Operation method of fire-fighting early warning system based on cloud platform

Abstract

The invention discloses an operation method of a fire-fighting early warning system based on a cloud platform, and the method mainly comprises the following steps: S1, collecting the temperature of inflammables in a workshop through a monitoring module, and inputting the comprehensive information of the inflammables and explosives in the workshop into a system; s2, starting to analyze the visual image and the inflammable temperature in the workshop, judging whether a fire occurs or not according to an analysis result, and performing early warning; s3, when the system detects that a fire occurs, the combustion characteristics of inflammables at a fire point and the distance between the inflammables around the fire point and the fire point are analyzed, and the fire hazard degree and the fire spreading trend are judged according to the analysis result; and S4, after the fire hazard degree is determined, analyzing and planning an escape route. The method has the characteristic of improving accuracy and safety.

Description

Technical Field

[0001] The present invention relates to the field of fire warning technology, and in particular to an operation method of a fire warning system based on a cloud platform. Background Art

[0002] In recent years, with the rapid development of the economy, the scale of my country's industrial parks has continued to expand, and fire warning is of great significance for fire prevention and control and personnel evacuation.

[0003] At present, the traditional fire warning method is to detect through fixed detection devices or manually discover and then alarm. However, since the combustion of flammable materials is easily affected by environmental factors, the existing technology is prone to misjudgment and missed judgment, resulting in poor accuracy of fire warning in the existing technology. Moreover, due to the urgency and uncertainty of the fire environment, the existing technology cannot accurately predict the fire and assess the degree of danger of the fire according to the fire environment, and thus cannot plan safe routes for workers to take emergency evacuations, resulting in low system safety and low rescue efficiency. Therefore, it is very necessary to design a cloud-based fire warning system to improve accuracy and safety. Summary of the invention

[0004] The purpose of the present invention is to provide a fire warning system based on a cloud platform and an operation method thereof to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a fire warning system based on a cloud platform, comprising a data acquisition module, a fire warning module and a personnel avoidance module, characterized in that: the data acquisition module is used to collect visual images of workshops in the park, the temperature of flammable materials in the workshops and comprehensive information of flammable materials, the fire warning module is used to detect fire characteristics, analyze whether a fire occurs in the area, further analyze the fire environment, and predict the fire, the personnel avoidance module is used to determine whether workers can evacuate according to the analysis results of the fire warning module, and plan escape routes for the workers, and the data acquisition module, the fire warning module and the personnel avoidance module are connected to each other in communication;

[0006] The fire monitoring module includes a visual detection submodule, a temperature detection submodule and a smoke detection submodule. The visual detection submodule is used to detect the flame characteristics and smoke characteristics in the workshop. The temperature detection submodule is used to detect the temperature of the flammable material and the temperature of the ignition point. The smoke detection submodule is used to detect the smoke concentration in the workshop after a fire occurs.

[0007] The environmental detection module includes a flammable material analysis submodule and a fire intensity analysis submodule. The flammable material analysis submodule is used to identify the flammable material at the fire point and analyze the combustion characteristics of the flammable material. The fire intensity analysis submodule is used to analyze the impact of environmental factors on the spread of fire, analyze the impact of flammable materials around the fire point on the fire, and predict the development trend of the fire based on the analysis results.

[0008] According to the above technical solution, the data acquisition module includes a monitoring module, a sensor module and an item comprehensive information entry module. The monitoring module is used to detect whether flames or smoke appear in the visual image and identify flammable materials in the image. The sensor module is used to detect the temperature of flammable materials and the smoke concentration on the escape route. The item comprehensive information entry module is used to enter the comprehensive information of flammable materials in the workshop into the system.

[0009] According to the above technical solution, the fire warning module includes a fire monitoring module and an environmental detection module. The fire monitoring module is used to detect whether there is flame or smoke in the workshop, and to detect the temperature and smoke concentration of flammable materials. The environmental detection module is used to detect the combustion conditions of the fire point in the workshop and the distance between the flammable materials near the fire point and the fire point.

[0010] According to the above technical solution, the personnel evacuation module includes a communication module, and the communication module is used to remind workers of the fire and guide the workers to escape.

