Tower crane lighting control method and system based on Kalman filtering and energy consumption optimization

Abstract

The present invention provides a tower crane lighting control method and system based on Kalman filtering and energy consumption optimization, which relates to the technical field of intelligent tower crane control. The method collects the mechanical vibration signals and attitude data of the tower crane in real time through vibration sensors and inclination sensors; performs pre-filtering of low-pass filtering on the vibration signals, performs wavelet transform on the filtered signals, suppresses the characteristic frequency band of gear meshing noise in the frequency domain, and obtains the filtered vibration components; models the vibration components as system state variables based on the improved extended Kalman filter, and outputs in real time the tower crane attitude estimation values including the position coordinates of the tower crane boom, the swing angle, and the attitude error after vibration compensation; inputs the historical energy consumption data, the hoisting task plan, and the real-time grid load data, and performs hybrid energy consumption prediction through a dynamic multi-objective energy consumption optimization model; divides the lighting area into priorities according to the boom position and the operation plan, and distributes the power according to the grid peak and valley according to the balanced load strategy, effectively reducing the energy consumption cost.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent tower crane control, and particularly relates to a tower crane lighting control method and system based on Kalman filtering and energy consumption optimization. Background Art

[0002] With the acceleration of the urbanization process, there are more and more high-rise buildings and large-scale infrastructure construction projects. As an indispensable important equipment in construction, the lighting management and energy consumption optimization of tower cranes are particularly important. Traditional tower crane lighting management systems usually adopt simple timed switching modes or automatic control based on light sensors, but these methods have many deficiencies when facing complex construction site environments:

[0003] 1. High data noise: During the operation of the tower crane, mechanical vibrations are frequent and intense, which easily lead to a large amount of noise mixed in the sensor data, affecting the judgment and control accuracy of the system.

[0004] 2. Difficult energy consumption optimization: Existing systems mostly adopt Kalman filtering algorithms with fixed parameters, which cannot adapt to the dynamically changing construction site environment, resulting in difficult energy consumption management to achieve the best effect.

[0005] 3. High grid load pressure: The grid load on construction sites changes frequently, especially during peak electricity consumption periods, and the power supply is unstable. Traditional lighting management systems lack the ability to interact with the grid and are difficult to optimize energy consumption according to the grid load status.

[0006] 4. Increasing environmental protection requirements: With the increasingly strict environmental protection regulations, traditional systems perform poorly in reducing energy consumption and carbon emissions and are difficult to meet the environmental protection requirements of modern construction.

[0007] In view of the above problems, there is an urgent need for an intelligent tower crane lighting management system that can work stably in a mechanical vibration environment. Combining an energy consumption optimization model, dynamically adjusting the tower crane lighting strategy can not only improve the anti-interference ability of the system but also optimize energy consumption management, thereby achieving energy conservation, consumption reduction, and environmental protection goals. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to propose a tower crane lighting control method and system based on Kalman filtering and energy consumption optimization. By combining an improved Kalman filtering algorithm and an energy consumption optimization model, the anti-interference ability of the system in a mechanical vibration environment is significantly improved, and the energy consumption cost is effectively reduced through a dynamic optimization strategy, meeting the intelligent and environmental protection requirements of modern construction.

[0009] To achieve the above purpose, the present invention provides the following technical solutions:

[0010] For the above purposes, in a first aspect, the present invention provides a tower crane lighting control method based on Kalman filtering and energy consumption optimization, including the following steps:

[0011] Collect the tower crane mechanical vibration signals and attitude data in real time through vibration sensors and inclination sensors, and use a band-stop filter to suppress noise in the preset vibration frequency band;

[0012] Dynamically set the process noise covariance matrix and the observation noise covariance matrix according to the vibration intensity, perform pre-filtering of the vibration signal with a low-pass filter, perform wavelet transform on the filtered signal, and suppress the characteristic frequency band of gear meshing noise in the frequency domain to obtain the filtered vibration component;

[0013] Model the vibration component as a system state variable based on the improved extended Kalman filter, and output in real time the tower crane attitude estimation value including the position coordinates of the tower crane boom, the swing angle, and the attitude error after vibration compensation;

[0014] Input historical energy consumption data, lifting task plans, and real-time grid load data, and perform hybrid energy consumption prediction through a dynamic multi-objective energy consumption optimization model;

[0015] According to the boom position and the operation plan, divide the lighting area into priorities, and allocate power according to the peak-valley of the power grid according to the balanced load strategy.

[0016] As a further solution of the present invention, using a band-stop filter to suppress noise in the preset vibration frequency band includes the following steps:

[0017] Divide the preset vibration frequency band into multiple sub-frequency bands according to the mechanical vibration characteristics, and design a band-stop filter for each sub-frequency band;

[0018] Use bilinear transformation to convert the analog filter into a digital filter. The original sensor data is first suppressed of vibration noise by the band-stop filter, and then input into the extended Kalman filter for state estimation of the tower crane displacement and attitude angle. Combine wavelet transform to analyze the filter residuals and dynamically adjust the parameters of the band-stop filter.

