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

Abstract

The invention provides a tower crane lighting control method and system based on Kalman filtering and energy consumption optimization, and relates to the technical field of intelligent tower crane control. According to the method, mechanical vibration signals and attitude data of a tower crane are collected in real time through a vibration sensor and a tilt angle sensor; performing pre-low-pass filtering on the vibration signal, performing wavelet transform on the filtered signal, and suppressing a characteristic frequency band of gear meshing noise in a frequency domain to obtain a filtered vibration component; modeling the vibration component into a system state variable based on improved extended Kalman filtering, and outputting a tower crane attitude estimation value containing a tower crane jib position coordinate, a swing angle and an attitude error after vibration compensation in real time; inputting historical energy consumption data, a hoisting task plan and power grid real-time load data, and performing hybrid energy consumption prediction through a dynamic multi-target energy consumption optimization model; according to the position of a suspension arm and an operation plan, lighting areas are divided into priorities, power is distributed according to peak and valley of a power grid and a load balancing strategy, and energy consumption cost is effectively reduced.

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 switch modes or automatic control based on light sensors, but these methods have many deficiencies when facing complex construction site environments: 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 being mixed into the sensor data, affecting the judgment and control accuracy of the system.

[0003] 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.

[0004] 3. Large 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.

[0005] 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.

[0006] 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 the goals of energy conservation, consumption reduction, and environmental protection. Summary of the Invention

[0007] 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.

[0008] To achieve the above purpose, the present invention provides the following technical solutions: Based on the above purpose, in the first aspect, the present invention provides a tower crane lighting control method based on Kalman filtering and energy consumption optimization, including the following steps: Collect the mechanical vibration signals and attitude data of the tower crane in real time through vibration sensors and inclination sensors, and use a band-stop filter to suppress noise in the preset vibration frequency band; 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 by 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; Based on the improved extended Kalman filter, model the vibration component as a system state variable, 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; 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; According to the boom position and operation plan, divide the lighting area into priorities, and allocate power according to the peak and valley of the power grid according to the balanced load strategy.

[0009] 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: 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; Use the bilinear transformation to convert the analog filter into a digital filter. The original data of the sensor 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.

[0010] 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:

[0011] 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.

[0012] 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: Initializing the process noise covariance matrix and the observation noise covariance matrix; Modeling the mechanical vibration as a periodic state variable, where:

[0013] In the formula, is the amplitude; is the main vibration frequency, characterizing 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; Performing frequency-domain analysis on the filtering residual based on wavelet transform, and dynamically adjusting the values of the noise covariance matrix and the observation noise covariance matrix value , where:

[0014]

[0015] 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 filtering residual through wavelet transform; is the variance of the observation residual, characterizing the time-varying characteristics of the sensor noise; Real-time output of the position coordinates of the tower crane boom , the swing angle and the attitude error after vibration compensation.

[0016] As a further solution of the present invention, when dividing the priority levels of the lighting areas according to the boom position and the operation plan, the lighting areas are divided into high priority (operation radius ± 3m), medium priority (adjacent areas), and low priority (non-operation areas).

[0017] As a further solution of the present invention, the power distribution according to the peak and valley of the power grid according to the balanced load strategy 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%; during the valley period (electricity price ≤ 0.5 yuan / kWh), the power is distributed according to the balanced load strategy.

[0018] 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, and triggering the recalibration of Kalman filter parameters and the iteration of the optimization model when it is detected that the main frequency deviation of mechanical vibration ≥ 10 Hz or the grid load suddenly changes; wherein, the triggering conditions for retraining the dynamic multi-objective energy consumption optimization model are: The mechanical vibration energy suddenly changes in the 120 Hz frequency band by more than 30% of the historical average; The grid load volatility ≥ 10% for three consecutive sampling periods; The deviation between the actual lighting energy consumption and the predicted value lasts for 5 minutes ≥ 15%.

[0019] In a second aspect, the present invention provides a tower crane lighting control system based on Kalman filtering and energy consumption optimization, which includes the following components: A data acquisition module for real-time collecting tower crane mechanical vibration signals and attitude data through vibration sensors and inclination sensors; 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 with a low-pass filter, performing wavelet transform on the filtered signal, and suppressing the characteristic frequency band of gear meshing noise in the frequency domain to obtain a filtered vibration component; An attitude estimation module for modeling the vibration component as a system state variable based on an improved extended Kalman filter, and real-time outputting 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; A multi-objective energy consumption optimization module for inputting historical energy consumption data, lifting task plans, and real-time grid load data, and performing hybrid energy consumption prediction through a dynamic multi-objective energy consumption optimization model; A lighting control execution module for dividing the lighting area into priorities according to the boom position and operation plan, and allocating power according to the grid peak and valley according to the balanced load strategy.

