Guardrail lamp brightness adjusting method, device and equipment and storage medium

Abstract

The invention relates to the technical field of illumination adjustment, in particular to a guardrail lamp brightness adjustment method and device, equipment and a storage medium, and the method comprises the steps: obtaining real-time brightness and traffic flow parameters, and carrying out the fuzzy processing of the real-time brightness and traffic flow parameters, and obtaining input parameters; obtaining a PID parameter adjustment amount based on the input parameter and a pre-constructed PID parameter lookup table, and optimizing a pre-constructed S dimming curve based on the PID parameter adjustment amount and the input parameter to generate a brightness adjustment scheme as a basis for adjusting the working state of the guardrail lamp; according to the method disclosed by the invention, accurate and reliable input parameters can be obtained by monitoring the brightness and the traffic flow in real time and performing data fuzzification processing; the PID parameter adjustment amount is obtained based on the input parameters and the pre-constructed PID parameter lookup table, so that rapid response to brightness adjustment is realized, and the matching between the generated brightness adjustment scheme and the environment and traffic conditions is ensured; in addition, the brightness is dynamically adjusted based on the optimized S dimming curve, energy consumption can be optimized, and energy conservation and emission reduction can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of lighting adjustment, and particularly to a method, device, equipment and storage medium for adjusting the brightness of guardrail lights. Background Art

[0002] As an urban infrastructure, road guardrail lights ensure the safety of night driving and reduce traffic accidents by providing functional road lighting; among many guardrail light products, guardrail lights with two lighting modes of yellow light and white light are highly regarded by the market due to their unique advantages.

[0003] In dual-mode guardrail lights, the yellow light mode is usually used to provide soft lighting and reduce the visual stimulation to drivers, while the white light mode provides brighter light to meet the lighting needs under different weather and traffic conditions; this dual-mode design not only improves the adaptability of guardrail lights, enabling them to cope with changing environmental conditions, but also performs well in energy conservation and environmental protection. Through intelligent control, the brightness and color can be adjusted according to actual needs, thus achieving the goal of energy conservation and emission reduction.

[0004] Although dual-mode guardrail lights have many advantages in design, in actual operation, there is a significant technical problem when switching lighting modes - the extension of the brightness transition time; this problem usually occurs during the process of switching from one lighting state to another, such as switching from a high-brightness mode to a low-brightness mode, or during the conversion between different color temperature settings; the extension of the transition time not only affects the user experience, but may also have an adverse impact on visual comfort, especially in scenarios where lighting needs to be quickly adjusted to adapt to different environments or tasks.

[0005] It can be seen that the existing technology still needs to be improved. Summary of the Invention

[0006] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a method for adjusting the brightness of guardrail lights, which uses a fuzzy PID controller to optimize the S dimming curve based on the real-time brightness and traffic flow, so as to dynamically adjust the brightness and achieve a rapid response to brightness adjustment.

[0007] The first aspect of the present invention provides a method for adjusting the brightness of guardrail lights, including: obtaining real-time brightness parameters and real-time traffic flow parameters, and respectively performing fuzzy processing to obtain input parameters; pre-constructing a PID parameter lookup table, and obtaining a PID parameter adjustment amount based on the input parameters and the pre-constructed PID parameter lookup table; optimizing the pre-constructed S dimming curve based on the PID parameter adjustment amount and the input parameters, and generating a brightness adjustment scheme based on the optimized S dimming curve; adjusting the working state of the guardrail lights based on the generated brightness adjustment scheme.

[0008] Optionally, in the first implementation manner of the first aspect of the present invention, obtaining the real-time brightness parameter and the real-time traffic flow parameter, and respectively performing fuzzification processing to obtain input parameters, including: obtaining the real-time brightness parameter and the real-time traffic flow parameter, where the real-time brightness parameter includes the real-time brightness deviation and the real-time deviation change rate; obtaining the pre-constructed fuzzy sets, where the pre-constructed fuzzy sets include the deviation fuzzy set, the change rate fuzzy set, and the traffic flow fuzzy set; and determining the input parameters based on the real-time brightness parameter, the real-time traffic flow parameter, and the pre-constructed fuzzy sets, where the input parameters include the brightness level, the deviation change rate level, and the traffic flow level.

[0009] Optionally, in the second implementation manner of the first aspect of the present invention, pre-constructing a PID parameter look-up table, and obtaining a PID parameter adjustment amount based on the input parameters and the pre-constructed PID parameter look-up table, including: obtaining historical lighting data, constructing a fuzzy rule base based on the historical lighting data, where the fuzzy rule base includes at least 50 groups of fuzzy three-dimensional rules; compiling the constructed fuzzy rule base into a PID parameter look-up table; and obtaining a PID parameter adjustment amount based on the input parameters and the pre-constructed PID parameter look-up table, where the PID parameter adjustment amount includes a proportional adjustment coefficient, an integral adjustment coefficient, and a differential adjustment coefficient.

