Hoisting Control System and Method for High-Power Fans in a Narrow Site under Low-Temperature Environment

Abstract

The present invention belongs to the technical field of fan hoisting, and specifically relates to a high-power fan hoisting control system and method based on a narrow site in a low-temperature environment. By collecting environmental data of the construction site, including real-time temperature and parameters related to temperature such as humidity and wind speed, the present invention constructs a high-precision temperature prediction model to achieve accurate temperature prediction of the construction environment. Based on this temperature prediction, the present invention can not only dynamically adjust the hoisting power parameters, but also intelligently plan and optimize the hoisting path by avoiding low-temperature sensitive areas in the hoisting path, ensuring efficient operation under low-temperature and narrow-site conditions. This comprehensive management method significantly improves the safety and efficiency of operation under extreme environmental conditions, effectively avoiding the decline in power system performance and operation errors caused by low temperature.

Description

Technical Field

[0001] The present invention belongs to the technical field of fan hoisting, and in particular relates to a control system and method for hoisting a high-power fan in a small space in a low-temperature environment. Background Art

[0002] As the global demand for renewable energy increases, wind power generation has become a key means to reduce carbon emissions and achieve sustainable energy development. As the core equipment for wind power generation, the demand for wind turbine installation is growing. With the continuous increase in the capacity of single units, high-power wind turbines have gradually become the mainstream. However, many high-quality wind energy resources are concentrated in remote and harsh areas, such as mountains, plateaus and polar regions. This leads to complex construction challenges such as high altitude and small sites when hoisting high-power wind turbines in these areas.

[0003] In the prior art, wind turbine hoisting systems mostly focus on hoisting safety control under normal circumstances. For example, the Chinese invention patent with publication number CN113321126B proposes a wind turbine hoisting platform and a hoisting method thereof, which uses pressure sensors and infrared detection devices installed at the connection position between the hoisting device and the hoisted equipment, as well as a control system that receives data from these sensors to judge the safety of the hoisting process, and adjust the hoisting position to improve safety when necessary.

[0004] However, although the above scheme has achieved basic safety monitoring and dynamic adjustment through real-time data collection, it has not fully considered the impact of low temperature and narrow site conditions on lifting operations. Specifically, it is reflected in the following aspects: 1. Performance degradation of the power system in low temperature environment: The power system (such as hydraulic drive and motor control) that the fan lifting relies on in low temperature environment has significantly reduced performance due to problems such as increased viscosity of hydraulic oil and changes in motor winding resistance. If the lifting power is not adaptively adjusted according to the temperature, it is easy to cause frequent failures of the lifting equipment or even mechanical damage, increasing the risk of lifting failure.

[0005] 2. Limited hoisting paths in narrow sites: The hoisting paths of high-power wind turbines in narrow sites are already limited. Ground frost heave and deformation under low temperature conditions will further compress the moving path of the hoisting equipment, making the originally limited safety redundant space even tighter. If the low temperature factor is not fully considered when adjusting the hoisting position, the risk of collision and the difficulty of adjusting the hoisting position will be increased invisibly, ultimately leading to poor hoisting adjustment effects. Summary of the invention

[0006] The present invention aims to solve the deficiencies in the prior art and proposes a high-power wind turbine hoisting control system and method based on a small site in a low-temperature environment, focusing on optimizing the hoisting control of high-power wind turbines under conditions of low temperature and limited space to improve the safety and efficiency of operations.

[0007] The object of the present invention can be achieved by the following technical solutions: In the first aspect of the present invention, a hoisting control system for a high-power fan in a narrow site under a low-temperature environment is provided, including the following modules: Data acquisition module: During the hoisting process, environmental sensors and a three-dimensional laser scanner are used to collect the temperature data of the construction site under the low-temperature environment in real time, the environmental factor data related to temperature, and the three-dimensional spatial point cloud data of the narrow site.

[0008] Temperature prediction module: Based on the historical temperature time series data and the historical environmental factor time series data related to temperature, a temperature prediction model is constructed to output the temperature prediction curve within a future set time window.

[0009] Power adjustment module: Under the mapping relationship between the temperature range and the power parameters, the power parameters of the hoisting equipment are adjusted according to the temperature prediction curve.

[0010] Path optimization module: Based on the three-dimensional spatial point cloud data, the distribution of obstacles in the construction site is identified, and the initial hoisting path is generated in combination with the fan size. During the hoisting operation according to the initial hoisting path, the path nodes are dynamically optimized according to the temperature prediction curve.

[0011] Early warning execution module: During the power adjustment and path optimization processes, it is configured to trigger a hierarchical early warning according to the comparison result between the predicted temperature and the warning threshold.

[0012] In the second aspect of the present invention, a hoisting control method for a high-power fan in a narrow site under a low-temperature environment is proposed, including the following steps: Step 1: The environmental sensors deployed at the boundary of the hoisting site and the key parts of the equipment are used to collect the real-time temperature data and environmental factor data under the low-temperature environment. At the same time, a three-dimensional laser scanner is used to obtain the three-dimensional spatial point cloud data of the narrow site.

[0013] Step 2: Based on the historical temperature time series data and the associated environmental factor data collected in Step 1, a temperature prediction model is constructed to output the temperature prediction curve within a future set time window.

[0014] Step 3: The temperature prediction curve generated in Step 2 is matched with the preset mapping relationship between the temperature range and the power parameters to adjust the power parameters of the hoisting equipment.

