Tunnel illumination operation and maintenance management system based on Internet of Things

Abstract

The invention discloses a tunnel lighting operation and maintenance management system based on the Internet of Things, relates to the technical field of Internet of Things and intelligent operation and maintenance management, and is used for solving the problems that the performance in the aspects of data acquisition precision and real-time response capability is limited, and fault diagnosis depends on preset rules and lacks intelligent prediction capability. Through four modules of region division, data acquisition, state evaluation and regulation and control execution, fine perception and dynamic regulation and control of an operation state are realized, a two-factor mechanism of illumination sensitivity and equipment health degree is adopted, region difference is quantified, and scheduling accuracy is improved; and a difference coefficient model is constructed to guide illumination and equipment regulation and control priorities, so that the resource allocation efficiency is improved. The system is based on closed-loop feedback control, guarantees lighting safety, energy conservation and efficient operation and maintenance, has the advantages of being intelligent, high in adaptability, good in expandability and the like, and is suitable for various tunnel lighting management scenes.

Description

Technical Field

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

[0002] Intelligent regulation technology refers to the use of advanced technical means and equipment to automatically or semi-automatically adjust system parameters to achieve efficient, precise, and flexible control. When applied to tunnel lighting operation and maintenance management, intelligent regulation technology can improve the operation efficiency and maintenance convenience of the system while ensuring lighting quality.

[0003] The existing technologies have the following deficiencies: At present, the tunnel lighting system is relatively sensitive to changes in light intensity, and multi-dimensional data such as lamp life and energy consumption optimization need to be comprehensively considered. The lighting requirements also vary under different environmental conditions. Due to the significant dynamic changes in lighting conditions in the tunnel lighting scenario, the existing systems have limited performance in data acquisition accuracy and real-time response capabilities, and fault diagnosis mostly relies on preset rules, lacking intelligent prediction capabilities. In addition, the existing technologies have insufficient capabilities in fusing and processing multi-source heterogeneous data, which may affect the overall performance and user experience of the system. Therefore, a tunnel lighting operation and maintenance management system based on the Internet of Things is proposed.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] To overcome the above-mentioned defects of the existing technologies, embodiments of the present invention provide a tunnel lighting operation and maintenance management system based on the Internet of Things. By dividing the tunnel area and the changes in dynamic lighting conditions, combining multi-source data collection and analysis, comprehensively evaluating the light intensity, lamp performance, and environmental factors in each divided area, obtaining the overall operating state of the tunnel lighting system, and calculating the light balance value to reduce the differences in lighting requirements under different areas and environmental conditions, thereby solving the problems raised in the background art.

[0006] To achieve the above object, the present invention provides the following technical solution: A tunnel lighting operation and maintenance management system based on the Internet of Things, including a regional division module, a data collection module, a status evaluation module, and a regulation execution module; The regional division module is used to divide the tunnel into several sub-areas according to the length and function, and 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 collection module; The data acquisition module is used to obtain the standard light intensity range, lamp life parameters, and energy consumption optimization goals of each sub-region of the tunnel, count the actual light output ratio of the lamps in each divided region, and detect the light decay rate and failure incidence rate of the lamps in each divided region; The status evaluation module calculates the light sensitivity of the corresponding divided region through the standard light intensity range of each sub-region of the tunnel, the actual light output ratio of the lamps in each divided region, and the light decay rate; calculates the equipment health of the corresponding divided region through the lamp life parameters of each sub-region of the tunnel, the actual light output ratio of the lamps in each divided region, and the failure incidence rate; analyzes the overall operating status of the tunnel lighting system based on the light sensitivity and equipment health of each divided region; The regulation execution module calculates the light difference coefficient and the equipment health difference coefficient using the light decay rate and the failure incidence rate of each divided region for comparison, selects the light or equipment regulation order according to the comparison result, and adjusts the light intensity or equipment status of the tunnel lighting system based on the light sensitivity or equipment health of the marked area and the overall operating status.

[0007] In a preferred embodiment, the data acquisition module accesses the public database to obtain the standard light intensity range and lamp life reference values of each sub-region of the tunnel, and obtains the full view of the tunnel lighting area through the distributed sensor network; Compare the characteristics of different types of lamps in the lamp image library, identify the lamp types in each divided region, and obtain the actual light output ratio of the lamps in the divided region; Mark and distinguish the positions of the lamps in each divided region.

[0008] In a preferred embodiment, when the data acquisition module obtains the light decay rate of the lamps in each divided region, two time points are selected as the light decay detection points, and the light intensity of the lamps at different marked positions in the divided region is detected at the two light decay detection points, and the light intensity difference between the two light decay detection points at the same marked position is calculated; Merge the light intensity differences at the same marked positions in different divided regions into a light intensity data set and calculate the average value as the light decay judgment value for the corresponding mark; Take the ratio of the light intensity of the lamps at the marked positions in each divided region to the light decay judgment value of the corresponding mark as the light decay rate of the lamps under the corresponding mark in the divided region; When the data acquisition module obtains the failure incidence rate of the lamps in each divided region, a random time point is selected as the failure detection time point; Detect the number of faulty lamps and the total number of lamps at different marked positions in each divided region at the failure detection time point; Take the ratio of the number of faulty lamps to the total number of lamps at the same marked position in each divided region as the failure incidence rate of the lamps under the corresponding mark.

