A networking communication method and system for smart street lights

Abstract

The present invention discloses a networking communication method and system for smart street lights, which relates to the technical field of smart street lights. The present invention includes an edge computing module, a regional computing module, and a fault recording module. The total communication module is connected to n regional computing modules, and each regional computing module is connected to an edge computing subsystem composed of a number of edge computing modules; the present invention realizes the capture of monitoring data through the cameras, backup batteries, and data transmission lines in the edge computing module; realizes preliminary data processing and secondary data processing through the edge central hub and the edge computing unit; performs adjustment operations based on the data processing results through the smart street lights and the backup batteries; the present invention optimizes the interconnection and intercommunication problems between street lights through the solution of the optimal route, improves the ability of remote control and troubleshooting of devices through data aggregation and analysis, and improves the data collection, information transmission, and intelligent control capabilities of the smart street light communication network.

Description

Technical Field

[0001] The present invention relates to the technical field of smart street lamps, and specifically to a networking communication method and system for smart street lamps. Background Technique

[0002] Smart street lamps are an important part of the construction of smart cities. Based on road lighting poles, they integrate multiple functions such as public security, traffic signals, communication, and traffic signs, achieving the integration of multiple poles into one, reducing the number of poles on the road surface, and releasing public space resources; smart street lamps not only have the lighting function of ordinary street lamps, but also can achieve energy conservation and reduce maintenance costs through technologies such as precise switching, lighting on demand, and real-time dimming; cameras installed on street lamp poles can monitor traffic flow and road conditions in real time, and use remote control technology to manage and monitor street lamps in real time through the Internet, realizing functions such as switching, brightness adjustment, and lighting range control, and can detect the presence of pedestrians and perform intelligent control, entering the standby mode to save energy when there are no pedestrians passing by, and automatically turning on to provide lighting when pedestrians pass by; smart street lamps are widely used in occasions such as smart cities, communities, scenic spots, industrial parks, digital villages, municipal administration, highways, and characteristic towns, providing intelligent solutions for various application scenarios; in the future, with the continuous development of technologies such as 5G, artificial intelligence, and the Internet of Things, smart street lamps will achieve more intelligent and automated control and management, bringing more convenience and comfort to urban management and residents' lives; at the same time, with the continuous advancement of the construction of smart cities, smart street lamps will become an indispensable part of urban infrastructure;

[0003] However, in the development process of existing smart street lamp technologies, there are still some challenges, such as the interconnection and interoperability issues between street lamps need to be optimized, the ability of device remote control and troubleshooting needs to be improved, and the ability of data collection, information transmission, and intelligent control needs to be improved, etc. Summary of the Invention

[0004] In order to overcome the problems in the background technique that the interconnection and interoperability between street lamps need to be optimized, the ability of device remote control and troubleshooting needs to be improved, and the ability of data collection, information transmission, and intelligent control needs to be improved, etc., the embodiments of the present invention provide a networking communication method and system for smart street lamps.

[0005] The object of the present invention can be achieved through the following technical solutions: A networking communication system for smart street lamps includes an edge computing module, a regional computing module, and a fault recording module. The total communication module is connected to n regional computing modules, and each regional computing module is connected to several edge computing modules.

[0006] Each edge computing module in the edge computing subsystem includes an edge computing unit, an edge center, a camera, a smart street lamp, and a backup battery. Each edge computing unit is connected to several edge centers, each edge center is connected to a camera, a smart street lamp, and a backup battery. The data of each edge center is aggregated through the edge computing unit, and two-way data transmission can be performed between each edge center.

[0007] As a preferred embodiment of the present invention, the camera in the edge computing module records road traffic images every preset time t, and obtains the number of pedestrians Xk and the number of vehicles Yk that appear in the road traffic images within the preset time based on motion analysis technology.

[0008] Calculate the difference between the number of pedestrians in the current preset time and the number of pedestrians in the previous preset time every preset time to obtain the change amount of the number of pedestrians ΔXk. Calculate the difference between the number of vehicles in the current preset time and the number of vehicles in the previous preset time every preset time to obtain the change amount of the number of vehicles ΔYk. When the values of both the change amount of the number of pedestrians ΔXk and the change amount of the number of vehicles ΔYk are 0, the camera generates a static signal and sends it to the edge center; when there is a non-zero value in either the change amount of the number of pedestrians ΔXk or the change amount of the number of vehicles ΔYk, the camera generates an analysis signal and sends it to the edge center. The camera obtains the image brightness Tk in the road traffic images within the preset time based on image analysis technology. An ellipse is constructed with the sum of the number of vehicles Yk and the number of pedestrians Xk as the major axis and the number of pedestrians Xk as the minor axis, and the area of the ellipse is extracted and recorded as the traffic volume parameter Sk, where k is the edge center number, k = 1, 2,..., p, and p is the total number of cameras in this edge computing module. Record the time t, the edge center number k, the traffic volume parameter Sk, and the image brightness T and form a camera data vector (t, k, Sk, Tk). The smart street lamp in the edge computing module obtains the input power Pk of the smart street lamp every preset time t, and records the time t, the edge center number k, and the input power P and forms a street lamp data vector (t, k, Pk). The backup battery in the edge computing module obtains the percentage c of the remaining power of the backup battery every preset time t, and records the time t, the edge center number k, and the percentage c of the remaining power of the backup battery to form a battery data vector (t, k, c).

[0009] As a preferred embodiment of the present invention, the edge center constructs a distribution model of natural light intensity: where t is the time; f(t) is the natural light intensity; A is a preset influence factor; σ is a preset scale factor, and μ is a preset position factor. Plot the function image of f(t). Obtain the light intensity f(t) in the distribution model of natural light intensity every preset time t. Through the formula get the lighting demand index αk, where λ1, λ2, and λ3 are preset influence factors. The edge center passes the formula Calculate the power supply demand index βk, where is the rated input power of the intelligent street lamp.

[0010] As a preferred embodiment of the present invention, for the transmission network topology of the edge central unit and the edge computing unit, each edge central unit is connected to the edge computing unit through a transmission line, and adjacent edge central units are connected through a transmission line. The three edge central units are numbered 1, 2, and 3 respectively; the edge computing unit is numbered 0. The transmission line passing through the edge central unit is denoted as the baseline; the transmission line not connected to the edge central unit is denoted as the secondary line, and each baseline and secondary line is marked with the starting point and ending point of the transmission line. Every preset time, each baseline and secondary line obtains its own bandwidth utilization percentage ω a-b . a is the starting point number of the transmission line, and b is the ending point label of the transmission line. The edge computing module obtains all transmission paths starting from the edge central unit and ending at the edge computing unit 0, denoted as optional lines, and calculates the line with the lowest sum of bandwidth utilization percentages in the optional lines through the formula MIN∑ω a-b , denoted as the optimal line.

