Street lamp intelligent energy-saving control system based on Internet of Things

Abstract

The invention discloses a street lamp intelligent energy-saving control system based on the Internet of Things, and relates to the technical field of street lamp regulation and control, the street lamp intelligent energy-saving control system comprises a dispatching center, an edge control module and street lamp modules, the dispatching center divides a covered area into a plurality of control areas, and each control area is internally provided with at least one group of street lamp modules; the number of the edge control modules is multiple, and each edge control module corresponds to one control area and controls each street lamp module in the corresponding control area. The edge control module comprises a control unit and a plurality of sensing units; each sensing unit is used for collecting environment information in the corresponding control area. Dynamic brightness adjustment is carried out based on the multi-dimensional environment data, the optimal brightness requirement of each control area is calculated through an intelligent algorithm by collecting the multi-dimensional environment information including the noise data, the flow data, the natural light brightness and the non-natural light source brightness in real time, and the brightness of the street lamp is accurately adjusted.

Description

Technical Field

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

[0002] With the continuous advancement of urbanization, street lights, as an important part of urban infrastructure, have multiple functions such as ensuring night travel safety and improving urban landscape. At present, traditional street light control systems mainly rely on timers or light sensors to control the switch and brightness of street lights. Although this simple control method can meet basic lighting needs, it has many limitations and is difficult to adapt to the needs of modern cities for energy saving and intelligent management.

[0003] After searching, a Chinese patent (publication number: CN108055726B) discloses an energy-saving lighting system and method for LED street lamps. The patent includes an embedded processing unit, which is respectively connected to an LED module, an image acquisition module, a brightness adjustment module, a brightness detection module, a wireless transmission module and a solar cell module; the image acquisition module faces the opposite direction of driving, is used to collect image information of pedestrians and vehicles, and transmits the data to the embedded processing unit; the embedded processing unit receives the brightness information of the brightness adjustment module and controls the switch and brightness of the street lamp according to the collected image information of pedestrians and vehicles.

[0004] In the prior art, street lamps are usually controlled on and off and their brightness according to fixed times or simple light intensity thresholds, resulting in high-brightness lighting in areas and time periods with sparse traffic or no pedestrians. In addition, traditional street lamp systems cannot comprehensively consider multiple environmental factors such as noise, traffic, and natural light brightness for dynamic adjustment, and cannot quickly adapt to lighting needs in different scenarios. Therefore, the present invention proposes an intelligent energy-saving control system for street lamps based on the Internet of Things. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent energy-saving control system for street lamps based on the Internet of Things to solve the problems mentioned in the above background technology.

[0006] The present invention can be implemented through the following technical solutions: an intelligent energy-saving control system for street lamps based on the Internet of Things, including a dispatching center, an edge control module, and a street lamp module;

[0007] The dispatch center divides the covered area into a plurality of control areas, each of which is provided with at least one group of street light modules;

[0008] There are multiple edge control modules, and each edge control module corresponds to a control area and controls each street light module in the corresponding control area;

[0009] The edge control module includes a control unit and a plurality of sensing units;

[0010] Each sensor unit is used to collect environmental information in the corresponding control area, including brightness data, noise data and flow data, and each sensor unit is connected to the control unit by wired or wireless communication;

[0011] The brightness data includes natural brightness and non-natural light source brightness, wherein the non-natural light source brightness includes the light source generated by buildings or equipment (such as shop advertising screens, LED display screens, street lighting, industrial equipment) and street light modules in the control area;

[0012] The control unit calculates the brightness adjustment data based on the environmental information in the corresponding control area, and based on the brightness adjustment data, the control unit adjusts the brightness of each street lamp module in the corresponding control area on the basis of meeting the brightness requirements of the corresponding control area to avoid excessive lighting of the street lamp module, thereby achieving energy saving.

