Artificial Intelligence-based Intelligent Control System for Emergency Lights

Abstract

The present invention discloses an intelligent control system for emergency lights based on artificial intelligence. The present invention relates to the field of emergency light control and includes an emergency light management platform, which is provided with a lamp management module, an environment monitoring module, a data analysis module, a correlation analysis module, and an intelligent control module; the lamp management module is used to set the lamp distribution image; the environment monitoring module is used to obtain the environment monitoring data and operation status data of the emergency lights; the data analysis module is used to analyze and process the environment monitoring data, judge whether there is an abnormality, and obtain the real-time lamp distribution image according to the judgment result; the correlation analysis module is used to set the correlation relationship according to the distribution of the judgment results of each lamp in the real-time lamp distribution image and obtain the correlation analysis result; the intelligent control module is used to perform intelligent management and control on the corresponding emergency lights according to the correlation analysis result; the present invention saves the response time of the emergency lights to a certain extent.

Description

Technical Field

[0001] The present invention relates to the field of emergency light control, and specifically to an intelligent control system for emergency lights based on artificial intelligence. Background Art

[0002] With the continuous expansion of the scale and the increasing complexity of modern buildings, the importance of emergency lighting systems in ensuring personnel safety has become increasingly prominent. Traditional emergency lighting systems usually rely on simple switch control and fixed power backup, lacking the ability to respond to environmental changes in real time and may not function effectively in emergencies; in recent years, with the continuous development of Internet of Things, artificial intelligence and sensor technologies, emergency light control systems are gradually developing towards intelligence, automation and high efficiency;

[0003] After retrieval, the publication number CN110461074A discloses an emergency light automatic detection multi-mode switching system, method and emergency light. Among them, an emergency light automatic detection multi-mode switching system includes: an intelligent control unit, a key trigger module and an acoustic-optic indication module. The present invention realizes the switching of the emergency light function scenarios by adopting the key trigger module and the acoustic-optic indication module, enabling maintenance personnel to react quickly and perform maintenance on the emergency light in the shortest time. By using an ADC sampling module, a charging control module and an LCD discharge control module, intelligent charge and discharge management of the emergency light is realized, and the situation where the emergency light cannot be used normally in an emergency is avoided through intelligent charge and discharge management.

[0004] In the event of an emergency, emergency lights can help people identify the safe path direction in the first time. However, emergency lights often realize their functions according to environmental changes. But as the environment changes, the probability of avoiding safety accidents when an emergency occurs is greater. Therefore, how to carry out danger warning as much as possible before the environment changes, save the response time of emergency lighting fixtures, and improve the accuracy during the response process is a problem we need to consider. For this reason, an intelligent control system for emergency lights based on artificial intelligence is provided herein. Summary of the Invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide an intelligent control system for emergency lights based on artificial intelligence.

[0006] The purpose of the present invention can be achieved by the following technical solutions: An intelligent control system for emergency lights based on artificial intelligence, including an emergency light management platform, wherein a lamp management module, an environment monitoring module, a data analysis module, a correlation analysis module and an intelligent control module are arranged in the emergency light management platform;

[0007] The lamp management module is used to obtain the basic information of the lamps and set the lamp distribution image according to the basic information of the lamps;

[0008] The environmental monitoring module is used to obtain the environmental monitoring data at the corresponding positions of each emergency light and the operation status data of each emergency light according to the light distribution image;

[0009] The data analysis module is used to analyze and process the obtained environmental monitoring data, judge whether there is an abnormality at the corresponding position of each emergency light in the light distribution image. If there is an abnormality, judge the corresponding type of abnormality, map the judgment result to the light distribution image, and obtain the real-time light distribution image;

[0010] The correlation analysis module is used to perform verification processing according to the distribution of the judgment results of each light in the real-time light distribution image, set the corresponding correlation relationship, and set the correlation analysis results of each emergency light according to the correlation relationship;

[0011] The intelligent control module is used to adjust the lighting mode of the corresponding emergency light in real time according to the correlation analysis result and the operation status data of the emergency light, and perform intelligent management and control on the corresponding light according to the adjustment information.

[0012] Further, the process of the lamp management module setting the light distribution image includes:

[0013] Set a lamp entry unit and an image distribution unit; the lamp entry unit is used to enter the basic lamp information, and the basic lamp information includes lamp parameter information and lamp installation information;

[0014] The image distribution unit is used to obtain the building structure information in the building, generate an initial building image according to the building structure information, obtain the distribution of each emergency light in the initial building image according to the lamp installation information, mark the coordinate information of the emergency light, and obtain the corresponding light distribution image according to the marking result.

