Self-adaptive control method of fire-fighting emergency lamp

Abstract

The invention discloses a self-adaptive control method for a fire-fighting emergency lamp, and relates to the technical field of illumination control, and the method comprises the steps: arranging a fire-fighting emergency lamp group with ceramic fluorescent powder, generating visible light with a predetermined color through blue laser excitation, and achieving nS-level fiber breaking control through the chromatographic reflection of the visible light, carrying out attenuation prediction and identification on the light conversion efficiency of the lamp in combination with a generative adversarial network; a building three-dimensional model is generated through simulation, a lamp identifier is mapped into the model, fixed-point optimization control analysis of the lamp is executed, laser parameters are dynamically adjusted, and self-adaptive regulation and control of emergency lighting of a target building are achieved. The technical problems that an existing fire-fighting emergency lighting system has electrical fire hazards and is insufficient in anti-interference capability and emergency response capability are solved, and the technical effects that the intrinsic safety, the anti-interference capability and the emergency response capability of the emergency lighting system are improved through dynamic optimization of the photoelectric separation technology and the generative adversarial network are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of lighting control, and in particular to an adaptive control method for fire emergency lighting. Background Art

[0002] Existing fire emergency lighting systems usually provide power and control signals to lamps through traditional wire connections. This design has the following technical defects and safety hazards: on the one hand, since wires are susceptible to electromagnetic interference, signal transmission may be unstable, thus affecting the normal startup and lighting effect of lamps; on the other hand, wire power supply has the risk of short circuit or electric sparks, which can easily cause fires or explosions in flammable and explosive places (such as chemical plants, gas stations) or ancient buildings. In addition, traditional emergency lighting systems still have deficiencies in light source performance prediction and dynamic control, and cannot be adjusted intelligently according to the actual environment and the attenuation of lamp performance, thus affecting the emergency response capability of the overall system. Summary of the invention

[0003] The present application provides an adaptive control method for fire emergency lighting, which is used to solve the technical problems that the existing fire emergency lighting system has electrical fire hazards and insufficient anti-interference ability and emergency response capability.

[0004] The present application provides an adaptive control method for fire emergency lamps, the method comprising: deploying predetermined fire emergency lighting equipment in a target building, wherein the predetermined fire emergency lighting equipment comprises a fire emergency lamp group, the fire emergency lamp group comprises a plurality of fire emergency lamps, each fire emergency lamp is installed with ceramic phosphor, and the ceramic phosphor can excite a predetermined color visible light greater than the wavelength of blue light under the irradiation of a blue laser; combining a generative adversarial network, according to a plurality of working characteristics, a plurality of working environment conditions and a plurality of element ratios of the plurality of fire emergency lamps, respectively predicting the attenuation of light conversion efficiency of the plurality of fire emergency lamps, obtaining a plurality of predicted light conversion efficiencies and marking the plurality of fire emergency lamps; combining a layout structure diagram of the target building with simulation to generate a three-dimensional building model, mapping and rendering a plurality of marked fire emergency lamps to the three-dimensional building model, and generating a three-dimensional building emergency lighting model; using the three-dimensional building emergency lighting model, performing fixed-point optimization control analysis of the predetermined fire emergency lighting equipment, and outputting a dynamic optimization control scheme to perform adaptive regulation of the emergency lighting of the target building.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages: The adaptive control method of fire emergency lighting provided by the present application relates to the field of lighting control technology. By deploying a fire emergency lighting group with ceramic phosphors, blue light is used to generate visible light of a predetermined color, and nS-level fiber break control is achieved through the chromatographic reflection of visible light, and light efficiency attenuation prediction is performed in combination with a generative adversarial network. Through three-dimensional building modeling and dynamic optimization control, an adaptive control scheme is output to achieve efficient, safe and intelligent management of emergency lighting, solving the technical problems of electrical fire hazards in existing fire emergency lighting systems, and insufficient anti-interference and emergency response capabilities, and achieving the technical effect of improving the inherent safety, anti-interference and emergency response capabilities of emergency lighting systems through optoelectronic separation technology and dynamic optimization of generative adversarial networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0007] Figure 1 A schematic flow chart of an adaptive control method for fire emergency lighting provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of adaptively regulating emergency lighting of a target building in the adaptive control method of fire emergency lighting provided in an embodiment of the present application. DETAILED DESCRIPTION

[0008] The present application provides an adaptive control method for fire emergency lighting, which is used to solve the technical problems that the existing fire emergency lighting system has electrical fire hazards and insufficient anti-interference ability and emergency response capability.

[0009] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0010] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0011] Embodiment 1, as Figure 1 As shown, the present application provides an adaptive control method for fire emergency lighting, the method comprising: P10: Deploy predetermined fire emergency lighting equipment in the target building, wherein the predetermined fire emergency lighting equipment includes a fire emergency lamp group, the fire emergency lamp group includes a plurality of fire emergency lamps, each fire emergency lamp is installed with ceramic phosphors, and the ceramic phosphors can excite a predetermined color visible light greater than the wavelength of blue light under the irradiation of blue laser.