[0013] A further technical improvement of the present invention is that: the brightness adjustment data is influenced by noise data and flow data, and the greater the noise data and flow data, the greater the brightness requirement of the control area;

[0014] And the control unit adjusts the brightness data by adjusting the lighting intensity of the street lamp module to achieve the energy saving purpose of the street lamp module. Therefore, the calculation formula of the brightness adjustment data is:

[0015] L d =min(L max ,max(L min ,L j +ω 1 N+ω 2 ·FL n -(1-γ)L a ));

[0016] L min =L j +α 1 N+α 2 ·F;

[0017] L max =L j +β 1 N+β 2 ·F;

[0018] Where, L d is the brightness requirement data of the current control area; L max , L min are the maximum and minimum brightness allowed in the control area; α 1 , α2 is the weight coefficient for adjusting the minimum brightness; β 1 , β 2 To adjust the weight coefficient of the maximum brightness, it is larger to ensure sufficient lighting in high-demand scenarios;

[0019] L j The preset minimum lighting intensity of the street light module in the corresponding control area; L d L is the brightness adjustment data in the control area; n is the natural brightness; L a is the brightness of the non-natural light source, which includes the brightness contribution provided by the street light module in the corresponding control area; N is the noise data; F is the flow data; ω 1 ,ω 2 are the weight coefficients of noise data and traffic data, respectively, which are used to adjust the influence ratio of brightness demand calculation; γ is the brightness adjustment coefficient, which is used to correct the calculation error.

[0020] A further technical improvement of the present invention is that when calculating the brightness adjustment data, the dispatch center changes the brightness intensity of the street light module in the corresponding control area and monitors the brightness change of the adjacent control area to adjust the brightness intensity of the street light module in the corresponding control area, and corrects the calculation formula of the brightness adjustment data to:

[0021]

[0022] Where, ΔL m ΔL is the brightness variation of the street light module in the current control area; s is the brightness data change amount of the adjacent control area; ε is a positive number to prevent division by zero.

[0023] A further technical improvement of the present invention is that: the sensing unit continuously collects noise data, flow data and brightness data based on a sliding time window, the total duration of the time window remains fixed, and each time new data is collected, the latest collected data will overwrite the earliest data, ensuring that the time window always contains the latest continuous data;

[0024] During the data update process, the sensor unit calculates the average values ​​of the corresponding noise data, flow data and brightness data in real time based on all the data in the current time window to reflect the real-time changes in the environment.

[0025] A further technical improvement of the present invention is that when each sensor unit collects environmental information in the corresponding control area, a first standard deviation is set for noise data, a second standard deviation is set for flow data, and a third standard deviation is set for brightness data;

[0026] In the process of the sensing unit continuously collecting noise data, flow data and brightness data based on the sliding time window, if the difference between the noise data, flow data or brightness data and the corresponding average value exceeds the corresponding first standard deviation, second standard deviation or third standard deviation, the sensing unit determines it as an abnormal value;

[0027] The sensor unit removes the noise data, flow data or brightness data that are judged as abnormal values, and then performs smoothing based on the remaining data in the sliding time window to ensure the continuity and stability of the data.

[0028] A further technical improvement of the present invention is that: when adjusting the brightness of multiple control areas, the control area with the largest brightness data is taken as the core, and each of its adjacent control areas sets a brightness reduction amplitude threshold through its own control unit to maintain the corresponding brightness difference, thereby avoiding excessive brightness differences between adjacent control areas;

[0029] For multi-level adjacent control areas, the brightness difference is smoothly adjusted in turn to form a brightness gradient transition zone to improve visual consistency.

[0030] A further technical improvement of the present invention is that: after the activation frequency of brightness adjustment of multiple control areas reaches a preset frequency threshold, the dispatch center determines whether there is continuity between the control areas;

[0031] On the basis of continuity, the dispatch center marks the corresponding control areas, and then the dispatch center collects the noise data and traffic data collected by each marked control area, and the dispatch center judges whether the collected noise data and traffic data are related according to the order of activation of the brightness adjustment of each marked control area.

[0032] A further technical improvement of the present invention is that: the dispatch center is provided with an adjacency matrix model based on the distribution relationship between the street light modules and the blocks and road networks;

[0033] The dispatch center matches each marked control area path with correlation between noise data and traffic data with the adjacency matrix model, that is, a series of marked control areas forming a continuous path are compared with the topological structure in the adjacency matrix model;

[0034] After a successful match, the dispatch center coordinates the brightness of each marked control area to avoid sudden changes in brightness between different areas, thereby improving visual comfort and energy saving.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention performs dynamic brightness adjustment based on multi-dimensional environmental data. By real-time collection of multi-dimensional environmental information including noise data, traffic data, natural light brightness and non-natural light source brightness, the optimal brightness requirement of each control area is calculated through an intelligent algorithm, and the brightness of street lamps is accurately adjusted to avoid unnecessary energy waste, thereby maximizing energy saving effects while ensuring lighting quality.