[0015] Further, the process of the environmental monitoring module obtaining the environmental monitoring data and operation status data of each emergency light includes:

[0016] An environmental monitoring sensor is arranged in the corresponding emergency light, and the environmental monitoring sensor is used to monitor the environmental data around the emergency light to obtain the corresponding environmental monitoring data. The environmental monitoring data includes light intensity data, humidity data, vibration intensity data, water level height data, smoke data and infrared thermal imaging data;

[0017] A hardware monitoring sensor is arranged in the corresponding emergency light, and the working state of the emergency light is monitored through the hardware monitoring sensor to obtain the corresponding operation status data. The operation status data includes power data information and lighting brightness data.

[0018] Further, the process of the data analysis module analyzing and processing the environmental monitoring data to determine whether there are abnormalities in the corresponding positions of each emergency light includes:

[0019] Set edge analysis nodes according to the lamp distribution image, and analyze and process the environmental monitoring data and operation status data obtained in the corresponding emergency lights through the edge analysis nodes;

[0020] Obtain the historical environmental monitoring data and historical operation status data in the emergency light management platform, statistically analyze the abnormality rates of different types of environmental monitoring data and operation status data in the historical environmental monitoring data, and respectively obtain the historical data abnormality rates of light intensity data, humidity data, vibration intensity data, water level height data, smoke data, infrared thermal imaging data, and power data information;

[0021] Sort the historical data abnormality rates of the obtained different types of data from high to low;

[0022] Set an environmental monitoring loop link according to the sorting result. The environmental monitoring loop link sequentially sets corresponding data processing sub-nodes according to the sorting result of different types of historical data abnormality rates. The data processing sub-nodes are used to analyze and process the corresponding type of data, determine whether there are abnormalities in the data corresponding to the corresponding emergency lights, and sequentially analyze and process different types of environmental monitoring data according to the sorting result of the data processing sub-nodes in the environmental monitoring loop link to obtain an analysis result. Package the analysis results of each data processing sub-node of the corresponding emergency light in the environmental monitoring loop link to obtain the environmental monitoring analysis data packet at the current moment.

[0023] Further, the process of the data analysis module obtaining the real-time lamp distribution image includes:

[0024] If there are no abnormalities in all types of environmental monitoring data in the environmental monitoring analysis data packet, there are no abnormalities in the surrounding environment of the emergency light;

[0025] If there is only one type of abnormal environmental monitoring data in the environmental monitoring analysis data packet, set the corresponding environmental monitoring data as the abnormal type;

[0026] If there are multiple types of abnormal environmental monitoring data in the environmental monitoring analysis data packet, obtain the abnormal deviation values of each type of environmental monitoring data, and obtain the abnormal type according to the abnormal deviation values of each environmental monitoring data;

[0027] Map the analysis results obtained for each emergency light to the corresponding lamp distribution image to obtain the real-time lamp distribution image, and send the obtained real-time lamp distribution image to the correlation analysis module.

[0028] Further, the process of the association analysis module verifying the judgment results of each emergency light includes:

[0029] Obtain the environmental monitoring analysis data packets corresponding to the abnormal emergency lights in the real-time lamp distribution image, and perform horizontal verification analysis and vertical verification analysis on the obtained environmental monitoring analysis data packets respectively;

[0030] Preset a horizontal verification period, obtain the environmental monitoring analysis data packets obtained by the emergency lights in each unit time within the horizontal verification period. The horizontal verification analysis is used to analyze and process the obtained environmental monitoring analysis data packets, obtain the change smoothing values of various types of data information within the horizontal verification period, and judge whether the corresponding data information is abnormal according to the change smoothing values;

[0031] Preset a vertical verification period, obtain the environmental monitoring analysis data packets obtained by the emergency lights adjacent to the abnormal emergency lights in each unit time within the vertical verification period, and obtain the abnormal types of the environmental monitoring data in the emergency lights being abnormal;

[0032] Construct corresponding isolation forest models according to different abnormal types, input the environmental monitoring data of the corresponding types within the vertical verification period in the adjacent and abnormal emergency lights into the isolation forest models, and judge whether there is data abnormality therein.