[0009] In a preferred embodiment, the state evaluation module respectively 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 area light quality value and the area equipment health value of the corresponding divided area; The weighted average of the actual light output ratio of the lamps in the divided area and the light decay rate or failure incidence rate of the corresponding lamps is respectively obtained to get the area light decay average value or the area failure average value of the corresponding divided area; The ratio of the area light quality value of the divided area to the average value of the area light quality of all divided areas is used as the light quality value ratio of the corresponding divided area, and the ratio of the area equipment health value of the divided area to the average value of the area equipment health quality of all divided areas is used as the equipment health value ratio of the corresponding divided area; The ratio of the area light decay average value of the divided area to the maximum value of the area light decay average values of all divided areas is used as the area light decay rate ratio of the corresponding divided area, and the ratio of the area failure average value of the divided area to the maximum value of the area failure average values of all divided areas is used as the area failure rate ratio of the corresponding divided area.

[0010] In a preferred embodiment, the light sensitivity of each divided area is analyzed and quantified by using the light quality value ratio and the area light decay rate ratio of each divided area. The specific steps are as follows: The absolute value of the difference between the light quality value ratio of the divided area and 1 is used as the light quality value difference, and the light quality value difference of the divided area and the area light decay rate ratio are used to analyze the light sensitivity by using the logistic regression method; Similarly, the equipment health of each divided area is analyzed and quantified by using the equipment health value ratio and the area failure rate ratio of each divided area to obtain the equipment health coefficient of the divided area; Among them, the linear combination of the input features is replaced by the summation result of the equipment health value difference and the area failure rate ratio of the corresponding divided area.

[0011] In a preferred embodiment, the average value of the light sensitivity coefficients of all divided areas is calculated as the average light sensitivity of the tunnel lighting system; The average value of the equipment health coefficients 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.

[0012] In a preferred embodiment, before determining the light intensity or device status of the tunnel lighting system, the regulation execution module selects the divided area with the minimum average regional light attenuation and the divided area with the lowest average regional failure rate from all divided areas as alternative area A and alternative area B respectively.

[0013] In a preferred embodiment, the difference between the average regional light attenuation in alternative area A and the median of the average regional light attenuation of all divided areas is used to obtain the light intensity difference coefficient of the tunnel lighting system. The difference between the average regional failure rate in alternative area B and the median of the average regional failure rate of all divided areas is used to obtain the device health difference coefficient of the tunnel lighting system. When the light intensity difference coefficient of the tunnel lighting system is less than the device health difference coefficient, first regulate the light intensity of the tunnel lighting system, and then regulate the device status of the tunnel lighting system. When the light intensity difference coefficient of the tunnel lighting system is greater than the device health difference coefficient, first regulate the device status of the tunnel lighting system, and then regulate the light intensity of the tunnel lighting system.

[0014] In a preferred embodiment, when the light intensity difference coefficient of the tunnel lighting system is less than the device health difference coefficient, the optimal regional light intensity of alternative area A is used as the light intensity of the tunnel lighting system, and the difference between the regional light sensitivity in alternative area A and the average regional light sensitivity is obtained as the sensitivity difference. The device health difference is calculated through a conversion formula in combination with a preset empirical conversion constant. The calculation formula is: device health difference = empirical conversion constant × sensitivity difference × light balance value. The light intensity of the tunnel lighting system is compared with a preset light threshold. When the light intensity of the tunnel lighting system exceeds the light threshold, the device health degree obtained by adding the optimal regional device health value of alternative area B and the device health difference is used as the device health degree of the tunnel lighting system. When the light intensity of the tunnel lighting system is lower than the light threshold, the optimal regional device health value of alternative area B is used as the device health degree of the tunnel lighting system.

[0015] In a preferred embodiment, when the light intensity difference coefficient of the tunnel lighting system is greater than the device health difference coefficient, the optimal regional device health value of alternative area B is used as the device health degree of the tunnel lighting system, and the difference between the regional device health degree in alternative area B and the average regional device health degree is obtained as the sensitivity difference. The light intensity difference is calculated through a conversion formula in combination with a preset empirical conversion constant. The calculation formula is: light intensity 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 lighting optimum value of the alternative area A as the lighting intensity of the tunnel lighting system; When the health of the tunnel lighting system equipment is lower than the equipment health threshold, use the lighting intensity obtained by subtracting the lighting difference from the regional lighting optimum value of the alternative area A as the lighting intensity of the tunnel lighting system.