[0011] When the edge central unit receives the analysis signal, it turns on the data transmission line and sends the lighting demand index αk and the power supply demand index βk to the edge computing unit and other edge central units through the optimal line every preset time; when the edge central unit receives the stationary signal, it stops searching for the optimal line data transmission line and terminates sending the lighting demand index αk and the power supply demand index βk to the edge computing unit and other edge central units.

[0012] As a preferred embodiment of the present invention, after receiving the lighting demand index αk, the edge computing unit performs arithmetic analysis. When the lighting demand index αk is greater than the preset lighting demand index threshold one , it sends a signal to turn on the lights to the edge central unit k through the optimal line; when the lighting demand index αk is greater than the lighting demand index threshold two , it sends a signal to amplify the lights to the edge central unit k and sends a high-demand signal to all edge central units. When the lighting demand index αk is less than the preset lighting demand index threshold one , it sends a signal to turn off the lights to the edge central unit k through the optimal line and sends a low-demand signal to all edge central units through the optimal line.

[0013] As a preferred embodiment of the present invention, when the edge central unit receives the signal to turn on the lights, it turns on the intelligent street lamp at a preset power one. When the edge central unit receives the signal to turn off the lights, it turns off the intelligent street lamp; when the edge central unit receives the signal to amplify the lights, it turns on the intelligent street lamp at a preset power two, and the preset power two is greater than the preset power one; when the edge central unit receives the high-demand signal, it multiplies the preset influence factors λ2 and λ3 by the preset weight factor Ω; when the edge central unit receives the low-demand signal, it divides the preset influence factors λ2 and λ3 by the preset weight factor Ω.

[0014] As a preferred embodiment of the present invention, when the edge computing unit detects a power outage, it performs arithmetic analysis on the received power supply demand index βk. When the power supply demand index is less than the preset power supply demand index threshold it sends a self-power supply signal to the edge center k through the optimal line. When the power supply demand index is greater than or equal to the preset power supply demand index threshold it sends a different-power supply signal to all edge centers through the optimal line. When the edge center k receives the self-power supply signal, it turns on the backup battery controlled by the edge center k to supply power to the intelligent street lights controlled by the edge center k. When the edge center k receives the different-power supply signal, it turns on the backup battery controlled by the edge center k and supplies power to the intelligent street lights controlled by the edge center with the highest power supply demand index βk.

[0015] As a preferred embodiment of the present invention, the area calculation module records the lighting demand index αk and the power supply demand index βk of all edge computing modules at preset time intervals t. Each edge computing module has a number i; i = 1, 2,..., m; each area calculation module has a number j; j = 1, 2,..., n; n is the total number of area calculation modules. The area calculation module forms a data vector (t, i, j, k, αk, βk) with time t, edge computing module number i, area calculation module number j, edge center number k, lighting demand index αk, and power supply demand index βk. The data vectors with the same area calculation module number i, edge computing module number j, and edge center number k are recorded as a monitoring group. The time t, lighting demand index αk, and power supply demand index βk in each monitoring group are extracted to generate two sets of data points (t, αk) and (t, βk). A two-dimensional rectangular coordinate system is established with time t as the horizontal axis and the lighting demand index αk as the vertical axis. All points (t, αk) are filled into the coordinate system, and then all points are fitted with a smooth curve to generate an image of the lighting demand index changing with time. The area of the part greater than the straight line in the image of the lighting demand index changing with time is extracted and denoted as the lighting anomaly index E1, where is the preset lighting demand index threshold two. When the lighting anomaly index E1 is greater than the preset threshold, the area calculation module number i, edge computing module number j, and edge center number k of the monitoring group are extracted to generate a lighting anomaly vector (E1, i, j, k). A two-dimensional rectangular coordinate system is established with time t as the horizontal axis and the power supply demand index βk as the vertical axis. All points (t, βk) are filled into the coordinate system, and then all points are fitted with a smooth curve to generate an image of the power supply demand index changing with time. The area of the part greater than the straight line in the image of the power supply demand index changing with time is extracted and denoted as the power supply anomaly parameter E2, where is a preset power supply demand index threshold. When the power supply anomaly parameter E2 is greater than the preset threshold, the area calculation module number i, edge calculation module number j, and edge center number k of the monitoring group are extracted to generate a power supply anomaly vector (E2, i, j, k).

[0016] The fault record module records all lighting anomaly vectors (E1, i, j, k) and power supply anomaly vectors (E2, i, j, k), and records all lighting anomaly vectors as a lighting anomaly group; records all power supply anomaly vectors as a power supply anomaly group, and displays all vectors in the lighting anomaly group and the power supply anomaly group on the display terminal by grouping.

[0017] As a preferred embodiment of the present invention, a networking communication method for intelligent street lights includes the following steps:

[0018] Step 1: Obtain monitoring data:

[0019] The camera in the edge calculation module records road traffic videos every preset time t, obtains the number of pedestrians Xk and the number of vehicles Yk that appear in the road traffic videos within the preset time based on motion analysis technology, and the camera obtains the image brightness Tk in the road traffic videos within the preset time based on image analysis technology; the backup battery in the edge calculation module obtains the percentage c of the remaining power of the backup battery every preset time t; the edge calculation module obtains all transmission paths starting from the edge center and ending at edge calculation unit 0, denoted as optional lines; every preset time, each baseline and sub-line that maintains data transmission between the edge center and the edge calculation unit obtains its own bandwidth utilization percentage ω a-b ;

[0020] Step 2: Preliminary data analysis:

[0021] The edge computing module calculates the difference between the number of pedestrians at a preset time and the number of pedestrians at the previous preset time every preset time to obtain the change in the number of pedestrians ΔXk, and calculates the difference between the number of vehicles at a preset time and the number of vehicles at the previous preset time every preset time to obtain the change in the number of vehicles ΔYk; when the values of both the change in the number of pedestrians ΔXk and the change in the number of vehicles ΔYk are 0, the camera generates a static signal and sends it to the edge center; when there is a non-zero value in either the change in the number of pedestrians ΔXk or the change in the number of vehicles ΔYk, the camera generates an analysis signal and sends it to the edge center; an ellipse is constructed with the sum of the number of vehicles Yk and the number of pedestrians Xk as the major axis and the number of pedestrians Xk as the minor axis, and the area of the ellipse is extracted and recorded as the traffic volume parameter Sk, where k is the edge center number, k = 1, 2,..., p, and p is the total number of cameras in this edge computing module; the time t, the edge center number k, the traffic volume parameter Sk, and the image brightness T are recorded and combined into a camera data vector (t, k, Sk, Tk); the intelligent street lights in the edge computing module obtain the input power Pk of the intelligent street lights every preset time t, and the time t, the edge center number k, and the input power P are recorded and combined into a street light data vector (t, k, Pk); the time t, the edge center number k, and the percentage c of the remaining power of the backup battery are recorded and combined into a battery data vector (t, k, c); the edge center constructs a distribution model of the natural light intensity: where t is the time; f(t) is the natural light intensity; A is a preset influence factor; σ is a preset scale factor, and μ is a preset position factor; the function image of f(t) is plotted; the light intensity f(t) is obtained in the distribution model of the natural light intensity every preset time t; through the formula the lighting demand index αk is obtained, where λ1, λ2, and λ3 are preset influence factors; the edge center calculates the power supply demand index βk through the formula where is the rated input power of the intelligent street light;

