unqualified; when detecting wind pressure, the wind pressure sensor collection head is replaced, the wind pressure sensor is placed at the air outlet, and the wind pressure value is calculated according to the pressure per unit area to realize the detection of wind pressure; the illumination sensor / noise sensor collection head is placed at the light source / noise source to detect its light source intensity / decibel value; the laser rangefinder and the inclination angle measuring instrument, whose output value is the measurement result; all front-end detection data are transmitted to the edge computing gateway via Bluetooth, and the edge computing gateway analyzes and calculates the results, and sends the detection data and calculation results to the cloud-based fire protection facility health situation analysis system through the edge computing gateway to realize centralized evaluation and situation analysis of the overall fire protection facility detection.

[0011] Preferably, the detection integrated device is a detection rod composed of multiple telescopic sections, with a handle section at the end, a telescopic section in the middle, and a detection section at the front end. A sleeve is mounted on the outside of the detection section, and a T-shaped screw is arranged on the side wall of the sleeve. The T-shaped screw presses and fixes the sleeve on the detection section. A fixing plate is welded to the outside of the sleeve, and a plurality of small holes are arranged on the fixing plate. Binding ropes are passed through the small holes, and the collection head is bound to the fixing plate with the binding ropes.

[0012] Preferably, the collection head that can be bound to the detection section includes a smoke / temperature simulation detection unit, a wind pressure detection unit, an illumination / noise detection unit and a laser ranging / tilt angle detection unit, and these four units all have Bluetooth communication functions with the handle section.

[0013] Preferably, the handle node is an edge computing gateway having the following functions:

[0014] 1) It has the function of controlling the front-end detection section to simulate smoke and temperature, and can interact with the monitoring data of the front-end detection section;

[0015] 2) It has the functions of receiving detection node detection data and edge computing and logical judgment;

[0016] 3) It has 4G / 5G wireless communication transmission function, which can send the front-end detection and edge computing results to the fire protection facility health status analysis software system.

[0017] Preferably, the transmission layer is composed of a fire protection facility detection data communication protocol, a 4G / 5G / NB wireless network or the Internet.

[0018] Preferably, the data layer is composed of IoT monitoring data, detection business data, fire protection facility basic data, other external data, and big data cleaning, storage, mining and analysis service components.

[0019] Preferably, the support layer is composed of cloud computing resources, storage resources, network resources, middleware, operating system, retrieval services, GIS services, security management services, visualization services and various application management interface components.

[0020] Preferably, the application layer consists of fire facility inspection report application, fault hazard analysis application, fault hazard disposal application, fire facility health status service and jurisdiction fire safety risk assessment service.

[0021] A fire protection facility health status detection and analysis system based on AIOT has a system working platform of six layers. The first layer is composed of the underlying fire protection facility monitoring Internet of Things terminal cluster (smoke / temperature simulator, wind pressure sensor, etc.); the second layer is composed of fire protection facility monitoring data communication protocol components; the third layer is composed of cloud-based WINDOWSSERVER2018 / UbuntuLinuxServer service operating system; the fourth layer is composed of MySQL database management system; the fifth layer is composed of Apache Tomcat 8.0WEB middleware; and the sixth layer is composed of fire protection facility health status analysis software system.

[0022] A fire protection facility health status detection and analysis system based on AIOT, and its fire protection facility health status evaluation method is as follows:

[0023] The integrity and effectiveness of building fire protection facilities is the most important indicator for fire safety evaluation. The dynamic quantitative evaluation of fire safety is to dynamically collect the working status, fault status, fault frequency and fault duration of fire protection facilities in a unit time, and use the fault-free working time of fire protection facilities, fire protection facility fault weight, fire protection facility fault frequency and fault duration in a unit time as quantitative evaluation indicators of fire safety;

[0024] Taking the health status evaluation of a single fire protection facility as an example, its potential safety hazard rate (fire safety dynamic evaluation value) is:

[0025]

[0026] Where W is the weight value of fire protection facilities (usually determined by the importance of fire prevention and fire extinguishing, and can be defined by the public security fire department), Fre is the fault frequency per unit time, FT is the fault duration, and MTBF is the mean time between failures of the system (obtained by the fire protection facility manufacturer through type testing);

[0027] Taking a social unit as an example, the social unit has n fire protection facilities such as automatic fire alarm system, automatic sprinkler system, fire hydrant system, gas fire extinguishing system, smoke exhaust system, and fire isolation facilities; the hidden danger rate of fire protection facilities in the social unit (dynamic evaluation value of fire safety) is:

[0028]

[0029] It is the arithmetic mean of the health status evaluation of all fire protection facilities in social units.