[0012] Among them, the predetermined fire emergency lighting equipment also includes a photoelectric lighting centralized power supply and an optical fiber module, wherein the photoelectric lighting centralized power supply is embedded with a laser generator, and the laser generator is used to emit blue laser; the optical fiber module is connected to the fire emergency lamp group, and the blue laser emitted by the laser generator is projected to the several fire emergency lamps to emit a predetermined color visible light greater than the wavelength of blue light, wherein the predetermined color is green, red or white.

[0013] It should be understood that the predetermined fire emergency lighting equipment is deployed in the target building, which includes a fire emergency lighting set, a photoelectric lighting centralized power supply, and an optical fiber module to achieve efficient and safe emergency lighting functions. The fire emergency lighting set consists of a plurality of independent fire emergency lighting fixtures, each of which is installed with ceramic phosphors. Ceramic phosphors are a special material that can be excited when irradiated by blue lasers to emit a predetermined color visible light greater than the wavelength of blue light. These visible lights include green, red or white, which can meet the different needs of evacuation instructions, emergency warnings and ambient lighting.

[0014] The photoelectric lighting centralized power supply is the core component of the entire system, which is embedded with a laser generator. The main function of the laser generator is to generate blue laser with a wavelength in the range of 450-490nm. This blue laser has the characteristics of high energy and high directionality, which is very suitable for long-distance and efficient transmission through optical fiber. In order to ensure the transmission effect of blue light, the system is designed with an optical fiber module, which is a device specially used for optical signal transmission. It has the characteristics of low loss and high reliability, and can stably transmit the blue laser emitted by the laser generator to each fire emergency lamp.

[0015] When the blue laser is projected onto the fire emergency lamp through the optical fiber module, the ceramic phosphor inside the lamp starts to work under the stimulation of the blue light, and emits visible light of a predetermined color through the energy conversion process. For example, green light can be used to mark evacuation routes and emergency exits, red light is used for danger warnings or emergency area guidance, and white light is used to provide high-brightness ambient lighting to ensure that personnel can clearly identify the surrounding environment and complete evacuation smoothly.

[0016] The above design makes full use of the technical principle of photoelectric separation, and realizes nS-level fiber break control technology through the reflection of the visible light spectrum, so that the entire system completely avoids the participation of current in the transmission path from the photoelectric lighting centralized power supply to the fire emergency lamps, effectively reducing the hidden dangers of electrical fires. In addition, through the combination of blue laser and ceramic phosphor, the lamps can achieve efficient and stable lighting to meet emergency needs in different scenarios. In short, by integrating fiber optic transmission, laser technology and fluorescence excitation principles, a safe, reliable and versatile fire emergency lighting solution can be created.

[0017] P20: Combined with the generative adversarial network, according to the working characteristics, working environment conditions and element ratios of the fire emergency lamps, the light conversion efficiency attenuation of the fire emergency lamps is predicted respectively, and the predicted light conversion efficiencies are obtained to mark the fire emergency lamps.

[0018] Furthermore, step P20 of the embodiment of the present application further includes: P21: Obtain several working characteristics and several working environment conditions of the several fire emergency lamps, and several element ratios of several ceramic phosphors, wherein the working characteristic is the cumulative turning-on time of the lamps, and the working environment conditions at least include the temperature mean, temperature difference mean, humidity mean and humidity difference mean in a predetermined historical time zone; P22: Randomly select the first working characteristic, the first working environment condition and the first element ratio of the first fire emergency lamp; P23: Combine the generative adversarial network pre-constructed efficiency attenuation prediction plug-in to perform attenuation prediction on the first working characteristic, the first working environment condition and the first element ratio, and output a first attenuation ratio; P24: Subtract the first attenuation ratio from 1 to obtain a first predicted light conversion efficiency, and add it to the several predicted light conversion efficiencies.

[0019] It should be understood that in the fire emergency lighting system, the generative adversarial network (GAN) can be combined to predict the light conversion efficiency attenuation of several fire emergency lamps to provide accurate performance status identification for system optimization.

[0020] First, obtain the working characteristics, working environment conditions and element ratio of ceramic phosphors of several fire emergency lamps. The working characteristics mainly refer to the cumulative opening time of the lamp, that is, the cumulative working time of the lamp from the first start to the current moment, which is a key influencing factor for the change of light conversion efficiency. The working environment conditions include the mean temperature, mean temperature difference, mean humidity and mean humidity difference in a predetermined historical time zone (such as the past 24 hours). These parameters can reflect the stability and coping ability of the operating environment of the lamp. The element ratio refers to the ratio of different components in the ceramic phosphor in the lamp, which directly affects the light efficiency and stability of the visible light after blue light excitation.

[0021] Subsequently, the first working characteristic, the first working environment condition and the first element ratio of a certain fire emergency lamp (such as the first lamp) are randomly selected as input data for attenuation prediction. The random selection mechanism here can effectively avoid data bias and ensure the universality and accuracy of the prediction results.

