Intelligent fire early warning system based on Internet of Things

Abstract

The invention relates to the technical field of fire early warning, and discloses an intelligent fire early warning system based on the Internet of Things. The fire early warning system comprises a sensor module, a communication module, a central processing module, an early warning module and a control module, the sensor module monitors the temperature, the smoke concentration and the flame condition in the environment in real time. The communication module transmits data collected by the sensor module to the central processing module. The central processing module analyzes and processes the received data, and judges whether a fire occurs or not according to a preset threshold value and an algorithm; the early warning module sends sound-light alarm, short message notification and mobile phone APP push to a user; the control module is used for controlling starting and stopping of fire fighting equipment. Through the multi-sensor fusion technology, the system can identify fire signals more accurately, the accuracy and timeliness of fire early warning are improved, and fire losses are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of fire warning, and in particular to an intelligent fire warning system based on the Internet of Things. Background Art

[0002] At present, the number of residential households is large and widely distributed, electrical equipment is increasing continuously, and high-rise residential buildings are springing up. In the increasingly complex urban environment, there is a situation where the fire supervision force cannot cover all areas and the fire prevention and control system is imperfect. Moreover, in places such as mines, forests, and factories, it is difficult to maintain the fire prevention and control system. Once a fire danger occurs and is not dealt with in time, it is easy to cause serious economic and property losses.

[0003] Traditional fire warning systems mainly rely on single-type sensor devices such as independent smoke detectors and temperature detectors. Although these devices can detect the occurrence of a fire to a certain extent, they have many limitations. First, the detection range of traditional sensors is limited. For example, a smoke detector usually can only trigger an alarm when the smoke reaches a certain concentration, and may not be able to detect it in time when the smoke concentration is low in the initial stage of a fire. The temperature detector is similar. It will only give an alarm when the temperature rises to a certain level, and may not be able to detect a slowly developing smoldering fire in time. Secondly, the information transmission of traditional systems is not timely and accurate enough. Many traditional fire warning devices can only give an audible and visual alarm locally, and cannot quickly and accurately convey the fire information to relevant personnel and the fire department. This may result in the rescue personnel not being able to reach the scene in time after a fire occurs, delaying the best fire extinguishing opportunity. Moreover, traditional systems lack intelligent data analysis and processing capabilities. They cannot predict the development trend of a fire, nor can they make adaptive adjustments according to different environmental factors and fire characteristics, and it is difficult to meet the actual application requirements of complex changes.

[0004] Therefore, there is an urgent need for a technology to replace the existing fire warning method to solve the problems of how to improve the reliability of fire detection, improve the accuracy and timeliness of fire warning, and reduce fire losses. Summary of the Invention

[0005] The embodiments of the present application provide an intelligent fire warning system based on the Internet of Things, thereby solving the problems in related technologies of improving the reliability of fire detection, improving the accuracy and timeliness of fire warning, and reducing fire losses.

[0006] Among them, an intelligent fire warning system based on the Internet of Things provided by the embodiments of the present application includes: a sensor module, a communication module, a central processing module, a warning module, and a control module;

[0007] The sensor module is used to monitor the temperature, gas, smoke concentration, and flame situation in the environment in real time;

[0008] The communication module transmits the data collected by the sensor module to the central processing module, and at the same time receives instructions from the central processing module and conveys the instructions to other modules;

[0009] The central processing module is used to receive and store the data collected by the sensor module and perform data analysis and processing;

[0010] The central processing module adopts a weighted fusion algorithm, and the preset importance weight of the temperature sensor is 0.4, the importance weight of the smoke sensor is 0.2, the importance weight of the gas sensor is 0.2, and the importance weight of the flame sensor is 0.2;

[0011] During operation, the measured values of each sensor are multiplied by the corresponding weights and then added together to obtain a comprehensive fire risk value ΔX. A comprehensive safety threshold X is set, and X > 0;

[0012] When ΔX < X, it is determined that the monitored environment is safe;

[0013] When ΔX ≥ X, it is determined that a fire has occurred in the detection environment, and warning and fire extinguishing instructions are issued to the warning module and the control module through the communication module according to the fire situation;

[0014] The warning module is used to send fire warning information to users and conduct linkage with the fire department and the property management center;

[0015] The control module is used to control fire-fighting equipment.