[0015] Step 4: Based on the three-dimensional spatial point cloud data obtained in Step 1, the distribution of obstacles in the construction site is identified and the low-temperature sensitive areas are marked. The initial hoisting path is generated in combination with the fan size parameters; a dynamic buffer area is allocated to the low-temperature sensitive areas according to the temperature prediction curve in Step 2, and thus the initial hoisting path is dynamically optimized.

[0016] Step 5: During the power adjustment and path optimization processes, it is configured to trigger a hierarchical early warning according to the comparison result between the predicted temperature and the warning threshold.

[0017] Combining all the above technical solutions, the positive effects of the present invention are as follows: 1. The present invention constructs an accurate temperature prediction model by collecting environmental data of the construction site to achieve accurate prediction of temperature changes in the construction environment. Based on this temperature prediction, the dynamic adjustment of hoisting power parameters is implemented, and the hoisting path is optimized to avoid key nodes in low-temperature sensitive areas, which not only fills the deficiencies of the prior art in hoisting scenarios under low temperature and narrow sites, but also significantly improves the safety and efficiency of operations under extreme environmental conditions.

[0018] 2. The present invention can timely remind hoisting operators to take control measures in advance by triggering a hierarchical warning mechanism based on the predicted temperature, significantly reducing the adverse effects of low temperature on the performance of hoisting equipment. This not only effectively guarantees the safety and reliability of hoisting operations, but also greatly extends the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on the following drawings without creative efforts.

[0020] Figure 1 It is a schematic diagram of the system module composition provided in Embodiment 1 of the present invention.

[0021] Figure 2 It is a flowchart of the implementation of the power adjustment module in the present invention.

[0022] Figure 3 It is a method step diagram provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0024] Embodiment 1

[0025] The present invention provides a high-power fan hoisting control system for narrow sites in low-temperature environments, including a data acquisition module, a temperature prediction module, a power adjustment module, a path optimization module, and a warning execution module.

[0026] See Figure 1 As shown, the module composition and relationship are as follows: The data acquisition module provides real-time environmental data and detailed three-dimensional spatial information for the temperature prediction module.

[0027] The temperature prediction module uses real-time environmental data to generate a temperature prediction curve and transmits it to the power adjustment module and the path optimization module for corresponding parameter adjustment and path optimization.

[0028] The power adjustment module outputs the adjusted power parameters and directly applies them to the hoisting equipment, and at the same time feeds back to the early warning execution module for evaluating whether it is necessary to trigger an early warning action.

[0029] The optimized hoisting path of the path optimization module is applied to the hoisting equipment for hoisting position adjustment, and at the same time feeds back to the early warning execution module for evaluating whether it is necessary to trigger an early warning action.

[0030] The early warning execution module outputs an early warning signal and sends it to the operator or the control system to guide the adoption of corresponding preventive measures or emergency responses.

[0031] The above-mentioned modules work together to achieve the comprehensive management and optimization of the hoisting operation of high-power fans under low-temperature and narrow-site conditions, ensuring the safety and efficiency of the operation. Each module not only independently completes its specific tasks, but also supports each other through data sharing and feedback mechanisms, forming a highly integrated intelligent control system.

[0032] The data acquisition module uses environmental sensors and 3D laser scanners to collect in real time the temperature data, temperature-related environmental factor data in the low-temperature environment, and 3D spatial point cloud data of the narrow site during the hoisting process.

[0033] In a specific embodiment of the above solution, the environmental sensors are deployed at the boundaries of the hoisting site and key parts of the equipment, integrating multiple sensors such as temperature sensors, humidity sensors, and wind speed sensors to comprehensively monitor the construction environment. Among them, the temperature sensor is specifically used to collect temperature data in the low-temperature environment, while the humidity sensor, wind speed sensor, etc. are responsible for obtaining environmental factor data such as humidity and wind speed related to the temperature. In addition, the 3D laser scanner precisely scans the spatial structure of the construction site to generate detailed 3D spatial point cloud data. Through these comprehensive data, the system can achieve a comprehensive perception of the construction environment.

[0034] The temperature prediction module is used to construct a temperature prediction model based on historical temperature time series data and historical environmental factor time series data related to temperature, and output a temperature prediction curve within a future set time window.

[0035] The specific implementation process of the above module is as follows: collect historical temperature time series data and historical environmental factor time series data of the area where the construction site is located.

[0036] Exemplarily, the historical environmental factor time series data includes, but is not limited to, historical humidity time series data, historical wind speed time series data, etc. The time series can be hourly or every ten minutes corresponding to each day.

[0037] It should be emphasized that when collecting historical temperature time series data and historical environmental factor time series data, try to cover data from multiple recent years to provide a richer sample size. By analyzing data over a longer period, potential trends and patterns can be identified, thereby improving the prediction accuracy of future temperature changes and reducing biases caused by a single abnormal year.

[0038] Clean and standardize the collected historical temperature time series data and historical environmental factor time series data.

[0039] In the specific operation of the above scheme, the main purpose of data cleaning is to remove outliers and other data problems that may affect the accuracy of the model. Common outliers include extreme or unreasonable readings caused by sensor failures, transmission errors, or other non-natural phenomena. Standardization processing is to align the timestamps of the cleaned data and convert it into a standard format suitable for modeling to ensure data consistency and comparability.

[0040] Use time series analysis methods (such as Fourier transform, wavelet transform, etc.) to extract the periodic change characteristics in the low-temperature environment from the historical temperature time series data. For example, identify the temperature fluctuation pattern within a day (such as a large temperature difference between morning and evening), and seasonal changes (such as a longer duration of low temperature in winter).

[0041] Analyze the correlation between temperature and environmental factors through statistical methods, and thereby screen out the environmental factors that are relevant to temperature prediction in the low-temperature environment, denoted as correlated environmental factors.

