Intelligent energy-saving lamp management method and system based on Internet of Things

Abstract

The invention relates to the technical field of intelligent street lamp control, and particularly discloses an intelligent lamp energy-saving management method and system based on the Internet of Things, and the method comprises the steps: obtaining a solar calendar date corresponding to a current moment, determining a sunrise moment and a sunset moment according to the solar calendar date, and determining a day time interval; obtaining a lunar calendar date corresponding to the current moment, determining a monthly rising moment and a monthly falling moment according to the lunar calendar date, and determining a monthly interval; determining the brightness of each moment in the day time interval based on the monthly time interval, querying the brightness corresponding to the current moment, further determining the reference brightness of the street lamp, and broadcasting the reference brightness of the street lamp to a controller of each street lamp; and road surface information is obtained based on the information collector closest to each street lamp, and the street lamp reference brightness is adjusted according to the road surface information. According to the invention, the reference brightness at night of each day is determined according to the calendar, on this basis, the brightness is further adjusted by means of the camera installed on the street lamp, and the cost is extremely low.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent street lamp control, and in particular to an intelligent energy-saving management method and system for lamps based on the Internet of Things. Background Art

[0002] With the widespread application of new generation information technologies such as the Internet of Things, the Internet, and cloud computing, smart cities have gradually become the transformation direction of many cities. As an infrastructure of urban roads, the intelligent expansion of street lights is the only way to build smart cities.

[0003] The existing intelligent adjustment process of street lights mostly involves installing sensors and microprocessors in the street lights. The sensors detect the light in real time, and the microprocessors identify the detected light to adjust the brightness. This is equivalent to migrating the brightness adaptive adjustment module of a mobile phone to the street light. This adjustment method is feasible, but the cost is relatively high. How to reduce the brightness adjustment cost of smart street lights is the technical problem that the technical solution of the present invention aims to solve. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent energy-saving management method and system for lamps based on the Internet of Things to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent energy-saving management method for lamps based on the Internet of Things, the method comprising:

[0007] Get the Gregorian calendar date corresponding to the current time, determine the sunrise and sunset times based on the Gregorian calendar date, and determine the day-time interval with the sunset time as the left endpoint and the sunrise time as the right endpoint;

[0008] Get the lunar date corresponding to the current time, determine the moonrise and moonset times based on the lunar date, and determine the monthly time interval with the moonrise time as the left endpoint and the moonset time as the right endpoint;

[0009] Determine the brightness of each moment in the daily time interval based on the monthly time interval, query the brightness corresponding to the current moment, determine the street lamp reference brightness according to the brightness corresponding to the current moment, and broadcast the street lamp reference brightness to the controller of each street lamp;

[0010] Road surface information is acquired based on an information collector closest to each street lamp, and the reference brightness of the street lamp is adjusted according to the road surface information; the road surface information includes audio information and video information.

[0011] As a further solution of the present invention, the steps of determining the brightness at each time in the daily time interval based on the monthly time interval, querying the brightness corresponding to the current time, determining the street lamp reference brightness according to the brightness corresponding to the current time, and broadcasting the street lamp reference brightness to the controllers of each street lamp include:

[0012] Read the lunar date within the month time interval, and query the moon size based on the lunar date, expressing it as a percentage; a full moon is 100%, and no moon is 0%;

[0013] Determine the peak brightness of the monthly time interval according to the percentage, and construct a Gaussian distribution function based on the peak brightness as the brightness distribution function of each moment in the monthly time interval;

[0014] Calculate the intersection of the daily time interval and the monthly time interval, and intercept the brightness distribution function based on the intersection as the brightness of the intersection within the daily time interval;

[0015] Calculating the complement of the intersection within the day-time interval, using zero as the brightness of the complement;

[0016] Query the brightness corresponding to the current moment, determine the street lamp reference brightness according to the brightness corresponding to the current moment, and broadcast the street lamp reference brightness to the controllers of each street lamp;

[0017] The streetlight reference brightness is inversely proportional to the brightness corresponding to the current moment, and the specific relationship is:

[0018] Where, L t is the reference brightness of the street light at time t, H t is the brightness corresponding to time t, where time t is the current time; L max is the maximum brightness of the street light.

[0019] As a further solution of the present invention, the step of obtaining road surface information based on an information collector closest to each street lamp and adjusting the reference brightness of the street lamp according to the road surface information includes:

[0020] For any streetlight, obtain the information collector closest to the streetlight, and use the information collector to obtain audio information in real time;

[0021] Taking the current moment as the starting point, intercept the audio before the preset time span, identify the intercepted audio, and determine the periodicity;

[0022] When periodicity occurs, obtain video information;

[0023] With the current streetlight as the center, all video information within the preset range is counted, all video information within the preset range is identified, and the current streetlight benchmark brightness is adjusted according to the identification results of the video information.

