Equipment operation optimization method and system based on edge computing

Abstract

The invention relates to the technical field of edge computing, and particularly discloses an equipment operation optimization method and system based on edge computing, and the method comprises the following steps: S1, deploying an edge computing module, detecting the network connection condition of intelligent lighting equipment, screening out to-be-adjusted equipment, determining a dimming region, and obtaining the initial illumination intensity of the to-be-adjusted equipment in the dimming region; s2, presetting a detection period, obtaining the number of pedestrians in the dimming area in the detection period and the standing time of the pedestrians, and calculating the pedestrian crowding degree in the dimming area; and S3, calculating an illumination intensity difference value and an influence coefficient, obtaining a pedestrian crowding degree and illumination intensity, calculating an ideal illumination difference value and first illumination intensity, and correcting the first illumination intensity into second illumination intensity through a second correction value. The equipment operation optimization method based on edge computing is used for solving the problem that current intelligent equipment excessively depends on cloud decision, and the real-time performance is improved.

Description

Technical Field

[0001] The present invention relates to the field of edge computing technology, and in particular to a device operation optimization method and system based on edge computing. Background Art

[0002] Edge computing is a distributed computing architecture that shifts data processing and analysis tasks from centralized cloud or data centers to "edge" devices (such as smartphones, sensors, routers, and gateways) close to the data source or user end. By performing computations at the edge of the network, edge computing can reduce data transmission latency, lower bandwidth pressure, and improve system real-time performance and reliability. By bringing computing power to the edge of the network, edge computing overcomes the latency, bandwidth, and reliability bottlenecks of cloud computing, becoming a key supporting technology in the era of the Internet of Things, artificial intelligence, and 5G.

[0003] There are some problems with current smart lighting devices. Some smart lighting devices rely on cloud-based decision centers (such as remote control and data storage), which leads to command delays. This phenomenon is more serious when the network is unstable. In addition, when the network is disconnected, the smart lighting devices in the disconnected area cannot be controlled, such as turning lights on and off or dimming them.

[0004] In the existing technology, smart lighting devices are overly dependent on cloud-based decision-making centers, resulting in low risk resistance. This leads to poor real-time performance, reduced user experience, and low reliability in key scenarios such as emergency lighting. Therefore, reducing the dependence of smart lighting devices on cloud-based decision-making and ensuring that smart lighting devices still perform well when the network is disconnected requires a device operation optimization method based on edge computing to solve the problem of current smart devices being overly dependent on cloud-based decision-making and improve real-time performance. Summary of the Invention

[0005] The purpose of the present invention is to provide a device operation optimization method and system based on edge computing to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The device operation optimization method based on edge computing includes the following steps:

[0008] S1: Deploy an edge computing module on the smart lighting device. Use the edge computing module to detect the network connection status of the current smart lighting device. If the quality of the network connection of the smart lighting device is unstable, mark the smart lighting device as a device to be adjusted.

[0009] Record the area covered by the light of the device to be adjusted as the dimming area, and obtain the initial light intensity LX in the dimming area when the device to be adjusted is working at rated power;

[0010] S2: Preset the detection period T, obtain the number of pedestrians N in the dimming area during the detection period T and the pedestrian stay time t in the dimming area, and calculate the pedestrian congestion degree in the dimming area Among them, t n represents the time the nth pedestrian stays in the dimming area, and S represents the area of ​​the road in the dimming area;

[0011] S3: Obtain the light intensity lx of the dimming area within the detection period T, calculate the light intensity difference Δlx = LX - lx, and calculate the influence coefficient Where Δlx i Represents the light intensity difference of the i-th detection cycle, Y i represents the pedestrian congestion degree in the dimming area during the i-th detection cycle;

[0012] Get the pedestrian congestion Y in the dimming area during the current detection cycle now and light intensity lx now , calculate the ideal illumination difference Δlx s =K*Y now , calculate the first light intensity lx_f=lx in the dimming area now +Δlx s ;

[0013] Calculate the second correction value M=Y now / Y ave , the first illumination intensity is corrected to the second illumination intensity lx_s=M*lx_f, where Y ave Represents the average pedestrian congestion within the dimming area.