[0022] Next, the above input data of the lamp is calculated and analyzed through the efficiency attenuation prediction plug-in pre-built by the Generative Adversarial Network (GAN). Generative Adversarial Network is a deep learning model that can capture hidden nonlinear relationships in complex, multi-dimensional data through adversarial training of generators and discriminators, thereby accurately predicting the attenuation trend of light conversion efficiency. Specifically, GAN generates an attenuation model based on the input data, and outputs the first attenuation ratio of the first lamp based on the operating performance of the lamp in the actual environment. The attenuation ratio reflects the degree to which the light conversion efficiency of the lamp is reduced due to the length of use, environmental impact and material aging.

[0023] Finally, the first predicted light conversion efficiency of the first lamp is calculated according to the predicted first attenuation ratio. The calculation formula may be: light conversion efficiency = 1-attenuation ratio. The predicted light conversion efficiency value thus obtained directly reflects the light efficiency status of the lamp, and the value is added to the predicted light conversion efficiency set of all lamps in the system. Through this set, the light efficiency status of all fire emergency lamps in the target building can be comprehensively identified.

[0024] This method effectively combines multi-dimensional working characteristics and environmental parameters through the introduction of GAN technology to achieve accurate prediction of lamp performance, which not only provides a scientific basis for the dynamic regulation of the system, but also ensures the reliability and intelligence of the fire emergency lighting system.

[0025] Further, the efficiency attenuation prediction plug-in is pre-built, and step P23 of the embodiment of the present application further includes: P23-1: Collect sample working feature sets, sample working environment condition sets and sample element ratio sets, and mark the light conversion efficiency attenuation ratios of lamps under different sample working features, sample working environment conditions and sample element ratios to obtain sample attenuation ratio sets; P23-2: Use the sample working feature set, sample working environment condition set, sample element ratio set and sample attenuation ratio set as training sets, and divide them into P equal parts of supervised training data, where P is an integer greater than 10; P23-3: Construct P efficiency attenuation prediction nodes in combination with a generative adversarial network, where each efficiency attenuation prediction node includes a generator and a discriminator; P23-4: Use the P parts of supervised training data to train the P efficiency attenuation prediction nodes respectively until the mean square error loss function converges, and obtain the trained P efficiency attenuation prediction nodes to form the efficiency attenuation prediction plug-in.

[0026] Optionally, in order to build a high-precision efficiency decay prediction plug-in, multiple efficient prediction nodes can be finally formed through sample collection, data annotation, training data division and generative adversarial network (GAN) training process.

[0027] First, collect the sample working feature set, sample working environment condition set and sample element ratio set, and annotate the light conversion efficiency attenuation ratio of the lamps under different sample conditions to generate a sample attenuation ratio set. Here, the sample working features include parameters such as the cumulative opening time of the lamp, the sample working environment conditions cover the historical temperature, humidity and their fluctuation range, and the sample element ratio is the specific information of the ratio of different materials in the ceramic phosphor. The annotation of these data is a key step. Through real experiments or historical data, the changes in the light efficiency of the lamps are quantitatively evaluated to form a systematic attenuation ratio benchmark.

[0028] Next, the above four types of sample data (working feature set, environmental condition set, element ratio set, and attenuation ratio set) are used as training sets and divided equally into P supervised training data to ensure that each data set has a balanced feature distribution and representativeness. Here, P is an integer greater than 10 to ensure sufficient sample distribution to support efficient training of the model.

[0029] Then, the generative adversarial network (GAN) technology is used to construct P efficiency decay prediction nodes. Each node consists of two key modules: the generator and the discriminator. The generator is responsible for generating efficiency decay prediction values ​​based on the input samples, while the discriminator evaluates and provides feedback by comparing the generated values ​​with the true values, gradually optimizing the generator's prediction ability. This adversarial training mechanism of GAN can capture complex nonlinear relationships, enabling the model to show excellent prediction accuracy under multi-dimensional input data.

[0030] Subsequently, the P pieces of supervised training data are used to train the P prediction nodes. During the training process, the mean square error (MSE) is used as the loss function, and the deviation between the prediction result and the true value is measured by minimizing the MSE value. The training is iterated continuously until the loss function of all nodes converges, that is, the prediction error of the model is reduced to an acceptable range.

[0031] Finally, all the P trained efficiency decay prediction nodes are integrated to form a complete efficiency decay prediction plug-in. This plug-in can accurately predict the light conversion efficiency decay of lamps in real time according to the input working characteristics, environmental conditions and element ratios, providing key data support for subsequent dynamic optimization control. Through this process, the efficiency decay prediction plug-in uses GAN technology combined with multi-dimensional training samples to achieve high-precision and high-robustness performance prediction goals, ensuring that the fire emergency lighting system can intelligently adapt to complex environmental changes in actual applications and provide stable and reliable lighting services.

