A fireproof electronic display screen intelligent control system

Abstract

This invention relates to the field of fire protection technology and discloses an intelligent control system for a fire-resistant electronic display screen. The system includes: a temperature monitoring module that uses a sensor array evenly distributed in an area behind the screen where the number of through-holes exceeds a preset number to collect real-time temperature data from surrounding areas; a smoke monitoring module that uses sensors installed at multiple through-hole locations behind the screen to collect real-time ambient smoke concentration; a probe monitoring module that uses high-definition probes at the screen edge or through-hole locations to acquire monitoring videos of the outside of the screen and the through-holes; a multi-source data analysis module that, based on temperature, smoke concentration, and internal and external monitoring videos, establishes a three-dimensional temperature field model, smoke diffusion trajectory, and fire source characteristic data; and an intelligent control module that, based on the above data and multi-level early warning rules, triggers a fire-fighting linkage system and simultaneously performs intelligent control of the display screen. This improves the intelligent level of fire protection for electronic display screens, enabling automatic and efficient response to fire hazards without constant human monitoring, thus enhancing management efficiency.

Description

Technical Field

[0001] This invention relates to the field of fire protection technology, and in particular to an intelligent control system for a fireproof electronic display screen. Background Technology

[0002] In modern society, electronic displays are widely used in commercial advertising, traffic signs, information dissemination, and many other fields. Especially with the development of visual culture, fully enclosed displays and immersive, fully-enclosed experience scenarios offer viewers a more immersive viewing experience. However, during operation, electronic displays may ignite due to external environmental factors, causing fires. Once a fire occurs, it not only damages the display itself but can also spread and cause larger safety incidents, posing a serious threat to life and property. Therefore, developing a fire-resistant intelligent control system for electronic displays is of great significance. This system monitors key indicators such as temperature and smoke in the surrounding environment of the electronic display in real time, and combines this with video surveillance to promptly detect potential indoor fire risks and take effective preventative and response measures. This not only protects the electronic display equipment and extends its lifespan but also greatly improves the safety of public places and various application scenarios. With increasing public awareness of safety issues, this intelligent control system has broad application prospects in various industries and is expected to become a standard technology for electronic display safety, driving the electronic display industry towards greater safety and reliability.

[0003] However, existing electronic display screen immersive space equipment has many shortcomings in its fire response methods. Regarding fire hazard monitoring, it cannot comprehensively analyze multi-source monitoring data, ultimately making it difficult to promptly trigger fire-fighting linkage systems and intelligently control electronic display screen equipment based on this key information and multi-level early warning rules. Consequently, it cannot effectively prevent and respond to indoor fire accidents.

[0004] Therefore, this invention proposes an intelligent control system for fire-resistant electronic displays. Summary of the Invention

[0005] This invention provides an intelligent control system for fire-resistant electronic displays. Based on various monitoring data, it establishes a three-dimensional spatial temperature field model, smoke diffusion trajectory, and fire source characteristic data. This allows for in-depth analysis of the spatial conditions surrounding the electronic display, integrating fragmented data into a systematic and intuitive information system to accurately locate fire hazard points and their development trends. Based on the analysis results and multi-level early warning rules, it triggers a fire-fighting linkage system, enabling rapid response to fire hazards and timely implementation of fire extinguishing and other fire-fighting measures to reduce fire losses. Simultaneously, it intelligently controls the fire-resistant electronic display, such as promptly shutting it down in the early stages of a fire to prevent electronic equipment malfunctions from exacerbating the fire risk. The overall system design improves the intelligence level of fire protection for electronic displays, eliminating the need for constant manual monitoring. It automatically and efficiently responds to fire hazards, saving labor costs and improving management efficiency. The system's comprehensive monitoring and intelligent control functions help ensure the stable and safe operation of the electronic display, extend its service life, and reduce equipment damage and economic losses caused by fires and other unexpected events.

[0006] This invention provides an intelligent control system for a fireproof electronic display screen, comprising:

[0007] The temperature monitoring module is used to collect temperature data of each surrounding area of ​​the screen in real time based on a temperature sensor array uniformly distributed in an area where the number of through holes on the back of the electronic display screen exceeds a preset number.

[0008] The smoke monitoring module is used to collect real-time smoke concentrations at multiple locations in the environment where the screen is located based on smoke sensors installed at multiple through-hole positions on the back of the screen with the sensing ports facing the airflow direction of the through-holes.

[0009] The probe monitoring module is used to acquire screen monitoring video based on a high-definition probe;

[0010] The multi-source data analysis module is used to establish a three-dimensional spatial temperature field model, smoke diffusion trajectory, and fire source characteristic data of the space where the electronic display screen is located, based on temperature data of various surrounding areas of the screen, smoke concentration in multiple locations in the environment where the screen is located, and monitoring video of the screen.

[0011] The intelligent control module is used to trigger the fire-fighting linkage system based on the three-dimensional spatial temperature field model, smoke diffusion trajectory, fire source characteristic data, and multi-level early warning rules in the space where the electronic display screen is located. At the same time, it performs intelligent control of the fire-resistant electronic display screen.

[0012] Preferably, the multi-source data analysis module includes:

[0013] The temperature field model building submodule is used to interpolate and build models of temperature data in various surrounding areas of the screen, generating a three-dimensional spatial temperature field model within the space where the electronic display screen is located.

[0014] The smoke diffusion model establishment submodule is used to generate smoke diffusion trajectories based on the smoke concentration at multiple locations around the screen.

[0015] The fire source feature analysis submodule is used to analyze fire source feature data from the screen monitoring video.

[0016] Preferably, the smoke diffusion model establishment submodule includes:

[0017] The first sorting unit is used to sort the monitoring positions of all smoke sensors according to the sorting principle of the smoke concentration in the environment where the screen is located from largest to smallest, based on the latest obtained smoke concentration data, to obtain the first position sequence.

[0018] The second sorting unit is used to calculate the concentration change trend value of each monitoring location based on the historical smoke concentration value sequence of the corresponding monitoring location monitored by each smoke sensor, and sort the monitoring locations of all smoke sensors according to the sorting principle of concentration change trend value from large to small to obtain the second location sequence.

[0019] The anomaly detection unit is used to determine the latest safety monitoring period based on the historical smoke concentration value sequence of the corresponding monitoring location detected by all smoke sensors, and to determine the earliest abnormal anchoring time and anomaly degree of each smoke sensor in the latest safety monitoring period based on all abnormal smoke concentration values ​​that appear in the historical smoke concentration value sequence of the corresponding monitoring location detected by each smoke sensor within the latest safety monitoring period.

[0020] The priority value calculation unit is used to calculate the priority value of the monitoring position of each smoke sensor based on the earliest abnormal anchoring time and abnormality degree of each smoke sensor in the latest safety monitoring cycle, the ranking value of the corresponding monitoring position in the first position sequence and the ranking value in the second position sequence.

[0021] The trajectory simulation unit is used to generate smoke diffusion trajectories based on the priority values ​​of the monitoring locations of all smoke sensors.

[0022] Preferably, the trajectory simulation unit includes:

[0023] The starting point and circular range determination subunit is used to take the monitoring position corresponding to the highest priority value among all the monitoring positions of the smoke sensors as the assumed starting point position, and construct the circular range area of ​​the false starting point position with the false starting point position as the center and the distance between the false starting point position and the monitoring position corresponding to the second highest priority value as the radius.

[0024] The first interpolation processing subunit is used to interpolate multiple interpolation positions within the circular range area when the circular range area contains monitoring positions other than the false starting position and the monitoring position corresponding to the second largest priority value. This is done based on the historical smoke concentration value sequence at the false starting position, the historical smoke concentration value sequence at the monitoring position corresponding to the second largest priority value, and the historical smoke concentration value sequences of all remaining monitoring positions within the circular range area other than the false starting position and the monitoring position corresponding to the second largest priority value, to obtain the historical smoke concentration value sequence of multiple interpolation positions within the current circular range area.

[0025] The priority value determination subunit is used to determine the current priority value of each anchoring location based on the historical smoke concentration value sequence of all interpolation locations and the historical smoke concentration value sequence of all monitoring locations.

[0026] The first trajectory fitting subunit is used to fit the smoke diffusion trajectory based on the current priority values ​​of all anchor positions.

[0027] Preferably, the first trajectory fitting subunit includes:

[0028] The starting point and circular range determination end is used to determine the latest false starting point position based on the current priority value of all anchor positions, and construct the circular range area of ​​the latest false starting point position with the latest false starting point position as the center and the distance between the latest false starting point position and the anchor position corresponding to the second largest current priority value as the radius;

[0029] In the trajectory fitting end, when the circular range of the latest false starting point position contains anchor positions other than the latest false starting point position and the anchor position corresponding to the second largest current priority value, interpolation processing and priority value calculation are performed on the circular range of the latest false starting point position based on the historical smoke concentration value sequence of the existing anchor positions to obtain the latest set of anchor positions. This process continues until the latest circular range obtained based on the latest set of anchor positions does not contain any remaining anchor positions other than the latest false starting point position and the anchor position corresponding to the second largest latest priority value. In this case, the latest false starting point position is connected to the anchor position corresponding to the second largest latest priority value as a partial smoke diffusion trajectory, and the anchor position corresponding to the second largest latest priority value is updated as the false starting point position. The next connection position of the partial smoke diffusion trajectory is then determined until all anchor positions are connected to obtain the smoke diffusion trajectory.

