Fire emergency lighting control method and device combined with remote assistance of auxiliary equipment

Abstract

The present application provides a fire emergency lighting control method and device combined with remote assistance of auxiliary equipment, which relates to the field of lighting control technology, including: using the emergency lighting demonstration space to analyze the influence of evacuation path characteristics, smoke concentration, crowd density and regional brightness, and establish the first, second and third mapping compensations; when a fire occurs, regularly read the real-time monitoring information and transmit it to multiple edge nodes; at the edge node, based on the first, second and third mapping compensations, perform brightness compensation analysis on the real-time monitoring information, generate brightness compensation parameters and perform brightness control of the lighting device. This application can solve the technical problem that the existing emergency lighting devices cannot quickly set the appropriate emergency lighting brightness according to the crowd evacuation situation, environmental conditions and dynamic changes of the fire when the fire occurs, resulting in poor emergency lighting effects, and can achieve the technical goal of precise lighting control and effectively improve the quality of emergency lighting.

Description

Technical Field

[0001] The present application relates to the field of lighting control technology, and in particular to a fire emergency lighting control method and device combined with remote assistance of auxiliary equipment. Background Art

[0002] Traditional emergency lighting devices usually use fixed brightness settings, which do not take into account environmental changes when a fire occurs and the real-time needs of personnel evacuation. Whether it is the width of the evacuation channel, changes in crowd density, or the obstruction of fire smoke, the lighting system cannot be adjusted in real time according to on-site conditions. This fixed brightness emergency lighting mode has many limitations, especially in emergency situations such as fires, which directly affects the efficiency and safety of personnel evacuation.

[0003] At present, the existing emergency lighting devices are unable to quickly set the appropriate emergency lighting brightness according to the crowd evacuation situation, environmental conditions and dynamic changes of the fire when a fire occurs, resulting in technical problems such as poor emergency lighting effects. Summary of the invention

[0004] The purpose of this application is to provide a fire emergency lighting control method and device combined with remote assistance of auxiliary equipment, so as to solve the technical problem that the existing emergency lighting devices are unable to quickly set the appropriate emergency lighting brightness according to the crowd evacuation situation, environmental conditions and dynamic changes of the fire when a fire occurs, resulting in poor emergency lighting effects.

[0005] In view of the above problems, the present application provides a fire emergency lighting control method and device combined with remote assistance of auxiliary equipment.

[0006] In the first aspect, the present application provides a fire emergency lighting control method combined with remote assistance of auxiliary equipment, which is implemented by a fire emergency lighting control device combined with remote assistance of auxiliary equipment, including: on a cloud server, an emergency lighting demonstration space is simulated and constructed based on building structure information, monitoring equipment layout information and lighting equipment layout information; using the emergency lighting demonstration space, the influence of evacuation path characteristics, smoke concentration, crowd density and regional brightness are analyzed respectively, and a first mapping compensation, a second mapping compensation and a third mapping compensation are established, and stored in an edge processing network, wherein the edge processing network includes multiple edge nodes, each edge node corresponds to an emergency lighting device; when a fire occurs, real-time monitoring information is read regularly, and the real-time monitoring information is mapped and transmitted to the multiple edge nodes; at the edge node, brightness compensation analysis is performed on the real-time monitoring information based on the first mapping compensation, the second mapping compensation and the third mapping compensation, and brightness compensation parameters are generated to perform adaptive brightness control of the emergency lighting device.

[0007] In the second aspect, the present application also provides a fire emergency lighting control device combined with remote assistance of auxiliary equipment, which is used to execute a fire emergency lighting control method combined with remote assistance of auxiliary equipment as described in the first aspect, including: a lighting demonstration space construction module, which is used to simulate and construct an emergency lighting demonstration space on a cloud server based on building structure information, monitoring equipment layout information and lighting equipment layout information; a brightness impact analysis module, which is used to use the emergency lighting demonstration space to perform impact analysis on evacuation path characteristics, smoke concentration, crowd density and regional brightness, establish a first mapping compensation, a second mapping compensation and a third mapping compensation, and store them to an edge processing network, wherein the edge processing network includes multiple edge nodes, each edge node corresponds to an emergency lighting device; an information transmission module, which is used to periodically read real-time monitoring information when a fire occurs, and map and transmit the real-time monitoring information to the multiple edge nodes; a brightness control module, which is used to perform brightness compensation analysis on the real-time monitoring information at the edge node based on the first mapping compensation, the second mapping compensation and the third mapping compensation, and generate brightness compensation parameters to perform adaptive brightness control of the emergency lighting device.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] Through the cloud server, an emergency lighting demonstration space is simulated and constructed based on the building structure information, the monitoring equipment layout information and the lighting equipment layout information; then, the emergency lighting demonstration space is used to analyze the influence of evacuation path characteristics, smoke concentration, crowd density and regional brightness, establish the first mapping compensation, the second mapping compensation and the third mapping compensation, and store them in the edge processing network, wherein the edge processing network includes multiple edge nodes, each edge node corresponds to an emergency lighting device; then, when a fire occurs, the real-time monitoring information is read regularly and mapped and transmitted to multiple edge nodes; finally, at the edge node, the real-time monitoring information is analyzed for brightness compensation based on the first mapping compensation, the second mapping compensation and the third mapping compensation, and the brightness compensation parameters are generated to perform adaptive brightness control of the emergency lighting device; through the above method, the lighting brightness can be adaptively controlled according to the real-time evacuation scene when the fire occurs, so as to achieve the technical goal of precise lighting control, thereby effectively improving the quality of emergency lighting and ensuring the safe evacuation of personnel in fire emergency situations.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 A flowchart of a fire emergency lighting control method combined with remote assistance of auxiliary equipment is provided for this application;

[0013] Figure 2 A schematic diagram of a flow chart of establishing a first mapping compensation in a fire emergency lighting control method combined with remote assistance of auxiliary equipment in this application;

[0014] Figure 3 This is a structural schematic diagram of a fire emergency lighting control device combined with remote assistance of auxiliary equipment in this application.

