Fire smoke detection method and device

Abstract

The invention provides a fire and smoke detection method and device, and the method comprises the steps: inputting shot plant video data into a trained flame detection algorithm and a trained smoke detection algorithm corresponding to each detection content, and obtaining the flame detection confidence and the smoke detection confidence of a plurality of video frames; determining a fire detection result according to the flame detection confidence and the smoke detection confidence; and if the fire detection result is that the fire occurs, triggering fire alarm of the plant. As the fire detection result is comprehensively obtained according to the flame detection confidence coefficient and the smoke detection confidence coefficient, the fire detection accuracy can be improved, and alarm resources are prevented from being wasted.

Description

Technical Field

[0001] The present application relates to the technical field of fire and smoke detection, and in particular to a fire and smoke detection method and device. Background Art

[0002] In the event of a fire in a building, especially a high-rise building, the existing fire alarm device can only realize regional (within the floor) alarm. When a fire occurs, it is necessary to manually contact the fire department, which affects the time of fire rescue. Building monitoring is a common safety monitoring method for buildings. The existing building monitoring can only monitor the picture and cannot warn of the burning flame, which will cause extremely tragic injuries. In order to be able to warn in time when a fire occurs, fire protection devices and fire alarm devices are usually installed in the building for early warning. Common fire alarm devices include smoke alarm equipment, flame detection equipment, etc. However, the above equipment has a relatively simple method of judging whether the flame is a fire, which is easy to cause misjudgment and waste alarm resources. Summary of the invention

[0003] The purpose of the present application is to provide a fire and smoke detection method and device that can overcome the shortcomings and deficiencies in the prior art.

[0004] A first aspect of an embodiment of the present application provides a fire and smoke detection method, comprising:

[0005] The captured factory video data is respectively input into the trained flame detection algorithm and smoke detection algorithm corresponding to each detection content to obtain the flame detection confidence and smoke detection confidence of multiple video frames;

[0006] Determining a fire detection result according to the flame detection confidence and the smoke detection confidence;

[0007] If the fire detection result is that a fire occurs, a fire alarm of the factory building is triggered.

[0008] Further, the flame detection confidence includes flame confidence; the smoke detection confidence includes smoke confidence;

[0009] The step of determining the fire detection result according to the flame detection confidence and the smoke detection confidence comprises:

[0010] Comparing the smoke detection confidence with a preset first confidence threshold;

[0011] If the smoke detection confidence is greater than the first confidence threshold, comparing the flame confidence with a preset second confidence threshold;

[0012] If the flame confidence is greater than the second confidence threshold, the fire detection result is determined to be the occurrence of a fire.

[0013] Further, after the step of comparing the smoke detection confidence with a preset first confidence threshold, the method further comprises:

[0014] If the smoke detection confidence is less than or equal to the first confidence threshold, comparing the flame confidence with a preset third confidence threshold; the third confidence threshold is less than the first confidence threshold;

[0015] If the flame confidence is greater than the third confidence threshold, the fire detection result is determined to be a fire.

[0016] Furthermore, the flame detection confidence also includes flame color; the smoke detection confidence also includes smoke color;

[0017] After the step of comparing the smoke detection confidence with a preset first confidence threshold, the method further comprises:

[0018] According to a preset correspondence relationship between flame color, smoke color and enhancement coefficient, obtaining the enhancement coefficient corresponding to the flame color and the smoke color;

[0019] The value of the flame confidence is increased according to the enhancement coefficient to update the flame confidence.

[0020] Furthermore, the correspondence relationship of the flame color-smoke color-enhancement coefficient is that a combination of a flame color and a smoke color corresponds to an enhancement coefficient.

[0021] Further, the smoke detection algorithm includes a trunk module, an enhancement module, a neck module and a head module;

[0022] The steps of inputting the captured factory video data into the trained smoke detection algorithm include:

[0023] Input the factory video data into the backbone module of the smoke detection algorithm for feature extraction processing to obtain the first feature of each video frame;

[0024] Inputting the first feature of each video frame into the enhancement module of the smoke detection algorithm for enhanced convolution processing to obtain the second feature of each video frame;

[0025] Inputting the second feature of each video frame into the neck module of the smoke detection algorithm for bidirectional feature convolution processing to obtain the third feature of each video frame;

[0026] The third feature of each video frame is input into the head module of the smoke detection algorithm for feature prediction processing to obtain detection results of the multiple video frames of the smoke detection algorithm.