[0030] Preferred options also include:

[0031] The second trajectory fitting subunit is used to connect the false starting position and the monitoring position corresponding to the second highest priority value as a part of the smoke diffusion trajectory when the circular range does not contain any remaining monitoring positions other than the false starting position and the monitoring position corresponding to the second highest priority value. The monitoring position corresponding to the second highest priority value is updated to the false starting position, and the next connection position of the part of the smoke diffusion trajectory is determined until all anchor positions are connected to obtain the smoke diffusion trajectory.

[0032] Preferably, the fire source feature analysis submodule includes:

[0033] The suspected flame area filtering unit is used to identify all irregular areas enclosed by irregular edges in each video frame of the screen monitoring video based on the edge detection algorithm, and to filter out the suspected flame areas in each video frame based on the preset saturation threshold and brightness threshold.

[0034] The physical space positioning unit is used to determine the three-dimensional coordinate range of each suspected flame area in the world coordinate system based on the triangulation algorithm, which serves as the physical space range of each suspected flame area.

[0035] The real flame region screening unit is used to obtain the signal intensity of the strong absorption peak signal within the preset wavelength range in the physical space of each suspected flame region based on the multispectral fusion monitoring method, and to determine whether the corresponding suspected flame region is a real flame region based on the signal intensity of the strong absorption peak signal within the preset wavelength range in the physical space of each suspected flame region, and to use the judgment result of the corresponding suspected flame region as the judgment result of all suspected flame regions. Based on the judgment results of all suspected flame regions, all real flame regions are screened out.

[0036] The inter-frame correspondence unit is used to perform inter-frame correspondence on all real flame regions in all video frames of the screen monitoring video to obtain the real flame region sequence of all real fire sources.

[0037] The fire source feature data extraction unit is used to extract fire source feature data based on the real flame region sequence of all real fire sources.

[0038] Preferably, the fire source feature data extraction unit includes:

[0039] The physical spatial positioning subunit is used to determine the location of each real fire source based on the triangulation method and the sequence of real flame areas of each real fire source;

[0040] The flame spread direction analysis subunit is used to perform pixel motion vector tracking on the real flame region sequence of each real fire source to obtain the flame spread direction of each real fire source.

[0041] The flicker frequency analysis subunit is used to track the periodic changes in regional brightness of the real flame region sequence for each real fire source and obtain the flicker frequency of each real fire source.

[0042] The diffusion rate analysis subunit is used to perform regional area diffusion trend analysis on the real flame region sequence of each real fire source to obtain the diffusion rate of each real fire source.

[0043] The multidimensional feature aggregation subunit is used to treat the location of all real fire sources, flame spread direction, flashing frequency, and spread speed as fire source feature data.

[0044] Preferably, the intelligent control module includes:

[0045] The fire intensity level calculation submodule is used to calculate the current fire intensity level based on the three-dimensional spatial temperature field model, smoke diffusion trajectory, and fire source characteristic data within the space where the electronic display screen is located.

[0046] The fire alarm linkage triggering submodule is used to trigger the fire alarm linkage system based on the current fire intensity level.

[0047] The intelligent control submodule is used to intelligently control the fire prevention electronic display screen based on the current fire intensity level.

[0048] Preferably, the intelligent control submodule includes:

[0049] The model building unit is used to build intelligent control models for electronic displays.

[0050] The intelligent control unit is used to input the current fire level value into the intelligent control model of the electronic display screen, obtain the current intelligent control command of the fire prevention electronic display screen, and perform intelligent control of the fire prevention electronic display screen based on the current intelligent control command.

[0051] The beneficial effects of this invention compared to existing technologies are as follows: Multi-module collaborative monitoring: The temperature monitoring module uses a temperature sensor array to collect real-time temperature data from various areas surrounding the screen; the smoke monitoring module obtains smoke concentration through smoke sensors at specific locations; and the probe monitoring module obtains monitoring video using high-definition probes. This achieves multi-dimensional, all-round real-time monitoring of the environment surrounding the electronic display screen, providing a rich data foundation for accurately identifying potential fire hazards. The multi-source data analysis module establishes a three-dimensional spatial temperature field model, smoke diffusion trajectory, and fire source characteristic data based on various monitoring data. This enables in-depth analysis of the spatial conditions of the electronic display screen, integrating scattered data into systematic and intuitive information to accurately locate fire hazard points and their development trends. The intelligent control module triggers the fire-fighting linkage system based on the analysis results and multi-level early warning rules, enabling rapid response to fire hazards and timely implementation of fire-fighting measures to reduce fire losses. Simultaneously, it provides intelligent control of the fire-resistant electronic display screen, such as timely shutdown of the display screen in the early stages of a fire to prevent electronic equipment malfunctions from exacerbating the fire risk. The overall system design improves the intelligent level of fire prevention for electronic displays, eliminating the need for constant manual monitoring. It can automatically and efficiently respond to fire hazards, saving labor costs and improving management efficiency. The system's comprehensive monitoring and intelligent control functions help ensure the stable and safe operation of electronic displays, extend their service life, and reduce equipment damage and economic losses caused by accidents such as fires.

[0052] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0053] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0054] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0055] Figure 1 This is a flowchart illustrating the implementation logic of an intelligent control system for a fireproof electronic display screen in an embodiment of the present invention. Detailed Implementation

[0056] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0057] Example 1: This invention provides an intelligent control system for a fireproof electronic display screen, referencing... Figure 1 ,include:

[0058] The temperature monitoring module is used to collect temperature data of each surrounding area of ​​the screen in real time based on a temperature sensor array uniformly distributed in an area where the number of through holes on the back of the electronic display screen exceeds a preset number.

[0059] The smoke monitoring module is used to collect real-time smoke concentrations at multiple locations in the environment where the screen is located based on smoke sensors installed at multiple through-hole positions on the back of the screen with the sensing ports facing the airflow direction of the through-holes.

[0060] The probe monitoring module is used to acquire screen monitoring video based on a high-definition probe;

[0061] The multi-source data analysis module is used to establish a three-dimensional spatial temperature field model, smoke diffusion trajectory and fire source characteristic data in the space where the electronic display screen is located, based on temperature data of various surrounding areas of the screen, smoke concentration in multiple locations in the environment where the screen is located, and monitoring video of the screen.

[0062] The intelligent control module is used to trigger the fire-fighting linkage system based on the three-dimensional spatial temperature field model, smoke diffusion trajectory, fire source characteristic data, and multi-level early warning rules in the space where the electronic display screen is located. At the same time, it performs intelligent control of the fire-resistant electronic display screen.

[0063] In this embodiment, the temperature data of each peripheral area of ​​the screen is collected by the temperature monitoring module based on the temperature sensor array in the area where the number of through holes on the back of the screen exceeds a preset number, which is the real-time temperature information of each area of ​​the electronic display screen.

[0064] In this embodiment, the inner side of the screen specifically refers to the inner space adjacent to or near the power supply box of the screen. Multiple smoke sensors are installed at this location in the smoke detection module, with the sensing ports facing the direction of airflow. This is to enable timely and accurate detection of fires caused by the surrounding environment igniting the screen.

[0065] In this embodiment, the smoke concentration at multiple locations in the environment where the screen is located is information collected in real time by the smoke monitoring module using multiple sets of smoke sensors installed at specific locations on the back of the screen.

[0066] In this embodiment, the screen monitoring video is acquired by a high-definition probe installed outside the screen, providing video information about whether a fire or smoke has occurred at the electronic display screen. Analysis of these videos can reveal whether a fire has occurred at the screen, or whether any unusual human activity poses a safety threat to the display screen.

[0067] In this embodiment, the three-dimensional spatial temperature field model, smoke diffusion trajectory, and fire source characteristic data within the space where the electronic display screen is located are as follows:

[0068] The three-dimensional spatial temperature field model visually displays the temperature distribution in the space where the electronic display screen is located in three dimensions, helping to identify potential overheating areas;

[0069] The smoke diffusion trajectory shows the path of smoke propagation in space, which is helpful in predicting the source and direction of fire spread;

[0070] Fire source characteristic data includes the location of the fire source, the direction of flame spread, the flashing frequency, and the spread speed.

[0071] In this embodiment, the fire-fighting linkage system is a collection of fire-related operations triggered by the intelligent control module based on the three-dimensional spatial temperature field model, smoke diffusion trajectory, fire source characteristic data, and multi-level early warning rules within the space where the electronic display screen is located. It may include activating fire-fighting equipment (such as sprinkler systems and fire extinguishers), issuing alarms to notify personnel to evacuate, and linking with other fire-fighting facilities (such as fireproof roller shutters and smoke extraction systems). The purpose is to quickly take effective measures to control the spread of fire, reduce losses caused by the fire, and protect the lives and property of personnel.