[0014] As a further solution of the present invention: in step S1, detecting the network connection status of the current smart lighting device based on the edge computing module includes:

[0015] The edge computing module is instructed to send a preset number of ICMP requests, and the packet loss rate PLR ​​between the edge computing module sending the ICMP request and receiving the ICMP request is obtained. If the packet loss rate PLR ​​is greater than 0.01%, it means that the quality of the network connection status of the current smart lighting device is unstable.

[0016] As a further solution of the present invention: in step S1, the method for obtaining the initial light intensity 1x in the dimming area includes:

[0017] Pre-set the light monitoring point, obtain the light intensity of the light monitoring point and calculate the mean light intensity lx ave , let the initial light intensity lx=lx ave .

[0018] As a further solution of the present invention: in the step S2, if the number of pedestrians N in the dimming area is 0, the corresponding detection cycle is recorded as an empty cycle. If the current detection cycle is an empty cycle, subsequent operations are stopped and the output power of the intelligent lighting device is reduced to half of the rated power.

[0019] As a further solution of the present invention: in step S1, the smart lighting device is turned off and the light intensity in the dimming area is detected. If the light intensity is greater than or equal to a preset light intensity threshold, the subsequent steps are stopped and the smart lighting device is turned off.

[0020] As a further solution of the present invention: in the step S3, the record of the light intensity difference Δlx=0 is eliminated and does not participate in the calculation of the influence coefficient K.

[0021] As a further solution of the present invention: in step S3, the maximum light intensity difference Δlx is calculated max =lx_s-lx now , if the maximum light intensity difference light intensity adjustment value Δlx max ≥5000: Stop adjusting the part of the intelligent lighting device that exceeds 5000.

[0022] The device operation optimization system based on edge computing includes:

[0023] Judgment module: Deploy an edge computing module on the smart lighting device side. Based on the edge computing module, the network connection status of the current smart lighting device is detected. If the quality of the network connection of the smart lighting device is unstable, the smart lighting device is marked as a device to be adjusted.

[0024] Record the area covered by the light of the device to be adjusted as the dimming area, and obtain the initial light intensity LX in the dimming area when the device to be adjusted is working at rated power;

[0025] Calculation module: Preset the detection period T, obtain the number of pedestrians N in the dimming area within the detection period T and the pedestrian stay time t in the dimming area, and calculate the pedestrian congestion in the dimming area Among them, t n represents the time the nth pedestrian stays in the dimming area, and S represents the area of ​​the road in the dimming area;

[0026] Adjustment module: obtain the light intensity lx of the dimming area within the detection period T, calculate the light intensity difference Δlx = LX-lx, and calculate the influence coefficient Where Δlx i Represents the light intensity difference of the i-th detection cycle, Y i represents the pedestrian congestion degree in the dimming area during the i-th detection cycle;

[0027] Get the pedestrian congestion Y in the dimming area during the current detection cycle now and light intensity lx now , calculate the ideal illumination difference Δlx s =K*Y now , calculate the first light intensity lx_f=lx in the dimming area now +Δlx s ;

[0028] Calculate the second correction value M=Y now / Y ave , the first illumination intensity is corrected to the second illumination intensity lx_s=M*lx_f, where Y ave Represents the average pedestrian congestion within the dimming area.