[0037] In addition, the adjacent control area in the present invention has the ability of multi-area collaborative control. By introducing the adjacency matrix model, it can automatically identify adjacent control areas based on the distribution relationship between street lights and blocks and road networks, and smoothly adjust the brightness difference to avoid visual discomfort caused by sudden brightness changes.

[0038] On the other hand, the present invention is designed based on the Internet of Things architecture to meet the needs of smart cities for the intelligent, information-based and sustainable development of street lamp systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0040] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0042] Example 1

[0043] See also Figure 1 As shown, the present invention provides an intelligent energy-saving control system for street lamps based on the Internet of Things, including a dispatching center, an edge control module, and a street lamp module;

[0044] The dispatch center divides the covered area into multiple control areas, and multiple groups of street light modules are arranged in each control area;

[0045] There are multiple edge control modules, and each edge control module corresponds to a control area and controls each street light module in the corresponding control area;

[0046] The edge control module includes a control unit and a plurality of sensing units;

[0047] Each sensor unit is used to collect environmental information in the corresponding control area, including brightness data, noise data and flow data, and each sensor unit is connected to the control unit by wired or wireless communication;

[0048] The sensor unit continuously collects noise data, flow data and brightness data based on a sliding time window. The total duration of the time window remains fixed. Whenever new data is collected, the latest collected data will overwrite the earliest data to ensure that the time window always contains the latest continuous data. That is, each time a new data is collected, the sensor unit will automatically remove the earliest data to ensure that the window always contains the latest data.

[0049] During the data update process, the sensor unit calculates the average values ​​of the corresponding noise data, flow data and brightness data in real time based on all the data in the current time window to reflect the real-time changes in the environment.

[0050] When each sensor unit collects environmental information in the corresponding control area, a first standard deviation is set for noise data, a second standard deviation is set for flow data, and a third standard deviation is set for brightness data;

[0051] In the process of the sensing unit continuously collecting noise data, flow data and brightness data based on the sliding time window, if the difference between the noise data, flow data or brightness data and the corresponding average value exceeds the corresponding first standard deviation, second standard deviation or third standard deviation, the sensing unit determines it as an abnormal value;

[0052] The sensor unit removes the noise data, flow data or brightness data that are judged as abnormal values, and then performs smoothing based on the remaining data in the sliding time window to ensure the continuity and stability of the data.

[0053] The brightness data includes natural brightness and non-natural light source brightness, wherein the non-natural light source brightness includes the light source generated by buildings or equipment (such as shop advertising screens, LED display screens, street lighting, industrial equipment) and street light modules in the control area;

[0054] The control unit calculates the brightness adjustment data based on the environmental information in the corresponding control area, and based on the brightness adjustment data, the control unit adjusts the brightness of each street lamp module in the corresponding control area on the basis of meeting the brightness requirements of the corresponding control area to avoid excessive lighting of the street lamp module, thereby achieving energy saving.

[0055] The brightness adjustment data is influenced by noise data and flow data. The greater the noise data and flow data, the greater the brightness requirement of the control area.

[0056] And the control unit adjusts the brightness data by adjusting the lighting intensity of the street lamp module to achieve the energy saving purpose of the street lamp module. Therefore, the calculation formula of the brightness adjustment data is:

[0057] L d =min(L max ,max(Lmin ,L j +ω 1 N+ω 2 ·FL n -(1-γ)L a ));

[0058] L min =L j +α 1 N+α 2 ·F;

[0059] L max =L j +β 1 N+β 2 ·F, and each control area is set with a corresponding maximum brightness threshold for L max The maximum value of is constrained;

[0060] Where, L d is the brightness requirement data of the current control area; L max , L min are the maximum and minimum brightness allowed in the control area; α 1 , α 2 is the weight coefficient for adjusting the minimum brightness; β 1 , β 2 To adjust the weight coefficient of the maximum brightness, it is larger to ensure sufficient lighting in high-demand scenarios;