[0022] Step Three: Solve the optimal route:

[0023] In the transmission network of the edge center and the edge computing unit, each edge center is connected to the edge computing unit through a transmission line, and adjacent edge centers are connected through a transmission line; the three edge centers are numbered 1, 2, and 3 respectively; the edge computing unit is numbered 0; the transmission line passing through the edge center is recorded as the baseline; the edge computing module obtains all transmission paths starting from the edge center and ending at the edge computing unit 0, which are recorded as optional routes, and calculates the route with the lowest sum of the bandwidth utilization percentages in the optional routes through the formula MIN∑ω a-b

[0024] Step Four: Data transmission:

[0025] ​When the edge central unit receives the analysis signal, it turns on the data transmission line and sends the lighting demand index αk and the power supply demand index βk to the edge computing unit and other edge central units through the optimal line at preset intervals; when the edge central unit receives the stationary signal, it stops searching for the optimal line and terminates sending the lighting demand index αk and the power supply demand index βk to the edge computing unit and other edge central units;

[0026] Step Five: Secondary data analysis and execution of the lighting demand index:

[0027] After receiving the lighting demand index αk, the edge computing unit performs arithmetic analysis. When the lighting demand index αk is greater than the preset lighting demand index threshold one At this time, it sends a light-on signal to the edge central unit k through the optimal line; when the lighting demand index αk is greater than the lighting demand index threshold two It sends a magnification signal to the edge central unit k and a high-demand signal to all edge central units; when the lighting demand index αk is less than the preset lighting demand index threshold one At this time, it sends a light-off signal to the edge central unit k through the optimal line and a low-demand signal to all edge central units through the optimal line; when the edge central unit receives the light-on signal, it turns on the smart street lamp at the preset power one; when the edge central unit receives the light-off signal, it turns off the smart street lamp; when the edge central unit receives the magnification signal, it turns on the smart street lamp at the preset power two, and the preset power two is greater than the preset power one; when the edge central unit receives the high-demand signal, it multiplies the preset influence factors λ2 and λ3 by the preset weight factor Ω; when the edge central unit receives the low-demand signal, it divides the preset influence factors λ2 and λ3 by the preset weight factor Ω;

[0028] Step Six: Secondary data analysis and execution of the power supply demand index:

[0029] When the edge computing unit detects a power outage, it performs arithmetic analysis on the received power supply demand index βk. When the power supply demand index is less than the preset power supply demand index threshold At this time, it sends a self-power supply signal to the edge central unit k through the optimal line; when the power supply demand index is greater than or equal to the preset power supply demand index threshold At this time, it sends a different-power supply signal to all edge central units through the optimal line; when the edge central unit k receives the self-power supply signal, it turns on the backup battery controlled by the edge central unit k to supply power to the smart street lamp controlled by the edge central unit k; when the edge central unit k receives the different-power supply signal, it turns on the backup battery controlled by the edge central unit k and supplies power to the smart street lamp controlled by the edge central unit with the highest power supply demand index βk;

[0030] Step Seven: Data summary and fault analysis:

[0031] The area calculation module records the lighting demand index αk and the power supply demand index βk of all edge computing modules every preset time t; each edge computing module has a number i; i = 1, 2,..., m; each area calculation module has a number j; j = 1, 2,..., n; n is the total number of area calculation modules. The area calculation module forms a data vector (t, i, j, k, αk, βk) with time t, the edge computing module number i, the area calculation module number j, the edge center number k, the lighting demand index αk, and the power supply demand index βk; the data vectors with the same area calculation module number i, edge computing module number j, and edge center number k are recorded as a monitoring group; extract the time t, the lighting demand index αk, and the power supply demand index βk in each monitoring group to generate two sets of data points (t, αk) and (t, βk); establish a two-dimensional rectangular coordinate system with time t as the horizontal axis and the lighting demand index αk as the vertical axis, fill all the points (t, αk) into the coordinate system, and then fit all the points with a smooth curve to generate an image of the lighting demand index changing with time; extract the area of the part greater than the straight line in the image of the lighting demand index changing with time and denote it as the lighting anomaly index E1, where is the preset lighting demand index threshold two; when the lighting anomaly index E1 is greater than the preset threshold, extract the area calculation module number i, the edge computing module number j, and the edge center number k of this monitoring group to generate a lighting anomaly vector (E1, i, j, k); establish a two-dimensional rectangular coordinate system with time t as the horizontal axis and the power supply demand index βk as the vertical axis, fill all the points (t, βk) into the coordinate system, and then fit all the points with a smooth curve to generate an image of the power supply demand index changing with time; extract the area of the part greater than the straight line in the image of the power supply demand index changing with time and denote it as the power supply anomaly parameter E2, where is the preset power supply demand index threshold; when the power supply anomaly parameter E2 is greater than the preset threshold, extract the area calculation module number i, the edge computing module number j, and the edge center number k of this monitoring group to generate a power supply anomaly vector (E2, i, j, k); the fault recording module records all the lighting anomaly vectors (E1, i, j, k) and the power supply anomaly vectors (E2, i, j, k) and denotes all the lighting anomaly vectors as the lighting anomaly group; denote all the power supply anomaly vectors as the power supply anomaly group, and display all the vectors in the lighting anomaly group and the power supply anomaly group on the display terminal according to the grouping.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] 1. The present invention optimizes the interconnection and intercommunication between street lamps by solving the optimal route, ensuring that data transmission can still be completed through other transmission paths even when the bandwidth burden of a single transmission path is relatively high. This enables the entire communication network to better adapt to external disturbances and internal changes, improves the reliability of data transmission in the entire system, optimizes the topological structure of the intelligent street lamp communication network, and enhances the robustness of the entire system.

[0034] 2. The present invention improves the remote control and troubleshooting capabilities of intelligent street lamp devices through data aggregation and fault analysis, accurately analyzes the cause of the fault, precisely locates the fault device number, and monitors the fault risk in real time.

