LED street lamp intelligent supervision control system based on Internet of Things

Abstract

The invention relates to the technical field of street lamp intelligent control, and particularly discloses an LED street lamp intelligent supervision and control system based on the Internet of Things. The first on-off time is obtained through optimization adjustment based on the astronomical algorithm and the historical actual operation condition of the LED street lamp, the second on-off time is determined based on the atmospheric environment condition, the optimal on-off time is determined by comparing the first on-off time with the second on-off time, the accuracy and rationality of on-off time setting are improved, and the working efficiency is improved. The control of the street lamp is more intelligent, the atmospheric environment data, the traffic environment data and the spatial feature data of the LED street lamp are subjected to feature extraction, a discrimination vector is constructed based on the feature extraction condition, whether the brightness needs to be regulated and controlled or not is intelligently judged by using a Logistic regression model, the regulation and control brightness value is further accurately calculated, and an instruction is generated; therefore, the brightness of the street lamp can be timely and accurately adjusted according to actual environment changes, and energy waste caused by too high brightness or influence on lighting effect caused by too low brightness is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of street lamps, and in particular to an intelligent monitoring and control system for LED street lamps based on the Internet of Things. Background Art

[0002] As cities continue to expand and modernize, urban lighting systems, as a crucial component of urban infrastructure, are becoming increasingly large. Traditional LED streetlight control methods often rely on timed switches or manual control, a crude management model with numerous drawbacks.

[0003] Traditional timing control methods often use a uniform time setting for streetlight on / off times, failing to fully consider the impact of different geographic locations, seasonal variations, and weather conditions on lighting needs. For example, in autumn and winter, when days are short and nights are long, a uniform off-time can lead to insufficient road lighting in the early morning, impacting traffic safety. Conversely, in spring and summer, when days are long and nights are short, fixed on-times can waste energy. Furthermore, weather changes such as rainy and foggy days can significantly reduce ambient light levels, but traditional methods lack the flexibility to adjust streetlight on / off based on real-time atmospheric conditions. This results in insufficient lighting in poor lighting conditions, or in maintaining high brightness even in good lighting conditions, leading to unnecessary energy consumption.

[0004] When it comes to streetlight brightness control, traditional control methods lack comprehensive consideration of multiple influencing factors. Operating solely based on fixed brightness settings, they fail to fully consider the differences in lighting requirements due to road type (such as expressways, main roads, branch roads, etc.), traffic flow, and the surrounding environment (buildings, vegetation, billboards, etc.). For example, on main roads with heavy traffic, low-brightness lighting may not meet the driver's visual needs, increasing the risk of traffic accidents; while on branch roads with light traffic, high-brightness lighting results in energy waste. At the same time, surrounding buildings, vegetation, and billboards may block streetlights, affecting the lighting effect, but traditional methods cannot dynamically adjust the brightness based on these actual conditions. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent monitoring and control system for LED street lamps based on the Internet of Things to solve the problems raised in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions: an intelligent monitoring and control system for LED street lights based on the Internet of Things, including a server, which is communicatively connected to a data acquisition module, an on / off time analysis module, an on / off time determination module, a feature extraction module, a brightness control judgment module, a brightness control analysis module and a remote control terminal.

[0007] The data acquisition module is used to obtain the geographic spatiotemporal data, atmospheric environment data, traffic environment data and spatial feature data of each LED street lamp in the target urban road area through different street lamp monitoring terminals such as GPS sensors, light intensity sensors, visibility meters, rain sensors, snow depth sensors, humidity sensors, and smart cameras.

[0008] The on / off time analysis module is used to analyze the geographic spatiotemporal data and the atmospheric environment data to obtain the first on / off time and the second on / off time of each LED street lamp in the target urban road area, wherein the on / off time includes the on time and the off time;

[0009] The on / off time determination module is used to determine the optimal on / off time of each LED street lamp in the target city road area based on the first on / off time and the second on / off time of each LED street lamp in the target city road area.

[0010] The feature extraction module is used to extract features from the atmospheric environment data, traffic environment data and spatial feature data of each LED street lamp in the target urban road area during a preset working time period, and obtain the first target feature impact value, the second target feature impact value and the third target feature impact value of each LED street lamp in the target urban road area during the preset working time period.

[0011] The brightness control determination module is used to construct a street lamp brightness control discriminant vector in a target urban road area, and then determine whether the street lamp brightness in the target urban road area needs to be controlled.

[0012] The brightness control analysis module is used to count the numbers corresponding to the LED street lights that need brightness control in the target urban road area, and analyze the control brightness values ​​corresponding to the LED street lights that need brightness control.