[0016] Technical effects and advantages of the present invention: By introducing four major modules of regional division, data collection, status evaluation, and regulation execution, the present invention constructs an Internet of Things-based full-life-cycle tunnel lighting operation and maintenance management system, realizing refined perception and dynamic regulation of the operating status of the tunnel lighting system. Among them, the dual-factor evaluation mechanism of light sensitivity and equipment health can quantify the key influence degrees of different regions in terms of light attenuation and equipment failures, enhancing the pertinence and accuracy of system operation and maintenance scheduling; at the same time, by constructing a comparison model of the light difference coefficient and the equipment health difference coefficient, it effectively guides the priority order of light adjustment and equipment maintenance, improving the allocation efficiency of regulation resources; the system performs closed-loop feedback control based on dynamic light conditions and equipment status during operation, ensuring that the tunnel lighting achieves the operation and maintenance goals of optimal energy consumption and minimal maintenance while meeting the requirements of safe passage. The overall technical solution has significant advantages of high intelligence, high adaptability, and high scalability, and is widely applicable to the intelligent lighting management needs of multi-scene and multi-type tunnels. Description of the Drawings

[0017] Figure 1 It is a schematic diagram of the module structure of the Internet of Things-based tunnel lighting operation and maintenance management system of the present invention.

[0018] Figure 2 It is a working logic flow chart of the regulation execution module in the Internet of Things-based tunnel lighting operation and maintenance management system of the present invention. Detailed Embodiments

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0020] The present invention provides an Internet of Things-based tunnel lighting operation and maintenance management system, and its specific implementation is as follows.

[0021] Combined with the attached Figure 1 and the attached Figure 2, the overall system includes a regional division module, a data acquisition module, a status evaluation module, and a regulation execution module. Through information transmission and logical operations among these modules, comprehensive management of the tunnel lighting system is achieved.

[0022] First, the regional division module divides the tunnel into several sub-regions according to its length and function, and marks and differentiates each sub-region according to the changes in dynamic lighting conditions.

[0023] For example, in a practical application scenario, assume that a certain tunnel is 500 meters long and is divided into three sub-regions, namely the entrance section, the transition section, and the exit section, according to the functional differences at the tunnel entrance, middle, and exit.

[0024] The boundaries of each sub-region are determined by the positions of sensor nodes installed in the tunnel. These nodes are connected to the regional division module through a wireless communication protocol and transmit data on dynamic lighting conditions in real time.

[0025] The regional division module passes the divided sub-regions and their corresponding lighting condition information to the data acquisition module for subsequent processing.

[0026] The data acquisition module is responsible for obtaining the standard lighting intensity range, lamp life parameters, and energy consumption optimization goals for each sub-region of the tunnel.

[0027] As shown in the appendix Figure 1 , the data acquisition module obtains the standard lighting intensity intervals and lamp life reference values for each sub-region of the tunnel by accessing a public database.

[0028] At the same time, the data acquisition module uses a distributed sensor network to obtain a full view of the tunnel lighting area, and compares the characteristics of different types of lamps in the lamp image library through image recognition technology, so as to identify the lamp types in each divided region and obtain the actual lighting output ratio of the lamps in the divided region.

[0029] In addition, the data acquisition module also marks and differentiates the positions of the lamps in each divided region.

[0030] For example, at the marked positions A1 and A2 in the entrance section, the data acquisition module detects that the actual lighting outputs of the lamps are 80% and 75% respectively, and records these data for subsequent analysis.

[0031] Furthermore, when the data acquisition module obtains the light decay speed of the lamps in each divided region, it selects two time points as the light decay detection points.

[0032] For example, at the marked position A1 in the entrance section, the first light decay detection point is 10 am and the second light decay detection point is 4 pm.

[0033] At these two time points, the data acquisition module respectively detects the illumination intensity of the lamps at the marked position A1 and calculates the difference between the two detection results.

[0034] Suppose the illumination intensity detected at 10 am is 800 lumens and the illumination intensity detected at 4 pm is 750 lumens. Then the light decay judgment value for this marked position is 50 lumens.

[0035] Subsequently, the data acquisition module combines the light decay judgment values of all marked positions into a light intensity data set and calculates the average value as the light decay judgment value for the corresponding mark.

[0036] On this basis, the data acquisition module takes the ratio of the illumination intensity of the lamps at the marked positions in each divided area to the light decay judgment value of the corresponding mark as the light decay speed of the lamps under the corresponding mark in the divided area.

[0037] Similarly, when the data acquisition module obtains the failure incidence rate of the lamps in each divided area, it randomly selects a time point as the failure detection time point.

[0038] For example, at the marked position B1 in the transition section, the failure detection time point is 12 noon. The data acquisition module detects that the number of faulty lamps at the marked position B1 is 2 and the total number is 20. Then the failure incidence rate of the lamps under this marked position is 10%.