[0072] In this embodiment, the fire alarm linkage system is triggered, and the fire prevention electronic display screen is intelligently controlled. If a fire occurs, the power is turned off, the emergency power is turned on, the emergency lights are turned on, the sprinkler system of the fire alarm linkage system is triggered to extinguish the fire at the source, and the screen on the roof opens like a door.

[0073] Example 2: Based on Example 1, the multi-source data analysis module includes:

[0074] The temperature field model building submodule is used to interpolate and build models of temperature data in various surrounding areas of the screen, generating a three-dimensional spatial temperature field model within the space where the electronic display screen is located.

[0075] The smoke diffusion model establishment submodule is used to generate smoke diffusion trajectories based on the smoke concentration at multiple locations in the environment where the screen is located.

[0076] The fire source feature analysis submodule is used to analyze fire source feature data from the screen monitoring video.

[0077] In this embodiment, interpolation is used because the temperature sensor array only collects temperature data from discrete regions of the screen, while the actual temperature in space is continuously changing. Interpolation methods allow for the estimation of temperatures at other locations between known discrete points. For example, using algorithms such as linear interpolation and spline interpolation, a series of approximate temperature values ​​at intermediate locations can be calculated between areas monitored by adjacent temperature sensors, thus providing a more comprehensive reflection of the temperature distribution across the entire back of the screen.

[0078] Model building involves integrating richer temperature data obtained through interpolation into a three-dimensional spatial temperature field model. This model visually presents the temperature conditions at various locations within the space where the electronic display screen is located in three dimensions, allowing relevant personnel to clearly see the temperature distribution and whether there are areas of abnormal temperature increases. For example, in the model, high-temperature areas may be represented in red, and low-temperature areas in blue. Through the visualization of different colors and heights, the temperature distribution becomes immediately apparent.

[0079] The beneficial effects of the above technologies are as follows: The temperature field modeling submodule interpolates and models temperature data to generate a three-dimensional spatial temperature field model, clearly presenting the temperature distribution and providing early warning of fire risks caused by overheating. The smoke diffusion modeling submodule generates diffusion trajectories based on smoke concentration, helping to determine the location and direction of fire spread, facilitating early prevention and control. The fire source characteristic analysis submodule acquires fire source characteristic data from monitoring videos, providing a basis for accurately triggering the fire-fighting linkage system and improving fire extinguishing efficiency. The detailed module breakdown makes data analysis more professional and efficient, enhancing the accuracy and reliability of judgments. Simultaneously, it improves system maintainability and scalability, facilitating long-term optimization and upgrades.

[0080] Example 3: Based on Example 2, a smoke diffusion model submodule is established, including:

[0081] The first sorting unit is used to sort the monitoring positions of all smoke sensors according to the sorting principle of multiple smoke concentrations in the environment where the screen is located from largest to smallest, based on the latest obtained smoke concentration data, to obtain the first position sequence.

[0082] The second sorting unit is used to calculate the concentration change trend value of each monitoring location based on the historical smoke concentration value sequence of the corresponding monitoring location monitored by each smoke sensor, and sort the monitoring locations of all smoke sensors according to the sorting principle of concentration change trend value from large to small to obtain the second location sequence.

[0083] The anomaly detection unit is used to determine the latest safety monitoring period based on the historical smoke concentration value sequence of the corresponding monitoring location detected by all smoke sensors, and to determine the earliest abnormal anchoring time and anomaly degree of each smoke sensor in the latest safety monitoring period based on all abnormal smoke concentration values ​​that appear in the historical smoke concentration value sequence of the corresponding monitoring location detected by each smoke sensor within the latest safety monitoring period.

[0084] The priority value calculation unit is used to calculate the priority value of the monitoring position of each smoke sensor based on the earliest abnormal anchoring time and abnormality degree of each smoke sensor in the latest safety monitoring cycle, the ranking value of the corresponding monitoring position in the first position sequence and the ranking value in the second position sequence.

[0085] The trajectory simulation unit is used to generate smoke diffusion trajectories based on the priority values ​​of the monitoring locations of all smoke sensors.

[0086] In this embodiment, based on the historical smoke concentration value sequence of the corresponding monitoring location monitored by each smoke sensor, the concentration change trend value of each monitoring location is calculated. For example, a common method is to calculate the slope between adjacent data points. Assume that the historical smoke concentration value sequence is C1, C2, C3, ..., Cn, and the corresponding time points are t1, t2, t3, ..., tn. Taking a linear change trend as an example, two adjacent data points (ti, Ci) and (ti+1, Ci+1) in the sequence can be selected, and the average rate of change of smoke concentration between these two time points can be calculated using the formula (Ci+1−Ci) / (ti+1−ti), which is used as an approximation of the concentration change trend value within this time period.

[0087] If the calculated concentration change trend value is positive and the value is large, it indicates that the smoke concentration at the corresponding monitoring location is rising rapidly during that time period, which may suggest that the fire is developing; if the concentration change trend value is negative, it indicates that the smoke concentration is decreasing, which may indicate that the fire is under control or the smoke is dissipating.

[0088] In this embodiment, the monitoring location refers to the specific point where the smoke sensor is placed.

[0089] In this embodiment, the historical smoke concentration value sequence is a sequence of smoke concentration data continuously monitored and recorded by each smoke sensor over a period of time.

[0090] In this embodiment, the latest safety monitoring cycle is determined based on the historical smoke concentration value sequence of the corresponding monitoring locations detected by all smoke sensors. This is done by analyzing the historical smoke concentration value sequence to find a period of time where the smoke concentration fluctuation is small and within the normal range, and then setting this period as the latest safety monitoring cycle.

[0091] In this embodiment, an abnormal smoke concentration value refers to a value detected by the smoke sensor that significantly deviates from the normal smoke concentration range within the latest safety monitoring cycle. The appearance of these abnormal values ​​may indicate a potential fire, as fires typically cause drastic changes in smoke concentration. For example, if the normal smoke concentration range at a monitoring location is between 0 and 5 units within the latest safety monitoring cycle, and a smoke concentration of 15 is suddenly detected, then the value 15 can be considered an abnormal smoke concentration value.

[0092] In this embodiment, based on all abnormal smoke concentration values ​​appearing in the latest safety monitoring period from the historical smoke concentration value sequence of the corresponding monitoring location monitored by each smoke sensor, the earliest abnormal anchoring time and abnormality level of each smoke sensor in the latest safety monitoring period are determined:

[0093] The earliest abnormal anchoring time indicates the time point at which an abnormal smoke concentration value first appears within the latest safety monitoring cycle;

[0094] The degree of abnormality is determined by calculating the difference between all abnormal smoke concentration values ​​and the upper limit of the normal range, or the average of their proportional relationships with the mean of the normal range.

[0095] In this embodiment, based on the earliest abnormal anchoring time and abnormality level of each smoke sensor in the latest safety monitoring cycle, the ranking value of the corresponding monitoring position in the first position sequence and the ranking value in the second position sequence, the priority value of the monitoring position of each smoke sensor is calculated:

[0096] The priority value of each smoke sensor's monitoring position is determined by the product of the ratio of the difference between the earliest abnormal anchoring time and the current time in the latest safety monitoring cycle and the duration of the latest safety monitoring cycle, the degree of abnormality, the ratio of the maximum ranking value in the first position sequence to the ranking value of the corresponding monitoring position in the first position sequence, and the ratio of the maximum ranking value in the second position sequence to the ranking value in the second position sequence.

[0097] The beneficial effects of the above technologies are as follows: The first sorting unit ranks the smoke sensor monitoring locations according to the latest smoke concentration, quickly locating areas with high smoke concentration and providing initial clues for determining the smoke source. The second sorting unit calculates and ranks the concentration change trends, clearly presenting the smoke concentration change patterns at each location and helping to capture areas of rapid smoke diffusion. The anomaly judgment unit calibrates the latest safety monitoring cycle and determines the earliest anomaly anchoring time and anomaly severity, helping to accurately analyze the onset time and severity of smoke anomalies, providing key time nodes and severity information for prevention and control. The priority value calculation unit calculates the priority value for each monitoring location by integrating multiple data points, comprehensively considering the impact of various factors on smoke diffusion, making the assessment of the importance of each location in smoke diffusion more accurate. The trajectory simulation unit generates a smoke diffusion trajectory based on the priority value, more accurately and intuitively displaying the smoke diffusion path, providing a more reliable basis for fire-fighting coordination and personnel evacuation, and greatly improving the comprehensiveness, accuracy, and practicality of the system's smoke diffusion analysis.

[0098] Example 4: Based on Example 3, the trajectory simulation unit includes:

[0099] The starting point and circular range determination subunit is used to take the monitoring position corresponding to the highest priority value among all the monitoring positions of the smoke sensors as the assumed starting point position, and construct the circular range area of ​​the false starting point position with the false starting point position as the center and the distance between the false starting point position and the monitoring position corresponding to the second highest priority value as the radius.