[0020] As a further solution of the present invention, it further includes a noise suppression module for suppressing noise of the vibration signal according to a preset vibration frequency band through a band-stop filter, and suppressing gear meshing noise and high-frequency interference.

[0021] Compared with the prior art, the 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: 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 and noise suppression rate, models the mechanical vibration component as a periodic state variable, estimates the amplitude, main frequency, and phase in real time, compensates for the boom positioning error, adaptively updates based on wavelet high-frequency energy and observation residual variance, and enhances the robustness to sudden noises (such as gear wear); and reduces lighting energy consumption and suppresses the peak load of the power grid through a dynamic multi-objective energy consumption model, combines historical operation data and real-time power grid load prediction for hybrid energy consumption demand, optimizes power distribution, divides the lighting area according to the operation priority (core area > transition area > non-operation area), reduces the power of non-critical areas during peak power grid periods, 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.

[0022] 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

[0023] 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: Figure 1 It is a flowchart of a tower crane lighting control method based on Kalman filter and energy consumption optimization according to an embodiment of the present invention.

[0024] 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 filter and energy consumption optimization according to an embodiment of the present invention.

[0025] 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 DESCRIPTION OF THE EMBODIMENTS

[0026] Next, in combination with the drawings and specific embodiments, 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 combined arbitrarily to form new embodiments.

[0027] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the following further elaborates on the embodiments of the present invention in detail with reference to specific embodiments and the accompanying drawings. It should be understood that the specific embodiments described herein are merely used to explain the present application and are not intended to limit the present application.

[0028] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are for distinguishing 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 "comprising" and "having" and any variations thereof 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.

[0029] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with 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 of the present application without creative efforts fall within the scope of protection of the present application.

[0030] The flowchart shown in the accompanying drawings is only an example illustration and does not necessarily include all contents and operations / steps, nor does it necessarily execute 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.

[0031] Next, some embodiments of the present application will be described in detail in conjunction with the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0032] 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 building 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, consumption reduction, and environmental protection.

[0033] See Figure 1 As shown, the embodiments of the present invention provide a tower crane lighting control method based on Kalman filtering and energy consumption optimization. The method includes the following steps: Step S10: Real-time collect the mechanical vibration signals and attitude data of the tower crane through vibration sensors and inclination sensors, and use a band-stop filter to suppress noise in the preset vibration frequency band.

[0034] In this step, as shown in Figure 2 , using a band-stop filter to suppress noise in the preset vibration frequency band includes the following steps: Step S101: Divide the preset vibration frequency band into multiple sub-bands according to the mechanical vibration characteristics, and design a band-stop filter for each sub-band; Step S102: Use bilinear transformation to convert the analog filter into a digital filter. The original sensor data is first suppressed from 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.

[0035] In this embodiment, 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 actually 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:

[0036] where is the 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. Calculate from the bandwidth, and are the stopband cut-off frequencies, is the stopband bandwidth.

[0037] 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 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, and the vibration residual is secondarily denoised by combining with 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 corresponding bandwidth of 108 - 132Hz for the 120Hz vibration, 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 drops 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 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%), and the parameters are adjusted. 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.

[0038] Step S20: Dynamically set the process noise covariance matrix and the observation noise covariance matrix according to the vibration intensity, perform pre-filtering on 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.

[0039] 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: Step S201: Initialize the process noise covariance matrix and the observation noise covariance matrix; Step S202: Model the mechanical vibration as a periodic state variable, where:

[0040] In the formula, is the amplitude; is the vibration main frequency, representing the core frequency of the periodic motion of the vibration; is the phase angle, is the discrete time step at the sampling moment, , is the sampling period; Step S203: Perform frequency-domain analysis on the filtered residuals based on wavelet transform to dynamically adjust the value of the noise covariance matrix and the value of the observation noise covariance matrix , where:

[0041]

[0042] 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; Step S204: Real-time output the position coordinates of the tower crane jib , the swing angle and the attitude error after vibration compensation; among them, the attitude error after vibration compensation ≤ 5%.

[0043] For example, the sudden noise scenario is: when the gear wears and 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%.

[0044] Step S30: Model the vibration component as a system state variable based on the improved extended Kalman filter, and real-time output 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.