[0015] Description of reference numerals:

[0016] Lighting demonstration space construction module 11, brightness impact analysis module 12, information transmission module 13, brightness control module 14. DETAILED DESCRIPTION

[0017] This application provides a fire emergency lighting control method and device combined with remote assistance of auxiliary equipment, which solves the technical problem that the existing emergency lighting devices cannot quickly set the appropriate emergency lighting brightness according to the crowd evacuation situation, environmental conditions and dynamic changes of the fire when the fire occurs, resulting in poor emergency lighting effects. The lighting brightness can be adaptively adjusted according to the real-time evacuation scene when the fire occurs, achieving the technical goal of precise lighting control, thereby effectively improving the quality of emergency lighting and ensuring the safe evacuation of personnel in fire emergencies.

[0018] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0019] For example, please refer to the attached Figure 1 The present application provides a fire emergency lighting control method combined with remote assistance of auxiliary equipment, which is applied to a fire emergency lighting control device combined with remote assistance of auxiliary equipment, and specifically includes the following steps:

[0020] S100: On the cloud server, an emergency lighting demonstration space is constructed by simulation based on the building structure information, the monitoring equipment layout information and the lighting equipment layout information.

[0021] Specifically, first, the building structure information, monitoring equipment layout information and lighting equipment layout information of the target building are collected. The building structure information includes floor layout, evacuation channel information, fire exit information and key location information (such as stairs, road intersections, etc.); the monitoring equipment includes at least smoke sensors, infrared sensors and photosensitive sensors. The infrared sensor is used to monitor the density of pedestrian flow, and the photosensitive sensor is used to monitor the ambient brightness. The monitoring equipment layout information includes the location information of each monitoring sensor; the lighting equipment layout information includes the specific distribution location of the emergency lighting devices, among which, in this application, it is assumed that all emergency lighting devices in the building are of the same type and specification.

[0022] Next, on the cloud server, the building's structural information, monitoring equipment layout, and lighting equipment layout are imported into the simulation platform, and a suitable simulation engine, such as Unity, FDS, etc., is selected to perform three-dimensional visualization of the building, monitoring equipment, and lighting equipment, and to construct an emergency lighting demonstration space. By constructing an emergency lighting demonstration space, the emergency lighting response effect in a fire scene can be simulated in real time, providing support for subsequent regional brightness impact analysis.

[0023] S200: Utilizing the emergency lighting demonstration space, respectively analyze the impact of evacuation path characteristics, smoke concentration, crowd density and regional brightness, establish a first mapping compensation, a second mapping compensation and a third mapping compensation, and store them in an edge processing network, wherein the edge processing network includes a plurality of edge nodes, and each edge node corresponds to an emergency lighting device.

[0024] Specifically, the emergency lighting demonstration space is used to perform an impact analysis on evacuation path characteristics and regional brightness, wherein the evacuation path characteristics include evacuation channel width, evacuation key points, and safety sign points. For example, the regional brightness requirements are calculated based on the width, length, and function of the evacuation channel. Wider channels require higher brightness and a larger coverage area. Higher brightness and special lighting designs should be used in corners, stairwells, exits, and other areas to improve the efficiency of personnel identifying directions and passage. The brightness around safety signs needs to be high enough for easy identification. A first mapping compensation of regional brightness is established based on the impact analysis results, that is, the light intensity of the lighting devices in the area is adjusted according to the regional characteristics to ensure that the lighting needs of different areas are met.

[0025] Then, the smoke concentration and regional brightness are analyzed to ensure that in the event of a fire or emergency, the visibility in the area does not decrease significantly due to the increase in smoke concentration. By analyzing the effect of smoke concentration on the scattering and absorption of light, the brightness of emergency lighting is dynamically adjusted. For example, when the smoke concentration reaches a medium level, the emergency lighting system needs to dynamically adjust the brightness according to the light attenuation coefficient to ensure that the light can be effectively transmitted in the smoke environment; in areas with high smoke concentration, the scattering and absorption of light will be extremely serious, so it is necessary to significantly increase the lighting brightness (such as increasing it to twice the original brightness or higher); based on the analysis results, a second mapping compensation of smoke concentration-brightness is established. On the other hand, the impact of crowd density and regional brightness is analyzed. When the crowd density is high, people will block part of the light source, resulting in the inability of lighting to fully cover the entire evacuation passage or staircase. This blocking effect will lead to insufficient lighting in the local area, reduce visibility, and increase the danger during the evacuation process. According to the impact of crowd density on lighting needs, a third mapping compensation is established to dynamically adjust the regional brightness to ensure that appropriate lighting support is provided under different crowd density conditions.

[0026] Based on the lighting equipment layout information, multiple emergency lighting devices are determined, and edge nodes are deployed for each lighting device. The edge node is a small computing unit deployed near the lighting device, which can process local data from sensors and perform lighting control tasks. The edge node enables each emergency lighting device to quickly respond to environmental changes and perform intelligent adaptive regulation, thereby improving the timeliness and accuracy of emergency lighting regulation. Multiple edge computing nodes are determined, wherein each edge computing node has the location coordinates of the lighting device, and then an edge processing network is built based on the multiple edge nodes and the multiple location coordinates, and then the first mapping compensation, the second mapping compensation and the third mapping compensation are stored in multiple edge nodes of the edge processing network, wherein the edge node receives real-time data (such as smoke concentration, crowd density, etc.) from each sensor, and dynamically adjusts the brightness of the lighting device according to the stored mapping compensation.