[0042] As a specific implementation of the above scheme, statistical methods can adopt the Pearson correlation coefficient or machine learning models (such as linear regression) to quantify the relationship strength between temperature and various environmental factors (such as humidity, wind speed, etc.).

[0043] Furthermore, a preset correlation threshold can be set for the screening of correlated environmental factors to determine which environmental factors have a substantial impact on temperature prediction in the low-temperature environment. Compare the correlation index between each environmental factor and temperature with this correlation threshold, and select those environmental factors whose correlation indexes reach or exceed the preset threshold as correlated environmental factors. These selected environmental factors represent the characteristic variables that have the most influence on temperature prediction in the low-temperature environment.

[0044] The above method based on relevance screening not only improves the selection accuracy of the model input features, but also reduces the impact of irrelevant or redundant features on the model performance. By focusing on the key associated environmental factors, a more concise and efficient temperature prediction model can be constructed, thereby enhancing its prediction accuracy and stability in complex low-temperature environments. This method ensures that the selected feature variables can most reflect the main driving factors of temperature changes, providing reliable temperature prediction support for the hoisting operation of high-power fans.

[0045] Construct a temperature prediction model based on the periodic change characteristics and associated environmental factors in the low-temperature environment to select the prediction algorithm.

[0046] When constructing the temperature prediction model above, by incorporating the periodic change characteristics and associated environmental factors in the low-temperature environment into the data characteristic analysis, the most suitable prediction algorithms (such as autoregressive integrated moving average model, random forest, support vector machine, long short-term memory network) can be selected, thereby enhancing the adaptability and prediction accuracy of the model.

[0047] The process of specifically selecting the prediction algorithm is described in detail in the existing literature and technical documents and will not be elaborated here.

[0048] Divide the historical data into a training set and a test set, and use the training set to train the temperature prediction model. The inputs of the model include the historical temperature sequence and its corresponding associated environmental factor sequence, and the output is the temperature prediction value within the target time period.

[0049] In the innovative implementation of the above solution, in order to adapt to the real-time changing environmental conditions, the rolling time window technology is adopted to dynamically update the model parameters. Specifically, the system will retrain the model at fixed time intervals (such as every 5 minutes), adding the latest temperature and associated environmental factor data to the training set, thereby improving the prediction accuracy.

[0050] After completing the model training, use the test set to verify it, and evaluate the prediction performance by calculating the prediction error.

[0051] The above evaluation of the prediction performance by calculating the prediction error can use mean square error and mean absolute error metrics. If the prediction error of the model is large, it indicates that its generalization ability and prediction accuracy are insufficient. At this time, it is necessary to further optimize the model structure or adjust the parameters. Specifically, the adjustment can optimize the hyperparameters of the model (such as learning rate, number of hidden layer nodes, time step, etc.) to enhance the generalization ability and prediction accuracy of the model.

[0052] After evaluating that the prediction performance meets the standard, based on the temperature data and environmental factor data collected in real time during the hoisting process, screen out the associated environmental factors, and combine them with the temperature data as inputs and pass them to the trained temperature prediction model.

[0053] The temperature prediction model generates a temperature prediction curve within a set future time window based on the input data.

[0054] It should be noted that since the hoisting of high-power fans usually takes one to two weeks, significant temperature changes may occur during this period. Therefore, the set future time window should not be too long to avoid increased uncertainty and decreased accuracy due to long-term prediction. For example, setting the time window to 2 hours can ensure higher accuracy and reliability of temperature prediction and timely reflect short-term temperature fluctuations.

[0055] It should be noted that relying solely on real-time temperature data for adjustment during fan hoisting in a low-temperature environment may lead to response lags and operation delays, which in turn may cause problems such as decreased equipment performance, power system failures, or hoisting path deviations. At the same time, it will also expose the hoisting equipment to harsh environments for a long time. Through temperature prediction, adverse conditions can be identified and avoided in advance, the hoisting path and power parameters can be optimized, effectively preventing the equipment from being in extreme environments, reducing operation risks and equipment damage, and enhancing the safety, efficiency, and overall controllability and reliability of the construction process.

[0056] It should be pointed out that when predicting the temperature of the construction site, a dedicated temperature prediction model is constructed instead of directly using the data provided by the meteorological station. This is mainly because although the latter can provide certain reference for fan hoisting operations, there may be deficiencies in refined management and safe operation. Specifically: The data of the meteorological station is usually based on the average value of a large area or the grid forecast results, and cannot accurately reflect the specific local microclimate conditions of the construction site. Higher resolution (such as hourly or shorter time intervals) and higher accuracy temperature prediction are often required at the construction site to ensure the safety and efficiency of operations. In contrast, a dedicated temperature prediction model can be updated and corrected in real time by combining the temperature data collected on site, providing more accurate short-term predictions. This method can not only capture sudden temperature changes, but also adjust the prediction frequency and accuracy according to actual needs, thus better supporting the dynamic management and decision-making at the construction site. In this way, the reliability and applicability of temperature prediction can be significantly improved.

[0057] As an implementation example of the above temperature prediction, assume that a construction site is located in a cold area in the north. The current time is 3 am in winter, the ambient temperature is -15°C, the wind speed is 8 m / s, and the humidity is 70%. The system makes temperature predictions based on the following information: The temperature change pattern during the early morning period is extracted from historical data, and it is found that the temperature drops by an average of 1°C per hour, but when the wind speed exceeds 5 m / s, the cooling rate may accelerate to 1.5°C per hour.

[0058] Statistical results show that wind speed is one of the main environmental factors affecting temperature changes, while the influence of humidity is relatively small.

