Method and system for device operation optimization based on edge computing

Abstract

This invention relates to the field of edge computing technology, specifically disclosing a device operation optimization method and system based on edge computing, comprising the following steps: S1: Deploying an edge computing module to detect the network connection status of smart lighting devices, filtering out devices to be adjusted, determining the dimming area, and obtaining the initial light intensity in the dimming area of ​​the devices to be adjusted; S2: Presetting a detection period, obtaining the number of pedestrians and their dwell time in the dimming area within the detection period, and calculating the pedestrian congestion level in the dimming area; S3: Calculating the light intensity difference and influence coefficient, obtaining the pedestrian congestion level and light intensity, calculating the ideal light difference and the first light intensity, and correcting the first light intensity to a second light intensity using a second correction value. This invention addresses the problem of current smart devices relying too heavily on cloud-based decision-making through an edge computing-based device operation optimization method, thereby improving real-time performance.

Description

Technical Field

[0001] This invention relates to the field of edge computing technology, and more specifically to a method and system for optimizing device operation based on edge computing. Background Technology

[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) closer to the data source or user. By performing computation at the network edge, edge computing reduces data transmission latency, alleviates bandwidth pressure, and improves system real-time performance and reliability. By pushing computing power down to the network edge, edge computing addresses the bottlenecks of cloud computing in terms of latency, bandwidth, and reliability, becoming a key supporting technology for the Internet of Things, artificial intelligence, and 5G era.

[0003] There are some problems with current smart lighting devices. Some smart lighting devices rely on cloud decision centers (such as remote control and data storage), which leads to instruction delays. This phenomenon is more serious when the network is unstable. Moreover, when the network is down, it is impossible to control the smart lighting devices in the area where the network is down, such as being unable to turn the lights on or off or dim them.

[0004] In existing technologies, smart lighting devices rely too heavily on cloud-based decision centers, resulting in low resilience to risks. This leads to poor real-time performance, a degraded user experience, and low reliability in critical scenarios such as emergency lighting. Therefore, to reduce the dependence of smart lighting devices on cloud-based decision-making and ensure that they still perform well when the network is down, an edge computing-based device operation optimization method is needed to address the problem of current smart devices relying too heavily on cloud-based decision-making and improve real-time performance. Summary of the Invention

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

[0006] The objective of this 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. Based on the edge computing module, detect the current network connection status of the smart lighting device. If the quality of the network connection status of the smart lighting device is unstable, mark the smart lighting device as a device to be adjusted.

[0009] The area covered by the light of the device to be adjusted is recorded as the dimming area, and the initial light intensity LX in the dimming area is obtained when the device to be adjusted is working at rated power.

[0010] S2: Set a preset detection period T, obtain the number of pedestrians N in the dimming area and the dwell time t of pedestrians in the dimming area within the detection period T, and calculate the pedestrian congestion level in the dimming area. Among them, t n The nth pedestrian stays in the dimming area for a period of time, and S represents the area of ​​the road in the dimming area.

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

[0012] Obtain the pedestrian congestion level Y within the dimming area during the current detection period. now and light intensity lx now Calculate the ideal illumination difference Δlx s =K*Y now Calculate the first illuminance lx_f = lx within 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 This represents the average level of pedestrian congestion within the dimming area.

[0014] As a further aspect 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. The packet loss rate (PLR) between sending and receiving ICMP requests is obtained. If the packet loss rate (PLR) is greater than 0.01%, it indicates that the network connection quality of the current smart lighting device is unstable.

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

[0017] Pre-set light monitoring points, acquire the light intensity at the light monitoring points, and calculate the average light intensity lx. ave Let the initial light intensity be lx = lx ave .

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

[0019] As a further aspect 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 aspect of the present invention: in step S3, records with light intensity difference Δlx = 0 are removed and not included in the calculation of influence coefficient K.

