CN117715275B - Intelligent subway tunnel illumination regulation and control system and method - Google Patents
Intelligent subway tunnel illumination regulation and control system and method Download PDFInfo
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Abstract
The application provides an intelligent regulation and control system and method for subway tunnel illumination, which relate to the technical field of tunnel illumination, and the method comprises the following steps: collecting time information, traffic information, sound information and image information, making an illumination brightness decision according to a plurality of traffic information, obtaining the rest degree of a current point according to the time information, obtaining the rest degree of a user according to a plurality of sound information, identifying the rest degree of the user in a tunnel area according to a plurality of image information, calculating a plurality of comprehensive rest degrees, performing brightness information correction calculation, performing discrimination compensation according to the change gradient of a plurality of traffic information, and obtaining a plurality of third illumination information for illumination control. The method mainly solves the problems that the accuracy and timeliness of the existing subway tunnel illumination regulation and control method are insufficient and dynamic regulation cannot be realized due to the fact that the existing subway tunnel illumination regulation and control method is too single. Through intelligent regulation and control, the situation of excessive illumination or insufficient illumination can be avoided, energy saving and emission reduction are realized, and subway riding experience of a user is improved.
Description
Technical Field
The application relates to the technical field of tunnel illumination, in particular to an intelligent subway tunnel illumination regulation and control system and method.
Background
Along with the rapid development of urban subway traffic, subway tunnel illumination plays an increasingly important role in ensuring the safe operation of subways and improving energy efficiency. However, the conventional subway tunnel illumination control manner generally adopts manual control or simple time sequence control, and cannot dynamically adjust illumination equipment according to real-time position information of a subway train, so that an illumination effect is poor and energy is wasted. Meanwhile, as the running time and the traffic flow of the subway train have uncertainty, the illumination requirement of the tunnel also changes.
However, in the process of implementing the technical scheme of the embodiment of the application, the above technology is found to have at least the following technical problems:
The existing subway tunnel illumination regulation and control method is too single, so that the accuracy and timeliness of the method are insufficient, and dynamic adjustment cannot be realized.
Disclosure of Invention
The method mainly solves the problems that the accuracy and timeliness of the existing subway tunnel illumination regulation and control method are insufficient and dynamic regulation cannot be realized due to the fact that the existing subway tunnel illumination regulation and control method is too single.
In view of the above problems, the present application provides an intelligent subway tunnel illumination control system and method, and in a first aspect, the present application provides an intelligent subway tunnel illumination control method, where the method includes: collecting time information, and traffic information, sound information and image information of a plurality of tunnel areas in the subway tunnel through a sensor array arranged in the subway tunnel; according to the traffic information, making illumination brightness decisions of the tunnel areas to obtain first illumination brightness information; analyzing and acquiring the rest degrees of the users in the tunnel areas according to the time information to obtain a first rest degree and a plurality of second rest degrees; according to the image information, performing image processing, identifying rest degrees of users in the tunnel areas, obtaining a plurality of third rest degrees, and combining the first rest degrees and the second rest degrees, and calculating to obtain a plurality of comprehensive rest degrees; correcting and calculating the first illumination brightness information according to the comprehensive rest degrees to obtain second illumination brightness information; and according to the gradient of the variation of the plurality of people flow rate information, judging and compensating the plurality of second illumination brightness information to obtain a plurality of third illumination brightness information, and performing illumination control.
In a second aspect, the application provides an intelligent subway tunnel illumination regulation and control system, which comprises: the time information acquisition module is used for acquiring time information, and traffic information, sound information and image information of a plurality of tunnel areas in the subway tunnel through a sensor array configured in the subway tunnel; the first illumination brightness information acquisition module is used for making illumination brightness decisions of the tunnel areas according to the traffic information to obtain first illumination brightness information; the second rest degree acquisition module is used for analyzing and acquiring the rest degree of the subway tunnel at the current time point according to the time information to obtain a first rest degree, and analyzing and acquiring the rest degrees of users in the tunnel areas according to a plurality of pieces of sound information to obtain a plurality of second rest degrees; the third rest degree acquisition module is used for carrying out image processing according to the plurality of image information, identifying the rest degrees of users in the plurality of tunnel areas, obtaining a plurality of third rest degrees, combining the first rest degrees and the plurality of second rest degrees, and calculating to obtain a plurality of comprehensive rest degrees; the second illumination brightness information acquisition module is used for correcting and calculating the plurality of first illumination brightness information according to the plurality of comprehensive rest degrees to obtain a plurality of second illumination brightness information; and the illumination control module is used for judging and compensating the plurality of second illumination brightness information according to the change gradient of the plurality of people flow information to obtain a plurality of third illumination brightness information and carrying out illumination control.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The application provides an intelligent regulation and control system and method for subway tunnel illumination, which relate to the technical field of tunnel illumination, and the method comprises the following steps: collecting time information, traffic information, sound information and image information, making an illumination brightness decision according to a plurality of traffic information, obtaining the rest degree of a current point according to the time information, obtaining the rest degree of a user according to a plurality of sound information, identifying the rest degree of the user in a tunnel area according to a plurality of image information, calculating a plurality of comprehensive rest degrees, performing brightness information correction calculation, performing discrimination compensation according to the change gradient of a plurality of traffic information, and obtaining a plurality of third illumination information for illumination control.