[0035] 3. The present invention analyzes the road condition changes through a camera, maintains the lighting power of the intelligent street lamp unchanged when the number of pedestrians and vehicles remains the same, prevents the captured monitoring data from entering the transmission path of the edge center and the edge computing unit, reduces the unnecessary data transmission burden, and ensures that there are enough path selection options during data transmission; when the number of pedestrians and vehicles remains the same, it prevents the captured monitoring data from entering unnecessary computing processes, reduces the waste of computing power of the edge computing module, and ensures that the entire system has sufficient computing power surplus when the road conditions change.

[0036] 4. The present invention improves the correlation of multiple intelligent street lamps in the same area by adjusting the influencing factors involved in the calculation process of the lighting demand index. When a single intelligent street lamp detects a rapid increase in road conditions, it amplifies the sensitivity of other intelligent street lamps to changes in the number of pedestrians, vehicles, and brightness, making all intelligent street lamps an integrated whole that jointly perceives and responds, and improving the synchronization of intelligent street lamp power management. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0038] Figure 1 It is the system block diagram of the present invention;

[0039] Figure 2 It is the schematic diagram of the topological structure of the edge computing module of the present invention;

[0040] Figure 3 It is the schematic diagram of the distribution model function of the natural light intensity of the present invention;

[0041] Figure 4 It is the schematic diagram of the topological structure of the distributed communication network of the edge computing module of the present invention;

[0042] Figure 5 It is the method flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0043] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the scope of protection of the present invention.

[0044] Please refer to Figure 1 As shown in the figure, a networking communication system for intelligent street lamps includes an edge computing module, a regional computing module, and a fault recording module. The total communication module is connected to n regional computing modules, and each regional computing module is connected to several edge computing modules.

[0045] Please refer to Figure 2 As shown in the figure, each edge computing module in the edge computing subsystem includes an edge computing unit, an edge center, a camera, an intelligent street lamp, and a backup battery. Each edge computing unit is connected to several edge centers, each edge center is connected to a camera, an intelligent street lamp, and a backup battery, the data of each edge center is aggregated through the edge computing unit, and two-way data transmission can be performed between each edge center.

[0046] The camera in the edge computing module records road traffic videos every preset time t, and obtains the number of pedestrians Xk and the number of vehicles Yk that appear in the road traffic videos within the preset time based on motion analysis technology. Calculate the difference between the number of pedestrians in each preset time and the number of pedestrians in the previous preset time to obtain the change in the number of pedestrians ΔXk. Calculate the difference between the number of vehicles in each preset time and the number of vehicles in the previous preset time to obtain the change in the number of vehicles ΔYk. When the values of both the change in the number of pedestrians ΔXk and the change in the number of vehicles ΔYk are 0, the camera generates a static signal and sends it to the edge center; when there is a non-zero value in either the change in the number of pedestrians ΔXk or the change in the number of vehicles ΔYk, the camera generates an analysis signal and sends it to the edge center. The camera obtains the image brightness Tk in the road traffic video within the preset time based on image analysis technology. Construct an ellipse with the sum of the number of vehicles Yk and the number of pedestrians Xk as the major axis and the number of pedestrians Xk as the minor axis, and extract the area of the ellipse and record it as the traffic volume parameter Sk, where k is the edge center number, k = 1, 2,..., p, and p is the total number of cameras in this edge computing module. Record the time t, the edge center number k, the traffic volume parameter Sk, and the image brightness Tk and form a video data vector (t, k, Sk, Tk). The intelligent street lights in the edge computing module obtain the input power Pk of the intelligent street lights every preset time t, record the time t, the edge center number k, and the input power Pk and form a street light data vector (t, k, Pk). The backup battery in the edge computing module obtains the percentage c of the remaining electrical energy of the backup battery every preset time t, records the time t, the edge center number k, and the percentage c of the remaining electrical energy of the backup battery and forms a battery data vector (t, k, c).

[0047] Please refer to Figure 3 as shown, the edge center constructs a distribution model of natural light intensity: where t is the time; f(t) is the natural light intensity; A is a preset influence factor; σ is a preset scale factor, and μ is a preset position factor. Draw the function image of f(t). Obtain the illumination intensity f(t) in the distribution model of natural light intensity every preset time t. Through the formula get the lighting demand index αk, where λ1, λ2, and λ3 are preset influence factors. The edge center calculates the power supply demand index βk through the formula where is the rated input power of the intelligent street light.

[0048] Please refer to Figure 4As shown in the figure, the transmission network topology of the edge central node and the edge computing unit. Each edge central node is connected to the edge computing unit through a transmission line, and adjacent edge central nodes are connected through a transmission line. The three edge central nodes are numbered 1, 2, and 3 respectively; the edge computing unit is numbered 0. The transmission line passing through the edge central node is denoted as the baseline; the transmission line that does not connect to the edge central node is denoted as the secondary line, and each baseline and secondary line is marked with the starting point and ending point of the transmission line. Every preset time, each baseline and secondary line obtains its own bandwidth utilization percentage ωa-b. Here, a is the number of the starting point of the transmission line, and b is the number of the ending point of the transmission line. The edge computing module obtains all the transmission paths starting from the edge central node and ending at the edge computing unit 0, which are denoted as the optional lines, and calculates the line with the lowest sum of the bandwidth utilization percentages in the optional lines through the formula MIN∑ω a-b Calculate the line with the lowest sum of the bandwidth utilization percentages in the optional lines. For example, starting from the edge central node 1 and ending at the edge computing unit 0, there are five optional lines, which are: Line 1: Baseline 0-1; Line 2: Secondary line 1-2 → Baseline 2-0; Line 3: Secondary line 1-2 → Secondary line 2-3 → Baseline 3-0; Line 4: Secondary line 1-3 → Baseline 3-0; Line 5: Secondary line 1-3 → Secondary line 3-2 → Baseline 2-0. Among them, the bandwidth utilization percentage ω a-b of each transmission line is respectively: ω 0-1 = 80%; ω 0-2 = 20%; ω 0-3 = 20%; ω 1-2 = 10%; ω 2-3 = 20%; ω 1-3 = 20%.

[0049] By calculation, the sum of the bandwidth utilization percentages of each route is obtained: Route 1: 80%; Route 2: 30%; Route 3: 50%; Route 4: 40%; Route 5: 60%. The optimal line is obtained as Route 2 through calculation.

[0050] When the edge central node receives the analysis signal, it turns on the data transmission line and sends the lighting demand index αk and the power supply demand index βk to the edge computing unit and other edge central nodes through the optimal line every preset time; when the edge central node receives the stationary signal, it stops looking for the optimal line and terminates sending the lighting demand index αk and the power supply demand index βk to the edge computing unit and other edge central nodes.