[0011] According to the above technical solution, the personnel evacuation module also includes an escape route planning module and an emergency evacuation module. The escape route planning module is used to plan an escape route for workers in the event of a fire, and the emergency evacuation module is used to guide workers to evacuate and save themselves when the fire is too large.

[0012] According to the above technical solution, the operation method of the fire warning system mainly includes the following steps:

[0013] Step S1: Using the monitoring module, real-time visual images of the workshop are captured; using the sensor module, the temperature of the flammable materials in the workshop is collected; and using the item comprehensive information entry module, the comprehensive information of the flammable and explosive materials in the workshop is entered into the system;

[0014] Step S2: After the data is entered into the system, the system starts the fire monitoring module and begins to analyze the visual images and the temperature of the flammable materials in the workshop. Based on the analysis results, it determines whether a fire has occurred and issues an early warning.

[0015] Step S3: When the system detects a fire, the system sends an electrical signal to trigger the environment detection module to start, and begins to analyze the combustion characteristics of the flammable materials at the fire point, the distance between the flammable materials around the fire point and the fire point, and determines the fire danger level and fire spread trend based on the analysis results;

[0016] Step S4: After determining the fire danger level, the personnel evacuation module is activated to analyze the fire environment of each workshop in the park and plan escape routes for the workers based on the analysis results.

[0017] According to the above technical solution, step S2 further includes the following steps:

[0018] Step S21: retrieve the visual image in the workshop, scan and identify the flammable material feature nodes, connect the feature nodes to build a flammable material feature model, retrieve the corresponding flammable material information in the data according to the flammable material feature model, obtain the combustion conditions and ignition point of the flammable material, retrieve the infrared thermal data corresponding to the flammable material, identify the temperature of the flammable material, and if the temperature of the flammable material is greater than the maximum threshold set by the system, mark the flammable material as having a risk of spontaneous combustion, and send the location of the flammable material to the management terminal, otherwise continue to detect;

[0019] Step S22: retrieve the visual image in the workshop, set a fixed image extraction frame rate, periodically capture pictures from the visual image according to the set extraction frame rate, scan and identify the RGB color value of each pixel in the image, compare the values ​​of the three RGB channels in the pixel, select the maximum value as the grayscale value of the pixel to grayscale the image, identify the color value of each pixel after grayscale processing, calculate the difference in color values ​​between adjacent pixels, and if the difference in color value is greater than the system set threshold, mark the current adjacent pixel as an edge pixel, otherwise continue detection;

[0020] Step S23: Identify the marks in the pixels, connect the edge pixels to construct the target area, compare the last frame rate screenshot, calculate the difference in color values ​​of corresponding pixels in the target area of ​​two adjacent frame rate screenshots, if the color value difference is greater than the system set threshold, mark the target area as an abnormal area, otherwise continue to detect, anchor the abnormal area in the screenshot, identify the color values ​​of the pixels in the abnormal area, calculate the average of the three channel values, if the average is greater than the maximum threshold, mark the pixel as a retained pixel, otherwise mark it as a discarded pixel, identify the marks of the pixels in the abnormal area, connect the retained pixels to construct a flame model, compare the database, if the similarity is greater than the system set threshold, mark the target area as a fire, otherwise the system continues to detect;

[0021] Step S24: retrieve the grayscaled captured image, anchor the floating particles in the image, analyze the number of pixels in the fine particles, and determine whether smoke is present based on the analysis results.

[0022] According to the above technical solution, in step S24, the captured image after grayscale processing is retrieved, the color value of the pixel is identified, the color value difference between the current pixel and the upper, lower, left and right pixels is calculated respectively, the current pixel is added with the color value difference between the upper, lower, left and right pixels respectively, and new images are respectively constructed, the images are overlapped and fused, the color value of the pixel of the fused image is identified, the color value difference between adjacent pixels is calculated, if the difference is greater than the threshold, the pixel is marked, otherwise the detection is continued, the marked pixels are connected clockwise to construct a floating particle model, the number of pixels in the floating particle model is identified, if the number of pixels is greater than the maximum threshold, the floating particles are marked as dust, otherwise the floating particles are marked as smoke, the total number M of floating particles in the current image is identified, and the smoke occupancy ratio in the image is calculated by the formula Where P represents the smoke occupancy ratio in the image, that is, the proportion of smoke in the floating particles, m represents the amount of smoke in the image, and α represents the error coefficient of the smoke occupancy ratio of fine dust in the air. If the smoke occupancy ratio in the image is greater than the maximum threshold, it is marked that a fire has occurred in the current workshop, otherwise the detection will continue.

