A fire recognition alarm method and fire detector based on AI image

Abstract

The present invention discloses a fire identification alarm method and a fire detector based on AI images, which belong to the field of fire identification technology. The method comprises: constructing a model library by a platform; identifying a target area according to a user's fire monitoring demand; matching a fire identification model that meets the fire monitoring demand from the model library and marking it as a main identification model; installing a monitoring device in the target area, performing a fire verification simulation in the target area according to the monitoring device, and obtaining various inaccurate data; performing temporary fire monitoring supplement according to various inaccurate data, and setting a corresponding fire comprehensive identification scheme; performing real-time monitoring of the target area to obtain monitoring data, processing the monitoring data according to the fire comprehensive identification scheme, and obtaining a fire identification result; adjusting the main identification model by the platform according to various inaccurate data, updating the adjusted main identification model at the user, and canceling the temporary fire monitoring supplement.

Description

Technical Field

[0001] The present invention belongs to the technical field of fire identification, and specifically relates to a fire identification alarm method and a fire detector based on AI images. Background Art

[0002] In the current field of image recognition technology, especially in the critical application of fire monitoring, traditional methods face many challenges. First of all, building an efficient and accurate image recognition model is a complex and resource-intensive task. This process not only requires a large amount of data collection and annotation work, but also requires powerful computing resources and professional technicians to conduct long-term model training and optimization. Even so, when the trained model is actually deployed to the user's site, it often suffers from uncontrollable factors such as the complexity of the field environment, changes in lighting conditions, and the presence of obstructions, resulting in a significant decrease in recognition accuracy, and then false alarms or missed alarms, which seriously affects the accuracy and reliability of fire monitoring.

[0003] In addition, when the model shows deficiencies on the user side, it becomes particularly difficult to adjust and optimize it. On the one hand, users may not have the professional technical knowledge and resources to directly modify the model; on the other hand, even if technical personnel are involved, the adjustment process is not only time-consuming but also inefficient due to the need to conduct a series of tedious steps such as field data collection and model training again, making it difficult to meet users' urgent needs for rapid response and high-precision monitoring in emergency situations.

[0004] In view of the above problems, the market urgently needs a more flexible, efficient and adaptable fire monitoring solution; based on this, the present invention provides a fire identification and alarm method and fire detector based on AI images. Summary of the invention

[0005] In order to solve the problems existing in the above scheme, the present invention provides a fire identification alarm method and a fire detector based on AI image.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A fire recognition alarm method based on AI image, the method comprising:

[0008] Step 1: The platform constructs a model library, which is used to store various fire identification models and model information corresponding to each fire identification model; the model information includes the scope of application scenarios and the accuracy of each application scenario.

[0009] Step 2: Obtain the user's fire monitoring needs, and identify the target area that needs to be monitored according to the fire monitoring needs; match the fire recognition model that meets the fire monitoring needs from the model library and mark it as the main recognition model.

[0010] Furthermore, the method of matching the main recognition model that meets the fire monitoring requirements from the model library includes:

[0011] Identify the user's monitoring requirements according to the fire monitoring requirements, and collect the scene information of the target area; input the scene information and monitoring requirements into the model library to match with each model information, obtain each matching fire recognition model, and mark it as a candidate recognition model;

[0012] The candidate recognition models are screened to obtain a main recognition model.

[0013] Furthermore, the method for screening each candidate recognition model includes:

[0014] Identify the application scenarios where the scene information matches each of the to-be-selected recognition models, and obtain the accuracy of each of the scene information;

[0015] Performing scene change adaptation analysis on the scene information and each of the candidate recognition models to obtain a scene adaptation value corresponding to each of the candidate recognition models, wherein the scene adaptation value has a value range of [1, 10];

[0016] The accuracy is marked as μ and the scene adaptation value is marked as CA;

[0017] According to the formula Calculating the screening value of each of the candidate recognition models;

[0018] Where: PA is the screening value; b1 and b2 are both proportional coefficients, with a value range of 0 <b1≤1,0<b2≤1;

[0019] The candidate recognition model with the largest screening value is selected as the main recognition model.