[0030] Beneficial effects of the present invention:

[0031] 1. Improve the detection efficiency of building fire protection facilities

[0032] The application of integrated detection devices for fire protection facilities has changed the traditional manual detection method of fire protection facilities. It uses the Internet of Things and AI artificial intelligence technologies to greatly improve the detection efficiency of building fire protection facilities.

[0033] 2. Improved detection accuracy

[0034] The integrated detection device for fire-fighting facilities is equipped with intelligent judgment functions, which avoids inaccurate or erroneous detection results caused by uneven technical levels of fire-fighting facility detection personnel and subjective factors, and improves the detection accuracy of fire-fighting facilities.

[0035] 3. Established electronic files for fire protection facility inspection

[0036] The detection data and results of this system are all stored in the cloud. Through statistical analysis of cloud-based detection data, an electronic file of fire protection facility detection can be established to facilitate query and statistics.

[0037] 4. The analysis and evaluation of the health status of fire protection facilities has been realized. Through the analysis of a large amount of test data, the health status of building fire protection facilities can be analyzed and evaluated, and targeted maintenance of fire protection facilities can be carried out to ensure that the fire protection facilities are intact and effective.

[0038] 5. Contribute to the construction of smart fire protection projects

[0039] In the construction of smart fire protection projects, there are usually only real-time monitoring data of fire protection facilities, but a lack of fire protection facility inspection and maintenance data. Adding the fire protection facility maintenance data in this project not only provides a health status analysis for the building fire protection facilities, but also lays the foundation for smart fire protection projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Attached Figure 1 This is the overall architecture diagram of the system of the present invention.

[0041] Attached Figure 2 This is a network topology diagram of the present invention.

[0042] Attached Figure 3 It is a schematic diagram of the structure of the working platform of the system of the present invention.

[0043] Attached Figure 4 It is a schematic diagram of the three-dimensional structure of the detection integrated device according to an embodiment of the present invention.

[0044] Attached Figure 5 It is a three-dimensional enlarged schematic diagram of the front side of the detection section of the detection integrated device according to an embodiment of the present invention.

[0045] Attached Figure 6 It is a three-dimensional enlarged schematic diagram of the back side of the detection section of the detection integrated device according to an embodiment of the present invention.

[0046] Attached Figure 7 It is a functional schematic diagram of the integrated detection device according to an embodiment of the present invention.

[0047] Attached Figure 1 —7, handle section 1, telescopic section 2, detection section 3, small hole 4, T-shaped screw 5, binding rope 6, fixing plate 7, sleeve 8, smoke / temperature simulation detection unit 9. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] See attached Figure 1 , 2 As shown, a fire protection facility health status detection and analysis system based on AIOT includes an acquisition layer, a transmission layer, a data layer, a support layer and an application layer; the acquisition layer uses a detection integrated device to detect the fire protection facilities, and sends the detection data to the transmission layer, the transmission layer transmits the data to the data layer, the data layer transmits the data to the support layer, the support layer transmits the data to the application layer, and finally the application layer generates an evaluation report.

[0050] The collection layer is composed of smoke / temperature monitoring, wind pressure monitoring, illumination / noise monitoring, distance / slope measurement sensors or detectors and an edge computing gateway; the collection layer uses a detection integrated device with a replaceable collection head to collect the above data, wherein the smoke / temperature simulator collection head simulates the rated temperature of the smoke, and detects whether the smoke detector and temperature alarm have an alarm within a certain period of time. If an alarm occurs, it will be deemed qualified, and if no alarm occurs, it will be deemed unqualified; when detecting wind pressure, replace the wind pressure sensor collection head, place the wind pressure sensor at the air outlet, calculate the wind pressure value according to the pressure per unit area, and realize the detection of wind pressure; place the illumination sensor / noise sensor collection head at the light source / noise source to detect its light source intensity / decibel value; the laser rangefinder and inclination angle measuring instrument, whose output value is the measurement result; all front-end detection data are transmitted to the edge computing gateway via Bluetooth, and the edge computing gateway analyzes and calculates the results, and sends the detection data and calculation results to the cloud-based fire protection facility health situation analysis system through the edge computing gateway to realize centralized evaluation and situation analysis of the overall fire protection facility detection.

[0051] See attached Figure 4 —7, the detection integrated device is a detection rod composed of multiple telescopic sections, with a handle section 1 at the end, a telescopic section 2 in the middle, and a detection section 3 at the front end. A sleeve 8 is mounted on the outside of the detection section 3, and a T-shaped screw 5 is arranged on the side wall of the sleeve 8. The T-shaped screw 5 presses and fixes the sleeve on the detection section 3. A fixing plate 7 is welded on the outside of the sleeve 8, and a plurality of small holes 4 are arranged on the fixing plate 7. Binding ropes 6 are passed through the small holes. The collection head smoke / temperature simulation detection unit 9 is bound to the fixing plate 7 with the binding rope 6.