[0039] The status evaluation module calculates the light sensitivity of the corresponding divided area through the standard illumination intensity range of each sub - area of the tunnel, the actual illumination output ratio of the lamps in each divided area, and the light decay speed, and calculates the equipment health of the corresponding divided area through the lamp life parameters of each sub - area of the tunnel, the actual illumination output ratio of the lamps in each divided area, and the failure incidence rate.

[0040] Specifically, the status evaluation module respectively performs weighted summation on the actual illumination output ratio of the lamps in the divided area and the standard illumination intensity range of the corresponding lamps to obtain the regional illumination merit value and the regional equipment health merit value of the corresponding divided area.

[0041] For example, in the entrance section, suppose the actual illumination output ratios of the marked positions A1 and A2 are 80% and 75% respectively, and the standard illumination intensity ranges of the corresponding lamps are 90% and 85% respectively. Then the regional illumination merit value of the entrance section is (80%×90% + 75%×85%) / 2 = 79.625%.

[0042] At the same time, the status evaluation module respectively performs weighted averaging on the actual illumination output ratio of the lamps in the divided area and the light decay speed or failure incidence rate of the corresponding lamps to obtain the regional light decay average value or the regional failure average value of the corresponding divided area.

[0043] For example, in the entrance section, assuming that the light attenuation rates at the marked positions A1 and A2 are 6% and 5% respectively, the average light attenuation of the area in the entrance section is (6% + 5%) / 2 = 5.5%.

[0044] The state evaluation module also takes the ratio of the area light quality optimum value of the divided area to the average value of the area light quality optimum values of all divided areas as the light quality optimum ratio of the corresponding divided area, and takes the ratio of the area equipment health optimum value of the divided area to the average value of the area equipment health optimum values of all divided areas as the equipment health optimum ratio of the corresponding divided area.

[0045] For example, assuming that the average value of the area light quality optimum values of all divided areas is 80%, then the light quality optimum ratio of the entrance section is 79.625% / 80% = 0.995.

[0046] Similarly, the state evaluation module takes the ratio of the average area light attenuation value of the divided area to the maximum value of the average area light attenuation values of all divided areas as the area light attenuation rate ratio of the corresponding divided area, and takes the ratio of the average area failure value of the divided area to the maximum value of the average area failure values of all divided areas as the area failure rate ratio of the corresponding divided area.

[0047] The state evaluation module further analyzes and quantifies the light sensitivity of each divided area by using the light quality optimum ratio and the area light attenuation rate ratio of each divided area.

[0048] The specific steps are as follows: Take the absolute value of the difference between the light quality optimum ratio of the divided area and 1 as the light quality optimum difference. For example, the light quality optimum difference of the entrance section is |0.995 - 1| = 0.005.

[0049] The smaller the light quality optimum difference, the closer the light quality optimum ratio of the divided area is to 1.

[0050] Subsequently, the state evaluation module analyzes the light sensitivity by using the light quality optimum difference of the divided area and the area light attenuation rate ratio through the logistic regression method.

[0051] Assuming that the area light attenuation 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.

[0052] Similarly, the state evaluation module analyzes and quantifies the equipment health of each divided area by using the equipment health optimum ratio and the area failure rate ratio of each divided area to obtain the equipment health coefficient of the divided area.

[0053] For example, assume that the equipment health optimal value ratio of the entrance section is 0.98 and the regional failure rate ratio is 0.8. Then the linear combination z of the input features is 0.98 + 0.8 = 1.78. Substituting it into the formula C = 1 / (1 + e^(-z)), the equipment health coefficient C of the entrance section is calculated to be approximately 0.85.

[0054] Before determining the light intensity or equipment status of the tunnel lighting system, the regulation execution module selects the partition area with the minimum average light attenuation value and the partition area with the lowest average failure value from all partition areas as alternative area A and alternative area B respectively.

[0055] For example, in the above scenario, assume that the average light attenuation value of the transition section is 4% and the average failure value is 8%, while the corresponding values of other partition areas are higher than this. Then the transition section is selected as alternative area A and alternative area B.

[0056] The regulation execution module subtracts the average light attenuation value in alternative area A from the median of the average light attenuation values of all partition areas to obtain the light difference coefficient of the tunnel lighting system.

[0057] For example, assume that the median of the average light attenuation values of all partition areas is 5%. Then the light difference coefficient of the tunnel lighting system is 4% - 5% = -1%.

[0058] Similarly, the regulation execution module subtracts the average failure value in alternative area B from the median of the average failure values of all partition areas to obtain the equipment health difference coefficient of the tunnel lighting system.

[0059] For example, assume that the median of the average failure values of all partition areas is 10%. Then the equipment health difference coefficient of the tunnel lighting system is 8% - 10% = -2%.

[0060] When the light difference coefficient of the tunnel lighting system is less than the equipment health difference coefficient, the regulation execution module first regulates the light intensity of the tunnel lighting system and then regulates the equipment status of the tunnel lighting system.