[0020] Step three: installing monitoring equipment in the target area, and performing a fire verification simulation in the target area according to the monitoring equipment to obtain various misalignment data.

[0021] Furthermore, the method of conducting fire verification simulation in the target area according to the monitoring equipment is as follows:

[0022] Setting each fire simulation condition according to the scene information; performing a fire simulation in the target area according to each fire simulation condition, and obtaining a monitoring simulation image corresponding to each fire simulation condition;

[0023] Analyze each of the monitored simulation images through the main recognition model to obtain the fire recognition results corresponding to each of the fire simulation conditions; check each of the fire recognition results to obtain a check result, where the check result includes correct recognition and misrecognition;

[0024] Integrate the fire simulation conditions, monitored images, and fire recognition results corresponding to misrecognition in the check result into misaligned data.

[0025] Further, the method for setting each fire simulation condition according to the scene information includes:

[0026] Summarize each scene cause, where the scene cause is the cause leading to misrecognition of the fire; conduct an equivalent evaluation of pairwise scene causes to obtain the equivalent evaluation results corresponding to the pairwise scene causes, where the equivalent evaluation result includes being regarded as equivalent and not being regarded as equivalent; screen each of the scene causes according to each equivalent evaluation result to obtain each primary cause;

[0027] Estimate the simulation cost and occurrence probability in the target area of each of the primary causes;

[0028] According to the formula Calculate the corresponding cause value;

[0029] In the formula: RV is the cause value; b3 and b4 are both proportionality coefficients, with the value range of 0 ≤ b3 ≤ 1 and 0 < b4 ≤ 1; YB is the simulation cost; η is the occurrence probability;

[0030] Screen each of the primary causes according to each of the cause values to obtain each target cause; set the fire simulation conditions according to each of the target causes.

[0031] Further, the method for conducting an equivalent evaluation of pairwise scene causes includes:

[0032] Establish an equivalent evaluation model, and the expression of the equivalent judgment model is:

[0033] ;

[0034] In the formula: s is the input data, and the input data is two scene causes for which an equivalent evaluation is conducted; the output data is the equivalent judgment value DT(s), and the equivalent judgment value is 1 or 0;

[0035] Analyze pairwise scene causes through the equivalent judgment model to obtain the equivalent judgment values corresponding to the pairwise scene causes;

[0036] When the equivalent judgment value is 1, the equivalent evaluation result is regarded as equivalent;

[0037] When the equivalent judgment value is 0, the equivalent evaluation result is not regarded as equivalent.

[0038] Step 4: Perform temporary fire monitoring supplements based on the inaccurate data and set up a corresponding comprehensive fire identification plan.

[0039] Furthermore, the method for performing temporary fire monitoring supplement according to each inaccurate data includes:

[0040] Identify the inaccuracy reasons of each of the inaccuracy data, analyze each of the inaccuracy reasons, and obtain each solution method corresponding to each of the inaccuracy reasons; define a combination standard, the combination standard is to solve the influence of each inaccuracy reason on fire identification; form each candidate combination method according to the combination standard and each of the solutions;

[0041] The candidate combinations are screened to obtain a target combination, and temporary fire monitoring is supplemented according to the target combination.

[0042] Step 5: Monitor the target area in real time to obtain monitoring data, process the monitoring data according to the comprehensive fire identification scheme to obtain fire identification results, and display the fire identification results to the user.

[0043] Step 6: The platform adjusts the main recognition model according to the inaccurate data, updates the adjusted main recognition model at the user's location, and cancels the temporary fire monitoring supplement.

[0044] Furthermore, the adjusted main recognition model is stored in a model library.

[0045] Carry out real-time monitoring of the target area and obtain corresponding monitoring data.