[0029] The beneficial effects of the present invention are as follows: first, by deploying an edge computing module on the smart lighting device end and judging the current network connection status to determine whether the smart lighting device needs to be replaced with an edge computing-based method, when the network connection status is unstable, not only will the delay be large, but network interruption is likely to occur. Then, it is necessary to calculate the impact of pedestrians on the light intensity in the dimming area. The more pedestrians there are, the greater the impact on the light intensity in the dimming area. The pedestrian congestion is calculated based on the number of pedestrians and the length of stay. This value is used to reflect the congestion in the dimming area. Subsequently, the influence coefficient is calculated. The influence coefficient is a value used to reflect the trend of the light intensity difference with the pedestrian congestion. Then, the ideal light difference is calculated based on the pedestrian congestion and light intensity in the dimming area within the current detection cycle, thereby obtaining a first light intensity. Then, the first light intensity is corrected to obtain a second light intensity. The purpose is to further solve the problem that too many pedestrians will block each other's light, so further correction is required. In summary, by optimizing the edge computing of smart lighting devices, the problem of current smart devices being overly dependent on cloud-based decision-making is solved and real-time performance is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention will be further described below with reference to the accompanying drawings.

[0031] Figure 1 It is a structural diagram of the device operation optimization method and system based on edge computing of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0033] See also Figure 1 As shown, the present invention is a device operation optimization method based on edge computing, comprising the following steps:

[0034] S1: Deploy an edge computing module on the smart lighting device. Use the edge computing module to detect the network connection status of the current smart lighting device. If the quality of the network connection of the smart lighting device is unstable, mark the smart lighting device as a device to be adjusted.

[0035] Record the area covered by the light of the device to be adjusted as the dimming area, and obtain the initial light intensity LX in the dimming area when the device to be adjusted is working at rated power;

[0036] S2: Preset the detection period T, obtain the number of pedestrians N in the dimming area during the detection period T and the pedestrian stay time t in the dimming area, and calculate the pedestrian congestion degree in the dimming area Among them, t n represents the time the nth pedestrian stays in the dimming area, and S represents the area of ​​the road in the dimming area;

[0037] S3: Obtain the light intensity lx of the dimming area within the detection period T, calculate the light intensity difference Δlx = LX - lx, and calculate the influence coefficient Where Δlx i Represents the light intensity difference of the i-th detection cycle, Y i represents the pedestrian congestion degree in the dimming area during the i-th detection cycle;

[0038] Get the pedestrian congestion Y in the dimming area during the current detection cycle now and light intensity lx now , calculate the ideal illumination difference Δlx s =K*Y now , calculate the first light intensity lx_f=lx in the dimming area now +Δlx s ;

[0039] Calculate the second correction value M=Y now / Y ave , the first illumination intensity is corrected to the second illumination intensity lx_s=M*lx_f, where Y ave Represents the average pedestrian congestion within the dimming area.

[0040] It should be noted that this invention is specifically designed for complex outdoor environments, such as urban parks and plazas, and other densely populated public areas. Its technical solutions are not applicable to indoor scenarios. In typical outdoor application scenarios, first, public spaces like parks have large and highly mobile foot traffic, generating dynamic lighting demands of hundreds of people per second during peak hours. Second, equipment is exposed to outdoor environments for long periods of time, facing accelerated aging factors such as high temperature, humidity, and salt spray corrosion, resulting in a higher hardware failure rate than indoor equipment. Furthermore, severe weather conditions such as heavy rain, snow, and strong winds can cause a surge in network base station load or damage communication lines. Actual measured data shows that in extreme weather conditions, wireless signal strength fluctuations can reach 40dB, with packet loss rates exceeding 30%.

[0041] In this context, traditional smart lighting systems that rely on centralized cloud-based decision-making have significant drawbacks. When network quality degrades to a latency of more than 200ms, the response time for lighting control commands exceeds human perception, resulting in a flickering visual experience. However, with an edge computing architecture, devices can autonomously complete over 90% of real-time control tasks. When network quality is detected to fall below a preset threshold, a switch to edge computing mode is immediately initiated.

[0042] During the control process of an intelligent lighting system, it is necessary to accurately monitor and analyze the specific area covered by the lighting of the device being adjusted. This area is referred to as the dimming zone. To properly adjust the light intensity, it is first necessary to obtain the initial light intensity in the dimming zone when the device being adjusted is operating at rated power. Obtaining this initial light intensity value is crucial, as it serves as a basic reference for subsequent light intensity adjustments and provides an accurate starting point for the entire adjustment process.

