Tunnel lighting operation and maintenance management system based on the Internet of Things

Abstract

This invention discloses an Internet of Things (IoT)-based tunnel lighting operation and maintenance management system, involving IoT technology and intelligent operation and maintenance management technologies. It addresses the issues of limited data collection accuracy and real-time response capabilities, as well as the reliance on preset rules for fault diagnosis and a lack of intelligent predictive capabilities. Through four modules: regional division, data collection, and status assessment and control execution, it achieves refined perception and dynamic control of operating status. A dual-factor mechanism, based on light sensitivity and equipment health, quantifies regional differences and improves scheduling accuracy. A variance coefficient model is constructed to guide lighting and equipment control priorities, improving resource allocation efficiency. Based on closed-loop feedback control, the system ensures lighting safety, energy conservation, and efficient operation and maintenance. It possesses the advantages of intelligence, strong adaptability, and good scalability, making it suitable for a variety of tunnel lighting management scenarios.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things technology and intelligent operation and maintenance management technology, and more specifically, to a tunnel lighting operation and maintenance management system based on the Internet of Things. Background Art

[0002] Intelligent control technology uses advanced technologies and equipment to automatically or semi-automatically adjust system parameters for efficient, precise, and flexible control. When applied to tunnel lighting operations and maintenance, intelligent control technology can improve system efficiency and ease of maintenance while ensuring lighting quality.

[0003] The existing technology has the following deficiencies:

[0004] Currently, tunnel lighting systems are sensitive to changes in light intensity and require comprehensive consideration of multi-dimensional data, such as lamp lifespan and energy optimization. Lighting requirements also vary under different environmental conditions. Due to the significant dynamic variations in lighting conditions within tunnel lighting scenarios, existing systems are limited in data acquisition accuracy and real-time response capabilities. Fault diagnosis often relies on pre-set rules and lacks intelligent predictive capabilities. Furthermore, existing technologies are insufficiently capable of fusing and processing heterogeneous data from multiple sources, potentially impacting overall system performance and user experience. Therefore, a tunnel lighting operation and maintenance management system based on the Internet of Things (IoT) is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] To overcome the above-mentioned shortcomings of the prior art, embodiments of the present invention provide an IoT-based tunnel lighting operation and maintenance management system. By dividing tunnel areas and dynamically adjusting lighting conditions, combined with multi-source data collection and analysis, a comprehensive assessment of the light intensity, lamp performance, and environmental factors in each divided area is performed to obtain the overall operating status of the tunnel lighting system. The system also calculates the lighting balance value to reduce the differences in lighting requirements in different areas and environmental conditions, thereby solving the problems raised in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a tunnel lighting operation and maintenance management system based on the Internet of Things, comprising a region division module, a data acquisition module, a status assessment module, and a control execution module;

[0008] The area division module is used to divide the tunnel into several sub-areas according to length and function, mark and distinguish each sub-area according to the changes in dynamic lighting conditions, and transmit each divided area and the corresponding lighting conditions to the data acquisition module;

[0009] The data acquisition module is used to obtain the standard light intensity range, lamp life parameters and energy consumption optimization targets for each sub-area of ​​the tunnel, calculate the actual light output ratio of the lamps in each divided area, and detect the light decay rate and failure rate of the lamps in each divided area;

[0010] The status assessment module calculates the light sensitivity of each tunnel sub-area based on the standard light intensity range, the actual light output ratio of the lamps in each sub-area, and the light decay rate. It also calculates the equipment health of each sub-area based on the lamp life parameters, the actual light output ratio of the lamps in each sub-area, and the failure rate. The overall operating status of the tunnel lighting system is analyzed based on the light sensitivity and equipment health of each sub-area.

[0011] The control execution module uses the light attenuation rate and fault occurrence rate of each divided area to calculate the light difference coefficient and the equipment health difference coefficient for comparison. Based on the comparison results, the control sequence of light or equipment is selected, and the light intensity or equipment status of the tunnel lighting system is adjusted based on the light sensitivity or equipment health of the marked area and the overall operating status.

[0012] In a preferred embodiment, the data acquisition module accesses a public database to obtain the standard light intensity intervals and lamp life reference values ​​for each sub-area of ​​the tunnel, and obtains a full view of the tunnel lighting area through a distributed sensor network;

[0013] Compare the features of different types of lamps in the lamp image library, identify the lamp type in each divided area, and obtain the actual light output ratio of the lamps in the divided area;

[0014] Mark and distinguish the positions of lamps in each divided area.