The method mainly solves the problems that the accuracy and timeliness of the existing subway tunnel illumination regulation and control method are insufficient and dynamic regulation cannot be realized due to the fact that the existing subway tunnel illumination regulation and control method is too single. Through intelligent regulation and control, the situation of excessive illumination or insufficient illumination can be avoided, energy saving and emission reduction are realized, and subway riding experience of a user is improved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an intelligent subway tunnel illumination regulation method according to an embodiment of the application;
fig. 2 is a schematic flow chart of a method for obtaining a plurality of comprehensive rest degrees in an intelligent regulation and control method for subway tunnel illumination according to an embodiment of the application;
Fig. 3 is a schematic flow chart of a method for obtaining a plurality of user image segmentation results in an intelligent subway tunnel illumination regulation and control method according to an embodiment of the application;
fig. 4 is a schematic structural diagram of an intelligent subway tunnel lighting control system according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a time information acquisition module 10, a first illumination brightness information acquisition module 20, a second rest degree acquisition module 30, a third rest degree acquisition module 40, a second illumination brightness information acquisition module 50 and an illumination control module 60.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The method mainly solves the problems that the accuracy and timeliness of the existing subway tunnel illumination regulation and control method are insufficient and dynamic regulation cannot be realized due to the fact that the existing subway tunnel illumination regulation and control method is too single. Through intelligent regulation and control, the situation of excessive illumination or insufficient illumination can be avoided, energy saving and emission reduction are realized, and subway riding experience of a user is improved.
For a better understanding of the foregoing technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments of the present invention:
example 1
The intelligent subway tunnel illumination regulation and control method as shown in fig. 1 comprises the following steps:
Collecting time information, and traffic information, sound information and image information of a plurality of tunnel areas in the subway tunnel through a sensor array arranged in the subway tunnel;
Specifically, accurate time information is acquired by a high-precision time server, GPS, or other device. The time server can provide nanosecond time precision, and accuracy of time information is ensured. The GPS can provide global positioning service to ensure the real-time and accuracy of the time information. The method comprises the steps of collecting people flow information of a plurality of tunnel areas in a subway tunnel, arranging people flow sensors or monitoring cameras in the tunnel, and detecting people flow by utilizing technical means such as infrared and microwave. Meanwhile, by combining the image recognition technology of the monitoring camera, people flow data can be counted more accurately. Sound information in a subway tunnel is collected, and sound data in the tunnel is collected by using an acoustic sensor, a microphone or the like. The sound sensor may detect the noise level in the tunnel and the microphone may collect more comprehensive sound information. And acquiring image information in the subway tunnel, and acquiring the images in the tunnel in real time by using equipment such as a high-speed linear array camera, an infrared camera or a common monitoring camera. More useful information can be extracted in combination with image processing and recognition techniques.
According to the traffic information, making illumination brightness decisions of the tunnel areas to obtain first illumination brightness information;
Specifically, the collected people flow data is transmitted to the lighting regulation and control center through a communication network. And the lighting regulation and control center calculates the lighting requirement of each area in real time according to the traffic data. The greater the traffic, the higher the required illumination intensity. According to the traffic data, the lighting control center uses a preset algorithm or model (such as a linear regression model, a neural network, etc.) to perform decision calculation of the lighting brightness. And the illumination regulation and control center generates illumination brightness instructions of each area according to the decision result. For example, the more people, the greater the brightness needs to be. According to the actual situation in the subway tunnel, a plurality of tunnel areas are divided into a plurality of stations, transfer channels, staircases and the like. Different lighting strategies are set according to the functions and requirements of different areas. For example: station area: and setting higher illumination brightness to meet the visual demands of passengers. Transfer channel: the illumination brightness is properly reduced, and the energy is saved. Stairwell: by adopting induction type illumination, pedestrians can automatically light up when passing by, and the pedestrians can automatically close when no one passes by. According to the time information and weather conditions, the illumination brightness of each area is dynamically adjusted so as to adapt to the requirements under different time periods and weather conditions. For example, the illumination brightness in the tunnel is appropriately increased during a sunny day or a daytime, and the illumination brightness is appropriately decreased during a cloudy day or a night time.
Analyzing and acquiring the rest degrees of the users in the tunnel areas according to the time information to obtain a first rest degree and a plurality of second rest degrees;
specifically, the rest degree of the subway tunnel at the current time point is obtained according to time information analysis, the collected time information is compared with a preset time table, and the time period of the current time point, such as the daytime, the evening, the night and the like, is identified. And according to the time period of the current time point, inquiring a preset rest degree value table, and determining a corresponding rest degree value. The rest value may be set according to actual requirements, e.g. 0 for the lowest activity level and 100 for the highest activity level. And taking the determined rest degree value as a first rest degree for subsequent illumination brightness decision. And acquiring the voice information of each tunnel region according to the rest degrees of the users in the tunnel regions by analyzing the voice information, wherein the voice information comprises parameters such as loudness and frequency of the voice. The collected sound information is compared with preset sound characteristics, and the type of sound or activity type, such as human voice, vehicle voice, construction voice and the like, is identified. And according to the identified sound type or activity type, inquiring a preset sound activity level table, and determining a corresponding rest degree value. Similarly, the rest degree value can be set according to actual requirements. And taking the determined rest degree value as a second rest degree of the area for subsequent illumination brightness decision. The rest degree of the subway tunnel at the current time point and the rest degrees of users in the tunnel areas can be accurately obtained according to the time information and the sound information, and a more comprehensive basis is provided for the decision of the illumination brightness. This helps realizing more accurate illumination adjustment, improves comfort and the energy efficiency of tunnel illumination.
