Emergency lamp intelligent control system based on artificial intelligence

Abstract

The invention discloses an intelligent emergency lamp control system based on artificial intelligence, and relates to the field of emergency lamp control, and the system comprises an emergency lamp 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 for setting a lamp distribution image; the environment monitoring module is used for acquiring environment monitoring data and operation state data of the emergency lamp; the data analysis module is used for analyzing and processing the environment monitoring data, judging whether abnormity exists or not, and acquiring a real-time lamp distribution image according to a judgment result; the association analysis module is used for setting an association relationship according to the distribution condition of each lamp judgment result in the real-time lamp distribution image, and obtaining an association analysis result; the intelligent control module is used for intelligently controlling the corresponding emergency lamp according to the correlation analysis result; according to the invention, the response time of the emergency lamp is saved to a certain extent.

Description

Technical Field

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

[0002] With the continuous expansion of the scale and increase in 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, lack the ability to respond to environmental changes in real time, and may not be able to function effectively in emergency situations. In recent years, with the continuous development of the Internet of Things, artificial intelligence and sensor technology, emergency lighting control systems are gradually developing towards intelligence, automation and efficiency.

[0003] After searching, 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 button trigger module and an audio-visual indication module. The present invention uses a button trigger module and an audio-visual indication module to achieve the switching of emergency light function scenes, so that maintenance personnel can respond quickly and take maintenance measures on the emergency light in the shortest time. The ADC sampling module, charging control module and LCD discharge control module are used to achieve intelligent charging and discharging management of the emergency light. Through intelligent charging and discharging management, the emergency light can be prevented from being unable to be used normally in an emergency.

[0004] When an emergency occurs, emergency lights can help people identify the direction of a safe path in the first place. However, emergency lights often realize their functions according to changes in the environment. As the environment changes, people are more likely to avoid safety accidents when an emergency occurs. Therefore, how to provide danger warnings as much as possible before the environment changes, save the response time of emergency lights, and improve the accuracy of the response process are issues we need to consider. To this end, we now provide an emergency light intelligent control system based on artificial intelligence. Summary of the invention

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

[0006] The purpose of the present invention can be achieved by the following technical solutions: an emergency light intelligent control system based on artificial intelligence, including an emergency light management platform, wherein 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;

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

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

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

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

[0011] The intelligent control module is used to adjust the lighting mode of the corresponding emergency lamps in real time according to the correlation analysis results and operation status data of the emergency lamps, and to intelligently manage and control the corresponding lamps according to the adjustment information.

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

[0013] A lamp input unit and an image distribution unit are provided; the lamp input unit is used to input basic information of the lamp, and the basic information of the lamp includes lamp parameter information and lamp installation information;

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

[0015] Furthermore, the process of the environmental monitoring module obtaining the environmental monitoring data and operating status data of each emergency lamp includes:

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

[0017] The corresponding emergency lamps are provided with hardware monitoring sensors, which monitor the working status of the emergency lamps to obtain corresponding operating status data, including power data information and lighting brightness data.

[0018] Furthermore, the data analysis module analyzes and processes the environmental monitoring data to determine whether there is an abnormality at the corresponding position of each emergency lamp, including:

[0019] An edge analysis node is set according to the lamp distribution image, and the environmental monitoring data and operating status data obtained in the corresponding emergency lamps are analyzed and processed through the edge analysis node;

[0020] Obtain the historical environmental monitoring data and historical operating status data in the emergency lighting management platform, conduct statistical analysis on the abnormal rates of different types of environmental monitoring data and operating status data in the historical environmental monitoring data, and obtain the historical data abnormal rates of light intensity data, humidity data, vibration intensity data, water level data, smoke data, infrared thermal imaging data, and power data information respectively;

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

[0022] An environmental monitoring loop link is set according to the sorting results. The environmental monitoring loop link sets corresponding data processing sub-nodes in sequence according to the sorting results of the abnormality rates of different types of historical data. The data processing sub-nodes are used to analyze and process the corresponding types of data, determine whether there are abnormalities in the data of the type corresponding to the corresponding emergency lighting fixture, and analyze and process different types of environmental monitoring data in sequence according to the sorting results of the data processing sub-nodes in the environmental monitoring loop link to obtain analysis results. The analysis results of the corresponding emergency lighting fixtures in each data processing sub-node in the environmental monitoring loop link are packaged to obtain the environmental monitoring analysis data packet at the current moment.