[0016] Furthermore, the sensor module includes a temperature sensor, a gas sensor, a smoke sensor, and a flame sensor; the temperature sensor can timely capture abnormal temperature increases based on the Seebeck effect, the gas sensor is used to detect changes in the concentration of combustible gases, the smoke sensor is used to detect the presence of smoke, and the flame sensor detects the presence of flame at the moment of open fire generation based on the optical characteristics of the flame combined with the flickering frequency of the flame.

[0017] Furthermore, the communication module is based on the IEEE802.11 standard, uses radio waves to transmit data in the air, realizes the connection between the device and the network through an access point, and encrypts the transmitted data at the same time.

[0018] Furthermore, the central processing module performs normalization processing on the data, and the normalization formula is:

[0019]

[0020] where, x norm is the result after normalization; x is the original data; x max is the maximum value in the original data.

[0021] Furthermore, the central processing module uses a neural network algorithm to construct a neural network model. Taking the data of temperature sensors, smoke sensors, and flame sensors as the input layer neurons, several hidden layers are set in the middle, and the output layer outputs the judgment result of whether a fire occurs and the fire risk level. Through training with a large amount of historical data, the weights and thresholds of the neural network are adjusted so that the neural network can accurately learn the fire occurrence pattern from the input data. During actual operation, when real-time sensor data is input and processed by the neural network calculation, if the output fire occurrence probability exceeds a certain threshold, it is determined that a fire has occurred.

[0022] Furthermore, the warning methods of the warning module include an audible and visual alarm, SMS notification, and mobile APP push.

[0023] Furthermore, the warning module is divided into primary warning, intermediate warning, and advanced warning according to the possibility of fire occurrence and the development trend of the fire;

[0024] The primary warning indicates that a slight difference in the fire information index is detected, indicating a potential fire risk;

[0025] The intermediate warning indicates that the possibility of fire occurrence is relatively high, and the fire information index has exceeded the set safety threshold and shows a continuous changing trend;

[0026] The advanced warning indicates that the fire is basically determined to have occurred and the fire has reached a relatively serious development stage.

[0027] Furthermore, the control module is connected to the starting device of the fire extinguisher. When receiving a start command, it triggers the opening of the fire extinguisher valve by sending an electrical signal, so that the fire extinguishing agent is released according to the preset spraying method and range to extinguish the incipient fire.

[0028] Furthermore, the intelligent fire warning system further includes a remote monitoring module for users to understand the operation status of each link of the fire warning system and fire data information in real time.

[0029] Furthermore, the control module controls key components such as the spray pump and valves in the sprinkler system. When a fire occurs, it will start the spray pump according to the fire size, spread range, and preset parameters, so that water is transported through the pipe network to each sprinkler for fire extinguishing by spraying water.

[0030] Furthermore, the control module is connected to components such as the electric door closer and sequencer of the fire door. When receiving a fire signal, the control module prompts the fire door to close automatically to ensure that it is closed tightly to form an effective fire separation.

[0031] Furthermore, the control module is connected to components of the smoke exhaust system such as smoke exhaust fans and smoke valves. When a fire occurs, the smoke exhaust fan is turned on, and the fan operates at a predetermined speed and air volume. At the same time, the corresponding smoke valve is opened to promote the thick smoke generated by the fire to be discharged outside the building through the smoke exhaust duct.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] Through the multi-sensor fusion technology, the system can more accurately identify fire signals, reduce the false alarm rate and missed alarm rate, thereby improving the reliability of fire detection, enhancing the accuracy and timeliness of fire early warning, and reducing fire losses; realizing remote monitoring and intelligent management, and reducing labor costs; promoting the rational allocation and efficient utilization of fire protection resources. And the system can process and analyze sensor data in real time, quickly make fire prediction and positioning decisions, thereby shortening the response time and winning precious time for fire extinguishing and rescue.

[0034] The technical solutions of the present application will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0035] The drawings forming a part of the specification depict embodiments of the present application and, together with the description, are used to explain the principles of the present application.