[0019] As a further solution of the present invention, the band-stop filter uses a Butterworth band-stop filter, and the center frequency of the band-stop filter is determined according to the peak value of the vibration spectrum measured by the sensor; the bandwidth of the band-stop filter is set to the peak frequency ±10%; the transfer function of the Butterworth band-stop filter is:

[0020]

[0021] Among them, is a complex frequency variable used to characterize the frequency domain characteristics of the filter, is the center angular frequency of the vibration noise that the filter needs to suppress, , is the center frequency of the stopband; is the quality factor, the larger the value, the narrower the stopband, the smaller the value, the wider the stopband, calculated from the bandwidth , and are the stopband cut-off frequencies, is the stopband bandwidth.

[0022] As a further solution of the present invention, when modeling the vibration component as a system state variable based on the improved extended Kalman filter, it further includes:

[0023] Initializing the process noise covariance matrix and the observation noise covariance matrix;

[0024] Modeling the mechanical vibration as a periodic state variable, where:

[0025]

[0026] In the formula, is the amplitude; is the main vibration frequency, representing the core frequency of the periodic vibration motion; is the phase angle, is the discrete time step at the sampling moment, , is the sampling period;

[0027] Performing frequency-domain analysis on the filter residual based on wavelet transform and dynamically adjusting the values of the noise covariance matrix and the value of the observation noise covariance matrix , where:

[0028]

[0029]

[0030] In the formula, is the initial process noise covariance; is the initial observation noise covariance; and are the adaptive weight coefficients; is the high-frequency band extracted from the filter residual through wavelet transform; is the variance of the observation residual, representing the time-varying characteristics of the sensor noise;

[0031] Real-time output of the position coordinates of the tower crane boom , the swing angle and the attitude error after vibration compensation.

[0032] As a further solution of the present invention, when dividing the priority levels of the lighting areas according to the position of the jib and the operation plan, the lighting areas are divided into high-priority areas (operation radius ±3m), medium-priority areas (adjacent areas), and low-priority areas (non-operation areas).

[0033] As a further solution of the present invention, the power distribution according to the balanced load strategy according to the peak and valley of the power grid includes: when the power grid is at peak (electricity price ≥ 1.0 yuan / kWh), the lighting power of the high-priority area is increased to 80%, and the low-priority area is reduced to 20%; when it is at valley (electricity price ≤ 0.5 yuan / kWh), the power is distributed according to the balanced load strategy.

[0034] As a further solution of the present invention, the tower crane lighting control method based on Kalman filtering and energy consumption optimization further includes retraining the dynamic multi-objective energy consumption optimization model according to a preset time period. When it is detected that the main frequency deviation of mechanical vibration ≥ 10Hz or the power grid load suddenly changes, trigger the recalibration of Kalman filter parameters and the iteration of the optimization model; among them, the triggering conditions for retraining the dynamic multi-objective energy consumption optimization model are:

[0035] The mechanical vibration energy suddenly changes in the 120Hz frequency band by more than 30% of the historical average;

[0036] The power grid load volatility is ≥ 10% for three consecutive sampling periods;

[0037] The deviation between the actual lighting energy consumption and the predicted value lasts for 5 minutes ≥ 15%.

[0038] In the second aspect, the present invention provides a tower crane lighting control system based on Kalman filtering and energy consumption optimization, including the following components:

[0039] A data acquisition module for real-time collecting the tower crane mechanical vibration signals and attitude data through vibration sensors and inclination sensors;

[0040] A covariance adjustment module for dynamically setting the process noise covariance matrix and the observation noise covariance matrix according to the vibration intensity, performing pre-filtering on the vibration signal by low-pass filtering, performing wavelet transform on the filtered signal, and suppressing the characteristic frequency band of gear meshing noise in the frequency domain to obtain the filtered vibration component;

[0041] An attitude estimation module for modeling the vibration component as a system state variable based on the improved extended Kalman filter, and real-time outputting the tower crane attitude estimation value including the position coordinates of the tower crane jib, the swing angle, and the attitude error after vibration compensation;

[0042] A multi-objective energy consumption optimization module for inputting historical energy consumption data, lifting task plans, and real-time power grid load data, and performing hybrid energy consumption prediction through a dynamic multi-objective energy consumption optimization model;

[0043] The lighting control execution module is used to divide the lighting area into priorities according to the boom position and operation plan, and allocate power according to the grid peak-valley based on the balanced load strategy.

[0044] As a further solution of the present invention, it further includes a noise suppression module, which is used to suppress the vibration signal noise through a band-stop filter according to the preset vibration frequency band, and suppress the gear meshing noise and high-frequency interference.