[0015] In a preferred embodiment, when the data acquisition module obtains the light decay rate of the lamps in each divided area, it selects two time points as light decay detection points, detects the light intensity of the lamps at different marked positions in the divided area at the two light decay detection points, and calculates the light intensity difference between the two light decay detection points at the same marked position;

[0016] The light intensity differences at the same marked positions in different divided areas are merged into a light intensity data set and the average value is calculated as the light attenuation judgment value of the corresponding mark;

[0017] The ratio of the light intensity of the lamp at the marked position in each divided area to the light decay judgment value of the corresponding mark is used as the light decay speed of the lamp at the corresponding mark in the divided area;

[0018] When the data acquisition module obtains the failure rate of lamps in each divided area, it randomly selects a time point as the fault detection time point;

[0019] At the fault detection time point, the number of faulty lamps and the total number of lamps in different marked positions in each divided area are detected;

[0020] The ratio of the number of faulty lamps to the total number of lamps at the same marked position in each divided area is taken as the failure rate of the lamps at the corresponding mark.

[0021] In a preferred embodiment, the status assessment module performs weighted summation on the actual light output ratio of the lamps in the divided area and the standard light intensity range of the corresponding lamps to obtain the regional lighting merit and regional equipment health merit of the corresponding divided area;

[0022] The actual light output ratio of the lamps in the divided area and the light decay speed or failure rate of the corresponding lamps are weighted and averaged to obtain the regional light decay mean or regional failure mean of the corresponding divided area;

[0023] The ratio of the regional illumination merit value of the divided area to the average regional illumination quality value of all divided areas is used as the illumination merit value ratio of the corresponding divided area, and the ratio of the regional equipment health merit value of the divided area to the average regional equipment health quality value of all divided areas is used as the equipment health merit value ratio of the corresponding divided area;

[0024] The ratio of the regional light attenuation mean of the divided area to the maximum regional light attenuation mean of all divided areas is taken as the regional light attenuation rate ratio of the corresponding divided area, and the ratio of the regional fault mean of the divided area to the maximum regional fault mean of all divided areas is taken as the regional fault rate ratio of the corresponding divided area.

[0025] In a preferred embodiment, the illumination sensitivity of each divided area is analyzed and quantified using the illumination figure of merit of each divided area and the regional light decay rate ratio. The specific steps are as follows:

[0026] The absolute value of the difference between the illumination merit ratio of the divided area and 1 is taken as the illumination merit difference. The illumination merit difference of the divided area and the regional light attenuation rate ratio are used to analyze the illumination sensitivity using the logistic regression method.

[0027] Similarly, the equipment health merit ratio and regional failure rate ratio of each divided area are used to analyze and quantify the equipment health coefficient of each divided area;

[0028] Among them, the linear combination of input features is replaced by the sum of the equipment health merit difference and the regional failure rate ratio of the corresponding divided area.

[0029] In a preferred embodiment, the average light sensitivity coefficients of all divided areas are calculated as the average light sensitivity of the tunnel lighting system;

[0030] The average equipment health coefficient of all divided areas is calculated as the average equipment health of the tunnel lighting system;

[0031] The average light sensitivity of the tunnel lighting system and the average equipment health of the tunnel lighting system are summed to obtain the light balance value of the current tunnel lighting system.

[0032] In a preferred embodiment, before determining the light intensity or equipment status of the tunnel lighting system, the control execution module selects the divided area with the smallest regional light attenuation mean and the divided area with the lowest regional fault mean from all divided areas as candidate area A and candidate area B, respectively.

[0033] In a preferred embodiment, the illumination difference coefficient of the tunnel lighting system is obtained by subtracting the regional light attenuation mean value in the candidate area A from the median of the regional light attenuation mean values ​​of all divided areas;

[0034] The equipment health difference coefficient of the tunnel lighting system is obtained by subtracting the regional fault mean in the candidate area B from the median of the regional fault mean of all divided areas;

[0035] When the illumination variation coefficient of the tunnel lighting system is less than the equipment health variation coefficient, the illumination intensity of the tunnel lighting system is regulated first, and then the equipment status of the tunnel lighting system is regulated;

[0036] When the illumination variation coefficient of the tunnel lighting system is greater than the equipment health variation coefficient, the equipment status of the tunnel lighting system is regulated first, and then the illumination intensity of the tunnel lighting system is regulated.

[0037] In a preferred embodiment, when the illumination variation coefficient of the tunnel lighting system is less than the equipment health variation coefficient, the regional illumination figure of merit of the candidate area A is used as the illumination intensity of the tunnel lighting system, and the sensitivity difference is obtained by subtracting the regional illumination sensitivity of the candidate area A from the regional average illumination sensitivity;

[0038] The device health difference is calculated by combining the preset empirical conversion constant with the conversion formula: Device health difference = empirical conversion constant × sensitivity difference × light balance value;

[0039] Compare the light intensity of the tunnel lighting system with the preset light threshold. When the light intensity of the tunnel lighting system exceeds the light threshold, the equipment health value of the candidate area B plus the equipment health difference is used as the equipment health of the tunnel lighting system.

[0040] When the illumination intensity of the tunnel lighting system is lower than the illumination threshold, the regional equipment health merit of the alternative area B is used as the equipment health of the tunnel lighting system.

[0041] In a preferred embodiment, when the illumination variation coefficient of the tunnel lighting system is greater than the equipment health variation coefficient, the regional equipment health merit of candidate region B is used as the equipment health of the tunnel lighting system, and the sensitivity difference is obtained by subtracting the regional equipment health of candidate region B from the regional average equipment health.