[0024] As a further solution of the present invention, the steps of taking the current street lamp as the center, collecting statistics of all video information within a preset range, identifying all video information within the preset range, and adjusting the current street lamp reference brightness according to the identification results of the video information include:

[0025] The current street lamp is taken as the center and the preset value is used as the radius to determine the circular range;

[0026] Query all video information of all information collectors within the circular range within the time span;

[0027] Convert the video information into an image queue, calculate the pixel mean of each image in the image queue, and convert the image queue into a mean array;

[0028] Calculating a standard deviation of the mean array, and when the standard deviation is less than a preset first threshold, marking an area corresponding to the corresponding video information;

[0029] Calculate the ratio of the marked area to the circular range, and determine the adjustment coefficient of the street lamp benchmark brightness based on the ratio;

[0030] The relationship between the adjustment coefficient and the ratio is:

[0031] Where α is the adjustment coefficient, S m is the area of ​​the mth marked region, M is the total number of marked regions; ε is the preset threshold; and r is the radius of the circular range.

[0032] As a further solution of the present invention: the step of calculating the pixel mean of each image in the image queue includes:

[0033] Read each image in the image queue and convert the image into a grayscale image;

[0034] Extracting a histogram of the grayscale image, and fitting the histogram based on a Gaussian distribution function to obtain a determined grayscale distribution function;

[0035] Read the mean of the grayscale distribution function and query the pixel mean in the image.

[0036] As a further embodiment of the present invention, the method further comprises:

[0037] Perform voice recognition on the audio information, and when the voice recognition is successful, convert the audio information into text;

[0038] Perform content recognition on the text to obtain keywords;

[0039] According to the keyword, the brightness is searched in a preset brightness table and displayed; the brightness searched in the brightness table has the highest priority;

[0040] When voice recognition fails, the failure duration is recorded. When the failure duration reaches the preset duration threshold, the adjusted street light reference brightness is displayed.

[0041] The voice recognition frequency is adjusted synchronously according to the failure duration.

[0042] The technical solution of the present invention also provides an intelligent energy-saving management system for lamps based on the Internet of Things, the system comprising:

[0043] The day-time interval determination module is used to obtain the Gregorian calendar date corresponding to the current time, determine the sunrise time and sunset time according to the Gregorian calendar date, and determine the day-time interval with the sunset time as the left endpoint and the sunrise time as the right endpoint;

[0044] The monthly time interval determination module is used to obtain the lunar calendar date corresponding to the current time, determine the moonrise time and moonset time according to the lunar calendar date, and determine the monthly time interval with the moonrise time as the left endpoint and the moonset time as the right endpoint;

[0045] A brightness broadcast module, configured to determine the brightness at each time in a daily time interval based on the monthly time interval, query the brightness corresponding to the current time, determine a streetlight reference brightness based on the brightness corresponding to the current time, and broadcast the streetlight reference brightness to the controllers of each streetlight;

[0046] The brightness adjustment module is used to obtain road surface information based on the information collector closest to each street lamp, and adjust the reference brightness of the street lamp according to the road surface information; the road surface information includes audio information and video information.

[0047] As a further solution of the present invention: the brightness broadcast module includes:

[0048] A moon size query unit is used to read the lunar date within the lunar time interval and query the moon size according to the lunar date, and express it as a percentage; a full moon is 100% and no moon is 0%;

[0049] a brightness distribution determining unit, configured to determine a peak brightness within a monthly time interval according to the percentage, and construct a Gaussian distribution function according to the peak brightness as the brightness distribution function at each moment within the monthly time interval;

[0050] An intersection determination unit is used to calculate the intersection of the daily time interval and the monthly time interval, and intercept the brightness distribution function based on the intersection as the brightness of the intersection within the daily time interval;

[0051] a complement determination unit, configured to calculate the complement of the intersection within a day-time interval, using zero as the brightness of the complement;

[0052] a broadcast execution unit, configured to query the brightness corresponding to the current moment, determine a reference brightness of the street lamp according to the brightness corresponding to the current moment, and broadcast the reference brightness of the street lamp to the controllers of each street lamp;

[0053] The streetlight reference brightness is inversely proportional to the brightness corresponding to the current moment, and the specific relationship is:

[0054] Where, L t is the reference brightness of the street light at time t, H t is the brightness corresponding to time t, where time t is the current time; L max is the maximum brightness of the street light.