[0045] Compared with the prior art, a tower crane lighting control method and system based on Kalman filtering and energy consumption optimization proposed by the present invention have the following beneficial effects:

[0046] The present invention fuses multi-sensor data through an improved extended Kalman filter, combines frequency-domain and time-domain collaborative filtering to achieve the vibration noise suppression rate, models the mechanical vibration component as a periodic state variable, and estimates the amplitude, main frequency and phase in real time to compensate for the boom positioning error. Based on the adaptive update of wavelet high-frequency energy and observation residual variance, the robustness to sudden noise (such as gear wear) is enhanced; and the lighting energy consumption is reduced through a dynamic multi-objective energy consumption model, the grid peak load is flattened, and the hybrid energy consumption demand is combined with historical operation data and real-time grid load prediction to optimize power distribution. The lighting area is divided according to the operation priority (core area > transition area > non-operation area), and the power of the non-critical area is reduced during the peak period of the power grid. It is applicable to scenarios such as port cranes and construction tower cranes, and can effectively achieve vibration suppression and energy consumption optimization, shorten the fault diagnosis response time, and be compatible with sudden working conditions.

[0047] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Brief Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following will briefly introduce the drawings required for the description of the exemplary embodiments or related technologies. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0049] Figure 1 It is a flowchart of a tower crane lighting control method based on Kalman filtering and energy consumption optimization according to an embodiment of the present invention.

[0050] Figure 2 It is a flowchart of using a band-stop filter to suppress noise in a preset vibration frequency band in a tower crane lighting control method based on Kalman filtering and energy consumption optimization according to an embodiment of the present invention.

[0051] Figure 3 It is a flowchart of modeling the vibration component as a system state variable based on an improved extended Kalman filter in a tower crane lighting control method based on Kalman filter and energy consumption optimization according to an embodiment of the present invention. Detailed implementation manners

[0052] Next, in combination with the accompanying drawings and specific implementation manners, the present application will be further described. It should be noted that, on the premise of no conflict, the following-described embodiments or technical features can be arbitrarily combined to form new embodiments.

[0053] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further details the embodiments of the present invention with reference to specific embodiments and the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] It should be noted that all the expressions using "first" and "second" in the embodiments of the present invention are used to distinguish two non-identical entities or non-identical parameters with the same name. It can be seen that "first" and "second" are only for the convenience of expression and should not be construed as a limitation on the embodiments of the present invention. In addition, the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units inherently includes other steps or units.

[0055] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0056] The flowchart shown in the accompanying drawings is only an example illustration, and does not necessarily include all the content and operations / steps, nor does it necessarily need to be executed in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged. Therefore, the actual execution order may change according to the actual situation.

[0057] Next, some implementation manners of the present application will be described in detail with reference to the accompanying drawings. Without conflict, the following-described embodiments and the features in the embodiments can be combined with each other.

[0058] Since a single state estimation algorithm or static energy consumption model in the prior art is difficult to solve the problems of high data noise, difficult energy consumption optimization, and large power grid load pressure, the present invention proposes a tower crane lighting control method and system based on Kalman filtering and energy consumption optimization. By combining an improved Kalman filtering algorithm and an energy consumption optimization model, the anti-interference ability of the system in a mechanical vibration environment is significantly improved, and the energy consumption cost is effectively reduced through a dynamic optimization strategy, meeting the intelligent and environmental protection requirements of modern construction. An intelligent tower crane lighting management system that can work stably in a mechanical vibration environment, combined with an energy consumption optimization model, dynamically adjusts the tower crane lighting strategy, which can not only improve the anti-interference ability of the system but also optimize the energy consumption management, thereby achieving the goals of energy conservation and environmental protection.

[0059] See Figure 1 As shown, an embodiment of the present invention provides a tower crane lighting control method based on Kalman filtering and energy consumption optimization, and the method includes the following steps:

[0060] Step S10: Real-time collect the tower crane mechanical vibration signals and attitude data through vibration sensors and inclination sensors, and use a band-stop filter to suppress noise in a preset vibration frequency band.

[0061] In this step, see Figure 2 As shown, using a band-stop filter to suppress noise in a preset vibration frequency band includes the following steps:

[0062] Step S101: Divide the preset vibration frequency band into multiple sub-frequency bands according to the mechanical vibration characteristics, and design a band-stop filter for each sub-frequency band;

[0063] Step S102: Use bilinear transformation to convert the analog filter into a digital filter. The original sensor data is first suppressed by the band-stop filter for vibration noise, and then input into the extended Kalman filter for state estimation of the tower crane displacement and attitude angle. Combine wavelet transform to analyze the filter residuals and dynamically adjust the parameters of the band-stop filter.

[0064] In this embodiment, the band-stop filter uses a Butterworth band-stop filter, and the center frequency of the band-stop filter is determined according to the peak value of the measured vibration spectrum of the sensor; the bandwidth of the band-stop filter is set to the peak frequency ±10%; the transfer function of the Butterworth band-stop filter is:

[0065]

[0066] Where is a complex frequency variable used to characterize the frequency domain characteristics of the filter, is the center angular frequency of the vibration noise that the filter needs to suppress, , is the center frequency of the stopband; is the quality factor， The larger the value, the narrower the stopband. The smaller the value, the wider the stopband. Calculated from the bandwidth , and are the stopband cut-off frequencies, is the stopband bandwidth.