[0013] The remote control terminal is used to receive the on / off instructions of each LED street lamp in the target city road area and perform corresponding switch control, receive the brightness control instructions and brightness control values ​​of each LED street lamp in the target city road area and perform corresponding brightness control.

[0014] The server is also used to store the atmospheric environment indicator triggering opening and closing intervals corresponding to each road type, store the first character segmentation rules, and the target brightness values ​​corresponding to the discrimination results of each logistics regression model.

[0015] Beneficial effects of the present invention:

[0016] The present invention accurately determines the start and close times that match the local day and night changes according to the specific geographical location of each LED street lamp in the target urban road area, so that the turning on and off of the street lamps is more in line with the actual lighting needs, avoiding the waste of resources or insufficient lighting caused by unified time setting. Combined with the actual historical operation of the street lamps, the start and close times obtained based on the astronomical algorithm are optimized and adjusted to obtain a first start and close time, and the second start and close time is determined based on the atmospheric environment conditions. The optimal start and close time is determined by comparing the first start and close time with the second start and close time, fully considering various factors such as astronomical time and atmospheric environment, and being able to provide the most appropriate start and close time for the street lamps in different scenarios, which not only meets the road lighting needs but also saves energy to the greatest extent, further improves the accuracy and rationality of the start and close time setting, and makes the control of the street lamps more intelligent.

[0017] The present invention extracts features from the atmospheric environment data, traffic environment data, and spatial feature data of each LED street lamp in the target urban road area during a preset working period, constructs a discriminant vector based on the feature extraction, and uses a logistic regression model to intelligently determine whether the brightness needs to be adjusted. The brightness adjustment value is further accurately calculated and an instruction is generated. This allows the brightness of the street lamps to be adjusted promptly and accurately according to actual environmental changes, avoiding excessively high brightness that wastes energy or excessively low brightness that affects the lighting effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below with reference to the accompanying drawings.

[0019] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] See also Figure 1 As shown, the present invention is an intelligent monitoring and control system for LED street lamps based on the Internet of Things, including a server, which is communicatively connected to a data acquisition module, an on / off time analysis module, an on / off time determination module, a feature extraction module, a brightness control judgment module, a brightness control analysis module and a remote control terminal.

[0022] The data acquisition module is based on the Internet of Things connection and obtains the geographic spatiotemporal data, atmospheric environment data, traffic environment data and spatial feature data of each LED street lamp in the target urban road area through different street lamp monitoring terminals such as GPS sensors, light intensity sensors, visibility meters, rain sensors, snow depth sensors, humidity sensors, and smart cameras.

[0023] The on / off time analysis module is used to analyze geographic spatiotemporal data and atmospheric environment data to obtain the first on / off time and the second on / off time of each LED street light in the target urban road area, where the on / off time includes the opening time and the closing time;

[0024] Specifically, the process of obtaining the first on / off time of each LED streetlight in the target urban road area is as follows:

[0025] The longitude, latitude, time zone, and current date sequence number of each LED streetlight in the target city's road area are obtained from the geographic spatiotemporal data, and the sunrise and sunset times corresponding to the locations of each LED streetlight in the target city's road area are calculated using an astronomical algorithm.

[0026] It should be noted that my country's time zone is East 8, that is, the time zone of all LED street lights in the target city road area is 8; the current date serial number refers to the sequence of the current date in the corresponding year, such as February 11th is 42; the astronomical algorithm specifically includes the sunrise time calculation formula and the sunset time calculation formula:

[0027]

[0028]

[0029] Obtain the on / off response durations of each LED streetlight in the target city road area within a preset historical time period, calculate the average of these durations, and obtain the average on / off response durations of each LED streetlight in the target city road area within a preset historical time period.

[0030] The average turn-on response time of each LED street lamp in the target city road area corresponding to the historical preset time period is used as the early turn-on time offset of each LED street lamp in the target city road area; the average turn-off response time of each LED street lamp in the target city road area corresponding to the historical preset time period is used as the early turn-off time offset of each LED street lamp in the target city road area;

[0031] Subtract the sunrise time corresponding to the location of each LED street lamp in the target city road area from the offset of the early closing time to obtain the first closing time of each LED street lamp in the target city road area; subtract the sunset time corresponding to the location of each LED street lamp in the target city road area from the offset of the early opening time to obtain the first opening time of each LED street lamp in the target city road area;

[0032] Thus, the first on-off time of each LED street lamp in the target urban road area is obtained.