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

[0024] If all types of environmental monitoring data in the environmental monitoring analysis data package do not contain abnormalities, then there are no abnormalities in the environment surrounding the emergency lighting fixture;

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

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

[0027] The analysis results obtained for each emergency lamp are mapped into the corresponding lamp distribution image, a real-time lamp distribution image is obtained, and the obtained real-time lamp distribution image is sent to the correlation analysis module.

[0028] Furthermore, the process of the correlation analysis module verifying the judgment results of each emergency lighting fixture includes:

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

[0030] Preset a horizontal verification cycle, obtain the environmental monitoring analysis data packet obtained by the emergency lighting in each unit time during the horizontal verification cycle, the horizontal verification analysis is used to analyze and process the obtained environmental monitoring analysis data packet, obtain the change smoothing value of each type of data information in the horizontal verification cycle, and judge whether the corresponding data information has an abnormality according to the change smoothing value;

[0031] Preset a vertical verification cycle, obtain the environmental monitoring analysis data packets obtained by the emergency lighting fixtures adjacent to the abnormal emergency lighting fixtures in each unit time during the vertical verification cycle, and obtain the abnormal type of the environmental monitoring data in the emergency lighting fixtures;

[0032] A corresponding lonely forest model is constructed according to the different types of anomalies, and the corresponding types of environmental monitoring data in the longitudinal verification cycle of adjacent and abnormal emergency lamps are input into the lonely forest model to determine whether there are data anomalies.

[0033] Furthermore, the process of the correlation analysis module setting the correlation analysis results of each emergency lighting fixture includes:

[0034] Obtain the location and abnormal deviation value of abnormal emergency lamps in the real-time lamp distribution image, perform abnormal area statistics based on the location information of each abnormal emergency lamp, and obtain abnormal area data;

[0035] Preset an association mapping function according to the abnormal type of the emergency lighting fixture, input the obtained abnormal area data and abnormal deviation value into the association mapping function, analyze and process the association mapping function, and output the association radius and the association coefficient of the corresponding association radius;

[0036] The obtained correlation radius and the correlation coefficient corresponding to the correlation radius are subjected to correlation analysis in the real-time lamp distribution image to obtain the correlation analysis result.

[0037] Furthermore, the process of the intelligent control module intelligently controlling the corresponding lamps includes:

[0038] Obtain the correlation analysis results and operation 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;

[0039] The obtained operating status data is compared and analyzed with the lighting mode, and intelligent regulation is performed based on the comparison and analysis results.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. Generate corresponding lamp distribution images through lamp basic information and building result information, obtain corresponding real-time lamp distribution images according to environmental monitoring data and operation status data of each emergency lamp in the lamp distribution image, conduct unified analysis on the real-time lamp distribution image, judge the association relationship of different emergency lamps in the building, and make corresponding association analysis results for other emergency lamps in the real-time lamp distribution image according to the association relationship, so as to realize intelligent management and control of other emergency lamps. Other emergency lamps do not need to respond to emergencies according to environmental monitoring results, thus saving response time;

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

[0043] 3. Verify and analyze the environmental monitoring analysis data packets obtained in the edge analysis node, and verify and analyze the abnormal data in the emergency lighting fixtures through horizontal verification analysis and vertical verification analysis, which reduces the errors in the environmental monitoring process to a certain extent and improves the accuracy of the emergency lighting response process. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of an emergency light intelligent control system based on artificial intelligence according to an embodiment of the present application. DETAILED DESCRIPTION

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

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

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

[0048] Set up the lighting fixture entry unit and image distribution unit;

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

[0050] The lamp parameter information includes various parameters of emergency lamps, 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 light;

[0052] The lighting installation information includes installation location information, installation method information, and electrical connection information;

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

[0054] An information entry window is provided, through which the building structure information and building use information in the corresponding building are entered, the building structure information includes a building plan and a building section, and the building use information is the use information of the corresponding building structure, including multiple types such as public places, commercial places or civil places;

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

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

[0057] Obtaining lamp installation information, obtaining the distribution of corresponding emergency lights in the initial building image according to the installation position information and installation method information in the lamp installation information, and marking the coordinates in the three-dimensional space coordinate system corresponding to the initial building image according to the distribution of the emergency lights;

[0058] The electrical connection information of the emergency lighting fixtures is obtained, and the obtained electrical connection information is electrically planned in the initial building image corresponding to the three-dimensional space coordinate system, and the distribution image of the lighting fixtures in the corresponding building is obtained according to the electrical planning result.