[0045] In this step, when modeling the state variables, the system state vector includes the position, swing angle, and vibration parameters. Under the 120Hz gear vibration, the boom position error without compensation is ±15cm, which is reduced to ±3cm after compensation. The improved extended Kalman filter converges to the steady state within 5 seconds, and the vibration parameter estimation error ≤ 2%. By modeling the vibration components and performing real-time error compensation, the tower crane attitude estimation accuracy is significantly improved, ensuring operation safety.

[0046] Step S40: Input the historical energy consumption data, hoisting task plan, and real-time grid load data, and perform hybrid energy consumption prediction through the dynamic multi-objective energy consumption optimization model.

[0047] In this step, based on the lighting power and ambient temperature in the input historical energy consumption data, and combined with the electricity price and carbon quota in the hoisting task plan and the accessed real-time grid load data, the dynamic multi-objective energy consumption optimization model predicts the lighting demand in the next 1 hour, and corrects the influence of temperature on energy consumption by combining the thermodynamic model. The prediction error ≤ 8%.

[0048] 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.

[0049] Among them, in the objective function and optimization objectives of the 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 at different time periods (such as the peak electricity price of 1.2 yuan / kWh and the valley electricity price of 0.4 yuan / kWh). Specifically, it optimizes the energy consumption cost by selecting different electricity price intervals. Especially during the peak electricity price period, it reduces the power in non-core areas appropriately to reduce the expenditure. Minimizing the grid peak load means that during the peak grid load period (at night or specific working hours), the 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, avoiding over-reliance on lighting equipment during a certain period, reducing load fluctuations and unnecessary energy peak consumption. Maximizing the energy consumption balance degree helps to maintain the long-term stability of the grid and reduce energy waste. The working process of the dynamic multi-objective energy consumption optimization model is as follows: (1) Input data: Historical energy consumption data: The hourly lighting power and ambient temperature in the past 30 days.

[0050] Lifting task plan: including information such as lifting operation time and operation area.

[0051] Grid real-time 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).

[0052] (2) Objective function: Minimize energy consumption cost: Adjust the distribution of lighting power according to the grid load fluctuation and electricity prices at different time periods.

[0053] 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.

[0054] (3) Optimization plan: 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.

[0055] (4) Output results: Lighting demand prediction: Predict the lighting power for the next 1 hour and give the corresponding power distribution plan for optimal energy consumption.

[0056] 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.

[0057] Response plan: For example, during the peak period of the grid load, reduce the grid load and save electricity costs by reducing the lighting power in non-core areas.

[0058] 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: (1) Input data (ambient temperature): Extract the ambient temperature data (20°C to 35°C) for the past 30 days from historical energy consumption data.

[0059] (2) Correction plan: For every 1°C increase, the energy consumption increases by 1.5%. Use the correction coefficient based on the physical characteristics of the equipment and the temperature relationship to adjust the energy consumption prediction value at different temperatures.

[0060] (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 rises 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%.

[0061] By combining the above dynamic multi-objective energy consumption optimization model and correcting the impact of temperature on energy consumption with the thermodynamic model, accurate energy consumption prediction can be made under the influence of various 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.

[0062] 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 in 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 valley hours) and carbon quota (0.8 kg CO2 / kWh); the optimization objective is to minimize the energy consumption cost and carbon emissions, search for the optimal power distribution plan, and adapt to the grid load fluctuations. The prediction result is: The predicted lighting demand for the next 1 hour is 12 kW. Considering the 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 bills. Through the multi-objective optimization model and real-time data fusion, high-precision energy consumption prediction and low-carbon scheduling are achieved.

[0063] Step S50: According to the boom position and operation plan, prioritize the lighting areas and allocate power according to the equal load strategy based on the peak and valley of the power grid.

[0064] In this step, when prioritizing the lighting areas according to the boom position and 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).

[0065] In this embodiment, allocating power according to the equal load strategy based on the peak and valley of the power grid includes: increasing the lighting power in the high-priority area to 80% and reducing the power in the low-priority area to 20% during the peak hours of the power grid (electricity price ≥ 1.0 yuan / kWh); allocating power according to the equal load strategy during the valley hours (electricity price ≤ 0.5 yuan / kWh).

[0066] For example, the specific power grid peak and valley strategy is as follows: Peak hours (18:00 - 22:00): High priority 80%, low priority 20%, total power reduced by 15%. Valley hours (00:00 - 06:00): Balanced distribution (high 60%, medium 40%, low 30%) to make full use of low - price electric energy.