[0032] Further, to predict the first attenuation ratio, step P23 of the embodiment of the present application further includes: P23-5: Obtain the first working characteristic and the first working environment condition, wherein the first working characteristic is the first cumulative opening time, and the first working environment condition includes the first temperature mean, the first temperature difference mean, the first humidity mean and the first humidity difference mean; P23-6: Perform lamp attenuation scale evaluation based on the first cumulative opening time, the first temperature mean, the first temperature difference mean, the first humidity mean and the first humidity difference mean, and output the first attenuation scale coefficient, wherein the attenuation scale coefficient is positively correlated with the cumulative opening time, the temperature mean, the temperature difference mean, the humidity mean and the humidity difference mean; P23-7: Calculate the coefficient ratio of the first attenuation scale coefficient to the maximum attenuation scale coefficient in the predetermined historical window, and multiply it by P and round it to get the first prediction node number; P23-8: Randomly select the efficiency attenuation prediction nodes of the first prediction node number from the P efficiency attenuation prediction nodes, perform attenuation prediction on the first working characteristic, the first working environment condition and the first element ratio, and output the first attenuation ratio after the mean calculation.

[0033] In a possible embodiment of the present application, in the process of predicting the first attenuation ratio, the embodiment of the present application realizes accurate and efficient light conversion efficiency prediction through in-depth analysis of the first working characteristics and the first working environment conditions, combined with the branch optimization selection of the lamp attenuation scale assessment and efficiency attenuation prediction node.

[0034] First, the first working characteristic and the first working environment condition are obtained, wherein the first working characteristic is the cumulative opening time of the lamp, which is one of the core factors of the lamp light efficiency decay and reflects the total use time of the lamp. The first working environment condition includes the first temperature mean, the first temperature difference mean, the first humidity mean and the first humidity difference mean. These environmental parameters can fully characterize the environmental changes and stability of the lamp operation, and have an important impact on the light efficiency decay.

[0035] Next, based on the above parameters, the lamp attenuation scale is evaluated and the first attenuation scale coefficient is output. The attenuation scale coefficient is an indicator that quantifies the trend of the lamp's light efficiency decline, and is positively correlated with the cumulative turn-on time, the average temperature, the average temperature difference, the average humidity, and the average humidity difference. For example, the longer the cumulative turn-on time and the more severe the environmental fluctuations (such as temperature difference and humidity difference), the greater the attenuation scale coefficient, reflecting that the degree to which the lamp's light efficiency is affected is higher.

[0036] Then, the ratio of the first attenuation scale coefficient to the maximum attenuation scale coefficient in the predetermined historical window is calculated to obtain the ratio of the current lamp to the worst attenuation condition in history. The ratio is multiplied by P (the total number of efficiency attenuation prediction nodes) and rounded to obtain the first prediction node number. This step optimizes the adaptability of the prediction by dynamically allocating the number of prediction nodes: the larger the attenuation scale, the more prediction nodes are allocated to ensure the accuracy of the prediction; when the attenuation scale is small, the number of allocated nodes is reduced accordingly, which improves the calculation efficiency of the system.

[0037] Subsequently, nodes of the first prediction node number are randomly selected from the P efficiency decay prediction nodes to perform decay prediction on the first working characteristic, the first working environment condition and the first element ratio. The calculation result of each prediction node reflects the independent evaluation capability of the node for the current input parameters, and finally the first decay ratio is obtained by calculating the mean of the output results of all selected nodes.

[0038] This ensemble learning method greatly improves the analysis efficiency while ensuring the prediction accuracy by dynamically selecting the number of prediction branches. By using only an appropriate number of prediction nodes to participate in the calculation, it avoids resource waste and ensures the model's full learning ability for complex input parameters. The first attenuation ratio of the final output provides reliable data support for subsequent light conversion efficiency calculation and optimization control, further improving the intelligence and robustness of the fire emergency lighting system.

[0039] P30: Generate a three-dimensional building model by simulating the layout structure diagram of the target building, and map and render a number of fire emergency lamps to the three-dimensional building model to generate a three-dimensional building emergency lighting model.

[0040] Specifically, in the process of simulating and generating a three-dimensional building model in combination with the layout structure diagram of the target building, the embodiment of the present application maps the data identifying the fire emergency lighting fixtures to the building model through digital modeling and rendering technology to construct a complete three-dimensional building emergency lighting model. First, obtain the layout structure diagram from the target building. The structure diagram is an important basic data reflecting the building space distribution and functional areas, including room location, channel distribution, exit location and height information. Figure 1 Generally, they are stored in CAD or BIM formats, which can accurately describe the geometric features and spatial relationships of buildings and are the key input for subsequent 3D modeling.

[0041] Next, the layout diagram is converted into a 3D building model through professional simulation modeling tools (such as Revit or 3D Max). In this process, the simulation tool uses the geometric information in the layout diagram (such as plan, section and elevation) to build a 3D digital model that reflects the actual structure of the building. This model not only includes the spatial layout of the building, but also reflects details such as wall material, door and window distribution based on the input data, making the model highly realistic.