[0010] Optionally, in the third implementation manner of the first aspect of the present invention, optimizing the pre-constructed S dimming curve based on the PID parameter adjustment amount and the input parameters, and generating a brightness adjustment scheme based on the optimized S dimming curve, including: determining the curve center point based on the real-time brightness deviation, and determining whether it is in the peak period based on the real-time traffic flow parameter; if it is in the peak period, obtaining the preset peak curve slope, and optimizing the pre-constructed S dimming curve based on the preset peak curve slope and the curve center point; if it is not in the peak period, calculating the curve slope based on the PID parameter adjustment amount, and optimizing the pre-constructed S dimming curve based on the calculated curve slope and the curve center point; and generating a brightness adjustment scheme based on the optimized S dimming curve.

[0011] Optionally, in the fourth implementation manner of the first aspect of the present invention, adjusting the working state of the guardrail lights based on the generated brightness adjustment scheme, and then including: obtaining the real-time working current information of the guardrail lights and performing preprocessing to obtain current characteristic information; inputting the current characteristic information into a pre-trained optical decay prediction model, and determining a risk warning scheme based on the optical decay risk result output by the optical decay prediction model.

[0012] Optionally, in the fifth implementation manner of the first aspect of the present invention, the obtaining the real-time working current information of the guardrail lamp and performing preprocessing to obtain current feature information includes: obtaining the real-time working current information of the guardrail lamp, performing wavelet threshold denoising processing and moving average filtering processing on the real-time working current information respectively to obtain preprocessed current information; calculating the total harmonic distortion rate, odd harmonic proportion, ripple peak-to-peak value and high-frequency energy density based on the preprocessed current information to construct a feature matrix; and performing normalization processing on the constructed feature matrix by using the Z-Score normalization formula to obtain current feature information.

[0013] Optionally, in the sixth implementation manner of the first aspect of the present invention, the inputting the current feature information into a pre-trained light decay prediction model and confirming a risk warning scheme according to the light decay risk result output by the light decay prediction model includes: inputting the current feature information into a pre-trained light decay prediction model, where the pre-trained light decay prediction model is an LSTM-RNN model, the LSTM-RNN model includes an input layer, a first LSTM layer, a second LSTM layer and an output layer connected in sequence, the first LSTM layer contains 64 neurons, the second LSTM layer contains 32 neurons, and the activation function of the output layer is a Sigmoid activation function; obtaining the light decay risk result output by the light decay prediction model, where the light decay risk result is the light decay percentage; confirming the warning level based on the light decay risk result, and obtaining a risk warning scheme corresponding to the warning level; and performing a risk warning action based on the risk warning scheme.

[0014] The second aspect of the present invention provides a guardrail lamp brightness adjustment device, including: a processing module, configured to obtain real-time brightness parameters and real-time traffic flow parameters, and perform fuzzification processing on them respectively to obtain input parameters; a lookup module, configured to pre-construct a PID parameter lookup table, and obtain a PID parameter adjustment amount based on the input parameters and the pre-constructed PID parameter lookup table; an optimization module, configured to optimize a pre-constructed S dimming curve based on the PID parameter adjustment amount and the input parameters, and generate a brightness adjustment scheme based on the optimized S dimming curve; and an adjustment module, configured to adjust the working state of the guardrail lamp based on the generated brightness adjustment scheme.

[0015] The third aspect of the present invention provides a guardrail lamp brightness adjustment device, where the guardrail lamp brightness adjustment device includes: a memory and at least one processor, and instructions are stored in the memory; at least one of the processors calls the instructions in the memory so that the guardrail lamp brightness adjustment device executes each step of the guardrail lamp brightness adjustment method described in any one of the above.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium, on which instructions are stored, and when the instructions are executed by a processor, each step of the guardrail lamp brightness adjustment method described in any one of the above is implemented.

[0017] In the technical solution of the present invention, by real-time monitoring of brightness and traffic flow and performing data fuzzification processing, accurate and reliable input parameters can be obtained. Based on the input parameters and a pre-constructed PID parameter lookup table, the PID parameter adjustment amount is obtained, realizing a rapid response to brightness adjustment and ensuring the matching of the generated brightness adjustment scheme with the environment and traffic conditions. In addition, based on the optimized S dimming curve, the brightness is dynamically adjusted, optimizing energy consumption and achieving the goal of energy conservation and carbon reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is the first flowchart of the guardrail lamp brightness adjustment method provided by the embodiment of the present invention;

[0019] Figure 2 It is the second flowchart of the guardrail lamp brightness adjustment method provided by the embodiment of the present invention;

[0020] Figure 3 It is the structural schematic diagram of the guardrail lamp brightness adjustment device provided by the embodiment of the present invention;