[0033] Further, the process of the association analysis module setting the association analysis results of each emergency light includes:

[0034] Obtain the positions and abnormal deviation values of the abnormal emergency lights in the real-time lamp distribution image, perform abnormal area statistics according to the position information of each abnormal emergency light, and obtain abnormal area data;

[0035] Preset an association mapping function according to the abnormal types of the emergency lights, input the obtained abnormal area data and abnormal deviation values into the association mapping function, and perform analysis and processing by the association mapping function to output the association radius and the correlation coefficient of the corresponding association radius;

[0036] Perform association analysis on the obtained association radius and the correlation coefficient corresponding to the association radius in the real-time lamp distribution image to obtain the association analysis result.

[0037] Further, the process of the intelligent control module performing intelligent management and control on the corresponding lamps includes:

[0038] Obtain the association analysis results and operating status data of each emergency light in the real-time lamp distribution image, and set the lighting modes of each emergency light in the real-time lamp distribution image according to the association analysis results;

[0039] Compare and analyze the obtained operating status data with the lighting mode, and perform intelligent regulation according to the comparison and analysis results.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] 1. Generate corresponding lamp distribution images based on the basic information of the lamps and the building structure information. Obtain the corresponding real-time lamp distribution images according to the environmental monitoring data and operating status data of each emergency lamp in the lamp distribution images. Conduct unified analysis on the real-time lamp distribution images to judge the association relationships of different emergency lamps in the building. Make corresponding association analysis results for other emergency lamps in the real-time lamp distribution images according to the association relationships, so as to realize the intelligent control of other emergency lamps. Other emergency lamps do not need to perform emergency responses according to the environmental monitoring results, thus saving response time;

[0042] 2. By setting edge analysis nodes, set corresponding environmental monitoring loop links in the edge analysis nodes, and sequentially analyze and process the environmental monitoring data obtained from the corresponding emergency lamps by the environmental monitoring loop links to obtain whether there are abnormalities in the corresponding data information. In this process, the efficiency of data analysis in the emergency lamps is improved;

[0043] 3. Conduct verification analysis on the environmental monitoring analysis data packets obtained in the edge analysis nodes. Verify the abnormal data in the emergency lamps through horizontal verification analysis and vertical verification analysis, which reduces the error in the environmental monitoring process to a certain extent and improves the accuracy in the emergency lamp response process. Description of the Drawings

[0044] Figure 1 It is the schematic diagram of the intelligent control system for emergency lights based on artificial intelligence in the embodiment of the present application. Detailed Embodiments

[0045] As Figure 1 shown, the intelligent control system for emergency lights based on artificial intelligence includes an emergency light management platform, and a lamp management module, an environmental monitoring module, a data analysis module, an association analysis module, and an intelligent control module are arranged in the emergency light management platform.

[0046] The emergency light management platform obtains corresponding emergency light devices, analyzes and processes the operation process of the obtained emergency light devices, and realizes the remote control and management of the emergency lights.

[0047] The lamp management module is used to obtain the basic information of the lamps, set the lamp distribution images according to the basic information of the lamps, and send the obtained lamp distribution images to the data analysis module. Its specific implementation process includes:

[0048] Set a lamp input unit and an image distribution unit;

[0049] The lamp input unit is used to input the basic information of the lamp. The basic information of the lamp includes lamp parameter information, battery characteristic information, and lamp installation information, where:

[0050] The lamp parameter information includes various parameters of the emergency lamp, such as rated power, luminous flux, lighting mode, lamp function information, lamp material information, etc.;

[0051] The battery characteristic information includes the battery type information and battery capacity information of the emergency lamp, etc.;

[0052] The lamp installation information includes installation location information, installation method information, and electrical connection information, etc.;

[0053] The image distribution unit is used to set the lamp distribution image according to the basic information of the lamp, store the obtained lamp distribution image, and send it to other modules;

[0054] An information input window is provided, and the building structure information and building use information of the corresponding building are input through the information input window. The building structure information includes the building floor plan and building section drawing, and the building use information is the use information of the corresponding building structure, including various types such as public places, commercial places, or civilian places;

[0055] Obtain the building structure information in the building, and generate an initial building image based on the building structure information;

[0056] Set up a three-dimensional space coordinate system, and map the initial building image into the three-dimensional space coordinate system;

[0057] Obtain the lamp installation information, obtain the distribution situation of the corresponding emergency lamp in the initial building image according to the installation location information and installation method information in the lamp installation information, and perform coordinate marking in the three-dimensional space coordinate system corresponding to the initial building image according to the distribution situation of the emergency lamp;

[0058] Obtain the electrical connection information of the emergency lamp, perform electrical planning on the obtained electrical connection information in the initial building image corresponding to the three-dimensional space coordinate system, and obtain the lamp distribution image in the corresponding building according to the electrical planning result.