[0059] Based on historical data, the system trained an LSTM model and verified its prediction error (MSE = 0.5°C) in the test set. The model performed well and met the prediction requirements.

[0060] The system takes the current temperature (-15°C) and wind speed (8 m / s) as inputs, and combines with the historical temperature drop pattern to generate a temperature prediction curve within the next 2 hours.

[0061] See Figure 2 As shown, the power parameter adjustment module is used to adjust the power parameters of the hoisting equipment according to the temperature prediction curve under the mapping relationship between the temperature range and the power parameters.

[0062] The specific implementation process of the above module is as follows: The working temperature range is divided into multiple temperature intervals according to the working characteristics of the wind turbine hoisting equipment.

[0063] For each temperature interval, an appropriate mapping relationship between it and the power parameters is established.

[0064] The above-mentioned power parameters include but are not limited to starting current, operating frequency, and hydraulic system pressure.

[0065] The appropriate mapping relationship between the power parameters and the temperature interval is obtained based on experimental data, manufacturer's recommended values, or historical operation experience to ensure that the equipment can operate efficiently and stably under various temperature conditions.

[0066] In the example of the above implementation plan, assume that we have pre-divided the working temperature range into the following three temperature intervals and mapped appropriate power parameters for each interval, as shown in Table 1.

[0067] Table 1: Mapping relationship between partial temperature intervals and appropriate power parameters

[0068]

[0069] Integrate the temperature data collected in real time during the hoisting process and the temperature prediction curve within the future set time window provided by the temperature prediction module into the current expected temperature range.

[0070] As an embodiment of the above solution, assume that the real-time temperature collected at a certain moment (T0) is -12°C.

[0071] The temperature prediction module generates a temperature prediction curve within the next 2 hours (set time window) based on historical data and current environmental conditions. The curve shows that at T0 + 30 minutes, the predicted temperature is -14°C.

[0072] At T0 + 60 minutes, the predicted temperature is -16°C.

[0073] At T0 + 90 minutes, the predicted temperature is -15°C.

[0074] At T0 + 120 minutes, the predicted temperature is -13°C.

[0075] Integrate the real-time temperature data with the temperature prediction curve within the future set time window to calculate the comprehensive temperature range covering the current and future periods.

[0076] The current real-time temperature is -12°C.

[0077] The lowest predicted temperature within the next 2 hours is -16°C, and the highest predicted temperature is -13°C.

[0078] Therefore, the current expected temperature range is from -16°C to -12°C.

[0079] Compare and analyze the current expected temperature range with the divided temperature intervals. If the current predicted temperature range is completely within a single temperature interval, adjust the current power parameters of the lifting equipment according to the appropriate power parameters mapped by that temperature interval.

[0080] If the current temperature range spans multiple temperature intervals, record these involved temperature intervals as the covered temperature intervals.

[0081] Select the covered temperature interval corresponding to the lowest temperature among all the covered temperature intervals as the severe temperature interval.

[0082] The following is an example of the mapping between the above temperature intervals and the appropriate power parameters. Assume the current expected temperature range is from -18°C to -28°C. Comparing the current expected temperature range with the divided temperature intervals shows that it spans the two temperature intervals of -20°C to -10°C and -30°C to -20°C. In this case, select the covered temperature interval corresponding to the lowest temperature, i.e., -30°C to -20°C, as the severe temperature interval.

[0083] Based on the appropriate mapping relationship between the temperature intervals and the power parameters, extract the appropriate power parameters corresponding to the severe temperature interval.

[0084] Adjust the current power parameters of the lifting equipment according to the appropriate power parameters mapped by the severe temperature interval.

[0085] It should be added that the adjustment of the current power parameters using the appropriate power parameters mentioned above aims to achieve a smooth transition from the current power parameters to the appropriate power parameters.

[0086] In the above solution, selecting the appropriate power parameters of the severe temperature interval for adjustment can ensure that the equipment can still operate normally under the most stringent operating environment. This method directly optimizes for the most unfavorable situation and avoids risks caused by underestimating the environmental impact.

[0087] When making hoisting operation adjustments, the present invention makes use of real-time temperature data and temperature prediction curves within a future set time window, comprehensively considering current and expected temperature changes, making the adjustments more comprehensive and forward-looking. This helps to make adjustment preparations in advance, reduces downtime caused by equipment failures or environmental factors, and can maintain the best performance of the equipment under harsh conditions such as low temperature, reducing the failure rate and accident risk.

[0088] The path optimization module identifies the distribution of construction site obstacles based on three-dimensional spatial point cloud data, generates an initial hoisting path in combination with the fan size, and dynamically optimizes the path nodes according to the temperature prediction curve during the hoisting operation executed according to the initial hoisting path.

[0089] Preferably, the initial hoisting path refers to the following generation process: constructing a point cloud model of the construction site according to the three-dimensional spatial point cloud data of the narrow site.

[0090] In the specific implementation of the preferred solution, the process of constructing the point cloud model of the construction site is as follows: preprocessing the three-dimensional spatial point cloud data of the narrow site through point cloud processing techniques (such as filtering, downsampling, and smoothing, etc.); subsequently, using a surface reconstruction algorithm (such as Poisson reconstruction or triangular mesh generation) to convert the point cloud data into a three-dimensional field point cloud model that accurately describes the terrain and structural characteristics of the construction site.

[0091] Based on the size parameters of the fan (including height, diameter, blade length, etc.), simulate the minimum safe envelope space during the hoisting of the fan in the point cloud model.