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

[0060] Setting up an environmental monitoring unit, a lighting monitoring unit and a data transmission unit;

[0061] The environmental monitoring unit is used to obtain environmental monitoring data at the corresponding lamp position. The corresponding emergency lamp is provided with an environmental monitoring sensor, and the environmental monitoring sensor is used to perform environmental monitoring to obtain environmental monitoring data;

[0062] Environmental monitoring sensors include light sensors, humidity sensors, vibration sensors, water level sensors, smoke sensors, and infrared sensors, among which:

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

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

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

[0066] The water level sensor is used to monitor the water level data in the environment surrounding the emergency lighting fixture;

[0067] The smoke sensor is used to monitor the smoke conditions 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] Mark the obtained light intensity data, humidity data, vibration intensity data, water level data, smoke data and infrared thermal imaging data as environmental monitoring data;

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

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

[0072] If it exists, the obtained emergency lighting fixture is associated with the emergency lighting fixture, and the obtained emergency lighting fixture is used to obtain the emergency lighting fixture associated with it according to the associated radius monitoring interval;

[0073] If it does not exist, then obtain the emergency lighting fixtures adjacent to the emergency lighting fixture, and obtain the emergency lighting fixtures associated with it;

[0074] The mutually related emergency lamps are marked as related emergency lamp groups, an environmental monitoring period is set, the obtained related emergency lamp groups are sorted by the distance from the origin position in the lamp distribution image, and the obtained related emergency lamp groups are respectively associated with the unit time in the environmental monitoring period according to the sorting result;

[0075] According to the association result, the collection time of all emergency lamps in the associated emergency lamp group is set in sequence;

[0076] The environmental monitoring unit acquires environmental monitoring data in the surrounding environment of the corresponding emergency lamps in the lamp distribution image according to the set acquisition time;

[0077] The lamp monitoring unit is used to obtain the operating status data corresponding to each emergency lamp. The corresponding emergency lamp is provided with a hardware monitoring sensor, and the operating status of the emergency lamp is monitored by the hardware monitoring sensor;

[0078] Hardware monitoring sensors include voltage sensors and brightness sensors;

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

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

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

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

[0083] A wireless communication terminal is provided in the corresponding emergency lighting fixture, and the wireless communication terminal is used to receive or send data information that needs to be transmitted in the emergency lighting fixture.

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

[0085] The data analysis module obtains the lamp distribution image, sets the edge analysis node according to the distribution of the emergency lamps in the lamp distribution image, and analyzes and processes the environmental monitoring data and operating status data obtained in the corresponding emergency lamps through the edge analysis node;

[0086] The edge analysis node obtains the historical environmental monitoring data and historical operating status data in the emergency lighting management platform, performs statistical analysis on the abnormal rates of different types of environmental monitoring data and operating status data in the historical environmental monitoring data, and obtains the historical data abnormal rates of light intensity data, humidity data, vibration intensity data, water level data, smoke data, infrared thermal imaging data, and power data information;

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

[0088] An environmental monitoring loop link is set according to the sorting results. The environmental monitoring loop link sequentially sets data processing sub-nodes according to the sorting results of abnormal rates of different types of historical data. The data processing sub-nodes are used to analyze and process the corresponding types of data. Different types of environmental monitoring data are analyzed and processed in sequence according to the sorting results of the data processing sub-nodes in the environmental monitoring loop link, wherein:

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

[0090] The horizontal coordinate in the two-dimensional standard coordinate system is the corresponding unit time, and the vertical coordinate is the corresponding data; the corresponding type of environmental monitoring data is mapped to the two-dimensional standard coordinate system corresponding to the data processing sub-node;