[0042] The light difference is calculated by the conversion formula based on the preset empirical conversion constant. The calculation formula is: light difference = empirical conversion constant × sensitivity difference × light balance value;

[0043] Compare the health of the tunnel lighting system equipment with the preset equipment health threshold. When the health of the tunnel lighting system equipment exceeds the equipment health threshold, use the regional illumination merit of the candidate area A as the illumination intensity of the tunnel lighting system.

[0044] When the equipment health of the tunnel lighting system is lower than the equipment health threshold, the illumination intensity obtained by subtracting the illumination difference from the regional illumination merit of the candidate area A is used as the illumination intensity of the tunnel lighting system.

[0045] Technical effects and advantages of the present invention:

[0046] The present invention constructs a full-life cycle tunnel lighting operation and maintenance management system based on the Internet of Things by introducing four major modules: area division, data collection, status assessment, and control execution. This achieves refined perception and dynamic control of the operating status of the tunnel lighting system. The dual-factor evaluation mechanism of light sensitivity and equipment health is used to quantify the critical impact of different regions on light attenuation and equipment failure, enhancing the pertinence and accuracy of system operation and maintenance scheduling. At the same time, by constructing a comparative model of the light difference coefficient and the equipment health difference coefficient, it effectively guides the priority of light adjustment and equipment maintenance, and improves the allocation efficiency of control resources. During operation, the system performs closed-loop feedback control based on dynamic lighting conditions and equipment status, ensuring that tunnel lighting achieves the operation and maintenance goals of optimal energy consumption and minimal maintenance while ensuring safe passage. The overall technical solution has the significant advantages of high intelligence, high adaptability, and high scalability, and is widely applicable to the intelligent lighting management needs of multiple scenarios and types of tunnels. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the module structure of the tunnel lighting operation and maintenance management system based on the Internet of Things of the present invention.

[0048] Figure 2 This is a working logic flow chart of the control execution module in the tunnel lighting operation and maintenance management system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0049] 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 creative efforts are within the scope of protection of the present invention. Example

[0050] The present invention provides a tunnel lighting operation and maintenance management system based on the Internet of Things, and its specific implementation methods are as follows.

[0051] Combined with attachment Figure 1 and attached Figure 2 The system as a whole includes area division module, data acquisition module, status evaluation module and control execution module. The comprehensive management of the tunnel lighting system is achieved through information transmission and logical operations between the modules.

[0052] First, the area division module divides the tunnel into several sub-areas according to length and function, and marks and distinguishes each sub-area according to changes in dynamic lighting conditions.

[0053] For example, in a practical application scenario, assuming that a tunnel is 500 meters long, it is divided into three sub-areas based on the functional differences of the tunnel entrance, middle and exit, namely the entrance section, transition section and exit section.

[0054] The boundaries of each sub-area are determined by the locations of sensor nodes installed in the tunnel. These nodes are connected to the area division module through a wireless communication protocol, transmitting data on dynamic lighting conditions in real time.

[0055] The area division module transmits the divided sub-areas and their corresponding lighting condition information to the data acquisition module for subsequent processing.

[0056] The data acquisition module is responsible for obtaining the standard light intensity range, lamp life parameters and energy consumption optimization targets of each sub-area of ​​the tunnel.

[0057] As attached Figure 1 As shown, the data acquisition module obtains the standard light intensity range of each sub-area of ​​the tunnel and the reference value of the lamp life by accessing the public database.

[0058] At the same time, the data acquisition module uses a distributed sensor network to obtain a full view of the tunnel lighting area, and uses image recognition technology to compare the characteristics of different types of lamps in the lamp image library, so as to identify the type of lamps in each divided area and obtain the actual light output ratio of the lamps in the divided area.

[0059] In addition, the data acquisition module also marks and distinguishes the positions of the lamps in each divided area.

[0060] For example, at the marked positions A1 and A2 of the entrance section, the data acquisition module detects that the actual light output of the lamp is 80% and 75% respectively, and records this data for subsequent analysis.

[0061] Furthermore, the data acquisition module selects two time points as light decay detection points when acquiring the light decay speed of the lamps in each divided area.

[0062] For example, at the marked position A1 of the entrance section, the first light attenuation detection point is 10 am, and the second light attenuation detection point is 4 pm.

[0063] At these two time points, the data acquisition module detects the light intensity of the lamp in the marked position A1 respectively, and calculates the difference between the two detection results.

[0064] Assuming that the light intensity detected at 10 a.m. is 800 lumens and the light intensity detected at 4 p.m. is 750 lumens, the light attenuation judgment value at the marked position is 50 lumens.

[0065] Subsequently, the data acquisition module combines the light attenuation judgment values ​​of all marked positions into a light intensity data set and calculates the average value as the light attenuation judgment value of the corresponding mark.