[0033] Specifically, the process of obtaining the second on / off time of each LED street light in the target urban road area is as follows:

[0034] The sunrise time and sunset time corresponding to the location of each LED street lamp in the target urban road area are deduced forward and backward according to the preset time period to obtain the sunrise collection time period and sunset collection time period corresponding to the location of each LED street lamp in the target urban road area;

[0035] Obtain the atmospheric environment index at each time point during the sunrise collection period corresponding to the location of each LED street lamp in the target urban road area, and match it with the atmospheric environment index interval corresponding to the atmospheric environment index to obtain the atmospheric environment index interval at each time point during the sunrise collection period corresponding to the location of each LED street lamp in the target urban road area;

[0036] It should be noted that atmospheric environment indicators include: natural light intensity, air visibility, rainfall, snowfall and humidity.

[0037] Obtain the road type corresponding to the location of each LED street lamp in the target city road area, match it with the environmental indicator trigger on / off interval corresponding to each road type stored in the server, and obtain the environmental indicator trigger on / off interval corresponding to the location of each LED street lamp in the target city road area;

[0038] It should be noted that road types include but are not limited to: expressways, main roads, branch roads and side roads.

[0039] Compare the atmospheric environment index intervals at each time point during the sunrise collection period corresponding to the location of each LED street lamp in the target urban road area with the atmospheric environment index trigger shutdown interval. If the atmospheric environment index interval corresponding to a certain LED street lamp in the target urban road area falls within the atmospheric environment index trigger shutdown interval, then determine that the LED street lamp needs to be shut down at that time point. Count the corresponding shutdown time points of each LED street lamp, and record the earliest shutdown time point of each LED street lamp as the second shutdown time point of each LED street lamp.

[0040] Compare the atmospheric environment index intervals at each time point during the sunset collection period corresponding to the location of each LED street lamp in the target urban road area with the atmospheric environment index trigger-on interval. If the atmospheric environment index interval corresponding to a certain LED street lamp in the target urban road area falls within the atmospheric environment index trigger-on interval, then determine that the LED street lamp needs to be turned on at that time point. Count the corresponding time points at which each LED street lamp needs to be turned on, and record the latest time point at which each LED street lamp needs to be turned on as the second turn-on time point of each LED street lamp.

[0041] Thus, the second on-off time of each LED street lamp in the target urban road area is obtained.

[0042] The on / off time determination module is used to determine the optimal on / off time of each LED street lamp in the target city road area based on the first on / off time and the second on / off time of each LED street lamp in the target city road area.

[0043] Specifically, the process of determining the optimal on / off time for each LED streetlight in the target urban road area is as follows:

[0044] When the first off time of a certain LED street lamp in the target urban road area is less than or equal to the second off time, the second off time of the LED street lamp is used as the optimal off time of the LED street lamp; when the first off time of a certain LED street lamp in the target urban road area is greater than the second off time, the first off time of the LED street lamp is used as the optimal off time of the LED street lamp, and a corresponding off instruction is generated;

[0045] When the first on-time of a certain LED street lamp in the target urban road area is less than or equal to the second on-time, the first on-time of the LED street lamp is used as the optimal on-time of the LED street lamp; when the first on-time of a certain LED street lamp in the target urban road area is greater than the second on-time, the second on-time of the LED street lamp is used as the optimal on-time of the LED street lamp, and a corresponding on-time instruction is generated;

[0046] The on / off instructions for each LED street light are sent to the remote control terminal via the transmitter.

[0047] In a specific embodiment, the present invention accurately determines the start and close times that match the local day and night changes based on the specific geographical locations of each LED street lamp in the target urban road area, so that the opening and closing of the street lamps are more in line with the actual lighting needs, avoiding the waste of resources or insufficient lighting caused by unified time settings. Combined with the actual historical operation of the street lamps, the start and close times obtained based on the astronomical algorithm are optimized and adjusted to obtain a first start and close time, and the second start and close time is determined based on the atmospheric environment conditions. The optimal start and close time is determined by comparing the first start and close time and the second start and close time, fully considering various factors such as astronomical time and atmospheric environment, and being able to provide the most appropriate start and close time for the street lamps in different scenarios, which not only meets the needs of road lighting but also saves energy to the greatest extent, further improves the accuracy and rationality of the start and close time settings, and makes the control of the street lamps more intelligent.

[0048] The feature extraction module is used to extract features from the atmospheric environment data, traffic environment data and spatial feature data of each LED street lamp in the target urban road area during a preset working time period, and obtain the first target feature impact value, second target feature impact value and third target feature impact value of each LED street lamp in the target urban road area during the preset working time period.