According to the image information, performing image processing, identifying rest degrees of users in the tunnel areas, obtaining a plurality of third rest degrees, and combining the first rest degrees and the second rest degrees, and calculating to obtain a plurality of comprehensive rest degrees;
Specifically, the professional cable is used for connecting the camera with the image collector, so that the stability and the instantaneity of data transmission are ensured. The digital image processing software is used for preprocessing the acquired image, including denoising, contrast enhancement, color balancing and other operations. If the image blur or dynamic range is too large, appropriate sharpening or exposure compensation is required. The processed image is converted into a standard format, such as JPG or PNG, for subsequent processing. And (3) performing target detection on the preprocessed image by using an image processing library (such as OpenCV), and identifying travelers and other dynamic objects. For pedestrians, feature extraction and classification algorithms may be used for recognition, such as Haar feature or deep learning based algorithms. For other dynamic objects, detection and tracking can be performed according to the motion trail and speed of the dynamic objects. And counting the number of targets and the activity frequency in each region according to the target detection and identification results. And analyzing the data, and evaluating the rest degree of each region by combining a preset rest degree evaluation model. This may include consideration of target density, activity frequency, etc. And comparing the estimated rest degree value with a preset standard value, and determining the lighting requirement of each area. The first rest degree, the plurality of second rest degrees and the plurality of third rest degrees are taken as input data. And carrying out weighted calculation or logic judgment on the input data by using a preset comprehensive rest degree calculation model to obtain the comprehensive rest degree value of each region. The comprehensive rest degree value reflects the overall activity level or activity degree of each region in the tunnel, and can provide more accurate basis for the decision of illumination brightness.
Correcting and calculating the first illumination brightness information according to the comprehensive rest degrees to obtain second illumination brightness information;
Specifically, the rule and weight of the correction calculation are determined from the calculation result of the comprehensive rest degree. For example, if the overall rest of a certain area is higher, indicating that the activity level of that area is higher, a higher illumination intensity is required to meet the demand. Therefore, a higher weight value can be given to a region of high comprehensive rest. And taking the first illumination brightness information of each area as input data, and calculating according to the established relation model of illumination brightness and rest degree and the determined correction calculation rule and weight. And obtaining second illumination brightness information of each area according to the calculation result. This may be an adjustment or increment of the original luminance information to accommodate the actual rest level requirements. And outputting the calculated second illumination brightness information to an illumination control system to realize dynamic adjustment of illumination. This can be achieved by wired or wireless communication techniques, ensuring stability and real-time of data transmission.
And according to the gradient of the variation of the plurality of people flow rate information, judging and compensating the plurality of second illumination brightness information to obtain a plurality of third illumination brightness information, and performing illumination control.
Specifically, the collected people flow information is analyzed in time series, and the rate or gradient of the change of the people flow in each time period is calculated. Trends and rules of the gradient of the variation of the human flow, such as whether to gradually increase or decrease, and the speed of the variation, are analyzed. And establishing a mathematical relation model between the traffic variation gradient and the illumination brightness according to the historical data and the experimental result. The model can be calibrated and adjusted according to actual demands so as to adapt to different people flow change conditions and tunnel environments. And taking the second illumination brightness information of each area as input data, and calculating according to the established relationship model of the traffic gradient and the illumination brightness. And correspondingly adjusting the second illumination brightness information according to the positive and negative conditions of the traffic gradient. And setting the maximum illumination brightness span according to the passenger flow change span, judging whether the maximum illumination brightness span is exceeded, and if so, carrying out supplementary adjustment to the maximum illumination brightness span, and carrying out the supplementary adjustment on the illumination brightness of a plurality of areas one by one. Third illumination brightness information of each region is obtained by discrimination compensation calculation. The calculated third illumination brightness information is transmitted to the illumination control system, and the illumination brightness of each area is adjusted by the system based on the information. The lighting control system may automatically or semi-automatically perform lighting adjustments according to a preset adjustment strategy or algorithm. For example, a PID controller, a fuzzy logic controller, or the like may be used. In the process of illumination adjustment, the state of illumination equipment needs to be monitored in real time, so that normal operation is ensured. If an abnormality or a fault occurs, the system should be able to alarm in time and take corresponding measures.
Furthermore, the method of the present application carries out the illumination brightness decision of the tunnel areas according to the traffic information to obtain the first illumination brightness information, and the method comprises the following steps:
According to the illumination control historical data in the subway tunnel, a sample people flow information set and a sample first illumination brightness information set are obtained, and the size of the sample people flow information is positively correlated with the size of the sample first illumination brightness information;
based on a decision tree, taking traffic information as decision input, and adopting the sample traffic information set and the sample first illumination brightness information set to construct an illumination brightness decision device;
and based on the illumination brightness decision period, carrying out decision classification on the plurality of people flow information to obtain the plurality of first illumination brightness information.