[0051] After receiving the lighting demand index αk, the edge computing unit performs operation and analysis. When the lighting demand index αk is greater than the preset lighting demand index threshold 1 , it sends a signal to turn on the light to the edge central node k through the optimal line; when the lighting demand index αk is greater than the lighting demand index threshold 2 Send an amplification signal to edge center k and a high-demand signal to all edge centers. When the lighting demand index αk is less than the preset lighting demand index threshold one Turn off the lights signal is sent to edge center k through the optimal line, and a low-demand signal is sent to all edge centers through the optimal line.

[0052] When the edge center receives the turn-on signal, it turns on the intelligent street lamp with a preset power one. When the edge center receives the turn-off signal, it turns off the intelligent street lamp; when the edge center receives the amplification signal, it turns on the intelligent street lamp with a preset power two, and the preset power two is greater than the preset power one; when the edge center receives the high-demand signal, it multiplies the preset influence factors λ2 and λ3 by the preset weight factor Ω; when the edge center receives the low-demand signal, it divides the preset influence factors λ2 and λ3 by the preset weight factor Ω. The preset influence factors λ2 and λ3 are the coefficients in the calculation formula of the lighting demand index αk where λ2 controls the influence of the number of pedestrians and vehicles in the road traffic camera on the lighting demand index, and λ3 controls the influence of the image brightness in the road traffic camera on the lighting demand index. The increase of λ2 and λ3 means the increase of the lighting demand index of all intelligent street lamps controlled by this edge computing unit, and then increases the sensitivity of the intelligent street lamp power to the number of pedestrians, the number of vehicles and the brightness.

[0053] When the edge computing unit detects a power outage, it performs arithmetic analysis on the received power supply demand index βk. When the power supply demand index is less than the preset power supply demand index threshold A self-power supply signal is sent to edge center k through the optimal line. When the power supply demand index is greater than or equal to the preset power supply demand index threshold An alternative power supply signal is sent to all edge centers through the optimal line. When edge center k receives the self-power supply signal, it turns on the backup battery controlled by edge center k to supply power to the intelligent street lamps controlled by edge center k. When edge center k receives the alternative power supply signal, it turns on the backup battery controlled by edge center k and supplies power to the intelligent street lamp controlled by the edge center with the highest power supply demand index βk.

[0054] The area calculation module records the lighting demand index αk and power supply demand index βk of all edge computing modules every preset time t. Each edge computing module has a number i; i = 1, 2,..., m; each area calculation module has a number j; j = 1, 2,..., n; n is the total number of area calculation modules. The area calculation module forms a data vector (t, i, j, k, αk, βk) with time t, edge computing module number i, area calculation module number j, edge center number k, lighting demand index αk, and power supply demand index βk. The data vectors with the same area calculation module number i, edge computing module number j, and edge center number k are recorded as a monitoring group. Extract the time t, lighting demand index αk, and power supply demand index βk in each monitoring group to generate two sets of data points (t, αk) and (t, βk). Establish a two-dimensional rectangular coordinate system with time t as the horizontal axis and lighting demand index αk as the vertical axis, fill all points (t, αk) into the coordinate system, and then use a smooth curve to fit all points to generate an image of the lighting demand index changing with time. Extract the area greater than the straight line in the image of the lighting demand index changing with time and denote it as the lighting anomaly index E1, where is the preset lighting demand index threshold two. When the lighting anomaly index E1 is greater than the preset threshold, extract the area calculation module number i, edge computing module number j, and edge center number k of this monitoring group to generate a lighting anomaly vector (E1, i, j, k). Establish a two-dimensional rectangular coordinate system with time t as the horizontal axis and power supply demand index βk as the vertical axis, fill all points (t, βk) into the coordinate system, and then use a smooth curve to fit all points to generate an image of the power supply demand index changing with time. Extract the area greater than the straight line in the image of the power supply demand index changing with time and denote it as the power supply anomaly parameter E2, where is the preset power supply demand index threshold. When the power supply anomaly parameter E2 is greater than the preset threshold, extract the area calculation module number i, edge computing module number j, and edge center number k of this monitoring group to generate a power supply anomaly vector (E2, i, j, k).

[0055] The fault recording module records all lighting anomaly vectors (E1, i, j, k) and power supply anomaly vectors (E2, i, j, k), and records all lighting anomaly vectors as the lighting anomaly group; records all power supply anomaly vectors as the power supply anomaly group, and displays all vectors in the lighting anomaly group and the power supply anomaly group on the display terminal according to the grouping.

[0056] Please refer to Figure 5 as shown, a networking communication method for smart street lights includes the following steps:

[0057] Step 1: Obtain monitoring data:

[0058] The camera in the edge computing module records road traffic videos every preset time t. Based on motion analysis technology, the number of pedestrians Xk and the number of vehicles Yk that appear in the road traffic videos within the preset time are obtained. The camera obtains the image brightness Tk in the road traffic videos within the preset time based on image analysis technology; the backup battery in the edge computing module obtains the percentage c of the remaining electrical energy of the backup battery every preset time t; the edge computing module obtains all transmission paths starting from the edge center and ending at edge computing unit 0, denoted as optional lines; every preset time, each baseline and sub-line that maintains data transmission between the edge center and the edge computing unit obtains its own bandwidth utilization percentage ω a-b ;

[0059] Step 2: Preliminary data analysis:

[0060] The edge computing module calculates the difference between the number of pedestrians in the current preset time and the number of pedestrians in the previous preset time every preset time to obtain the change in the number of pedestrians ΔXk, and calculates the difference between the number of vehicles in the current preset time and the number of vehicles in the previous preset time every preset time to obtain the change in the number of vehicles ΔYk; when the values of both the change in the number of pedestrians ΔXk and the change in the number of vehicles ΔYk are 0, the camera generates a static signal and sends it to the edge center; when there is a non-zero value in either the change in the number of pedestrians ΔXk or the change in the number of vehicles ΔYk, the camera generates an analysis signal and sends it to the edge center; an ellipse is constructed with the sum of the number of vehicles Yk and the number of pedestrians Xk as the major axis and the number of pedestrians Xk as the minor axis, and the area of the ellipse is extracted and denoted as the traffic volume parameter Sk, where k is the edge center number, k = 1, 2,..., p, and p is the total number of cameras in this edge computing module; the time t, the edge center number k, the traffic volume parameter Sk, and the image brightness T are recorded and composed into a camera data vector (t, k, Sk, Tk); the smart street lights in the edge computing module obtain the input power Pk of the smart street lights every preset time t, and the time t, the edge center number k, and the input power P are recorded and composed into a street light data vector (t, k, Pk); the time t, the edge center number k, and the percentage c of the remaining electrical energy of the backup battery are recorded and composed into a battery data vector (t, k, c); the edge center constructs a distribution model of natural light intensity: where t is the time; f(t) is the natural light intensity; A is a preset influence factor; σ is a preset scale factor, and μ is a preset position factor; the function image of f(t) is plotted; the light intensity f(t) is obtained in the natural light intensity distribution model every preset time t; through the formula the lighting demand index αk is obtained, where λ1, λ2, and λ3 are preset influence factors; the edge center calculates the power supply demand index βk through the formula where is the rated input power of the smart street light;