[0066] On this basis, the data acquisition module uses the ratio of the light intensity of the lamp at the marked position in each divided area to the light decay judgment value of the corresponding mark as the light decay speed of the lamp under the corresponding mark in the divided area.

[0067] Similarly, when the data acquisition module obtains the failure rate of the lamps in each divided area, it randomly selects a time point as the fault detection time point.

[0068] For example, at the marked position B1 of the transition section, the fault detection time point is 12 noon. The data acquisition module detects that the number of faulty lamps in the marked position B1 is 2, and the total number is 20. Then the failure rate of the lamps at this marked position is 10%.

[0069] The status assessment module calculates the light sensitivity of the corresponding divided area through the standard light intensity range of each sub-area of ​​the tunnel, the actual light output ratio of the lamps in each divided area, and the light decay rate. It also calculates the equipment health of the corresponding divided area through the lamp life parameters of each sub-area of ​​the tunnel, the actual light output ratio of the lamps in each divided area, and the failure rate.

[0070] Specifically, the status assessment module performs weighted summation on the actual light output ratio of the lamps in the divided area and the standard light intensity range of the corresponding lamps to obtain the regional lighting merit and regional equipment health merit of the corresponding divided area.

[0071] For example, in the entrance section, assuming that the actual light output of marked positions A1 and A2 accounts for 80% and 75% respectively, and the standard light intensity ranges of the corresponding lamps are 90% and 85% respectively, the regional lighting figure of merit of the entrance section is (80%×90%+75%×85%) / 2=79.625%.

[0072] At the same time, the status assessment module takes the weighted average of the actual light output ratio of the lamps in the divided area and the light decay speed or failure rate of the corresponding lamps to obtain the regional light decay mean or regional failure mean of the corresponding divided area.

[0073] For example, at the entrance section, assuming that the light attenuation rates at marked positions A1 and A2 are 6% and 5% respectively, the regional light attenuation average of the entrance section is (6%+5%) / 2=5.5%.

[0074] The status assessment module also takes the ratio of the regional lighting merit of the divided area to the average regional lighting quality of all divided areas as the lighting merit ratio of the corresponding divided area, and takes the ratio of the regional equipment health merit of the divided area to the average regional equipment health quality of all divided areas as the equipment health merit ratio of the corresponding divided area.

[0075] For example, assuming that the average regional illumination quality ratio of all divided areas is 80%, the illumination quality ratio of the entrance section is 79.625% / 80%=0.995.

[0076] Similarly, the status assessment module uses the ratio of the regional optical attenuation mean of the divided area to the maximum regional optical attenuation mean of all divided areas as the regional optical attenuation rate ratio of the corresponding divided area, and uses the ratio of the regional fault mean of the divided area to the maximum regional fault mean of all divided areas as the regional fault rate ratio of the corresponding divided area.

[0077] The state assessment module further uses the illumination figure of merit ratio and regional light attenuation rate ratio of each divided area to analyze and quantify the illumination sensitivity of each divided area.

[0078] The specific steps are as follows: subtract the illumination merit ratio of the divided area from 1 and take the absolute value as the illumination merit difference. For example, the illumination merit difference of the entrance section is |0.995-1|=0.005.

[0079] The smaller the illumination merit difference is, the closer the illumination merit ratio of the divided area is to 1.

[0080] Subsequently, the state assessment module uses the logistic regression method to analyze the light sensitivity of the divided area based on the light merit difference and the regional light decay rate ratio.

[0081] Assuming that the regional light decay rate ratio of the entrance section is 0.9, the linear combination z of the input features is 0.005-0.9=-0.895. Substituting it into the formula C=1 / (1+e^(-z)), the light sensitivity coefficient C of the entrance section is calculated to be approximately 0.29.

[0082] Similarly, the status assessment module uses the equipment health merit ratio and regional failure rate ratio of each divided area to analyze and quantify the equipment health of each divided area to obtain the equipment health coefficient of the divided area.

[0083] For example, assuming that the equipment health merit ratio of the inlet section is 0.98 and the regional failure rate ratio is 0.8, the linear combination of the input features z is 0.98+0.8=1.78. Substituting it into the formula C=1 / (1+e^(-z)), the equipment health coefficient C of the inlet section is calculated to be approximately 0.85.

[0084] Before determining the light intensity or equipment status of the tunnel lighting system, the control execution module selects the divided area with the smallest regional light attenuation mean and the divided area with the lowest regional fault mean from all divided areas as candidate area A and candidate area B, respectively.

[0085] For example, in the above scenario, assuming that the average regional optical attenuation value of the transition section is 4%, the average regional fault value is 8%, and the corresponding values ​​of other divided areas are higher than this value, the transition section is selected as candidate area A and candidate area B.

[0086] The control execution module obtains the illumination difference coefficient of the tunnel lighting system by subtracting the regional light attenuation mean value in the candidate area A from the median of the regional light attenuation mean values ​​of all divided areas.

[0087] For example, assuming that the median of the average light attenuation of all divided areas is 5%, the illumination variation coefficient of the tunnel lighting system is 4%-5%=-1%.