[0027] Furthermore, the backbone module includes a feature extraction layer and a plurality of sequentially cascaded combined convolutional layers; wherein the combined convolutional layer includes a first convolutional network and a second convolutional network;

[0028] The step of inputting the factory video data into the backbone module of the smoke detection algorithm for feature extraction processing to obtain the first feature of each video frame includes:

[0029] Inputting the factory video data into the feature extraction layer for feature extraction processing to obtain the fourth feature of each video frame;

[0030] Inputting the fourth feature of each video frame into the first convolutional network of the first combined convolutional layer for convolution processing to obtain the fifth feature of each video frame;

[0031] Inputting the fifth feature of each video frame into the second convolution network of the same combined convolution layer for convolution processing to obtain the sixth feature of each video frame;

[0032] The sixth feature of each video frame is input into the next-level combined convolution layer for convolution processing, and the sixth feature of each video frame output by the last-level combined convolution layer is determined as the first feature.

[0033] Further, the second convolutional network includes a plurality of phantom convolution units and convolution fusion units cascaded in sequence;

[0034] The step of inputting the fifth feature of each video frame into the second convolution network of the same combined convolution layer for convolution processing to obtain the sixth feature of each video frame includes:

[0035] Inputting the fifth feature into a plurality of phantom convolution units cascaded in sequence for convolution processing, to obtain a seventh feature output by each phantom convolution unit;

[0036] The fifth feature and the seventh feature output by each phantom convolution unit are input into the convolution fusion unit for convolution fusion to obtain the sixth feature.

[0037] Furthermore, the step of inputting the fifth feature of each video frame into the second convolutional network of the same combined convolutional layer for convolution processing to obtain the sixth feature of each video frame includes:

[0038] The sixth characteristic is obtained by the following formula:

[0039]

[0040] Y is the sixth feature, x is the fifth feature, cat(·) is the data concatenation function, y irepresents the seventh feature output by i cascaded phantom convolution units, and n is the number of cascaded phantom convolution units.

[0041] A second aspect of an embodiment of the present application provides a fire and smoke detection device, comprising:

[0042] The confidence acquisition module is used to input the captured factory video data into the trained flame detection algorithm and smoke detection algorithm corresponding to each detection content, and obtain the flame detection confidence and smoke detection confidence of multiple video frames;

[0043] A fire detection result acquisition module, used to determine the fire detection result according to the flame detection confidence and the smoke detection confidence;

[0044] The fire alarm module is used to trigger the fire alarm of the factory building if the fire detection result is a fire.

[0045] The present application obtains flame detection confidence and smoke detection confidence of multiple video frames by inputting the captured factory video data into the trained flame detection algorithm and smoke detection algorithm corresponding to each detection content, and then determines the fire detection result according to the flame detection confidence and the smoke detection confidence to judge whether to trigger a fire alarm in the factory. Since the fire detection result is obtained based on the flame detection confidence and the smoke detection confidence, the accuracy of fire detection can be improved and the waste of alarm resources can be prevented.

[0046] In order to provide a clearer understanding of the present application, the specific implementation of the present application will be described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of a fire and smoke detection method according to an embodiment of the present application.

[0048] Figure 2 A schematic diagram of an algorithm model of a smoke detection algorithm according to an embodiment of the present application.

[0049] Figure 3 A schematic diagram of a second convolutional network of a backbone module of a smoke detection algorithm according to an embodiment of the present application.

[0050] Figure 4 This is a module connection diagram of a fire and smoke detection device according to an embodiment of the present application.

[0051] 1. Confidence acquisition module; 2. Fire detection result acquisition module; 3. Fire alarm module. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0053] It should be clear that the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the embodiments of the present application.

[0054] When the following description relates to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. The singular forms of "a", "said" and "the" used in the present application and the appended claims are also intended to include the majority form, unless the context clearly indicates other meanings. The words "if" / "if" used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0055] In addition, in the description of this application, unless otherwise specified, "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0056] See also Figure 1 , which is a flow chart of a fire and smoke detection method according to an embodiment of the present application, comprising:

[0057] S1: Input the captured factory video data into the trained flame detection algorithm and smoke detection algorithm corresponding to each detection content, and obtain the flame detection confidence and smoke detection confidence of multiple video frames.