[0092] It should be noted that the minimum safe envelope space refers to the minimum safe envelope space required during the hoisting of the fan. This envelope space not only considers the physical size of the fan itself but also includes the additional clearance required to ensure operation safety to avoid collisions with surrounding obstacles.

[0093] Apply geometric topology analysis methods to identify the distribution of obstacles in the construction site point cloud model containing the minimum safe envelope space of the fan, thereby marking the passable areas and obstacle areas in the construction site.

[0094] It should be understood that geometric topology analysis methods combine the principles of geometry and topology to analyze spatial structures and shape characteristics. Geometry focuses on the size, shape, and relative position of objects, while topology studies the continuity and connectivity of space. Through this combination, geometric topology analysis methods can effectively process point cloud models and extract useful spatial information from them.

[0095] In the wind turbine hoisting operation, the point cloud model of the construction site contains detailed terrain and obstacle information. Geometric topology analysis methods can be used to identify these obstacles. The specific identification process is described in detail in the existing technical literature and will not be elaborated here.

[0096] In the specific implementation of the above solution, the process of extracting the passable area is to determine the passable area where the hoisting equipment can move freely in the point cloud model of the construction site based on the position information of the obstacles.

[0097] Combining the identified passable area and obstacle area with the kinematic constraints of the hoisting equipment (such as maximum steering angle, minimum turning radius, and maximum climbing ability) to generate an initial hoisting path.

[0098] In the innovative implementation of the above solution, in order to cope with the real-time changing environmental conditions (such as newly added obstacles), a dynamic adjustment mechanism is introduced, allowing the system to instantaneously update the path planning scheme according to the latest sensor feedback to ensure the safety and efficiency of the hoisting operation.

[0099] The above process constructs an accurate point cloud model of the construction site, simulates the minimum safety envelope space for wind turbine hoisting, and uses advanced geometric topology analysis methods to identify the passable area and obstacle distribution. Finally, combining the kinematic constraints of the hoisting equipment to generate the optimal initial hoisting path, which not only improves the safety and success rate of the hoisting operation, but also enhances the flexibility and adaptability of the system, and can effectively cope with the complex and changeable construction site environment.

[0100] Further preferably, the dynamic optimization of the path nodes according to the temperature prediction curve includes the following: Mark the low-temperature sensitive area in the point cloud model of the construction site.

[0101] The process of marking the low-temperature sensitive area mentioned above is as follows: Collect the historical performance failure records of the hoisting equipment operating in the low-temperature environment, and extract the positions where the hoisting equipment fails from the historical performance failure records.

[0102] It should be noted that the historical performance failure records mentioned above refer to the failure event records of the hoisting equipment due to performance degradation. Using the historical performance failure records to mark the low-temperature sensitive area is because the historical performance failure records provide the actual operating performance of the equipment under specific environmental conditions (such as low temperature). These data reflect the performance and potential problems of the equipment in the real operating environment. Compared with theoretical analysis or simulation, the actual failure records can more accurately reflect which areas are prone to performance degradation or failure, thus improving the accuracy of marking.

[0103] For each historical performance failure record, use a spatial clustering algorithm to group the failure locations of the lifting equipment into categories of similar locations to form low-temperature sensitive areas. These areas may have a significant impact on the operation of the lifting equipment in low-temperature environments due to terrain features (such as low-lying areas being prone to water accumulation and icing), etc.

[0104] Use a visualization tool to mark the low-temperature sensitive areas in the construction site point cloud model.

[0105] Specifically, when marking the low-temperature sensitive areas, the marking accuracy of the low-temperature sensitive areas can be iteratively optimized through historical lifting data.

[0106] Calculate the proportion of each low-temperature sensitive area in all historical performance failure records, and assign a priority label to each low-temperature sensitive area according to this proportion.

[0107] The specific example of assigning the priority label is as follows: Set a threshold based on the calculated proportion to divide different priority labels. For example: Areas with a proportion greater than a certain higher threshold (such as 50%) are marked as high priority.

[0108] Areas with a proportion between medium thresholds (such as 10% - 50%) are marked as medium priority.

[0109] Areas with a proportion lower than a certain lower threshold (such as less than 10%) are marked as low priority.

[0110] It should be explained that the priority labels assigned to the low-temperature sensitive areas above reflect the risk levels of the low-temperature sensitive areas. Higher-priority low-temperature sensitive areas usually mean a larger proportion of equipment failures occur in these areas, indicating a higher risk in these areas.

[0111] Assign a buffer zone to the low-temperature sensitive areas in the path planning according to the temperature prediction curve.

[0112] It should be noted that the buffer zone actually refers to an additional spatial area set around the low-temperature sensitive area. Its main purpose is to provide an additional safety margin for the lifting path. Even if there are small operation errors or environmental changes, it can prevent the equipment from directly entering the low-temperature sensitive area during the lifting task, thus reducing potential risks.

[0113] The specific assignment process is as follows: Assign an initial buffer zone to each low-temperature sensitive area according to the priority label.

[0114] The initial buffer zone assignment operation above is as follows: 1) Set a basic buffer zone width as the starting value for all low-temperature sensitive areas. This width can be determined according to actual requirements such as equipment size and operation error range, and then define an expansion factor to adjust the width of the buffer zone according to the priority.

[0115] 2) For the low-temperature sensitive areas marked as high priority, apply a larger expansion factor (such as 2 times or more), multiply the base buffer width by this expansion factor to obtain the final buffer width.

[0116] For example, if the base buffer width is 5 meters and the expansion factor is 2, the buffer width of the high-priority area is 10 meters.