[0023] According to the above technical solution, step S3 further includes the following steps:

[0024] Step S31: Anchor the fire point, identify the characteristics of the flammable materials around the fire point, retrieve the corresponding combustion characteristics and ignition points in the database according to the characteristics of the flammable materials, and if the flammable materials are explosive materials, notify the workers to evacuate quickly through the communication module, otherwise calculate the temperature radiated from the fire point to the surrounding flammable materials;

[0025] Step S32: retrieve the temperature of the ignition point, calculate the distance between the flammable material and the ignition point using the distance formula, and calculate the temperature radiated from the ignition point to the surrounding flammable materials using the formula Where Q represents the temperature radiated from the fire point to the surrounding flammable materials, C represents the outer temperature of the fire point, L represents the distance between the fire point and the flammable material, and β represents the attenuation coefficient of the distance between the fire point and the flammable material to the radiation temperature of the fire point. If the radiation temperature Q is less than the ignition point of the flammable material, the fire point is marked as a treatable fire, and the nearest worker is notified through the communication module to use a fire extinguisher to deal with it. Otherwise, the worker is notified through the communication module to evacuate.

[0026] According to the above technical solution, step S4 further includes the following steps:

[0027] Step S41: When a fire occurs in a workshop, the system notifies the workers to evacuate through the communication module, retrieves the infrared sensor data, and identifies the infrared thermal image. If the temperature in the workshop is greater than the system-set threshold, the current workshop is marked as impassable, otherwise it is marked as an alternative route. The harmful gas sensor data is retrieved to identify the concentration of harmful gases in the air. If the harmful gas is greater than the limit threshold, the workshop is marked as impassable, otherwise it is marked as an alternative route. The captured image is retrieved to identify the smoke occupancy ratio. If the smoke occupancy ratio is greater than the limit threshold, the workshop is marked as impassable, otherwise it is marked as an alternative route. The mark of each workshop is identified, and the alternative routes are marked in the park building model. If there is a wall between adjacent alternative routes, the workers are guided to the emergency shelter for shelter through the communication module, otherwise the workers are guided to evacuate through the planned escape route. At the same time, when a fire occurs, the system obtains the positioning information of the workers in the park building in real time through the communication module, and sends the positioning information to the rescue center.

[0028] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by detecting whether the temperature of the flammable material is greater than a threshold value, can prevent the flammable material from spontaneously combusting when the temperature reaches the ignition point, reduce the possibility of spontaneous combustion of the flammable material, and thereby improve the safety of the workshop; by graying the captured image, marking abnormal areas, detecting abnormal areas and further eliminating pixels, and finally comparing the database model, the influence of flames reflected from walls or buildings around the fire point on the accuracy of fire point judgment can be reduced, thereby greatly improving the accuracy of fire detection; by detecting the occupancy rate of smoke in the air, a fire can be detected before the smoke alarm sounds an alarm, thereby reducing the hysteresis of the system detection, and at the same time preventing the smoldering of the flammable material. There is no open flame, causing the system to fail to identify the fire, resulting in increased losses to the company, further improving safety and accuracy, by calculating the temperature of the national power radiation to the flammable materials, and then analyzing whether the flammable materials will be ignited, it can quickly deal with smaller fires, and then curb the spread of the fire, reduce corporate losses, and further improve the safety of the company, by planning escape routes for workers in the event of a fire, and detecting the escape routes in real time, and guiding workers to safe havens for shelter when the escape passage is impassable, it can avoid workers missing the best escape time due to excessive smoke and inability to find escape passages in the event of a fire, and at the same time can guide workers to safe havens for self-protection when they cannot escape, thereby greatly improving the safety of workers. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0030] Figure 1It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0032] See also Figure 1 , the present invention provides a technical solution: a fire warning system based on a cloud platform, including a data acquisition module, a fire warning module and a personnel avoidance module, characterized in that: the data acquisition module is used to collect visual images of workshops in the park, the temperature of flammable materials in the workshops and comprehensive information of flammable materials, the fire warning module is used to detect fire characteristics, analyze whether a fire occurs in the area, further analyze the fire environment, and predict the fire, the personnel avoidance module is used to determine whether workers can evacuate according to the analysis results of the fire warning module, and plan escape routes for the workers, and the data acquisition module, the fire warning module and the personnel avoidance module are connected to each other in communication;