[0061] Step 3: Solve the optimal line

[0062] In the transmission network connecting the central and edge computing units, each edge central node is connected to the edge computing unit through a transmission line, and adjacent edge central nodes are connected through a transmission line; the three edge central nodes are numbered 1, 2, and 3 respectively; the edge computing unit is numbered 0; the transmission line passing through the edge central node is denoted as the baseline; the edge computing module obtains all transmission paths starting from the edge central node and ending at the edge computing unit 0, denoted as optional lines, and calculates the line with the lowest sum of bandwidth utilization percentages in the optional lines through the formula MIN∑ω a-b Calculate the line with the lowest sum of bandwidth utilization percentages in the optional lines;

[0063] Step Four: Data Transmission:

[0064] When the edge central node receives the analysis signal, it turns on the data transmission line and sends the lighting demand index αk and the power supply demand index βk to the edge computing unit and other edge central nodes through the optimal line at preset intervals; when the edge central node receives the stationary signal, it stops searching for the optimal line data transmission line and terminates sending the lighting demand index αk and the power supply demand index βk to the edge computing unit and other edge central nodes;

[0065] Step Five: Secondary Data Analysis of Lighting Demand Index:

[0066] After receiving the lighting demand index αk, the edge computing unit performs arithmetic analysis. When the lighting demand index αk is greater than the preset lighting demand index threshold one When it is, it sends a signal to turn on the lights to the edge central node k through the optimal line; when the lighting demand index αk is greater than the lighting demand index threshold two Send an amplification signal to the edge central node k and a high-demand signal to all edge central nodes; when the lighting demand index αk is less than the preset lighting demand index threshold one When it is, it sends a signal to turn off the lights to the edge central node k through the optimal line and sends a low-demand signal to all edge central nodes through the optimal line; when the edge central node receives the signal to turn on the lights, it turns on the smart street lamp at the preset power one; when the edge central node receives the signal to turn off the lights, it turns off the smart street lamp; when the edge central node receives the amplification signal, it turns on the smart street lamp at the preset power two, and the preset power two is greater than the preset power one; when the edge central node receives the high-demand signal, it multiplies the preset influence factors λ2 and λ3 by the preset weight factor Ω; when the edge central node receives the low-demand signal, it divides the preset influence factors λ2 and λ3 by the preset weight factor Ω;

[0067] Step Six: Secondary Data Analysis and Execution of Power Supply Demand Index:

[0068] When the edge computing unit detects a power outage, it performs arithmetic analysis on the received power supply demand index βk. When the power supply demand index is less than the preset power supply demand index threshold When the power supply demand index is less than the preset power supply demand index threshold, a self-power supply signal is sent to the edge center k through the optimal line; when the power supply demand index is greater than or equal to the preset power supply demand index threshold a different power supply signal is sent to all edge centers through the optimal line; when the edge center k receives the self-power supply signal, the backup battery controlled by the edge center k is turned on to supply power to the intelligent street lights controlled by the edge center k; when the edge center k receives the different power supply signal, the backup battery controlled by the edge center k is turned on to supply power to the intelligent street lights controlled by the edge center with the highest power supply demand index βk;

[0069] Step Seven: Data aggregation, fault analysis and execution:

[0070] The area calculation module records the lighting demand index αk and the power supply demand index βk of all edge calculation modules every preset time t; each edge calculation module has a number i; i = 1, 2,..., m; each area calculation module has a number j; j = 1, 2,..., n; n is the total number of area calculation modules. The area calculation module forms a data vector (t, i, j, k, αk, βk) with time t, edge calculation module number i, area calculation module number j, edge center number k, lighting demand index αk and power supply demand index βk; the data vectors with the same area calculation module number i, edge calculation module number j and edge center number k are recorded as a monitoring group; extract the time t, lighting demand index αk and power supply demand index βk in each monitoring group to generate two sets of data points (t, αk) and (t, βk); establish a two-dimensional rectangular coordinate system with time t as the horizontal axis and lighting demand index αk as the vertical axis, fill all points (t, αk) into the coordinate system, and then fit all points with a smooth curve to generate an image of the lighting demand index changing with time; extract the area greater than the straight line in the image of the lighting demand index changing with time and denote it as the lighting anomaly index E1, where is the preset lighting demand index threshold two; when the lighting anomaly index E1 is greater than the preset threshold, extract the area calculation module number i, edge calculation module number j and edge center number k of this monitoring group to generate a lighting anomaly vector (E1, i, j, k); establish a two-dimensional rectangular coordinate system with time t as the horizontal axis and power supply demand index βk as the vertical axis, fill all points (t, βk) into the coordinate system, and then fit all points with a smooth curve to generate an image of the power supply demand index changing with time; extract the area greater than the straight line in the image of the power supply demand index changing with time and denote it as the power supply anomaly parameter E2, where is a preset power supply demand index threshold; when the power supply anomaly parameter E2 is greater than the preset threshold, the area calculation module number i, edge calculation module number j, and edge central number k of the monitoring group are extracted to generate a power supply anomaly vector (E2, i, j, k); the fault recording module records all lighting anomaly vectors (E1, i, j, k) and power supply anomaly vectors (E2, i, j, k) and records all lighting anomaly vectors as a lighting anomaly group; all power supply anomaly vectors are recorded as a power supply anomaly group, and all vectors in the lighting anomaly group and the power supply anomaly group are displayed on the display terminal by grouping.