[0055] As a further solution of the present invention: the brightness adjustment module includes:

[0056] An audio acquisition unit is used to obtain the information collector closest to any street lamp, and the information collector obtains audio information in real time;

[0057] An audio recognition unit is used to intercept the audio before a preset time span starting from the current time, recognize the intercepted audio, and determine the periodicity;

[0058] A video acquisition unit, configured to acquire video information when periodicity occurs;

[0059] The video recognition unit is used to count all video information within a preset range with the current street lamp as the center, identify all video information within the preset range, and adjust the current street lamp reference brightness according to the recognition result of the video information.

[0060] As a further solution of the present invention: the video recognition unit includes:

[0061] The circle creation subunit is used to determine the circular range with the current street lamp as the center and the preset value as the radius;

[0062] The video reading subunit is used to query all video information of all information collectors within the circular range within the time span;

[0063] An image recognition subunit is used to convert video information into an image queue, calculate the pixel mean of each image in the image queue, and convert the image queue into a mean array;

[0064] a region marking subunit, configured to calculate a standard deviation of the mean array, and mark a region corresponding to the corresponding video information when the standard deviation is less than a preset first threshold;

[0065] A coefficient calculation subunit is used to calculate the ratio of the marked area to the circular range, and determine the adjustment coefficient of the street lamp reference brightness according to the ratio;

[0066] The relationship between the adjustment coefficient and the ratio is:

[0067] Where α is the adjustment coefficient, S m is the area of ​​the mth marked region, m is the total number of marked regions; ε is the preset threshold; and r is the radius of the circular range.

[0068] Compared with the existing technology, the beneficial effect of the present invention is: the present invention determines the baseline brightness of each night according to the calendar, and on this basis, uses the camera installed on the street lamp to further adjust the brightness, wherein the camera belongs to the infrastructure and is used to obtain the urban road conditions. The brightness adjustment and recognition task is transferred to the microprocessor of the camera. The street lamp only needs to receive instructions and adjust the brightness. From the perspective of the street lamp itself, only a controller with information receiving function is needed, and the cost is extremely low. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0070] Figure 1 This is a flowchart of the intelligent energy-saving management method for lighting based on the Internet of Things.

[0071] Figure 2 This is the first sub-process flowchart of the intelligent energy-saving management method for lighting based on the Internet of Things.

[0072] Figure 3 This is the second sub-process flowchart of the intelligent energy-saving management method for lighting fixtures based on the Internet of Things.

[0073] Figure 4 This is a structural block diagram of the lighting intelligent energy-saving management system based on the Internet of Things. DETAILED DESCRIPTION

[0074] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] Figure 1 This is a flowchart of an intelligent energy-saving management method for lighting fixtures based on the Internet of Things. In an embodiment of the present invention, an intelligent energy-saving management method for lighting fixtures based on the Internet of Things includes:

[0076] Step S100: Obtain the Gregorian calendar date corresponding to the current time, determine the sunrise time and sunset time according to the Gregorian calendar date, and determine the day-time interval with the sunset time as the left endpoint and the sunrise time as the right endpoint;

[0077] In the current technical context, the relationship between the Gregorian calendar date and the sunrise and sunset times is known data that can be obtained. Common weather service apps will indicate the sunrise and sunset times of each day. The time interval from sunset to sunrise, that is, the time interval with sunset as the left endpoint and sunrise as the right endpoint, is called the daily time interval.

[0078] Step S200: Obtain the lunar date corresponding to the current time, determine the moonrise time and moonset time according to the lunar date, and determine the lunar time interval with the moonrise time as the left endpoint and the moonset time as the right endpoint;

[0079] The lunar calendar date is related to the rising and setting of the moon. The time interval determined by the moonrise time as the left endpoint and the moonset time as the right endpoint is called the monthly time interval. The monthly time interval reflects the rising and setting of the moon.

[0080] Step S300: Determine the brightness at each time in the daily time interval based on the monthly time interval, query the brightness corresponding to the current time, determine the street lamp reference brightness based on the brightness corresponding to the current time, and broadcast the street lamp reference brightness to the controllers of each street lamp;

[0081] The monthly time interval represents the movement trajectory of the moon, and the daily time interval represents the stage without the sun, that is, the night period. Combined with the monthly time interval, the ambient brightness at each moment in the night period can be determined. Based on the ambient brightness, the baseline brightness of the street lamp can be determined and then broadcast to each street lamp.

[0082] In summary, this application determines the ambient brightness during the night period based on the known movement trajectories of the moon and the sun (the moon is equivalent to the light source at this time), and determines the baseline brightness of the street lamp based on the ambient brightness. The higher the ambient brightness, the lower the baseline brightness of the street lamp.