[0049] Specifically, the process of obtaining the first target feature influence value and the second target feature influence value is as follows:

[0050] Obtain atmospheric environment data for each LED street lamp in a target urban road area at each time stamp during a preset working time period, split the atmospheric environment data using a preset splitting rule to obtain first atmospheric environment data and second atmospheric environment data for each LED street lamp in the target urban road area at each time stamp during the preset working time period; wherein the first atmospheric environment data consists of a plurality of numeric characters and mainly includes quantifiable atmospheric index data; and the second atmospheric environment data consists of a plurality of alphabetic characters and is used to describe information such as attributes or units of the atmospheric environment;

[0051] It should be noted that atmospheric environment data includes but is not limited to: air humidity, light intensity, and haze concentration.

[0052] Acquire multiple characters from the first atmospheric environment data and the second atmospheric environment data according to a preset target character combination rule, and combine them to obtain a target character combination;

[0053] Rearranging the standard encoding table according to the target character combination to obtain a first encoding table, and decoding the first environment data according to the first encoding table to obtain first decoded data;

[0054] Performing index processing on the first decoded data using the first character segmentation rule stored in the server to obtain a plurality of first identification character combinations;

[0055] It should be noted that the server stores a mapping relationship between the first decoding data and the character segmentation rule.

[0056] identifying a first representation character combination including the first character from the segmented first representation character combinations, and using the first representation character combination as a target identification character combination;

[0057] Thus, the target identification character combination of each LED street lamp in the target city road area at each time stamp in the preset working time period is obtained, and the target identification character combination is matched with the first target feature influence value corresponding to each target identification character combination stored in the server, so as to obtain the first target feature influence value of each LED street lamp in the target city road area at each time stamp in the preset working time period, and the average value is calculated to obtain the calculated result as the first target feature influence value of each LED street lamp in the target city road area in the preset working time period;

[0058] It needs to be further explained that if the environmental data obtained is "251876AHUM75TRA120", it is split into the first environmental data "25187675120" (which contains quantitative data such as light intensity value, humidity value, haze concentration, etc.) and the second environmental data "AHUMTRA" ("A" represents light intensity, "HUM" represents humidity, and "TRA" represents haze concentration, which are characters used to describe data attributes or units).

[0059] Assume that the rule is to take the first three digits and the last three digits from the first environment data, and take all the characters from the second environment data, and obtain the target character combination "251120AHUMTRA" by combining "251", "120" and "AHUMTRA".

[0060] The original coding table is A=1, B=2,... Since the target character combination contains special characters such as "A", "H", and "U", after rearrangement, it may become A=5, H=12, U=20,... The coding rules of digital characters are also adjusted according to the characteristics of the target character combination.

[0061] For the first environment data "25187675120", decoding using the new encoding table may result in "5122081518".

[0062] Set the target character to "L", and among "L=251, M=876, N=75, T=120", identify the combination containing "L", that is, "L=251" as the target identification character combination.

[0063] Similarly, based on the traffic environment data of each LED street lamp in the target city road area at each time stamp in the preset working time period, the second target feature impact value of each LED street lamp in the target city road area in the preset working time period is analyzed;

[0064] It should be noted that traffic environment data includes but is not limited to: vehicle flow, driving speed, passenger flow, vehicle type ratio, and flow ratio.

[0065] It should be further explained that the vehicle model ratio refers to the ratio of vehicle models with license plates of different colors, that is, the ratio of vehicle models with yellow plates, green plates, blue plates and white plates.

[0066] Specifically, the process of obtaining the influence value of the third target feature is as follows:

[0067] Obtain the light source height of each LED street lamp at the location of the target urban road area, match it with the preset reference lighting area corresponding to each light source height, obtain the reference lighting area at the location of each LED street lamp in the target urban road area, match it with the allowed building characteristic value, allowed vegetation characteristic value, allowed billboard characteristic value, and allowed road condition characteristic value corresponding to each preset reference lighting area, and obtain the allowed building characteristic value, allowed vegetation characteristic value, allowed billboard characteristic value, and allowed road condition characteristic value at the location of each LED street lamp in the target urban road area;

[0068] The building characteristic value of each LED street lamp in the target urban road area is calculated by subtracting the building characteristic value from the allowed building characteristic value, and the ratio of the difference to the allowed building characteristic value is calculated to obtain the building light impact coefficient of each LED street lamp in the target urban road area;

[0069] Similarly, the vegetation light impact coefficient, billboard light impact coefficient and road condition light impact coefficient of each LED street lamp in the target urban road area are analyzed;

[0070] The building light impact coefficient, vegetation light impact coefficient, billboard light impact coefficient and road condition light impact coefficient are input into the graphics processor, which converts them into numerical values ​​according to a certain ratio and inputs them into the line graph to obtain the corresponding four points. The four points are connected in sequence by line segments to obtain a broken line. The two endpoints of the broken line are made perpendicular to the X-axis respectively, so that the broken line and the two perpendicular lines form a closed figure with the X-axis. The area of ​​the closed figure is identified, and the numerical value of its area is used as the third target feature impact value.