[0036] Referring to the drawings, the present application can be more clearly understood from the following detailed description, wherein:

[0037] Figure 1 is a structural block diagram of an intelligent fire early warning system based on the Internet of Things proposed by the present application. Detailed Embodiments

[0038] Various exemplary embodiments of the present application will now be described in detail with reference to the drawings. It should be noted that: unless otherwise specifically stated, the relative arrangements, numerical expressions, and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present application.

[0039] At the same time, it should be understood that, for the sake of convenience of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationships.

[0040] The following description of at least one exemplary embodiment is merely illustrative in nature and is not intended as any limitation on the present application or its application or use.

[0041] Techniques, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and devices should be regarded as part of the specification.

[0042] It should be noted that like reference numerals and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

[0043] In addition, the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on what can be achieved by those of ordinary skill in the art. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0044] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present application are only used to explain the relative positional relationship and movement conditions between components in a certain specific posture (as shown in the figures). If the specific posture changes, the directional indications will also change accordingly.

[0045] The following Figure 1 is used to describe an Internet of Things-based intelligent fire warning system according to an exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown for the convenience of understanding the spirit and principle of the present application, and the embodiments of the present application are not limited in this regard. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0046] The present application proposes an Internet of Things-based intelligent fire warning system.

[0047] Among them, an Internet of Things-based intelligent fire warning system provided according to an embodiment of the present application includes: a sensor module, a communication module, a central processing module, a warning module, and a control module;

[0048] The sensor module is used to monitor the temperature, gas, smoke concentration, and flame conditions in the environment in real time;

[0049] The communication module transmits the data collected by the sensor module to the central processing module, and at the same time receives the instructions from the central processing module and conveys the instructions to other modules;

[0050] The central processing module is used to receive and store the data collected by the sensor module and perform data analysis and processing;

[0051] The central processing module adopts a weighted fusion algorithm, presetting the importance weight of the temperature sensor to be 0.4, the importance weight of the smoke sensor to be 0.2, the importance weight of the gas sensor to be 0.2, and the importance weight of the flame sensor to be 0.2;

[0052] During the operation, the measured values of each sensor are multiplied by the corresponding weights and then added together to obtain a comprehensive fire risk value ΔX. A comprehensive safety threshold X is set, and X > 0;

[0053] When ΔX < X, it is determined that the monitored environment is safe;

[0054] When ΔX ≥ X, it is determined that a fire has occurred in the detection environment, and warning and fire extinguishing instructions are issued to the warning module and the control module according to the fire situation through the communication module;

[0055] The warning module is used to send fire warning information to users and is linked with the fire department and the property management center;

[0056] The control module is used to control fire-fighting equipment.

[0057] Specifically, the system uses Internet of Things technology to connect multiple sensors (such as smoke sensors, temperature sensors, gas sensors, etc.) to the central processing unit to achieve real-time data collection and analysis. In addition, the system can also be linked with the fire department, the property management center, etc. to achieve rapid response and disposal.

[0058] It can be understood that the sensor module can monitor the temperature, gas, smoke concentration and flame conditions in the environment in real time, and comprehensively monitor possible fire hazards from multiple angles. Different types of sensors can capture the characteristics of different stages of a fire. For example, a temperature sensor can detect abnormal temperature rise in the early stage, a smoke sensor is sensitive to the appearance of smoke, a gas sensor can detect changes in the concentration of combustible gases, and a flame sensor can quickly respond when a fire appears, thus providing a rich source of information for fire warning;

[0059] The central processing module adopts a weighted fusion algorithm to reasonably allocate the weights of different sensors. The temperature sensor has a higher weight because temperature change is often one of the important indicators in the early stage of a fire; smoke, gas and flame sensors also have their important roles and jointly participate in the calculation of the comprehensive fire risk value. This algorithm makes the system more scientific and accurate in judging the fire risk, avoids the limitations of a single sensor, and improves the reliability of the warning.

[0060] Specifically, the sensor module includes a temperature sensor, a gas sensor, a smoke sensor and a flame sensor; the temperature sensor can timely capture the abnormal rise of temperature based on the Seebeck effect, the gas sensor is used to detect changes in the concentration of combustible gases, the smoke sensor is used to detect the presence of smoke, and the flame sensor is based on the optical characteristics of the flame combined with the flicker frequency of the flame to detect the presence of the flame at the moment of the fire generation.