[0117] 3) For the low-temperature sensitive areas marked as medium priority, apply a medium-sized expansion factor (such as 1.5 times), multiply the base buffer width by this expansion factor to obtain the final initial buffer width.

[0118] For example, if the base buffer width is 5 meters and the expansion factor is 1.5, the initial buffer width of the medium-priority area is 7.5 meters.

[0119] 4) For the low-temperature sensitive areas marked as low priority, apply a smaller expansion factor (such as 1 time or slightly greater than 1), multiply the base buffer width by this expansion factor to obtain the final initial buffer width.

[0120] For example, if the base buffer width is 5 meters and the expansion factor is 1.2, the initial buffer width of the low-priority area is 6 meters.

[0121] Set the dynamic adjustment trigger conditions at the buffer boundary as follows: a) When the predicted temperature obtained from the temperature prediction curve is higher than the primary warning threshold, reduce the initial buffer according to the set ratio.

[0122] It can be understood that when the predicted temperature is lower than the primary warning threshold, it indicates that the current ambient temperature is relatively high and has not reached the low-temperature range that requires warning. In this case, the possibility of the equipment performance being affected by low temperature is small, the risk is low, there is no need to expand the initial buffer, and the initial buffer can be reduced. For example, the base buffer can be maintained, and reducing the buffer helps to improve the space utilization rate of the hoisting operation and reduce the path restriction problem caused by too large a buffer.

[0123] b) When the predicted temperature is lower than the primary warning threshold and higher than the intermediate warning threshold, maintain the initial buffer.

[0124] It can be understood that when the predicted temperature is lower than the primary warning threshold but higher than the intermediate warning threshold, it indicates that the ambient temperature has entered the low-temperature range but has not reached a high risk level. At this time, the equipment performance may begin to be affected to a certain extent, but it is still within the controllable range. In this temperature range, further reducing or expanding the buffer may lead to unnecessary operation complexity or resource waste.

[0125] c) When the predicted temperature is lower than the intermediate warning threshold and higher than the high warning threshold, the initial buffer is expanded according to a set ratio.

[0126] It can be understood that when the predicted temperature is lower than the intermediate warning threshold but higher than the high warning threshold, it indicates that the ambient temperature has entered the low-temperature high-risk range. At this time, the equipment performance may be significantly affected, and the failure risk increases significantly. Expanding the buffer is to provide a larger safety margin around the low-temperature sensitive area to reduce the possibility of the equipment being exposed to a high-risk environment.

[0127] The above-mentioned primary warning threshold, intermediate warning threshold, and high warning threshold are mentioned in the warning execution module, and the primary warning threshold > intermediate warning threshold > high warning threshold.

[0128] It should be added that the situation where the predicted temperature is lower than the high warning threshold is not considered during the buffer allocation process. The reason is that when the predicted temperature drops below this threshold, the warning execution module will trigger a high-level warning and execute an emergency braking measure. At this time, the lifting operation has been suspended, so there is no need to design an additional buffer for this situation. This processing method ensures that the safety mechanism of the system can give priority to ensuring the safety of equipment and personnel under extreme low-temperature conditions and avoid risks caused by continued operation.

[0129] It can be seen from the above operations that as the predicted temperature value decreases, the buffer range is appropriately expanded to increase the safety margin. Through the real-time updated temperature prediction information, the size of the buffer around the low-temperature sensitive area is dynamically adjusted to ensure that it always adapts to the current and expected temperature conditions.

[0130] Identify path nodes on the initial lifting path that are located in or near the low-temperature sensitive buffer.

[0131] When the temperature prediction shows that the temperature is lower than the set warning threshold within the future set time window, shorten the turning radius for path nodes located in or near the low-temperature sensitive buffer.

[0132] The main reason for shortening the turning radius of path nodes located in or near the low-temperature sensitive buffer as mentioned above is that the low-temperature sensitive buffer often contains relatively high risk factors. Shortening the turning radius can help the equipment better avoid these risk factors, which helps reduce the time of the equipment being exposed to high-risk areas for a long time under low-temperature conditions, thereby reducing the risk of equipment performance degradation. In addition, a smaller turning radius usually means that the torque and power required by the equipment during the turning process are lower, thus reducing the burden on the power system.

[0133] It should be noted that the above-mentioned warning threshold is the primary warning threshold. When the temperature prediction shows that the temperature will be lower than the primary warning threshold in the future, it indicates that the equipment performance may start to be affected, although it has not reached the high-risk state. At this time, taking preventive measures (such as shortening the turning radius) can avoid potential risks in advance and prevent the situation from deteriorating to the extent that a higher-level response is required. In addition, taking overly conservative measures too early (such as significantly adjusting the path when the temperature has not dropped significantly) may lead to unnecessary waste of resources and increased operational complexity.

[0134] Preferably, while shortening the turning radius, it is necessary to ensure the smoothness and continuity of the entire path to avoid operational difficulties or other safety hazards caused by sudden path changes.

[0135] The early warning execution module is configured to trigger hierarchical early warnings according to the comparison result between the predicted temperature and the warning threshold during the power adjustment and path optimization processes.

[0136] The specific operations for triggering the hierarchical early warnings are as follows: When the predicted temperature is lower than the primary warning threshold, trigger a primary warning and push an equipment status check instruction to the visualization interface.

[0137] When the predicted temperature is lower than the intermediate warning threshold, trigger an intermediate warning and automatically reduce the lifting speed.

[0138] When the predicted temperature exceeds the high warning threshold, trigger a high warning and perform an emergency brake.