[0061] L j The preset minimum lighting intensity of the street light module in the corresponding control area; L d L is the brightness adjustment data in the control area; n is the natural brightness; L a It is the brightness of non-natural light source, which includes the brightness contribution provided by the street light module in the corresponding control area. By changing the brightness of each street light module in the corresponding control area, L a The value of; N is the noise data; F is the flow data; ω 1 ,ω 2 are the weight coefficients of noise data and flow data, respectively, which are used to adjust the influence ratio of brightness demand calculation; γ is the brightness adjustment coefficient, which is used to correct the calculation error;

[0062] And α 1 , α 2 , β 1 , β 2 ,ω 1 and ω 2The value of is obtained based on historical data and experimental methods. Different weight combinations are compared experimentally in actual environments to observe the pros and cons of the control effect, so as to select the most appropriate weight value.

[0063] When adjusting the brightness of multiple control areas, the control area with the largest brightness data is taken as the core, and each adjacent control area sets the brightness reduction threshold through its own control unit to maintain the corresponding brightness difference and avoid excessive brightness difference between adjacent control areas;

[0064] For multi-level adjacent control areas, the brightness difference is smoothly adjusted in turn to form a brightness gradient transition zone to improve visual consistency.

[0065] Example 2

[0066] An intelligent energy-saving control system for street lamps based on the Internet of Things, comprising a dispatching center, an edge control module, and a street lamp module;

[0067] The dispatch center divides the covered area into multiple control areas, and multiple groups of street light modules are arranged in each control area;

[0068] There are multiple edge control modules, and each edge control module corresponds to a control area and controls each street light module in the corresponding control area;

[0069] The edge control module includes a control unit and a plurality of sensing units;

[0070] Each sensor unit is used to collect environmental information in the corresponding control area, including brightness data, noise data and flow data, and each sensor unit is connected to the control unit by wired or wireless communication;

[0071] The sensor unit continuously collects noise data, flow data and brightness data based on a sliding time window. The total duration of the time window remains fixed. Whenever new data is collected, the latest collected data will overwrite the earliest data to ensure that the time window always contains the latest continuous data. That is, each time a new data is collected, the sensor unit will automatically remove the earliest data to ensure that the window always contains the latest data.

[0072] During the data update process, the sensor unit calculates the average values ​​of the corresponding noise data, flow data and brightness data in real time based on all the data in the current time window to reflect the real-time changes in the environment.

[0073] When each sensor unit collects environmental information in the corresponding control area, a first standard deviation is set for noise data, a second standard deviation is set for flow data, and a third standard deviation is set for brightness data;

[0074] In the process of the sensing unit continuously collecting noise data, flow data and brightness data based on the sliding time window, if the difference between the noise data, flow data or brightness data and the corresponding average value exceeds the corresponding first standard deviation, second standard deviation or third standard deviation, the sensing unit determines it as an abnormal value;

[0075] The sensor unit removes the noise data, flow data or brightness data that are judged as abnormal values, and then performs smoothing based on the remaining data in the sliding time window to ensure the continuity and stability of the data.

[0076] The brightness data includes natural brightness and non-natural light source brightness, wherein the non-natural light source brightness includes the light source generated by buildings or equipment (such as shop advertising screens, LED display screens, street lighting, industrial equipment) and street light modules in the control area;

[0077] The control unit calculates the brightness adjustment data based on the environmental information in the corresponding control area, and based on the brightness adjustment data, the control unit adjusts the brightness of each street lamp module in the corresponding control area on the basis of meeting the brightness requirements of the corresponding control area to avoid excessive lighting of the street lamp module, thereby achieving energy saving.

[0078] The brightness adjustment data is influenced by noise data and flow data. The greater the noise data and flow data, the greater the brightness requirement of the control area.