[0067] In this embodiment, according to the mechanical vibration characteristics (gearbox main frequency 120Hz, motor fundamental frequency 50Hz), a band-stop filter is used to eliminate the noise in the motor vibration frequency band of 50 - 200Hz. The 50 - 500Hz is divided into three sub-bands: 50 - 100Hz (low-frequency band, suppressing the motor fundamental frequency noise), 100 - 300Hz (main band, suppressing the gear meshing noise), 300 - 500Hz (high-frequency band, suppressing the mechanical impact noise). A band-stop filter is designed for each sub-band, and the vibration is modeled as a periodic state variable through the state extended Kalman filter (EKF) for compensation. The process noise covariance (initial value 0.01) and the observation noise covariance (initial value 0.1) are dynamically adjusted. The vibration residual is secondarily denoised by combining the frequency-domain wavelet transform; among them, the center frequency of the band-stop filter is the 120Hz gear vibration, and the bandwidth is the bandwidth corresponding to the 120Hz vibration, 108 - 132Hz, which can avoid excessive attenuation of the effective signal. By adding an anti-aliasing low-pass filter (cut-off frequency ≥500Hz) at the sensor signal acquisition end, at the gearbox main frequency of 120Hz, the noise amplitude attenuation is ≥30dB, and the boom positioning error after vibration compensation is reduced from ±2° to ±0.5°. Avoiding the aliasing of high-frequency vibration components to the low-frequency band can effectively suppress the mechanical vibration noise in the range of 50 - 500Hz, improve the tower crane state estimation accuracy and system robustness. If the measured spectrum shows that the motor vibration main frequency shifts to 55Hz, the center frequency of the band-stop filter is dynamically adjusted to 55Hz, and the bandwidth is adjusted to 11Hz (±10%). After parameter adjustment, through multi-band band-stop filtering and anti-aliasing preprocessing, the mechanical vibration noise is effectively suppressed, providing a high signal-to-noise ratio signal for subsequent state estimation.

[0068] Step S20: Dynamically set the process noise covariance matrix and the observation noise covariance matrix according to the vibration intensity, perform pre-filtering of the vibration signal with a low-pass filter, perform wavelet transform on the filtered signal, and suppress the characteristic frequency band of the gear meshing noise in the frequency domain to obtain the filtered vibration components.

[0069] In this step, referring to Figure 3 as shown, when modeling the vibration components as system state variables based on the improved extended Kalman filter, it further includes:

[0070] Step S201: Initialize the process noise covariance matrix and the observation noise covariance matrix;

[0071] Step S202: Model mechanical vibration as a periodic state variable, where:

[0072]

[0073] In the formula, is the amplitude; is the main vibration frequency, representing the core frequency of the periodic vibration motion; is the phase angle, is the discrete time step at the sampling moment, , is the sampling period;

[0074] Step S203: Perform frequency-domain analysis on the filtered residuals based on wavelet transform, and dynamically adjust the values of the noise covariance matrix and the observation noise covariance matrix value , where:

[0075]

[0076]

[0077] In the formula, is the initial process noise covariance; is the initial observation noise covariance; and are adaptive weight coefficients; is the high-frequency band extracted from the filtered residuals through wavelet transform; is the variance of the observation residuals, representing the time-varying characteristics of the sensor noise;

[0078] Step S204: Real-time output the position coordinates of the tower crane boom , the swing angle and the attitude error after vibration compensation; among them, the attitude error after vibration compensation ≤ 5%.

[0079] For example, the sudden noise scenario is: when gear wear causes a sudden increase in high-frequency energy = 0.8, increases from 0.01 to 0.01×(1 + 0.5×0.8) = 0.014, enhancing the model's adaptability to uncertainty; the observation interference scenario is: if the sensor is affected by electromagnetic interference resulting in the residual variance: = 0.6, increases from 0.1 to 0.1×(1 + 0.3×0.6) = 0.118, reducing the weight of abnormal measurements; through wavelet frequency-domain analysis and noise covariance adaptive adjustment, the robustness of the improved extended Kalman filter in complex working conditions is significantly improved, and the attitude error is stabilized at ≤ 5%.

[0080] Step S30: Model the vibration components as system state variables based on the improved extended Kalman filter, and output in real time the tower crane attitude estimation values including the position coordinates of the tower crane boom, the swing angle, and the attitude error after vibration compensation.

[0081] In this step, when modeling the state variables, the system state vector includes position, swing angle, and vibration parameters. Under the 120Hz gear vibration, the boom position error without compensation is ±15cm, and it drops to ±3cm after compensation. The improved extended Kalman filter converges to the steady state within 5 seconds, and the vibration parameter estimation error ≤2%. Through vibration component modeling and real-time error compensation, the tower crane attitude estimation accuracy is significantly improved, ensuring operation safety.

[0082] Step S40: Input the historical energy consumption data, the hoisting task plan, and the real-time grid load data, and perform hybrid energy consumption prediction through the dynamic multi-objective energy consumption optimization model.