[0071] It should be noted that the building characteristic values, vegetation characteristic values, and billboard characteristic values ​​are obtained as follows:

[0072] Obtain the building area, building height, and building density within the reference lighting area of ​​each LED street lamp location in the target urban road area, perform normalization processing, and multiply the building area, building height, and building density within the reference lighting area of ​​each LED street lamp location after normalization processing by their corresponding building light impact factors, and calculate the sum to obtain the building characteristic value of each LED street lamp location in the target urban road area;

[0073] Obtain the values ​​of vegetation height, vegetation density, and vegetation type within the reference lighting area at the location of each LED street lamp in the target urban road area, perform normalization processing, and multiply the normalized values ​​of vegetation height, vegetation density, and vegetation type within the reference lighting area at the location of each LED street lamp by their corresponding vegetation light impact factors, and calculate the sum of the products to obtain the vegetation characteristic values ​​at the location of each LED street lamp in the target urban road area;

[0074] It should be further explained that the vegetation type value refers to a quantitative indicator of the vegetation type. Different types of vegetation have different transmittance and reflectivity of light.

[0075] The height, area, relative position and material value of each billboard in the reference lighting area at the location of each LED street lamp in the target urban road area are obtained and normalized. The height, area, relative position and material value of the billboard in the reference lighting area at the location of each LED street lamp after normalization are respectively multiplied by the corresponding vegetation light impact factor and the sum is calculated to obtain the characteristic value of the billboard at the location of each LED street lamp in the target urban road area.

[0076] It should be further explained that the relative position of the billboard refers to the azimuth angle of the billboard with the street lamp source as the center. For example, at 45° to the left front of the street lamp, billboards at different azimuth angles have different effects on the direction of light propagation. The billboard material value refers to a numerical indicator that quantifies the properties of the material used to make the billboard. Different billboard materials have different light absorbance values.

[0077] Obtain the road condition indicators corresponding to the locations of the LED street lamps in the target city road area, multiply them with the preset road condition light impact factors corresponding to each road condition indicator, and calculate the sum to obtain the road condition characteristic values ​​of the locations of the LED street lamps in the target city road area.

[0078] It should be noted that the various road condition indicators include but are not limited to: road type value, number of road lanes, road curvature, road waterlogging area, and road waterlogging depth.

[0079] The brightness control determination module is used to construct a street light brightness control discriminant vector for the target urban road area, and then determine whether the street light brightness in the target urban road area needs to be controlled.

[0080] Specifically, determining whether the brightness of streetlights in a target urban road area needs to be adjusted includes:

[0081] Extract the first target impact value, second target impact value and third target impact value of each LED street lamp in the target city road area during the preset working time period, and record them as α i , β i , γ i , construct the street light brightness control discriminant vector x in the target urban road area i =(α i ,β i ,γ i );

[0082] Through the logistics regression model Determine whether the brightness of street lights in the target city road area needs to be regulated. The dependent variable y represents whether the brightness of street lights in the target city road area needs to be regulated. It is a binary variable. y = 1 means that it needs to be regulated, and y = 0 means that it does not need to be regulated. P(y = 1 | x i ) is the discriminant vector x i The probability that the dependent variable y takes the value 1 is, is the set fitting model parameter and the discriminant vector x i The dot product of is the set fitting model parameter, x i Indicates the number of the fitting model parameters, i=0,1,2,3.

[0083] Specifically,

[0084] If P(y=1|x i )≥0.5, it is determined that the streetlight brightness in the target urban road area needs to be adjusted.

[0085] If P(y=1|x i )<0.5, it is determined that the brightness of street lights in the target urban road area does not need to be adjusted.

[0086] The brightness control analysis module is used to count the numbers of LED street lights that need brightness control in the target urban road area and analyze the control brightness values ​​corresponding to each LED street light that needs brightness control.