[0059] The environmental monitoring module is used to obtain the environmental monitoring data and operating status data at the corresponding lamp position according to the lamp distribution image, and send the obtained environmental monitoring data to the data analysis module. Its specific implementation process includes:

[0060] Set up an environmental monitoring unit, a lamp monitoring unit, and a data transmission unit;

[0061] The environmental monitoring unit is used to obtain environmental monitoring data at the position of the corresponding lamp. An environmental monitoring sensor is provided in the corresponding emergency lamp, and environmental monitoring is carried out through the environmental monitoring sensor to obtain environmental monitoring data;

[0062] The environmental monitoring sensor includes a light sensor, a humidity sensor, a vibration sensor, a water level sensor, a smoke sensor, and an infrared sensor, where:

[0063] The light sensor is used to monitor the light intensity data of the environment around the emergency lamp;

[0064] The humidity sensor is used to monitor the humidity data at the location of the emergency lamp;

[0065] The vibration sensor is used to monitor the vibration intensity data in the environment around the emergency lamp;

[0066] The water level sensor is used to monitor the water level height data in the environment around the emergency lamp;

[0067] The smoke sensor is used to monitor the smoke situation in the environment around the lamp and obtain the corresponding smoke data;

[0068] The infrared sensor is used to monitor the infrared thermal imaging data within the lighting range of the lamp;

[0069] The obtained light intensity data, humidity data, vibration intensity data, water level height data, smoke data, and infrared thermal imaging data are marked as environmental monitoring data;

[0070] Obtain the distribution of each lamp in the lamp distribution image, and set an associated radius monitoring interval according to the distribution image of each lamp;

[0071] Obtain the emergency lamp at the corresponding coordinate marked position, and determine whether there is an emergency lamp separated from it within the associated radius monitoring interval:

[0072] If there is, associate the obtained emergency lamp with this emergency lamp, and obtain the emergency lamp associated with it according to the associated radius monitoring interval;

[0073] If not, obtain the adjacent emergency lamp to this emergency lamp, and obtain the emergency lamp associated with it from the adjacent one;

[0074] Mark the associated emergency lamps as an associated emergency lamp group, set an environmental monitoring period, sort the obtained associated emergency lamp group according to the distance from the origin position in the lamp distribution image, and associate the obtained associated emergency lamp group with the unit time within the environmental monitoring period according to the sorting result;

[0075] Set the acquisition time of all emergency lights in the associated emergency light group in sequence according to the association result;

[0076] The environmental monitoring unit obtains the environmental monitoring data in the environment around the corresponding emergency light in the light distribution image according to the set acquisition time;

[0077] The lamp monitoring unit is used to obtain the operation status data corresponding to each emergency light. A hardware monitoring sensor is set in the corresponding emergency light, and the working status of the emergency light is monitored through the hardware monitoring sensor;

[0078] The hardware monitoring sensor includes a voltage sensor and a brightness sensor;

[0079] The voltage sensor is used to monitor the input voltage of the emergency light in real time and obtain the power data information of the emergency light;

[0080] The brightness sensor is used to obtain the illumination brightness data of the corresponding emergency light during operation;

[0081] Mark the obtained power data information and illumination brightness data as the operation status data of the corresponding emergency light;

[0082] The data transmission unit is used to transmit the environmental monitoring data and operation status data of each emergency light obtained in the environmental monitoring unit and the lamp monitoring unit in real time, and send them to the data analysis module;

[0083] A wireless communication terminal is set in the corresponding emergency light, and the wireless communication terminal is used to receive or send the data information required to be transmitted in the emergency light.