[0021] As a further aspect 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 is the light intensity adjustment value Δlx max ≥5000 will stop adjusting the portion of the intelligent lighting device that exceeds 5000.

[0022] Edge computing-based device operation optimization system includes:

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

[0024] The area covered by the light of the device to be adjusted is recorded as the dimming area, and the initial light intensity LX in the dimming area is obtained when the device to be adjusted is working at rated power.

[0025] Calculation module: Presets a detection period T, obtains the number of pedestrians N in the dimming area and the dwell time t of pedestrians in the dimming area within the detection period T, and calculates the pedestrian congestion level in the dimming area. Among them, t n The nth pedestrian stays in the dimming area for a period of time, and S represents the area of ​​the road in the dimming area.

[0026] Adjustment module: Obtains the illumination intensity lx of the dimming area within the detection period T, calculates the illumination intensity difference Δlx = LX - lx, and calculates the influence coefficient. Wherein, Δlx i Y represents the difference in light intensity during the i-th detection period. i This represents the pedestrian congestion level in the dimming area during the i-th detection cycle;

[0027] Obtain the pedestrian congestion level Y within the dimming area during the current detection period. now and light intensity lx now Calculate the ideal illumination difference Δlx s =K*Y now Calculate the first illuminance lx_f = lx within 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 This represents the average level of pedestrian congestion within the dimming area.

[0029] The beneficial effects of this invention are as follows: First, by deploying an edge computing module on the smart lighting device, and judging whether the smart lighting device needs to be replaced with an edge computing-based method by judging the current network connection status, 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 level is calculated based on the number of pedestrians and their dwell time. This value is used to reflect the congestion level in the dimming area. Then, the influence coefficient is calculated, which is used to reflect the trend of the light intensity difference with the pedestrian congestion level. Then, the ideal light difference is calculated based on the pedestrian congestion level and light intensity in the dimming area in the current detection period, thus obtaining the first light intensity. Then, the first light intensity is corrected to obtain the 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 needed. In summary, by optimizing the edge computing method for smart lighting devices, the problem of current smart devices relying too much on cloud decision-making is solved and the real-time performance is improved. Attached Figure Description

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

[0031] Figure 1 This is a schematic diagram of the device operation optimization method and system based on edge computing of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see 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. Based on the edge computing module, detect the current network connection status of the smart lighting device. If the quality of the network connection status of the smart lighting device is unstable, mark the smart lighting device as a device to be adjusted.

[0035] The area covered by the light of the device to be adjusted is recorded as the dimming area, and the initial light intensity LX in the dimming area is obtained when the device to be adjusted is working at rated power.

[0036] S2: Set a preset detection period T, obtain the number of pedestrians N in the dimming area and the dwell time t of pedestrians in the dimming area within the detection period T, and calculate the pedestrian congestion level in the dimming area. Among them, t n The nth pedestrian stays in the dimming area for a period of time, and S represents the area of ​​the road in the dimming area.

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

[0038] Obtain the pedestrian congestion level Y within the dimming area during the current detection period. now and light intensity lx now Calculate the ideal illumination difference Δlx s =K*Y now Calculate the first illuminance lx_f = lx within 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 This represents the average level of pedestrian congestion within the dimming area.

[0040] It should be noted that this invention is specifically designed for complex outdoor environments such as densely populated public areas like city parks and squares, and its technical solution is not applicable to indoor scenarios. In typical outdoor application scenarios, firstly, open spaces like parks have high and mobile pedestrian traffic, potentially generating dynamic lighting demands from hundreds of people per second during peak periods; secondly, equipment exposed to the outdoor environment for extended periods faces factors that accelerate aging, such as high temperatures, humidity, and salt spray corrosion, leading to a higher hardware failure rate compared to indoor equipment; furthermore, severe weather conditions such as heavy rain, blizzards, and strong winds can cause a surge in network base station load or damage to communication lines. Actual measurement data shows that under extreme weather conditions, wireless signal strength fluctuations can reach 40dB, and packet loss rates exceed 30%.