[0027] By building an edge processing network for adaptive regulation of lighting devices, the intelligence and response speed of the emergency lighting system can be effectively improved. The edge nodes have independent data processing and decision-making capabilities, and can dynamically adjust the light intensity of the lighting devices according to real-time environmental changes, ensuring that the system can provide the best lighting support in emergency situations. At the same time, the distributed architecture of the edge processing network makes the system more stable and reliable, with strong fault tolerance and efficient data processing performance.

[0028] S300: When a fire occurs, regularly read the real-time monitoring information, and map and transmit the real-time monitoring information to the multiple edge nodes.

[0029] Specifically, first obtain the coverage of various sensors, including the sensing areas of smoke sensors, infrared sensors and photosensitive sensors, to ensure the comprehensiveness and accuracy of monitoring data. Then, in the emergency lighting demonstration space, combined with the coverage of sensors and the layout of lighting devices, establish a communication mapping between sensors and edge nodes, that is, according to the geographical location of sensors and edge nodes, bind the monitoring data of each sensor to the nearest edge node to ensure that the sensor data can be transmitted to the nearest edge node in time for processing. Then, when a fire occurs, regularly collect real-time data from smoke sensors, infrared sensors and photosensitive sensors, and the frequency of data collection can be adjusted according to the severity of the fire and the sensor response speed; further based on the communication mapping between sensors and edge nodes, the real-time monitoring information is mapped and transmitted to the multiple edge nodes. By establishing a communication mapping between sensors and edge nodes to perform the transmission of monitoring information, it can be ensured that when a fire occurs, the real-time data of the sensor can be quickly and stably obtained and transmitted to the edge node for processing, where each edge node can make independent decisions based on these real-time data and dynamically adjust the brightness of the emergency lighting device.

[0030] S400: At the edge node, based on the first mapping compensation, the second mapping compensation and the third mapping compensation, a brightness compensation analysis is performed on the real-time monitoring information, and a brightness compensation parameter is generated to perform adaptive brightness control of the emergency lighting device.

[0031] Specifically, real-time monitoring information is received through the edge node, including real-time smoke concentration, real-time crowd density and real-time ambient brightness. Then, the location information of the edge node is determined, and the location information is input into the first mapping compensation for matching, and the first compensation parameter is determined. The first compensation parameter reflects the adjustment of the lighting brightness required by the evacuation path characteristics of the location. The real-time smoke concentration is input into the second mapping compensation for matching, and the second compensation parameter is determined; the real-time crowd density is input into the third mapping compensation for matching, and the third compensation parameter is output. Finally, for the purpose of meeting the expected lighting brightness, a brightness compensation analysis is performed according to the first compensation parameter, the second compensation parameter, the third compensation parameter and the real-time ambient brightness, that is, the expected lighting brightness is subtracted from the real-time ambient brightness, and the first compensation parameter, the second compensation parameter and the third compensation parameter are added to obtain the expected compensation parameter; finally, the initial lighting brightness is updated and corrected according to the expected compensation parameter, and the brightness compensation parameter is obtained to execute the brightness adaptive control of the emergency lighting device.

[0032] By performing brightness compensation analysis on real-time monitoring information based on the first, second, and third mapping compensations at the edge node, generating real-time dynamic brightness compensation parameters, and adaptively adjusting the emergency lighting device according to the compensation parameters, the emergency lighting device can respond quickly in emergency situations such as fire, dynamically adjust the lighting brightness, and ensure the visibility of the evacuation channel and the safety of personnel.

[0033] The fire emergency lighting control method combined with remote assistance of auxiliary equipment is applied to a fire emergency lighting control device combined with remote assistance of auxiliary equipment, which can solve the technical problem that the existing emergency lighting devices cannot quickly set the appropriate emergency lighting brightness according to the crowd evacuation situation, environmental conditions and dynamic changes of the fire when a fire occurs, resulting in poor emergency lighting effects. Through the cloud server, an emergency lighting demonstration space is simulated and constructed based on the building structure information, the monitoring equipment layout information and the lighting equipment layout information; then, the emergency lighting demonstration space is used to analyze the influence of evacuation path characteristics, smoke concentration, crowd density and regional brightness, establish the first mapping compensation, the second mapping compensation and the third mapping compensation, and store them in the edge processing network, wherein the edge processing network includes multiple edge nodes, each edge node corresponds to an emergency lighting device; then, when a fire occurs, the real-time monitoring information is read regularly and mapped and transmitted to multiple edge nodes; finally, at the edge node, the real-time monitoring information is analyzed for brightness compensation based on the first mapping compensation, the second mapping compensation and the third mapping compensation, and the brightness compensation parameters are generated to perform adaptive brightness control of the emergency lighting device; through the above method, the lighting brightness can be adaptively controlled according to the real-time evacuation scene when the fire occurs, so as to achieve the technical goal of precise lighting control, thereby effectively improving the quality of emergency lighting and ensuring the safe evacuation of personnel in fire emergency situations.

[0034] Furthermore, the present application includes:

[0035] The monitoring equipment includes a smoke sensor, an infrared sensor and a photosensitive sensor, wherein the infrared sensor is used to monitor the density of human flow, and the photosensitive sensor is used to monitor the brightness of the environment.