[0084] The data analysis module is used to analyze and process the obtained environmental monitoring data, judge whether there is an abnormality in the light distribution image corresponding to the corresponding lamp. If there is an abnormality, judge the corresponding abnormality type, and send the obtained judgment result to the association analysis module. Its specific implementation process includes:

[0085] The data analysis module obtains the light distribution image, sets edge analysis nodes according to the distribution of emergency lights in the light distribution image, and analyzes and processes the environmental monitoring data and operation status data obtained in the corresponding emergency lights through the edge analysis nodes;

[0086] The edge analysis node obtains the historical environmental monitoring data and historical operation status data in the emergency light management platform, statistically analyzes the abnormality rates of different types of environmental monitoring data and operation status data in the historical environmental monitoring data, and respectively obtains the historical data abnormality rates of light intensity data, humidity data, vibration intensity data, water level height data, smoke data, infrared thermal imaging data, and power data information;

[0087] Sort the historical data anomaly rates of different types of obtained data from high to low;

[0088] Set up an environmental monitoring loop link according to the sorting result. The environmental monitoring loop link sequentially sets data processing sub-nodes according to the sorting result of the historical data anomaly rates of different types. The data processing sub-nodes are used to analyze and process the corresponding type of data, and sequentially analyze and process different types of environmental monitoring data according to the sorting result of the data processing sub-nodes in the environmental monitoring loop link, where:

[0089] In the data processing sub-nodes corresponding to different types of light intensity data, humidity data, vibration intensity data, water level height data, smoke data, and power data information, two-dimensional standard coordinate systems of the corresponding type of data with respect to time are respectively set;

[0090] In the two-dimensional standard coordinate system, the abscissa is the corresponding unit time, and the ordinate is the corresponding data; map the corresponding type of environmental monitoring data into the two-dimensional standard coordinate system corresponding to the data processing sub-node;

[0091] In the two-dimensional standard coordinate system, a standard threshold interval of the corresponding environmental monitoring data is set. Compare and analyze the mapping result of the environmental monitoring data in the two-dimensional standard coordinate system with the corresponding standard threshold interval, and judge whether there is an anomaly in the corresponding data processing sub-node according to the comparison and analysis result;

[0092] If the corresponding environmental monitoring data belongs to the standard threshold interval, the corresponding environmental monitoring data has no anomaly;

[0093] If the corresponding environmental monitoring data does not belong to the standard threshold interval, the corresponding environmental monitoring data has an anomaly, and obtain the anomaly deviation value between the corresponding environmental monitoring data and the standard threshold interval;

[0094] Unify and store the obtained analysis results and anomaly deviation values;

[0095] In the data processing sub-node corresponding to the infrared thermal imaging data, an infrared thermal imaging analysis model is set. The infrared thermal imaging analysis model is used to analyze and process the corresponding infrared thermal imaging analysis model and judge the corresponding infrared ratio data;

[0096] Set a corresponding standard threshold interval according to the infrared ratio data, compare and analyze the obtained infrared ratio data with the standard threshold interval, and judge whether there is an anomaly;

[0097] The edge analysis node packs the analysis results of each data processing sub-node in the environmental monitoring loop link of the corresponding emergency lamp into an environmental monitoring analysis data packet at the current moment;

[0098] Analyze and process the environmental monitoring analysis data packets obtained by the edge analysis nodes. The specific implementation process includes:

[0099] If there are no abnormalities in all types of environmental monitoring data in the environmental monitoring analysis data packet, then there are no abnormalities in the surrounding environment of the emergency lamp;

[0100] If there is only one type of abnormal environmental monitoring data in the environmental monitoring analysis data packet, set the corresponding environmental monitoring data as the abnormal type;

[0101] If there are multiple types of abnormal environmental monitoring data in the environmental monitoring analysis data packet, obtain the abnormal deviation values of each type of environmental monitoring data, and obtain the abnormal type according to the abnormal deviation values of each environmental monitoring data;

[0102] Analyze and process according to the historical environmental monitoring data and historical operating status data in the emergency lamp management platform, and build a component association model based on the deep learning algorithm. The component association model is used to analyze and process the abnormal working states of other environments caused by the abnormalities of different environmental monitoring data, and obtain the abnormal weight factor;

[0103] Obtain the comprehensive abnormal data of the emergency lamp according to the obtained abnormal weight factor and abnormal deviation value, and judge the abnormal type of the emergency lamp according to the comprehensive abnormal data;

[0104] Map the obtained analysis results to the corresponding lamp distribution image to obtain the real-time lamp distribution image, and send the obtained real-time lamp distribution image to the correlation analysis module.