[0043] In practical applications, a reasonable detection cycle is required to more scientifically and effectively adjust light intensity. During this pre-set detection cycle, various sensors and other monitoring devices installed in the dimming area can comprehensively and accurately obtain information on the number of pedestrians in the dimming area. Furthermore, the duration of pedestrians' stay in the dimming area must be accurately recorded. These two key data points can comprehensively reflect the pedestrian congestion level in the dimming area.

[0044] Specifically, the more pedestrians there are and the longer they stay in the dimming area, the higher the pedestrian congestion in that area. This is because when more pedestrians gather in one area, not only will the propagation and distribution of light be affected due to the blocking and reflection of light by their bodies, but the activity status and position changes of different pedestrians will further change the lighting environment in the dimming area. For example, when a large number of pedestrians frequently move around or stay for a long time in the dimming area, the scattering and absorption of light in different directions will become more complicated, resulting in a deviation between the actual lighting effect and the theoretical lighting effect at the rated power of the device.

[0045] Conversely, when pedestrians are few and their stay time is short, their impact on the dimming zone's light intensity is relatively small. Therefore, pedestrian congestion, as an important indicator that directly reflects the actual changes in lighting needs in the dimming zone, is a key factor in adjusting the light intensity of intelligent lighting equipment. By monitoring and analyzing pedestrian congestion in real time, the intelligent lighting system can dynamically adjust light intensity based on actual conditions, achieving efficient energy utilization while meeting pedestrian lighting needs.

[0046] After acquiring the initial light intensity and monitoring the number of pedestrians and their dwell time, the next step is to obtain the actual light intensity in the dimming area during the detection cycle. This actual light intensity data is collected and aggregated in real time by multiple high-precision light sensors distributed throughout the dimming area. These sensors can accurately perceive light changes at different locations, providing comprehensive and accurate data support for subsequent analysis.

[0047] After obtaining the initial light intensity and actual light intensity during the detection cycle, a specific mathematical calculation method is used to calculate the difference between these two values, namely the light intensity difference. This difference intuitively reflects the degree of change in the actual light intensity of the dimming area compared to the initial state due to the combined effects of various factors during the detection cycle.

[0048] To further explore the inherent relationship between pedestrian congestion and light intensity differences, we need to introduce a key metric, the influence coefficient, for quantitative analysis. The influence coefficient is calculated based on a large number of real-world data samples, using complex mathematical models and statistical methods. It accurately reflects how changes in pedestrian congestion affect light intensity differences, revealing the objective laws between the two.

[0049] An in-depth analysis of the impact coefficients reveals several phenomena and patterns with practical implications. First, when pedestrian congestion increases, the number of pedestrians within the dimming zone increases and their distribution becomes denser, leading to inevitable shadowing. This shadowing effect significantly impacts ground visibility, causing uneven light distribution. Previously clearly visible areas may become shadowed or dimmed, causing inconvenience for pedestrians and potentially increasing safety risks.

[0050] From a more general perspective, the greater the number of pedestrians, the higher the pedestrian congestion within the dimming zone. In this case, light will be more obstructed and scattered during propagation, resulting in less light contributing to effective illumination and ultimately lowering the overall light intensity within the dimming zone. For example, in a crowded shopping mall corridor or subway platform, even if the lighting equipment is operating at rated power when a large number of pedestrians gather, the actual perceived light intensity will be significantly lower than when there are fewer pedestrians due to factors such as shadows.

[0051] Given this complex relationship and the potential volatility of data under different circumstances, a method for calculating influence coefficients and replacing them with their averages is employed to make the analysis more accurate, reliable, universal, and representative. By calculating and averaging the influence coefficients over multiple testing cycles, we can effectively eliminate interference from individual anomalies or special cases, resulting in a stable value that reflects general patterns. This approach provides a more scientific and accurate basis for formulating intelligent lighting system adjustment strategies, ensuring that the lighting system can make appropriate adjustments based on actual conditions, achieving both pedestrian lighting needs and efficient energy utilization.