[0079] And the control unit adjusts the brightness data by adjusting the lighting intensity of the street lamp module to achieve the energy saving purpose of the street lamp module;

[0080] Compared with Example 1, when calculating the brightness adjustment data in Example 2, the dispatch center changes the brightness intensity of the street light module in the corresponding control area and monitors the brightness change of the adjacent control area. Based on the monitoring results of the brightness change of the adjacent control area, the influence of the brightness change of the street light module in the control area on the brightness change of the adjacent control area is analyzed, and the brightness amplitude of the street light module in the control area is adjusted, and the calculation formula of the brightness adjustment data is corrected to:

[0081]

[0082] Where, L d is the brightness requirement data of the current control area; L max , L min are the maximum and minimum brightness allowed in the control area; α 1 , α 2 is the weight coefficient for adjusting the minimum brightness; β 1 , β 2 To adjust the weight coefficient of the maximum brightness, it is larger to ensure sufficient lighting in high-demand scenarios;

[0083] L j The preset minimum lighting intensity of the street light module in the corresponding control area; L d L is the brightness adjustment data in the control area; n is the natural brightness; L a It is the brightness of non-natural light source, which includes the brightness contribution provided by the street light module in the corresponding control area. By changing the brightness of each street light module in the corresponding control area, L a The value of; N is the noise data; F is the flow data; ω 1 ,ω 2 are the weight coefficients of noise data and flow data, respectively, which are used to adjust the influence ratio of brightness demand calculation; γ is the brightness adjustment coefficient, which is used to correct the calculation error;

[0084] ΔL m ΔL is the brightness variation of the street light module in the current control area; s is the brightness data change amount of the adjacent control area; ε is a positive number to prevent division by zero;

[0085] like Then the brightness data of the adjacent control area covers the contribution of the street light module in the current control area;

[0086] like This indicates that the brightness of this area is greatly affected by other light sources, and the contribution of the street light module is relatively small;

[0087] like Then the brightness of the street light module partially contributes to the overall brightness of the control area;

[0088] When adjusting the brightness of multiple control areas, the control area with the largest brightness data is taken as the core, and each adjacent control area sets the brightness reduction threshold through its own control unit to maintain the corresponding brightness difference and avoid excessive brightness difference between adjacent control areas;

[0089] For multi-level adjacent control areas, the brightness difference is smoothly adjusted in turn to form a brightness gradient transition zone to improve visual consistency.

[0090] After the activation frequency of brightness adjustment of multiple control areas reaches a preset frequency threshold, the dispatch center determines whether there is continuity between the control areas, that is, whether the control areas have a continuous order;

[0091] On the basis of continuity, the dispatch center marks the corresponding control areas, and then the dispatch center collects the noise data and flow data collected by each marked control area, and the dispatch center judges whether each collected noise data and flow data are related according to the order in which the brightness adjustment of each marked control area is activated;

[0092] In this embodiment, a noise data association threshold Z and a flow data difference threshold Q are set, and the correlation between noise data is judged by using the Pearson correlation coefficient, and the formula used is:

[0093] Where N i and N j are the noise data collected from control area i and control area j respectively; σN i and σN j are the standard deviations of the noise data in control area i and control area j respectively; Cov(N i ,N j ) is the covariance;

[0094] Among them, -1≤ρN≤1;

[0095] And the difference between the flow data is calculated by the formula ΔF = |F i -F j |;

[0096] If ρN≥Z and ΔF≤Q, the dispatch center determines that the noise data and traffic data of the marked control area are correlated.

[0097] The dispatch center is provided with an adjacency matrix model based on the distribution relationship between the street light modules and the blocks and road networks;

[0098] The dispatch center matches each marked control area path with correlation between noise data and traffic data with the adjacency matrix model, that is, a series of marked control areas forming a continuous path are compared with the topological structure in the adjacency matrix model;

[0099] After a successful match, the dispatch center coordinates the brightness of each marked control area to avoid sudden changes in brightness between different areas, thereby improving visual comfort and energy saving.

[0100] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An intelligent energy-saving control system for street lamps based on the Internet of Things, comprising a dispatching center, an edge control module, and a street lamp module, characterized in that: The dispatch center divides the covered area into a plurality of control areas, each of which is provided with at least one group of street light modules; There are multiple edge control modules, and each edge control module corresponds to a control area and controls each street light module in the corresponding control area; The edge control module includes a control unit and a plurality of sensing units; Each sensor unit is used to collect environmental information in the corresponding control area, including brightness data, noise data and flow data, and each sensor unit is connected to the control unit by wired or wireless communication; And the brightness data includes natural brightness and non-natural light source brightness, and the non-natural light source brightness includes the brightness generated by buildings or equipment and street light modules in the control area; The control unit calculates brightness adjustment data based on environmental information in the corresponding control area, and based on the brightness adjustment data, the control unit adjusts the brightness of each street lamp module in the corresponding control area on the basis of meeting the brightness requirements of the corresponding control area.