[0083] The reference brightness of the street lamp is adjusted by the controller. Therefore, after the master control terminal determines the reference brightness of the street lamp, it broadcasts the reference brightness to all street lamps.

[0084] Step S400: acquiring road surface information based on an information collector closest to each street lamp, and adjusting the reference brightness of the street lamp according to the road surface information; the road surface information includes audio information and video information;

[0085] Step S400 is applied to the street lamp end. Each street lamp in this application is an independent device. After receiving the street lamp benchmark brightness broadcast by the master control end, it displays the street lamp benchmark brightness; at the same time, the road surface information is obtained based on the information collector closest to each street lamp. The road surface information includes audio information and video information. The road surface information is identified, and then the street lamp benchmark brightness is adjusted to determine a dynamic brightness; the simplest advantage of the dynamic brightness is that the brightness can be reduced during unmanned periods, thereby achieving an energy-saving effect.

[0086] Figure 2 This is a flowchart of the first sub-process of the IoT-based intelligent energy-saving management method for lamps. The steps of determining the brightness at each time in the daily time interval based on the monthly time interval, querying the brightness corresponding to the current time, determining the street lamp reference brightness based on the brightness corresponding to the current time, and broadcasting the street lamp reference brightness to the controllers of each street lamp include:

[0087] Step S301: Read the lunar date in the lunar time interval, and query the moon size according to the lunar date, and express it as a percentage; a full moon is 100%, and no moon is 0%;

[0088] Step S302: determining the peak brightness of the monthly time interval according to the percentage, and constructing a Gaussian distribution function according to the peak brightness as the brightness distribution function of each moment in the monthly time interval;

[0089] Step S303: Calculate the intersection of the daily time interval and the monthly time interval, and intercept the brightness distribution function based on the intersection as the brightness of the intersection within the daily time interval;

[0090] Step S304: Calculate the complement of the intersection within the day-time interval, and use zero as the brightness of the complement;

[0091] Step S305: querying the brightness corresponding to the current moment, determining the street lamp reference brightness according to the brightness corresponding to the current moment, and broadcasting the street lamp reference brightness to the controllers of each street lamp;

[0092] The street lamp reference brightness is inversely proportional to the brightness corresponding to the current moment.

[0093] The above content provides a detailed description of step S300. The moon's size varies on different dates, with the largest being in the middle of each month and the smallest on the first day of the month. The moon also rises and sets at different times (relative to the Earth). This information is stored in existing moon phase diagrams and is known data. The lunar date within the lunar time interval is read, and the moon's size is queried based on the lunar date, expressed as a percentage; a full moon is 100% and no moon is 0%.

[0094] The peak brightness within the monthly time interval is determined according to the percentage, and the peak brightness is obtained by multiplying the percentage by the maximum brightness queried in advance; since the ambient brightness brought by the moon in the process of rising and setting is similar to the normal distribution, a Gaussian distribution function (normal distribution) is determined based on the peak brightness, which is used as the brightness distribution function for each moment within the monthly time interval; it should be noted that knowing only the peak brightness parameter is not enough, and another parameter is also needed. The other parameter can select the brightness of other moments within the monthly time interval that are not the middle moments, thereby determining the Gaussian distribution function; in addition, a certain parameter (the μ term in the Gaussian distribution function) can be set in advance, and when the peak brightness is obtained, another parameter (the σ term in the Gaussian distribution function) is determined according to the peak brightness.

[0095] Calculate the intersection of the daily time interval and the monthly time interval. The intersection represents the time period when there is a moon at night. At this time, intercept the ambient brightness of the monthly time interval; calculate the complement of the intersection within the daily time interval. The complement represents the time period when there is no moon at night. At this time, set the ambient brightness to zero; finally, obtain the brightness of all moments in the daily time interval. From the above content, it can be seen that the daily time interval corresponds to the night.

[0096] Finally, the streetlight reference brightness can be determined based on the brightness at all times during the day and time interval. The greater the brightness at all times during the day and time interval, the smaller the streetlight reference brightness. In other words, the streetlight reference brightness is inversely proportional to the brightness corresponding to the current moment. One feasible inverse relationship is:

[0097] Where, L t is the reference brightness of the street light at time t, H t is the brightness corresponding to time t, where time t is the current time; L max is the maximum brightness of the street light.

[0098] Less than 1, also less than one, multiply it by the maximum brightness of the street light to get the display brightness; L t With H t Inversely proportional.