[0021] Figure 4 It is the structural schematic diagram of the guardrail lamp brightness adjustment equipment provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The present invention provides a guardrail lamp brightness adjustment method, device, equipment and storage medium. In the present invention, the terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above drawings are used to distinguish similar objects and do not have to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order different from that illustrated or described here. In addition, the term "comprising" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or equipment comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0023] For ease of understanding, the specific process of the embodiment of the present invention is described below. Please refer to Figure 1 , an embodiment of the guardrail lamp brightness adjustment method in the embodiment of the present invention includes:

[0024] 101. Obtain the real-time brightness parameter and the real-time traffic flow parameter, and perform fuzzy processing on them respectively to obtain the input parameters;

[0025] 102. Pre-build a PID parameter lookup table, and obtain the PID parameter adjustment amount based on the input parameters and the pre-built PID parameter lookup table;

[0026] In this embodiment, by obtaining the real-time brightness parameter and the real-time traffic flow parameter and performing fuzzy processing on them, the input parameters can more accurately reflect the changes in ambient light and traffic conditions, thereby providing more reliable data support for finding the PID parameter adjustment amount; at the same time, obtaining the PID parameter adjustment amount based on the real-time input parameters and the pre-built PID parameter lookup table can achieve a rapid response to brightness adjustment, ensuring that the brightness of the guardrail lights always matches the actual situation.

[0027] 103. Optimize the pre-built S dimming curve based on the PID parameter adjustment amount and the input parameters, and generate a brightness adjustment plan based on the optimized S dimming curve;

[0028] In this embodiment, through the pre-built S dimming curve and the optimization algorithm, the S dimming curve can be dynamically optimized according to the real-time input parameters and the PID parameter adjustment amount, thereby generating a more reasonable brightness adjustment plan; it can not only meet the traffic lighting requirements, but also minimize the energy consumption of the guardrail lights on the premise of ensuring the lighting effect, achieving the goal of energy conservation and emission reduction.

[0029] 104. Adjust the working state of the guardrail lights based on the generated brightness adjustment plan;

[0030] In this embodiment, by adjusting the brightness of the guardrail lights in real time, the lighting effect under different lighting and traffic conditions can be ensured, thereby improving the safety of road driving; at the same time, this technical solution also enhances the stability and reliability of the guardrail lights during operation through the optimization algorithm and intelligent control means, reducing the safety risks caused by failures.

[0031] This application discloses a method for adjusting the brightness of guardrail lights. By collecting brightness and traffic flow data in real time and performing fuzzy processing, the accuracy and real-time performance of the guardrail light brightness adjustment are improved, ensuring that the generated brightness adjustment plan matches the environment and traffic conditions; at the same time, through pre-building a PID parameter lookup table and an optimization method to adjust and optimize the pre-built S dimming curve, not only the accuracy of dimming is improved, but also a differential brightness adjustment plan for different scenarios and requirements is realized, making the lighting effect of the guardrail lights more in line with the environmental requirements; in addition, this technical solution improves the intelligent control level and automation degree of the guardrail lights, and through real-time brightness adjustment, enhances the safety and reliability of the guardrail lights during operation, reducing potential safety hazards.

[0032] Further, in this embodiment, obtaining the real-time brightness parameter and the real-time traffic flow parameter, and respectively performing fuzzification processing, the obtained input parameters include:

[0033] 201. Obtain the real-time brightness parameter and the real-time traffic flow parameter, where the real-time brightness parameter includes the real-time brightness deviation and the real-time deviation change rate;

[0034] In this embodiment, the real-time brightness deviation is the difference between the target brightness and the actual brightness, with the unit of Lux. The real-time brightness deviation reflects the gap between the current state of the guardrail lamp and the ideal value, that is, it provides static error information, and this static error information determines the dimming direction.

[0035] In this embodiment, the real-time deviation change rate is the change rate of the brightness deviation over time, with the unit of Lux / s, which is used to describe how fast the brightness value deviates from a certain reference value and reflects the dynamic characteristics of the guardrail lamp, and can suppress overshoot; in the actual calculation of the deviation change rate, first, record a series of consecutive time-point brightness values in sequence, and set the brightness value at the first time point as the reference reference value; subsequently, for two adjacent time points, calculate the difference between the brightness value at the latter time point and the reference brightness value to obtain the brightness deviation value; finally, divide the calculated brightness deviation value by the time interval between the two time points, thereby obtaining the rate of change of the brightness deviation over time during this time period, that is, the deviation change rate.

[0036] In this embodiment, the real-time traffic flow parameter is obtained through a radar / camera and is the real-time traffic flow percentage. The real-time traffic flow parameter reflects the road load; in addition, the real-time traffic flow parameter, as an external disturbance variable, corrects the priority of the dimming rate. Specifically, it responds quickly during peak hours and gives priority to energy conservation during off-peak hours.