[0061] For example, in the above scenario, since the light difference coefficient is -1% and the equipment health difference coefficient is -2%, the light intensity is regulated first.

[0062] The regulation execution module takes the regional light optimal value of alternative area A as the light intensity of the tunnel lighting system, and subtracts the regional light sensitivity in alternative area A from the average regional light sensitivity to obtain the sensitivity difference.

[0063] For example, assume that the regional light optimal value of alternative area A is 80%, the regional light sensitivity is 0.29, and the average regional light sensitivity is 0.3. Then the sensitivity difference is 0.29 - 0.3 = -0.01.

[0064] Calculate the equipment health difference through a conversion formula in combination with a preset experience conversion constant. The calculation formula is: Equipment health difference = Experience conversion constant × Sensitivity difference × Light balance value.

[0065] For example, assume the experience conversion constant is 2 and the light balance value is 1.5. Then the equipment health difference is 2 × (-0.01) × 1.5 = -0.03.

[0066] The regulation execution module compares the light intensity of the tunnel lighting system with a preset light threshold. When the light intensity of the tunnel lighting system exceeds the light threshold, the equipment health degree obtained by adding the equipment health difference to the regional equipment health optimal value of alternative area B is used as the equipment health degree of the tunnel lighting system. When the light intensity of the tunnel lighting system is lower than the light threshold, the regional equipment health optimal value of alternative area B is used as the equipment health degree of the tunnel lighting system.

[0067] For example, assume the light threshold is 75%. Then the light intensity of the tunnel lighting system is 80%, which exceeds the light threshold. The regional equipment health optimal value of alternative area B is 0.85, and the equipment health difference is -0.03. Then the equipment health degree of the tunnel lighting system is 0.85 + (-0.03) = 0.82.

[0068] When the light difference coefficient of the tunnel lighting system is greater than the equipment health difference coefficient, the regulation execution module first regulates the equipment state of the tunnel lighting system and then regulates the light intensity of the tunnel lighting system.

[0069] For example, assume that in another scenario, the light difference coefficient is -2% and the equipment health difference coefficient is -1%. Then the equipment state is regulated first.

[0070] The regulation execution module uses the regional equipment health optimal value of alternative area B as the equipment health degree of the tunnel lighting system, and calculates the sensitivity difference by taking the difference between the regional equipment health degree in alternative area B and the regional average equipment health degree.

[0071] For example, assume the regional equipment health optimal value of alternative area B is 0.85, the regional equipment health degree is 0.82, and the regional average equipment health degree is 0.8. Then the sensitivity difference is 0.82 - 0.8 = 0.02.

[0072] Calculate the light difference through a conversion formula in combination with a preset experience conversion constant. The calculation formula is: Light difference = Experience conversion constant × Sensitivity difference × Light balance value.

[0073] For example, assume the experience conversion constant is 2 and the light balance value is 1.5. Then the light difference is 2 × 0.02 × 1.5 = 0.06.

[0074] 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 lighting optimum value of the alternative area A is used as the lighting intensity of the tunnel lighting system; When the health of the tunnel lighting system equipment is lower than the equipment health threshold, the lighting intensity obtained by subtracting the lighting difference from the regional lighting optimum value of the alternative area A is used as the lighting intensity of the tunnel lighting system.

[0075] For example, assuming that the equipment health threshold is 0.8, the health of the tunnel lighting system equipment is 0.82, which exceeds the equipment health threshold, and the regional lighting optimum value of the alternative area A is 80%, then the lighting intensity of the tunnel lighting system is 80%.

[0076] To enable the relevant personnel in the technical field to fully understand and implement the present invention better, the following further supplements the specific implementation principles of the present invention in combination with a specific application scenario.

[0077] In the actual operation of the tunnel lighting operation and maintenance management system, it is first necessary to divide the tunnel into regions. Assume that a certain tunnel is 500 meters long and is divided into three sub-regions: the entrance section, the transition section, and the exit section according to the differences in function and lighting requirements.

[0078] The regional division module collects the dynamic lighting conditions of each sub-region in real time through the sensor nodes installed in the tunnel and transmits this data to the data collection module.

[0079] The positions of the sensor nodes are located by the wireless communication protocol to ensure that the boundaries of each sub-region are clear and can reflect the actual lighting changes.

[0080] Subsequently, the regional division module transmits the division results and their corresponding lighting condition information to the data collection module for subsequent processing.

[0081] After receiving the regional division information, the data collection module starts to obtain the standard lighting intensity range, lamp life parameters, and energy consumption optimization goals of each sub-region.

[0082] For example, at the marked positions A1 and A2 in the entrance section, the data collection module detects that the actual lighting outputs of the lamps are 80% and 75% respectively through the distributed sensor network.