[0052] After adjusting the light intensity, a second correction value must be calculated to further optimize the lighting effect and ensure the accuracy and stability of the adjustment. This second correction value is calculated based on the adjusted light intensity and the actual environmental changes within the dimming area, through comprehensive monitoring and analysis of multiple relevant parameters. These parameters include not only indicators of pedestrian congestion, such as the number of pedestrians and dwell time, but may also include factors such as natural light changes within the dimming area and fluctuations in the device's own performance.

[0053] The purpose of the second correction value is to further fine-tune the light intensity for more precise lighting control. It can make detailed corrections to the light intensity previously adjusted based on the influence coefficient according to real-time environmental changes and device operating status. For example, if after adjusting the light intensity, the actual lighting effect within the dimming area is still not ideal due to a sudden increase or decrease in external natural light, the second correction value will come into play. Based on the size and direction of the second correction value, the system will further fine-tune the output intensity of the smart lighting device, enabling it to better adapt to complex and changing environmental conditions and always maintain the light intensity in the dimming area at an optimal state.

[0054] Through this light intensity adjustment based on the influence coefficient and further optimization and adjustment of the second correction value, the intelligent lighting system can achieve precise control of the output intensity, while ensuring pedestrian lighting needs and visual comfort, maximizing energy utilization efficiency and realizing intelligent and efficient lighting management.

[0055] In another preferred embodiment of the present invention, detecting the current network connection status of the smart lighting device based on the edge computing module includes:

[0056] The edge computing module is instructed to send a preset number of ICMP requests, and the packet loss rate PLR ​​between the edge computing module sending the ICMP request and receiving the ICMP request is obtained. If the packet loss rate PLR ​​is greater than 0.01%, it means that the quality of the network connection status of the current smart lighting device is unstable.

[0057] It's worth noting that packet loss rate, a key indicator of network transmission quality, can directly reflect data loss during network transmission. In smart lighting systems, various control commands, status information, and sensor data need to be transmitted in real time over the network. When this data is transmitted from the sender to the receiver, some packets may be lost due to various potential factors such as network congestion, signal interference, and device failure. The packet loss rate is calculated by counting and calculating the total number of packets transmitted and the number of packets successfully received within a certain period of time. It clearly displays the extent of data loss as a percentage.

[0058] Therefore, by regularly calculating the packet loss rate and conducting real-time monitoring and analysis, we can promptly understand the network connection status of smart lighting devices. Once the packet loss rate is found to be outside the normal range, we can quickly take appropriate measures to troubleshoot and repair it, such as optimizing the network configuration, increasing the network bandwidth, and checking the network equipment, etc., to ensure the stability of the network connection and the reliable operation of the smart lighting system.

[0059] In another preferred embodiment of the present invention, the method for obtaining the initial light intensity 1x in the dimming area includes:

[0060] Pre-set the light monitoring point, obtain the light intensity of the light monitoring point and calculate the mean light intensity lx ave , let the initial light intensity lx=lx ave .

[0061] It is understandable that this method detects the initial light intensity in the dimming zone, which has a significant representative advantage and can provide a solid and reliable data foundation for subsequent lighting control, thereby effectively avoiding overall data errors caused by a specific value and ensuring the accuracy and stability of the entire intelligent lighting system. Through high-frequency sampling, it can capture small changes in light intensity over a short period of time, ensuring that no key information that may affect the overall light level is missed. At the same time, a sufficiently long sampling time can ensure that the collected data is fully representative, reflecting the stable lighting characteristics of the dimming zone under normal conditions, and reducing the impact of momentary interference or accidental factors on the measurement results.