[0037] 202. Obtain the pre-constructed fuzzy sets, where the pre-constructed fuzzy sets include a deviation fuzzy set, a change rate fuzzy set, and a traffic flow fuzzy set;

[0038] In this embodiment, the deviation fuzzy set includes 7 levels, which are: NB, extremely large negative deviation; NM, medium negative deviation; NS, tiny negative deviation; ZO, zero deviation; PS, tiny positive deviation; PM, medium positive deviation; and PB, extremely large positive deviation.

[0039] In this embodiment, the change rate fuzzy set includes 5 levels, which are: N, rapid negative change; NS, slow negative change; ZO, stable; PS, slow positive change; and P, rapid positive change.

[0040] In this embodiment, the traffic fuzzy set includes 3 levels, namely: Low, 0 - 30%, Medium, 20 - 80% and High, 70 - 100%.

[0041] 203. Confirm input parameters based on real - time brightness parameters, real - time traffic flow parameters and a pre - constructed fuzzy set. The input parameters include brightness level, deviation change rate level and traffic flow level;

[0042] In this embodiment, based on real - time parameters and a pre - constructed fuzzy set, input parameters, including brightness level, deviation change rate level and traffic flow level, can be accurately confirmed, thereby realizing intelligent control of road lighting brightness, improving the quality and efficiency of road lighting, and reducing energy consumption and environmental pollution at the same time.

[0043] Further, in this embodiment, for the pre - constructed PID parameter look - up table, obtain the PID parameter adjustment amount based on the input parameters and the pre - constructed PID parameter look - up table, including:

[0044] 301. Obtain historical lighting data and construct a fuzzy rule base based on the historical lighting data. The fuzzy rule base includes at least 50 groups of fuzzy three - dimensional rules;

[0045] In this embodiment, the historical lighting data covers various scenarios, including measured data in foggy days, heavy rain, peak traffic hours and late at night, etc. The fuzzy rule base includes at least 50 groups of fuzzy three - dimensional rules, enabling the system to more comprehensively consider various possible input situations, thereby improving the accuracy and robustness of control.

[0046] In this embodiment, the construction of the fuzzy three - dimensional rules is based on the following adjustment logic:

[0047] Proportional coefficient: When the deviation is large, quickly eliminate the error by enhancing the proportional action. For example, when the brightness deviation is a large positive deviation, the proportional coefficient increases by 0.8;

[0048] Integral coefficient: When there is a steady - state error, reduce the error by enhancing the integral action. For example, when the brightness deviation is stable, the integral coefficient increases by 0.1.

[0049] Differential coefficient: When the change rate is too fast, suppress the excessive change by enhancing the differential action. For example, when the brightness deviation change rate is a rapid negative change, the differential coefficient increases by 0.2.

[0050] 302. Compile the constructed fuzzy rule base into a PID parameter look - up table;

[0051] In this embodiment, the PID parameter look-up table is a 512-byte look-up table. The design and application of this table significantly improve the inference speed of the system. Specifically, compared with the case where this look-up table is not used, the inference speed is increased by 3 times. This improvement is of great significance for enhancing the system performance and efficiency.

[0052] 303. Obtain the PID parameter adjustment amount based on the input parameter and the pre-constructed PID parameter look-up table. The PID parameter adjustment amount includes a proportional adjustment coefficient, an integral adjustment coefficient, and a differential adjustment coefficient.

[0053] Further, in this embodiment, during the monthly periodic optimization process, the NSGA-II multi-objective genetic algorithm is used to precisely adjust the weights of the three-dimensional rules, aiming to achieve a balance between the response speed and energy consumption. When using the NSGA-II multi-objective genetic algorithm for weight adjustment, its constraint conditions are that the transition time ≤ 6.7 seconds and the dimming energy consumption ≤ 120% of the nominal value. Through this algorithm, the optimal weight combination can be found to ensure effective control of energy consumption while maintaining the fast response of the system, thus achieving the best balance between the two.

[0054] Further, in this embodiment, optimizing the pre-constructed S dimming curve based on the PID parameter adjustment amount and the input parameter, and generating a brightness adjustment plan based on the optimized S dimming curve, includes:

[0055] 401. Confirm the center point of the curve based on the real-time brightness deviation, and determine whether it is in the peak period based on the real-time traffic flow parameter.

[0056] In this embodiment, confirming the center point of the curve based on the real-time brightness deviation ensures that 50% of the brightness change occurs in the middle of the transition, thereby ensuring the accuracy of the dimming curve and avoiding problems such as insufficient lighting or over-illumination caused by brightness deviation, thus improving the comfort and safety of road lighting. When confirming the center point of the curve, the Hi3D centroid method principle can be used to achieve it. The specific steps are as follows: First, set the brightness threshold to distinguish the bright and dark parts to ensure accurate calculation. Then, collect the brightness data along the curve direction. The data can be obtained by sensors or image devices. Then, calculate the centroid coordinates of the brightness in the area above the brightness threshold, through integration or summation, and determine according to the collection method and accuracy requirements. The centroid position of the brightness is used as a preliminary estimate of the center point of the curve. Next, adjust the centroid position of the brightness according to the curve shape and brightness distribution to ensure uniform brightness change. Finally, verify the center point position through observation or measurement to ensure that the brightness change meets the expectations, that is, 50% of the brightness change occurs in the middle of the transition.