[0071] It should be understood that the terms "comprising" and "including" as used in the specification and claims of this disclosure indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0072] It should also be understood that the terms used in the specification of this disclosure are for the purpose of describing specific embodiments only and are not intended to limit this disclosure. As used in the specification and claims of this disclosure, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms. It should further be understood that the term "and / or" as used in the specification and claims of this disclosure refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations;

[0073] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A networking communication system for intelligent street lights, comprising a number of edge computing modules, area computing modules and fault recording modules, characterized in that: Each edge computing module includes an edge computing unit, an edge center, a camera, an intelligent street light and a backup battery; each edge computing unit is connected to a number of edge centers, each edge center is connected to a camera, an intelligent street light and a backup battery, the data of each edge center is aggregated through the edge computing unit, and two-way data transmission can be carried out between each edge center; Data collection and preliminary data analysis are carried out on the camera and the backup battery every preset time to obtain the lighting demand index and the power supply demand index; The difference between the number of pedestrians at each preset time and the number of pedestrians at the previous preset time is calculated to obtain the change in the number of pedestrians, and the difference between the number of vehicles at each preset time and the number of vehicles at the previous preset time is calculated to obtain the change in the number of vehicles; Whether to generate an analysis signal and a stationary signal is judged according to the values of the change in the number of pedestrians and the change in the number of vehicles; The traffic volume parameter Sk is obtained through the operation of the number of vehicles and the number of pedestrians, and the time, edge center number, traffic volume parameter and image brightness are recorded and composed into a camera data vector; The intelligent street lights in the edge computing module obtain the input power of the intelligent street lights every preset time, and record the time, edge center number and input power and compose them into a street light data vector; Record the time, edge center number and the percentage of the remaining power of the backup battery to form a battery data vector; A distribution model of natural light intensity is constructed, and the light intensity f(t) is obtained in the distribution model of natural light intensity every preset time t; The lighting demand index is obtained through operation, and there are several preset influencing factors in the operation process. The influencing factors are positively correlated with the lighting demand index, and the values of the influencing factors can be dynamically adjusted according to the quadratic operation analysis results of the lighting demand index; Quadratic operation analysis is carried out for the lighting demand index; The power supply demand index is obtained through operation, and quadratic operation analysis is carried out for the power supply demand index; In the transmission network topology of the edge center and the edge computing unit, each edge center is connected to the edge computing unit through a transmission line, and two adjacent edge centers are connected through a transmission line; The edge center numbers are k = 1, 2,..., K; K is the total number of edge centers; The edge computing unit number is 0; The transmission line passing through the edge center is recorded as the baseline; The transmission line that does not connect the edge center is recorded as the secondary line, and each baseline and secondary line are marked with the starting point and ending point of the transmission line; Every preset time, each baseline and secondary line obtains its own bandwidth utilization percentage; The edge computing module obtains all transmission paths starting from the edge center and ending at the edge computing unit 0, which are recorded as optional lines, and obtains the line with the lowest sum of bandwidth utilization percentages in the optional lines through formula operation, which is recorded as the optimal line; When the edge center receives the analysis signal, it turns on the data transmission line, and sends the lighting demand index and the power supply demand index to the edge computing unit and other edge centers through the optimal line every preset time; When the edge central control unit receives the static signal, it stops searching for the optimal line data transmission line and terminates sending the lighting demand index and the power supply demand index to the edge computing unit and other edge central control units; when the edge computing unit receives the lighting demand index, it performs secondary operation analysis on the lighting demand index; when a power outage is detected, it performs secondary operation analysis on the received power supply demand index. Execute control operations according to the results of the secondary operation analysis: When the edge central control unit receives the turn-on signal, it turns on the intelligent street lamp at a preset power one; when the edge central control unit receives the turn-off signal, it turns off the intelligent street lamp; when the edge central control unit receives the amplification signal, it turns on the intelligent street lamp at a preset power two, and the preset power two is greater than the preset power one. When the edge central control unit receives the high demand signal, it amplifies the preset influence factor by multiplying the preset influence factor by the preset weight factor. When the edge central control unit receives the low demand signal, it reduces the preset influence factor by dividing the preset influence factor by the preset weight factor. When the edge central control unit receives the self-power supply signal, it turns on the backup battery controlled by the edge central control unit to supply power to the intelligent street lamps controlled by the edge central control unit. When the edge central control unit receives the different power supply signal, it turns on the backup battery controlled by the edge central control unit to supply power to the intelligent street lamp with the highest power supply demand index among those controlled by the edge central control unit. The area calculation module records the lighting demand index and power supply demand index of all edge computing modules at preset time intervals; each edge computing module has a number i, and each area computing module has a number j, generating a data vector (t, i, j, k, , ); the data vectors with the same area computing module number, edge computing module number, and edge center number are recorded as a monitoring group; two sets of data points (t, ) and (t, ) are generated in the same monitoring group; an image of the change of the lighting demand index over time is generated; the area of the part of the image of the change of the lighting demand index over time that is greater than the straight line = the preset lighting demand index threshold two is extracted and denoted as the lighting anomaly index E1; When the lighting anomaly index is greater than the preset threshold, extract the area calculation module number i, edge computing module number j, and edge central number k of the monitoring group to generate a lighting anomaly vector (E1, i, j, k); generate an image of the power supply demand index changing over time; extract the area greater than the straight line = the preset power supply demand index threshold part and denote it as the power supply anomaly parameter E2; when the power supply anomaly parameter is greater than the preset threshold, extract the area calculation module number, edge computing module number, and edge central number of the monitoring group to generate a power supply anomaly vector (E2, i, j, k); where is the lighting demand index; is the power supply demand index; The fault recording module records all lighting anomaly vectors (E1, i, j, k) and power supply anomaly vectors (E2, i, j, k), and records all lighting anomaly vectors as a lighting anomaly group; records all power supply anomaly vectors as a power supply anomaly group, and displays all vectors in the lighting anomaly group and the power supply anomaly group on the display terminal according to the grouping.

2. The networking communication system for intelligent street lights according to claim 1, wherein The specific process of data collection is as follows: Record the road traffic camera every preset time, and obtain the number of pedestrians and vehicles appearing in the road traffic camera within the preset time based on motion analysis technology; the camera obtains the image brightness in the road traffic camera within the preset time based on image analysis technology, and the backup battery in the edge computing module obtains the percentage of the remaining power of the backup battery every preset time.

3. A networking communication system for smart street lights according to claim 1, characterized in that, The secondary operation analysis for the lighting demand index is specifically as follows: When the lighting demand index is greater than the first preset lighting demand index threshold, send a turn-on signal to the edge central control unit to which the lighting demand index belongs through the optimal line; when the lighting demand index is greater than the lighting demand index threshold two, send an amplification signal to the edge central control unit to which the lighting demand index belongs, and send a high demand signal to all edge central control units; when the lighting demand index is less than the first preset lighting demand index threshold, send a turn-off signal to the edge central control unit to which the lighting demand index belongs through the optimal line, and send a low demand signal to all edge central control units through the optimal line.

4. A networking communication system for intelligent street lights according to claim 3, characterized in that, The secondary operation analysis for the power supply demand index is specifically as follows: When the power supply demand index is less than the preset power supply demand index threshold, send a self-power supply signal to the edge central control unit k through the optimal line; when the power supply demand index is greater than or equal to the preset power supply demand index threshold, send a different power supply signal to all edge central control units through the optimal line.