[0052] The collection heads that can be bound to the detection section include a smoke / temperature simulation detection unit, a wind pressure detection unit, an illumination / noise detection unit, and a laser ranging / tilt angle detection unit. These four units all have Bluetooth communication functions with the handle section.

[0053] The handle node is an edge computing gateway with the following functions:

[0054] 1) It has the function of controlling the front-end detection section to simulate smoke and temperature, and can interact with the monitoring data of the front-end detection section;

[0055] 2) It has the functions of receiving detection node detection data and edge computing and logical judgment;

[0056] 3) It has 4G / 5G wireless communication transmission function, which can send the front-end detection and edge computing results to the fire protection facility health status analysis software system.

[0057] The transmission layer is composed of fire protection facility detection data communication protocol, 4G / 5G / NB wireless network or the Internet.

[0058] The data layer is composed of IoT monitoring data, detection business data, fire protection facility basic data, other external data, and big data cleaning, storage, mining and analysis service components.

[0059] The support layer is composed of cloud computing resources, storage resources, network resources, middleware, operating system, retrieval services, GIS services, security management services, visualization services and various application management interface components.

[0060] The application layer consists of fire protection facility inspection report application, fault hazard analysis application, fault hazard disposal application, fire protection facility health status service and jurisdiction fire safety risk assessment service.

[0061] See attached Figure 3 A fire protection facility health status detection and analysis system based on AIOT has a total of six layers in its system working platform. The first layer is composed of the underlying fire protection facility monitoring IoT terminal cluster (smoke / temperature simulator, wind pressure sensor, etc.); the second layer is composed of the fire protection facility monitoring data communication protocol component; the third layer is composed of the cloud WINDOWSSERVER2018 / UbuntuLinuxServer service operating system; the fourth layer is composed of the MySQL database management system; the fifth layer is composed of the Apache Tomcat 8.0WEB middleware, and the sixth layer is composed of the fire protection facility health status analysis software system.

[0062] A fire protection facility health status detection and analysis system based on AIOT, and its fire protection facility health status evaluation method is as follows:

[0063] The integrity and effectiveness of building fire protection facilities is the most important indicator for fire safety evaluation. The dynamic quantitative evaluation of fire safety is to dynamically collect the working status, fault status, fault frequency and fault duration of fire protection facilities in a unit time, and use the fault-free working time of fire protection facilities, fire protection facility fault weight, fire protection facility fault frequency and fault duration in a unit time as quantitative evaluation indicators of fire safety;

[0064] Taking the health status evaluation of a single fire protection facility as an example, its potential safety hazard rate (fire safety dynamic evaluation value) is:

[0065]

[0066] Where W is the weight value of fire protection facilities (usually determined by the importance of fire prevention and fire extinguishing, and can be defined by the public security fire department), Fre is the fault frequency per unit time, FT is the fault duration, and MTBF is the mean time between failures of the system (obtained by the fire protection facility manufacturer through type testing);

[0067] Taking a social unit as an example, the social unit has n fire protection facilities such as automatic fire alarm system, automatic sprinkler system, fire hydrant system, gas fire extinguishing system, smoke exhaust system, and fire isolation facilities; the hidden danger rate of fire protection facilities in the social unit (dynamic evaluation value of fire safety) is:

[0068]

[0069] It is the arithmetic mean of the health status evaluation of all fire protection facilities in social units.

[0070] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An AIOT-based fire protection facility health status detection and analysis system, characterized by: It includes an acquisition layer, a transmission layer, a data layer, a support layer and an application layer; the acquisition layer uses a detection integrated device to detect fire-fighting facilities, sends the detection data to the transmission layer, the transmission layer transmits the data to the data layer, the data layer transmits the data to the support layer, the support layer transmits the data to the application layer, and finally the application layer generates an evaluation report.

2. According to the AIOT-based fire protection facility health status detection and analysis system of claim 1, it is characterized by: The collection layer is composed of smoke / temperature monitoring, wind pressure monitoring, illumination / noise monitoring, distance / slope measurement sensors or detectors and an edge computing gateway; the collection layer uses a detection integrated device with a replaceable collection head to collect the above data, wherein the smoke / temperature simulator collection head simulates the rated temperature of the smoke, and detects whether the smoke detector and temperature alarm have an alarm within a certain period of time. If an alarm occurs, it will be deemed qualified, and if no alarm occurs, it will be deemed unqualified; when detecting wind pressure, replace the wind pressure sensor collection head, place the wind pressure sensor at the air outlet, calculate the wind pressure value according to the pressure per unit area, and realize the detection of wind pressure; place the illumination sensor / noise sensor collection head at the light source / noise source to detect its light source intensity / decibel value; the laser rangefinder and inclination angle measuring instrument, whose output value is the measurement result; all front-end detection data are transmitted to the edge computing gateway via Bluetooth, and the edge computing gateway analyzes and calculates the results, and sends the detection data and calculation results to the cloud-based fire protection facility health situation analysis system through the edge computing gateway to realize centralized evaluation and situation analysis of the overall fire protection facility detection.