[0067] Then, when the construction site tower crane reduces the lighting power of the non - operating area from 50% to 20% during peak hours (electricity price is 1.2 yuan / kWh), the daily electricity cost savings is 85 yuan. When the main vibration frequency shifts to 130Hz (mutation ≥ 10Hz), the extended Kalman filter parameter recalibration is triggered to ensure the compensation accuracy. Through dynamic priority division and grid load response, the dual optimization of lighting energy consumption and operation cost is achieved.

[0068] 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 main mechanical vibration frequency shifts ≥ 10Hz or the 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: The mechanical vibration energy mutates by more than 30% of the historical mean in the 120Hz frequency band; The 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%.

[0069] If the main vibration frequency of the tower crane shifts to 130Hz due to sudden gusts, the system triggers recalibration, and the EKF converges within 10 minutes, and the attitude error is restored to ≤ 5%.

[0070] The tower crane lighting control method based on Kalman filter and energy consumption optimization of the present invention can enhance the anti - vibration interference ability. The observation noise caused by mechanical vibration is effectively suppressed through the filter layer, the signal - to - noise ratio of the data is improved, the estimation accuracy is increased. The optimized process noise covariance and observation noise covariance balance the weights of prediction and observation, avoiding over - reliance on noise data, maintaining stable state estimation in a vibration environment (such as industrial robots, drones, vehicle systems), and the robustness is improved. The filtering strategy can be customized according to vibration characteristics (such as designing a band - stop filter for a specific frequency), and it is applicable to the load distribution of the distribution network.

[0071] 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 restrictive 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. Additionally, it is also easy to understand that these processes can be executed synchronously or asynchronously in, for example, multiple modules.

[0072] 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, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, some 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 other steps or steps or stages in other steps.

[0073] 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: A data acquisition module, configured to collect tower crane mechanical vibration signals and attitude data in real time through vibration sensors and inclination sensors; 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 by a low-pass filter, 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; 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; A multi-objective energy consumption optimization module, configured to input historical energy consumption data, a hoisting task plan, and real-time grid load data, and perform hybrid energy consumption prediction through a dynamic multi-objective energy consumption optimization model; A lighting control execution module, configured 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.

[0074] In this embodiment, a noise suppression module is further included, configured to suppress 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.

[0075] 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.

[0076] 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, and realize the vibration noise suppression rate by combining frequency-domain and time-domain collaborative filtering. The mechanical vibration component is modeled as a periodic state variable, and the amplitude, main frequency and phase are estimated 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, the hybrid energy consumption demand is combined with historical operation data and real-time grid load prediction, the power distribution is optimized, 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 grid period. 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.

[0077] 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 as defined by the claims. The functions, steps, and / or actions of the method claims according to the disclosed embodiments herein need not be performed in any specific order. In addition, although the elements disclosed by the embodiments of the present invention can be described or claimed in individual form, they can also be understood as plural unless explicitly limited to the singular.

[0078] 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 related listed items. The serial numbers of the above disclosed embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0079] Those of ordinary skill in the art should understand that: the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the embodiments disclosed by the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features between the above embodiments or different embodiments can also be combined, and there are many other variations in different aspects of the above embodiments of the present invention, which 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 comprises the following steps: Vibration sensors and inclination sensors are used to collect mechanical vibration signals and attitude data of the tower crane in real time, and band-stop filters are used to suppress noise in the preset vibration frequency band; The process noise covariance matrix and the observation noise covariance matrix are dynamically set according to the vibration intensity, the vibration signal is pre-low-pass filtered, the filtered signal is subjected to wavelet transform, the characteristic frequency band of the gear meshing noise is suppressed in the frequency domain, and the filtered vibration component is obtained; Based on the improved extended Kalman filter, the vibration component is modeled as a system state variable, and the estimated value of the tower crane attitude including the position coordinates of the tower crane arm, the swing angle and the attitude error after vibration compensation is output in real time; Input historical energy consumption data, lifting task plan and real-time grid load data, and use dynamic multi-objective energy consumption optimization model to make mixed energy consumption forecast; According to the boom position and operation plan, the lighting areas are prioritized and power is allocated according to the balanced load strategy based on the peak and valley of the power grid.