[0061] It is understandable that different types of sensors can play roles at different stages of fire development, thus achieving early warning. Temperature sensors and gas sensors can detect abnormal situations before a fire occurs, while smoke sensors and flame sensors can quickly issue alarms after a fire breaks out. This can greatly shorten the time from the occurrence of a fire to its discovery and improve the timeliness of early warning.

[0062] Moreover, the diversified design of the sensor module improves the reliability of the system. Even if a certain sensor fails, other sensors can still continue to work, ensuring that the system can continuously monitor the fire risk. At the same time, different types of sensors can verify each other, further improving the reliability of fire early warning.

[0063] Specifically, the communication module is based on the IEEE802.11 standard, uses radio waves to transmit data in the air, realizes the connection between the device and the network through an access point, and encrypts the transmitted data at the same time.

[0064] It is understandable that the communication module is based on the IEEE802.11 standard, which is a widely used, mature and stable wireless communication standard. It ensures the reliability and compatibility of communication, encrypts the transmitted data, and effectively protects the security and privacy of the data. In the fire early warning system, the data collected by the sensors may contain sensitive information, such as the location of the place, environmental parameters, etc. Encrypted transmission can prevent the data from being stolen or tampered with, ensuring the safe operation of the system.

[0065] Specifically, the central processing module normalizes the data, and the normalization formula is:

[0066]

[0067] where, x norm is the result after normalization; x is the original data; x max is the maximum value in the original data.

[0068] Specifically, during the normalization process, the data is usually preprocessed to identify and process outliers. This helps to reduce the impact of outliers on the system and avoid misjudgment or system instability caused by individual abnormal data. When the smoke sensor shows extremely high outliers due to failure or special environmental factors, the normalization process can limit it within a reasonable range, preventing this outlier from having too much interference on the entire fire risk assessment.

[0069] It can be understood that the normalized data is more convenient and accurate for comparative analysis in different regions and different time periods. When comparing the fire risks in different environments at the same time, or analyzing the changing trends of fire risks in the same region in different seasons, the normalized data can be directly compared without considering the dimension and range differences of the original data, improving the efficiency and reliability of data comparative analysis.

[0070] Specifically, the central processing module uses a neural network algorithm to construct a neural network model. Taking the data from temperature sensors, smoke sensors, and flame sensors as the input layer neurons, several hidden layers are set in the middle, and the output layer outputs the judgment result of whether a fire occurs and the fire risk level. Through training with a large amount of historical data, the weights and thresholds of the neural network are adjusted so that the neural network can accurately learn the fire occurrence pattern from the input data. During actual operation, when real-time sensor data is input and processed by the neural network calculation, if the output fire occurrence probability exceeds a certain threshold, it is determined that a fire has occurred.

[0071] Specifically, the neural network algorithm can adapt to corresponding application scenarios through targeted training according to the respective characteristics and fire occurrence laws of different places (such as homes, factories, shopping malls, etc.).

[0072] It can be understood that the neural network has a powerful non-linear mapping ability and can handle the complex non-linear relationships between the data of temperature sensors, smoke sensors, and flame sensors. The occurrence of a fire is often accompanied by the interaction of multiple factors, and its characteristic performance is not a simple linear law. The neural network can accurately capture the fire occurrence characteristics hidden behind the data by learning the complex patterns in a large amount of historical data, so as to more accurately judge whether a fire has occurred. Compared with the traditional judgment methods based on simple thresholds or linear rules, the accuracy of judgment is greatly improved.

[0073] Specifically, the warning methods of the warning module include audible and visual alarms, text message notifications, and mobile APP push notifications.

[0074] It can be understood that in different site environments, these several warning methods each have their own advantages and complement each other. For example, in noisy industrial factories, shopping malls with a large number of people, etc., the strong warning effect of the audible and visual alarm can quickly attract the attention of many on-site personnel; while in some relatively quiet but sparsely populated places, such as office buildings, schools, etc., text message notifications and mobile APP push notifications can ensure that everyone can receive the information in a timely manner, avoiding the situation of missing the warning due to the limitation of on-site sound propagation. For the complex situation of different regions and functional partitions in large sites, mobile APP push notifications can also achieve accurate push by region, enabling personnel in specific regions to obtain the fire information corresponding to the region, which is more targeted.