[0088] Similarly, the control execution module obtains the equipment health difference coefficient of the tunnel lighting system by subtracting the regional fault mean in the candidate area B from the median of the regional fault mean of all divided areas.

[0089] For example, assuming that the median mean of regional failures for all divided areas is 10%, the equipment health variance coefficient of the tunnel lighting system is 8%-10%=-2%.

[0090] When the illumination difference coefficient of the tunnel lighting system is less than the equipment health difference coefficient, the control execution module first controls the illumination intensity of the tunnel lighting system and then controls the equipment status of the tunnel lighting system.

[0091] For example, in the above scenario, since the illumination variance coefficient is -1% and the equipment health variance coefficient is -2%, the illumination intensity is adjusted first.

[0092] The control execution module uses the regional illumination merit of the candidate area A as the illumination intensity of the tunnel lighting system, and obtains the sensitivity difference by subtracting the regional illumination sensitivity in the candidate area A from the regional average illumination sensitivity.

[0093] For example, assuming that the regional illumination merit of device selection area A is 80%, the regional illumination sensitivity is 0.29, and the regional average illumination sensitivity is 0.3, the sensitivity difference is 0.29-0.3=-0.01.

[0094] The device health difference is calculated using the conversion formula combined with the preset empirical conversion constant. The calculation formula is: device health difference = empirical conversion constant × sensitivity difference × light balance value.

[0095] For example, assuming the empirical conversion constant is 2 and the light balance value is 1.5, the device health difference is 2×(-0.01)×1.5=-0.03.

[0096] The control execution module compares the tunnel lighting system's light intensity with a preset light threshold. When the tunnel lighting system's light intensity exceeds the light threshold, the device health value obtained by adding the device health difference to the regional device health merit of candidate area B is used as the tunnel lighting system's device health value.

[0097] When the illumination intensity of the tunnel lighting system is lower than the illumination threshold, the regional equipment health merit of the alternative area B is used as the equipment health of the tunnel lighting system.

[0098] For example, assuming the illumination threshold is 75%, the tunnel lighting system illumination intensity exceeds the illumination threshold 80%, the regional equipment health merit of alternative area B is 0.85, and the equipment health difference is -0.03. Then the tunnel lighting system equipment health is 0.85 + (-0.03) = 0.82.

[0099] When the illumination difference coefficient of the tunnel lighting system is greater than the equipment health difference coefficient, the control execution module first controls the equipment status of the tunnel lighting system and then controls the illumination intensity of the tunnel lighting system.

[0100] For example, assuming that the illumination variance coefficient in another scene is -2% and the device health variance coefficient is -1%, the device status is regulated first.

[0101] The control execution module uses the regional equipment health merit of the alternative area B as the equipment health of the tunnel lighting system, and subtracts the regional equipment health in the alternative area B from the regional average equipment health to obtain the sensitivity difference.

[0102] For example, if the regional device health merit of the device selected in area B is 0.85, the regional device health is 0.82, and the regional average device health is 0.8, then the sensitivity difference is 0.82-0.8=0.02.

[0103] The light difference is calculated by the conversion formula based on the preset empirical conversion constant, and the calculation formula is: light difference = empirical conversion constant × sensitivity difference × light balance value.

[0104] For example, assuming the empirical conversion constant is 2 and the light balance value is 1.5, the light difference is 2×0.02×1.5=0.06.

[0105] The control execution module compares the health of the tunnel lighting system equipment with the preset equipment health threshold. When the health of the tunnel lighting system equipment exceeds the equipment health threshold, the regional illumination merit of the candidate area A is used as the tunnel lighting system illumination intensity.

[0106] When the equipment health of the tunnel lighting system is lower than the equipment health threshold, the illumination intensity obtained by subtracting the illumination difference from the regional illumination merit of the candidate area A is used as the illumination intensity of the tunnel lighting system.

[0107] For example, assuming the device health threshold is 0.8, the tunnel lighting system device health of 0.82 exceeds the device health threshold, and the regional illumination merit of alternative area A is 80%, so the tunnel lighting system illumination intensity is 80%.

[0108] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is further supplemented below with reference to a specific application scenario.

[0109] In the actual operation of a tunnel lighting operation and maintenance management system, the tunnel must first be divided into zones. For example, a 500-meter-long tunnel can be divided into three sub-zones: the entrance, transition, and exit sections, based on their functions and lighting requirements.

[0110] The area division module collects the dynamic lighting conditions of each sub-area in real time through the sensor nodes installed in the tunnel, and transmits this data to the data acquisition module.

[0111] The position of the sensor node is determined by the wireless communication protocol to ensure that the boundary of each sub-area is clear and can reflect the actual light changes.

[0112] Subsequently, the region division module passes the division results and the corresponding lighting condition information to the data acquisition module for subsequent processing.

[0113] After receiving the area division information, the data acquisition module begins to obtain the standard light intensity range, lamp life parameters and energy consumption optimization targets of each sub-area.