[0046] A fire detector comprises a monitoring module, an analysis module and a transmission module;

[0047] The monitoring module monitors the target area and obtains monitoring data;

[0048] The analysis module is used to analyze the monitoring data to obtain fire identification results;

[0049] The transmission module is used for data transmission.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention realizes the centralized storage, rapid deployment and dynamic adjustment of fire identification models by constructing a centralized model library platform. Specifically, the platform is responsible for collecting, establishing and maintaining a fire identification model library that contains multiple scene adaptability. These models have been strictly tested and optimized and have high recognition accuracy and generalization ability. When a user proposes a fire monitoring demand, the platform can quickly analyze, match and recommend the most suitable fire identification model according to the user's needs. Then, according to the actual scene simulation, potential false alarm and missed alarm risk points are identified and predicted. For these risk points, the platform can immediately formulate and implement cross-complementary measures, such as adding cross-monitoring points, introducing multiple side analysis algorithms, etc., to minimize the false alarm rate and missed alarm rate. At the same time, once the temporary supplementary measures achieve the expected effect, the temporary supplementary measures can be cancelled to achieve efficient layout and high-precision recognition of fire identification; and the platform will have sufficient time to adjust the fire identification model applied by the user, improve the recognition accuracy and the work continuity of the platform's technical personnel, and reduce the volatility of the technical personnel's work. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 is a flow chart of the method of the present invention;

[0054] Figure 2 This is an example diagram of a mobile phone fire simulation of the present invention;

[0055] Figure 3 This is an example diagram of a lamp fire simulation of the present invention;

[0056] Figure 4 This is an example diagram of a vehicle headlight fire simulation according to the present invention. DETAILED DESCRIPTION

[0057] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] like Figures 1 to 4 As shown, a fire recognition alarm method based on AI image, the method includes:

[0059] Step 1: The platform builds a model library, which is used to store various fire recognition models established by the platform or obtained from other channels, such as fire recognition models adjusted and set according to the needs of previous users; and sets corresponding model information for each fire recognition model. The model information includes relevant model information such as the scope of application scenarios and the accuracy of each application scenario. The simulation information is set according to its actual usage records and verification conditions. The fire recognition model is used to identify fires in collected high-definition images; it is generally established based on deep neural networks.

[0060] Step 2: Obtain the user's fire monitoring requirements. The fire monitoring requirements are used by the user to describe which areas to monitor for fire and the corresponding monitoring requirements; determine the target area that needs to be monitored for fire and the scene information corresponding to the target area based on the fire monitoring requirements; match the corresponding fire recognition model from the model library based on the scene information and monitoring requirements, and mark it as the main recognition model; that is, first eliminate those that do not meet the monitoring requirements, and then match them through the application scenario range to obtain various fire recognition models, screen them, and obtain the main recognition model.

[0061] In one embodiment, the fire identification models may be screened based on existing screening methods; for example, based on the accuracy of each application scenario.

[0062] In one embodiment, each fire recognition model that needs to be screened is marked as a candidate recognition model, and a method for screening each candidate recognition model includes:

[0063] Identify the accuracy of application scenarios that each candidate recognition model matches;

[0064] The scene information of the target area is analyzed for changes, that is, in the subsequent scene environment changes, the more complex the scene is, the greater the impact of the corresponding changes is, and some fire identification models have low adaptability to changes, resulting in a large adjustment workload after being applied to the actual scene; statistical analysis is performed on a large amount of historical record data, and a scene adaptation value is set, with a value range of [1, 10]. The more adaptable it is to its changes, the higher its scene adaptation value is. A value equal to 1 indicates that it is within the scope of the application scene, and a value smaller than 1 indicates that it is not within the scope of the application scene; the adaptation adjustment workload after various changes that may be possible is determined based on the historical record data, and they are sorted. The adaptation value of the performance that does not need to be adjusted (no adjustment workload) is 10, and the corresponding scene adaptation value is inserted according to the difference in the adjustment workload; subsequently, the corresponding scene adaptation value can be estimated by estimating the adjustment workload of each fire identification model in different application scenarios, or statistical simulation is performed based on the historical record data to determine the scene adaptation value corresponding to each application scenario of each fire identification model within the scope of the application scenario; in one embodiment, because the subsequent adjustment is mainly performed by the staff of the platform, the scene adaptation value of each fire identification model in different application scenarios can be directly set manually, and the setting is performed from the perspective of the staff.