Specifically, history data including traffic information and illumination brightness information of each area is acquired from an illumination control system in a subway tunnel. Sample data, such as data for a particular time period, region or condition, is screened from the historical data. Classifying and sorting the screened sample data according to the traffic information and the illumination brightness information to form a sample traffic information set and a sample first illumination brightness information set. The input variables of the decision tree, i.e. the people flow information, are determined. The output variables of the decision tree, i.e. the illumination intensity information, are determined. And constructing a decision tree model by using the sample people flow information set and the sample first illumination brightness information set. Training and optimizing the decision tree to ensure that the decision tree can accurately predict illumination brightness information according to people flow information. A plurality of people flow information is input into a trained lighting intensity decision maker. And the decision maker classifies and predicts each region according to the characteristics of the people flow information and the rules in the historical data. And obtaining first illumination brightness information of each area according to the output of the decision maker. This information can be used directly for adjustment of the lighting control system.
Further, according to the method of the present application, according to the time information, the rest degrees of the subway tunnel at the current time point are obtained by analysis, a first rest degree is obtained, and according to a plurality of pieces of sound information, the rest degrees of the users in the tunnel areas are obtained by analysis, a plurality of second rest degrees are obtained, including:
Acquiring a sample time information set according to illumination control historical data in a subway tunnel, and acquiring resting proportion of users under different sample time information to obtain a first resting degree set of the sample;
constructing a first rest degree identification branch by adopting the sample time information set and the sample first rest degree set;
acquiring a sample sound information set, acquiring resting proportion of users under different sample sound information, and acquiring a sample second resting degree set;
constructing a second rest degree identification branch by adopting the sample sound information set and the sample second rest degree set;
And identifying the time information and the plurality of sound information based on the first rest degree identification branch and the second rest degree identification branch to obtain a first rest degree and a plurality of second rest degrees.
Specifically, history data including user activity data and illumination brightness data for each time period is acquired from an illumination control system within a subway tunnel. And extracting time information, classifying and sorting according to time, and forming a sample time information set. And analyzing the user activity data in each time period, and calculating the resting proportion of the users, namely the ratio of the resting number to the total number. And sorting the rest proportion of the users in each time period into a set to form a first rest degree set of the sample. An input variable, i.e., time information, of the first break identifying branch is determined. And establishing different branches and decision rules according to the relation between the time information and the first rest degree set. Training and optimizing the first rest degree identification branch to ensure that the first rest degree identification branch can accurately identify the rest degree of the user according to time information. And arranging sound sensors in the subway tunnel, and collecting sound information of each area. And classifying and sorting the collected sound information according to the intensity, frequency and other characteristics of the sound to form a sample sound information set. And analyzing the user activity data under each sound characteristic, and calculating the rest proportion of the user. And sorting the rest proportion of the users of each sound characteristic into a set to form a second rest degree set of the sample. An input variable of the second rest degree recognition branch, i.e. sound information, is determined. And establishing different branches and decision rules according to the relation between the sound information and the second rest degree set. Training and optimizing the second rest degree recognition branch to ensure that the second rest degree recognition branch can accurately recognize the rest degree of the user according to the sound information. The time information and the sound information acquired in real time are input into a first rest degree identification branch and a second rest degree identification branch which are already trained. The two branches are respectively identified and judged according to the input information, and the corresponding first rest degree and second rest degree are obtained. The first rest degree and the second rest degree can be fused or weighted and averaged to obtain a more comprehensive rest degree value for adjusting the illumination brightness.
Further, as shown in fig. 2, the method of the present application performs image processing according to a plurality of image information, identifies rest degrees of users in a plurality of tunnel areas, and obtains a plurality of third rest degrees, including:
user image segmentation processing is respectively carried out on the plurality of image information, and a plurality of user image sets are obtained through recognition segmentation;
Identifying and classifying the user images in the user image sets to obtain a plurality of rest user sets and a plurality of non-rest user sets;
According to the rest user sets and the non-rest user sets, carrying out statistical calculation to obtain user rest proportions in the tunnel areas and obtain a plurality of third rest degrees;
And respectively carrying out weighted calculation by adopting the first rest degree and combining the second rest degrees and the third rest degrees to obtain the comprehensive rest degrees.
Specifically, the division processing is performed for each image information using an image processing technique such as edge detection, region division, or the like. Each image information is divided into a plurality of user images by a division process, forming a user image set. Each set of user images is further identified and categorized. Whether each user is resting is determined based on features and attributes in the image, such as gestures, actions, facial expressions, etc. Classifying the user images judged to be resting into resting user sets, and classifying the rest user images into non-resting user sets. And counting the rest user set and the non-rest user set of each area. The user rest proportion, i.e. the ratio of the number of rest users to the total number of users, in each area is calculated. The user rest proportion of each area is taken as the third rest degree of the area. And determining weight values of the first rest degree, the second rest degree and the third rest degree according to actual requirements and scenes. The second rest degree and the third rest degree of each region are multiplied by the corresponding weight values using a weighted calculation method. And summarizing the weighted results to obtain the total comprehensive rest degree of each region.
Further, as shown in fig. 3, the method of the present application performs a user image segmentation process on the plurality of image information, and identifies and segments the plurality of image information to obtain a plurality of user image sets, including:
Acquiring a sample image information set, and carrying out manual identification, segmentation and labeling to obtain a sample user image segmentation result set;
Based on semantic segmentation, constructing a user image segmentation branch, and training to convergence by adopting the sample image information set and a sample user image segmentation result set;
based on the converged user image segmentation branches, the image information is identified and segmented to obtain a plurality of user image segmentation results, and the user image sets are obtained through division.