[0033] The fire monitoring module includes a visual detection submodule, a temperature detection submodule and a smoke detection submodule. The visual detection submodule is used to detect the flame characteristics and smoke characteristics in the workshop. The temperature detection submodule is used to detect the temperature of the flammable material and the temperature of the ignition point. The smoke detection submodule is used to detect the smoke concentration in the workshop after a fire occurs.

[0034] The environmental detection module includes a flammable material analysis submodule and a fire intensity analysis submodule. The flammable material analysis submodule is used to identify the flammable materials at the fire point and analyze the combustion characteristics of the flammable materials. The fire intensity analysis submodule is used to analyze the impact of environmental factors on the spread of fire, analyze the impact of flammable materials around the fire point on the fire, and predict the development trend of the fire based on the analysis results.

[0035] The data acquisition module includes a monitoring module, a sensor module and an item comprehensive information entry module. The monitoring module is used to detect whether there is flame or smoke in the visual image and identify flammable materials in the image. The sensor module is used to detect the temperature of flammable materials and the smoke concentration on the escape route. The item comprehensive information entry module is used to enter the comprehensive information of flammable materials in the workshop into the system.

[0036] The fire warning module includes a fire monitoring module and an environmental detection module. The fire monitoring module is used to detect whether there is flame or smoke in the workshop, and to detect the temperature and smoke concentration of flammable materials. The environmental detection module is used to detect the combustion conditions of the fire point in the workshop and the distance between the flammable materials near the fire point and the fire point.

[0037] The personnel evacuation module includes a communication module, which is used to remind workers of fire and guide them to escape.

[0038] The personnel evacuation module also includes an escape route planning module and an emergency evacuation module. The escape route planning module is used to plan escape routes for workers in the event of a fire, and the emergency evacuation module is used to guide workers to evacuate and save themselves when the fire is too large.

[0039] The operation method of the fire warning system mainly includes the following steps:

[0040] Step S1: Using the monitoring module, real-time visual images of the workshop are captured; using the sensor module, the temperature of the flammable materials in the workshop is collected; and using the item comprehensive information entry module, the comprehensive information of the flammable and explosive materials in the workshop is entered into the system;

[0041] Step S2: After the data is entered into the system, the system starts the fire monitoring module and begins to analyze the visual images and the temperature of the flammable materials in the workshop. Based on the analysis results, it determines whether a fire has occurred and issues an early warning.

[0042] Step S3: When the system detects a fire, the system sends an electrical signal to trigger the environment detection module to start, and begins to analyze the combustion characteristics of the flammable materials at the fire point, the distance between the flammable materials around the fire point and the fire point, and determines the fire danger level and fire spread trend based on the analysis results;

[0043] Step S4: After determining the fire danger level, the personnel evacuation module is activated to analyze the fire environment of each workshop in the park and plan escape routes for the workers based on the analysis results.

[0044] Step S2 further comprises the following steps:

[0045] Step S21: retrieve the visual image in the workshop, scan and identify the flammable material feature nodes, connect the feature nodes to build a flammable material feature model, retrieve the corresponding flammable material information in the data according to the flammable material feature model, obtain the combustion conditions and ignition point of the flammable material, retrieve the infrared thermal data corresponding to the flammable material, and identify the temperature of the flammable material. If the temperature of the flammable material is greater than the maximum threshold set by the system, the flammable material is marked as having a risk of spontaneous combustion, and the location of the flammable material is sent to the management terminal. Otherwise, the system continues to detect. By detecting whether the temperature of the flammable material is greater than the threshold, it is possible to avoid spontaneous combustion of the flammable material when the temperature reaches the ignition point, reduce the possibility of spontaneous combustion of the flammable material, and thus improve the safety of the workshop;