[0105] The correlation analysis module is used to set the correlation relationship according to the distribution of the judgment results of each lamp in the lamp distribution image, and set the corresponding correlation analysis result according to the correlation relationship. The specific implementation process includes:

[0106] A data verification unit and a correlation analysis unit are set in the correlation analysis module;

[0107] The data verification unit is used to obtain the real-time lamp distribution image and analyze and process the environmental monitoring analysis data packets corresponding to the edge analysis nodes corresponding to each emergency lamp in the real-time lamp distribution image;

[0108] Obtain the environmental monitoring analysis data packets corresponding to the abnormal emergency lamps in the real-time lamp distribution image, and perform horizontal verification analysis and vertical verification analysis on the obtained environmental monitoring analysis data packets respectively. The specific implementation process includes:

[0109] The horizontal verification analysis is used to analyze and process the environmental monitoring analysis data packets obtained by the abnormal emergency lamps at each unit time;

[0110] Preset a horizontal verification period, obtain the environmental monitoring analysis data packets obtained by the emergency lights within each unit time during the horizontal verification period, and analyze and process the verification results of the two-dimensional coordinate system corresponding to the abnormal environmental monitoring data types;

[0111] Obtain the mapping results of the two-dimensional coordinate system corresponding to each unit time during the horizontal verification period, and analyze and process the obtained mapping results to determine whether there are any abnormalities:

[0112] Mark the unit time corresponding to the horizontal verification period as t, and mark the environmental monitoring data corresponding to the t moment as y t , set a variation coefficient α, where 0 < α < 1, obtain the variation smoothing value, and mark it as S t , where:

[0113] S t = αy t +(1 + α)S t-1 ;

[0114] Preset a smoothing threshold, and mark it as N;

[0115] If |y t - S t | > N, then y t is abnormal data, and mark the corresponding unit time;

[0116] If |y t - S t | ≤ N, then y t is not abnormal data;

[0117] The longitudinal verification analysis is used to analyze and process the environmental monitoring analysis data packets obtained from the emergency lights adjacent to the abnormal emergency lights in the real-time lamp distribution image. The specific implementation process includes:

[0118] Preset a longitudinal verification period, obtain the environmental monitoring analysis data packets obtained by the emergency lights adjacent to the abnormal emergency lights within each unit time during the longitudinal verification period, and obtain the abnormal types of the environmental monitoring data in the emergency lights;

[0119] Construct corresponding isolation forest models according to different abnormal types, input the environmental monitoring data of the corresponding types within the longitudinal verification period in the adjacent and abnormal emergency lights into the isolation forest models, and determine whether there are any data abnormalities;

[0120] If there is abnormal data, set the corresponding environmental monitoring data as abnormal points and mark them;

[0121] If there is no abnormal data, the corresponding environmental monitoring data will not be set as an abnormal point;

[0122] The data verification unit obtains the horizontal verification analysis result and the vertical verification analysis result corresponding to the emergency lighting environmental monitoring analysis data packet in the corresponding real-time lighting distribution image, and comprehensively analyzes the horizontal verification analysis result and the vertical verification analysis result;

[0123] If there are abnormalities in both the horizontal verification analysis result and the vertical verification analysis result, the abnormal marking result of the emergency lighting will be revoked;

[0124] If there is an abnormality in the horizontal verification analysis result or the vertical verification analysis result, the data acquisition time in the emergency lighting will be adjusted, and the corresponding environmental monitoring data will be obtained again for analysis and processing to determine whether there is an abnormality in the environment around the emergency lighting;

[0125] If there are no abnormalities in the horizontal verification analysis result and the vertical verification analysis result, there is an abnormality in the environment around the emergency lighting;

[0126] The correlation analysis unit obtains the real-time lighting distribution image after verification analysis, and analyzes and processes the emergency lighting with abnormalities in the real-time lighting distribution image;

[0127] Obtain the locations of the emergency lighting with the same abnormal type in the real-time lighting distribution image for statistical analysis. The specific implementation process includes:

[0128] According to the location information of the emergency lighting with the corresponding abnormal type, the abnormal area is statistically analyzed based on the location information of each emergency lighting, and the abnormal level interval of the environmental monitoring data corresponding to the abnormal type in each emergency lighting;

[0129] Analyze and process the obtained abnormal area statistical result and the abnormal level interval of the environmental monitoring data of each abnormal type;

[0130] According to the preset correlation mapping function for the abnormal type, input the obtained abnormal area statistical result and the abnormal deviation value of the emergency lighting marked as abnormal into the correlation mapping function, and the correlation mapping function performs analysis and processing to output the correlation radius and the correlation coefficient of the corresponding correlation radius. The specific implementation process includes:

[0131] Among them, R is the correlation radius, x is the abnormal deviation value, μ is the abnormal area data, and σ is the standard coefficient;

[0132] According to the obtained correlation radius, with the abnormal emergency lighting as the center, sequentially obtain the correlation coefficients from the center to the endpoints of the corresponding correlation radius;

[0133] Perform correlation analysis on the obtained correlation radius and the correlation coefficient corresponding to the correlation radius within the real-time lamp distribution image. Preset a correlation analysis library, which stores the correlation radius corresponding to different abnormal types and the emergency lamp lighting modes corresponding to the correlation coefficients. Input the obtained correlation radius and correlation coefficient into the correlation analysis library to obtain the correlation analysis results of each emergency lamp on the correlation radius. The correlation analysis results include the emergency lamp lighting modes corresponding to the relevant emergency lamps;

[0134] It should be further noted that in the specific implementation process, the emergency lamp lighting mode includes the corresponding lighting light intensity and lighting function mode, and the lighting light intensity and lighting function mode corresponding to different emergency lamp lighting modes are different.

[0135] The intelligent control module is used to perform real-time adjustment on the corresponding emergency lamp lighting mode according to the correlation analysis results, environmental monitoring data, and operating status data of the lamps, and perform intelligent management and control on the corresponding lamps according to the adjustment information. The specific implementation process includes:

[0136] Obtain the correlation analysis results and operating status data of each emergency lamp in the real-time lamp distribution image, and set the lighting mode of each emergency lamp in the real-time lamp distribution image according to the correlation analysis results;

[0137] Obtain the operating status data of the corresponding emergency lamp, and perform a comparative analysis on the obtained operating status data and the lighting mode:

[0138] If the operating status data is consistent with the lighting mode, no analysis and processing are performed on it;

[0139] If the operating status data is inconsistent with the lighting mode, adjust the operating parameters of the emergency lamp until the lighting mode is consistent with the operating status data.