Specifically, image information is extracted from a monitoring video in a subway tunnel, and a sample image information set is formed. And carrying out manual identification and segmentation labeling on each piece of image information, dividing the image information into a plurality of user images, and forming a sample user image segmentation result set. Semantic segmentation algorithms, such as FCN, U-Net, etc., are selected for constructing user image segmentation branches. And taking the sample image information set and the sample user image segmentation result set as input data for training the user image segmentation branches. The user image segmentation branches are trained using a suitable optimization algorithm (e.g., gradient descent) and a loss function (e.g., cross entropy loss function). And continuously and iteratively updating the model parameters until a convergence state is reached, namely, the performance of the model on the verification set reaches the optimal performance. The trained user image segmentation branches are used for identifying and segmenting a plurality of image information acquired in real time. And obtaining a user image segmentation result of each image information through processing of a user image segmentation branch. And dividing each image into a plurality of user images according to the user image segmentation result to form a plurality of user image sets.
Furthermore, the method of the present application performs correction calculation on the plurality of first illumination brightness information according to the plurality of comprehensive rest degrees to obtain a plurality of second illumination brightness information, and includes:
acquiring the average resting degree of the subway tunnel;
And correcting and calculating the first illumination brightness information according to the comprehensive rest degrees and the average rest degrees to obtain second illumination brightness information, wherein the comprehensive rest degrees and the corrected illumination brightness information are inversely related.
Specifically, the user rest proportion in the history data in the subway tunnel is counted and analyzed, and the average rest degree is calculated. The average rest degree represents the average rest condition of the users in the tunnel and can be adjusted according to actual requirements and data. Based on the integrated rest level for each region, it is compared to the average rest level. If the overall resting degree of a certain area is higher than the average resting degree, the user in the area is more resting, and the illumination brightness needs to be reduced to create a more comfortable resting environment. If the integrated rest level of a certain area is lower than the average rest level, the user has less rest in the area, and the illumination brightness needs to be increased to improve the visibility and remind the user. And (3) using a proper algorithm or model, and performing correction calculation on the illumination brightness information of each area according to the magnitude relation of the comprehensive rest degree and the average rest degree. The result of the correction calculation is the second illumination brightness information of each area. The magnitude of the integrated rest degree and the magnitude of the corrected illumination brightness information are inversely related, which means that the higher the integrated rest degree is, the lower the corrected illumination brightness information is, and the lower the integrated rest degree is, the higher the corrected illumination brightness information is. Therefore, the illumination brightness can be reduced in the area with more rest of the user, and the illumination brightness can be increased in the area with more activities of the user, so that more intelligent and accurate illumination control is realized.
Further, the method of the present application performs discrimination and compensation on the plurality of second illumination brightness information according to the gradient of variation of the plurality of traffic information to obtain a plurality of third illumination brightness information, including:
Calculating to obtain a plurality of flow change gradients according to the plurality of people flow information;
Acquiring a sample flow change gradient set and acquiring a sample maximum illumination brightness span set;
Constructing an illumination brightness span identifier by adopting the sample flow change gradient set and the sample maximum illumination brightness span set, and identifying the flow change gradients to obtain a plurality of maximum illumination brightness spans;
And calculating to obtain a plurality of illumination brightness spans according to the second photo brightness information, respectively judging whether the illumination brightness spans are larger than the maximum illumination brightness spans, if so, adjusting and compensating according to the maximum illumination brightness spans, and if not, not processing to obtain a plurality of third illumination brightness information.
Specifically, a plurality of pieces of traffic information are analyzed, and the rate of change of traffic per area with time is calculated. The flow rate change rate of people in each area is taken as the flow rate change gradient of the area. And acquiring a plurality of flow change gradients for subsequent illumination brightness span identification and adjustment. Sample data of the flow gradient and the maximum illumination intensity span are extracted from the historical data. And sorting the sample data to form a sample flow change gradient set and a sample maximum illumination brightness span set. An appropriate algorithm or model, such as a decision tree, neural network, etc., is selected for constructing the illumination intensity span identifier. The identifier is trained and optimized using the sample flow gradient set and the sample maximum illumination intensity span set. Ensuring that the identifier is able to predict the maximum illumination intensity span from the flow gradient. A plurality of flow rate variation gradients are input into the already trained illumination intensity span identifier. The identifier predicts according to the input flow change gradient to obtain the maximum illumination brightness span of each area. And calculating the illumination brightness span of each region according to the second illumination brightness information of the region. The illumination brightness span represents the range or magnitude of variation of the illumination brightness. The illumination intensity span of each region is compared to a corresponding maximum illumination intensity span. If the illumination brightness span of a certain area is larger than the maximum illumination brightness span, adjustment compensation is needed. If the illumination brightness span of a certain area is less than or equal to the maximum illumination brightness span, no adjustment compensation is needed. And for the area needing adjustment compensation, adjusting the second illumination brightness information according to the maximum illumination brightness span. The method of adjustment may be to decrease or increase the illumination intensity, depending on whether the maximum illumination intensity span is exceeded. The adjusted result is the third illumination brightness information of the area.
