An intelligent energy-saving control system for street lamps based on the Internet of Things

Abstract

The present invention discloses an intelligent energy-saving control system for street lamps based on the Internet of Things, which relates to the field of street lamp control technology. The control system includes a dispatching center, an edge control module, and a street lamp module. The dispatching center divides the covered area into multiple control areas, each of which is provided with at least one group of street lamp modules. The edge control modules are provided in multiple numbers, and each edge control module corresponds to a control area and controls each street lamp module within the corresponding control area. The edge control module includes a control unit and multiple sensor units, each of which is used to collect environmental information within the corresponding control area. The present invention performs dynamic brightness adjustment based on multi-dimensional environmental data. By collecting multi-dimensional environmental information including noise data, traffic data, natural light brightness, and non-natural light source brightness in real time, the optimal brightness requirement of each control area is calculated through an intelligent algorithm, and the street lamp brightness 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, streetlights, as a vital component of urban infrastructure, shoulder multiple functions, including ensuring nighttime travel safety and enhancing the urban landscape. Currently, traditional streetlight control systems rely primarily on timers or light sensors to control the on / off and brightness of streetlights. While this simple control method can meet basic lighting needs, it has numerous limitations and is difficult to adapt to the energy-saving and intelligent management needs of modern cities.

[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 the LED module, image acquisition module, brightness adjustment module, brightness detection module, wireless transmission module and solar cell module; the image acquisition module faces the opposite direction of driving and is used to collect pedestrian and vehicle image information and transmit the data to the embedded processing unit; the embedded processing unit receives the brightness information of the brightness adjustment module and controls the street lamp switch and brightness based on the collected pedestrian and vehicle image information.

[0004] In the prior art, street lamps are usually controlled on and off and in accordance with fixed time or simple light intensity thresholds. This results in high-brightness lighting in areas and periods with low traffic or no pedestrians. In addition, traditional street lamp systems are unable to dynamically adjust by comprehensively considering multiple environmental factors such as noise, traffic, and natural light brightness, and are unable to 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, comprising a dispatching center, an edge control module, and a street lamp module;

[0007] The dispatch center divides the covered area into multiple 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 through wired or wireless communication;

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

[0012] 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, so as to avoid excessive lighting of the street lamp module and achieve 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] The control unit adjusts the brightness data by adjusting the lighting intensity of the street light module to achieve the energy saving purpose of the street light module. Therefore, the calculation formula of the brightness adjustment data is:

[0015] L d =min(L max ,max(L min ,L j +ω1·N+ω2·FL n -(1-γ)L a ));

[0016] L min =L j +α1·N+α2·F;

[0017] L max =L j +β1·N+β2·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 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, which are larger to ensure sufficient lighting in high-demand scenarios;

[0019] L jThe preset minimum lighting intensity of the street light module in the corresponding control area; L d L is the brightness adjustment data within the control area; n is natural brightness; L a is the brightness of non-natural light sources, which includes the brightness contribution provided by the street light modules in the corresponding control area; N is the noise data; F is the traffic data; ω1 and ω2 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 calculation errors.

[0020] A further technical improvement of the present invention is that when calculating the brightness adjustment data, the dispatch center adjusts the brightness intensity of the street light modules in the corresponding control area by changing the brightness intensity of the street light modules in the corresponding control area and monitoring the brightness changes in the adjacent control areas. The calculation formula of the brightness adjustment data is modified 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 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;

[0024] 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 data in the current time window to reflect real-time changes in the environment.

[0025] A further technical improvement of the present invention is that: when each sensing unit collects environmental information within 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 continuously collecting noise data, flow data and brightness data based on the sliding time window, the sensing unit determines that 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 that it is an abnormal value;

[0027] The sensing 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 within 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 used as the core, and each of its adjacent control areas sets a brightness reduction 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 sequence 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 correlated according to the order in which the brightness adjustment of each marked control area is activated.

[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 streetlight 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 brightness changes between different areas, improve visual comfort and energy saving.

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

[0036] The present invention dynamically adjusts brightness based on multi-dimensional environmental data. By collecting multi-dimensional environmental information including noise data, traffic data, natural light brightness and non-natural light source brightness in real time, the intelligent algorithm is used to calculate the optimal brightness requirement for each control area, accurately adjust the brightness of street lights, avoid unnecessary energy waste, and maximize energy saving while ensuring lighting quality.

[0037] Furthermore, the adjacent control areas in the present invention have the ability to coordinate multi-area control. By introducing the adjacency matrix model, it can automatically identify adjacent control areas based on the distribution relationship between streetlights, 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 based on the Internet of Things architecture design to meet the needs of smart cities for intelligent, information-based and sustainable development of street lighting systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0041] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, 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 set 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 through 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 oldest data, ensuring that the time window always contains the latest continuous data. That is, every time a new data is collected, the sensor unit automatically removes the oldest data to ensure that the window always contains the latest data.

[0049] 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 data in the current time window to reflect real-time changes in the environment.