[0036] Specifically, the monitoring equipment includes smoke sensors, infrared sensors and photosensitive sensors. The smoke sensors are used to detect changes in smoke concentration when a fire occurs; the infrared sensors are used to monitor the density of human traffic and detect the flow of people in evacuation passages or specific areas; the photosensitive sensors are used to monitor the ambient brightness and provide real-time feedback on the light level of the current area. Through the combined use of smoke sensors, infrared sensors and photosensitive sensors, the emergency lighting system can comprehensively consider the smoke concentration, human traffic density and ambient brightness in the fire environment. At the same time, these real-time monitoring data are processed by edge nodes to provide a dynamic control basis for the lighting system, ensuring the best lighting support in emergency situations such as fires, and helping people evacuate safely.

[0037] Further, a first mapping compensation is established, as shown in the attached Figure 2 As shown, this application includes:

[0038] Based on the building structure information, evacuation path features of the area covered by the lighting device are extracted to obtain a path feature set, wherein the evacuation path features include evacuation channel width, evacuation key points and safety sign points; based on the path feature set, regional brightness impact analysis is performed according to the evacuation channel width, evacuation key points and safety sign points to determine multiple brightness impact coefficients; based on the multiple brightness impact coefficients, reverse calculation is performed to obtain multiple first brightness compensation coefficients, and a first mapping compensation of area-brightness is established.

[0039] Specifically, the evacuation path features are configured, where the evacuation path features include the width of the evacuation channel, key evacuation points, and safety sign points. The width of the evacuation channel refers to the lateral size of the internal channel of the building (such as corridors, stairwells, exit channels, etc.), and the channel width directly affects the demand for emergency lighting. Wider channels require stronger brightness to ensure that all channel areas are effectively illuminated, while narrow channels can use lower brightness; key evacuation points include corners, stairwells, doorways, exits, etc. in the evacuation path. These locations are usually crowded areas in the evacuation path. These locations are prone to confusion due to the large flow of people, so higher lighting brightness is required to ensure that people can quickly identify directions and exits; safety sign points include emergency exit signs, evacuation path indicator lights, fire extinguisher signs, etc. These signs need to be clearly visible in an emergency, and the lighting device needs to ensure that the brightness around these signs is high enough to be quickly identified in an emergency. Then, based on the building structure information, the evacuation path feature extraction of the lighting device coverage area is performed to obtain a path feature set, in which the path feature and the lighting device coverage area have a mapping relationship.

[0040] Based on the path feature set, the regional brightness impact analysis is performed according to the evacuation channel width, evacuation key points and safety sign points. For example, the wider the channel, the higher the brightness. According to the different channel widths, the lighting brightness needs to be adjusted accordingly; the key points in the evacuation path (such as exits, stairways, corners, etc.) need to be centrally illuminated to ensure the visibility of the crowd in these important areas, so the lighting requirements of the key points are relatively high; the safety signs need to be illuminated to ensure that the signs are clearly visible in an emergency, so the lighting requirements are relatively high; according to the analysis results, multiple brightness impact coefficients are obtained by reverse calculation according to the multiple brightness impact coefficients. For example, according to the sample data, the influence association between the brightness impact coefficient and the brightness compensation coefficient is established, and the influence association is reversely calculated to obtain multiple first brightness compensation coefficients. Then, based on the mapping relationship between the lighting coverage area and the first brightness compensation coefficient, according to the multiple lighting coverage areas and the multiple first brightness compensation coefficients. The first mapping compensation of area-brightness is constructed. By constructing the first mapping compensation, the emergency lighting device can dynamically adapt to the actual needs of the evacuation passage, ensuring that the best lighting conditions are provided for each important area in emergency situations such as fire, which significantly improves evacuation efficiency and personnel safety.

[0041] Further, according to the evacuation channel width, evacuation key points and safety sign points, regional brightness impact analysis is performed, and the application includes:

[0042] Call historical firefighting cases and retrieve a sample data set, wherein the sample data includes a sample channel width, a sample point type, a sample mark coordinate, and a sample brightness influence coefficient, and the sample brightness influence coefficient is determined based on a comprehensive combination of a regional brightness influence coefficient and a safety mark brightness influence coefficient; use the sample data set to train a BP neural network to obtain a brightness influence analysis channel; randomly select a first path feature from the path feature set, input the first path feature into the brightness influence analysis channel, and output a first brightness influence degree; generate a first brightness influence coefficient based on the first brightness influence degree, and add it to the multiple brightness influence coefficients.

[0043] Specifically, firstly, the relevant sample data set is retrieved through the historical fire case database, where the sample data includes the sample channel width, the sample point type, the sample sign coordinates and the sample brightness influence coefficient. The sample brightness influence coefficient is determined based on the regional brightness influence coefficient and the safety sign brightness influence coefficient. The sample sign coordinates record the specific location coordinates of the safety signs (such as exit signs, emergency evacuation lights, etc.), which are used to analyze the brightness requirements of the area near the safety signs.

[0044] Then, a brightness impact analysis channel is constructed based on the BP neural network. The brightness impact analysis channel is a neural network model that can be iteratively optimized in machine learning, including an input layer, a hidden layer, and an output layer, wherein the input data of the input layer are channel width, point type, and marker coordinates, and the output data of the output layer are brightness impact coefficients. Then, supervised training is performed on the brightness impact analysis channel using the sample data set, that is, training is performed using the sample data set, inputting data such as sample channel width, sample point type, and sample marker coordinates, and outputting the corresponding brightness impact coefficient. At the same time, the BP algorithm is used for error back propagation, and the weights and bias values ​​of the network are adjusted through multiple iterations, so that the model can accurately predict the brightness impact coefficients under different paths, and a brightness impact analysis channel that meets the expected convergence conditions is obtained.