[0046] Step S22: retrieve the visual image in the workshop, set a fixed image extraction frame rate, periodically capture pictures from the visual image according to the set extraction frame rate, scan and identify the RGB color value of each pixel in the image, compare the values ​​of the three RGB channels in the pixel, select the maximum value as the grayscale value of the pixel to grayscale the image, identify the color value of each pixel after grayscale processing, calculate the difference in color values ​​between adjacent pixels, and if the difference in color value is greater than the system set threshold, mark the current adjacent pixel as an edge pixel, otherwise continue detection;

[0047] Step S23: Identify the mark in the pixel, connect the edge pixel to construct the target area, compare the last frame rate intercepted picture, calculate the difference of the color value of the corresponding pixel in the target area of ​​the two adjacent frame rate intercepted pictures, if the color value difference is greater than the system set threshold, then mark the target area as an abnormal area, otherwise continue to detect, anchor the abnormal area in the intercepted picture, identify the color value of the pixel in the abnormal area, calculate the average of the three channel values, if the average is greater than the maximum threshold, then mark the pixel as a retained pixel, otherwise mark it as a discarded pixel, identify the mark of the pixel in the abnormal area, connect the retained pixel to construct a flame model, compare the database, if the similarity is greater than the system set threshold, then mark the target area for fire, otherwise the system continues to detect, by graying the intercepted picture, marking the abnormal area, detecting the abnormal area to further remove the pixels, and finally comparing the database model, it can reduce the influence of the reflected flames from the wall or the buildings around the fire point on the accuracy of the fire point judgment, and greatly improve the accuracy of fire detection;

[0048] Step S24: retrieve the grayscaled captured image, anchor the floating particles in the image, analyze the number of pixels in the fine particles, and determine whether smoke is present based on the analysis results.

[0049] In step S24, the captured image after grayscale processing is retrieved, the color value of the pixel is identified, the color value difference between the current pixel and the upper, lower, left and right pixels is calculated respectively, the current pixel is added with the color value difference between the upper, lower, left and right pixels respectively, and new images are respectively constructed, the images are overlapped and fused, the color value of the pixel of the fused image is identified, the color value difference between adjacent pixels is calculated, if the difference is greater than the threshold, the pixel is marked, otherwise the detection is continued, the marked pixels are connected clockwise to construct a floating particle model, the number of pixels in the floating particle model is identified, if the number of pixels is greater than the maximum threshold, the floating particles are marked as dust, otherwise the floating particles are marked as smoke, the total number M of floating particles in the current image is identified, and the smoke occupancy ratio in the image is calculated by the formula In the formula, P represents the smoke occupancy ratio in the image, that is, the proportion of smoke in the floating particles, m represents the amount of smoke in the image, and α represents the error coefficient of the fine dust in the air to the smoke occupancy ratio. If the smoke occupancy ratio in the image is greater than the maximum threshold, the current workshop is marked as having a fire, otherwise the detection continues. By detecting the smoke occupancy ratio in the air, a fire can be detected before the smoke alarm sounds, thereby reducing the lag in system detection. At the same time, it can avoid the situation where there is no open flame when flammable materials are smoldering, causing the system to fail to determine the fire, resulting in increased losses to the company, further improving safety and accuracy.

[0050] Step S3 further comprises the following steps:

[0051] Step S31: Anchor the fire point, identify the characteristics of the flammable materials around the fire point, retrieve the corresponding combustion characteristics and ignition points in the database according to the characteristics of the flammable materials, and if the flammable materials are explosive materials, notify the workers to evacuate quickly through the communication module, otherwise calculate the temperature radiated from the fire point to the surrounding flammable materials;

[0052] Step S32: retrieve the temperature of the ignition point, calculate the distance between the flammable material and the ignition point using the distance formula, and calculate the temperature radiated from the ignition point to the surrounding flammable materials using the formula Where Q represents the temperature radiated from the fire point to the surrounding flammable materials, C represents the outer temperature of the fire point, L represents the distance between the fire point and the flammable material, and β represents the attenuation coefficient of the distance between the fire point and the flammable material to the radiation temperature of the fire point. If the radiation temperature Q is less than the ignition point of the flammable material, the fire point is marked as a treatable fire, and the nearest worker is notified through the communication module to use a fire extinguisher to deal with it. Otherwise, the worker is notified through the communication module to evacuate. By calculating the temperature radiated from the flammable material, it is analyzed whether the flammable material will be ignited, and a smaller fire can be quickly handled, thereby curbing the spread of the fire, reducing corporate losses, and further improving corporate safety.