[0042] Subsequently, the identified fire emergency lamps are mapped to the 3D building model. The identification data of each fire emergency lamp includes the lamp's location coordinates, light conversion efficiency, lighting range, and predetermined colors (such as green for evacuation instructions, red for warnings, and white for ambient lighting). During the mapping process, the lamps are accurately placed at the actual installation location in the model by matching the lamp identification data with the specific coordinate points in the 3D building model.

[0043] In order to further enhance the visualization effect of the model, rendering technology is used to dynamically express the fire emergency lighting. For example, the functional types of different lighting fixtures can be marked by color, the lighting range of the lighting fixture can be visualized in the form of a light cone, or the lighting effect of the lighting fixture in different environments (such as darkness, smoke, etc.) can be dynamically simulated. This visualization process not only improves the intuitiveness of the 3D model, but also provides a reliable basis for the subsequent optimization of the lighting system.

[0044] Finally, a complete 3D model of building emergency lighting is generated. The model integrates the building structure, lamp distribution and its performance status, and can dynamically reflect the working status of the emergency lighting system in the building. Through the above steps, the construction of the 3D model of building emergency lighting not only realizes digital management, but also provides an accurate analysis tool for the optimization and regulation of the fire emergency system, which helps to improve the efficiency and reliability of emergency lighting.

[0045] P40: Utilize the three-dimensional model of building emergency lighting to perform fixed-point optimization control analysis of the predetermined fire emergency lighting equipment, and output a dynamic optimization control solution to perform adaptive regulation of the emergency lighting of the target building.

[0046] Further, such as Figure 2 As shown, step P40 of the embodiment of the present application also includes: P41: Receive fire sensor data at a fixed point, and simulate the spread of fire in a predetermined time zone according to the fire sensor data in the three-dimensional model of the building, and output a predicted fire spread area; P42: Perform lighting intensity analysis of several fire emergency lamps according to the predicted fire spread area, and output several expected lighting intensities; P43: Utilize the three-dimensional model of building emergency lighting, combine several predicted light conversion efficiencies to compensate for the several expected lighting intensities, and determine several target lighting intensities; P44: Based on the several target lighting intensities, perform emission control parameter optimization analysis of the laser generator in the predetermined time zone, output an optimization control scheme, and perform laser emission control in the predetermined time zone according to the optimization control scheme; P45: Continue to receive fire sensor data to perform fixed-point optimization control analysis, and output a dynamic optimization control scheme to perform adaptive regulation of emergency lighting of the target building.

[0047] It should be understood that when using the three-dimensional model of building emergency lighting to perform fixed-point optimization control analysis of predetermined fire emergency lighting equipment, the embodiment of the present application forms a real-time adaptive control solution through the dynamic linkage of fire sensor data and the lighting system.

[0048] First, fire sensor data is received at fixed points in the building 3D model. Fire sensor data includes information such as temperature, smoke concentration and flame position, which are provided in real time by sensors distributed in the building. Based on this data, the spread of fire in a predetermined time zone is simulated, and the fire spread path and area in the building are predicted using simulation algorithms. Fire spread simulation provides a key environmental basis for subsequent emergency lighting optimization.

[0049] Next, based on the predicted fire spread area, the system analyzes the lighting intensity of the relevant fire emergency lamps. By calculating the lighting demand in the fire-affected area, combined with the personnel evacuation path and visibility requirements, it outputs several expected lighting intensities. For example, lamps in high-risk areas of fire need to provide stronger lighting to ensure clear visibility of the evacuation passage.

[0050] Then, using the three-dimensional model of building emergency lighting, combined with several previously calculated predicted light conversion efficiencies, these expected lighting intensities are compensated. Light conversion efficiency compensation is based on the current performance status of the lamp (such as attenuation ratio) to ensure that the actual lighting effect matches the expected demand. Through calculation, the target lighting intensity of each lamp is determined to meet the emergency lighting needs.

[0051] Furthermore, based on the target lighting intensity, the system optimizes and analyzes the emission control parameters of the laser generator. The control parameters include laser power, emission direction, and distribution intensity. Through optimization analysis, an optimization control scheme is generated, and the working state of the laser generator in the predetermined time zone is adjusted accordingly. For example, the laser power is increased in areas that require high brightness, while the power is reduced in areas with low demand, optimizing energy consumption while ensuring the lighting quality of key areas.

[0052] Finally, the fire sensor data is continuously received and the optimization plan is adjusted in real time. This dynamic optimization continuously updates the control strategy through continuous monitoring of the spread of fire, combined with the deviation analysis between the current lighting intensity and the target intensity, to ensure that the intensity deviation of the overall lighting system is minimized and the best adaptive control effect is achieved.