[0083] In this step, through the lighting power and ambient temperature in the input historical energy consumption data, and combining the electricity price and carbon quota in the hoisting task plan and the accessed real-time grid load data, predict the lighting demand in the next 1 hour through the dynamic multi-objective energy consumption optimization model, and correct the influence of temperature on energy consumption in combination with the thermodynamic model, with the prediction error ≤8%.

[0084] In this embodiment, the framework of the dynamic multi-objective energy consumption optimization model uses the NSGA-III (Non-dominated Sorting Genetic Algorithm III) algorithm to process the high-dimensional objective space for multi-objective optimization. Among them, the optimization objectives include minimizing the total energy consumption cost, minimizing the grid peak load, and maximizing the energy consumption balance degree.

[0085] Among them, in the objective function and optimization objectives of this dynamic multi-objective energy consumption optimization model, minimizing the total energy consumption cost adjusts the power distribution of the lighting system according to the grid load fluctuation and the electricity price in different time periods (such as the peak-time electricity price of 1.2 yuan / kWh and the valley-time electricity price of 0.4 yuan / kWh). Specifically, optimize the energy consumption cost by selecting different electricity price intervals. Especially during the electricity price peak period, reduce the power in non-core areas appropriately to reduce the expenditure. Minimizing the grid peak load means that during the peak period of the grid load (at night or during specific working hours), this dynamic multi-objective energy consumption optimization model will try to reduce the overall lighting power, especially the load in non-core areas, to avoid overloading the grid. This helps the stable operation of the grid and reduces energy waste during peak loads. Maximizing the energy consumption balance degree optimizes the distribution of energy consumption to make the lighting demand as balanced as possible, avoid over-reliance on lighting equipment during a certain period, reduce load fluctuations and unnecessary energy peak consumption. Maximizing the energy consumption balance degree helps to maintain the grid stability in the long term and reduce energy waste. The workflow of this dynamic multi-objective energy consumption optimization model is as follows:

[0086] (1) Input data:

[0087] Historical energy consumption data: hourly lighting power and ambient temperature for the past 30 days.

[0088] Lifting task plan: includes information such as lifting operation time, operation area, etc.

[0089] Real-time grid load data: real-time electricity price (peak time electricity price is 1.2 yuan / kWh, valley time electricity price is 0.4 yuan / kWh) and carbon quota (0.8 kg CO2 / kWh).

[0090] (2) Objective function:

[0091] Minimize energy consumption cost: Adjust the distribution of lighting power according to the grid load fluctuation and electricity prices at different time periods.

[0092] Minimize carbon emissions: By reducing the use of high-carbon electricity, optimizing the carbon quota, and simultaneously optimizing multiple objectives through the NSGA-III algorithm, find the optimal power distribution plan that can balance energy consumption cost, carbon emissions, and grid load while meeting the lighting requirements.

[0093] (3) Optimization plan:

[0094] This dynamic multi-objective energy consumption optimization model adapts to the grid load fluctuation by searching for the optimal power distribution plan. During the peak period of the grid load, reduce the lighting power in non-core areas to reduce the total energy consumption and cost.

[0095] (4) Output results:

[0096] Lighting demand prediction: Predict the lighting power for the next 1 hour and give the corresponding power distribution plan for optimal energy consumption.

[0097] Power distribution plan: Combine the grid load fluctuation, adjust the time period distribution of lighting power to achieve minimizing energy consumption cost, balancing the grid load, and optimizing carbon emission control. The lighting power is dynamically adjusted to cope with the grid load fluctuation and electricity price change, thereby reducing the energy consumption cost and optimizing carbon emissions.

[0098] Response plan: For example, during the peak period of the grid load, reduce the lighting power in non-core areas to reduce the grid load and save electricity costs.

[0099] In this embodiment, a thermodynamic model is used to correct the influence of temperature on energy consumption. Among them, the energy consumption of lighting equipment is affected by the ambient temperature. Especially in a high-temperature environment, lighting equipment needs to consume more energy to maintain the required brightness. Therefore, the workflow for correction using the thermodynamic model is as follows:

[0100] (1)Input data (ambient temperature): Extract the ambient temperature data (20°C to 35°C) for the past 30 days from the historical energy consumption data.

[0101] (2)Correction scheme: For every 1°C increase, the energy consumption increases by 1.5%. Adjust the energy consumption prediction values at different temperatures using a correction factor based on the physical characteristics of the equipment and the temperature relationship.

[0102] (3)Correction result: When calculating the lighting demand, considering the impact of ambient temperature on energy consumption, the actual energy consumption will be adjusted. If the predicted lighting power is 12 kW and the temperature increases by 1°C, the corrected actual energy consumption demand is 12.3 kW. Through this correction, the accuracy of the energy consumption prediction result is improved, ensuring that the error between the actual energy consumption and the predicted value is less than 8%.