[0041] Against this backdrop, traditional smart lighting systems that rely on centralized cloud-based decision-making have significant drawbacks: when network quality drops to a latency of over 200ms, the response time of lighting adjustment commands will exceed the human perception threshold, resulting in a visual flickering sensation. However, with an edge computing architecture, the device can autonomously complete over 90% of real-time control tasks. When network quality is detected to be below a preset threshold, the edge computing mode is immediately switched.

[0042] In the process of controlling an intelligent lighting system, it is necessary to accurately monitor and analyze the specific area covered by the light from the device to be adjusted, and designate this specific area as the dimming zone. To achieve reasonable adjustment of light intensity, it is first necessary to obtain the initial light intensity in the dimming zone when the device is operating at its rated power. Obtaining this initial light intensity value is crucial; it serves as the basic reference value for subsequent light intensity adjustments, providing an accurate starting point for the entire adjustment process.

[0043] In practical applications, a reasonable detection cycle needs to be preset to adjust light intensity more scientifically and effectively. Within this preset detection cycle, various sensors and other monitoring devices installed in the dimming area comprehensively and accurately acquire information on the number of pedestrians in the dimming area. Simultaneously, the dwell time of pedestrians in the dimming area must be accurately recorded. These two key data points 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 level in that area. This is because a large number of pedestrians gathered in one area not only affects the propagation and distribution of light due to the blocking and reflection of light by their own bodies, but also the activity status and positional changes of different pedestrians further alter the lighting environment within the dimming area. For example, when a large number of pedestrians move frequently or stay in the dimming area for extended periods, the scattering and absorption of light in different directions becomes more complex, leading to a deviation between the actual lighting effect and the theoretical lighting effect under the rated power of the equipment.

[0045] Conversely, when the number of pedestrians is small and their dwell time is short, their impact on the light intensity of the dimming area is relatively small. Therefore, pedestrian congestion, as an important indicator that can directly reflect changes in the actual light demand of the dimming area, is a key factor in adjusting the light intensity of intelligent lighting equipment. Through real-time monitoring and analysis of pedestrian congestion, intelligent lighting systems can dynamically adjust the light intensity according to the actual situation, achieving efficient energy utilization while meeting the lighting needs of pedestrians.

[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 of the dimming area within the detection period. This actual light intensity data is obtained in real time by collecting and aggregating multiple high-precision light sensors distributed throughout the dimming area. These sensors can accurately sense changes in light intensity at different locations, thus providing comprehensive and accurate data support for subsequent analysis.

[0047] After obtaining the initial and actual light intensity during the detection period, the difference between these two values, i.e., the light intensity difference, is calculated using a specific mathematical method. This difference directly reflects the degree of change in the actual light intensity of the dimming area compared to the initial state during the detection period, due to the combined effects of various factors.

[0048] To delve deeper into the intrinsic relationship between pedestrian congestion and the difference in light intensity, it is necessary to introduce the key indicator of influence coefficient for quantitative analysis. The influence coefficient is calculated based on a large sample of actual data, derived through complex mathematical models and statistical methods. It accurately reflects how changes in pedestrian congestion affect the difference in light intensity, revealing the objective laws governing the relationship between the two.

[0049] By analyzing the impact coefficients in depth, some phenomena and patterns with practical guiding significance can be clearly observed. First, when pedestrian congestion shows an upward trend, due to the increased number and relatively dense distribution of pedestrians within the dimming area, mutual occlusion between shadows is inevitable. This occlusion effect significantly impacts ground visibility, causing uneven light distribution on the ground. Areas that were originally clearly visible may become shadowed or poorly lit, thus inconveniencing pedestrians and potentially increasing safety hazards.