[0053] Furthermore, step P44 of the embodiment of the present application also includes: P44-1: Acquire the emission control parameter space of the laser generator, and randomly select multiple initial emission control parameters, wherein the emission control parameters include at least laser power, laser beam divergence angle and laser frequency; P44-2: Perform lighting simulation according to the multiple initial emission control parameters, and output multiple simulated lighting intensity sets, wherein each simulated lighting intensity set includes several simulated lighting intensities; P44-3: Taking the several target lighting intensities as references, respectively perform mapping deviation calculations on the multiple simulated lighting intensity sets, output multiple lighting intensity deviation sets, and calculate multiple intensity deviation means; P44-4: Perform lighting fitness evaluation on the multiple initial emission control parameters according to the multiple intensity deviation means, and output multiple lighting fitnesses, wherein the lighting fitness is negatively correlated with the intensity deviation mean; P44-5: Taking the emission control parameter space as a constraint, perform emission control parameter optimization analysis based on the multiple lighting fitnesses and the multiple initial emission control parameters until convergence, and output the optimal emission control parameters, which are set as the optimization control scheme.

[0054] Optionally, in the process of optimizing the emission control parameters of the laser generator, the embodiment of the present application ultimately determines the optimal emission control parameters through exploration of the emission parameter space, illumination simulation and deviation evaluation, and fitness-based optimization analysis.

[0055] First, the emission control parameter space of the laser generator is obtained. The emission control parameter space is a set of all feasible parameters, including at least three key dimensions: laser power, laser beam divergence angle, and laser frequency. Among them, laser power determines the intensity of light, the laser beam divergence angle affects the illumination coverage, and the laser frequency plays a key role in the energy distribution and stability of the laser. Multiple initial emission control parameters are randomly selected from this parameter space as the starting point of the optimization process.

[0056] Next, lighting simulation is performed based on the selected multiple initial emission control parameters. The lighting simulation reproduces the actual lighting effect of each set of emission control parameters in the three-dimensional model of building emergency lighting through simulation technology, and outputs multiple simulated lighting intensity sets. Each simulated lighting intensity set includes several simulated lighting intensities, corresponding to the lighting effects of different areas in the building.

[0057] Then, the mapping deviation calculation is performed for each set of simulated lighting intensity, taking the target lighting intensity predetermined by the system as the benchmark. The mapping deviation calculation is used to evaluate the difference between the simulated lighting intensity and the target lighting intensity, output multiple lighting intensity deviation sets, and further calculate the mean intensity deviation of each deviation set. The mean deviation reflects the overall lighting deviation of the current emission parameter combination and is a key indicator for measuring parameter adaptability.

[0058] Subsequently, the lighting adaptability is evaluated based on the calculated multiple intensity deviation means. Lighting adaptability is an indicator for evaluating the effect of the emission control parameter combination, and is negatively correlated with the intensity deviation mean, that is, the smaller the deviation mean, the higher the adaptability. In this way, the adaptability is used to rank the advantages and disadvantages of the initial emission control parameters, providing a basis for subsequent optimization.

[0059] Finally, with the emission control parameter space as the constraint condition, based on multiple illumination fitness and initial emission control parameters, the emission control parameter optimization analysis is performed. The optimization analysis uses an iterative algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) to explore in the emission control parameter space and gradually adjust the parameter combination to improve the fitness. Through multiple iterative calculations until the fitness converges, the optimal emission control parameters are finally output. This parameter combination is the optimization control scheme, which can be directly used for the emission control of the laser generator.

[0060] Furthermore, step P44-5 of the embodiment of the present application also includes: P44-51: Arrange multiple initial emission control parameters from large to small according to lighting adaptability to generate an initial solution sequence, and select the first predetermined proportion of solutions in the initial solution sequence as preferred individuals to obtain a preferred individual population; P44-52: According to a predetermined plan, perform individual crossover and individual mutation on the preferred individual population to obtain an updated individual population; P44-53: Repeat the selection, crossover, and mutation steps until the preset number of iterations is reached, and output the individual with the largest fitness in the current updated individual population as the optimal emission control parameter.

[0061] Specifically, in the process of determining the optimal emission control parameters, an intelligent search for the emission control parameters is further achieved through a crossover and mutation optimization mechanism based on a preferred individual population, and ultimately an optimal solution with the highest fitness is output.

[0062] First, according to the previously calculated illumination fitness, multiple initial emission control parameters are arranged in order from large to small to generate an initial solution sequence. Each solution in the solution sequence represents a set of emission control parameter combinations, including laser power, laser beam divergence angle, and laser frequency. The fitness of these parameter combinations reflects the degree to which they match the target illumination intensity. Subsequently, several solutions with the highest fitness are selected from the initial solution sequence according to a predetermined ratio (such as the top 30% or 50%) to form a preferred individual population. The preferred individual population represents the emission control parameter combination with better performance in the current solution space, providing a high-quality foundation for subsequent optimization.

[0063] Next, for the preferred individual population, individual crossover and individual mutation operations are performed according to a predetermined plan. The crossover operation refers to selecting some parameters from two preferred individuals for exchange, such as combining the laser power of one individual with the laser beam divergence angle of another individual to generate a new solution. This method can explore new areas of the solution space and avoid falling into local optimality. The mutation operation is to make small random adjustments to individual parameters, such as slightly increasing the laser frequency or reducing the laser power, thereby introducing diversity in the solution space. Through crossover and mutation operations, a new updated individual population is generated, and its fitness is recalculated and evaluated.