Example two
Based on the same inventive concept as the subway tunnel illumination intelligent regulation and control method in the foregoing embodiment, as shown in fig. 4, the present application provides a subway tunnel illumination intelligent regulation and control system, which includes:
a time information acquisition module 10, wherein the time information acquisition module 10 is used for acquiring time information, and traffic information, sound information and image information of a plurality of tunnel areas in a subway tunnel through a sensor array configured in the subway tunnel;
The first illumination brightness information acquisition module 20 is configured to perform illumination brightness decisions of the plurality of tunnel areas according to the plurality of traffic information, and obtain a plurality of first illumination brightness information;
The second resting degree obtaining module 30 is configured to obtain, according to the time information, a resting degree of the subway tunnel at a current time point by analyzing, to obtain a first resting degree, and according to a plurality of sound information, obtain resting degrees of users in the plurality of tunnel regions by analyzing, to obtain a plurality of second resting degrees;
a third rest degree obtaining module 40, where the third rest degree obtaining module 40 is configured to perform image processing according to the plurality of image information, identify rest degrees of users in the plurality of tunnel areas, obtain a plurality of third rest degrees, and calculate and obtain a plurality of comprehensive rest degrees by combining the first rest degree and the plurality of second rest degrees;
The second illumination brightness information obtaining module 50, where the second illumination brightness information obtaining module 50 is configured to perform correction calculation on the plurality of first illumination brightness information according to the plurality of comprehensive rest degrees to obtain a plurality of second illumination brightness information;
The lighting control module 60 is configured to perform discrimination and compensation on the plurality of second lighting brightness information according to the gradient of the variation of the plurality of traffic information, obtain a plurality of third lighting brightness information, and perform lighting control.
Further, the system further comprises:
the system comprises a sample set acquisition module, a first illumination module and a second illumination module, wherein the sample set acquisition module is used for acquiring a sample traffic information set and a sample first illumination brightness information set according to illumination control historical data in a subway tunnel, and the size of the sample traffic information is positively correlated with the size of the sample first illumination brightness information;
the brightness decision device constructing module is used for constructing an illumination brightness decision device by taking traffic information as decision input and adopting the sample traffic information set and the sample first illumination brightness information set based on a decision tree;
And the decision classification module is used for carrying out decision classification on the plurality of people flow information based on the illumination brightness decision period to obtain the plurality of first illumination brightness information.
Further, the system further comprises:
The rest proportion acquisition module is used for acquiring a sample time information set according to the lighting control historical data in the subway tunnel, acquiring the rest proportion of the user under different sample time information and acquiring a first sample rest degree set;
The first rest degree identification branch construction module is used for constructing a first rest degree identification branch by adopting the sample time information set and the sample first rest degree set;
The sample second rest degree set acquisition module is used for acquiring a sample sound information set and acquiring rest proportions of users under different sample sound information to acquire a sample second rest degree set;
the second rest degree identification branch construction module is used for constructing a second rest degree identification branch by adopting the sample sound information set and the sample second rest degree set;
and the information identification module is used for identifying the time information and the plurality of sound information based on the first rest degree identification branch and the second rest degree identification branch to obtain a first rest degree and a plurality of second rest degrees.
Further, the system further comprises:
the user image set acquisition module is used for respectively carrying out user image segmentation processing on the plurality of image information and identifying and segmenting to obtain a plurality of user image sets;
The identification classification module is used for identifying and classifying the user images in the user image sets to obtain a plurality of rest user sets and a plurality of non-rest user sets;
The third rest degree acquisition module is used for carrying out statistical calculation according to the rest user sets and the non-rest user sets to obtain user rest proportions in the tunnel areas and obtain a plurality of third rest degrees;
And the comprehensive rest degree acquisition module is used for adopting the first rest degree to respectively combine the plurality of second rest degrees and the plurality of third rest degrees to perform weighted calculation so as to acquire the plurality of comprehensive rest degrees.
Further, the system further comprises:
the image segmentation result set acquisition module is used for acquiring a sample image information set, and carrying out manual identification segmentation labeling to acquire a sample user image segmentation result set;
The image segmentation branch construction module is used for constructing user image segmentation branches based on semantic segmentation, and training the user image segmentation branches to convergence by adopting the sample image information set and the sample user image segmentation result set;
And the user image set acquisition module is used for identifying and dividing the plurality of image information based on the converged user image dividing branches to obtain a plurality of user image dividing results and dividing the plurality of user image sets.
Further, the system further comprises:
The average rest degree acquisition module is used for acquiring the average rest degree of the subway tunnel;
And the second illumination brightness information acquisition module is used for carrying out correction calculation on the plurality of first illumination brightness information according to the plurality of comprehensive rest degrees and the average rest degrees to obtain a plurality of second illumination brightness information, wherein the magnitude of the comprehensive rest degrees is inversely related to the magnitude of the corrected illumination brightness information.