[0050] When each sensor unit collects environmental information within 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 continuously collecting noise data, flow data and brightness data based on the sliding time window, the sensing unit determines that 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 that it is an abnormal value;

[0052] The sensing 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 within 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, where the non-natural light source brightness includes the light generated by buildings or equipment (such as store advertising screens, LED displays, street lighting, industrial equipment) and street light modules in the control area;

[0054] 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, so as to avoid excessive lighting of the street lamp module and achieve 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] The control unit adjusts the brightness data by adjusting the lighting intensity of the street light module to achieve the energy saving purpose of the street light module. Therefore, the calculation formula of the brightness adjustment data is:

[0057] L d =min(L max ,max(L min ,L j +ω1·N+ω2·FL n -(1-γ)L a ));

[0058] L min =L j +α1·N+α2·F;

[0059] L max =L j +β1·N+β2·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 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, which are 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 within the control area; n is natural brightness; L a It is the brightness of non-natural light source, which includes the brightness contribution provided by the street light modules in the corresponding control area. By changing the brightness of each street light module in the corresponding control area, L a N is the noise data; F is the flow data; ω1 and ω2 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] The values ​​of α1, α2, β1, β2, ω1 and ω2 are obtained based on historical data and experimental methods. Different weight combinations are compared experimentally in a real environment to observe the pros and cons of the control effects, so as to select the most appropriate weight values.

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

[0064] For multi-level adjacent control areas, the brightness difference is smoothly adjusted in sequence 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, including 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 set 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 through 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 oldest data, ensuring that the time window always contains the latest continuous data. That is, every time a new data is collected, the sensor unit automatically removes the oldest data to ensure that the window always contains the latest data.

[0072] 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 data in the current time window to reflect real-time changes in the environment.

[0073] When each sensor unit collects environmental information within 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 continuously collecting noise data, flow data and brightness data based on the sliding time window, the sensing unit determines that 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 that it is an abnormal value;

[0075] The sensing 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 within 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, where the non-natural light source brightness includes the light generated by buildings or equipment (such as store advertising screens, LED displays, street lighting, industrial equipment) and street light modules in the control area;

[0077] 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, so as to avoid excessive lighting of the street lamp module and achieve 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 light module to achieve the energy saving purpose of the street light module;

[0080] Compared to Example 1, when calculating the brightness adjustment data in Example 2, 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 areas. Based on the monitoring results of the brightness changes in the adjacent control areas, the dispatch center analyzes the impact of the brightness changes of the street light modules in the control area on the brightness changes in the adjacent control areas, and then adjusts the brightness amplitude of the street light modules in the control area. The calculation formula of the brightness adjustment data is revised 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 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, which are 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 within the control area; n is natural brightness; L a It is the brightness of non-natural light source, which includes the brightness contribution provided by the street light modules in the corresponding control area. By changing the brightness of each street light module in the corresponding control area, L a N is the noise data; F is the flow data; ω1 and ω2 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 contributes partially 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 used as the core. Each adjacent control area sets a brightness reduction threshold through its own control unit to maintain the corresponding brightness difference and avoid excessive brightness differences between adjacent control areas.

[0089] For multi-level adjacent control areas, the brightness difference is smoothly adjusted in sequence 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 collects the noise data and flow data collected by each marked control area. The dispatch center also determines whether the collected noise data and flow data are correlated 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. The correlation between noise data is determined by 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 equipped with an adjacency matrix model based on the distribution relationship between streetlight modules and 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 brightness changes between different areas, improve visual comfort and energy saving.

[0100] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still 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 multiple 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 through wired or wireless communication; The brightness data includes natural brightness and non-natural light source brightness. 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, adjusts the brightness of each street light module in the corresponding control area on the basis of meeting the brightness requirements of the corresponding control area. By collecting multi-dimensional environmental information including noise data, traffic data, natural light brightness and non-natural light source brightness in real time, the control unit calculates the optimal brightness requirement of each control area through an intelligent algorithm and accurately adjusts the brightness of the street lights. The brightness adjustment data is influenced by noise data and flow data; And the control unit adjusts the brightness data by adjusting the lighting intensity of the street light module. The calculation formula is: ； ； ； Where, It is the brightness requirement data of the current control area; , They are the maximum brightness and minimum brightness allowed in the control area; , To adjust the weight coefficient of minimum brightness; , To adjust the weight coefficient of maximum brightness; The preset minimum lighting intensity for the street light modules in the corresponding control area; To control the brightness adjustment data in the area; For natural brightness; is the brightness of non-natural light sources, including the brightness contribution provided by the street light modules in the corresponding control area; N is the noise data; F is the traffic data; 、 are the weight coefficients of noise data and flow data respectively; is the brightness adjustment coefficient; 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 areas to adjust the brightness intensity of the street light modules in the corresponding control area. The calculation formula is: ； Where, The brightness variation of the street light module in the current control area; is the brightness data change amount of the adjacent control area; is a positive number that prevents division by zero; 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 data in the current time window; 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 continuously collecting noise data, flow data and brightness data based on the sliding time window, the sensing unit determines that 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 that it is 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 within the sliding time window.

2. The intelligent energy-saving control system for street lamps based on the Internet of Things according to claim 1 is characterized in that: When adjusting the brightness of multiple control areas, the control area with the largest brightness data is used 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 smoothly adjusted in sequence to form a brightness gradient transition zone.

3. The intelligent energy-saving control system for street lamps based on the Internet of Things according to claim 2 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 the dispatch center collects the noise data and flow data collected by each marked control area. The dispatch center judges whether the collected noise data and flow data are correlated by the Pearson correlation coefficient according to the order in which the brightness adjustment of each marked control area is activated.

4. The intelligent energy-saving control system for street lamps based on the Internet of Things according to claim 3 is characterized in that: The dispatch center is equipped with an adjacency matrix model based on the distribution relationship between streetlight modules and blocks and road networks; The dispatch center matches each marked control area path with 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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