[0065] The accuracy is marked as μ and the scene adaptation value is marked as CA;

[0066] According to the formula Calculate the screening value of each candidate recognition model;

[0067] Where: PA is the screening value; b1 and b2 are both proportional coefficients, with a value range of 0 <b1≤1,0<b2≤1;

[0068] The candidate recognition model with the largest screening value is selected as the main recognition model.

[0069] Step three: Install monitoring equipment in the target area, including corresponding acquisition equipment, transmission equipment and other hardware and software equipment, which are used to capture images of the target area and perform corresponding fire identification analysis, such as color images 1080P, near-infrared images 1080P, etc.; adapt to all-day image acquisition; after the monitoring equipment is installed, conduct fire verification simulation in the target area to determine the inaccurate data, which are various situation data corresponding to false alarms and missed alarms.

[0070] In one embodiment, the method for performing fire verification simulation in the target area is:

[0071] According to the scene information, various possible fire situations in the target area and the scene situations corresponding to various fire situations are determined and integrated into various fire simulation conditions; Figures 2 to 4 As shown, scenario simulations under various interference conditions;

[0072] Perform fire simulations in the target area according to various fire simulation conditions, collect corresponding monitoring images, label them as monitoring simulation images, analyze the monitoring simulation images through the main recognition model to obtain corresponding fire recognition results; check each fire recognition result to obtain a check result, and the check result includes correct recognition and misrecognition.

[0073] Identify the fire simulation conditions, monitoring images, and fire recognition results corresponding to misrecognition in the recognition check result, and integrate the corresponding fire simulation conditions, monitoring images, and fire recognition results into misalignment data.

[0074] In one embodiment, the method of setting each fire simulation condition according to the scene information is to set it through existing methods, such as setting it according to the work experience of the staff.

[0075] In one embodiment, the method of setting each fire simulation condition according to the scene information includes:

[0076] Based on existing big data technology, obtain various scene reasons for false alarms, missed alarms, etc. in image-based fire recognition, such as various reasons like light interference, smoke interference, occlusion, etc.; conduct equivalent evaluations on pairwise scene reasons, including AB and BA, that is, reverse evaluations; obtain corresponding equivalent evaluation results, and the equivalent evaluation results include being regarded as equivalent and not being regarded as equivalent; screen each scene reason according to each equivalent evaluation result, and mark the remaining scene reasons as primary selection reasons; being regarded as equivalent means assuming that if a fire can be recognized under this scene reason, it can also be recognized under another scene reason, then the above two scene reasons are regarded as equivalent, and one is retained. If it is not equivalent when analyzed from another angle, then remove this scene reason and retain the one with a larger scope.

[0077] Estimate the occurrence probability of each primary selection reason in the target area, and estimate the simulation cost of simulating this primary selection reason, mainly the costs of corresponding simulation equipment, materials, etc.; all are predicted using existing prediction technologies.

[0078] According to the formula Calculate the corresponding reason value.

[0079] In the formula: RV is the reason value; b3 and b4 are both proportionality coefficients, and the value range is 0 ≤ b3 ≤ 1, 0 < b4 ≤ 1; YB is the simulation cost; η is the occurrence probability.

[0080] Mark the primary selection reasons with a reason value greater than the threshold X1 as target reasons; set fire simulation conditions according to each target reason, and the fire simulation conditions are the corresponding conditions for simulating this target reason.

[0081] In one embodiment, pairwise scene reasons can be equivalently evaluated based on existing technologies.

[0082] In one embodiment, a method for performing an equivalence evaluation on the causes of two scenarios includes:

[0083] An equivalent evaluation model is established. The equivalent evaluation model is used to simulate and judge the causes of the two corresponding scenarios to determine whether they meet the equivalent judgment criteria. Specifically, a large amount of existing historical fire identification data is used to make judgments to understand whether fire identification can be performed under the cause of scenario A, and whether fire identification can also be performed under the cause of scenario B. Then, it is determined whether the equivalent judgment criteria are met. The judgment can also be made based on other intelligent technologies. The expression of the equivalent judgment model is: ; Where: s is the input data, the input data is the two scene reasons for the equivalent evaluation; the output data is the equivalent judgment value DT(s);

[0084] Analyze the causes of each pair of scenarios through the equivalent judgment model to obtain the equivalent judgment values ​​corresponding to the causes of each pair of scenarios;

[0085] When the equivalence judgment value is 1, the equivalence assessment result is considered equivalent;

[0086] When the equivalence judgment value is 0, the equivalence evaluation result is not considered equivalent.