[0058] The flame detection algorithm and the smoke detection algorithm are trained by the following steps:

[0059] Obtain multiple training samples; each training sample is annotated with a corresponding flame training confidence and a smoke training confidence;

[0060] The initial detection algorithms of the laboratory server cluster are trained according to the training samples to obtain the flame detection algorithm and the smoke detection algorithm.

[0061] S2: Determine a fire detection result according to the flame detection confidence and the smoke detection confidence;

[0062] S3: If the fire detection result is that a fire has occurred, triggering a fire alarm in the factory building.

[0063] In a feasible embodiment, the flame detection confidence includes flame confidence; the smoke detection confidence includes smoke confidence;

[0064] The step S2: determining the fire detection result according to the flame detection confidence and the smoke detection confidence comprises:

[0065] S21: comparing the smoke detection confidence with a preset first confidence threshold;

[0066] S22: If the smoke detection confidence is greater than the first confidence threshold, comparing the flame confidence with a preset second confidence threshold;

[0067] S23: If the flame confidence is greater than the second confidence threshold, determine that the fire detection result is the occurrence of fire.

[0068] In a feasible embodiment, after the step of comparing the smoke detection confidence with a preset first confidence threshold in S21, the following steps are included:

[0069] S24: if the smoke detection confidence is less than or equal to the first confidence threshold, compare the flame confidence with a preset third confidence threshold; the third confidence threshold is less than the first confidence threshold;

[0070] S25: If the flame confidence is greater than the third confidence threshold, determining that the fire detection result is a fire

[0071] In a feasible embodiment, the flame detection confidence also includes flame color; the smoke detection confidence also includes smoke color;

[0072] After the step of comparing the smoke detection confidence with a preset first confidence threshold in S21, the method further comprises:

[0073] S2101: According to a preset correspondence relationship between flame color, smoke color and enhancement coefficient, obtaining an enhancement coefficient corresponding to the flame color and the smoke color.

[0074] S2102: Raise the value of the flame confidence according to the enhancement coefficient to update the flame confidence.

[0075] Among them, the corresponding relationship between flame color-smoke color-enhancement coefficient can be set by the user according to the corresponding relationship between the flame color of a special color and the smoke color of a special color, that is, when the flame color and the smoke color are special colors, the enhancement coefficient can be obtained according to the corresponding relationship between flame color-smoke color-enhancement coefficient to improve the flame confidence, so that it is easier to trigger the fire alarm of the factory. For example, when the flame color is yellow-green and the smoke color is yellow-green, the corresponding enhancement coefficient is 3, which can greatly increase the value of the flame confidence, so that the fire alarm of the factory can be triggered more easily, so that the personnel in the factory can be reminded to evacuate earlier through the fire alarm, thereby improving the personal safety of the personnel in the factory.

[0076] Among them, the execution order of steps S2101-S2102 precedes step S22 and step S24.

[0077] In a feasible embodiment, the corresponding relationship of flame color-smoke color-enhancement coefficient is that a combination of flame color and smoke color corresponds to an enhancement coefficient.

[0078] The present application obtains flame detection confidence and smoke detection confidence of multiple video frames by inputting the captured factory video data into the trained flame detection algorithm and smoke detection algorithm corresponding to each detection content, and then determines the fire detection result according to the flame detection confidence and the smoke detection confidence to judge whether to trigger the fire alarm of the factory. Since the fire detection result is obtained based on the flame detection confidence and the smoke detection confidence, the accuracy of fire detection can be improved and the waste of alarm resources can be prevented.

[0079] See also Figure 2 ,In a feasible embodiment, the smoke detection algorithm includes a trunk module, an enhancement module, a neck module and a head module;

[0080] The steps of inputting the captured factory video data into the trained smoke detection algorithm include:

[0081] Input the factory video data into the backbone module of the smoke detection algorithm for feature extraction processing to obtain the first feature of each video frame;

[0082] Inputting the first feature of each video frame into the enhancement module of the smoke detection algorithm for enhanced convolution processing to obtain the second feature of each video frame;

[0083] Inputting the second feature of each video frame into the neck module of the smoke detection algorithm for bidirectional feature convolution processing to obtain the third feature of each video frame;

[0084] The third feature of each video frame is input into the head module of the smoke detection algorithm for feature prediction processing to obtain detection results of the multiple video frames of the smoke detection algorithm.