2. According to the Internet of Things-based street lamp intelligent energy-saving control system of claim 1, it is characterized in that: The brightness adjustment data uses noise data and flow data as influencing factors; And the control unit adjusts the brightness data by adjusting the lighting intensity of the street light module. The calculation formula is: L d =min(L max ,max(L min ,L j +ω1·N+ω2·FL n -(1-γ)L a )); L min =L j +α1·N+α2·F; L max =L j +β1·N+β2·F; Where, L d is the brightness requirement data of the current control area; L max , L min are the maximum and minimum brightness allowed in the control area respectively; α1 and α2 are the weight coefficients for adjusting the minimum brightness; β1 and β2 are the weight coefficients for adjusting the maximum brightness; L j The preset minimum lighting intensity of the street light module in the corresponding control area; L d L is the brightness adjustment data in the control area; n is the natural brightness; L a is the brightness of non-natural light sources, including the brightness contribution provided by the street lamp module in the corresponding control area; N is the noise data; F is the flow data; ω1 and ω2 are the weight coefficients of noise data and flow data respectively; γ is the brightness adjustment coefficient.

3. According to the Internet of Things-based street lamp intelligent energy-saving control system of claim 2, it is characterized in that: When calculating the brightness adjustment data, the dispatch center changes the brightness intensity of the street light modules in the corresponding control area and monitors the brightness changes in the adjacent control area to adjust the brightness intensity of the street light modules in the corresponding control area. The calculation formula is: Where, ΔL m ΔL is the brightness variation of the street light module in the current control area; s is the brightness data change amount of the adjacent control area; ε is a positive number to prevent division by zero.

4. According to the Internet of Things-based street lamp intelligent energy-saving control system of claim 1, it is characterized in that: The sensing unit continuously collects noise data, flow data and brightness data based on a sliding time window. The total duration of the time window remains fixed. Whenever new data is collected, the latest collected data will overwrite the earliest data, ensuring that the time window always contains the latest continuous data. During the data update process, the sensing unit calculates the average values ​​of the corresponding noise data, flow data and brightness data in real time based on all the data in the current time window.

5. According to the Internet of Things-based street lamp intelligent energy-saving control system of claim 4, it is characterized in that: When each sensor unit collects environmental information in the corresponding control area, a first standard deviation, a second standard deviation and a third standard deviation are set for noise data, flow data and brightness data respectively; In the process of the sensing unit continuously collecting noise data, flow data and brightness data based on the sliding time window, if the difference between the noise data, flow data or brightness data and the corresponding average value exceeds the corresponding first standard deviation, second standard deviation or third standard deviation, the sensing unit determines it as an abnormal value; The sensing unit removes the noise data, flow data or brightness data that are determined to be abnormal values, and then performs smoothing based on the remaining data in the sliding time window.

6. According to the Internet of Things-based street lamp intelligent energy-saving control system of claim 1, it is characterized in that: When adjusting the brightness of multiple control areas, the control area with the largest brightness data is taken as the core, and each adjacent control area sets the brightness reduction threshold through its own control unit; For multi-level adjacent control areas, the brightness difference is adjusted smoothly in turn to form a brightness gradient transition zone.

7. The intelligent energy-saving control system for street lamps based on the Internet of Things according to claim 6 is characterized in that: After the activation frequency of brightness adjustment of multiple control areas reaches a preset frequency threshold, the dispatch center determines whether there is continuity between the control areas; On the basis of continuity, the dispatch center marks the corresponding control areas, and then collects the noise data and traffic data collected by each marked control area. The dispatch center judges whether the collected noise data and traffic data are correlated through the Pearson correlation coefficient according to the order of activation of the brightness adjustment of each marked control area.

8. The intelligent energy-saving control system for street lamps based on the Internet of Things according to claim 7 is characterized in that: The dispatch center is provided with an adjacency matrix model based on the distribution relationship between the street light modules and the blocks and road networks; The dispatch center matches each marked control area path having correlation between noise data and traffic data with an adjacency matrix model; After the match is successful, the dispatch center coordinates the brightness of each marked control area.

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