[0062] In another preferred embodiment of the present invention, if the number of pedestrians N in the dimming area is 0, the corresponding detection cycle is recorded as an empty cycle. If the current detection cycle is an empty cycle, subsequent operations are stopped and the output power of the intelligent lighting device is reduced to half of the rated power.

[0063] It should be noted that if there are no pedestrians in the dimming area, in order to reduce energy consumption, the output power will be reduced to half of the rated power. This method can reduce unnecessary resource waste of the equipment.

[0064] In another preferred embodiment of the present invention, the smart lighting device is turned off and the light intensity in the dimming area is detected. If the light intensity is greater than or equal to a preset light intensity threshold, the subsequent steps are stopped and the smart lighting device is turned off.

[0065] It should be noted that turning off smart lighting devices when there is sufficient light will help save electricity resources.

[0066] In another preferred embodiment of the present invention, the records with the light intensity difference Δlx=0 are discarded and do not participate in the calculation of the influence coefficient K.

[0067] It is understandable that eliminating the influence of erroneous parameters on the final adjustment result makes the final adjustment result more accurate.

[0068] In another preferred embodiment of the present invention, the maximum light intensity difference Δlx is calculated. max =lx_s-lx now , if the maximum light intensity difference light intensity adjustment value Δlx max ≥5000: Stop adjusting the part of the intelligent lighting device that exceeds 5000.

[0069] It is worth noting that the purpose is to control the speed of light changes to prevent rapid changes in light from affecting pedestrians. The purpose of control is for safety reasons.

[0070] The device operation optimization system based on edge computing includes:

[0071] Judgment module: Deploy an edge computing module on the smart lighting device side. Based on the edge computing module, the network connection status of the current smart lighting device is detected. If the quality of the network connection of the smart lighting device is unstable, the smart lighting device is marked as a device to be adjusted.

[0072] Record the area covered by the light of the device to be adjusted as the dimming area, and obtain the initial light intensity LX in the dimming area when the device to be adjusted is working at rated power;

[0073] Calculation module: Preset the detection period T, obtain the number of pedestrians N in the dimming area within the detection period T and the pedestrian stay time t in the dimming area, and calculate the pedestrian congestion in the dimming area Among them, t n represents the time the nth pedestrian stays in the dimming area, and S represents the area of ​​the road in the dimming area;

[0074] Adjustment module: obtain the light intensity lx of the dimming area within the detection period T, calculate the light intensity difference Δlx = LX-lx, and calculate the influence coefficient Among them, Δlxi represents the light intensity difference of the i-th detection cycle, Y i represents the pedestrian congestion degree in the dimming area during the i-th detection cycle;

[0075] Get the pedestrian congestion Y in the dimming area during the current detection cycle now and light intensity lx now , calculate the ideal illumination difference Δlx s =K*Y now , calculate the first light intensity lx_f=lx in the dimming area now +Δlx s ;

[0076] Calculate the second correction value M=Y now / Y ave , the first illumination intensity is corrected to the second illumination intensity lx_s=M*lx_f, where Y ave Represents the average pedestrian congestion within the dimming area.

[0077] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A device operation optimization method based on edge computing, characterized in that: The following steps are involved: S1: Deploy an edge computing module on the smart lighting device. Use the edge computing module to detect the network connection status of the current smart lighting device. If the quality of the network connection of the smart lighting device is unstable, mark the smart lighting device as a device to be adjusted. Record the area covered by the light of the device to be adjusted as the dimming area, and obtain the initial light intensity LX in the dimming area when the device to be adjusted is working at rated power; S2: Preset the detection period T, obtain the number of pedestrians N in the dimming area during the detection period T and the pedestrian stay time t in the dimming area, and calculate the pedestrian congestion degree in the dimming area Among them, t n represents the time the nth pedestrian stays in the dimming area, and S represents the area of ​​the road in the dimming area; S3: Obtain the light intensity lx of the dimming area within the detection period T, calculate the light intensity difference Δlx = LX - lx, and calculate the influence coefficient Where Δlx i Represents the light intensity difference of the i-th detection cycle, Y i represents the pedestrian congestion degree in the dimming area during the i-th detection cycle; Get the pedestrian congestion Y in the dimming area during the current detection cycle now and light intensity lx now , calculate the ideal illumination difference Δlx s =K*Y now , calculate the first light intensity lx_f=lx in the dimming area now +Δlx s ; Calculate the second correction value M=Y now / Y ave , the first illumination intensity is corrected to the second illumination intensity lx_s=M*lx_f, where Y ave Represents the average pedestrian congestion within the dimming area.