[0045] Then, any one path feature is randomly selected from the path feature set as the first path feature, and the first path feature is input into the brightness impact analysis channel for analysis to output a first brightness impact degree; further, a first brightness impact coefficient is generated based on the first brightness impact degree and added to the multiple brightness impact coefficients.

[0046] Further, based on the first brightness influence degree, a first brightness influence coefficient is generated. The present application includes:

[0047] According to the first path feature, area identification is performed in the emergency lighting demonstration space to determine a first identified area; using the emergency lighting demonstration space, lighting simulation is performed on the first identified area based on a variety of evacuation scenarios to determine a plurality of simulated brightness influence coefficients, wherein each evacuation scenario has different crowd density and ambient brightness; the plurality of simulated brightness influence coefficients are averaged to obtain a second brightness influence degree, and the first brightness influence coefficient is determined based on the first brightness influence degree and the second brightness influence degree.

[0048] Specifically, first, the area is marked in the emergency lighting demonstration space according to the first path feature, that is, the area corresponding to the first path feature is marked using the emergency lighting demonstration space to determine the first marked area, which represents the actual building space area under the first path feature, including evacuation passages, exits, key points and safety signs. Then, a variety of evacuation scenes are set, each scene has different parameters, such as crowd density and ambient brightness, and these scenes simulate different conditions that may occur in the building in emergency situations such as fire. Then, the emergency lighting demonstration space is used to perform lighting simulation on the first marked area based on a variety of evacuation scenes, that is, in each evacuation scene, the emergency lighting demonstration space is used to perform lighting simulation on the first marked area, and by adjusting the crowd density and ambient brightness in the simulated scene, the lighting demand changes in the area are observed. At the same time, according to the simulation results of different scenes, the brightness demand in each scene is determined, and a simulated brightness influence coefficient is generated to obtain multiple simulated brightness influence coefficients.

[0049] Then, the plurality of simulated brightness influence coefficients are averaged, and the average calculation result is used as the second brightness influence degree; finally, the first brightness influence degree and the second brightness influence degree are respectively configured with credible weights, which can be set according to the analysis accuracy of the model and the simulation accuracy of the demonstration space, and the first brightness influence degree and the second brightness influence degree are weightedly calculated according to the credible weights to obtain the first brightness influence coefficient. By combining the first brightness influence degree and the second brightness influence degree to obtain the first brightness influence coefficient, the accuracy and reliability of the setting of the first brightness influence coefficient can be improved.

[0050] Further, the second mapping compensation and the third mapping compensation are established, and the present application includes:

[0051] Query historical firefighting cases, retrieve and obtain a second sample set and a third sample set, wherein the second sample set includes a sample smoke concentration set and a sample brightness influence coefficient set, and the third sample set includes a sample crowd density set and a sample brightness influence coefficient set; use the sample smoke concentration set and the sample brightness influence coefficient set to perform linear correlation analysis, and establish a second mapping compensation of smoke concentration-brightness; use the sample crowd density set and the sample brightness influence coefficient set to perform linear correlation analysis, and establish a third mapping compensation of crowd density-brightness.

[0052] Specifically, first, historical fire cases are queried to retrieve the second sample set and the third sample set, where the second sample set is used to analyze the relationship between smoke concentration and brightness influence coefficient to help the emergency lighting system automatically adjust the brightness according to the smoke concentration, including a sample smoke concentration set and a sample brightness influence coefficient set. The sample smoke concentration set contains smoke concentration data under different scenarios extracted from historical fire cases. These concentration values ​​can be recorded based on real-time data from sensors or actual smoke diffusion conditions of the fire in the case; the sample brightness influence coefficient set is a set of brightness influence coefficients corresponding to smoke concentrations, which reflects the emergency lighting system's need to adjust lighting brightness under different smoke concentrations to ensure sufficient lighting when smoke is pervasive. The third sample set is used to analyze the relationship between crowd density and brightness influence coefficient, and to help the emergency lighting system automatically adjust the brightness according to the crowd density. It includes a sample crowd density set and a sample brightness influence coefficient set. The sample crowd density set contains personnel density data in different areas in historical cases, reflecting the distribution and density of the crowd during the fire evacuation process; the sample brightness influence coefficient set is a brightness influence coefficient set corresponding to the crowd density, reflecting the brightness adjustment that the lighting system needs to make under different crowd densities. The greater the crowd density, the higher the brightness required by the system to ensure sufficient lighting in crowded areas.

[0053] Then, the sample smoke concentration set and the sample brightness influence coefficient set are used to perform a linear correlation analysis to find out the linear relationship between smoke concentration and lighting brightness requirements, and a second mapping compensation of smoke concentration-brightness is established based on the linear relationship analysis results. The second mapping compensation can automatically adjust the brightness according to the real-time smoke concentration to ensure that sufficient lighting is provided in a high smoke concentration environment. The sample crowd density set and the sample brightness influence coefficient set are used to perform a linear correlation analysis to find out the linear relationship between crowd density and lighting brightness requirements, and a third mapping compensation of crowd density-brightness is established based on the linear relationship analysis results. The third mapping compensation is used in the emergency lighting system to dynamically adjust the lighting brightness according to the real-time crowd density.