[0057] In this embodiment, when the traffic flow level is High: 70 - 100%, it indicates that it is in the peak period.

[0058] 402. If it is during the peak period, obtain the preset peak curve slope, and optimize the pre-constructed S dimming curve based on the preset peak curve slope and the curve center point;

[0059] In this embodiment, in order to ensure traffic safety and improve road lighting effects, the control strategy of the guardrail lights must be able to be dynamically adjusted according to changes in traffic flow; this adjustment involves the concept of the forced curve slope, that is, when adjusting the duty cycle of white light and yellow light in the guardrail lights, the rate of change of brightness; during the peak period, in order to quickly respond to the increase in traffic flow, the forced curve slope k is set to be not less than 1.5. This parameter ensures that the brightness change of the guardrail lights must be fast enough to limit the transition time within 4 seconds to avoid visual interference to drivers; in addition, in order to provide the clearest and strongest signal indication and ensure the lighting effect, the white light power is preferentially increased to 100%. This priority setting ensures that during the traffic peak period, the guardrail lights can provide the best lighting conditions, helping drivers to see the road conditions more clearly, thereby improving driving safety.

[0060] 403. If it is not during the peak period, calculate the curve slope based on the PID parameter adjustment amount, and optimize the pre-constructed S dimming curve based on the calculated curve slope and the curve center point;

[0061] In this embodiment, the curve slope is calculated in real time by fuzzy PID. Specifically, the proportional adjustment coefficient, integral adjustment coefficient, and differential adjustment coefficient are adjustment amounts, and the curve slope can be regarded as the rate of change between the adjustment amount and time, which reflects the degree of inclination of the curve at a certain point; the calculated curve slope range is 0.5 - 2.0, corresponding to a transition duration of 6.7 - 2.5 seconds; dimming based on the S curve optimized by the calculated curve slope range has the advantages of smooth transition, energy saving and high efficiency, extending the lamp life, and improving the user experience; by precisely controlling the change of light intensity, this dimming method can effectively reduce energy consumption while ensuring the lighting quality, reduce the thermal stress and mechanical stress generated by the sudden change of light intensity of the lamp, thereby extending the lamp life; at the same time, it can also flexibly adjust the light intensity and transition time according to different environments and requirements, providing a more comfortable and personalized lighting experience for users.

[0062] 404. Generate a brightness adjustment plan based on the optimized S dimming curve;

[0063] In this embodiment, the mathematical model of the pre-constructed S dimming curve is:

[0064]

[0065] where, L initial is the current brightness value, L target is the target brightness value, k is the curve slope, t is the time point, t0 is the center point of the curve;

[0066] After substituting the curve slope calculated based on the PID parameter adjustment amount or the preset peak curve slope into the mathematical model of the S dimming curve, the adjusted brightness L(t) corresponding to different moments t can be calculated to obtain a brightness adjustment scheme.

[0067] Further, please refer to Figure 2 , the second embodiment of the guardrail lamp brightness adjustment method in the embodiment of the present invention includes:

[0068] 501. Obtain the real-time working current information of the guardrail lamp and perform preprocessing to obtain current characteristic information;

[0069] In this embodiment, the real-time working current information of the guardrail lamp can be obtained through a current transformer, and the real-time working current information is hardware-filtered through a second-order RC low-pass filter to suppress high-frequency interference and eliminate environmental interference.

[0070] 502. Input the current characteristic information into a pre-trained optical decay prediction model, and confirm a risk warning scheme according to the optical decay risk result output by the optical decay prediction model.

[0071] In this embodiment, through the real-time monitoring and fine preprocessing of the guardrail lamp working current information, the current characteristic information can be accurately extracted. Subsequently, the detailed current characteristic information is input into the optical decay prediction model trained and optimized with a large amount of data. The model will perform in-depth analysis and calculation based on these characteristics and output an accurate optical decay risk result; according to the optical decay risk result, the system will intelligently confirm and activate corresponding risk warning schemes, such as performing lamp maintenance in advance, adjusting the lighting strategy or issuing a replacement prompt, etc.; this series of measures not only effectively extends the service life of the guardrail lamp, ensures its continuous and stable lighting effect, but also greatly improves road safety and driving comfort, providing strong technical support for urban lighting management.