[0099] Figure 3 This is a second sub-flow diagram of the method for intelligent energy-saving management of lamps based on the Internet of Things. The step of obtaining road surface information based on the information collector closest to each street lamp and adjusting the reference brightness of the street lamp according to the road surface information includes:

[0100] Step S401: For any street lamp, obtain the information collector closest to the street lamp, and obtain audio information in real time by the information collector;

[0101] Step S402: Taking the current time as the starting point, intercepting the audio before the preset time span, identifying the intercepted audio, and determining the periodicity;

[0102] Step S403: when periodicity occurs, obtain video information;

[0103] Step S404: With the current street lamp as the center, all video information within a preset range is counted, all video information within the preset range is identified, and the reference brightness of the street lamp at the current moment is adjusted according to the identification result of the video information.

[0104] In an example of the technical solution of the present invention, a specific description is given of the autonomous brightness adjustment process of the street lamp end. A video collector is installed at the street lamp end, and the video collector has an audio collection function. For any street lamp, the nearest information collector is queried, and audio information is obtained in real time based on the nearest information collector. The energy consumed in obtaining and identifying audio information is very little. Therefore, this application first obtains audio and judges the audio. When the audio becomes periodic, the video information is obtained and identified and judged, and then the brightness is further adjusted based on the baseline brightness of the street lamp.

[0105] Specifically, the steps of taking the current street lamp as the center, counting all video information within a preset range, identifying all video information within the preset range, and adjusting the current street lamp reference brightness according to the identification results of the video information include:

[0106] The current street lamp is taken as the center and the preset value is used as the radius to determine the circular range;

[0107] Query all video information of all information collectors within the circular range within the time span;

[0108] Convert the video information into an image queue, calculate the pixel mean of each image in the image queue, and convert the image queue into a mean array;

[0109] Calculating a standard deviation of the mean array, and when the standard deviation is less than a preset first threshold, marking an area corresponding to the corresponding video information;

[0110] Calculate the ratio of the marked area to the circular range, and determine the adjustment coefficient of the street lamp benchmark brightness based on the ratio;

[0111] The relationship between the adjustment coefficient and the ratio is:

[0112] Where α is the adjustment coefficient, S m is the area of ​​the mth marked region, M is the total number of marked regions; ε is the preset threshold; and r is the radius of the circular range.

[0113] The adjustment coefficient multiplied by the street lamp baseline brightness is the final display brightness. If the proportion of unmanned areas (marked areas) is larger, the final display brightness will be lowered and the adjustment coefficient will be smaller. If the proportion of unmanned areas is very small, it means that there are many moving objects within the range. At this time, the brightness will not be reduced and the adjustment coefficient will be 1.

[0114] The above content provides a specific video information recognition process. The periodicity of audio information can be recognized and judged using existing technologies, which will not be described in detail.

[0115] With the current streetlight as the center, a preset value is used as the radius to determine the circular range. All video information of all information collectors within the circular range within the time span is queried to reflect all activity information around the streetlight within a period of time. In this application, the process of obtaining activity information only needs to determine whether there is a moving object. The judgment method is:

[0116] The video information is converted into an image queue. For each image in the image queue, the pixel mean of the image is calculated. The image queue is converted into a mean array. The standard deviation of the mean array is calculated. When there is no moving object, all videos are almost static videos, and the standard deviation will be very small. Based on this, if the standard deviation is less than the preset first threshold, then there is no moving object in the area corresponding to the video (near a street lamp). At this time, the corresponding area is marked as an unmanned area; the ratio of the marked unmanned area to the entire circular range is obtained, and the adjustment coefficient can be determined according to the ratio to adjust the baseline brightness of the street lamp.

[0117] Furthermore, the step of calculating the pixel mean of each image in the image queue includes:

[0118] Read each image in the image queue and convert the image into a grayscale image;

[0119] Extracting a histogram of the grayscale image, and fitting the histogram based on a Gaussian distribution function to obtain a determined grayscale distribution function;

[0120] Read the mean of the grayscale distribution function and query the pixel mean in the image.

[0121] The pixel mean of the image is determined based on the histogram. First, the image is converted into a grayscale image, the histogram of the grayscale image is extracted, and the histogram is fitted with a Gaussian distribution function (the second application in this application) to obtain a grayscale distribution function. This process is essentially to convert the histogram into a function expression, calculate the mean of the function, and obtain the number of pixels corresponding to the pixel mean (the vertical axis of the histogram is the number of pixels for each pixel value). The pixel mean can be queried based on the number of pixels.

[0122] It is worth mentioning that the pixel value queried based on the number of pixels may not be unique, so the closest pixel value can be selected as the pixel mean.