[0100] The first interpolation processing subunit is used to interpolate multiple interpolation positions within the circular range area when the circular range area contains monitoring positions other than the false starting position and the monitoring position corresponding to the second largest priority value. This is done based on the historical smoke concentration value sequence at the false starting position, the historical smoke concentration value sequence at the monitoring position corresponding to the second largest priority value, and the historical smoke concentration value sequences of all remaining monitoring positions within the circular range area other than the false starting position and the monitoring position corresponding to the second largest priority value, to obtain the historical smoke concentration value sequence of multiple interpolation positions within the current circular range area.

[0101] The priority value determination subunit is used to determine the current priority value of each anchoring location based on the historical smoke concentration value sequence of all interpolation locations and the historical smoke concentration value sequence of all monitoring locations.

[0102] The first trajectory fitting subunit is used to fit the smoke diffusion trajectory based on the current priority values ​​of all anchor positions.

[0103] In this embodiment, interpolation processing is performed on multiple interpolation locations within the circular range based on the historical smoke concentration value sequence at the spurious starting point, the historical smoke concentration value sequence at the monitoring location corresponding to the second highest priority value, and the historical smoke concentration value sequences of all remaining monitoring locations within the circular range (excluding the spurious starting point and the monitoring location corresponding to the second highest priority value). This yields the historical smoke concentration value sequence for multiple interpolation locations within the current circular range. After determining a circular range with the spurious starting point as the center and a specific interval as the radius, since there may be other monitoring locations within this range besides the spurious starting point and the monitoring location corresponding to the second highest priority value, these discrete monitoring locations cannot fully reflect the continuous changes in smoke concentration within the range. Therefore, the above three related historical smoke concentration value sequences are used to interpolate multiple interpolation locations within the circular range. In this way, the smoke concentration at these interpolation locations at different points in the past period can be inferred, thereby obtaining the historical smoke concentration value sequence for multiple interpolation locations within the current circular range. For example, assuming that the smoke concentrations at a certain moment are A, B, C, etc., at the false starting point, the monitoring position corresponding to the second highest priority value, and other remaining monitoring positions, the smoke concentration at that moment at the interpolation position within the circle is calculated using a specific interpolation algorithm (such as linear interpolation, spline interpolation, etc.), forming a data point in the historical smoke concentration value sequence of that interpolation position. Repeating this process yields historical smoke concentration value sequences for multiple interpolation positions.

[0104] In this embodiment, based on the historical smoke concentration value sequences of all interpolation locations and the historical smoke concentration value sequences of all monitoring locations, the current priority value of each anchoring location is determined:

[0105] Anchoring locations include interpolated locations within the circular area and the original monitoring locations. By comprehensively considering the historical smoke concentration sequences of all interpolated and monitoring locations, and analyzing these sequences in conjunction with factors previously used to determine priority values ​​(such as the earliest abnormal anchoring time, the degree of abnormality, and the ranking value in different location sequences), the current priority value of each anchoring location is re-determined. This means that, with the inclusion of richer data after interpolation, the importance of each location in the smoke diffusion process is reassessed. For example, the priority value of a monitoring location might have been determined solely based on its own monitoring data, but after incorporating data from interpolated locations, it might be found that the monitoring location has a close relationship with the smoke concentration changes of surrounding interpolated locations, and its current priority value may need to be adjusted.

[0106] The beneficial effects of the above technologies are as follows: The starting point and circular range determination subunit, by using the monitoring position corresponding to the highest priority value as the assumed starting point and constructing a circular range area according to specific rules, delineates the key range for subsequent analysis, enabling focus on the core area of ​​smoke diffusion, making the analysis more targeted and improving efficiency. When the circular range area includes other monitoring positions, the first interpolation processing subunit performs interpolation processing to obtain historical smoke concentration value sequences for multiple interpolation positions, effectively filling data gaps within the area, enriching data details, and making the analysis of smoke concentration changes in the area more accurate. The priority value determination subunit combines the historical smoke concentration value sequences of the interpolation positions and monitoring positions to determine the current priority value of each anchored position, comprehensively considering multiple data points, making the assessment of the importance of each position in smoke diffusion more comprehensive and reasonable. The first trajectory fitting subunit fits the smoke diffusion trajectory based on the current priority values ​​of all anchor positions. By utilizing the optimized priority value information, it can present a smoke diffusion path that is more in line with the actual situation, providing a more reliable and accurate reference for fire-fighting decisions and personnel evacuation planning. This significantly improves the accuracy and practicality of the system's smoke diffusion trajectory simulation, and further enhances the ability of the entire fire-prevention electronic display intelligent control system to cope with fire hazards.

[0107] Example 5: Based on Example 4, the first trajectory fitting subunit includes:

[0108] The starting point and circular range determination end is used to determine the latest false starting point position based on the current priority value of all anchor positions, and construct the circular range area of ​​the latest false starting point position with the latest false starting point position as the center and the distance between the latest false starting point position and the anchor position corresponding to the second largest current priority value as the radius;

[0109] In the trajectory fitting end, when the circular range of the latest false starting point position contains anchor positions other than the latest false starting point position and the anchor position corresponding to the second largest current priority value, interpolation processing and priority value calculation are performed on the circular range of the latest false starting point position based on the historical smoke concentration value sequence of the existing anchor positions to obtain the latest set of anchor positions. This process continues until the latest circular range obtained based on the latest set of anchor positions does not contain any remaining anchor positions other than the latest false starting point position and the anchor position corresponding to the second largest latest priority value. In this case, the latest false starting point position is connected to the anchor position corresponding to the second largest latest priority value as a partial smoke diffusion trajectory, and the anchor position corresponding to the second largest latest priority value is updated as the false starting point position. The next connection position of the partial smoke diffusion trajectory is then determined until all anchor positions are connected to obtain the smoke diffusion trajectory.

[0110] In this embodiment, the latest pseudo-starting point position is determined based on the current priority values ​​of all anchor positions. This is to continuously update the starting point of the smoke diffusion trajectory simulation. By comparing the current priority values ​​of all anchor positions (including previously determined interpolation positions and monitoring positions), the anchor position with the highest current priority value is selected as the latest pseudo-starting point position.

[0111] In this embodiment, interpolation and priority value calculation are performed on the circular region of the latest pseudo-starting point based on the historical smoke concentration value sequence of existing anchoring positions to obtain the latest set of anchoring positions. After determining the latest pseudo-starting point, a circular region is constructed with this position as the center and the distance between it and the anchoring position corresponding to the second largest current priority value as the radius. Then, using the historical smoke concentration value sequence of existing anchoring positions, interpolation is performed on this circular region. Similar to the previous processing of other circular regions, the smoke concentration of more locations within the circular region is estimated through the interpolation algorithm, resulting in new interpolated positions and their historical smoke concentration value sequences. Next, based on these new interpolated positions and the original anchoring positions, the priority value is recalculated by comprehensively considering various factors (such as abnormal situations, concentration change trends, etc.) to obtain the latest set of anchoring positions. This process continuously refines and updates the information of smoke diffusion-related locations, making the simulation of smoke diffusion more accurate. For example, within a new circular area, smoke concentration information for multiple new locations is obtained through interpolation. By combining the relationship between these locations and other anchor locations, as well as the changes in smoke concentration, their priority values ​​are calculated, thereby determining the latest set of anchor locations.

[0112] In this embodiment, a condition is that the latest circular range obtained based on the latest set of anchor positions does not contain any remaining anchor positions other than the latest pseudo-starting point position and the anchor position corresponding to the second largest latest priority value. When only the latest pseudo-starting point position and the anchor position corresponding to the second largest latest priority value remain within the constructed latest circular range, and there are no other remaining anchor positions, it indicates that the construction of the smoke diffusion trajectory within this local area has reached a relatively complete state at the current stage, and the next connection operation can be performed. This condition helps determine when to stop interpolation and priority value calculation operations within this circular range and enter the trajectory connection stage to gradually complete the construction of the entire smoke diffusion trajectory.

[0113] In this embodiment, the anchor position corresponding to the second largest latest priority value is updated as a pseudo-starting point position, and the next connection position for part of the smoke diffusion trajectory is determined until all anchor positions are connected to obtain the smoke diffusion trajectory. When the above judgment condition is met, the anchor position corresponding to the second largest latest priority value is set as a new pseudo-starting point position, and then the next anchor position connected to this position is determined as the next connection position for part of the smoke diffusion trajectory. In this way, the operation is repeated continuously, each time constructing a circular range area with the newly determined pseudo-starting point position, performing interpolation processing, priority value calculation, judgment conditions, and connection operations, until all anchor positions are connected, finally forming a complete smoke diffusion trajectory. This process is progressive, from local to global, accurately depicting the diffusion path of smoke in space, representing the approximate trajectory of smoke diffusion, and intuitively showing the possible direction and range of smoke propagation.