Further, the system further comprises:
The change gradient calculation acquisition module is used for calculating and acquiring a plurality of flow change gradients according to the plurality of people flow information;
The span set acquisition module is used for acquiring a sample flow change gradient set and acquiring a sample maximum illumination brightness span set;
The maximum illumination brightness span acquisition module is used for constructing an illumination brightness span identifier by adopting the sample flow change gradient set and the sample maximum illumination brightness span set, and identifying the flow change gradients to obtain a plurality of maximum illumination brightness spans;
and the adjustment compensation module is used for calculating and obtaining a plurality of illumination brightness spans according to the second photo brightness information, judging whether the illumination brightness spans are larger than the maximum illumination brightness spans or not respectively, if yes, performing adjustment compensation according to the maximum illumination brightness spans, and if not, not performing processing to obtain a plurality of third illumination brightness information.
Through the foregoing detailed description of the intelligent subway tunnel illumination regulation and control method, those skilled in the art can clearly understand that an intelligent subway tunnel illumination regulation and control system in this embodiment, for the system disclosed in the embodiment, the description is relatively simple because it corresponds to the embodiment disclosure device, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An intelligent regulation and control system for subway tunnel illumination, which is characterized by comprising:
The time information acquisition module is used for acquiring time information, and traffic information, sound information and image information of a plurality of tunnel areas in the subway tunnel through a sensor array configured in the subway tunnel;
The first illumination brightness information acquisition module is used for making illumination brightness decisions of the tunnel areas according to the traffic information to obtain first illumination brightness information;
The second rest degree acquisition module is used for analyzing and acquiring the rest degree of the subway tunnel at the current time point according to the time information to obtain a first rest degree, and analyzing and acquiring the rest degrees of users in the tunnel areas according to a plurality of pieces of sound information to obtain a plurality of second rest degrees;
The third rest degree acquisition module is used for carrying out image processing according to the plurality of image information, identifying the rest degrees of users in the plurality of tunnel areas, obtaining a plurality of third rest degrees, combining the first rest degrees and the plurality of second rest degrees, and calculating to obtain a plurality of comprehensive rest degrees;
The second illumination brightness information acquisition module is used for correcting and calculating the plurality of first illumination brightness information according to the plurality of comprehensive rest degrees to obtain a plurality of second illumination brightness information;
and the illumination control module is used for judging and compensating the plurality of second illumination brightness information according to the change gradient of the plurality of people flow information to obtain a plurality of third illumination brightness information and carrying out illumination control.
2. The system of claim 1, wherein making a decision on illumination intensity for the plurality of tunnel regions based on the plurality of traffic information to obtain a plurality of first illumination intensity information comprises:
the system comprises a sample set acquisition module, a first illumination module and a second illumination module, wherein the sample set acquisition module is used for acquiring a sample traffic information set and a sample first illumination brightness information set according to illumination control historical data in a subway tunnel, and the size of the sample traffic information is positively correlated with the size of the sample first illumination brightness information;
the brightness decision device constructing module is used for constructing an illumination brightness decision device by taking traffic information as decision input and adopting the sample traffic information set and the sample first illumination brightness information set based on a decision tree;
And the decision classification module is used for carrying out decision classification on the plurality of people flow information based on the illumination brightness decision device to obtain the plurality of first illumination brightness information.
3. The system of claim 1, wherein analyzing the degree of rest of the subway tunnel at the current time point according to the time information to obtain a first degree of rest, analyzing the degree of rest of the user in the plurality of tunnel areas according to a plurality of pieces of sound information to obtain a plurality of second degrees of rest, comprises:
The rest proportion acquisition module is used for acquiring a sample time information set according to the lighting control historical data in the subway tunnel, acquiring the rest proportion of the user under different sample time information and acquiring a first sample rest degree set;
The first rest degree identification branch construction module is used for constructing a first rest degree identification branch by adopting the sample time information set and the sample first rest degree set;
The sample second rest degree set acquisition module is used for acquiring a sample sound information set and acquiring rest proportions of users under different sample sound information to acquire a sample second rest degree set;
the second rest degree identification branch construction module is used for constructing a second rest degree identification branch by adopting the sample sound information set and the sample second rest degree set;
and the information identification module is used for identifying the time information and the plurality of sound information based on the first rest degree identification branch and the second rest degree identification branch to obtain a first rest degree and a plurality of second rest degrees.
4. The system of claim 1, wherein performing image processing based on the plurality of image information to identify a degree of rest for the user in the plurality of tunnel regions and obtain a plurality of third degrees of rest comprises:
the user image set acquisition module is used for respectively carrying out user image segmentation processing on the plurality of image information and identifying and segmenting to obtain a plurality of user image sets;
The identification classification module is used for identifying and classifying the user images in the user image sets to obtain a plurality of rest user sets and a plurality of non-rest user sets;
The third rest degree acquisition module is used for carrying out statistical calculation according to the rest user sets and the non-rest user sets to obtain user rest proportions in the tunnel areas and obtain a plurality of third rest degrees;
And the comprehensive rest degree acquisition module is used for adopting the first rest degree to respectively combine the plurality of second rest degrees and the plurality of third rest degrees to perform weighted calculation so as to acquire the plurality of comprehensive rest degrees.
5. The system of claim 4, wherein the user image segmentation process is performed on the plurality of image information, respectively, and the identifying the segmentation to obtain the plurality of user image sets comprises:
the image segmentation result set acquisition module is used for acquiring a sample image information set, and carrying out manual identification segmentation labeling to acquire a sample user image segmentation result set;
The image segmentation branch construction module is used for constructing user image segmentation branches based on semantic segmentation, and training the user image segmentation branches to convergence by adopting the sample image information set and the sample user image segmentation result set;
And the user image set acquisition module is used for identifying and dividing the plurality of image information based on the converged user image dividing branches to obtain a plurality of user image dividing results and dividing the plurality of user image sets.