[0103] By combining the above dynamic multi-objective energy consumption optimization model and correcting the impact of temperature on energy consumption by combining with the thermodynamic model, accurate energy consumption predictions can be made under the influence of multiple variables (such as temperature changes, grid fluctuations, electricity price changes, etc.), effectively achieving the dual optimization of energy consumption cost and carbon emissions, ensuring the load balance and stability of the power grid, improving the energy efficiency of the lighting system, and achieving efficient energy consumption management and scheduling.

[0104] For example: In the energy consumption model input, the historical energy consumption data is the lighting power and ambient temperature (20 - 35°C) per hour for the past 30 days; the real-time grid load data is the real-time electricity price (1.2 yuan / kWh during peak hours, 0.4 yuan / kWh during off-peak hours) and carbon quota (0.8 kgCO2 / kWh); the optimization objective is to minimize energy consumption cost and carbon emissions, search for the optimal power distribution plan, and adapt to grid load fluctuations. The prediction result is: The predicted lighting demand for the next 1 hour is 12 kW. Considering temperature correction (for every 1°C increase in temperature, the energy consumption increases by 1.5%), the actual energy consumption is 12.3 kW, and the error of 1.5% < 8%; the grid response is to reduce the power in the non-core area to 20% during peak hours, saving 30 yuan per hour in electricity costs. Through the multi-objective optimization model and real-time data fusion, high-precision energy consumption prediction and low-carbon scheduling are achieved.

[0105] Step S50: According to the position of the boom and the operation plan, prioritize the lighting areas and distribute power according to the balanced load strategy based on the peak and valley of the power grid.

[0106] In this step, when prioritizing the lighting areas according to the position of the boom and the operation plan, the lighting areas are divided into high priority (the operation radius of the boom projection area ±3 m, brightness 100%, ensuring operation safety), medium priority (adjacent area ±5 m, brightness 60%, auxiliary lighting), and low priority (non-operation area, brightness 20%, only basic lighting).

[0107] In this embodiment, the power distribution according to the balanced load strategy based on the peak and valley of the power grid includes: when the power grid is at peak (electricity price ≥ 1.0 yuan / kWh), the lighting power in the high-priority area is increased to 80%, and the low-priority area is reduced to 20%; when it is at valley (electricity price ≤ 0.5 yuan / kWh), the power is distributed according to the balanced load strategy.

[0108] For example, the specific peak and valley strategy of the power grid is as follows:

[0109] At peak time (18:00 - 22:00): 80% for high priority and 20% for low priority, and the total power is reduced by 15%;

[0110] At valley time (00:00 - 06:00): Balanced distribution (60% for high, 40% for medium, 30% for low), making full use of low-cost electric energy.

[0111] Then, when the construction site tower crane is at peak (electricity price 1.2 yuan / kWh), the lighting power in the non-operation area is reduced from 50% to 20%, saving 85 yuan in electricity bills per day. When the vibration main frequency shifts to 130 Hz (mutation ≥ 10 Hz), the extended Kalman filter parameter recalibration is triggered to ensure the compensation accuracy. Through dynamic priority division and power grid load response, the dual optimization of lighting energy consumption and operation cost is achieved.

[0112] In some embodiments, the tower crane lighting control method based on Kalman filter and energy consumption optimization further includes retraining the dynamic multi-objective energy consumption optimization model according to a preset time period. When it is detected that the mechanical vibration main frequency shifts ≥ 10 Hz or the power grid load mutates, the Kalman filter parameter recalibration and the optimization model iteration are triggered; among them, the triggering conditions for retraining the dynamic multi-objective energy consumption optimization model are:

[0113] The mechanical vibration energy mutates by more than 30% of the historical mean in the 120 Hz frequency band;

[0114] The power grid load volatility is ≥ 10% for three consecutive sampling periods;

[0115] The deviation between the actual lighting energy consumption and the predicted value lasts for 5 minutes ≥ 15%.

[0116] If the main frequency of the tower crane's vibration shifts to 130 Hz due to sudden wind, the system triggers recalibration, and the EKF converges within 10 minutes, and the attitude error is restored to ≤ 5%.

[0117] The tower crane lighting control method based on Kalman filtering and energy consumption optimization of the present invention can enhance the anti-vibration interference ability. By means of a filtering layer, it effectively suppresses the observation noise caused by mechanical vibration, improves the data signal-to-noise ratio, enhances the estimation accuracy. The optimized process noise covariance and observation noise covariance balance the weights of prediction and observation, avoid over-reliance on noise data, maintain stable state estimation in a vibration environment (such as industrial robots, drones, vehicle-mounted systems), and improve the robustness. It can customize the filtering strategy according to vibration characteristics (such as designing a band-stop filter for a specific frequency) and is applicable to the load distribution of the power distribution network.

[0118] It should be noted that the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be executed synchronously or asynchronously, for example, in multiple modules.

[0119] It should be understood that although the above is described in a certain order, these steps are not necessarily executed in the above order successively. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, a part of the steps of this embodiment may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of the steps or stages in other steps or other steps.