[0139] It should be pointed out that the reason for setting different temperature warning thresholds is that the power system of the lifting equipment shows different performance characteristics under low-temperature conditions. Especially in the hydraulic system, the hydraulic oil is affected by low temperature, and the viscosity of the hydraulic oil increases significantly, resulting in an increase in the flow resistance of the hydraulic system and showing different physical properties. The inflection point temperature at which the equipment performance changes significantly can be obtained through experiments on the hydraulic oil in the hydraulic system of the lifting equipment under low-temperature temperature gradients. These inflection point temperatures can be used as the basis for dividing different warning thresholds; in addition, the key parameters regarding the performance of the hydraulic oil at different temperatures provided in the technical manual when the lifting equipment leaves the factory can also be extracted as the basis for dividing different warning thresholds to ensure that the early warning mechanism can respond in a timely manner when the performance of the hydraulic system begins to be affected.

[0140] By triggering a hierarchical early warning mechanism based on the predicted temperature, the present invention can timely remind the lifting operator to take control measures in advance, significantly reducing the adverse impact of low temperature on the performance of the lifting equipment. This not only effectively guarantees the safety and reliability of the lifting operation, but also greatly extends the service life of the equipment.

[0141] It should be understood that the main reason for triggering the hierarchical warning based on the predicted temperature rather than the real-time temperature when triggering the hierarchical warning is that the real-time temperature reflects the environmental conditions at the current moment, and the adjustment of the hoisting speed and position usually takes a certain amount of time to complete. If the warning operation is only dependent on the real-time temperature, it may lead to the system being unable to take effective measures in time when the problem is identified. However, performing the warning operation based on the predicted temperature can provide sufficient preparation time for these operations, ensuring that the system can complete the necessary adjustments before the actual temperature reaches the dangerous range, improving the forward-looking and response efficiency of the system. In addition, the real-time temperature may cause false alarms or missed alarms due to local fluctuations or measurement errors, while the predicted temperature can effectively reduce the occurrence of this situation through the modeling and analysis of trends, thereby improving the stability of the warning system.

[0142] Embodiment 2

[0143] See Figure 3 As shown, the present invention proposes a hoisting control method for high-power fans in a narrow site under a low-temperature environment, including the following steps: Step 1: Collect real-time temperature data and environmental factor data in the low-temperature environment through environmental sensors deployed at the boundary of the hoisting site and key parts of the equipment, and at the same time use a three-dimensional laser scanner to obtain three-dimensional spatial point cloud data of the narrow site.

[0144] Step 2: Construct a temperature prediction model based on the historical temperature time-series data and associated environmental factor data collected in Step 1, and output a temperature prediction curve within a future set time window.

[0145] Step 3: Match the temperature prediction curve generated in Step 2 with the preset mapping relationship between the temperature range and the power parameters to adjust the power parameters of the hoisting equipment.

[0146] Step 4: Based on the three-dimensional spatial point cloud data obtained in Step 1, identify the distribution of obstacles in the construction site and mark the low-temperature sensitive areas, and generate an initial hoisting path in combination with the fan size parameters; allocate a dynamic buffer area for the low-temperature sensitive areas according to the temperature prediction curve in Step 2, and thus dynamically optimize the initial hoisting path.

[0147] Step 5: Configure to trigger a hierarchical warning according to the comparison result between the predicted temperature and the warning threshold during the power adjustment and path adjustment processes.

[0148] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A hoisting control system for a high-power fan in a narrow space under a low-temperature environment, characterized in that, It includes the following modules: Data acquisition module: Using environmental sensors and 3D laser scanners to collect real-time temperature data, temperature-related environmental factor data, and 3D spatial point cloud data of the construction site in a low-temperature environment during the hoisting process; Temperature prediction module: Construct a temperature prediction model based on historical temperature time series data and historical environmental factor time series data related to temperature, and output a temperature prediction curve within a future set time window; Power adjustment module: Adjust the power parameters of the hoisting equipment according to the temperature prediction curve under the mapping relationship between temperature intervals and power parameters; Path optimization module: Identify the distribution of construction site obstacles based on 3D spatial point cloud data, generate an initial hoisting path in combination with the fan size, and dynamically optimize the path nodes according to the temperature prediction curve during the hoisting operation performed according to the initial hoisting path; Early warning execution module: Configured to trigger hierarchical early warnings according to the comparison result of the predicted temperature and the warning threshold during the power adjustment and path optimization processes; The specific implementation process of the temperature prediction module is as follows: Collect historical temperature time series data and historical environmental factor time series data related to temperature in the area where the construction site is located; Clean and standardize the collected historical temperature time series data and historical environmental factor time series data; Extract the periodic change characteristics in the low-temperature environment from the historical temperature time series data; Analyze the correlation between temperature and environmental factors through statistical methods, and thus screen out the environmental factors that are relevant to temperature prediction in the low-temperature environment, denoted as correlated environmental factors; Select a prediction algorithm based on the periodic change characteristics in the low-temperature environment and correlated environmental factors to construct a temperature prediction model; Divide the historical data into a training set and a test set, use the training set to train the temperature prediction model, the input of the model includes the historical temperature sequence and its corresponding correlated environmental factor sequence, and the output is the temperature prediction value within the target time period; After completing the model training, use the test set to verify it, and evaluate the prediction performance by calculating the prediction error; After evaluating that the prediction performance meets the standard, based on the real-time temperature data and environmental factor data collected during the hoisting process, screen out the correlated environmental factors, and combine them with the temperature data as the input and pass it to the trained temperature prediction model; The temperature prediction model generates a temperature prediction curve within a future set time window according to the input data.