[0072] Further, in this embodiment, the obtaining the real-time working current information of the guardrail lamp and performing preprocessing to obtain current characteristic information includes:

[0073] 601. Obtain the real-time working current information of the guardrail lamp, and perform wavelet threshold denoising processing and moving average filtering processing on the real-time working current information respectively to obtain preprocessed current information;

[0074] In this embodiment, the real-time working current information of the guardrail lamp is obtained, and wavelet threshold denoising processing and moving average filtering processing are performed on it to effectively remove noise and fluctuations, so as to extract effective ripple characteristics, thereby obtaining more accurate preprocessed current information.

[0075] 602. Calculate the total harmonic distortion rate, odd harmonic ratio, ripple peak-to-peak value, and high-frequency energy density based on the preprocessed current information to construct a feature matrix;

[0076] In this embodiment, the calculation of the total harmonic distortion rate (THD) includes the steps:

[0077] 1. Obtain the preprocessed current signal data, denoted as I(t);

[0078] 2. Perform Fourier transform on I(t) to obtain the frequency spectrum information;

[0079] 3. Extract the fundamental wave amplitude I1 and the amplitudes of each harmonic In (n = 2, 3,...) from the frequency spectrum information;

[0080] 4. Calculate the total harmonic distortion rate according to the formula THD = √(Σ(In^2) / I1^2) - 1, where Σ represents the sum of the squares of all harmonic amplitudes.

[0081] In this embodiment, the calculation of the odd harmonic ratio includes the steps:

[0082] 1. Extract the amplitudes of all odd harmonics from the frequency spectrum information, denoted as Iodd_n (n = 1, 3, 5,...);

[0083] 2. Calculate the sum of the squares of all odd harmonic amplitudes Σ(Iodd_n^2);

[0084] 3. Calculate the sum of the squares of all harmonic amplitudes Σ(In^2);

[0085] 4. Calculate the odd harmonic ratio according to the formula odd harmonic ratio = Σ(Iodd_n^2) / Σ(In^2).

[0086] In this embodiment, the calculation of the ripple peak-to-peak value includes the steps:

[0087] 1. Extract a complete cycle signal from the preprocessed current signal I(t);

[0088] 2. Find the maximum value Imax and the minimum value Imin in this cycle signal;

[0089] 3. Calculate the ripple peak-to-peak value according to the formula ripple peak-to-peak value = Imax - Imin.

[0090] In this embodiment, the calculation of the high-frequency energy density includes the steps:

[0091] 1. Set a high-frequency range, for example, starting from a certain harmonic to the highest harmonic;

[0092] 2. Extract the amplitudes of all harmonics within this high-frequency range from the frequency spectrum information, denoted as Ih_n;

[0093] 3. Calculate the sum of squares Σ(Ih_n^2) of the amplitudes of all harmonics in the high-frequency range;

[0094] 4. Calculate the high-frequency energy density according to the formula high-frequency energy density = Σ(Ih_n^2) / total energy, where the total energy is the sum of squares Σ(In^2) of the amplitudes of all harmonics.

[0095] In this embodiment, the calculated total harmonic distortion rate, odd harmonic ratio, ripple peak-to-peak value, and high-frequency energy density are used as feature vectors to construct a feature matrix. Each column represents the feature vector of a sample, and each row represents a feature dimension; among them, the total harmonic distortion rate and the ripple peak-to-peak value directly reflect the aging of the lamp board chip, and the odd harmonic ratio and the high-frequency energy density are related to the life of the drive circuit.

[0096] 603. Use the Z-Score normalization formula to normalize the constructed feature matrix to obtain current feature information;

[0097] In this embodiment, the Z-Score normalization formula is used to normalize the feature matrix to obtain current feature information; the normalization process can eliminate the dimensional differences between different parameters, improve the accuracy and reliability of model analysis, and can also improve the model convergence speed.

[0098] Furthermore, in this embodiment, inputting the current feature information into a pre-trained optical decay prediction model and confirming a risk warning plan according to the optical decay risk result output by the optical decay prediction model includes:

[0099] 701. Input the current feature information into a pre-trained optical decay prediction model. The pre-trained optical decay prediction model is an LSTM-RNN model. The LSTM-RNN model includes an input layer, a first LSTM layer, a second LSTM layer, and an output layer connected in sequence. The first LSTM layer contains 64 neurons, the second LSTM layer contains 32 neurons, and the activation function of the output layer is the Sigmoid activation function;

[0100] In this embodiment, by inputting the current feature information into a pre-trained LSTM-RNN optical decay prediction model, accurate prediction of the optical decay risk is achieved; the LSTM-RNN optical decay prediction model has a reasonable structure, including an input layer, two LSTM layers, and an output layer. Among them, the first LSTM layer has 64 neurons, and the second LSTM layer has 32 neurons. Such a design can fully capture the temporal dependence relationship in the current feature information and improve the prediction accuracy; secondly, the output layer uses the Sigmoid activation function, so that the output optical decay risk result is the optical decay percentage. This result is intuitive and easy to understand, facilitating subsequent risk assessment and management.