[0064] Subsequently, the optimization steps of selection, crossover, and mutation are repeated. In each iteration, the quality of the selected individual population continues to improve, and the new updated population gradually approaches a solution with higher fitness. The system records the individual with the highest fitness in the current population in each iteration until the preset number of iterations is reached, ensuring that the optimization process fully explores the solution space while controlling the computational cost.

[0065] Finally, after reaching the preset number of iterations, the individual with the highest fitness in the current updated individual population is set as the optimal emission control parameter. After multiple rounds of optimization, this parameter combination can achieve the best balance in dimensions such as laser power, divergence angle and frequency, ensuring that the system achieves the optimal configuration of energy efficiency and performance while meeting the target lighting intensity. Through this optimization strategy, efficient search in a complex multi-dimensional parameter space, the optimal emission control parameters finally output can significantly improve the performance and reliability of the fire emergency lighting system, while ensuring flexibility and stability in practical applications.

[0066] In summary, the embodiments of the present application have at least the following technical effects: This application uses photoelectric separation technology to completely eliminate the risk of electrical fires such as short circuits and sparks caused by traditional wire power supply methods, significantly improving the safety of the system in flammable and explosive places and special environments. Generative adversarial networks and building three-dimensional models are used to achieve dynamic optimization control of emergency lighting systems, adjust laser parameters in real time according to environmental changes, ensure accurate matching of lighting coverage and intensity, and improve emergency response capabilities.

[0067] The technical effect of improving the inherent safety, anti-interference and emergency response capabilities of the emergency lighting system through dynamic optimization of optoelectronic separation technology and generative adversarial networks has been achieved.

[0068] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0070] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The adaptive control method of fire emergency lighting is characterized in that: Methods include: Deploy predetermined fire emergency lighting equipment in the target building, wherein the predetermined fire emergency lighting equipment includes a fire emergency lamp group, the fire emergency lamp group includes a plurality of fire emergency lamps, each of which is equipped with ceramic phosphors, and the ceramic phosphors can excite a predetermined color visible light greater than the wavelength of blue light under the irradiation of a blue laser; In combination with a generative adversarial network, according to a number of working characteristics, a number of working environment conditions and a number of element ratios of the number of fire emergency lamps, respectively predict the light conversion efficiency attenuation of the number of fire emergency lamps, obtain a number of predicted light conversion efficiencies to mark the number of fire emergency lamps; Combined with the layout structure diagram of the target building, a three-dimensional building model is generated by simulation, and a plurality of identification fire emergency lamps are mapped and rendered to the three-dimensional building model to generate a three-dimensional building emergency lighting model; The three-dimensional model of building emergency lighting is used to perform fixed-point optimization control analysis of the predetermined fire emergency lighting equipment, and a dynamic optimization control scheme is output to perform adaptive regulation of the emergency lighting of the target building.

2. The adaptive control method of fire emergency lighting according to claim 1, characterized in that: The predetermined fire emergency lighting equipment also includes a photoelectric lighting centralized power supply and an optical fiber module, wherein the photoelectric lighting centralized power supply is embedded with a laser generator, and the laser generator is used to emit a blue laser; The optical fiber module is connected to the fire emergency lamp group, and emits a predetermined color visible light greater than the wavelength of the blue light by projecting the blue laser emitted by the laser generator to the plurality of fire emergency lamps, wherein the predetermined color is green, red or white.

3. The adaptive control method of fire emergency lighting according to claim 1, characterized in that: Combined with the generative adversarial network, according to the working characteristics, working environment conditions and element ratios of the fire emergency lamps, the light conversion efficiency attenuation of the fire emergency lamps is predicted respectively to obtain a number of predicted light conversion efficiencies, including: Obtaining several working characteristics and several working environment conditions of the several fire emergency lamps, and several element ratios of several ceramic phosphors, wherein the working characteristic is the cumulative opening time of the lamps, and the working environment conditions at least include the temperature mean, temperature difference mean, humidity mean, and humidity difference mean in a predetermined historical time zone; Randomly select a first working characteristic, a first working environment condition and a first element ratio of a first fire emergency lighting fixture; In combination with the pre-built efficiency attenuation prediction plug-in of the generative adversarial network, attenuation prediction is performed on the first working characteristic, the first working environment condition and the first element ratio, and a first attenuation ratio is output; The first attenuation ratio is subtracted from 1 to obtain a first predicted light conversion efficiency, which is added to the plurality of predicted light conversion efficiencies.