[0085] See also Figure 3 In a feasible embodiment, the backbone module includes a feature extraction layer and a plurality of sequentially cascaded combined convolutional layers; wherein the combined convolutional layer includes a first convolutional network and a second convolutional network;

[0086] The step of inputting the factory video data into the backbone module of the smoke detection algorithm for feature extraction processing to obtain the first feature of each video frame includes:

[0087] Inputting the factory video data into the feature extraction layer for feature extraction processing to obtain the fourth feature of each video frame;

[0088] Inputting the fourth feature of each video frame into the first convolutional network of the first combined convolutional layer for convolution processing to obtain the fifth feature of each video frame;

[0089] Inputting the fifth feature of each video frame into the second convolution network of the same combined convolution layer for convolution processing to obtain the sixth feature of each video frame;

[0090] The sixth feature of each video frame is input into the next-level combined convolution layer for convolution processing, and the sixth feature of each video frame output by the last-level combined convolution layer is determined as the first feature.

[0091] In a feasible embodiment, the second convolutional network includes a plurality of phantom convolution units and convolution fusion units cascaded in sequence;

[0092] The step of inputting the fifth feature of each video frame into the second convolution network of the same combined convolution layer for convolution processing to obtain the sixth feature of each video frame includes:

[0093] Inputting the fifth feature into a plurality of phantom convolution units cascaded in sequence for convolution processing, to obtain a seventh feature output by each phantom convolution unit;

[0094] The fifth feature and the seventh feature output by each phantom convolution unit are input into the convolution fusion unit for convolution fusion to obtain the sixth feature.

[0095] In a feasible embodiment, the step of inputting the fifth feature of each video frame into the second convolutional network of the same combined convolutional layer for convolution processing to obtain the sixth feature of each video frame includes:

[0096] The sixth characteristic is obtained by the following formula:

[0097]

[0098] Y is the sixth feature, x is the fifth feature, cat(·) is the data concatenation function, y i represents the seventh feature output by i cascaded phantom convolution units, and n is the number of cascaded phantom convolution units.

[0099] See also Figure 4 The second embodiment of the present application provides a fire and smoke detection device, comprising:

[0100] Confidence acquisition module 1 is used to input the captured factory video data into the trained flame detection algorithm and smoke detection algorithm corresponding to each detection content, and obtain the flame detection confidence and smoke detection confidence of multiple video frames;

[0101] A fire detection result acquisition module 2, used to determine the fire detection result according to the flame detection confidence and the smoke detection confidence;

[0102] The fire alarm module 3 is used to trigger a fire alarm in the factory building if the fire detection result indicates that a fire has occurred.

[0103] The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application. Ordinary technicians in this field can understand and implement it without creative work.

[0104] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0105] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the function selected in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 function selected in a box or multiple boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 steps for the function selected in a box or multiple boxes.

[0107] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0108] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0109] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0110] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0111] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A fire and smoke detection method, characterized in that: include: The captured factory video data is respectively input into the trained flame detection algorithm and smoke detection algorithm corresponding to each detection content to obtain the flame detection confidence and smoke detection confidence of multiple video frames; Determining a fire detection result according to the flame detection confidence and the smoke detection confidence; If the fire detection result is that a fire occurs, a fire alarm of the factory building is triggered.

2. The fire and smoke detection method according to claim 1, characterized in that: The flame detection confidence includes flame confidence; the smoke detection confidence includes smoke confidence; The step of determining the fire detection result according to the flame detection confidence and the smoke detection confidence comprises: Comparing the smoke detection confidence with a preset first confidence threshold; If the smoke detection confidence is greater than the first confidence threshold, comparing the flame confidence with a preset second confidence threshold; If the flame confidence is greater than the second confidence threshold, the fire detection result is determined to be the occurrence of a fire.