[0120] In the second aspect of the embodiments of the present invention, the present invention also provides a tower crane lighting control system based on Kalman filtering and energy consumption optimization, including:

[0121] A data acquisition module, configured to collect tower crane mechanical vibration signals and attitude data in real time through vibration sensors and inclination sensors;

[0122] A covariance adjustment module, configured to dynamically set the process noise covariance matrix and the observation noise covariance matrix according to the vibration intensity, perform pre-filtering of the vibration signal with a low-pass filter, perform wavelet transform on the filtered signal, and suppress the characteristic frequency band of gear meshing noise in the frequency domain to obtain the filtered vibration component;

[0123] An attitude estimation module, configured to model the vibration component as a system state variable based on an improved extended Kalman filter, and output in real time a tower crane attitude estimation value including the position coordinates of the tower crane boom, the swing angle, and the attitude error after vibration compensation;

[0124] The multi-objective energy consumption optimization module is used to input historical energy consumption data, hoisting task plans, and real-time grid load data, and perform hybrid energy consumption prediction through a dynamic multi-objective energy consumption optimization model;

[0125] The lighting control execution module is used to prioritize the lighting areas according to the boom position and operation plan, and allocate power according to the peak and valley of the power grid according to the balanced load strategy.

[0126] In this embodiment, a noise suppression module is further included, which is used to suppress the noise of the vibration signal according to a preset vibration frequency band through a band-stop filter, and suppress gear meshing noise and high-frequency interference.

[0127] Through the above detailed steps, the tower crane lighting control system based on Kalman filtering and energy consumption optimization of the present invention is used to execute the steps of the tower crane lighting control method based on Kalman filtering and energy consumption optimization in the above embodiment, which will not be elaborated here.

[0128] The tower crane lighting control method and system based on Kalman filtering and energy consumption optimization of the present invention fuse multi-sensor data through an improved extended Kalman filter, realize the vibration noise suppression rate through frequency-domain and time-domain collaborative filtering, model the mechanical vibration component as a periodic state variable, and estimate the amplitude, main frequency and phase in real time, compensate for the boom positioning error, adaptively update based on wavelet high-frequency energy and observation residual variance, and enhance the robustness to sudden noise (such as gear wear); and reduce the lighting energy consumption through a dynamic multi-objective energy consumption model, flatten the peak load of the power grid, combine historical operation data and real-time power grid load prediction of hybrid energy consumption demand, optimize power distribution, divide the lighting area according to the operation priority (core area > transition area > non-operation area), and reduce the power of non-critical areas during peak power grid hours. It is applicable to scenarios such as port cranes and construction tower cranes, can effectively achieve vibration suppression and energy consumption optimization, shorten the fault diagnosis response time, and be compatible with sudden working conditions.

[0129] The above are exemplary embodiments disclosed by the present invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments disclosed by the present invention defined by the claims. The functions, steps, and / or actions of the method claims according to the disclosed embodiments herein do not need to be performed in any specific order. In addition, although the elements disclosed in the embodiments of the present invention can be described or claimed in individual form, they can also be understood as multiple unless explicitly limited to the singular.

[0130] It should be understood that, as used herein, unless the context clearly supports exceptions, the singular form "a" is also intended to include the plural form. It should also be understood that the "and / or" used herein refers to any and all possible combinations of one or more of the associated listed items. The serial numbers of the disclosed embodiments of the present invention above are only for description and do not represent the advantages and disadvantages of the embodiments.

[0131] Those of ordinary skill in the art should understand that any discussion of the above embodiments is merely exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other variations in different aspects of the embodiments of the present invention as above, and they are not provided in detail for the sake of brevity. Therefore, any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present invention shall be included in the protection scope of the embodiments of the present invention.

Claims

1. A tower crane lighting control method based on Kalman filtering and energy consumption optimization, characterized in that, The method includes the following steps: Vibration signals and attitude data of the tower crane are collected in real time through vibration sensors and inclination sensors, and a band-stop filter is used to suppress noise in a preset vibration frequency band; The process noise covariance matrix and the observation noise covariance matrix are dynamically set according to the vibration intensity. A pre-filtering low-pass filter is applied to the vibration signal, and wavelet transform is performed on the filtered signal to suppress the characteristic frequency band of gear meshing noise in the frequency domain, obtaining a filtered vibration component; Based on the improved extended Kalman filter, the vibration component is modeled as a system state variable, and the attitude estimation value of the tower crane including the position coordinates of the tower crane boom, the swing angle, and the attitude error after vibration compensation is output in real time; Historical energy consumption data, lifting task plans, and real-time grid load data are input, and hybrid energy consumption prediction is performed through a dynamic multi-objective energy consumption optimization model; According to the boom position and operation plan, the lighting areas are prioritized, and power is distributed according to the peak-valley of the power grid according to the balanced load strategy; Among them, using a band-stop filter to suppress noise in a preset vibration frequency band includes the following steps: The preset vibration frequency band is divided into multiple sub-frequency bands according to mechanical vibration characteristics, and a band-stop filter is designed for each sub-frequency band; The analog filter is converted into a digital filter by bilinear transformation. The original data of the sensor is first suppressed by the band-stop filter to suppress vibration noise, and then input into the extended Kalman filter for state estimation of the tower crane displacement and attitude angle. The wavelet transform is combined to analyze the filter residual error, and the parameters of the band-stop filter are dynamically adjusted; Among them, the band-stop filter uses a Butterworth band-stop filter. The center frequency of the band-stop filter is determined according to the peak value of the measured vibration spectrum of the sensor; the bandwidth of the band-stop filter is set to the peak frequency ±10%; the transfer function of the Butterworth band-stop filter is: Among them, is a complex frequency variable, used to characterize the frequency-domain characteristics of the filter, is the central angular frequency of the vibration noise that the filter needs to suppress, , is the center frequency of the stopband; is the quality factor, The larger the value, the narrower the stopband, The smaller the value, the wider the stopband. Calculated from the bandwidth , and are the stopband cut-off frequencies, is the stopband bandwidth.