[0114] For example, at the marked positions A1 and A2 of the entrance section, the data acquisition module detects through the distributed sensor network that the actual light output of the lamp is 80% and 75% respectively.

[0115] At the same time, the data acquisition module extracts the standard light intensity range and lamp life reference values ​​of these locations from the public database, and uses image recognition technology to compare the features in the lamp image library to identify the lamp type and its performance parameters.

[0116] In addition, the data acquisition module also selected two time points as light attenuation detection points, and tested the light intensity of the lamp at the entrance section mark position A1 at 10 am and 4 pm respectively.

[0117] Assuming that the light intensity detected at 10 a.m. is 800 lumens and the light intensity detected at 4 p.m. is 750 lumens, the light attenuation judgment value at this location is 50 lumens.

[0118] The data acquisition module combines the light attenuation judgment values ​​of all marked positions into a light intensity data set and calculates the average value as the light attenuation judgment value of the corresponding mark.

[0119] On this basis, the data acquisition module further calculates the ratio of the light intensity of the lamp at the marked position in each divided area to the light decay judgment value to obtain the light decay speed of the lamp.

[0120] Similarly, at the marked position B1 of the transition section, the data acquisition module randomly selects 12 noon as the fault detection time point, and detects that the number of faults of the lamps at this position is 2, and the total number is 20, so the fault incidence rate is calculated to be 10%.

[0121] The status assessment module conducts quantitative analysis of the light sensitivity and equipment health of each divided area based on the multi-source data provided by the data acquisition module.

[0122] Taking the entrance section as an example, the state assessment module performs a weighted summation of the actual light output ratios of marked positions A1 and A2 with the standard light intensity ranges of the corresponding lamps, and obtains the regional lighting merit of the entrance section as (80%×90%+75%×85%) / 2=79.625%.

[0123] At the same time, the state assessment module calculates the weighted average of the light attenuation rates at marked positions A1 and A2, and obtains the regional light attenuation average of the entrance section as (6% + 5%) / 2 = 5.5%.

[0124] Subsequently, the status assessment module calculates the ratio of the regional illumination merit of the entrance section to the average regional illumination merit of all divided areas, and obtains the illumination merit ratio of the entrance section as 79.625% / 80%=0.995.

[0125] Similarly, the state assessment module calculates the regional light attenuation rate ratio of the entrance section as 5.5% / 6%=0.92.

[0126] On this basis, the state assessment module analyzes the light sensitivity of the entrance section through the logistic regression method. The light figure of merit difference |0.995-1|=0.005 and the regional light decay rate ratio 0.92 are substituted into the formula C=1 / (1+e^(-z)), where z=0.005-0.92=-0.915. The calculated light sensitivity coefficient C of the entrance section is approximately 0.29.

[0127] Similarly, the condition assessment module analyzes the equipment health at the inlet section. Assuming the equipment health figure of merit ratio is 0.98 and the regional failure rate ratio is 0.8, the linear combination of the input features z = 0.98 + 0.8 = 1.78. Substituting this into the formula, the equipment health coefficient C of the inlet section is approximately 0.85.

[0128] After completing the status assessment, the control execution module selects candidate area A and candidate area B based on the average light attenuation and fault average of each divided area.

[0129] For example, in the above scenario, assuming that the average regional optical attenuation value of the transition section is 4%, the average regional fault value is 8%, and the corresponding values ​​of other divided areas are higher than this value, the transition section is selected as candidate area A and candidate area B.

[0130] The control execution module subtracts the regional light attenuation mean value of the alternative area A from the median of the regional light attenuation mean values ​​of all divided areas, and obtains the illumination difference coefficient of the tunnel lighting system as 4%-5%=-1%.

[0131] Similarly, the control execution module subtracts the regional fault mean of alternative area B from the median of the regional fault mean of all divided areas, and obtains the equipment health difference coefficient of the tunnel lighting system as 8%-10%=-2%.

[0132] When the illumination variation coefficient of the tunnel lighting system is less than the equipment health variation coefficient, the control execution module prioritizes regulating the illumination intensity.

[0133] For example, in the above scenario, since the illumination variance coefficient is -1% and the equipment health variance coefficient is -2%, the illumination intensity is adjusted first.

[0134] The control execution module uses the regional illumination merit of the alternative area A as the illumination intensity of the tunnel lighting system, and calculates the difference between the regional illumination sensitivity of the alternative area A and the regional average illumination sensitivity, and obtains a sensitivity difference of 0.29-0.3=-0.01.

[0135] Combined with the preset empirical conversion constant 2 and the light balance value 1.5, the device health difference is calculated using the formula: device health difference = empirical conversion constant × sensitivity difference × light balance value, and the device health difference is 2 × (-0.01) × 1.5 = -0.03.

[0136] The control execution module compares the light intensity of the tunnel lighting system with the preset light threshold. Assuming the light threshold is 75%, the light intensity of the tunnel lighting system exceeds the light threshold by 80%. The regional equipment health merit of alternative area B is 0.85, and the equipment health difference is -0.03. The final conclusion is that the equipment health of the tunnel lighting system is 0.85 + (-0.03) = 0.82.