4. The adaptive control method of fire emergency lighting according to claim 3, characterized in that: Pre-built efficiency decay prediction plugins, including: Collecting a sample working characteristic set, a sample working environment condition set and a sample element ratio set, and marking the light conversion efficiency attenuation ratio of lamps under different sample working characteristics, sample working environment conditions and sample element ratios to obtain a sample attenuation ratio set; The sample working feature set, the sample working environment condition set, the sample element ratio set and the sample attenuation ratio set are used as training sets, and are equally divided into P supervised training data, where P is an integer greater than 10; Combined with the generative adversarial network, P efficiency decay prediction nodes are constructed, where each efficiency decay prediction node includes a generator and a discriminator; The P pieces of supervised training data are used to train the P efficiency decay prediction nodes respectively until the mean square error loss function converges, and the trained P efficiency decay prediction nodes are obtained to form the efficiency decay prediction plug-in.

5. The adaptive control method of fire emergency lighting according to claim 4, characterized in that: Performing attenuation prediction on the first working characteristic, the first working environment condition and the first element ratio, and outputting a first attenuation ratio, includes: Acquire a first working characteristic and a first working environment condition, wherein the first working characteristic is a first accumulated opening time, and the first working environment condition includes a first temperature mean, a first temperature difference mean, a first humidity mean, and a first humidity difference mean; Perform lamp attenuation scale assessment according to the first cumulative opening time, the first temperature average, the first temperature difference average, the first humidity average and the first humidity difference average, and output a first attenuation scale coefficient, wherein the attenuation scale coefficient is positively correlated with the cumulative opening time, the temperature average, the temperature difference average, the humidity average and the humidity difference average; Calculate the coefficient ratio of the first attenuation scale coefficient to the maximum attenuation scale coefficient in a predetermined historical window, multiply the coefficient by P and round it to obtain the first prediction node quantity; Efficiency attenuation prediction nodes of the first number of prediction nodes are randomly selected from the P efficiency attenuation prediction nodes, attenuation prediction is performed on the first working characteristic, the first working environment condition and the first element ratio, and a first attenuation ratio is output after mean calculation.

6. The adaptive control method of fire emergency lighting according to claim 2, characterized in that: Using the three-dimensional model of building emergency lighting, executing the fixed-point optimization control analysis of the predetermined fire emergency lighting equipment, and outputting a dynamic optimization control scheme to perform adaptive regulation of the emergency lighting of the target building, including: receiving fire sensor data at a fixed point, performing a fire spread simulation within a predetermined time zone in the three-dimensional building model according to the fire sensor data, and outputting a predicted fire spread area; Performing lighting intensity analysis of a plurality of fire emergency lamps according to the predicted fire spread area, and outputting a plurality of expected lighting intensities; Using the three-dimensional model of building emergency lighting, combined with a plurality of predicted light conversion efficiencies, the plurality of expected lighting intensities are compensated to determine a plurality of target lighting intensities; Based on the illumination intensities of the several targets, performing an optimization analysis of emission control parameters of the laser generator in the predetermined time zone, outputting an optimization control scheme, and performing laser emission control in the predetermined time zone according to the optimization control scheme; Continue to receive fire sensor data to perform fixed-point optimization control analysis, and output dynamic optimization control solutions to perform adaptive regulation of emergency lighting in target buildings.

7. The adaptive control method of fire emergency lighting according to claim 6, characterized in that: Based on the illumination intensities of the several targets, an optimization analysis of emission control parameters of the laser generator in the predetermined time zone is performed, and an optimization control scheme is output, including: Acquire the emission control parameter space of the laser generator, and randomly select a plurality of initial emission control parameters, wherein the emission control parameters at least include laser power, laser beam divergence angle and laser frequency; Performing lighting simulation according to the multiple initial emission control parameters, and outputting multiple simulated lighting intensity sets, wherein each simulated lighting intensity set includes a plurality of simulated lighting intensities; Taking the several target lighting intensities as references, respectively performing mapping deviation calculations on the multiple simulated lighting intensity sets, outputting multiple lighting intensity deviation sets, and calculating multiple intensity deviation mean values; Performing lighting adaptability evaluation on the multiple initial emission control parameters according to the multiple intensity deviation mean values, and outputting multiple lighting adaptability values, wherein the lighting adaptability value is negatively correlated with the intensity deviation mean value; Taking the emission control parameter space as a constraint, based on the multiple lighting adaptability and multiple initial emission control parameters, an emission control parameter optimization analysis is performed until convergence, and the optimal emission control parameters are output and set as the optimization control scheme.

8. The adaptive control method of fire emergency lighting according to claim 7, characterized in that: Based on the multiple lighting adaptability and the multiple initial emission control parameters, an emission control parameter optimization analysis is performed until convergence, and an optimal emission control parameter is output, including: Arrange multiple initial emission control parameters from large to small according to the lighting adaptability to generate an initial solution sequence, and select the first predetermined proportion of solutions in the initial solution sequence as preferred individuals to obtain a preferred individual population; According to a predetermined plan, performing individual crossover and individual mutation on the preferred individual population to obtain an updated individual population; The steps of selection, crossover, and mutation are repeatedly performed until a preset number of iterations is reached, and the individual with the largest fitness in the current updated individual population is output as the optimal emission control parameter.

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