[0140] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent control system for emergency lights based on artificial intelligence, including an emergency light management platform, characterized in that, The emergency light management platform is provided with a lamp management module, an environment monitoring module, a data analysis module, a correlation analysis module and an intelligent control module; The lamp management module is used to obtain the basic lamp information and set the lamp distribution image according to the basic lamp information; The environment monitoring module is used to obtain the environment monitoring data at the corresponding positions of each emergency lamp and the operation status data of each emergency lamp according to the lamp distribution image; The data analysis module is used to analyze and process the obtained environment monitoring data, judge whether there is an abnormality at the corresponding position of each emergency lamp in the lamp distribution image, if there is an abnormality, judge the corresponding abnormality type, map the judgment result to the lamp distribution image, and obtain the real-time lamp distribution image; The correlation analysis module is used to perform verification processing according to the distribution of the judgment results of each emergency lamp in the real-time lamp distribution image, set the corresponding correlation relationship, and set the correlation analysis results of other emergency lamps according to the correlation relationship; The intelligent control module is used to adjust the lighting mode of the corresponding emergency lamp in real time according to the correlation analysis result and the operation status data of the emergency lamp, and perform intelligent control on the corresponding emergency lamp; The process of the lamp management module setting the lamp distribution image includes: Inputting the basic lamp information of the emergency lamp and the building structure information in the building, where the basic lamp information includes lamp parameter information and lamp installation information; generating an initial building image according to the building structure information, obtaining the distribution of each emergency lamp in the initial building image according to the lamp installation information, marking the coordinate information of the emergency lamp, and obtaining the corresponding lamp distribution image according to the marking result; The process of the environment monitoring module obtaining the environment monitoring data and the operation status data of each emergency lamp includes: The corresponding emergency lamp is provided with an environment monitoring sensor and a hardware monitoring sensor; The environment monitoring sensor is used to monitor the environment around the emergency lamp and obtain the corresponding environment monitoring data. The environment monitoring data includes light intensity data, humidity data, vibration intensity data, water level height data, smoke data, infrared thermal imaging data and radar data. The radar data includes the number data, human body status data and human heart rate data of the human bodies existing around the emergency lamp; The hardware monitoring sensor is used to monitor the working state of the emergency lamp and obtain the corresponding operation status data. The operation status data includes power data information, lighting brightness data and environment monitoring sensor status data; The process of the data analysis module analyzing and processing the environment monitoring data and judging whether there is an abnormality at the corresponding position of each emergency lamp includes: Setting edge analysis nodes according to the lamp distribution image, and analyzing and processing the environment monitoring data and operation status data obtained in the corresponding emergency lamp through the edge analysis nodes; Obtain the historical environmental monitoring data and historical operating status data in the emergency light management platform, and conduct statistical analysis on the abnormal rates of different types of environmental monitoring data and operating status data in the historical environmental monitoring data, respectively obtaining the historical data abnormal rates of light intensity data, humidity data, vibration intensity data, water level height data, smoke data, infrared thermal imaging data, radar data, and power data information; Sort the historical data abnormal rates of different types of data obtained from high to low; Set up an environmental monitoring loop link according to the sorting result. The environmental monitoring loop link sequentially sets corresponding data processing sub-nodes according to the sorting result of different types of historical data abnormal rates. The data processing sub-nodes are used to analyze and process the corresponding type of data, determine whether there is an abnormality in the data corresponding to the corresponding emergency light, and sequentially analyze and process different types of environmental monitoring data according to the sorting result of the data processing sub-nodes in the environmental monitoring loop link to obtain an analysis result. Package the analysis results of each data processing sub-node of the corresponding emergency light in the environmental monitoring loop link to obtain an environmental monitoring analysis data packet at the current moment; The process by which the data analysis module obtains the real-time lamp distribution image includes: If there are no abnormalities in all types of environmental monitoring data in the environmental monitoring analysis data packet, there are no abnormalities in the surrounding environment of the corresponding emergency light; If there is only one type of abnormal environmental monitoring data in the environmental monitoring analysis data packet, set the corresponding environmental monitoring data as the abnormal type; If there are multiple types of abnormal environmental monitoring data in the environmental monitoring analysis data packet, obtain the abnormal deviation values of each type of environmental monitoring data, and obtain the abnormal type according to the abnormal deviation values of each environmental monitoring data; Map the analysis results obtained for each emergency light to the corresponding lamp distribution image to obtain a real-time lamp distribution image, and send the obtained real-time lamp distribution image to the correlation analysis module; The process by which the correlation analysis module verifies and processes the judgment results of each emergency light includes: Obtain the environmental monitoring analysis data packet corresponding to the abnormal emergency light in the real-time lamp distribution image, and conduct horizontal verification analysis and vertical verification analysis on the obtained environmental monitoring analysis data packet respectively; Preset a horizontal verification period, and obtain the environmental monitoring analysis data packets obtained by the emergency light within each unit time during the horizontal verification period. The horizontal verification analysis is used to analyze and process the obtained environmental monitoring analysis data packet to obtain the change smoothing value of each type of data information during the horizontal verification period, and determine whether there is an abnormality in the corresponding data information according to the change smoothing value; Preset a vertical verification period, obtain the environmental monitoring analysis data packets obtained by the emergency lights adjacent to the abnormal emergency light within each unit time during the vertical verification period, and obtain the abnormal type of the environmental monitoring data in the emergency light that is abnormal; Construct corresponding isolation forest models according to different types of anomalies, and input the environmental monitoring data of corresponding types within the longitudinal verification period of adjacent and anomalous emergency lighting fixtures into the isolation forest models to determine whether there are data anomalies among them.

2. The intelligent control system for emergency lights based on artificial intelligence according to claim 1, wherein The process of the association analysis module setting the association analysis results of each emergency lighting fixture includes: Obtain the positions and anomaly deviation values of the emergency lighting fixtures with anomalies in the real-time lighting fixture distribution image, perform anomaly area statistics based on the position information of each anomalous emergency lighting fixture, and obtain anomaly area data; Preset an association mapping function according to the anomaly type of the emergency lighting fixture, input the obtained anomaly area data and anomaly deviation values into the association mapping function, and perform analysis and processing by the association mapping function to output the association radius and the correlation coefficient corresponding to the association radius; Perform association analysis on the obtained association radius and the correlation coefficient corresponding to the association radius in the real-time lighting fixture distribution image to obtain the association analysis result.

3. The intelligent control system for emergency lights based on artificial intelligence according to claim 2, wherein The process of the intelligent control module performing intelligent management and control on the corresponding lighting fixtures includes: Obtain the association analysis results and operating status data of each emergency lighting fixture in the real-time lighting fixture distribution image, and set the lighting modes of each emergency lighting fixture in the real-time lighting fixture distribution image according to the association analysis results; Compare and analyze the obtained operating status data with the lighting modes, and perform intelligent regulation according to the comparison and analysis results.

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