[0101] 702. Obtain the light attenuation risk result output by the light attenuation prediction model, where the light attenuation risk result is the light attenuation percentage.

[0102] 703. Confirm the warning level based on the light attenuation risk result, and obtain the risk warning plan corresponding to the warning level.

[0103] In this embodiment, confirming the warning level based on the light attenuation risk result and obtaining the risk warning plan corresponding to the warning level realizes the hierarchical management of risks, and can take corresponding warning measures according to different risk levels, improving the pertinence and effectiveness of risk management. Specifically, the risk levels can include level one, level two, and level three. Among them, the triggering condition for level one is that the light attenuation risk < 5%, and the corresponding warning action is: mark for observation, and the detection frequency is increased to 1 time / week; the triggering condition for level two is that the light attenuation risk is 5% - 10%, and the corresponding warning action is: generate a work order, and conduct on-site review within 2 weeks; the triggering condition for level three is that the light attenuation risk > 10%, and the corresponding warning action is: replace immediately, and synchronously calibrate the surrounding lamps.

[0104] 704. Execute the risk warning action based on the risk warning plan.

[0105] In this embodiment, executing the risk warning action based on the risk warning plan ensures that a timely response can be made when the light attenuation risk occurs, and effective measures are taken for intervention, thereby ensuring the stable operation of the guardrail lights and extending the service life of the guardrail lights.

[0106] The method for adjusting the brightness of guardrail lights in the embodiment of the present invention has been described above. Next, the device for adjusting the brightness of guardrail lights in the embodiment of the present invention will be described. Please refer to Figure 3 , an embodiment of the device for adjusting the brightness of guardrail lights in the embodiment of the present invention includes:

[0107] A processing module 801, configured to obtain real-time brightness parameters and real-time traffic flow parameters, and perform fuzzy processing on them respectively to obtain input parameters.

[0108] A lookup module 802, configured to pre-build a PID parameter lookup table, and obtain the PID parameter adjustment amount based on the input parameters and the pre-built PID parameter lookup table.

[0109] An optimization module 803, configured to optimize the pre-built S dimming curve based on the PID parameter adjustment amount and the input parameters, and generate a brightness adjustment plan based on the optimized S dimming curve.

[0110] An adjustment module 804, configured to adjust the working state of the guardrail lights based on the generated brightness adjustment plan.

[0111] Based on the same idea as the method in the above embodiments, the device provided in this application can implement the method of the above embodiments.

[0112] Above Figure 3 The guardrail lamp brightness adjustment device in the embodiments of the present invention is described in detail from the perspective of modular functional entities. Below, the guardrail lamp brightness adjustment device in the embodiments of the present invention is described in detail from the perspective of hardware processing.

[0113] Figure 4 FIG. 9 is a schematic structural diagram of a guardrail lamp brightness adjustment device provided by an embodiment of the present invention. The guardrail lamp brightness adjustment device 900 may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 910 (for example, one or more processors) and a memory 920, and one or more storage media 930 (for example, one or more mass storage devices) for storing application programs 933 or data 932. Among them, the memory 920 and the storage media 930 may be transient storage or persistent storage. The program stored in the storage media 930 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the guardrail lamp brightness adjustment device 900. Further, the processor 910 may be configured to communicate with the storage media 930 and execute a series of instruction operations in the storage media 930 on the guardrail lamp brightness adjustment device 900 to implement the steps of the guardrail lamp brightness adjustment method provided in the above method embodiments.

[0114] The guardrail lamp brightness adjustment device 900 may further include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input / output interfaces 960, and / or one or more operating systems 931, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that Figure 4 The shown structural diagram of the guardrail lamp brightness adjustment device does not limit the guardrail lamp brightness adjustment device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0115] The present invention also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium, or may also be a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the guardrail lamp brightness adjustment method.

[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, or units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0117] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or 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 causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. 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.

[0118] Finally, it should be noted that the above are only preferred examples of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for adjusting the brightness of a guardrail lamp, characterized in that: include: Obtain real-time brightness parameters and real-time vehicle flow parameters, and perform fuzzy processing on them respectively to obtain input parameters; A PID parameter lookup table is pre-built to obtain a PID parameter adjustment amount based on input parameters and the pre-built PID parameter lookup table; Optimize the pre-built S dimming curve based on the PID parameter adjustment amount and the input parameters, and generate a brightness adjustment scheme based on the optimized S dimming curve; The working state of the guardrail light is adjusted based on the generated brightness adjustment scheme.