[0123] As a preferred embodiment of the technical solution of the present invention, the method further includes:

[0124] Perform voice recognition on the audio information, and when the voice recognition is successful, convert the audio information into text;

[0125] Perform content recognition on the text to obtain keywords;

[0126] According to the keyword, the brightness is searched in a preset brightness table and displayed; the brightness searched in the brightness table has the highest priority;

[0127] When voice recognition fails, the failure duration is recorded. When the failure duration reaches the preset duration threshold, the adjusted street light reference brightness is displayed.

[0128] The voice recognition frequency is adjusted synchronously according to the failure duration.

[0129] The above content mainly provides a solution for reducing brightness during unoccupied periods. In actual scenarios, there is also a need to increase the brightness. For example, when someone is playing nearby, higher brightness is often required. Therefore, this application also introduces a personnel determination solution based on voice recognition technology.

[0130] Perform voice recognition on the audio information. When the voice recognition is successful, convert the audio information into text, perform content recognition on the text, and obtain keywords. The keywords include "dark" and "unclear", etc., which are specifically determined in advance by the management. When the keywords are detected, directly query the brightness in the table and display them with a higher priority.

[0131] When there is no human voice, the duration of the absence of the human scene is recorded. When the duration is long enough, the brightness read from the brightness table is deleted and the original brightness adjustment process is restored.

[0132] Figure 4 The following is a structural block diagram of a lighting intelligent energy-saving management system based on the Internet of Things. In an embodiment of the present invention, a lighting intelligent energy-saving management system based on the Internet of Things, the system 10 includes:

[0133] The day-time interval determination module 11 is used to obtain the Gregorian calendar date corresponding to the current time, determine the sunrise time and sunset time according to the Gregorian calendar date, and determine the day-time interval with the sunset time as the left endpoint and the sunrise time as the right endpoint;

[0134] A monthly time interval determination module 12 is configured to obtain the lunar date corresponding to the current time, determine the moonrise time and moonset time based on the lunar date, and determine the monthly time interval with the moonrise time as the left endpoint and the moonset time as the right endpoint;

[0135] The brightness broadcast module 13 is used to determine the brightness of each time in the daily time interval based on the monthly time interval, query the brightness corresponding to the current time, determine the street lamp reference brightness according to the brightness corresponding to the current time, and broadcast the street lamp reference brightness to the controller of each street lamp;

[0136] The brightness adjustment module 14 is used to obtain road surface information based on the information collector closest to each street lamp, and adjust the reference brightness of the street lamp according to the road surface information; the road surface information includes audio information and video information.

[0137] Furthermore, the brightness broadcasting module 13 includes:

[0138] A moon size query unit is used to read the lunar date within the lunar time interval and query the moon size according to the lunar date, and express it as a percentage; a full moon is 100% and no moon is 0%;

[0139] a brightness distribution determining unit, configured to determine a peak brightness within a monthly time interval according to the percentage, and construct a Gaussian distribution function according to the peak brightness as the brightness distribution function at each moment within the monthly time interval;

[0140] An intersection determination unit is used to calculate the intersection of the daily time interval and the monthly time interval, and intercept the brightness distribution function based on the intersection as the brightness of the intersection within the daily time interval;

[0141] a complement determination unit, configured to calculate the complement of the intersection within a day-time interval, using zero as the brightness of the complement;

[0142] a broadcast execution unit, configured to query the brightness corresponding to the current moment, determine a reference brightness of the street lamp according to the brightness corresponding to the current moment, and broadcast the reference brightness of the street lamp to the controllers of each street lamp;

[0143] The streetlight reference brightness is inversely proportional to the brightness corresponding to the current moment, and the specific relationship is:

[0144] Where, L t is the reference brightness of the street light at time t, H t is the brightness corresponding to time t, where time t is the current time; L max is the maximum brightness of the street light.

[0145] Specifically, the brightness adjustment module 14 includes:

[0146] An audio acquisition unit is used to obtain the information collector closest to any street lamp, and the information collector obtains audio information in real time;

[0147] An audio recognition unit is used to intercept the audio before a preset time span starting from the current time, recognize the intercepted audio, and determine the periodicity;

[0148] A video acquisition unit, configured to acquire video information when periodicity occurs;

[0149] The video recognition unit is used to count all video information within a preset range with the current street lamp as the center, identify all video information within the preset range, and adjust the current street lamp reference brightness according to the recognition result of the video information.

[0150] Wherein, the video recognition unit includes:

[0151] The circle creation subunit is used to determine the circular range with the current street lamp as the center and the preset value as the radius;

[0152] The video reading subunit is used to query all video information of all information collectors within the circular range within the time span;

[0153] An image recognition subunit is used to convert video information into an image queue, calculate the pixel mean of each image in the image queue, and convert the image queue into a mean array;

[0154] a region marking subunit, configured to calculate a standard deviation of the mean array, and mark a region corresponding to the corresponding video information when the standard deviation is less than a preset first threshold;

[0155] A coefficient calculation subunit is used to calculate the ratio of the marked area to the circular range, and determine the adjustment coefficient of the street lamp reference brightness according to the ratio;

[0156] The relationship between the adjustment coefficient and the ratio is:

[0157] Where α is the adjustment coefficient, S m is the area of ​​the mth marked region, M is the total number of marked regions; ε is the preset threshold; and r is the radius of the circular range.