[0091] A standard threshold interval of the corresponding environmental monitoring data is set in the two-dimensional standard coordinate system, and a comparative analysis is performed between the mapping result of the environmental monitoring data in the two-dimensional standard coordinate system and the corresponding standard threshold interval, and whether the corresponding data processing sub-node has an abnormality is determined according to the comparative analysis result;

[0092] If the corresponding environmental monitoring data falls within the standard threshold range, then there is no abnormality in the corresponding environmental monitoring data;

[0093] If the corresponding environmental monitoring data does not belong to the standard threshold interval, then the corresponding environmental monitoring data is abnormal, and the abnormal deviation value between the corresponding environmental monitoring data and the standard threshold interval is obtained;

[0094] The obtained analysis results and abnormal deviation values ​​are uniformly stored;

[0095] An infrared thermal imaging analysis model is provided in the data processing sub-node corresponding to the infrared thermal imaging data, and the infrared thermal imaging analysis model is used to analyze and process the corresponding infrared thermal imaging analysis model to determine the corresponding infrared proportion data;

[0096] Set the corresponding standard threshold interval according to the infrared proportion data, compare and analyze the obtained infrared proportion data with the standard threshold interval to determine whether there is an abnormality;

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

[0098] The environmental monitoring and analysis data packets obtained by the edge analysis node are analyzed and processed. The specific implementation process includes:

[0099] If all types of environmental monitoring data in the environmental monitoring analysis data package do not contain abnormalities, then there are no abnormalities in the environment surrounding the emergency lighting fixture;

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

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

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

[0103] Obtaining comprehensive abnormal data of the emergency lighting fixture according to the obtained abnormal weight factor and abnormal deviation value, and determining the abnormal type of the emergency lighting fixture according to the comprehensive abnormal data;

[0104] The obtained analysis results are mapped into the corresponding lamp distribution image, the real-time lamp distribution image is obtained, and the obtained real-time lamp distribution image is sent to the correlation analysis module.

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

[0106] The association analysis module is provided with a data verification unit and an association analysis unit;

[0107] The data verification unit is used to obtain a 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 and 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 and analysis data packets. 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 emergency lighting with abnormalities in each unit time;

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

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

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

[0113] S t =αy t +(1+α)S t-1 ;

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

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

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

[0117] The longitudinal verification analysis is used to analyze and process the environmental monitoring analysis data packets obtained from the emergency lighting fixture with abnormalities and the emergency lighting fixtures adjacent to it in the real-time lighting fixture distribution image, and the specific implementation process includes:

[0118] Preset a vertical verification cycle, obtain the environmental monitoring analysis data packets obtained by the emergency lighting fixtures adjacent to the abnormal emergency lighting fixtures in each unit time during the vertical verification cycle, and obtain the abnormal type of the environmental monitoring data in the emergency lighting fixtures;

[0119] According to the different types of abnormalities, a corresponding lonely forest model is constructed, and the corresponding types of environmental monitoring data in the longitudinal verification cycle of the adjacent emergency lighting fixtures with abnormalities are input into the lonely forest model to determine whether there are data abnormalities;

[0120] If there is abnormal data, the corresponding environmental monitoring data will be set as an abnormal point and marked;

[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 results and the vertical verification analysis results corresponding to the emergency lighting environment monitoring analysis data packet in the corresponding real-time lighting distribution image, and performs a comprehensive analysis on the horizontal verification analysis results and the vertical verification analysis results;

[0123] If there are abnormalities in both the horizontal verification analysis results and the vertical verification analysis results, the abnormal marking results 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 collection time in the emergency lighting fixture is adjusted, and the corresponding environmental monitoring data is re-acquired for analysis and processing to determine whether there is an abnormality in the environment around the emergency lighting fixture;

[0125] If there is no abnormality in the horizontal verification analysis results and the vertical verification analysis results, then there is an abnormality in the surrounding environment of the emergency lighting fixture;

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

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

[0128] According to the location information of the emergency lighting fixtures of the corresponding abnormal type, the abnormal area is counted according to the location information of each emergency lighting fixture, and the abnormal level interval of the environmental monitoring data of the corresponding abnormal type in each emergency lighting fixture is calculated;

[0129] Analyze and process the obtained abnormal area statistics and the abnormal level intervals of each abnormal type of environmental monitoring data;