[0050] From a more general, objective perspective, a larger number of pedestrians means a higher level of pedestrian congestion within the dimming area. In this situation, light is subject to more obstruction and scattering during propagation, resulting in less light capable of contributing to effective illumination and ultimately lower overall light intensity in the dimming area. For example, in a crowded shopping mall corridor or subway platform, even if the lighting equipment operates at its rated power, the actual perceived light intensity will be significantly lower than when there are fewer pedestrians due to factors such as shadows.

[0051] Taking into account this complex relationship and the potential fluctuations in data under different circumstances, a method of calculating influence coefficients and using their average values ​​as a substitute is adopted to make the analysis results more accurate, reliable, universal, and representative. By calculating and averaging the influence coefficients over multiple detection periods, interference from individual abnormal data or special cases can be effectively eliminated, thus obtaining a stable value that reflects general patterns. This approach provides a more scientific and accurate basis for formulating adjustment strategies for intelligent lighting systems, ensuring that the lighting system can make reasonable adjustments according to actual conditions, achieving both pedestrian lighting needs and energy-efficient utilization.

[0052] After adjusting the light intensity, a second correction value needs to 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 actual environmental changes within the dimming area, through comprehensive monitoring and analysis of multiple relevant parameters. These parameters include not only indicators reflecting pedestrian congestion such as the number of pedestrians and dwell time, but may also involve factors such as changes in natural lighting within the dimming area and fluctuations in the equipment's own performance.

[0053] The second correction value further fine-tunes the light intensity to achieve more precise lighting control. It meticulously corrects the light intensity previously adjusted based on the influence coefficient, according to real-time environmental changes and equipment operating status. For example, if, after adjusting the light intensity, a sudden increase or decrease in ambient light results in an unsatisfactory lighting effect within the dimming area, the second correction value comes into play. The system will then fine-tune the output intensity of the intelligent lighting equipment again based on the magnitude and direction of the second correction value, enabling it to better adapt to complex and changing environmental conditions and consistently maintain optimal light intensity within the dimming area.

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

[0055] In another preferred embodiment of the present invention, detecting the network connectivity status of the current 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. The packet loss rate (PLR) between sending and receiving ICMP requests is obtained. If the packet loss rate (PLR) is greater than 0.01%, it indicates that the network connection quality of the current smart lighting device is unstable.

[0057] It's worth noting that packet loss rate, as a crucial indicator of network transmission quality, directly reflects the extent of data loss during network transmission. In intelligent lighting systems, various control commands, status information, and sensor data all need to be transmitted in real-time via the network. When this data is transmitted from the sender to the receiver, due to various potential factors such as network congestion, signal interference, and equipment failure, some data packets may be lost. The packet loss rate is calculated by statistically analyzing the total number of data packets transmitted and the number of successfully received data packets within a certain period, clearly displaying the degree of data loss as a percentage.

[0058] Therefore, by regularly calculating the packet loss rate and monitoring and analyzing it in real time, we can understand the network connection status of smart lighting devices in a timely manner. Once the packet loss rate is found to be outside the normal range, we can quickly take corresponding measures to investigate and repair it, such as optimizing network configuration, increasing network bandwidth, and checking network equipment, so as to ensure the stability of network connection and guarantee the reliable operation of smart lighting system.

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

[0060] Pre-set light monitoring points, acquire the light intensity at the light monitoring points, and calculate the average light intensity lx. ave Let the initial light intensity be lx = lx ave .

[0061] Understandably, the method detects the initial illuminance in the dimming area, offering significant representativeness and providing a solid and reliable data foundation for subsequent lighting control. This effectively avoids overall data errors caused by a single specific value, ensuring the accuracy and stability of the entire intelligent lighting system. High-frequency sampling captures minute changes in illuminance over short periods, ensuring no critical information that could affect the overall illuminance level is missed. Simultaneously, a sufficiently long sampling duration guarantees the data's representativeness, reflecting the stable illuminance characteristics of the dimming area 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 period is recorded as an empty period. If the current detection period is an empty period, the subsequent operation is 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, the output power should be reduced to half of the rated power in order to reduce energy consumption. This method can reduce unnecessary waste of equipment resources.