[0083] At the same time, the data collection module extracts the standard lighting intensity intervals and lamp life reference values of these positions from the public database, and uses image recognition technology to compare the features in the lamp image library to identify the lamp types and their performance parameters.

[0084] In addition, the data acquisition module also selects two time points as the light decay detection points, and detects the light intensity of the lamps at the marked position A1 in the entrance section at 10:00 am and 4:00 pm respectively.

[0085] Suppose the light intensity detected at 10:00 am is 800 lumens, and the light intensity detected at 4:00 pm is 750 lumens. Then the light decay judgment value at this position is 50 lumens.

[0086] The data acquisition module combines the light decay judgment values of all marked positions into a light intensity data set, and calculates the average value as the light decay judgment value corresponding to the mark.

[0087] On this basis, the data acquisition module further calculates the ratio of the light intensity of the lamps at the marked positions in each divided area to the light decay judgment value to obtain the light decay speed of the lamps.

[0088] Similarly, at the marked position B1 in the transition section, the data acquisition module randomly selects 12:00 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. Thus, the calculated fault incidence rate is 10%.

[0089] Based on the multi-source data provided by the data acquisition module, the status evaluation module conducts a quantitative analysis on the light sensitivity and equipment health of each divided area.

[0090] Taking the entrance section as an example, the status evaluation module weights and sums the actual light output ratios of the marked positions A1 and A2 to the standard light intensity ranges of the corresponding lamps respectively, and obtains the regional light quality value of the entrance section as (80%×90% + 75%×85%) / 2 = 79.625%.

[0091] At the same time, the status evaluation module weights and averages the light decay speeds of the marked positions A1 and A2 to obtain the regional light decay average value of the entrance section as (6% + 5%) / 2 = 5.5%.

[0092] Subsequently, the status evaluation module calculates the ratio of the regional light quality value of the entrance section to the average value of the regional light quality of all divided areas, and obtains the light quality value ratio of the entrance section as 79.625% / 80% = 0.995.

[0093] Similarly, the status evaluation module calculates the regional light decay rate ratio of the entrance section as 5.5% / 6% = 0.92.

[0094] On this basis, the status evaluation module analyzes the light sensitivity of the entrance section through the logistic regression method, substituting the light quality difference |0.995 - 1| = 0.005 and the regional light attenuation rate ratio 0.92 into the formula C = 1 / (1 + e^(-z)), where z = 0.005 - 0.92 = -0.915, and calculates that the light sensitivity coefficient C of the entrance section is approximately 0.29.

[0095] Similarly, the status evaluation module analyzes the equipment health of the entrance section. Assuming the equipment health quality ratio is 0.98 and the regional failure rate ratio is 0.8, then the linear combination of input features z = 0.98 + 0.8 = 1.78. Substituting it into the formula for calculation, the equipment health coefficient C of the entrance section is approximately 0.85.

[0096] After completing the status evaluation, the regulation execution module selects alternative regions A and B according to the average light attenuation and average failure of each divided region.

[0097] For example, in the above scenario, assuming the average regional light attenuation of the transition section is 4% and the average regional failure is 8%, and the corresponding values of other divided regions are higher than this value, then the transition section is selected as alternative regions A and B.

[0098] The regulation execution module subtracts the average regional light attenuation of alternative region A from the median of the average regional light attenuation of all divided regions, and the light difference coefficient of the tunnel lighting system is 4% - 5% = -1%.

[0099] Similarly, the regulation execution module subtracts the average regional failure of alternative region B from the median of the average regional failure of all divided regions, and the equipment health difference coefficient of the tunnel lighting system is 8% - 10% = -2%.

[0100] When the light difference coefficient of the tunnel lighting system is less than the equipment health difference coefficient, the regulation execution module preferentially regulates the light intensity.

[0101] For example, in the above scenario, since the light difference coefficient is -1% and the equipment health difference coefficient is -2%, the light intensity is regulated first.

[0102] The regulation execution module takes the regional light quality of alternative region A as the light intensity of the tunnel lighting system, and calculates the difference between the regional light sensitivity of alternative region A and the average regional light sensitivity, and the sensitivity difference is 0.29 - 0.3 = -0.01.

[0103] Combined with the preset empirical conversion constant 2 and the light balance value 1.5, the equipment health difference is calculated through the formula equipment health difference = empirical conversion constant × sensitivity difference × light balance value, and the equipment health difference is 2×(-0.01)×1.5 = -0.03.

[0104] The regulation execution module compares the light intensity of the tunnel lighting system with a preset light threshold. Assuming the light threshold is 75%, the light intensity of the tunnel lighting system is 80% exceeding the light threshold. The regional equipment health optimum value of alternative area B is 0.85, and the equipment health deficiency is -0.03. Finally, the equipment health degree of the tunnel lighting system is obtained as 0.85 + (-0.03) = 0.82.

[0105] When the light difference coefficient of the tunnel lighting system is greater than the equipment health difference coefficient, the regulation execution module preferentially regulates the equipment status.