[0114] The beneficial effects of the above technology are as follows: The starting point and circular range determination end re-determines the latest pseudo-starting point position and constructs the circular range area based on the current priority values ​​of all anchor positions. This dynamic updating of the starting point and range allows for continuous adjustment of the analysis focus area based on real-time data, better tracking the core path of smoke diffusion and making the smoke diffusion trajectory simulation more closely reflect actual dynamic changes. In the trajectory fitting end, when other anchor positions exist within the circular range area, interpolation processing and priority value calculation are performed to continuously optimize the anchor position set and refine trajectory details, ensuring a more accurate smoke diffusion trajectory and providing more accurate information for judging the specific direction of smoke diffusion. Through continuous iteration until all anchor positions are connected to obtain a complete smoke diffusion trajectory, this gradual refinement and improvement makes the generation process of the smoke diffusion trajectory more rigorous, fully considering the relationships between various positions and the complexities of the smoke diffusion process. This series of operations effectively improves the accuracy and completeness of the smoke diffusion trajectory simulation, providing a more reliable basis for firefighters to formulate scientific and reasonable response strategies, helping to improve the efficiency and effectiveness of fire prevention and control, and enhancing the comprehensiveness and depth of the entire intelligent control system for fire prevention electronic displays in analyzing fire hazards.

[0115] Example 6: Based on Example 1, it also includes:

[0116] The second trajectory fitting subunit is used to connect the false starting position and the monitoring position corresponding to the second highest priority value as a part of the smoke diffusion trajectory when the circular range does not contain any remaining monitoring positions other than the false starting position and the monitoring position corresponding to the second highest priority value. The monitoring position corresponding to the second highest priority value is updated to the false starting position, and the next connection position of the part of the smoke diffusion trajectory is determined until all anchor positions are connected to obtain the smoke diffusion trajectory.

[0117] In this embodiment, the monitoring position corresponding to the second highest priority value is updated as a pseudo-starting point position, and the next connection position for part of the smoke diffusion trajectory is determined until all anchor positions are connected to obtain the smoke diffusion trajectory. During the smoke diffusion trajectory simulation, this operation is performed when the circular area determined in a certain step does not contain any remaining monitoring positions other than the pseudo-starting point position and the monitoring position corresponding to the second highest priority value. The monitoring position corresponding to the second highest priority value is updated as a pseudo-starting point position because it has a high priority in the current stage of the smoke diffusion simulation and may be an important node in the smoke diffusion. For example, smoke may diffuse from a previous pseudo-starting point position to this position and then continue to diffuse from this position as a new "center." Determining the next connection position for part of the smoke diffusion trajectory requires certain rules or algorithms; for example, based on the priority values ​​of surrounding anchor positions, the distance from the current pseudo-starting point position, and the trend of smoke concentration changes. For example, the anchor position closest to the current pseudo-starting point position and with a high priority value is selected as the next connection position. By repeatedly performing this operation—connecting the updated pseudo-starting point to the next connection point each time to form a partial smoke diffusion trajectory, and then using the newly connected position as the pseudo-starting point for the next round—the search for the next connection point continues. This cycle continues until all anchoring positions are connected, ultimately yielding the complete smoke diffusion trajectory.

[0118] The beneficial effects of the above technology are as follows: When there are no other redundant monitoring positions within the circular area, it can quickly connect the dummy starting point position with the monitoring position corresponding to the second highest priority value to form a partial smoke diffusion trajectory. This allows the system to quickly construct the basic framework of the smoke diffusion trajectory under specific conditions, avoiding interruptions in trajectory generation due to data distribution. By updating the monitoring position corresponding to the second highest priority value to the dummy starting point position and continuously determining the next connection position, all anchor positions are gradually connected to obtain a complete trajectory. This coherent processing method ensures the integrity of the smoke diffusion trajectory generation, ensuring that the entire smoke diffusion process is presented reasonably. This helps operators fully understand the smoke diffusion path and obtain reliable trajectory information even when the data distribution is relatively simple. Moreover, this unit works in conjunction with other related modules, enriching the response strategies for smoke diffusion trajectory generation, improving the system's adaptability to different data conditions, and providing more comprehensive and flexible support for subsequent decisions based on smoke diffusion trajectories, such as fire rescue deployment and personnel evacuation planning. This enhances the overall ability of the intelligent control system for fire prevention electronic displays to cope with various scenarios.

[0119] Example 7: Based on Example 2, the fire source feature analysis submodule includes:

[0120] The suspected flame area filtering unit is used to identify all irregular areas enclosed by irregular edges in each video frame of the screen monitoring video based on the edge detection algorithm, and to filter out the suspected flame areas in each video frame based on the preset saturation threshold and brightness threshold.

[0121] The physical space positioning unit is used to determine the three-dimensional coordinate range of each suspected flame area in the world coordinate system based on the triangulation algorithm, which serves as the physical space range of each suspected flame area.

[0122] The real flame region screening unit is used to obtain the signal intensity of the strong absorption peak signal within the preset wavelength range in the physical space of each suspected flame region based on the multispectral fusion monitoring method, and to determine whether the corresponding suspected flame region is a real flame region based on the signal intensity of the strong absorption peak signal within the preset wavelength range in the physical space of each suspected flame region, and to use the judgment result of the corresponding suspected flame region as the judgment result of all suspected flame regions. Based on the judgment results of all suspected flame regions, all real flame regions are screened out.

[0123] The inter-frame correspondence unit is used to perform inter-frame correspondence on all real flame regions in all video frames of the screen monitoring video to obtain the real flame region sequence of all real fire sources.

[0124] The fire source feature data extraction unit is used to extract fire source feature data based on the real flame region sequence of all real fire sources.

[0125] In this embodiment, the edge detection algorithm is, for example, the Canny edge detection operator.

[0126] In this embodiment, suspected flame regions in each video frame are filtered out from all irregular regions based on preset saturation and brightness thresholds. This further filters the irregular regions identified by the edge detection algorithm. Flames typically exhibit high saturation and brightness characteristics, and the preset saturation and brightness thresholds are numerical standards set based on these typical characteristics of flames. For each irregular region obtained by the edge detection algorithm, the system calculates its average saturation and brightness values ​​and compares these values ​​with the preset thresholds. If the average saturation of an irregular region is greater than the saturation threshold and the average brightness is greater than the brightness threshold, then the region is considered to have some characteristics of a flame and is thus filtered out as a suspected flame region. For example, assuming the preset saturation threshold is 0.8 and the brightness threshold is 150, when the average saturation of an irregular region is 0.85 and the average brightness is 160, it will be identified as a suspected flame region. This filtering method can effectively exclude some non-flame irregular regions, such as the edge regions of ordinary objects, further focusing on possible flame regions and improving the accuracy of flame detection.

[0127] In this embodiment, a triangulation algorithm is used to determine the three-dimensional coordinate range of each suspected flame area in the world coordinate system. The triangulation algorithm utilizes the principles of geometric trigonometry to determine the spatial location of an object. In this system, monitoring video is acquired through at least two cameras with different perspectives (e.g., high-definition cameras at the edge of a screen and at perforated locations). For each suspected flame area, the triangulation algorithm is used to calculate the three-dimensional coordinate range of that area in the world coordinate system based on the camera's position and angle, as well as the location information of the suspected flame area in the image. This allows for accurate identification of the specific physical location of suspected flames, providing crucial information for the precise implementation of subsequent firefighting measures. For example, it helps firefighters quickly locate the source of a fire or accurately guides firefighting equipment towards the fire source in an automatic fire suppression system.

[0128] In this embodiment, a multispectral fusion monitoring method is used to acquire the signal intensity of strong absorption peak signals within a preset wavelength range in the physical space of each suspected flame region. This method comprehensively utilizes information from multiple different spectral bands for monitoring. Flames produce strong absorption peak signals within certain specific wavelength ranges. By analyzing the signal intensity within these wavelength ranges, the authenticity of the flame can be further confirmed. The system uses corresponding spectral sensors to acquire spectral information within a preset wavelength range in the physical space of each suspected flame region, then analyzes this spectral data to identify strong absorption peak signals and measures their signal intensity. For example, for certain types of flames, there will be obvious strong absorption peaks at certain wavelengths in the infrared band. By detecting the signal intensity at these wavelengths, more accurate information about the flame characteristics can be obtained, providing a stronger basis for determining whether the suspected flame region is a real flame.

[0129] In this embodiment, the preset wavelength range is, for example, 200-280nm.

[0130] In this embodiment, the final confirmation step for a suspected flame region is to determine whether it is a real flame region based on the signal intensity of the strong absorption peak signal within a preset wavelength range in the physical space of each suspected flame region. Since a real flame produces a strong absorption peak signal with specific intensity characteristics within the preset wavelength range, the intensity of the obtained strong absorption peak signal can be compared with a pre-set standard. If the signal intensity is within the expected range and matches the characteristics of a real flame within that wavelength range, then the suspected flame region is likely a real flame region; conversely, if the signal intensity differs significantly from the standard, it is determined to be a non-real flame region (possibly a false fire source such as a light or reflector). For example, the strong absorption peak signal intensity of a real flame within the preset wavelength range is typically between 80 and 120 units. If the signal intensity detected in a suspected flame region within this wavelength range is 100 units, then it can be preliminarily determined that the region is a real flame region.