[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 helps to save electricity.

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

[0067] It is understandable that eliminating the influence of erroneous parameters on the final adjustment result will make 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 is the light intensity adjustment value Δlx max ≥5000 will stop adjusting the portion 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 excessively rapid changes in lighting from affecting pedestrians; the control is for safety reasons.

[0070] Edge computing-based device operation optimization system includes:

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

[0072] The area covered by the light of the device to be adjusted is recorded as the dimming area, and the initial light intensity LX in the dimming area is obtained when the device to be adjusted is working at rated power.

[0073] Calculation module: Presets a detection period T, obtains the number of pedestrians N in the dimming area and the dwell time t of pedestrians in the dimming area within the detection period T, and calculates the pedestrian congestion level in the dimming area. Among them, t n The nth pedestrian stays in the dimming area for a period of time, and S represents the area of ​​the road in the dimming area.

[0074] Adjustment module: Obtains the illumination intensity lx of the dimming area within the detection period T, calculates the illumination intensity difference Δlx = LX - lx, and calculates the influence coefficient. Where Δlxi represents the light intensity difference in the i-th detection period, Y i This represents the pedestrian congestion level in the dimming area during the i-th detection cycle;

[0075] Obtain the pedestrian congestion level Y within the dimming area during the current detection period. now and light intensity lx now Calculate the ideal illumination difference Δlx s =K*Y now Calculate the first illuminance lx_f = lx within 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 This represents the average level of pedestrian congestion within the dimming area.

[0077] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

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

2. The device operation optimization method based on edge computing according to claim 1, characterized in that, In step S1, detecting the network connectivity 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. The packet loss rate (PLR) between sending and receiving ICMP requests is obtained. If the packet loss rate (PLR) is greater than 0.01%, it indicates that the network connection quality 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 illumination intensity LX in the dimming area includes: Pre-set light monitoring points, acquire the light intensity at the light monitoring points, and calculate the average 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 period is recorded as an empty period. If the current detection period is an empty period, the subsequent operation is stopped and the output power of the intelligent 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 the 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, records with a light intensity difference Δlx=0 are removed and are not included 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 is the light intensity adjustment value Δlx max ≥5000 will stop adjusting the portion of the intelligent lighting device that exceeds 5000.

8. A device operation optimization system based on edge computing, characterized in that, include: Judgment Module: Deploy an edge computing module on the smart lighting device. Based on the edge computing module, detect the current network connection status of the smart lighting device. If the quality of the network connection status of the smart lighting device is unstable, mark the smart lighting device as a device to be adjusted. The area covered by the light of the device to be adjusted is recorded as the dimming area, and the initial light intensity LX in the dimming area is obtained when the device to be adjusted is working at rated power. Calculation module: Presets a detection period T, obtains the number of pedestrians N in the dimming area and the dwell time t of pedestrians in the dimming area within the detection period T, and calculates the pedestrian congestion level in the dimming area. , where t n The nth pedestrian stays in the dimming area for a period of time, and S represents the area of ​​the road in the dimming area. Adjustment module: Obtains the illumination intensity lx of the dimming area within the detection period T, calculates the illumination intensity difference Δlx=LX-lx, and calculates the influence coefficient. , where Δlx i Y represents the difference in light intensity during the i-th detection period. i This represents the pedestrian congestion level in the dimming area during the i-th detection cycle; Obtain the pedestrian congestion level Y within the dimming area during the current detection period. now and light intensity lx now Calculate the ideal illumination difference Calculate the first illuminance lx_f=lx within the dimming area. now +Δlx s ; Calculate the second correction value M=Y now / Y ave The first light intensity is corrected to the second light intensity. , where Y ave This represents the average level of pedestrian congestion within the dimming area.

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