2. The tower crane lighting control method based on Kalman filtering and energy consumption optimization according to claim 1, characterized in that When modeling the vibration component as a system state variable based on the improved extended Kalman filter, it also includes: Initializing the process noise covariance matrix and the observation noise covariance matrix; Modeling the mechanical vibration as a periodic state variable, where: Wherein, is the amplitude; is the main vibration frequency, representing the core frequency of the periodic vibration motion; is the phase angle, is the discrete time step at the sampling moment, , is the sampling period; Perform frequency-domain analysis on the filtering residuals based on wavelet transform and dynamically adjust the value of the noise covariance matrix and the value of the observation noise covariance matrix , where: In the formula, is the initial process noise covariance; is the initial observation noise covariance; and are adaptive weight coefficients; is the high-frequency band obtained by extracting the filtering residual through wavelet transform; is the variance of the observation residual, representing the time-varying characteristics of the sensor noise; Real-time output of the position coordinates of the tower crane boom , swing angle and the attitude error after vibration compensation.

3. The tower crane lighting control method based on Kalman filtering and energy consumption optimization according to claim 1, characterized in that, When prioritizing the lighting areas according to the boom position and operation plan, the lighting areas are divided into high priority, medium priority, and low priority.

4. The tower crane lighting control method based on Kalman filtering and energy consumption optimization according to claim 1, characterized in that Distributing power according to the peak-valley of the power grid according to the balanced load strategy includes: increasing the lighting power of the high-priority area to 80% and decreasing the low-priority area to 20% during the peak of the power grid; distributing power according to the balanced load strategy during the valley.

5. The tower crane lighting control method based on Kalman filtering and energy consumption optimization according to claim 4, characterized in that, The tower crane lighting control method based on Kalman filter and energy consumption optimization further includes retraining the dynamic multi-objective energy consumption optimization model at preset time intervals. When it is detected that the main vibration frequency deviation of the machine vibration is ≥10Hz or the power grid load suddenly changes, the Kalman filter parameter recalibration and optimization model iteration are triggered.

6. The tower crane lighting control method based on Kalman filtering and energy consumption optimization according to claim 5, characterized in that, The triggering conditions for retraining the dynamic multi-objective energy consumption optimization model are: The mechanical vibration energy mutates by more than 30% of the historical average in the 120Hz frequency band; The power grid load volatility is ≥10% for three consecutive sampling periods; The deviation between the actual lighting energy consumption and the predicted value lasts for 5 minutes ≥15%.

7. A tower crane lighting control system based on Kalman filtering and energy consumption optimization, characterized in that, For implementing the tower crane lighting control method based on Kalman filter and energy consumption optimization according to any one of claims 1-6, the system includes: The data acquisition module is used to collect the tower crane mechanical vibration signals and attitude data in real time through vibration sensors and inclination sensors; The covariance adjustment module is used to dynamically set the process noise covariance matrix and the observation noise covariance matrix according to the vibration intensity, perform pre - low - pass filtering on the vibration signal, perform wavelet transform on the filtered signal, suppress the characteristic frequency band of gear meshing noise in the frequency domain, and obtain the filtered vibration component; The attitude estimation module is used to model the vibration component as a system state variable based on the improved extended Kalman filter, and output in real time the tower crane attitude estimation value including the position coordinates of the tower crane boom, the swing angle and the attitude error after vibration compensation; The multi - objective energy consumption optimization module is used to input historical energy consumption data, hoisting task plans and real - time grid load data, and perform hybrid energy consumption prediction through a dynamic multi - objective energy consumption optimization model; The lighting control execution module is used to divide the lighting area into priorities according to the boom position and the operation plan, and allocate power according to the peak - valley of the power grid according to the balanced load strategy.

8. The tower crane lighting control system based on Kalman filtering and energy consumption optimization according to claim 7, wherein, It also includes a noise suppression module, which is used to suppress the noise of the vibration signal according to the preset vibration frequency band through a band - stop filter, and suppress gear meshing noise and high - frequency interference.

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