2. The device operation optimization method based on edge computing according to claim 1 is characterized in that: In step S1, detecting the network connection status of the current smart lighting device based on the edge computing module includes: The edge computing module is instructed to send a preset number of ICMP requests, and the packet loss rate PLR ​​between the edge computing module sending the ICMP request and receiving the ICMP request is obtained. If the packet loss rate PLR ​​is greater than 0.01%, it means that the quality of the network connection status of the current smart lighting device is unstable.

3. The device operation optimization method based on edge computing according to claim 1, characterized in that: In step S1, the method for obtaining the initial light intensity 1x in the dimming area includes: Pre-set the light monitoring point, obtain the light intensity of the light monitoring point and calculate the mean light intensity lx ave , let the initial light intensity lx=lx ave .

4. The device operation optimization method based on edge computing according to claim 1, characterized in that: In step S2, if the number of pedestrians N in the dimming area is 0, the corresponding detection cycle is recorded as an empty cycle. If the current detection cycle is an empty cycle, subsequent operations are stopped and the output power of the smart lighting device is reduced to half of the rated power.

5. The device operation optimization method based on edge computing according to claim 1, characterized in that: In step S1, the smart lighting device is turned off and the light intensity in the dimming area is detected. If the light intensity is greater than or equal to a preset light intensity threshold, the subsequent steps are stopped and the smart lighting device is turned off.

6. The device operation optimization method based on edge computing according to claim 1, characterized in that: In step S3, the records with the light intensity difference Δlx=0 are discarded and do not participate in the calculation of the influence coefficient K.

7. The device operation optimization method based on edge computing according to claim 1, characterized in that: In step S3, the maximum light intensity difference Δlx is calculated. max =lx_s-lx now , if the maximum light intensity difference light intensity adjustment value Δlx max ≥5000, stops adjusting the part of the intelligent lighting device that exceeds 5000.

8. The device operation optimization system based on edge computing is characterized by: include: Judgment module: Deploy an edge computing module on the smart lighting device side. Based on the edge computing module, the network connection status of the current smart lighting device is detected. If the quality of the network connection of the smart lighting device is unstable, the smart lighting device is marked as a device to be adjusted. Record the area covered by the light of the device to be adjusted as the dimming area, and obtain the initial light intensity LX in the dimming area when the device to be adjusted is working at rated power; Calculation module: Preset the detection period T, obtain the number of pedestrians N in the dimming area within the detection period T and the pedestrian stay time t in the dimming area, and calculate the pedestrian congestion in the dimming area Among them, t n represents the time the nth pedestrian stays in the dimming area, and S represents the area of ​​the road in the dimming area; Adjustment module: obtain the light intensity lx of the dimming area within the detection period T, calculate the light intensity difference Δlx = LX-lx, and calculate the influence coefficient Where Δlx i Represents the light intensity difference of the i-th detection cycle, Y i represents the pedestrian congestion degree in the dimming area during the i-th detection cycle; Get the pedestrian congestion Y in the dimming area during the current detection cycle now and light intensity lx now , calculate the ideal illumination difference Δlx s =K*Y now , calculate the first light intensity lx_f=lx in the dimming area now +Δlx s ; Calculate the second correction value M=Y now / Y ave , the first illumination intensity is corrected to the second illumination intensity lx_s=M*lx_f, where Y ave Represents the average pedestrian congestion within the dimming area.

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