[0054] By constructing the second mapping compensation and the third mapping compensation, the lighting brightness can be automatically adjusted according to the real-time smoke concentration and crowd density data to ensure sufficient lighting intensity in emergency situations such as fire, thereby improving the efficiency and safety of personnel evacuation.

[0055] Furthermore, the present application also includes:

[0056] Obtain the coverage of smoke sensors, infrared sensors and photosensors; use the emergency lighting demonstration space to establish a communication mapping between sensors and edge nodes according to the coverage, and perform monitoring information transmission based on the communication mapping.

[0057] Specifically, first, obtain the coverage of smoke sensors, infrared sensors and photosensors. The smoke sensor is used to detect the smoke concentration in the air during a fire. The infrared sensor is used to detect the crowd density and personnel movement, and monitor the crowd flow by sensing the infrared radiation emitted by the human body. The photosensitive sensor is used to monitor the ambient brightness to ensure that the emergency lighting system can automatically adjust the lighting brightness according to changes in ambient light. The coverage of the sensor is set based on the actual status of the equipment.

[0058] Then, using the emergency lighting demonstration space, according to the coverage and physical location of the sensor, the communication mapping relationship between each edge node and the sensor it is responsible for monitoring is determined, where each edge node is responsible for the monitoring data collection and processing of sensors in a certain area. The sensor is first associated with the nearest edge node to ensure that the monitoring data can be transmitted to the edge node with the lowest delay. In addition, if the coverage of the sensor overlaps with multiple edge nodes, redundant mapping can be used, and the sensor data can be sent to multiple edge nodes at the same time to ensure that when a node fails, other nodes can still receive data. Finally, real-time monitoring information transmission is performed according to the communication mapping.

[0059] Further, based on the first mapping compensation, the second mapping compensation and the third mapping compensation, the real-time monitoring information is subjected to brightness compensation analysis. The present application includes:

[0060] Read the real-time monitoring information received by the edge node, wherein the real-time monitoring information includes real-time smoke concentration, real-time crowd density and real-time ambient brightness; based on the first mapping compensation, determine a first compensation coefficient according to the position information of the edge node; based on the second mapping compensation and the real-time smoke concentration, determine a second compensation coefficient, and based on the third mapping compensation and the real-time crowd density, determine a third compensation coefficient; based on the expected lighting brightness, correct the initial lighting brightness according to the first compensation coefficient, the second compensation coefficient, the third compensation coefficient and the real-time ambient brightness to obtain the brightness compensation parameter.

[0061] Specifically, first, read the real-time monitoring information received by the edge node, wherein the real-time monitoring information includes real-time smoke concentration, real-time crowd density and real-time ambient brightness. Then, based on the first mapping compensation, determine the first compensation coefficient according to the position information of the edge node, wherein each edge node has fixed position information, which is related to the emergency lighting equipment area it is responsible for controlling, usually including the width of the evacuation channel, key evacuation points (such as corners, exits), etc. On the other hand, input the real-time smoke concentration into the second mapping compensation for matching, and determine the second compensation parameter; input the real-time crowd density into the third mapping compensation for matching, and output the third compensation parameter. Finally, for the purpose of meeting the expected lighting brightness, perform brightness compensation analysis according to the first compensation parameter, the second compensation parameter, the third compensation parameter and the real-time ambient brightness, that is, subtract the real-time ambient brightness from the expected lighting brightness, add the first compensation parameter, the second compensation parameter and the third compensation parameter, and obtain the expected compensation parameter; finally, update and correct the initial lighting brightness according to the expected compensation parameter, and obtain the brightness compensation parameter to perform brightness adaptive regulation of the emergency lighting device.

[0062] In summary, the fire emergency lighting control method combined with remote assistance of auxiliary equipment provided by this application has the following technical effects:

[0063] Through the cloud server, an emergency lighting demonstration space is simulated and constructed based on the building structure information, the monitoring equipment layout information and the lighting equipment layout information; then, the emergency lighting demonstration space is used to analyze the influence of evacuation path characteristics, smoke concentration, crowd density and regional brightness, establish the first mapping compensation, the second mapping compensation and the third mapping compensation, and store them in the edge processing network, wherein the edge processing network includes multiple edge nodes, each edge node corresponds to an emergency lighting device; then, when a fire occurs, the real-time monitoring information is read regularly and mapped and transmitted to multiple edge nodes; finally, at the edge node, the real-time monitoring information is analyzed for brightness compensation based on the first mapping compensation, the second mapping compensation and the third mapping compensation, and the brightness compensation parameters are generated to perform adaptive brightness control of the emergency lighting device; through the above method, the lighting brightness can be adaptively controlled according to the real-time evacuation scene when the fire occurs, so as to achieve the technical goal of precise lighting control, thereby effectively improving the quality of emergency lighting and ensuring the safe evacuation of personnel in fire emergency situations.

[0064] Embodiment 2: Based on the fire emergency lighting control method combined with remote assistance of auxiliary equipment in the above embodiment, the present application also provides a fire emergency lighting control device combined with remote assistance of auxiliary equipment, as shown in the attached Figure 3 ,include:

[0065] A lighting demonstration space construction module 11 is used to simulate and construct an emergency lighting demonstration space on a cloud server based on building structure information, monitoring equipment layout information and lighting equipment layout information; a brightness impact analysis module 12 is used to use the emergency lighting demonstration space to perform impact analysis on evacuation path characteristics, smoke concentration, crowd density and regional brightness, establish a first mapping compensation, a second mapping compensation and a third mapping compensation, and store them in an edge processing network, wherein the edge processing network includes multiple edge nodes, each edge node corresponds to an emergency lighting device; an information transmission module 13 is used to periodically read real-time monitoring information when a fire occurs, and map and transmit the real-time monitoring information to the multiple edge nodes; a brightness control module 14 is used to perform brightness compensation analysis on the real-time monitoring information at the edge node based on the first mapping compensation, the second mapping compensation and the third mapping compensation, and generate brightness compensation parameters to perform adaptive brightness control of the emergency lighting device.