[0131] In this embodiment, inter-frame correspondence is performed on all real flame regions in all video frames of the screen monitoring video to obtain a sequence of real flame regions for all real fire sources. This step is to continuously track the changes of real flames over time. In different video frames, the position and shape of the flames may differ due to dynamic changes. Through inter-frame correspondence technology, the system can match and associate real flame regions in adjacent video frames to determine whether they originate from the same real fire source. For example, image feature matching algorithms (such as SIFT, SURF, etc.) are used to compare the shape, texture, position, and other features of real flame regions in different frames, identifying regions with similar features as manifestations of the same fire source at different times. By processing the real flame regions in all video frames in this way, a sequence of real flame regions for all real fire sources can be obtained. This sequence records the flame region information of each real fire source at different points in time, demonstrating the development process of the flame, such as its spread and flickering dynamic changes. It provides a continuous information basis for analyzing fire source characteristic data (such as flame spread direction, flickering frequency, and spread speed), helping to more comprehensively understand the development trend of a fire and providing more detailed and accurate information for fire prevention decisions.

[0132] The beneficial effects of the above technologies are as follows: The suspected flame area screening unit identifies irregular areas using edge detection algorithms and filters suspected flame areas by combining preset saturation and brightness thresholds, narrowing the analysis range, improving processing efficiency, avoiding interference from a large number of irrelevant areas, and quickly focusing on potential fire source areas. The physical space positioning unit uses triangulation algorithms to determine the three-dimensional coordinate range of suspected flame areas in the world coordinate system, i.e., the physical space range, accurately locating the spatial position of the fire source, providing key information for firefighters to quickly reach the fire source, and greatly improving the accuracy and timeliness of rescue operations. The real flame area screening unit uses a multispectral fusion monitoring method to judge the authenticity of flames based on the intensity of strong absorption peak signals within a preset wavelength range, effectively reducing misjudgments, accurately identifying real flame areas, providing a reliable basis for subsequent decision-making, and avoiding resource waste or delays in rescue due to misjudgments. The inter-frame correspondence unit performs inter-frame correspondence of real flame areas in each video frame to form a sequence of real flame areas, continuously presenting the dynamic changes of the fire source, enabling analysts to grasp the development trend of the fire source, and providing strong support for planning response strategies in advance. The fire source characteristic data extraction unit extracts data based on the flame area sequence, providing rich key data for triggering the fire linkage system. This helps to accurately formulate fire extinguishing plans, improve the targeting and efficiency of fire extinguishing operations, comprehensively enhance the fire protection system's ability to analyze and respond to fire sources, and effectively protect the safety of people's lives and property.

[0133] Example 8: Based on Example 7, the fire source feature data extraction unit includes:

[0134] The physical spatial positioning subunit is used to determine the location of each real fire source based on the triangulation method and the sequence of real flame areas of each real fire source;

[0135] The flame spread direction analysis subunit is used to perform pixel motion vector tracking on the real flame region sequence of each real fire source to obtain the flame spread direction of each real fire source.

[0136] The flicker frequency analysis subunit is used to track the periodic changes in regional brightness of the real flame region sequence for each real fire source and obtain the flicker frequency of each real fire source.

[0137] The diffusion rate analysis subunit is used to perform regional area diffusion trend analysis on the real flame region sequence of each real fire source to obtain the diffusion rate of each real fire source.

[0138] The multidimensional feature aggregation subunit is used to treat the location of all real fire sources, flame spread direction, flashing frequency, and spread speed as fire source feature data.

[0139] In this embodiment, the location of each real fire source is determined based on triangulation and a sequence of real flame areas. Triangulation utilizes geometric principles and video information acquired from different angles by multiple cameras (such as high-definition probes at the screen edge and perforation locations). The sequence of real flame areas contains information about the flame area of ​​each real fire source in different video frames. Based on the camera positions and angles, and the pixel positions of the real flame areas in each video frame, the system can accurately determine the three-dimensional coordinates of the real fire source in actual space, i.e., its location, through triangulation calculations. For example, just as a target point is determined by the intersection of rays from different directions in a space, by capturing the flame area from different camera perspectives, the specific location of the fire source near the electronic display screen can be clearly identified through triangulation calculations. This is crucial for firefighters to quickly locate and extinguish the fire.

[0140] In this embodiment, pixel motion vector tracking is performed on the sequence of real flame areas for each real fire source to obtain the flame spread direction of each real fire source. This process is achieved by analyzing the movement of pixels in the flame area within each video frame of the real flame area sequence. The system tracks the displacement of pixels within the flame area between adjacent video frames and calculates the pixel motion vector. The combined direction of a large number of pixel motion vectors represents the flame spread direction. For example, if most pixels within the flame area move towards the upper right, then the flame spread direction is upper right. Accurately obtaining the flame spread direction helps in advance planning evacuation routes, deploying fire extinguishing equipment, and preventing personnel and equipment from being in the path of flame spread.

[0141] In this embodiment, the periodic changes in brightness of the actual flame region sequence for each real fire source are tracked to obtain the flicker frequency of each real fire source. Flame flickering manifests as periodic changes in regional brightness. The system monitors the brightness of the flame region in each video frame of the real flame region sequence and records the brightness changes. By analyzing the brightness fluctuations over time, the time interval between the peak and the next peak is determined, thereby calculating the flicker frequency. For example, if the flame region brightness peaks every 0.5 seconds, the flicker frequency is 2 times / second. The flicker frequency can serve as a reference for judging the nature and intensity of the fire source; different types of fire sources may have different flicker frequency characteristics.

[0142] In this embodiment, the system analyzes the diffusion trend of the actual flame region sequence for each real fire source to obtain the diffusion rate of each real fire source. The system measures the area of ​​the flame region in each video frame of the actual flame region sequence and compares the changes in the flame region area across different video frames. The diffusion rate is obtained by calculating the increase in the flame region area per unit time. For example, if the flame region area increases from 100 square pixels to 150 square pixels in 1 second, then the diffusion rate is 50 square pixels per second. Understanding the diffusion rate helps assess the speed of fire development, allowing for timely adjustments to firefighting strategies and the rational allocation of resources to respond to the fire.

[0143] The beneficial effects of the above technologies are as follows: The physical spatial positioning subunit uses triangulation combined with a sequence of real flame areas to determine the actual location of the fire source, providing accurate fire source location information for fire rescue, enabling rescue forces to quickly and accurately reach the fire point and improving rescue efficiency. The flame spread direction analysis subunit tracks the sequence of real flame areas through pixel motion vectors to obtain the flame spread direction, helping firefighters to predict the flame spread trend in advance, rationally plan evacuation routes and deploy firefighting forces, and effectively reduce the losses caused by the fire. The flicker frequency analysis subunit tracks the periodic changes in regional brightness to obtain the flicker frequency of the real fire source. This feature can help determine the type of fire source, the size of the fire, etc., providing a basis for formulating targeted firefighting strategies. The spread speed analysis subunit analyzes the spread trend of the regional area to obtain the real fire source spread speed, enabling fire departments to promptly grasp the speed of fire development, thereby rationally allocating resources and taking more effective firefighting measures. The multi-dimensional feature aggregation subunit summarizes the location of the fire, the direction of flame spread, the flashing frequency, and the spread speed into fire source feature data, forming a comprehensive and systematic set of fire source information. This provides rich and accurate data support for the comprehensive decision-making of the intelligent control system for fire prevention electronic displays, and comprehensively improves the system's ability to respond to fires and the scientific nature of its decision-making.

[0144] Example 9: Based on Example 1, the intelligent control module includes:

[0145] The fire intensity level calculation submodule is used to calculate the current fire intensity level based on the three-dimensional spatial temperature field model, smoke diffusion trajectory, and fire source characteristic data within the space where the electronic display screen is located.

[0146] The fire alarm linkage triggering submodule is used to trigger the fire alarm linkage system based on the current fire intensity level.

[0147] The intelligent control submodule is used to intelligently control the fire prevention electronic display screen based on the current fire intensity level.

[0148] In this embodiment, the current fire intensity level is calculated based on the three-dimensional spatial temperature field model, smoke diffusion trajectory, and fire source characteristic data within the space where the electronic display screen is located. For example, by setting specific algorithms and weights, parameters such as the area of ​​the high-temperature region, the smoke diffusion rate, and the flame diffusion rate are calculated to obtain a value representing the current severity of the fire, i.e., the current fire intensity level.

[0149] First, determine the parameters involved in the calculation and their meanings:

[0150] Area of ​​high-temperature region (A): Obtained from the three-dimensional temperature field model, in square meters. It is assumed that the high-temperature region is defined as the area where the temperature exceeds a certain set threshold (e.g., 80℃).

[0151] Smoke diffusion velocity (Vsmoke): Determined by analyzing the smoke diffusion trajectory, measured in meters per minute.

[0152] Flame spread rate (Vflame): Extracted from fire source characteristic data, in meters per minute.