[0087] Step 4: Perform temporary fire monitoring supplements based on the inaccurate data and set up corresponding comprehensive fire identification plans.

[0088] When temporary fire monitoring is supplemented, the coordination relationship with the main identification model can be determined, thereby realizing fire identification of the monitoring data and solving various inaccuracy causes.

[0089] In one embodiment, temporary fire monitoring supplement is performed based on each inaccurate data, and the supplement is performed manually, that is, based on the inaccurate data, it is determined under what circumstances the main recognition model cannot meet the fire recognition requirements, and the staff selects a supplementary monitoring method to overcome the cause; illustratively, such as the cause of light interference, it is possible to supplement the existing model for light recognition, and comprehensively identify the fire with the main recognition model, and eliminate the light in the recognition of the main recognition model; it is also possible to supplement monitoring images from other angles and remove the influence through multi-angle monitoring.

[0090] In one embodiment, the method for performing temporary fire monitoring supplement according to each misaligned data includes:

[0091] Identify each inaccuracy data, determine each inaccuracy cause based on each inaccuracy data, and determine the inaccuracy cause in combination with the target cause corresponding to the fire simulation condition; analyze various solutions that can solve the corresponding inaccuracy cause, combine them according to each solution, and obtain each candidate combination method. Each combination can solve each inaccuracy cause, that is, combine them based on solving all inaccuracy causes to form each candidate combination method; screen each candidate combination method to obtain the target combination method, and perform temporary fire monitoring supplement according to the target combination method.

[0092] In one embodiment, the selection of the combinations can be performed based on various existing selection methods, such as cost, implementation difficulty, and the like.

[0093] Step 5: Conduct real-time monitoring of the target area, obtain corresponding monitoring data, process the monitoring data according to the comprehensive fire identification plan, obtain corresponding fire identification results, and display the obtained fire identification results to the user.

[0094] Step 6: The platform adjusts the main recognition model according to the inaccurate data. When the main recognition model can overcome the inaccurate causes corresponding to the inaccurate data, the adjusted main recognition model will be updated at the user's location; the temporary fire monitoring supplement will be canceled, and the adjusted main recognition model will be stored in the model library.

[0095] A fire detector comprises: a monitoring module, an analysis module and a transmission module;

[0096] The monitoring module monitors the target area and obtains monitoring data;

[0097] The analysis module is used to analyze the monitoring data to obtain fire identification results;

[0098] The transmission module is used for data transmission, such as wireless or wired data transmission.