2. The tower crane lighting control method based on Kalman filtering and energy consumption optimization as claimed in claim 1, characterized in that: Using a band-stop filter to suppress noise in a preset vibration frequency band includes the following steps: Divide the preset vibration frequency band into multiple sub-bands according to the mechanical vibration characteristics, and design a band-stop filter for each sub-band; Bilinear transformation is used to convert the analog filter into a digital filter. The raw data of the sensor is first passed through a band-stop filter to suppress vibration noise, and then input into the extended Kalman filter to estimate the displacement and attitude angle of the tower crane. The filter residual is analyzed by wavelet transform, and the parameters of the band-stop filter are dynamically adjusted.

3. The tower crane lighting control method based on Kalman filtering and energy consumption optimization as claimed in claim 2, characterized in that: The band-stop filter adopts 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: in, 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 stopband center frequency; is the quality factor, The larger the value, the narrower the stopband. The smaller the value, the wider the stopband, calculated by the bandwidth , and is the stopband cutoff frequency, is the stopband bandwidth.

4. The tower crane lighting control method based on Kalman filtering and energy consumption optimization as claimed in claim 3 is characterized in that: When modeling vibration components as system state variables based on the improved extended Kalman filter, it also includes: Initialize the process noise covariance matrix and the observation noise covariance matrix; Model the mechanical vibration as a periodic state variable where: In the formula, is the amplitude; It is the main frequency of vibration, which characterizes the core frequency of the periodic motion of vibration; is the phase angle, is the discrete time step at the sampling instant, , is the sampling period; Perform frequency domain analysis on filter residuals based on wavelet transform and dynamically adjust the noise covariance matrix value and the observed noise covariance matrix value ,in: In the formula, is the initial process noise covariance; is the initial observation noise covariance; and Adaptive weight coefficient; To extract the high frequency band of the filter residual by wavelet transform; is the variance of the observed residual, which characterizes the time-varying characteristics of the sensor noise; Output the crane arm position coordinates in real time , Swing angle And the attitude error after vibration compensation.

5. The tower crane lighting control method based on Kalman filtering and energy consumption optimization as claimed in claim 1, characterized in that: When prioritizing the lighting areas based on the boom position and the work plan, the lighting areas are divided into high priority, medium priority, and low priority.

6. The tower crane lighting control method based on Kalman filtering and energy consumption optimization as claimed in claim 1, characterized in that: Power is distributed according to the balanced load strategy based on the peak and valley of the power grid, including: during the peak of the power grid, the lighting power in the high-priority area is increased to 80%, and the lighting power in the low-priority area is reduced to 20%; during the valley period, power is distributed according to the balanced load strategy.

7. The tower crane lighting control method based on Kalman filtering and energy consumption optimization as claimed in claim 6, characterized in that: The tower crane lighting control method based on Kalman filtering and energy consumption optimization also includes retraining the dynamic multi-objective energy consumption optimization model according to a preset time period, and triggering Kalman filter parameter recalibration and optimization model iteration when a mechanical vibration main frequency deviation of ≥10Hz or a sudden change in grid load is detected.

8. The tower crane lighting control method based on Kalman filtering and energy consumption optimization as claimed in claim 7, characterized in that: The triggering conditions for retraining the dynamic multi-objective energy consumption optimization model are: Mechanical vibration energy in the 120Hz frequency band suddenly exceeds the historical average by 30%; The grid load fluctuation rate is ≥ 10% for three consecutive sampling periods; The deviation between the actual lighting energy consumption and the predicted value lasts for 5 minutes or more.

9. A tower crane lighting control system based on Kalman filtering and energy consumption optimization, characterized in that: The system is used to execute the tower crane lighting control method based on Kalman filtering and energy consumption optimization as described in any one of claims 1 to 8, and comprises: The data acquisition module is used to collect the mechanical vibration signal and posture data of the tower crane 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 a pre-low-pass filter on the vibration signal, perform a wavelet transform on the filtered signal, suppress the characteristic frequency band of the 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 the tower crane attitude estimation value including the tower crane arm position coordinates, swing angle and attitude error after vibration compensation in real time; The multi-objective energy consumption optimization module is used to input historical energy consumption data, lifting task plans and real-time grid load data, and perform mixed energy consumption prediction through a dynamic multi-objective energy consumption optimization model; The lighting control execution module is used to prioritize lighting areas according to the boom position and operation plan, and allocate power according to the balanced load strategy based on the peak and valley of the power grid.

10. The tower crane lighting control system based on Kalman filtering and energy consumption optimization as claimed in claim 9, characterized in that: 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, thereby suppressing the gear meshing noise and high-frequency interference.

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