[0153] Flame spread direction consistency (D): Analyzes the flame spread direction and calculates the degree of consistency in each direction. For example, if most of the flame spreads in the same direction, the consistency is high; if the flame spreads in a chaotic manner, the consistency is low. The value range is set to [0,1], where 0 represents no inconsistency and 1 represents complete consistency.

[0154] Flame flicker frequency (Fflicker): Obtained from fire source characteristic data, measured in flickers per second.

[0155] Then, set specific algorithms and weights:

[0156] Weighting of high-temperature area: wA=0.3, weighting of smoke diffusion speed: wsmoke=0.2, weighting of flame diffusion speed: wflame=0.2, weighting of flame diffusion direction consistency: wD=0.15, weighting of flame flicker frequency: wflicker=0.15;

[0157] These weights are set based on the relative importance of each parameter in assessing the severity of the fire, and can be adjusted in practical applications based on a large amount of experimental data and fire characteristic analysis.

[0158] Redefine the normalization function:

[0159] Normalization of high-temperature area: Assuming that the maximum area of ​​the high-temperature area in previous monitoring data is Amax = 15 square meters, the normalized high-temperature area Anorm is calculated as follows: Anorm = A ÷ Amax.

[0160] Normalization of smoke diffusion velocity: Assume the maximum smoke diffusion velocity is Vsmokemax = 8 m / min. Then the normalized smoke diffusion velocity Vsmokenorm = Vsmoke ÷ Vsmokemax.

[0161] Flame spread velocity normalization: Assume the maximum flame spread velocity is Vflamemax = 4 m / min. Then the normalized flame spread velocity Vflamenorm = Vflame ÷ Vflamemax.

[0162] Flame flicker frequency normalization: Assume the maximum flame flicker frequency in the historical data is Fflickermax = 10 times / second. Then the normalized flame flicker frequency Fflickernorm = Fflicker ÷ Fflickermax.

[0163] Then, the current fire intensity level (F) is calculated by weighted summation:

[0164] F=wA×Anorm+wsmoke×Vsmokenorm+wflame×Vflamenorm+wD×D+wflicker×Fflickernorm;

[0165] For example, in a certain monitoring session:

[0166] If the area of ​​the high-temperature region is A = 9 square meters, then Anorm = 9 ÷ 15 = 0.6;

[0167] If the smoke diffusion speed Vsmoke = 5 m / min, then Vsmokenorm = 5 ÷ 8 = 0.625;

[0168] If the flame spread rate Vflame = 2.5 m / min, then Vflamenorm = 2.5 ÷ 4 = 0.625;

[0169] Analysis of the flame propagation direction yielded a flame propagation direction consistency D = 0.8;

[0170] If the flame flicker frequency Fflicker = 6 times / second, then Fflickernorm = 6 ÷ 10 = 0.6;

[0171] Substituting these values ​​into the formula, we get:

[0172] F=0.3×0.6+0.2×0.625+0.2×0.625+0.15×0.8+0.15×0.6=0.18+0.125+0.125+0.12+0.09=0.64;

[0173] The value of 0.64 represents the current fire intensity level calculated in this study. It comprehensively considers multiple factors, including the area of ​​the high-temperature zone, the rate of smoke diffusion, the rate of flame diffusion, the consistency of the flame diffusion direction, and the frequency of flame flickering, all of which indicate the severity of the fire. Similarly, different thresholds can be set according to the actual situation to classify the fire intensity level. For example, 0-0.4 represents a low fire intensity level, 0.4-0.7 represents a medium fire intensity level, and 0.7-1 represents a high fire intensity level, so that the system can take corresponding fire-fighting measures based on different fire intensity levels.

[0174] In this embodiment, triggering the fire alarm system based on the current fire intensity level means that the system takes corresponding fire-fighting measures according to the calculated fire intensity level. When the fire intensity level reaches a certain threshold, a series of fire-related operations will be automatically initiated. For example, if the fire intensity level is low, the alarm system may be triggered first to notify nearby personnel; if the fire intensity level is high, in addition to the alarm, fire-fighting equipment such as automatic sprinkler systems and gas extinguishing devices will be activated, and other fire-fighting facilities such as fireproof roller shutters and smoke exhaust systems will be activated. These measures are used to control the spread of the fire and ensure the safety of personnel and property. This method of precisely triggering the fire alarm system based on the fire intensity level can rationally allocate fire-fighting resources and improve the efficiency of fire response.

[0175] The beneficial effects of the above technologies are as follows: The fire intensity calculation submodule calculates the current fire intensity value by integrating the three-dimensional spatial temperature field model of the space where the electronic display screen is located, the smoke diffusion trajectory, and fire source characteristic data. This multi-dimensional data fusion calculation method can more comprehensively and accurately reflect the actual fire situation, providing a reliable basis for subsequent decision-making. The fire linkage triggering submodule triggers the fire linkage system based on the calculated current fire intensity value, enabling precise activation of corresponding fire-fighting measures according to the fire intensity, avoiding over- or under-response, improving the efficiency of fire-fighting resource utilization, and timely and effectively controlling the spread of fire, ensuring the safety of people's lives and property. The intelligent control submodule intelligently controls the fire-resistant electronic display screen based on the current fire intensity value, enabling the display screen to dynamically adjust the displayed content according to the fire intensity. For example, it can display evacuation guidance information when the fire is small, and highlight warning information when the fire is severe, providing more effective information support for personnel evacuation and rescue command. The three submodules work together to build a complete and intelligent fire response system, enhancing the comprehensive response capability of the fire-resistant electronic display screen intelligent control system to fire scenarios, and improving the intelligence level and practicality of the entire system.

[0176] Example 10: Based on Example 9, the intelligent control submodule includes:

[0177] The model building unit is used to build intelligent control models for electronic displays.

[0178] The intelligent control unit is used to input the current fire level value into the intelligent control model of the electronic display screen, obtain the current intelligent control command of the fire prevention electronic display screen, and perform intelligent control of the fire prevention electronic display screen based on the current intelligent control command.

[0179] In this embodiment, building an intelligent control model for the electronic display screen involves constructing a mathematical model or algorithm framework for intelligent control of the fire-resistant electronic display screen. This model is trained using numerous fire prevention and control examples within the spaces where the electronic display screens are located (calculating the current fire intensity level based on data from these examples) and intelligent control commands for the electronic display screens determined by fire control personnel based on the actual fire conditions recorded in these examples. Specifically, the current fire intensity level calculated from the data in the fire prevention and control examples is used as the model input, while the intelligent control commands for the electronic display screens determined by fire control personnel based on the actual fire conditions recorded in the examples are used as the model output.

[0180] For example, the model might output that when the fire is small, the electronic display screen switches to displaying evacuation instructions; when the fire intensifies, it switches to displaying high-brightness warnings. By integrating the logical relationships between these factors, the model can accurately output control commands to the electronic display screen based on the input current fire intensity level. It provides a standardized and intelligent foundation for realizing intelligent control of electronic displays in fire scenarios, making the control process more scientific and efficient.

[0181] In this embodiment, the current intelligent control command is a specific operation instruction calculated by the electronic display screen's intelligent control model based on input parameters such as the current fire intensity level. This command clarifies how the fire-resistant electronic display screen should respond to changes in the fire intensity. For example, the command might be "adjust the display screen brightness to the highest level and display 'Fire Hazard, Evacuate Immediately'", or "turn off some non-critical display areas to reduce power consumption and focus on displaying emergency warning information," etc.

[0182] In this embodiment, intelligent control of the fire-resistant electronic display screen based on current intelligent control commands means performing corresponding operations on the electronic display screen according to the commands obtained from the intelligent control model. After receiving the current intelligent control commands, the control system of the electronic display screen adjusts various parameters and display content of the display screen according to the requirements of the commands. For example, if the command requires changing the display brightness and content, the control system will control the backlight brightness adjustment module and the display content output module of the display screen to adjust the brightness and display specific information. In this way, the fire-resistant electronic display screen can respond to changes in fire intensity in real time, providing necessary information support for personnel evacuation and firefighting work, and improving the overall fire response capability.

[0183] The beneficial effects of the above technologies are as follows: The model building unit constructs an intelligent control model for the electronic display screen, providing a standardized and intelligent framework for the intelligent control of fire-resistant electronic display screens. This model can integrate various relevant factors and logical relationships, laying the foundation for the generation of precise control commands. The intelligent control unit inputs the current fire level value into the constructed intelligent control model to obtain the current intelligent control commands for the fire-resistant electronic display screen. This model-based command generation method is efficient and accurate, and can quickly and accurately provide control commands based on the fire situation. Intelligent control of the fire-resistant electronic display screen based on the current intelligent control commands enables the display screen to respond appropriately to changes in the fire situation in real time. For example, it can display different emergency level indicators and escape route guidance information according to different fire levels, greatly enhancing the pertinence and effectiveness of information transmission in fire scenarios. Through the close cooperation between model building and intelligent control, the intelligence level of the fire-resistant electronic display screen intelligent control system is significantly improved, providing personnel with more scientific and reasonable information guidance during fires, and further improving the overall effectiveness of the entire system in responding to fires.