[0099] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0100] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A fire recognition and alarm method based on AI image, characterized in that: The method includes: The platform constructs a model library for storing various fire recognition models and the model information corresponding to each fire recognition model; the model information includes the application scenario range and the accuracy of each application scenario. Obtain the user's fire monitoring requirements, and identify the target area that needs to be monitored for fires according to the fire monitoring requirements; match the fire recognition model that meets the fire monitoring requirements from the model library, and mark it as the main recognition model. Install monitoring equipment in the target area, and perform fire verification simulation in the target area according to the monitoring equipment to obtain each misalignment data. Conduct temporary fire monitoring supplementation according to each misalignment data, and set corresponding fire comprehensive recognition schemes. Conduct real-time monitoring on the target area to obtain monitoring data, process the monitoring data according to the fire comprehensive recognition scheme to obtain a fire recognition result, and display the fire recognition result to the user. The platform adjusts the main recognition model according to each misalignment data, updates the adjusted main recognition model at the user's side, and cancels the temporary fire monitoring supplementation. The method for matching the main recognition model that meets the fire monitoring requirements from the model library includes: Identify the user's monitoring requirements according to the fire monitoring requirements, and collect the scene information of the target area; input the scene information and monitoring requirements into the model library to match with each model information, and obtain each matched fire recognition model, which is marked as the candidate recognition model. Screen each candidate recognition model to obtain the main recognition model. The method for performing fire verification simulation in the target area according to the monitoring equipment is: Set each fire simulation condition according to the scene information; perform fire simulation in the target area according to each fire simulation condition to obtain the monitoring simulation images corresponding to each fire simulation condition. Analyze each monitoring simulation image through the main recognition model to obtain the fire recognition results corresponding to each fire simulation condition; check each fire recognition result to obtain a check result, and the check result includes correct recognition and misalignment recognition. Integrate the fire simulation conditions, monitoring images, and fire recognition results corresponding to the misalignment recognition in the check result into misalignment data. The method for setting each fire simulation condition according to the scene information includes: Summarize each scene cause, where the scene cause is the cause leading to misalignment in fire recognition; perform equivalent evaluation on every two scene causes to obtain the equivalent evaluation results corresponding to every two scene causes, and the equivalent evaluation results include being regarded as equivalent and not being regarded as equivalent; screen each scene cause according to each equivalent evaluation result to obtain each primary cause. Estimate the simulation cost and occurrence probability in the target area of each primary cause. According to the formula Calculate the corresponding cause value; In the formula: RV is the cause value; b3 and b4 are both proportionality coefficients, and the value range is 0 ≤ b3 ≤ 1, 0 < b4 ≤ 1; YB is the simulation cost; η is the occurrence probability. Screen each primary cause according to each cause value to obtain each target cause; set fire simulation conditions according to each target cause. The method for performing equivalent evaluation on every two scene causes includes: Establish an equivalent evaluation model, and the expression of the equivalent judgment model is: ； Where: s is the input data, which is the two scenario reasons for the equivalent evaluation; the output data is the equivalent judgment value DT(s), which is 1 or 0; Analyze the causes of the two scenarios by using the equivalent judgment model to obtain equivalent judgment values ​​corresponding to the causes of the two scenarios; When the equivalence judgment value is 1, the equivalence assessment result is deemed to be equivalent; When the equivalence judgment value is 0, the equivalence evaluation result is not considered equivalent.

2. The fire identification and alarm method based on AI image according to claim 1 is characterized in that: Methods for screening the candidate recognition models include: Identify the application scenarios where the scene information matches each of the to-be-selected recognition models, and obtain the accuracy of each of the scene information; Performing scene change adaptation analysis on the scene information and each of the candidate recognition models to obtain a scene adaptation value corresponding to each of the candidate recognition models, wherein the scene adaptation value has a value range of [1, 10]; The accuracy is marked as μ and the scene adaptation value is marked as CA; According to the formula Calculating the screening value of each of the candidate recognition models; Where: PA is the screening value; b1 and b2 are both proportional coefficients, with a value range of 0 <b1≤1,0<b2≤1; The candidate recognition model with the largest screening value is selected as the main recognition model.

3. The fire identification and alarm method based on AI image according to claim 1 is characterized in that: Methods for supplementing temporary fire monitoring based on inaccurate data include: Identify the inaccuracy reasons of each of the inaccuracy data, analyze each of the inaccuracy reasons, and obtain each solution method corresponding to each of the inaccuracy reasons; define a combination standard, the combination standard is to solve the influence of each inaccuracy reason on fire identification; form each candidate combination method according to the combination standard and each of the solutions; The candidate combinations are screened to obtain a target combination, and temporary fire monitoring is supplemented according to the target combination.

4. The fire recognition alarm method based on AI image according to claim 1 is characterized in that: The adjusted main recognition model is stored in the model library.

5. A fire detector, characterized in that: A fire identification and alarm method based on AI image according to any one of claims 1 to 4 is implemented, comprising a monitoring module, an analysis module and a transmission module; The monitoring module monitors the target area and obtains monitoring data; The analysis module is used to analyze the monitoring data to obtain fire identification results; The transmission module is used for data transmission.

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