6. The system of claim 4, wherein performing a correction calculation on the plurality of first illumination intensity information according to the plurality of integrated rest degrees to obtain a plurality of second illumination intensity information comprises:
The average rest degree acquisition module is used for acquiring the average rest degree of the subway tunnel;
And the second illumination brightness information acquisition module is used for carrying out correction calculation on the plurality of first illumination brightness information according to the plurality of comprehensive rest degrees and the average rest degrees to obtain a plurality of second illumination brightness information, wherein the magnitude of the comprehensive rest degrees is inversely related to the magnitude of the corrected illumination brightness information.
7. The system of claim 1, wherein the performing discrimination compensation on the plurality of second illumination intensity information according to the gradient of variation of the plurality of traffic information to obtain a plurality of third illumination intensity information comprises:
The change gradient calculation acquisition module is used for calculating and acquiring a plurality of flow change gradients according to the plurality of people flow information;
The span set acquisition module is used for acquiring a sample flow change gradient set and acquiring a sample maximum illumination brightness span set;
the maximum illumination brightness span acquisition module is used for constructing an illumination brightness span identifier by adopting the sample flow change gradient set and the sample maximum illumination brightness span set, and identifying the flow change gradients to obtain a plurality of maximum illumination brightness spans;
And the adjustment compensation module is used for calculating and obtaining a plurality of illumination brightness spans according to the plurality of second illumination brightness information, judging whether the illumination brightness spans are larger than the plurality of maximum illumination brightness spans or not respectively, if yes, performing adjustment compensation according to the maximum illumination brightness spans, and if not, not performing processing to obtain a plurality of third illumination brightness information.
8. The intelligent subway tunnel illumination regulation and control method is characterized by comprising the following steps of:
Collecting time information, and traffic information, sound information and image information of a plurality of tunnel areas in the subway tunnel through a sensor array arranged in the subway tunnel;
According to the traffic information, making illumination brightness decisions of the tunnel areas to obtain first illumination brightness information;
analyzing and acquiring the rest degrees of the users in the tunnel areas according to the time information to obtain a first rest degree and a plurality of second rest degrees;
According to the image information, performing image processing, identifying rest degrees of users in the tunnel areas, obtaining a plurality of third rest degrees, and combining the first rest degrees and the second rest degrees, and calculating to obtain a plurality of comprehensive rest degrees;
Correcting and calculating the first illumination brightness information according to the comprehensive rest degrees to obtain second illumination brightness information;
And according to the gradient of the variation of the plurality of people flow rate information, judging and compensating the plurality of second illumination brightness information to obtain a plurality of third illumination brightness information, and performing illumination control.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107120607A (en) * | 2017-07-12 | 2017-09-01 | 许昌虹榕节能电器设备有限公司 | A kind of street lamp that can be automatically adjusted according to flow of the people size |
| CN108207059A (en) * | 2017-12-29 | 2018-06-26 | 戴琪 | A kind of city energy-conserving road lamp |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2687724B2 (en) * | 1990-11-30 | 1997-12-08 | 松下電器産業株式会社 | Lighting equipment |
| US7965859B2 (en) * | 2006-05-04 | 2011-06-21 | Sony Computer Entertainment Inc. | Lighting control of a user environment via a display device |
| KR102004351B1 (en) * | 2017-12-28 | 2019-07-29 | 장승익 | Vehicle-controlled tunnel lighting control device |
| KR102009207B1 (en) * | 2019-03-05 | 2019-10-21 | 주식회사 리산테크 | Integrated system for controlling tunnel lighting based on different kinds data |
| CN113490312B (en) * | 2021-07-14 | 2024-03-12 | 广州市坤龙信息系统有限公司 | Intelligent illumination dimming method and system for expressway tunnel |
| CN113905474B (en) * | 2021-09-07 | 2023-10-31 | 中国电建集团华东勘测设计研究院有限公司 | Urban tunnel intelligent lighting system and dimming method |
| CN217985481U (en) * | 2022-07-20 | 2022-12-06 | 中南建筑设计院股份有限公司 | Indoor intelligent temperature controller |
| CN116249248A (en) * | 2023-04-08 | 2023-06-09 | 广州柏曼光电科技有限公司 | Intelligent illumination control method and system |
| CN116683470A (en) * | 2023-05-06 | 2023-09-01 | 山东高速集团有限公司 | A load regulation method, system and terminal based on crowd density |
| CN116761309B (en) * | 2023-06-14 | 2024-02-02 | 贵州中南交通科技有限公司 | Tunnel energy-saving intelligent management system and method |
| CN117355013A (en) * | 2023-10-07 | 2024-01-05 | 深圳高力特通用电气有限公司 | LED intelligent lighting control system based on visual perception |
-
2024
- 2024-01-24 CN CN202410103512.4A patent/CN117715275B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107120607A (en) * | 2017-07-12 | 2017-09-01 | 许昌虹榕节能电器设备有限公司 | A kind of street lamp that can be automatically adjusted according to flow of the people size |
| CN108207059A (en) * | 2017-12-29 | 2018-06-26 | 戴琪 | A kind of city energy-conserving road lamp |
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