3. According to claim 2, a fire protection facility health status detection and analysis system based on AIOT is characterized by: The detection integrated device is a detection rod composed of multiple telescopic sections, with a handle section at the end, a telescopic section in the middle, and a detection section at the front end. A sleeve is mounted on the outside of the detection section, and a T-shaped screw is arranged on the side wall of the sleeve. The T-shaped screw presses and fixes the sleeve on the detection section. A fixing plate is welded on the outside of the sleeve, and a plurality of small holes are arranged on the fixing plate. Binding ropes are passed through the small holes, and the collection head is bound to the fixing plate with the binding ropes.

4. According to claim 3, a fire protection facility health status detection and analysis system based on AIOT is characterized in that: The collection heads that can be bound to the detection section include a smoke / temperature simulation detection unit, a wind pressure detection unit, an illumination / noise detection unit, and a laser ranging / tilt angle detection unit. These four units all have Bluetooth communication functions with the handle section.

5. According to the AIOT-based fire protection facility health status detection and analysis system of claim 1, it is characterized by: The handle node is an edge computing gateway with the following functions: 1) It has the function of controlling the front-end detection section to simulate smoke and temperature, and can interact with the monitoring data of the front-end detection section; 2) It has the functions of receiving detection node detection data and edge computing and logical judgment; 3) It has 4G / 5G wireless communication transmission function, which can send the front-end detection and edge computing results to the fire protection facility health status analysis software system.

6. The AIOT-based firefighting facility health status detection and analysis system according to claim 1 is characterized by: The transmission layer is composed of fire protection facility detection data communication protocol, 4G / 5G / NB wireless network or the Internet.

7. The fire protection facility health status detection and analysis system based on AIOT according to claim 1 is characterized by: The data layer is composed of IoT monitoring data, detection business data, fire protection facility basic data, other external data, and big data cleaning, storage, mining and analysis service components.

8. The AIOT-based firefighting facility health status detection and analysis system according to claim 1 is characterized by: The support layer is composed of cloud computing resources, storage resources, network resources, middleware, operating system, retrieval services, GIS services, security management services, visualization services and various application management interface components.

9. The AIOT-based firefighting facility health status detection and analysis system according to claim 1 is characterized by: The application layer consists of fire protection facility inspection report application, fault hazard analysis application, fault hazard disposal application, fire protection facility health status service and jurisdiction fire safety risk assessment service.

10. A fire protection facility health status detection and analysis system based on AIOT, whose system working platform consists of six layers. The first layer is composed of the underlying fire protection facility monitoring Internet of Things terminal cluster (smoke / temperature simulator, wind pressure sensor, etc.); the second layer is composed of the fire protection facility monitoring data communication protocol component; the third layer is composed of the cloud-based WINDOWSSERVER2018 / UbuntuLinuxServer service operating system; the fourth layer is composed of the MySQL database management system; the fifth layer is composed of the Apache Tomcat 8.0WEB middleware, and the sixth layer is composed of the fire protection facility health status analysis software system.

11. A fire protection facility health status detection and analysis system based on AIOT, and its fire protection facility health status evaluation method is as follows: The integrity and effectiveness of building fire protection facilities is the most important indicator for fire safety evaluation. The dynamic quantitative evaluation of fire safety is to dynamically collect the working status, fault status, fault frequency and fault duration of fire protection facilities in a unit time, and use the fault-free working time of fire protection facilities, fire protection facility fault weight, fire protection facility fault frequency and fault duration in a unit time as quantitative evaluation indicators of fire safety; The potential safety hazard rate of a single fire-fighting facility (dynamic fire safety evaluation value) is: Where W is the weight value of fire protection facilities (usually determined by the importance of fire prevention and fire extinguishing, and can be defined by the public security fire department), Fre is the fault frequency per unit time, FT is the fault duration, and MTBF is the mean time between failures of the system (obtained by the fire protection facility manufacturer through type testing); The social unit has a total of n fire protection facilities, including automatic fire alarm system, automatic sprinkler system, fire hydrant system, gas fire extinguishing system, smoke exhaust system, and fire isolation facilities. The hidden danger rate of fire protection facilities in the social unit (dynamic evaluation value of fire safety) is: It is the arithmetic mean of the health status evaluation of all fire protection facilities in social units.

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