[0053] Step S4 further comprises the following steps:

[0054] Step S41: When a fire occurs in a workshop, the system notifies the workers to evacuate through the communication module, retrieves the infrared sensor data, identifies the infrared thermal image, and if the temperature in the workshop is greater than the system-set threshold, marks the current workshop as inaccessible, otherwise it is marked as an alternative route, retrieves the harmful gas sensor data, identifies the concentration of harmful gases in the air, and if the harmful gas is greater than the limit threshold, marks the workshop as inaccessible, otherwise it is marked as an alternative route, retrieves the intercepted image, identifies the smoke occupancy ratio, and if the smoke occupancy ratio is greater than the limit threshold, marks the workshop as inaccessible, otherwise it is marked as an alternative route, identifies the mark of each workshop, and marks the alternative route in the park building model. If the corresponding When there is a wall between adjacent alternative routes, the communication module is used to guide the workers to the emergency shelter for shelter. Otherwise, the workers are guided to evacuate through the planned escape route. At the same time, when a fire occurs, the system obtains the positioning information of the workers in the park building in real time through the communication module, and sends the positioning information to the rescue center. By planning escape routes for workers in the event of a fire, detecting the escape routes in real time, and guiding workers to the shelter for shelter when the escape passage is impassable, it can avoid that when a fire occurs, the workers cannot find the escape passage due to excessive smoke and miss the best escape time. At the same time, it can guide workers to the shelter for self-protection when they cannot escape, thereby greatly improving the safety of the workers.

[0055] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device.