[0137] When the illumination difference coefficient of the tunnel lighting system is greater than the equipment health difference coefficient, the control execution module prioritizes regulating the equipment status.

[0138] For example, assuming that the illumination variance coefficient in another scene is -2% and the device health variance coefficient is -1%, the device status is regulated first.

[0139] The control execution module uses the regional equipment health merit of alternative area B as the equipment health of the tunnel lighting system, and calculates the difference between the regional equipment health of alternative area B and the regional average equipment health, and obtains a sensitivity difference of 0.82-0.8=0.02.

[0140] Combined with the preset empirical conversion constant 2 and the light balance value 1.5, the light difference is calculated as 2×0.02×1.5=0.06 using the formula: light difference = empirical conversion constant × sensitivity difference × light balance value.

[0141] The control execution module compares the health of the tunnel lighting system equipment with the preset equipment health threshold. Assuming the equipment health threshold is 0.8, the tunnel lighting system equipment health of 0.82 exceeds the equipment health threshold, and the regional illumination merit of alternative area A is 80%. Finally, it is concluded that the tunnel lighting system light intensity is 80%.

[0142] Through the above steps, the present invention realizes comprehensive management and intelligent regulation of the tunnel lighting system.

[0143] The area division module and data acquisition module ensure the precise collection and integration of multi-source heterogeneous data. The status assessment module provides an overall operational status assessment of the system through quantitative analysis of light sensitivity and equipment health. The control execution module dynamically adjusts the light intensity and equipment status based on the comparison results of the light difference coefficient and the equipment health difference coefficient, thereby effectively improving the operating efficiency and maintenance convenience of the tunnel lighting system.

[0144] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0145] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0146] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0147] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0148] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0150] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0151] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0152] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0153] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The tunnel lighting operation and maintenance management system based on the Internet of Things is characterized by: It includes area division module, data acquisition module, status assessment module and control execution module; The area division module is used to divide the tunnel into several sub-areas according to length and function, mark and distinguish each sub-area according to the changes in dynamic lighting conditions, and transmit each divided area and the corresponding lighting conditions to the data acquisition module; The data acquisition module is used to obtain the standard light intensity range, lamp life parameters and energy consumption optimization targets for each sub-area of ​​the tunnel, calculate the actual light output ratio of the lamps in each divided area, and detect the light decay rate and failure rate of the lamps in each divided area; The status assessment module calculates the light sensitivity of the corresponding divided area based on the standard light intensity range of each sub-area of ​​the tunnel, the actual light output ratio of the lamps in each divided area, and the light decay rate; The equipment health of each divided area is calculated based on the lamp life parameters of each sub-area of ​​the tunnel, the actual light output ratio of the lamps in each divided area, and the failure rate; Before determining the light intensity or equipment status of the tunnel lighting system, the control execution module selects the area with the smallest average regional light attenuation value and the area with the lowest average regional fault value from all the divided areas as candidate area A and candidate area B, respectively; The illumination difference coefficient of the tunnel lighting system is obtained by subtracting the regional light attenuation mean value in the candidate area A from the median of the regional light attenuation mean values ​​of all divided areas; The equipment health difference coefficient of the tunnel lighting system is obtained by subtracting the regional fault mean in the candidate area B from the median of the regional fault mean of all divided areas; The illumination difference coefficient and the equipment health difference coefficient are compared, and the illumination or equipment control sequence is selected based on the comparison results. The illumination intensity or equipment status of the tunnel lighting system is adjusted based on the illumination sensitivity or equipment health of the marked area.

2. The tunnel lighting operation and maintenance management system based on the Internet of Things according to claim 1 is characterized by: The data acquisition module accesses the public database to obtain the standard light intensity range and lamp life reference value of each sub-area of ​​the tunnel, and obtains a full view of the tunnel lighting area through the distributed sensor network; Compare the features of different types of lamps in the lamp image library, identify the lamp type in each divided area, and obtain the actual light output ratio of the lamps in the divided area; Mark and distinguish the positions of lamps in each divided area.

3. The tunnel lighting operation and maintenance management system based on the Internet of Things according to claim 2 is characterized by: When the data acquisition module obtains the light decay speed of the lamps in each divided area, it selects two time points as light decay detection points, detects the light intensity of the lamps at different marked positions in the divided area at the two light decay detection points, and calculates the light intensity difference between the two light decay detection points at the same marked position; The light intensity differences at the same marked positions in different divided areas are merged into a light intensity data set and the average value is calculated as the light attenuation judgment value of the corresponding mark; The ratio of the light intensity of the lamp at the marked position in each divided area to the light decay judgment value of the corresponding mark is used as the light decay speed of the lamp at the corresponding mark in the divided area; When the data acquisition module obtains the failure rate of lamps in each divided area, it randomly selects a time point as the fault detection time point; At the fault detection time point, the number of faulty lamps and the total number of lamps in different marked positions in each divided area are detected; The ratio of the number of faulty lamps to the total number of lamps at the same marked position in each divided area is taken as the failure rate of the lamps at the corresponding mark.