[0066] Furthermore, the fire emergency lighting control device combined with remote assistance of auxiliary equipment is also used for:

[0067] The monitoring equipment includes a smoke sensor, an infrared sensor and a photosensitive sensor, wherein the infrared sensor is used to monitor the density of human flow, and the photosensitive sensor is used to monitor the brightness of the environment.

[0068] Furthermore, the fire emergency lighting control device combined with remote assistance of auxiliary equipment is also used for:

[0069] Based on the building structure information, evacuation path features of the area covered by the lighting device are extracted to obtain a path feature set, wherein the evacuation path features include evacuation channel width, evacuation key points and safety sign points; based on the path feature set, regional brightness impact analysis is performed according to the evacuation channel width, evacuation key points and safety sign points to determine multiple brightness impact coefficients; based on the multiple brightness impact coefficients, reverse calculation is performed to obtain multiple first brightness compensation coefficients, and a first mapping compensation of area-brightness is established.

[0070] Furthermore, the fire emergency lighting control device combined with remote assistance of auxiliary equipment is also used for:

[0071] Call historical firefighting cases and retrieve a sample data set, wherein the sample data includes a sample channel width, a sample point type, a sample mark coordinate, and a sample brightness influence coefficient, and the sample brightness influence coefficient is determined based on a comprehensive combination of a regional brightness influence coefficient and a safety mark brightness influence coefficient; use the sample data set to train a BP neural network to obtain a brightness influence analysis channel; randomly select a first path feature from the path feature set, input the first path feature into the brightness influence analysis channel, and output a first brightness influence degree; generate a first brightness influence coefficient based on the first brightness influence degree, and add it to the multiple brightness influence coefficients.

[0072] Furthermore, the fire emergency lighting control device combined with remote assistance of auxiliary equipment is also used for:

[0073] According to the first path feature, area identification is performed in the emergency lighting demonstration space to determine a first identified area; using the emergency lighting demonstration space, lighting simulation is performed on the first identified area based on a variety of evacuation scenarios to determine a plurality of simulated brightness influence coefficients, wherein each evacuation scenario has different crowd density and ambient brightness; the plurality of simulated brightness influence coefficients are averaged to obtain a second brightness influence degree, and the first brightness influence coefficient is determined based on the first brightness influence degree and the second brightness influence degree.

[0074] Furthermore, the fire emergency lighting control device combined with remote assistance of auxiliary equipment is also used for:

[0075] Query historical firefighting cases, retrieve and obtain a second sample set and a third sample set, wherein the second sample set includes a sample smoke concentration set and a sample brightness influence coefficient set, and the third sample set includes a sample crowd density set and a sample brightness influence coefficient set; use the sample smoke concentration set and the sample brightness influence coefficient set to perform linear correlation analysis, and establish a second mapping compensation of smoke concentration-brightness; use the sample crowd density set and the sample brightness influence coefficient set to perform linear correlation analysis, and establish a third mapping compensation of crowd density-brightness.

[0076] Furthermore, the fire emergency lighting control device combined with remote assistance of auxiliary equipment is also used for:

[0077] Obtain the coverage of smoke sensors, infrared sensors and photosensors; use the emergency lighting demonstration space to establish a communication mapping between sensors and edge nodes according to the coverage, and perform monitoring information transmission based on the communication mapping.

[0078] Furthermore, the fire emergency lighting control device combined with remote assistance of auxiliary equipment is also used for:

[0079] Read the real-time monitoring information received by the edge node, wherein the real-time monitoring information includes real-time smoke concentration, real-time crowd density and real-time ambient brightness; based on the first mapping compensation, determine a first compensation coefficient according to the position information of the edge node; based on the second mapping compensation and the real-time smoke concentration, determine a second compensation coefficient, and based on the third mapping compensation and the real-time crowd density, determine a third compensation coefficient; based on the expected lighting brightness, correct the initial lighting brightness according to the first compensation coefficient, the second compensation coefficient, the third compensation coefficient and the real-time ambient brightness to obtain the brightness compensation parameter.