[0184] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A fireproof electronic display screen intelligent control system, characterized in that, include: The temperature monitoring module is used to collect temperature data of each surrounding area of ​​the screen in real time based on a temperature sensor array uniformly distributed in an area where the number of through holes on the back of the electronic display screen exceeds a preset number. The smoke monitoring module is used to collect real-time smoke concentrations at multiple locations in the environment where the screen is located based on smoke sensors installed at multiple through-hole positions on the back of the screen with the sensing ports facing the airflow direction of the through-holes. The probe monitoring module is used to acquire screen monitoring video based on a high-definition probe; The multi-source data analysis module is used to establish a three-dimensional spatial temperature field model, smoke diffusion trajectory, and fire source characteristic data of the space where the electronic display screen is located, based on temperature data of various surrounding areas of the screen, smoke concentration in multiple locations in the environment where the screen is located, and monitoring video of the screen. The intelligent control module is used to trigger the fire-fighting linkage system based on the three-dimensional spatial temperature field model, smoke diffusion trajectory, fire source characteristic data and multi-level early warning rules in the space where the electronic display screen is located, and at the same time, to intelligently control the fire-resistant electronic display screen. The multi-source data analysis module includes: The temperature field model building submodule is used to interpolate and build models of temperature data in various surrounding areas of the screen, generating a three-dimensional spatial temperature field model within the space where the electronic display screen is located. The smoke diffusion model establishment submodule is used to generate smoke diffusion trajectories based on the smoke concentration at multiple locations around the screen. The fire source feature analysis submodule is used to analyze fire source feature data from the screen monitoring video; The smoke diffusion model establishment submodule includes: The first sorting unit is used to sort the monitoring positions of all smoke sensors according to the sorting principle of the smoke concentration in the environment where the screen is located from largest to smallest, based on the latest obtained smoke concentration, to obtain the first position sequence. The second sorting unit is used to calculate the concentration change trend value of each monitoring location based on the historical smoke concentration value sequence of the corresponding monitoring location monitored by each smoke sensor, and sort the monitoring locations of all smoke sensors according to the sorting principle of concentration change trend value from large to small to obtain the second location sequence. The anomaly detection unit is used to determine the latest safety monitoring period based on the historical smoke concentration value sequence of the corresponding monitoring location detected by all smoke sensors, and to determine the earliest abnormal anchoring time and anomaly degree of each smoke sensor in the latest safety monitoring period based on all abnormal smoke concentration values ​​that appear in the historical smoke concentration value sequence of the corresponding monitoring location detected by each smoke sensor within the latest safety monitoring period. The priority value calculation unit is used to calculate the priority value of the monitoring position of each smoke sensor based on the earliest abnormal anchoring time and abnormality degree of each smoke sensor in the latest safety monitoring cycle, the ranking value of the corresponding monitoring position in the first position sequence and the ranking value in the second position sequence. The trajectory simulation unit is used to generate smoke diffusion trajectories based on the priority values ​​of the monitoring locations of all smoke sensors.

2. The intelligent control system for fireproof electronic displays according to claim 1, characterized in that, The trajectory simulation unit includes: The starting point and circular range determination subunit is used to take the monitoring position corresponding to the highest priority value among all the monitoring positions of the smoke sensors as the false starting point position, and construct the circular range area of ​​the false starting point position with the false starting point position as the center and the distance between the false starting point position and the monitoring position corresponding to the second highest priority value as the radius. The first interpolation processing subunit is used to interpolate multiple interpolation positions within the circular range area when the circular range area contains monitoring positions other than the false starting position and the monitoring position corresponding to the second largest priority value. This is done based on the historical smoke concentration value sequence at the false starting position, the historical smoke concentration value sequence at the monitoring position corresponding to the second largest priority value, and the historical smoke concentration value sequences of all remaining monitoring positions within the circular range area other than the false starting position and the monitoring position corresponding to the second largest priority value, to obtain the historical smoke concentration value sequence of multiple interpolation positions within the current circular range area. The priority value determination subunit is used to determine the current priority value of each anchoring location based on the historical smoke concentration value sequence of all interpolation locations and the historical smoke concentration value sequence of all monitoring locations. The first trajectory fitting subunit is used to fit the smoke diffusion trajectory based on the current priority values ​​of all anchor positions.

3. The intelligent control system for fireproof electronic displays according to claim 2, characterized in that, The first trajectory fitting subunit includes: The starting point and circular range determination end is used to determine the latest false starting point position based on the current priority value of all anchor positions, and construct the circular range area of ​​the latest false starting point position with the latest false starting point position as the center and the distance between the latest false starting point position and the anchor position corresponding to the second largest current priority value as the radius; In the trajectory fitting end, when the circular range of the latest false starting point position contains anchor positions other than the latest false starting point position and the anchor position corresponding to the second largest current priority value, interpolation processing and priority value calculation are performed on the circular range of the latest false starting point position based on the historical smoke concentration value sequence of the existing anchor positions to obtain the latest set of anchor positions. This process continues until the latest circular range obtained based on the latest set of anchor positions does not contain any remaining anchor positions other than the latest false starting point position and the anchor position corresponding to the second largest latest priority value. In this case, the latest false starting point position is connected to the anchor position corresponding to the second largest latest priority value as a partial smoke diffusion trajectory, and the anchor position corresponding to the second largest latest priority value is updated as the false starting point position. The next connection position of the partial smoke diffusion trajectory is then determined until all anchor positions are connected to obtain the smoke diffusion trajectory.

4. The intelligent control system for fireproof electronic displays according to claim 3, characterized in that, Also includes: The second trajectory fitting subunit is used to connect the false starting position and the monitoring position corresponding to the second highest priority value as a part of the smoke diffusion trajectory when the circular range does not contain any remaining monitoring positions other than the false starting position and the monitoring position corresponding to the second highest priority value. The monitoring position corresponding to the second highest priority value is updated to the false starting position, and the next connection position of the part of the smoke diffusion trajectory is determined until all anchor positions are connected to obtain the smoke diffusion trajectory.

5. The intelligent control system for fireproof electronic displays according to claim 1, characterized in that, The fire source characteristic analysis submodule includes: The suspected flame area filtering unit is used to identify all irregular areas enclosed by irregular edges in each video frame of the screen monitoring video based on the edge detection algorithm, and to filter out the suspected flame areas in each video frame based on the preset saturation threshold and brightness threshold. The physical space positioning unit is used to determine the three-dimensional coordinate range of each suspected flame area in the world coordinate system based on the triangulation algorithm, which serves as the physical space range of each suspected flame area. The real flame region screening unit is used to obtain the signal intensity of the strong absorption peak signal within the preset wavelength range in the physical space of each suspected flame region based on the multispectral fusion monitoring method, and to determine whether the corresponding suspected flame region is a real flame region based on the signal intensity of the strong absorption peak signal within the preset wavelength range in the physical space of each suspected flame region, and to use the judgment result of the corresponding suspected flame region as the judgment result of all suspected flame regions. Based on the judgment results of all suspected flame regions, all real flame regions are screened out. The inter-frame correspondence unit is used to perform inter-frame correspondence on all real flame regions in all video frames of the screen monitoring video to obtain the real flame region sequence of all real fire sources. The fire source feature data extraction unit is used to extract fire source feature data based on the real flame region sequence of all real fire sources.

6. The intelligent control system for fireproof electronic displays according to claim 5, characterized in that, The fire source feature data extraction unit includes: The physical spatial positioning subunit is used to determine the location of each real fire source based on the triangulation method and the sequence of real flame areas of each real fire source; The flame spread direction analysis subunit is used to perform pixel motion vector tracking on the real flame region sequence of each real fire source to obtain the flame spread direction of each real fire source. The flicker frequency analysis subunit is used to track the periodic changes in regional brightness of the real flame region sequence for each real fire source and obtain the flicker frequency of each real fire source. The diffusion rate analysis subunit is used to perform regional area diffusion trend analysis on the real flame region sequence of each real fire source to obtain the diffusion rate of each real fire source. The multidimensional feature aggregation subunit is used to treat the location of all real fire sources, flame spread direction, flashing frequency, and spread speed as fire source feature data.

7. The intelligent control system for fireproof electronic displays according to claim 1, characterized in that, The intelligent control module includes: The fire intensity level calculation submodule is used to calculate the current fire intensity level based on the three-dimensional spatial temperature field model, smoke diffusion trajectory, and fire source characteristic data within the space where the electronic display screen is located. The fire alarm linkage triggering submodule is used to trigger the fire alarm linkage system based on the current fire intensity level. The intelligent control submodule is used to intelligently control the fire prevention electronic display screen based on the current fire intensity level.

8. The intelligent control system for fireproof electronic displays according to claim 7, characterized in that, The intelligent control submodule includes: The model building unit is used to build intelligent control models for electronic displays. The intelligent control unit is used to input the current fire level value into the intelligent control model of the electronic display screen, obtain the current intelligent control command of the fire prevention electronic display screen, and perform intelligent control of the fire prevention electronic display screen based on the current intelligent control command.

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