5. A networking communication method for intelligent street lights, characterized in that, The specific steps are as follows: Step 1: Obtain monitoring data: The camera obtains the number of pedestrians, the number of vehicles, and the image brightness at preset intervals; the backup battery obtains the percentage of the remaining power of the backup battery at preset intervals; the edge computing module obtains all transmission paths starting from the edge central unit and ending at the edge computing unit, denoted as optional lines; each data transmission line between the edge central unit and the edge computing unit obtains its own bandwidth utilization percentage; Step 2. Preliminary data analysis: The change in the number of pedestrians and the change in the number of vehicles are obtained through calculations; whether a stationary signal and an analysis signal are generated are judged based on the values of the change in the number of pedestrians and the change in the number of vehicles; the traffic volume parameter is obtained through calculations of the number of vehicles and the number of pedestrians; Record the road traffic camera at preset intervals t, and obtain the number of pedestrians Xk and the number of vehicles Yk that appear in the road traffic camera within the preset time based on motion analysis technology; Calculate the difference between the number of pedestrians at the current preset time and the number of pedestrians at the previous preset time to obtain the change in the number of pedestrians Xk. Calculate the difference between the number of vehicles at the current preset time and the number of vehicles at the previous preset time to obtain the change in the number of vehicles Yk; When the change in the number of pedestrians Xk and the change in the number of vehicles Yk are both 0, the camera generates a static signal and sends it to the edge center; When there is a non-zero value in the change in the number of pedestrians Xk or the change in the number of vehicles Yk, the camera generates an analysis signal and sends it to the edge center; The camera obtains the image brightness Tk in the road traffic camera within the preset time based on image analysis technology, constructs an ellipse with the sum of the number of vehicles Yk and the number of pedestrians Xk as the major axis and the number of pedestrians Xk as the minor axis, and extracts the area of the ellipse as the traffic volume parameter Sk, where k is the edge center number, k = 1, 2,..., K, and K is the total number of edge centers; Record the time t, the edge center number k, the traffic volume parameter Sk, and the image brightness Tk and form a camera data vector (t, k, Sk, Tk); The smart streetlights in the edge computing module obtain the input power Pk of the smart streetlights every preset time t, record the time t, the edge center number k, and the input power Pk and form a streetlight data vector (t, k, Pk); The backup battery in the edge computing module obtains the percentage c of the remaining electrical energy of the backup battery every preset time t, and records the time t, the edge center number k, and the percentage c of the remaining electrical energy of the backup battery to form a battery data vector (t, k, c); Generate a camera data vector (t, k, Sk, Tk), a street lamp data vector (t, k, Pk), and a battery data vector (t, k, c); construct a distribution model of natural light intensity, and obtain the illumination intensity in the distribution model of natural light intensity at preset intervals t; obtain the lighting demand index through calculations, and there are several preset influencing factors in the calculation process; obtain the power supply demand index through calculations; Step 3. Solve the optimal line: The edge computing module obtains all transmission paths starting from the edge central unit and ending at the edge computing unit 0, denoted as optional lines, and calculates the line with the lowest sum of bandwidth utilization percentages in the optional lines, denoted as the optimal line; Step 4. Data transmission: When the edge central unit receives the analysis signal, it turns on the data transmission line, and sends the lighting demand index and the power supply demand index to the edge computing unit and other edge central units through the optimal line at preset intervals; When the edge central unit receives the stationary signal, it stops searching for the optimal line and terminates sending the lighting demand index and the power supply demand index to the edge computing unit and other edge central units; Step 5. Secondary data analysis and execution of the lighting demand index: When the lighting demand index is greater than the preset lighting demand index threshold 1, send a signal to turn on the lights to the edge central unit to which the lighting demand index belongs through the optimal line; when the lighting demand index is greater than the lighting demand index threshold 2, send a signal to amplify to the edge central unit to which the lighting demand index belongs, and send a high demand signal to all edge central units; when the lighting demand index is less than the preset lighting demand index threshold 1, send a signal to turn off the lights to the edge central unit to which the lighting demand index belongs through the optimal line, and send a low demand signal to all edge central units through the optimal line; when the edge central unit receives the signal to turn on the lights, turn on the intelligent street lamp at the preset power 1; when the edge central unit receives the signal to turn off the lights, turn off the intelligent street lamp; when the edge central unit receives the signal to amplify, turn on the intelligent street lamp at the preset power 2, and the preset power 2 is greater than the preset power 1; when the edge central unit receives the high demand signal, amplify the preset influencing factor and multiply it by the preset weight factor; when the edge central unit receives the low demand signal, reduce the preset influencing factor and divide it by the preset weight factor; Step 6. Secondary data analysis and execution of the power supply demand index: When the edge computing unit detects a power outage, it performs arithmetic analysis on the received power supply demand index. When the power supply demand index is less than the preset power supply demand index threshold, it sends a self-power supply signal to the edge central hub to which the power supply demand index belongs through the optimal line; when the power supply demand index is greater than or equal to the preset power supply demand index threshold, it sends a different power supply signal to all edge central hubs through the optimal line; when the edge central hub receives the self-power supply signal, it turns on the backup battery controlled by the edge central hub to supply power to the intelligent street lights controlled by the edge central hub; when the edge central hub receives the different power supply signal, it turns on the backup battery controlled by the edge central hub to supply power to the intelligent street lights controlled by the edge central hub with the highest power supply demand index. Step 7: Data aggregation and fault analysis: The area calculation module records the lighting demand index and power supply demand index of all edge computing modules at preset time intervals; each edge computing module has a number i, and each area calculation module has a number j. The area calculation module combines the time t, the edge computing module number i, the area calculation module number j, the edge central number k, the lighting demand index and the power supply demand index to form a data vector (t, i, j, k, , ); the data vectors with the same area calculation module number, edge computing module number, and edge central number are recorded as a monitoring group; two data points (t, ) and (t, ) are generated in the same monitoring group; an image of the lighting demand index changing with time is generated; the area greater than the straight line = the preset lighting demand index threshold in the image of the lighting demand index changing with time is extracted and recorded as the lighting anomaly index E1; When the lighting anomaly index is greater than a preset threshold, extract the area calculation module number i, edge calculation module number j, and edge central number k of the monitoring group to generate a lighting anomaly vector (E1, i, j, k); generate an image of the power supply demand index changing over time; extract the area greater than the straight line = the area of the part of the preset power supply demand index threshold in the image of the power supply demand index changing over time and denote it as the power supply anomaly parameter E2; when the power supply anomaly parameter is greater than the preset threshold, extract the area calculation module number, edge calculation module number, and edge central number of the monitoring group to generate a power supply anomaly vector (E2, i, j, k); The fault recording module records all lighting anomaly vectors (E1, i, j, k) and power supply anomaly vectors (E2, i, j, k), and records all lighting anomaly vectors as a lighting anomaly group; records all power supply anomaly vectors as a power supply anomaly group, and displays all vectors in the lighting anomaly group and the power supply anomaly group on the display terminal according to the grouping.

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