[0158] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent energy-saving management method for lamps based on the Internet of Things, characterized in that: The method comprises: Get the Gregorian calendar date corresponding to the current time, determine the sunrise and sunset times according to the Gregorian calendar date, and determine the day-time interval with the sunset time as the left endpoint and the sunrise time as the right endpoint; Get the lunar date corresponding to the current time, determine the moonrise time and moonset time according to the lunar date, and determine the monthly time interval with the moonrise time as the left endpoint and the moonset time as the right endpoint; Determine the brightness of each moment in the daily time interval based on the monthly time interval, query the brightness corresponding to the current moment, determine the reference brightness of the street lamp according to the brightness corresponding to the current moment, and broadcast the reference brightness of the street lamp to the controllers of each street lamp; Road surface information is acquired based on an information collector closest to each street lamp, and the reference brightness of the street lamp is adjusted according to the road surface information; the road surface information includes audio information and video information.

2. The intelligent energy-saving management method for lighting fixtures based on the Internet of Things according to claim 1 is characterized in that: The steps of determining the brightness at each moment in the daily time interval based on the monthly time interval, querying the brightness corresponding to the current moment, determining the street lamp reference brightness according to the brightness corresponding to the current moment, and broadcasting the street lamp reference brightness to the controllers of each street lamp include: Read the lunar date in the month time interval, and query the moon size according to the lunar date, expressed as a percentage; a full moon is 100%, and no moon is 0%; Determine the peak brightness of the monthly time interval according to the percentage, and construct a Gaussian distribution function according to the peak brightness as the brightness distribution function of each moment in the monthly time interval; Calculate the intersection of the daily time interval and the monthly time interval, and intercept the brightness distribution function based on the intersection as the brightness of the intersection within the daily time interval; Calculate the complement of the intersection within the day-time interval, using zero as the brightness of the complement; Query the brightness corresponding to the current moment, determine the reference brightness of the street lamp according to the brightness corresponding to the current moment, and broadcast the reference brightness of the street lamp to the controllers of each street lamp; The street lamp reference brightness is inversely proportional to the brightness corresponding to the current moment, and the specific relationship is: Where, L t is the reference brightness of the street lamp at time t, H t is the brightness corresponding to time t, where time t is the current time; L max is the maximum brightness of the street light.

3. The intelligent energy-saving management method for lamps based on the Internet of Things according to claim 1 is characterized in that: The step of acquiring road surface information based on the information collector closest to each street lamp and adjusting the reference brightness of the street lamp according to the road surface information comprises: For any street lamp, obtain the information collector closest to the street lamp, and use the information collector to obtain audio information in real time; Taking the current time as the starting point, intercept the audio before the preset time span, identify the intercepted audio, and determine the periodicity; When periodicity occurs, obtain video information; With the current street lamp as the center, all video information within the preset range is counted, all video information within the preset range is identified, and the reference brightness of the street lamp at the current moment is adjusted according to the identification result of the video information.

4. The intelligent energy-saving management method for lamps based on the Internet of Things according to claim 3 is characterized in that: The steps of taking the current street lamp as the center, counting all video information within a preset range, identifying all video information within the preset range, and adjusting the current street lamp reference brightness according to the identification result of the video information include: The current street lamp is taken as the center and the preset value is used as the radius to determine the circular range; Query all video information of all information collectors within the circular range within the time span; Convert the video information into an image queue, calculate the pixel mean of each image in the image queue, and convert the image queue into a mean array; Calculating the standard deviation of the mean array, and when the standard deviation is less than a preset first threshold, marking the area corresponding to the corresponding video information; Calculate the ratio of the marked area to the circular range, and determine the adjustment coefficient of the street lamp reference brightness according to the ratio; The relationship between the adjustment coefficient and the ratio is: In the formula, α is the adjustment coefficient, S m is the area of ​​the mth marked region, m is the total number of marked regions; ε is the preset threshold; r is the radius of the circular range.

5. The intelligent energy-saving management method for lamps based on the Internet of Things according to claim 4 is characterized in that: The step of calculating the pixel mean of each image in the image queue comprises: Read each image in the image queue and convert the image into a grayscale image; Extracting a histogram of the grayscale image, and fitting the histogram based on a Gaussian distribution function to obtain a determined grayscale distribution function; Read the mean of the grayscale distribution function and query the pixel mean in the image.