4. The tunnel lighting operation and maintenance management system based on the Internet of Things according to claim 1 is characterized by: The status assessment module performs weighted summation on the actual light output ratio of the lamps in the divided area and the standard light intensity range of the corresponding lamps to obtain the regional lighting merit and regional equipment health merit of the corresponding divided area; The actual light output ratio of the lamps in the divided area and the light decay speed or failure rate of the corresponding lamps are weighted and averaged to obtain the regional light decay mean or regional failure mean of the corresponding divided area; The ratio of the regional illumination merit value of the divided area to the average regional illumination quality value of all divided areas is used as the illumination merit value ratio of the corresponding divided area, and the ratio of the regional equipment health merit value of the divided area to the average regional equipment health quality value of all divided areas is used as the equipment health merit value ratio of the corresponding divided area; The ratio of the regional light attenuation mean of the divided area to the maximum regional light attenuation mean of all divided areas is taken as the regional light attenuation rate ratio of the corresponding divided area, and the ratio of the regional fault mean of the divided area to the maximum regional fault mean of all divided areas is taken as the regional fault rate ratio of the corresponding divided area.

5. The tunnel lighting operation and maintenance management system based on the Internet of Things according to claim 4 is characterized by: The illumination sensitivity of each divided area is analyzed and quantified using the illumination figure of merit ratio and regional light attenuation rate ratio of each divided area. The specific steps are as follows: The absolute value of the difference between the illumination merit ratio of the divided area and 1 is taken as the illumination merit difference. The illumination merit difference of the divided area and the regional light attenuation rate ratio are used to analyze the illumination sensitivity using the logistic regression method. Similarly, the equipment health merit ratio and regional failure rate ratio of each divided area are used to analyze and quantify the equipment health coefficient of each divided area; Among them, the linear combination of input features is replaced by the sum of the equipment health merit difference and the regional failure rate ratio of the corresponding divided area.

6. The tunnel lighting operation and maintenance management system based on the Internet of Things according to claim 5 is characterized by: The average light sensitivity coefficient of all divided areas is calculated as the average light sensitivity of the tunnel lighting system; The average equipment health coefficient of all divided areas is calculated as the average equipment health of the tunnel lighting system; The average light sensitivity of the tunnel lighting system and the average equipment health of the tunnel lighting system are summed to obtain the light balance value of the current tunnel lighting system.

7. The tunnel lighting operation and maintenance management system based on the Internet of Things according to claim 1 is characterized by: When the illumination variation coefficient of the tunnel lighting system is less than the equipment health variation coefficient, the illumination intensity of the tunnel lighting system is regulated first, and then the equipment status of the tunnel lighting system is regulated; When the illumination variation coefficient of the tunnel lighting system is greater than the equipment health variation coefficient, the equipment status of the tunnel lighting system is regulated first, and then the illumination intensity of the tunnel lighting system is regulated.

8. The tunnel lighting operation and maintenance management system based on the Internet of Things according to claim 7 is characterized by: When the illumination difference coefficient of the tunnel lighting system is less than the equipment health difference coefficient, the regional illumination merit of the candidate area A is used as the illumination intensity of the tunnel lighting system, and the sensitivity difference is obtained by subtracting the regional illumination sensitivity of the candidate area A from the regional average illumination sensitivity; The device health difference is calculated by combining the preset empirical conversion constant with the conversion formula: Device health difference = empirical conversion constant × sensitivity difference × light balance value; Compare the light intensity of the tunnel lighting system with the preset light threshold. When the light intensity of the tunnel lighting system exceeds the light threshold, the equipment health value of the candidate area B plus the equipment health difference is used as the equipment health of the tunnel lighting system. When the illumination intensity of the tunnel lighting system is lower than the illumination threshold, the regional equipment health merit of the alternative area B is used as the equipment health of the tunnel lighting system.

9. The tunnel lighting operation and maintenance management system based on the Internet of Things according to claim 7 is characterized by: When the illumination difference coefficient of the tunnel lighting system is greater than the equipment health difference coefficient, the regional equipment health merit of the candidate area B is used as the equipment health of the tunnel lighting system, and the sensitivity difference is obtained by subtracting the regional equipment health of the candidate area B from the regional average equipment health. The light difference is calculated by the conversion formula based on the preset empirical conversion constant. The calculation formula is: light difference = empirical conversion constant × sensitivity difference × light balance value; Compare the health of the tunnel lighting system equipment with the preset equipment health threshold. When the health of the tunnel lighting system equipment exceeds the equipment health threshold, use the regional illumination merit of the candidate area A as the illumination intensity of the tunnel lighting system. When the equipment health of the tunnel lighting system is lower than the equipment health threshold, the illumination intensity obtained by subtracting the illumination difference from the regional illumination merit of the candidate area A is used as the illumination intensity of the tunnel lighting system.

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