[0075] Specifically, the early warning module is divided into primary early warning, intermediate early warning, and advanced early warning according to the likelihood of a fire occurring and the development trend of the fire;

[0076] The primary early warning indicates that a slight difference in the fire information index is detected, indicating a potential fire risk;

[0077] The intermediate early warning indicates that the likelihood of a fire occurring is relatively high, the fire information index has exceeded the set safety threshold and shows a continuous changing trend;

[0078] The advanced early warning indicates that the fire is basically determined to have occurred and the fire has reached a relatively serious stage of development.

[0079] It can be understood that for different early warning levels, human, material and other resources can be reasonably allocated. In the primary early warning stage, since only a slight difference in the fire information index is detected and there is a potential risk, only a small number of staff or security personnel need to be arranged to go to the relevant area for further inspection and detection to confirm whether there is a real fire hazard, so as to avoid wasting a large amount of resources due to excessive use. In the intermediate early warning stage, as the likelihood of a fire occurring increases and the index exceeds the threshold and shows a changing trend, more relevant personnel can be notified, such as the property management team, part-time firefighters, etc., to let them make corresponding preparations, such as preparing some fire fighting equipment and organizing the preliminary preparation for the orderly evacuation of nearby personnel. When entering the advanced early warning stage, when the fire is basically determined and the fire is serious, all resources such as the fire department, professional rescue teams and all available fire fighting equipment can be fully mobilized to carry out all-out fire fighting and large-scale personnel evacuation and rescue work, ensuring that resources can be accurately invested in the corresponding stage and play the greatest role.

[0080] Specifically, the intelligent fire early warning system also includes a remote monitoring module for users to understand the operation status of each link of the fire early warning system and fire data information in real time.

[0081] Specifically, in addition to the data during a fire, the operation status of each link of the system can also be monitored in real time at ordinary times, including the working status of sensors, whether the communication is normal, whether the fire fighting equipment is in a standby state, etc. Once abnormal data or equipment failure prompts appear in a certain link, relevant personnel can detect it in time, arrange personnel to conduct inspections and repairs in advance, eliminate potential fire hazards in the bud, prevent problems before they occur, and effectively reduce the probability of a fire occurring.

[0082] Specifically, the control module is connected to the starting device of the fire extinguisher. When receiving a start command, it triggers the opening of the fire extinguisher valve by sending an electrical signal, so that the fire extinguishing agent is released according to the preset spraying method and range to fight the initial fire.

[0083] Specifically, in the initial stage of a fire, the fire is often small and relatively easy to control. Promptly activating a fire extinguisher can quickly extinguish the open fire and prevent the fire from spreading further. In places such as offices and computer rooms where the space is relatively small and the fire hazards are mostly caused by electricity or small flammable materials, automatically controlling the activation of the fire extinguisher can play a fire extinguishing role at the first moment when the fire is unnoticed.

[0084] Specifically, the control module manipulates key components such as the spray pump and valves in the sprinkler system. When a fire occurs, it will start the spray pump according to the size of the fire, the spreading range, and the preset parameters, so that water is transported through the pipe network to each sprinkler for fire extinguishing by spraying water.

[0085] Specifically, in the case where the fire spreads quickly and over a wide range, it can quickly cover the fire area, reduce the temperature, and isolate the air, playing a role in extinguishing the fire and controlling the spread of the fire. Moreover, through the intelligent control of the control module, its fire extinguishing efficiency and rationality can be improved.

[0086] Specifically, the control module is connected to components such as the electric door closer and sequencer of the fire door. When receiving a fire signal, the control module prompts the fire door to close automatically, ensuring that it closes tightly to form an effective fire separation.

[0087] Specifically, the control module is connected to components of the smoke exhaust system such as the smoke exhaust fan and smoke exhaust valve. When a fire occurs, the smoke exhaust fan is turned on, so that the fan operates at a preset speed and air volume, and at the same time, the corresponding smoke exhaust valve is opened, prompting the thick smoke generated by the fire to be discharged outside the building through the smoke exhaust pipe.

[0088] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present application are pointed out by the following claims.