[0087] Specifically, the process of analyzing the corresponding brightness control value of each LED street lamp that needs brightness control is as follows:

[0088] Extract the target brightness value corresponding to each logistics regression model discrimination result stored in the server, and match the discrimination result of each logistics regression model corresponding to each LED street lamp that needs brightness control to obtain the target brightness value corresponding to each LED street lamp that needs brightness control;

[0089] Extract the actual brightness value corresponding to each LED street lamp that needs brightness control. When the actual brightness value corresponding to a LED street lamp that needs brightness control is greater than the target brightness value, it is determined that the LED street lamp that needs brightness control needs to increase or decrease brightness control, and a corresponding brightness reduction control instruction is generated;

[0090] When the actual brightness value corresponding to a certain LED street lamp requiring brightness control is less than the target brightness value, it is determined that the brightness of the LED street lamp requiring brightness control needs to be increased, and a corresponding brightness increase control instruction is generated;

[0091] The difference between the actual brightness value and the target brightness value of each LED street lamp that needs brightness control is used as the control brightness value corresponding to each LED street lamp that needs brightness control;

[0092] Thus, the control instructions and control brightness values ​​corresponding to each LED street lamp that needs brightness control are sent to the remote control terminal through the transmitter.

[0093] In a specific embodiment, the present invention extracts features from the atmospheric environment data, traffic environment data, and spatial feature data of each LED street lamp in the target urban road area during a preset working period, constructs a discriminant vector based on the feature extraction, and uses a logistic regression model to intelligently determine whether the brightness needs to be adjusted. The brightness adjustment value is further accurately calculated and instructions are generated. This allows the brightness of the street lamps to be adjusted in a timely and accurate manner according to actual environmental changes, avoiding excessively high brightness that wastes energy or excessively low brightness that affects the lighting effect.

[0094] The remote control terminal is used to receive the on / off instructions of each LED street lamp in the target city road area and perform corresponding switch control, receive the brightness control instructions and brightness control values ​​of each LED street lamp in the target city road area and perform corresponding brightness control.

[0095] The server is also used to store the atmospheric environment indicator trigger opening and closing intervals corresponding to each road type, store the first character segmentation rules, and the target brightness values ​​corresponding to the judgment results of each logistics regression model.

[0096] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. An intelligent monitoring and control system for LED street lights based on the Internet of Things, characterized in that: include: The on / off time analysis module is used to analyze geographic spatiotemporal data and atmospheric environment data to obtain the first on / off time and the second on / off time of each LED street light in the target urban road area, where the on / off time includes the opening time and the closing time; An on / off time determination module, configured to determine the optimal on / off time of each LED street lamp in the target city road area based on the first on / off time and the second on / off time of each LED street lamp in the target city road area; A feature extraction module is used to extract features from the atmospheric environment data, traffic environment data, and spatial feature data of each LED street lamp in the target urban road area during a preset working time period, and obtain a first target feature impact value, a second target feature impact value, and a third target feature impact value of each LED street lamp in the target urban road area during the preset working time period; A brightness control determination module is used to construct a streetlight brightness control discriminant vector for the target urban road area, and then determine whether the streetlight brightness in the target urban road area needs to be controlled; The brightness control analysis module is used to count the numbers of LED street lights that need brightness control in the target urban road area and analyze the control brightness values ​​corresponding to each LED street light that needs brightness control.

2. The LED street light intelligent monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: Also included is a server, which is communicatively connected to the data acquisition module and the remote control terminal; The data acquisition module obtains the geographic spatiotemporal data, atmospheric environment data, traffic environment data and spatial characteristic data of each LED street lamp in the target urban road area through different street lamp monitoring terminals; The remote control terminal is used to receive the on / off instructions of each LED street lamp in the target city road area and perform corresponding switch control, receive the brightness control instructions and brightness control values ​​of each LED street lamp in the target city road area and perform corresponding brightness control.

3. The LED street light intelligent monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: The process of obtaining the first on / off time of each LED street lamp in the target urban road area is as follows: Obtain the longitude, latitude, time zone, and current date sequence number of each LED street light in the target city's road area, and calculate the sunrise and sunset times corresponding to each LED street light in the target city's road area using an astronomical algorithm; Obtain the on / off response durations of each LED streetlight in the target city road area within a preset historical time period, calculate the average of these durations, and obtain the average on / off response durations of each LED streetlight in the target city road area within a preset historical time period. The average turn-on response time of each LED street lamp in the target city road area corresponding to the historical preset time period is used as the early turn-on time offset of each LED street lamp in the target city road area; the average turn-off response time of each LED street lamp in the target city road area corresponding to the historical preset time period is used as the extended turn-off time offset of each LED street lamp in the target city road area; Subtract the sunrise time corresponding to the location of each LED street lamp in the target city road area from the extended off-time offset to obtain the first off-time of each LED street lamp in the target city road area; subtract the sunset time corresponding to the location of each LED street lamp in the target city road area from the early on-time offset to obtain the first on-time of each LED street lamp in the target city road area; Thus, the first on-off time of each LED street lamp in the target urban road area is obtained.