[0106] For example, assuming that in another scenario, the light difference coefficient is -2% and the equipment health difference coefficient is -1%, the equipment status is regulated first.

[0107] The regulation execution module takes the regional equipment health optimum value of alternative area B as the equipment health degree of the tunnel lighting system, and calculates the difference between the regional equipment health degree of alternative area B and the regional average equipment health degree, obtaining a sensitivity difference of 0.82 - 0.8 = 0.02.

[0108] Combined with a preset empirical conversion constant 2 and a light balance value 1.5, the light difference is calculated through the formula light difference = empirical conversion constant × sensitivity difference × light balance value as 2 × 0.02 × 1.5 = 0.06.

[0109] The regulation execution module compares the equipment health degree of the tunnel lighting system with a preset equipment health threshold. Assuming the equipment health threshold is 0.8, the equipment health degree of the tunnel lighting system 0.82 exceeds the equipment health threshold, and the regional light optimum value of alternative area A is 80%. Finally, the light intensity of the tunnel lighting system is obtained as 80%.

[0110] Through the above steps, the present invention realizes the comprehensive management and intelligent regulation of the tunnel lighting system.

[0111] The area division module and the data acquisition module ensure the accurate acquisition and fusion of multi-source heterogeneous data. The status evaluation module provides an overall operation status evaluation of the system through the quantitative analysis of light sensitivity and equipment health degree, while the regulation execution module dynamically adjusts the light intensity and equipment status according to the comparison result of the light difference coefficient and the equipment health difference coefficient, thereby effectively improving the operation efficiency and maintenance convenience of the tunnel lighting system.

[0112] The above formulas are all calculated by taking the numerical value without dimension. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0113] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. 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 programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired or wireless (such as 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 a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0114] It should be understood that in various embodiments of the present application, the sequence numbers of the above processes do not imply the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0115] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0116] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0117] In several embodiments provided in the present 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 illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0118] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0120] If the functions are implemented in the form of software function 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, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing 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 methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0121] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An Internet of Things-based tunnel lighting operation and maintenance management system, characterized in that: It includes a region division module, a data acquisition module, a status evaluation module, and a regulation execution module; The region division module is used to divide the tunnel into several sub-regions according to the length and function, and mark and distinguish each sub-region according to the change of dynamic lighting conditions, and transmit each divided region and the corresponding lighting conditions to the data acquisition module; The data acquisition module is used to obtain the standard lighting intensity range, lamp life parameters, and energy consumption optimization objectives of each sub-region of the tunnel, count the actual lighting output ratio of the lamps in each divided region, and detect the light decay speed and failure incidence rate of the lamps in each divided region; The status evaluation module calculates the lighting sensitivity of the corresponding divided region through the standard lighting intensity range of each sub-region of the tunnel, the actual lighting output ratio of the lamps in each divided region, and the light decay speed; Calculate the equipment health of the corresponding divided region through the lamp life parameters of each sub-region of the tunnel, the actual lighting output ratio of the lamps in each divided region, and the failure incidence rate; Analyze the overall operation status of the tunnel lighting system according to the lighting sensitivity and equipment health of each divided region; The regulation execution module calculates the lighting difference coefficient and the equipment health difference coefficient by using the light decay speed and the failure incidence rate of each divided region for comparison, selects the lighting or equipment regulation sequence according to the comparison result, and adjusts the lighting intensity or equipment status of the tunnel lighting system by integrating the lighting sensitivity or equipment health of the marked region and the overall operation status.

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

3. The Internet of Things-based tunnel lighting operation and maintenance management system according to claim 2, characterized in that: When the data acquisition module obtains the light decay speed of the lamps in each divided region, two time points are selected as the light decay detection points, and the lighting intensity of the lamps at different marked positions in the divided region is detected at the two light decay detection points, and the lighting intensity difference between the two light decay detection points at the same marked position is calculated; Merge the lighting intensity differences at the same marked position in different divided regions into a light intensity data set and calculate the average value as the light decay judgment value of the corresponding mark; Take the ratio of the lighting intensity of the lamp at the marked position in each divided region 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 region; When the data acquisition module obtains the failure incidence rate of the lamps in each divided region, a random time point is selected as the failure detection time point; Detect the number of faulty lamps and the total number of lamps at different marked positions in each divided region at the failure detection time point; Take the ratio of the number of faulty lamps to the total number of lamps at the same marked position in each divided region as the failure incidence rate of the lamps under the corresponding mark.