[0089] It should be understood that the present application is not limited to the precise structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. An intelligent fire warning system based on the Internet of Things, characterized in that, Including: a sensor module, a communication module, a central processing module, an early warning module, and a control module; The sensor module is used to monitor the temperature, gas, smoke concentration, and flame situation in the environment in real time; The communication module transmits the data collected by the sensor module to the central processing module, and at the same time receives the instructions from the central processing module and conveys the instructions to other modules; The central processing module is used to receive and store the data collected by the sensor module and perform data analysis and processing; The central processing module adopts a weighted fusion algorithm, presetting the importance weight of the temperature sensor to be 0.4, the importance weight of the smoke sensor to be 0.2, the importance weight of the gas sensor to be 0.2, and the importance weight of the flame sensor to be 0.2; Multiply the measured values of each sensor by the corresponding weights and then add them up to obtain a comprehensive fire risk value ΔX, set a comprehensive safety threshold X, and X > 0; When ΔX < X, it is determined that the monitored environment is safe; When ΔX ≥ X, it is determined that a fire has occurred in the monitored environment, and warning and fire extinguishing instructions are issued to the early warning module and the control module through the communication module according to the fire situation; The early warning module is used to send fire warning information to users and is linked with the fire department and the property management center; The control module is used to control fire-fighting equipment.

2. The intelligent fire warning system of the Internet of Things according to claim 1, characterized in that, The sensor module includes a temperature sensor, a gas sensor, a smoke sensor, and a flame sensor; the temperature sensor can timely capture the abnormal rise of temperature based on the Seebeck effect, the gas sensor is used to detect the change of combustible gas concentration, the smoke sensor is used to detect the presence of smoke, and the flame sensor is based on the optical characteristics of the flame combined with the flicker frequency of the flame to detect the presence of the flame at the moment of the generation of open fire.

3. The intelligent fire warning system of the Internet of Things according to claim 1, characterized in that, The communication module is based on the IEEE802.11 standard, uses radio waves to transmit data in the air, realizes the connection between the device and the network through an access point, and encrypts the transmitted data at the same time.

4. The intelligent fire warning system of the Internet of Things according to claim 1, characterized in that, The central processing module performs normalization processing on the data, and the normalization formula is: where x norm is the normalized result; x is the original data; x max is the maximum value in the original data.

5. The intelligent fire warning system of the Internet of Things according to claim 1, characterized in that The central processing module adopts a neural network algorithm to construct a neural network model. Using the data of the temperature sensor, smoke sensor, and flame sensor as the input layer neurons, several hidden layers are set in the middle, and the output layer outputs the judgment result of whether a fire has occurred and the fire risk level. Through training with a large amount of historical data, the weights and thresholds of the neural network are adjusted so that the neural network can accurately learn the fire occurrence pattern from the input data. During actual operation, real-time sensor data is input, and after being calculated and processed by the neural network, if the output fire occurrence probability exceeds a certain threshold, it is determined that a fire has occurred.

6. The intelligent fire warning system of the Internet of Things according to claim 1, characterized in that, The warning methods of the early warning module include an audible and visual alarm, a text message notification, and a mobile APP push.

7. The intelligent fire warning system of the Internet of Things according to claim 6, characterized in that, The early warning module is divided into primary warning, intermediate warning, and advanced warning according to the possibility of fire occurrence and the development trend of the fire; The primary warning indicates that a slight difference in the fire information index is detected, and there is a potential fire risk; The intermediate warning indicates that the possibility of a fire occurring is relatively high, the fire information index has exceeded the set safety threshold and there is a continuous changing trend; The advanced warning indicates that the fire is basically determined to have occurred and the fire has reached a relatively serious development stage.

8. The intelligent fire warning system of the Internet of Things according to claim 1, characterized in that, The control module is connected to the starting device of the fire extinguisher. When receiving a start instruction, it triggers the opening of the valve of the fire extinguisher by sending an electrical signal, so that the fire extinguishing agent is released according to the preset spraying method and range.

9. The intelligent fire warning system of the Internet of Things according to claim 1, characterized in that, The intelligent fire warning system further includes a remote monitoring module for users to understand the operation status of each link of the fire warning system and the fire data information in real time.

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