2. The high-power fan hoisting control system based on a narrow site in a low-temperature environment according to claim 1, wherein: The implementation process of the power adjustment module is as follows: Divide the working temperature range into multiple temperature intervals according to the working characteristics of the fan hoisting equipment; Establish a suitable mapping relationship between each temperature interval and the power parameters; Integrate the real-time temperature data collected during the hoisting process and the temperature prediction curve within the future set time window provided by the temperature prediction module into the current expected temperature range; Compare and analyze the current expected temperature range with the divided temperature intervals. If the current predicted temperature range is completely within a single temperature interval, adjust the current power parameters of the hoisting equipment according to the suitable power parameters mapped by the temperature interval.

3. The high-power fan hoisting control system based on a narrow site in a low-temperature environment according to claim 2, wherein: The power adjustment module also includes the following content: If the current expected temperature range spans multiple temperature intervals, record these involved temperature intervals as covered temperature intervals; Among all the covered temperature intervals, select the covered temperature interval corresponding to the lowest temperature as the severe temperature interval; Extract the appropriate dynamic parameters corresponding to the severe temperature interval based on the appropriate mapping relationship between the temperature interval and the dynamic parameters; Adjust the current dynamic parameters of the lifting equipment according to the appropriate dynamic parameters mapped by the severe temperature interval.

4. The hoisting control system for high-power fans in a narrow space under low-temperature environment according to claim 1, wherein: The generation of the initial lifting path is as follows: Construct a construction site point cloud model based on the three-dimensional spatial point cloud data of the narrow site; Simulate the minimum safety envelope space during the lifting of the wind turbine in the point cloud model based on the size of the wind turbine; Apply the geometric topology analysis method to identify the obstacle distribution in the construction site point cloud model containing the minimum safety envelope space of the wind turbine, and thus mark the passable area and the obstacle area in the construction site; Generate the initial lifting path by combining the marked passable area and obstacle area with the kinematic constraints of the lifting equipment.

5. The high-power fan hoisting control system based on a narrow site in a low-temperature environment according to claim 1, wherein: The dynamic optimization of the path nodes according to the temperature prediction curve includes the following: Mark the low-temperature sensitive areas in the construction site point cloud model; Allocate buffers for the low-temperature sensitive areas in the path planning according to the temperature prediction curve; Identify the path nodes located in or near the low-temperature sensitive buffer on the initial lifting path; When the temperature prediction shows that the temperature is lower than the set warning threshold within the future set time window, shorten the turning radius for the path nodes located in or near the low-temperature sensitive buffer.

6. The high-power fan hoisting control system based on a narrow site in a low-temperature environment according to claim 5, wherein: The process of marking the low-temperature sensitive areas in the construction site point cloud model is as follows: Collect the historical performance failure records of the lifting equipment operating in the low-temperature environment, and extract the failure occurrence locations of the lifting equipment from the historical performance failure records; Use the spatial clustering algorithm to classify the failure points at similar locations in each historical performance failure record of the lifting equipment failure occurrence location into one category to form a low-temperature sensitive area; Mark the low-temperature sensitive areas in the construction site point cloud model using a visualization tool; Calculate the proportion of each low-temperature sensitive area in all historical performance failure records, and assign a priority label to each low-temperature sensitive area according to this proportion.

7. The high-power fan hoisting control system based on a narrow site in a low-temperature environment according to claim 6, wherein: The triggering of the hierarchical warning includes the following process: When the predicted temperature is lower than the primary warning threshold, trigger the primary warning and push the equipment status check instruction to the visualization interface; When the predicted temperature is lower than the intermediate warning threshold, trigger the intermediate warning and automatically reduce the lifting speed; When the predicted temperature exceeds the high warning threshold, trigger the high warning and perform an emergency brake.

8. The hoisting control system of a high-power fan based on a narrow site in a low-temperature environment according to claim 7, wherein: The buffer includes the following allocation process: Allocate the initial buffer for each low-temperature sensitive area according to the priority label; Set the dynamic adjustment trigger conditions at the buffer boundary as follows: a) When the predicted temperature obtained according to the temperature prediction curve is higher than the primary warning threshold, reduce the initial buffer according to the set proportion; b) When the predicted temperature is lower than the primary warning threshold and higher than the intermediate warning threshold, maintain the initial buffer; c) When the predicted temperature is lower than the intermediate warning threshold and higher than the high warning threshold, expand the initial buffer according to the set proportion.

9. A hoisting control method for a high-power fan in a narrow site under a low-temperature environment, which is executed by the hoisting control system according to any one of claims 1-8, characterized in that, It includes the following steps: Step 1: Collect real-time temperature data and environmental factor data in a low-temperature environment through environmental sensors deployed at the boundary of the hoisting site and key parts of the equipment. At the same time, use a three-dimensional laser scanner to obtain three-dimensional spatial point cloud data of a narrow site; Step 2: Based on the historical temperature time-series data and associated environmental factor data collected in Step 1, construct a temperature prediction model and output a temperature prediction curve within a future set time window; Step 3: Match the preset mapping relationship between temperature intervals and power parameters according to the temperature prediction curve generated in Step 2, and adjust the power parameters of the hoisting equipment accordingly; Step 4: Based on the three-dimensional spatial point cloud data obtained in Step 1, identify the distribution of obstacles in the construction site and mark the low-temperature sensitive areas, and generate an initial hoisting path in combination with the fan size parameters; allocate a dynamic buffer zone for the low-temperature sensitive areas according to the temperature prediction curve in Step 2, and dynamically optimize the initial hoisting path accordingly; Step 5: Configure to trigger hierarchical early warnings according to the comparison results between the predicted temperature and the warning threshold during the power adjustment and path optimization processes.

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