3. The fire and smoke detection method according to claim 2, characterized in that: After the step of comparing the smoke detection confidence with a preset first confidence threshold, the method further comprises: If the smoke detection confidence is less than or equal to the first confidence threshold, comparing the flame confidence with a preset third confidence threshold; the third confidence threshold is less than the first confidence threshold; If the flame confidence is greater than the third confidence threshold, it is determined that the fire detection result is the occurrence of a fire.

4. The fire and smoke detection method according to claim 2 or 3, characterized in that: The flame detection confidence also includes flame color; the smoke detection confidence also includes smoke color; After the step of comparing the smoke detection confidence with a preset first confidence threshold, the method further comprises: According to a preset correspondence relationship between flame color, smoke color and enhancement coefficient, obtaining the enhancement coefficient corresponding to the flame color and the smoke color; The value of the flame confidence is increased according to the enhancement coefficient to update the flame confidence.

5. The fire and smoke detection method according to claim 4, characterized in that: The corresponding relationship of flame color-smoke color-enhancement coefficient is that a combination of flame color and smoke color corresponds to an enhancement coefficient.

6. The fire and smoke detection method according to claim 1, characterized in that: The smoke detection algorithm includes a trunk module, an enhancement module, a neck module and a head module; The steps of inputting the captured factory video data into the trained smoke detection algorithm include: Input the factory video data into the backbone module of the smoke detection algorithm for feature extraction processing to obtain the first feature of each video frame; Inputting the first feature of each video frame into the enhancement module of the smoke detection algorithm for enhanced convolution processing to obtain the second feature of each video frame; Inputting the second feature of each video frame into the neck module of the smoke detection algorithm for bidirectional feature convolution processing to obtain the third feature of each video frame; The third feature of each video frame is input into the head module of the smoke detection algorithm for feature prediction processing to obtain detection results of the multiple video frames of the smoke detection algorithm.

7. The fire and smoke detection method according to claim 6, characterized in that: The backbone module includes a feature extraction layer and a plurality of sequentially cascaded combined convolutional layers; wherein the combined convolutional layer includes a first convolutional network and a second convolutional network; The step of inputting the factory video data into the backbone module of the smoke detection algorithm for feature extraction processing to obtain the first feature of each video frame includes: Inputting the factory video data into the feature extraction layer for feature extraction processing to obtain the fourth feature of each video frame; Inputting the fourth feature of each video frame into the first convolutional network of the first combined convolutional layer for convolution processing to obtain the fifth feature of each video frame; Inputting the fifth feature of each video frame into the second convolution network of the same combined convolution layer for convolution processing to obtain the sixth feature of each video frame; The sixth feature of each video frame is input into the next-level combined convolution layer for convolution processing, and the sixth feature of each video frame output by the last-level combined convolution layer is determined as the first feature.

8. The fire and smoke detection method according to claim 7, characterized in that: The second convolutional network includes a plurality of phantom convolution units and convolution fusion units cascaded in sequence; The step of inputting the fifth feature of each video frame into the second convolution network of the same combined convolution layer for convolution processing to obtain the sixth feature of each video frame includes: Inputting the fifth feature into a plurality of phantom convolution units cascaded in sequence for convolution processing, to obtain a seventh feature output by each phantom convolution unit; The fifth feature and the seventh feature output by each phantom convolution unit are input into the convolution fusion unit for convolution fusion to obtain the sixth feature.

9. The fire and smoke detection method according to claim 6, characterized in that: The step of inputting the fifth feature of each video frame into the second convolution network of the same combined convolution layer for convolution processing to obtain the sixth feature of each video frame includes: The sixth characteristic is obtained by the following formula: Y is the sixth feature, x is the fifth feature, cat(·) is the data concatenation function, y i represents the seventh feature output by i cascaded phantom convolution units, and n is the number of cascaded phantom convolution units.

10. A fire and smoke detection device, characterized in that: include: The confidence acquisition module is used to input the captured factory video data into the trained flame detection algorithm and smoke detection algorithm corresponding to each detection content, and obtain the flame detection confidence and smoke detection confidence of multiple video frames; A fire detection result acquisition module, used to determine the fire detection result according to the flame detection confidence and the smoke detection confidence; The fire alarm module is used to trigger the fire alarm of the factory building if the fire detection result is a fire.

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