2. The method for adjusting the brightness of a guardrail lamp according to claim 1, characterized in that: The real-time brightness parameter and the real-time vehicle flow parameter are obtained and fuzzy processed respectively to obtain input parameters, including: Acquire real-time brightness parameters and real-time vehicle flow parameters, wherein the real-time brightness parameters include real-time brightness deviation and real-time deviation change rate; Acquire pre-constructed fuzzy sets, wherein the pre-constructed fuzzy sets include a deviation fuzzy set, a change rate fuzzy set, and a flow fuzzy set; Input parameters are confirmed based on the real-time brightness parameter, the real-time vehicle flow parameter and the pre-constructed fuzzy set, wherein the input parameters include brightness level, deviation change rate level and vehicle flow level.

3. The method for adjusting the brightness of a guardrail lamp according to claim 1, characterized in that: The pre-constructed PID parameter lookup table obtains the PID parameter adjustment amount based on the input parameter and the pre-constructed PID parameter lookup table, including: Acquire historical lighting data, and construct a fuzzy rule base based on the historical lighting data, wherein the fuzzy rule base includes at least 50 groups of fuzzy three-dimensional rules; Compile the constructed fuzzy rule base into a PID parameter lookup table; A PID parameter adjustment amount is obtained based on input parameters and a pre-constructed PID parameter lookup table, wherein the PID parameter adjustment amount includes a proportional adjustment coefficient, an integral adjustment coefficient, and a differential adjustment coefficient.

4. The method for adjusting the brightness of a guardrail lamp according to claim 2, characterized in that: The method of optimizing the pre-built S dimming curve based on the PID parameter adjustment amount and the input parameters, and generating a brightness adjustment scheme based on the optimized S dimming curve, includes: Confirm the center point of the curve based on the real-time brightness deviation, and determine whether it is in the peak period based on the real-time traffic flow parameters; If it is during the peak period, the preset peak curve slope is obtained, and the pre-built S dimming curve is optimized based on the preset peak curve slope and the center point of the curve; If it is not during the peak period, the curve slope is calculated based on the PID parameter adjustment amount, and the pre-constructed S dimming curve is optimized based on the calculated curve slope and the center point of the curve; Generate a brightness adjustment solution based on the optimized S dimming curve.

5. The method for adjusting the brightness of a guardrail lamp according to claim 1, characterized in that: The step of adjusting the working state of the guardrail lamp based on the generated brightness adjustment scheme comprises: Obtain the real-time working current information of the guardrail lamp and perform preprocessing to obtain the current characteristic information; The current characteristic information is input into the pre-trained light decay prediction model, and the risk warning plan is confirmed according to the light decay risk result output by the light decay prediction model.

6. The method for adjusting the brightness of a guardrail lamp according to claim 5, characterized in that: The real-time working current information of the guardrail lamp is obtained and preprocessed to obtain current characteristic information, including: Acquire the real-time working current information of the guardrail lamp, perform wavelet threshold denoising and sliding average filtering on the real-time working current information, and obtain pre-processed current information; Based on the preprocessed current information, the total harmonic distortion rate, odd harmonic proportion, ripple peak-to-peak value and high-frequency energy density are calculated to construct a feature matrix; The constructed characteristic matrix is ​​normalized using the Z-Score standardization formula to obtain the current characteristic information.

7. The method for adjusting the brightness of a guardrail lamp according to claim 5, characterized in that: The step of inputting the current characteristic information into the pre-trained light decay prediction model and confirming the risk warning scheme according to the light decay risk result output by the light decay prediction model includes: Inputting the current characteristic information into a pre-trained light decay prediction model, wherein the pre-trained light decay prediction model is an LSTM-RNN model, wherein the LSTM-RNN model comprises an input layer, a first LSTM layer, a second LSTM layer, and an output layer connected in sequence, wherein the first LSTM layer comprises 64 neurons, the second LSTM layer comprises 32 neurons, and the activation function of the output layer is a Sigmoid activation function; Obtaining a light decay risk result output by a light decay prediction model, wherein the light decay risk result is a light decay percentage; Confirm the warning level based on the light attenuation risk results and obtain the risk warning plan corresponding to the warning level; Execute risk warning actions based on risk warning plans.

8. A guardrail lamp brightness adjustment device, characterized in that: include: A processing module is used to obtain real-time brightness parameters and real-time vehicle flow parameters, and perform fuzzy processing on them respectively to obtain input parameters; A search module, used for pre-building a PID parameter search table, and obtaining a PID parameter adjustment amount based on an input parameter and the pre-built PID parameter search table; An optimization module, used for optimizing a pre-built S dimming curve based on a PID parameter adjustment amount and an input parameter, and generating a brightness adjustment scheme based on the optimized S dimming curve; The adjustment module is used to adjust the working state of the guardrail light based on the generated brightness adjustment scheme.

9. A guardrail lamp brightness adjustment device, characterized in that: The guardrail lamp brightness adjustment device comprises: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors calls the instructions in the memory so that the guardrail lamp brightness adjustment device performs each step of the guardrail lamp brightness adjustment method as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by the processor, the various steps of the guardrail lamp brightness adjustment method as described in any one of claims 1-7 are implemented.

Images