[0080] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The fire emergency lighting control method combined with remote assistance of auxiliary equipment and the specific examples in the aforementioned embodiment 1 are also applicable to a fire emergency lighting control device combined with remote assistance of auxiliary equipment in this embodiment. Through the aforementioned detailed description of a fire emergency lighting control method combined with remote assistance of auxiliary equipment, those skilled in the art can clearly know the fire emergency lighting control device combined with remote assistance of auxiliary equipment in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0081] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0082] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A fire emergency lighting control method combined with remote assistance of auxiliary equipment, characterized in that: include: On the cloud server, an emergency lighting demonstration space is simulated and constructed based on the building structure information, monitoring equipment layout information, and lighting equipment layout information; Using the emergency lighting demonstration space, the influence analysis of evacuation path characteristics, smoke concentration, crowd density and regional brightness is respectively performed, and a first mapping compensation, a second mapping compensation and a third mapping compensation are established and stored in an edge processing network, wherein the edge processing network includes a plurality of edge nodes, and each edge node corresponds to an emergency lighting device; When a fire occurs, regularly read the real-time monitoring information, and map and transmit the real-time monitoring information to the multiple edge nodes; At the edge node, based on the first mapping compensation, the second mapping compensation and the third mapping compensation, the real-time monitoring information is analyzed for brightness compensation, and brightness compensation parameters are generated to perform adaptive brightness control of the emergency lighting device; Establishing a first mapping compensation includes: Extract the evacuation path features of the lighting device coverage area based on the building structure information to obtain a path feature set, wherein the evacuation path features include evacuation channel width, evacuation key points and safety sign points; Based on the path feature set, regional brightness impact analysis is performed according to the evacuation channel width, evacuation key points and safety sign points to determine multiple brightness impact coefficients; Reverse calculation is performed based on the plurality of brightness influence coefficients to obtain a plurality of first brightness compensation coefficients, and a first region-brightness mapping compensation is established; Establishing the second mapping compensation and the third mapping compensation includes: Query historical firefighting cases, retrieve and obtain a second sample set and a third sample set, wherein the second sample set includes a sample smoke concentration set and a sample brightness influence coefficient set, and the third sample set includes a sample crowd density set and a sample brightness influence coefficient set; Performing linear correlation analysis using the sample smoke concentration set and the sample brightness influence coefficient set to establish a second mapping compensation of smoke concentration-brightness; The sample crowd density set and the sample brightness influence coefficient set are used to perform linear correlation analysis to establish a third mapping compensation of crowd density-brightness.

2. The fire emergency lighting control method combined with remote assistance of auxiliary equipment according to claim 1 is characterized in that: The monitoring equipment includes a smoke sensor, an infrared sensor and a photosensitive sensor, wherein the infrared sensor is used to monitor the density of human flow, and the photosensitive sensor is used to monitor the brightness of the environment.

3. The fire emergency lighting control method combined with remote assistance of auxiliary equipment according to claim 1 is characterized in that: The regional brightness impact analysis is performed based on the evacuation channel width, evacuation key points and safety sign points, including: Calling historical firefighting cases, retrieving and acquiring a sample data set, wherein the sample data includes a sample channel width, a sample point type, a sample sign coordinate, and a sample brightness influence coefficient, wherein the sample brightness influence coefficient is comprehensively determined based on a regional brightness influence coefficient and a safety sign brightness influence coefficient; Using the sample data set to train a BP neural network to obtain a brightness impact analysis channel; Randomly selecting a first path feature from the path feature set, inputting the first path feature into the brightness impact analysis channel, and outputting a first brightness impact degree; A first brightness influence coefficient is generated based on the first brightness influence degree, and is added to the plurality of brightness influence coefficients.

4. The fire emergency lighting control method combined with remote assistance of auxiliary equipment according to claim 3 is characterized in that: Generating a first brightness influence coefficient based on the first brightness influence degree includes: Performing area identification in the emergency lighting demonstration space according to the first path feature to determine a first identification area; Using the emergency lighting demonstration space, the first identification area is respectively subjected to lighting simulation based on a plurality of evacuation scenarios to determine a plurality of simulated brightness influence coefficients, wherein each evacuation scenario has a different crowd density and environmental brightness; A mean operation is performed on the multiple simulated brightness influence coefficients to obtain a second brightness influence degree, and the first brightness influence coefficient is determined based on the first brightness influence degree and the second brightness influence degree.

5. The fire emergency lighting control method combined with remote assistance of auxiliary equipment according to claim 2 is characterized in that: The method also includes: Get the coverage of smoke sensors, infrared sensors and light sensors; The emergency lighting demonstration space is utilized to establish a communication mapping between sensors and edge nodes according to the coverage range, and monitoring information transmission is performed based on the communication mapping.

6. The fire emergency lighting control method combined with remote assistance of auxiliary equipment according to claim 1, characterized in that: Performing brightness compensation analysis on the real-time monitoring information based on the first mapping compensation, the second mapping compensation, and the third mapping compensation includes: Reading real-time monitoring information received by the edge node, wherein the real-time monitoring information includes real-time smoke concentration, real-time crowd density and real-time ambient brightness; Based on the first mapping compensation, determining a first compensation coefficient according to the position information of the edge node; Determine a second compensation coefficient based on the second mapping compensation and the real-time smoke density, and determine a third compensation coefficient based on the third mapping compensation and the real-time crowd density; Taking the expected lighting brightness as a reference, the initial lighting brightness is corrected according to the first compensation coefficient, the second compensation coefficient, the third compensation coefficient and the real-time environment brightness to obtain the brightness compensation parameter.

7. Fire emergency lighting control device combined with remote assistance of auxiliary equipment, characterized in that: The steps for implementing the fire emergency lighting control method combined with remote assistance of auxiliary equipment as described in any one of claims 1 to 6 include: A lighting demonstration space construction module is used to simulate and construct an emergency lighting demonstration space on a cloud server based on building structure information, monitoring equipment layout information, and lighting equipment layout information; A brightness impact analysis module is used to use the emergency lighting demonstration space to respectively analyze the impact of evacuation path characteristics, smoke concentration, crowd density and regional brightness, establish a first mapping compensation, a second mapping compensation and a third mapping compensation, and store them in an edge processing network, wherein the edge processing network includes a plurality of edge nodes, each edge node corresponding to an emergency lighting device; An information transmission module, used for periodically reading real-time monitoring information when a fire occurs, and mapping and transmitting the real-time monitoring information to the multiple edge nodes; The brightness control module is used to perform brightness compensation analysis on the real-time monitoring information at the edge node based on the first mapping compensation, the second mapping compensation and the third mapping compensation, generate brightness compensation parameters and perform adaptive brightness control of the emergency lighting device.

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