4. The LED street light intelligent monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: The process of obtaining the second on / off time of each LED street lamp in the target urban road area is as follows: The sunrise time and sunset time corresponding to the location of each LED street lamp in the target urban road area are deduced forward and backward according to the preset time period to obtain the sunrise collection time period and sunset collection time period corresponding to the location of each LED street lamp in the target urban road area; Obtain the atmospheric environment index at each time point during the sunrise collection period corresponding to the location of each LED street lamp in the target urban road area, and match it with the atmospheric environment index interval corresponding to the atmospheric environment index to obtain the atmospheric environment index interval at each time point during the sunrise collection period corresponding to the location of each LED street lamp in the target urban road area; Obtain the road type corresponding to the location of each LED street lamp in the target city road area, match it with the preset environmental indicator trigger on / off interval corresponding to each road type, and obtain the environmental indicator trigger on / off interval corresponding to the location of each LED street lamp in the target city road area; Compare the atmospheric environment index intervals at each time point during the sunrise collection period corresponding to the location of each LED street lamp in the target urban road area with the atmospheric environment index trigger shutdown interval. If the atmospheric environment index interval corresponding to a certain LED street lamp in the target urban road area falls within the atmospheric environment index trigger shutdown interval, then determine that the LED street lamp needs to be shut down at that time point. Count the corresponding shutdown time points of each LED street lamp, and record the earliest shutdown time point of each LED street lamp as the second shutdown time point of each LED street lamp. Compare the atmospheric environment index intervals at each time point during the sunset collection period corresponding to the location of each LED street lamp in the target urban road area with the atmospheric environment index trigger-on interval. If the atmospheric environment index interval corresponding to a certain LED street lamp in the target urban road area falls within the atmospheric environment index trigger-on interval, then determine that the LED street lamp needs to be turned on at that time point. Count the corresponding time points at which each LED street lamp needs to be turned on, and record the latest time point at which each LED street lamp needs to be turned on as the second turn-on time point of each LED street lamp. Thus, the second on-off time of each LED street lamp in the target urban road area is obtained.

5. The LED street light intelligent monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: The process of determining the optimal on / off time for each LED street light in the target urban road area is as follows: When the first off time of a certain LED street lamp in the target urban road area is less than or equal to the second off time, the second off time of the LED street lamp is used as the optimal off time of the LED street lamp; when the first off time of a certain LED street lamp in the target urban road area is greater than the second off time, the first off time of the LED street lamp is used as the optimal off time of the LED street lamp, and a corresponding off instruction is generated; When the first on-time of a certain LED street lamp in the target urban road area is less than or equal to the second on-time, the first on-time of the LED street lamp is used as the optimal on-time of the LED street lamp; when the first on-time of a certain LED street lamp in the target urban road area is greater than the second on-time, the second on-time of the LED street lamp is used as the optimal on-time of the LED street lamp, and a corresponding on-time instruction is generated; The on / off instructions for each LED street light are sent to the remote control terminal via the transmitter.

6. The LED street light intelligent monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: The process of obtaining the first target feature influence value and the second target feature influence value is as follows: Obtain atmospheric environment data of each LED street lamp in the target city road area at each time stamp within a preset working time period, split the atmospheric environment data according to a preset splitting rule, and obtain first atmospheric environment data and second atmospheric environment data of each LED street lamp in the target city road area at each time stamp within the preset working time period; Acquire multiple characters from the first atmospheric environment data and the second atmospheric environment data according to a preset target character combination rule, and combine them to obtain a target character combination; Rearranging the standard encoding table according to the target character combination to obtain a first encoding table, and decoding the first environment data according to the first encoding table to obtain first decoded data; Performing index processing on the first decoded data using the first character segmentation rule stored in the server to obtain a plurality of first identification character combinations; identifying a first representation character combination including the first character from the segmented first representation character combinations, and using the first representation character combination as a target identification character combination; Thus, the target identification character combination of each LED street lamp in the target city road area at each time stamp in the preset working time period is obtained, and the target identification character combination is matched with the first target feature influence value corresponding to each target identification character combination stored in the server, and the first target feature influence value of each LED street lamp in the target city road area at each time stamp in the preset working time period is obtained, and the average value is calculated to obtain the calculated result as the first target feature influence value of each LED street lamp in the target city road area in the preset working time period; Similarly, based on the traffic environment data of each LED street lamp in the target city road area at each time stamp within the preset working time period, the second target characteristic impact value of each LED street lamp in the target city road area in the preset working time period is analyzed.