[0130] According to the abnormal type, the association mapping function is preset, and the obtained abnormal area statistics and the abnormal deviation value of the emergency lighting marked as abnormal are input into the association mapping function, which is analyzed and processed by the association mapping function to output the association radius and the association coefficient of the corresponding association 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, taking the abnormal emergency lighting fixture as the center, sequentially obtain the correlation coefficients of the corresponding correlation radius from the center to the end point;

[0133] The obtained correlation radius and the correlation coefficient corresponding to the correlation radius are subjected to correlation analysis in the real-time lamp distribution image, and a correlation analysis library is preset, in which the correlation radius corresponding to different abnormal types and the emergency lamp lighting modes corresponding to the correlation coefficient are stored, and the obtained correlation radius and the correlation coefficient are input into the correlation analysis library to obtain the correlation analysis results of each emergency lamp on the correlation radius, and the correlation analysis results include the emergency lamp lighting modes corresponding to the relevant emergency lamps;

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

[0135] The intelligent control module is used to adjust the lighting mode of the corresponding emergency lamps in real time according to the correlation analysis results of the lamps, environmental monitoring data and operation status data, and to intelligently control the corresponding lamps according to the adjustment information. The specific implementation process includes:

[0136] Obtain the correlation analysis results and operation 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 lighting fixtures, and compare and analyze the obtained operating status data with the lighting mode:

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

[0139] If the operating status data does not match the lighting mode, adjust the operating parameters of the emergency lighting fixture until the lighting mode matches the operating status data.

[0140] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An emergency light intelligent control system based on artificial intelligence, including an emergency light management platform, characterized in that: The emergency light management platform is provided with a light 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 basic information of lamps and set a lamp distribution image according to the basic information of lamps; The environmental monitoring module is used to obtain environmental monitoring data at corresponding positions of each emergency lamp and operating status data of each emergency lamp according to the lamp distribution image; The data analysis module is used to analyze and process the obtained environmental monitoring data, determine whether there is an abnormality at the corresponding position of each emergency lamp in the lamp distribution image, and if there is an abnormality, determine the corresponding abnormality type, map the judgment result to the lamp distribution image, and obtain a real-time lamp distribution image; The association 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, and set the corresponding association relationship, and set the association analysis results of other emergency lamps according to the association relationship; The intelligent control module is used to adjust the lighting mode of the corresponding emergency lamps in real time according to the correlation analysis results and operation status data of the emergency lamps, and to perform intelligent management and control of the corresponding emergency lamps.

2. The emergency light intelligent control system based on artificial intelligence according to claim 1 is characterized in that: The process of setting the lighting distribution image in the lighting management module includes: Input basic information of emergency lamps and building structure information in the building, wherein the basic information includes lamp parameter information and lamp installation information; generate an initial building image according to the building structure information, obtain the distribution of each emergency lamp in the initial building image according to the lamp installation information, mark the coordinate information of the emergency lamps, and obtain the corresponding lamp distribution image according to the marking result.

3. The emergency light intelligent control system based on artificial intelligence according to claim 2 is characterized in that: The process of the environmental monitoring module obtaining the environmental monitoring data and operating status data of each emergency lamp includes: The corresponding emergency lighting fixtures are equipped with environmental monitoring sensors and hardware monitoring sensors; The environmental monitoring sensor is used to monitor the environment around the emergency lighting fixture and obtain corresponding environmental monitoring data, which includes light intensity data, humidity data, vibration intensity data, water level data, smoke data and infrared thermal imaging data; The hardware monitoring sensor is used to monitor the working status of the emergency lighting fixture and obtain corresponding operating status data, wherein the operating status data includes power data information and lighting brightness data.