4. The Internet of Things-based tunnel lighting operation and maintenance management system according to claim 1, characterized in that: The state evaluation module respectively 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 area light quality value and the area equipment health quality value of the corresponding divided area; The actual light output ratio of the lamps in the divided area and the light decay rate or failure incidence rate of the corresponding lamps are respectively weighted and averaged to obtain the area light decay mean value or the area failure mean value of the corresponding divided area; The ratio of the area light quality value of the divided area to the average value of the area light quality of all divided areas is used as the light quality ratio of the corresponding divided area, and the ratio of the area equipment health quality value of the divided area to the average value of the area equipment health quality of all divided areas is used as the equipment health ratio of the corresponding divided area; The ratio of the area light decay mean value of the divided area to the maximum value of the area light decay mean values of all divided areas is used as the area light decay rate ratio of the corresponding divided area, and the ratio of the area failure mean value of the divided area to the maximum value of the area failure mean values of all divided areas is used as the area failure rate ratio of the corresponding divided area.

5. The Internet of Things-based tunnel lighting operation and maintenance management system according to claim 4, characterized in that: The light sensitivity of each divided area is analyzed and quantified by using the light quality ratio and the area light decay rate ratio of each divided area. The specific steps are as follows: The absolute value of the difference between the light quality ratio of the divided area and 1 is used as the light quality difference, and the light quality difference of the divided area and the area light decay rate ratio are used to analyze the light sensitivity by using the logistic regression method; Similarly, the equipment health of each divided area is analyzed and quantified by using the equipment health ratio and the area failure rate ratio of each divided area to obtain the equipment health coefficient of the divided area; Among them, the linear combination of the input features is replaced by the summation result of the equipment health difference and the area failure rate ratio of the corresponding divided area.

6. The Internet of Things-based tunnel lighting operation and maintenance management system according to claim 5, characterized in that: The average value of the light sensitivity coefficients of all divided areas is calculated as the average light sensitivity of the tunnel lighting system; The average value of the equipment health coefficients 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 Internet of Things-based tunnel lighting operation and maintenance management system according to claim 4, characterized in that: Before determining the light intensity or equipment status of the tunnel lighting system, the regulation execution module selects the divided area with the smallest area light decay mean value and the divided area with the lowest area failure mean value from all divided areas as the alternative area A and the alternative area B respectively.

8. The Internet of Things-based tunnel lighting operation and maintenance management system according to claim 7, characterized in that: The difference between the area light decay mean value in the alternative area A and the median value of the area light decay mean values of all divided areas is used as the light difference coefficient of the tunnel lighting system; 、 The difference between the regional fault mean value in the alternative area B and the median of the regional fault mean values of all divided areas is used to obtain the equipment health difference coefficient of the tunnel lighting system; When the light intensity difference coefficient of the tunnel lighting system is less than the equipment health difference coefficient, first adjust the light intensity of the tunnel lighting system, and then adjust the equipment state of the tunnel lighting system; When the light intensity difference coefficient of the tunnel lighting system is greater than the equipment health difference coefficient, first adjust the equipment state of the tunnel lighting system, and then adjust the light intensity of the tunnel lighting system.

9. The Internet of Things-based tunnel lighting operation and maintenance management system according to claim 8, characterized in that: When the light intensity difference coefficient of the tunnel lighting system is less than the equipment health difference coefficient, the regional light intensity optimum value of the alternative area A is used as the light intensity of the tunnel lighting system, and the difference between the regional light sensitivity in the alternative area A and the regional average light sensitivity is obtained to get the sensitivity difference; The equipment health difference is calculated through a conversion formula in combination with a preset empirical conversion constant, and its calculation formula is: equipment health difference = empirical conversion constant × sensitivity difference × light balance value; The light intensity of the tunnel lighting system is compared with a preset light intensity threshold. When the light intensity of the tunnel lighting system exceeds the light intensity threshold, the equipment health degree obtained by adding the equipment health optimum value of the alternative area B and the equipment health difference is used as the equipment health degree of the tunnel lighting system; When the light intensity of the tunnel lighting system is lower than the light intensity threshold, the equipment health optimum value of the alternative area B is used as the equipment health degree of the tunnel lighting system.

10. The Internet of Things-based tunnel lighting operation and maintenance management system according to claim 8, characterized in that: When the light intensity difference coefficient of the tunnel lighting system is greater than the equipment health difference coefficient, the equipment health optimum value of the alternative area B is used as the equipment health degree of the tunnel lighting system, and the difference between the regional equipment health degree in the alternative area B and the regional average equipment health degree is obtained to get the sensitivity difference; The light intensity difference is calculated through a conversion formula in combination with a preset empirical conversion constant, and its calculation formula is: light intensity difference = empirical conversion constant × sensitivity difference × light balance value; The equipment health degree of the tunnel lighting system is compared with a preset equipment health threshold. When the equipment health degree of the tunnel lighting system exceeds the equipment health threshold, the regional light intensity optimum value of the alternative area A is used as the light intensity of the tunnel lighting system; When the equipment health degree of the tunnel lighting system is lower than the equipment health threshold, the light intensity obtained by subtracting the light intensity difference from the regional light intensity optimum value of the alternative area A is used as the light intensity of the tunnel lighting system.

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