7. The LED street light intelligent monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: The process of obtaining the third target feature influence value is as follows: Obtain the light source height of each LED street lamp at the location of the target urban road area, match it with the preset reference lighting area corresponding to each light source height, obtain the reference lighting area at the location of each LED street lamp in the target urban road area, match it with the allowed building characteristic value, allowed vegetation characteristic value, allowed billboard characteristic value, and allowed road condition characteristic value corresponding to each preset reference lighting area, and obtain the allowed building characteristic value, allowed vegetation characteristic value, allowed billboard characteristic value, and allowed road condition characteristic value at the location of each LED street lamp in the target urban road area; The building characteristic value of each LED street lamp in the target urban road area is calculated by subtracting the building characteristic value from the allowed building characteristic value, and the ratio of the difference to the allowed building characteristic value is calculated to obtain the building light impact coefficient of each LED street lamp in the target urban road area; Similarly, the vegetation light impact coefficient, billboard light impact coefficient and road condition light impact coefficient of each LED street lamp in the target urban road area are analyzed; The building light impact coefficient, vegetation light impact coefficient, billboard light impact coefficient and road condition light impact coefficient are input into the graphics processor, which converts them into numerical values ​​according to a certain ratio and inputs them into the line graph to obtain the corresponding four points. The four points are connected in sequence by line segments to obtain a broken line. The two endpoints of the broken line are made perpendicular to the X-axis respectively, so that the broken line and the two perpendicular lines form a closed figure with the X-axis. The area of ​​the closed figure is identified, and the numerical value of its area is used as the third target feature impact value.

8. The LED street light intelligent monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: The determining whether the brightness of street lamps in the target urban road area needs to be adjusted includes: Extract the first target feature influence value, second target feature influence value and third target feature influence value of each LED street lamp in the target city road area during the preset working time period, and record them as α i , β i , γ i , construct the street light brightness control discriminant vector x in the target urban road area i =(α i ,β i ,γ i ); Through the logistics regression model Determine whether the brightness of street lights in the target city road area needs to be regulated. The dependent variable y represents whether the brightness of street lights in the target city road area needs to be regulated. It is a binary variable. y = 1 means that it needs to be regulated, and y = 0 means that it does not need to be regulated. P(y = 1 | x i ) is the discriminant vector x i The probability that the dependent variable y takes the value 1 is, is the set fitting model parameter and the discriminant vector x i The dot product of is the set fitting model parameter, x i Indicates the number of the fitting model parameter, i = 0, 1, 2, 3; If P(y=1|x i )≥0.5, it is determined that the streetlight brightness in the target urban road area needs to be adjusted; If P(y=1|x i )<0.5, it is determined that the brightness of street lights in the target urban road area does not need to be adjusted.

9. The LED street light intelligent monitoring and control system based on Internet of Things according to claim 1, characterized in that: The process of analyzing the brightness control value corresponding to each LED street lamp that needs brightness control is as follows: Extract the target brightness value corresponding to each logistics regression model discrimination result stored in the server, and match the discrimination result of each logistics regression model corresponding to each LED street lamp that needs brightness control to obtain the target brightness value corresponding to each LED street lamp that needs brightness control; Extract the actual brightness value corresponding to each LED street lamp that needs brightness control. When the actual brightness value corresponding to a LED street lamp that needs brightness control is greater than the target brightness value, it is determined that the LED street lamp that needs brightness control needs to increase or decrease brightness control, and a corresponding brightness reduction control instruction is generated; When the actual brightness value corresponding to a certain LED street lamp requiring brightness control is less than the target brightness value, it is determined that the brightness of the LED street lamp requiring brightness control needs to be increased, and a corresponding brightness increase control instruction is generated; The difference between the actual brightness value and the target brightness value of each LED street lamp that needs brightness control is used as the control brightness value corresponding to each LED street lamp that needs brightness control; Thus, the control instructions and control brightness values ​​corresponding to each LED street lamp that needs brightness control are sent to the remote control terminal through the transmitter.

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