4. The emergency light intelligent control system based on artificial intelligence according to claim 3 is characterized in that: The data analysis module analyzes and processes the environmental monitoring data to determine whether there are abnormalities at the corresponding positions of each emergency lamp. The process includes: An edge analysis node is set according to the lamp distribution image, and the environmental monitoring data and operating status data obtained in the corresponding emergency lamps are analyzed and processed through the edge analysis node; Obtain the historical environmental monitoring data and historical operating status data in the emergency lighting management platform, conduct statistical analysis on the abnormal rates of different types of environmental monitoring data and operating status data in the historical environmental monitoring data, and obtain the historical data abnormal rates of light intensity data, humidity data, vibration intensity data, water level data, smoke data, infrared thermal imaging data, and power data information respectively; Sort the historical data anomaly rates of different types of data obtained from high to low; An environmental monitoring loop link is set according to the sorting results. The environmental monitoring loop link sets corresponding data processing sub-nodes in sequence according to the sorting results of the abnormality rates of different types of historical data. The data processing sub-nodes are used to analyze and process the corresponding types of data, determine whether there are abnormalities in the data of the type corresponding to the corresponding emergency lighting fixture, and analyze and process different types of environmental monitoring data in sequence according to the sorting results of the data processing sub-nodes in the environmental monitoring loop link to obtain analysis results. The analysis results of the corresponding emergency lighting fixtures in each data processing sub-node in the environmental monitoring loop link are packaged to obtain the environmental monitoring analysis data packet at the current moment.

5. The emergency light intelligent control system based on artificial intelligence according to claim 4 is characterized in that: The process of the data analysis module obtaining real-time lighting distribution images includes: If all types of environmental monitoring data in the environmental monitoring analysis data package do not contain abnormalities, then there are no abnormalities in the surrounding environment of the corresponding emergency lighting fixtures; If there is only one type of abnormal environmental monitoring data in the environmental monitoring analysis data packet, the corresponding environmental monitoring data is set as the abnormal type; If there are multiple types of environmental monitoring data anomalies in the environmental monitoring analysis data packet, obtain the anomaly deviation value of each type of environmental monitoring data, and obtain the anomaly type according to the anomaly deviation value of each type of environmental monitoring data; The analysis results obtained for each emergency lamp are mapped into the corresponding lamp distribution image, a real-time lamp distribution image is obtained, and the obtained real-time lamp distribution image is sent to the correlation analysis module.

6. The emergency light intelligent control system based on artificial intelligence according to claim 5 is characterized in that: The process of the correlation analysis module verifying the judgment results of each emergency lighting fixture includes: Obtain environmental monitoring and analysis data packets corresponding to abnormal emergency lamps in the real-time lamp distribution image, and perform horizontal verification analysis and vertical verification analysis on the obtained environmental monitoring and analysis data packets; Preset a horizontal verification cycle, obtain the environmental monitoring analysis data packet obtained by the emergency lighting in each unit time during the horizontal verification cycle, the horizontal verification analysis is used to analyze and process the obtained environmental monitoring analysis data packet, obtain the change smoothing value of each type of data information in the horizontal verification cycle, and judge whether the corresponding data information has an abnormality according to the change smoothing value; Preset a vertical verification cycle, obtain the environmental monitoring analysis data packets obtained by the emergency lighting fixtures adjacent to the abnormal emergency lighting fixtures in each unit time during the vertical verification cycle, and obtain the abnormal type of the environmental monitoring data in the emergency lighting fixtures; A corresponding lonely forest model is constructed according to the different types of anomalies, and the corresponding types of environmental monitoring data in the longitudinal verification cycle of adjacent and abnormal emergency lamps are input into the lonely forest model to determine whether there are data anomalies.

7. The emergency light intelligent control system based on artificial intelligence according to claim 6 is characterized in that: The process of setting the correlation analysis results of each emergency lighting fixture in the correlation analysis module includes: Obtain the location and abnormal deviation value of abnormal emergency lamps in the real-time lamp distribution image, perform abnormal area statistics based on the location information of each abnormal emergency lamp, and obtain abnormal area data; Preset an association mapping function according to the abnormal type of the emergency lighting fixture, input the obtained abnormal area data and abnormal deviation value into the association mapping function, analyze and process the association mapping function, and output the association radius and the association coefficient of the corresponding association radius; The obtained correlation radius and the correlation coefficient corresponding to the correlation radius are subjected to correlation analysis in the real-time lamp distribution image to obtain the correlation analysis result.

8. The emergency light intelligent control system based on artificial intelligence according to claim 7 is characterized in that: The process of intelligent control module for intelligently controlling the corresponding lamps includes: Obtain the correlation analysis results and operation 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; The obtained operating status data is compared and analyzed with the lighting mode, and intelligent regulation is performed based on the comparison and analysis results.

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