6. The intelligent energy-saving management method for lighting fixtures based on the Internet of Things according to claim 1 is characterized in that: The method further comprises: Perform voice recognition on the audio information, and when the voice recognition is successful, convert the audio information into text; Perform content recognition on the text to obtain keywords; According to the keyword, the brightness is searched in a preset brightness table and displayed; the brightness searched in the brightness table has the highest priority; When voice recognition fails, the failure duration is recorded. When the failure duration reaches the preset duration threshold, the adjusted street lamp reference brightness is displayed; The frequency of voice recognition is adjusted synchronously according to the failure duration.

7. An intelligent energy-saving management system for lamps based on the Internet of Things, characterized in that: The system comprises: A day-time interval determination module is used to obtain the solar calendar date corresponding to the current time, determine the sunrise time and sunset time according to the solar calendar date, and determine the day-time interval with the sunset time as the left endpoint and the sunrise time as the right endpoint; A monthly time interval determination module is used to obtain the lunar date corresponding to the current time, determine the moon rise time and moon set time according to the lunar date, and determine the monthly time interval with the moon rise time as the left endpoint and the moon set time as the right endpoint; A brightness broadcasting module, used to determine the brightness at each time in the daily time interval based on the monthly time interval, query the brightness corresponding to the current time, determine the street lamp reference brightness according to the brightness corresponding to the current time, and broadcast the street lamp reference brightness to the controllers of each street lamp; The brightness adjustment module is used to obtain road surface information based on the information collector closest to each street lamp, and adjust the reference brightness of the street lamp according to the road surface information; the road surface information includes audio information and video information.

8. The intelligent energy-saving management system for lighting fixtures based on the Internet of Things according to claim 7 is characterized in that: The brightness broadcast module comprises: The moon size query unit is used to read the lunar date in the lunar time interval, and query the moon size according to the lunar date, which is expressed as a percentage; a full moon is 100%, and no moon is 0%; A brightness distribution determination unit, used to determine the peak brightness of the monthly time interval according to the percentage, and construct a Gaussian distribution function according to the peak brightness as the brightness distribution function of each moment in the monthly time interval; An intersection determination unit, used to calculate the intersection of the daily time interval and the monthly time interval, and intercept the brightness distribution function based on the intersection as the brightness of the intersection within the daily time interval; A complement determination unit, used to calculate the complement of the intersection within the day and time interval, using zero as the brightness of the complement; A broadcast execution unit, used to query the brightness corresponding to the current moment, determine the reference brightness of the street lamp according to the brightness corresponding to the current moment, and broadcast the reference brightness of the street lamp to the controllers of each street lamp; The street lamp reference brightness is inversely proportional to the brightness corresponding to the current moment, and the specific relationship is: Where, L t is the reference brightness of the street lamp at time t, H t is the brightness corresponding to time t, where time t is the current time; L max is the maximum brightness of the street light.

9. The intelligent energy-saving management system for lighting fixtures based on the Internet of Things according to claim 8 is characterized in that: The brightness adjustment module comprises: An audio acquisition unit is used to acquire the information collector closest to any street lamp, and the information collector acquires audio information in real time; The audio recognition unit is used to intercept the audio before the preset time span with the current time as the starting point, recognize the intercepted audio, and determine the periodicity; A video acquisition unit, used to acquire video information when periodicity occurs; The video recognition unit is used to count all video information within a preset range with the current street lamp as the center, recognize all video information within the preset range, and adjust the street lamp reference brightness at the current moment according to the recognition result of the video information.

10. The intelligent energy-saving management system for lighting fixtures based on the Internet of Things according to claim 9 is characterized in that: The video recognition unit comprises: The circle building subunit is used to determine the circular range with the current street lamp as the center and the preset value as the radius; The video reading subunit is used to query all video information of all information collectors within the circular range within the time span; The image recognition subunit is used to convert the video information into an image queue, calculate the pixel mean of each image in the image queue, and convert the image queue into a mean array; A region marking subunit, used for calculating a standard deviation of the mean value array, and marking a region corresponding to the corresponding video information when the standard deviation is less than a preset first threshold; A coefficient calculation subunit, used to calculate the ratio of the marked area to the circular range, and determine the adjustment coefficient of the reference brightness of the street lamp according to the ratio; The relationship between the adjustment coefficient and the ratio is: In the formula, α is the adjustment coefficient, S m is the area of ​​the mth marked area, M is the total number of marked areas; ε is the preset threshold; r is the radius of the circular range.

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