[0056] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for operating a fire warning system based on a cloud platform, characterized in that: The main steps include: Step S1: Using the monitoring module, real-time visual images of the workshop are captured; using the sensor module, the temperature of the flammable materials in the workshop is collected; and using the item comprehensive information entry module, the comprehensive information of the flammable and explosive materials in the workshop is entered into the system; Step S2: After the data is entered into the system, the system starts the fire monitoring module and begins to analyze the visual images and the temperature of the flammable materials in the workshop. Based on the analysis results, it determines whether a fire has occurred and issues an early warning. Step S3: When the system detects a fire, the system sends an electrical signal to trigger the environment detection module to start, and begins to analyze the combustion characteristics of the flammable materials at the fire point, the distance between the flammable materials around the fire point and the fire point, and determines the fire danger level and fire spread trend based on the analysis results; Step S4: After determining the fire danger level, the personnel evacuation module is activated to analyze the fire environment of each workshop in the park and plan escape routes for the workers based on the analysis results; The step S2 further comprises the following steps: Step S21: retrieve the visual image in the workshop, scan and identify the flammable material feature nodes, connect the feature nodes to build a flammable material feature model, retrieve the corresponding flammable material information in the data according to the flammable material feature model, obtain the combustion conditions and ignition point of the flammable material, retrieve the infrared thermal data corresponding to the flammable material, identify the temperature of the flammable material, and if the temperature of the flammable material is greater than the maximum threshold set by the system, mark the flammable material as having a risk of spontaneous combustion, and send the location of the flammable material to the management terminal, otherwise continue to detect; Step S22: retrieve the visual image in the workshop, set a fixed image extraction frame rate, periodically capture pictures from the visual image according to the set extraction frame rate, scan and identify the RGB color value of each pixel in the image, compare the values ​​of the three RGB channels in the pixel, select the maximum value as the grayscale value of the pixel to grayscale the image, identify the color value of each pixel after grayscale processing, calculate the difference in color values ​​between adjacent pixels, and if the difference in color value is greater than the system set threshold, mark the current adjacent pixel as an edge pixel, otherwise continue detection; Step S23: Identify the marks in the pixels, connect the edge pixels to construct the target area, compare the last frame rate screenshot, calculate the difference in color values ​​of corresponding pixels in the target area of ​​two adjacent frame rate screenshots, if the color value difference is greater than the system set threshold, mark the target area as an abnormal area, otherwise continue to detect, anchor the abnormal area in the screenshot, identify the color values ​​of the pixels in the abnormal area, calculate the average of the three channel values, if the average is greater than the maximum threshold, mark the pixel as a retained pixel, otherwise mark it as a discarded pixel, identify the marks of the pixels in the abnormal area, connect the retained pixels to construct a flame model, compare the database, if the similarity is greater than the system set threshold, mark the target area as a fire, otherwise the system continues to detect; Step S24: retrieve the grayscaled captured image, anchor the floating particles in the image, analyze the number of pixels in the fine particles, and determine whether smoke is present based on the analysis results; In the step S24, the captured image after grayscale processing is retrieved, the color value of the pixel is identified, the color value difference between the current pixel and the upper, lower, left and right pixels is calculated respectively, the current pixel is added with the color value difference between the upper, lower, left and right pixels respectively, and new images are respectively constructed, the images are overlapped and fused, the color value of the pixel of the fused image is identified, the color value difference between adjacent pixels is calculated, if the difference is greater than a threshold, the pixel is marked, otherwise the detection is continued, the marked pixels are connected clockwise to construct a floating particle model, the number of pixels in the floating particle model is identified, if the number of pixels is greater than a maximum threshold, the floating particles are marked as dust, otherwise the floating particles are marked as smoke, the total number M of floating particles in the current image is identified, and the smoke occupancy ratio in the image is calculated by a formula In the formula, P represents the smoke occupancy ratio in the image, that is, the proportion of smoke in the floating particles, m represents the amount of smoke in the image, and α represents the error coefficient of the fine dust in the air to the smoke occupancy ratio. If the smoke occupancy ratio in the image is greater than the maximum threshold, it is marked that a fire has occurred in the current workshop, otherwise the detection will continue; The step S3 further comprises the following steps: Step S31: Anchor the fire point, identify the characteristics of the flammable materials around the fire point, retrieve the corresponding combustion characteristics and ignition points in the database according to the characteristics of the flammable materials, and if the flammable materials are explosive materials, notify the workers to evacuate quickly through the communication module, otherwise calculate the temperature radiated from the fire point to the surrounding flammable materials; Step S32: retrieve the temperature of the ignition point, calculate the distance between the flammable material and the ignition point using the distance formula, and calculate the temperature radiated from the ignition point to the surrounding flammable materials using the formula In the formula, Q represents the temperature radiated from the fire point to the surrounding flammable materials, C represents the outer temperature of the fire point, L represents the distance between the fire point and the flammable material, and β represents the attenuation coefficient of the distance between the fire point and the flammable material to the radiation temperature of the fire point. If the radiation temperature Q is less than the ignition point of the flammable material, the fire point is marked as a treatable fire, and the nearest worker is notified through the communication module to use a fire extinguisher to deal with it. Otherwise, the worker is notified through the communication module to evacuate. The step S4 further comprises the following steps: Step S41: When a fire occurs in a workshop, the system notifies the workers to evacuate through the communication module, retrieves the infrared sensor data, and identifies the infrared thermal image. If the temperature in the workshop is greater than the system-set threshold, the current workshop is marked as impassable, otherwise it is marked as an alternative route. The harmful gas sensor data is retrieved to identify the concentration of harmful gases in the air. If the harmful gas is greater than the limit threshold, the workshop is marked as impassable, otherwise it is marked as an alternative route. The captured image is retrieved to identify the smoke occupancy ratio. If the smoke occupancy ratio is greater than the limit threshold, the workshop is marked as impassable, otherwise it is marked as an alternative route. The mark of each workshop is identified, and the alternative routes are marked in the park building model. If there is a wall between adjacent alternative routes, the workers are guided to the emergency shelter for shelter through the communication module, otherwise the workers are guided to evacuate through the planned escape route. At the same time, when a fire occurs, the system obtains the positioning information of the workers in the park building in real time through the communication module, and sends the positioning information to the rescue center.

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