A control method and device of a large module jacking system and a medium

Abstract

The application provides a control method and device of a large module jacking system and a medium, relates to the technical field of large module jacking system control, and the method comprises the following steps: obtaining predicted overall stress data QA and simulated overall stress data QB; determining the corresponding simulated overall stress size fluctuation rate λ1 of the building module in T according to XL3, and determining the corresponding simulated overall stress direction fluctuation rate λ2 of the building module in T according to XL4; if λ1 > θ and λ2 > θ, then determining the corresponding wind field complexity σ of the building module according to λ1 and λ2; determining the corresponding basic deceleration ratio τ of σ according to the preset wind field complexity and basic deceleration ratio mapping table QR; adjusting τ according to η to obtain a target deceleration ratio ε; and reducing the jacking speed of the jacking system according to ε; the application can realize reasonable speed reduction of the jacking system, so that precise balance between safety control and construction efficiency is realized.

Description

Technical Field

[0001] This invention relates to the field of control technology for large module lifting systems, and in particular to a control method, equipment and medium for a large module lifting system. Background Technology

[0002] During the jacking of thousand-ton building modules, it is common practice to detect any abnormalities in the jacking system and assess their severity. For significant anomalies, the system can be stopped immediately. For minor anomalies, the jacking speed can be reduced to mitigate the problem. However, current technologies typically employ a fixed-ratio speed reduction. In windy conditions, a fixed reduction may be insufficient to offset the impact of sudden wind load changes, increasing the risk of module tilting or jack overload. Conversely, excessive speed reduction can severely impair construction efficiency. Therefore, achieving a precise balance between safety control and construction efficiency by rationally reducing the jacking system's speed based on the degree of anomaly and the complexity of the wind field is a pressing technical challenge. Summary of the Invention

[0003] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0004] According to a first aspect of this application, a control method for a large module lifting system is provided, the method comprising the following steps:

[0005] Q100: Obtain the predicted overall stress data QA and simulated overall stress data QB of the jacked building module within the target historical time period T. QA is obtained through the working data of the jacking system within T, and QB is obtained by simulating the three-dimensional model of the building module based on the wind force and wind direction experienced by the building module within T. QB includes the simulated overall stress magnitude sequence XL3 and the simulated overall stress direction sequence XL4 of the building module within T.

[0006] Q200, if the similarity η between QA and QB is less than or equal to the first preset similarity threshold and greater than the second preset similarity threshold, then determine the fluctuation rate λ1 of the simulated overall force magnitude of the building module within T according to XL3, and determine the fluctuation rate λ2 of the simulated overall force direction of the building module within T according to XL4.

[0007] Q300, if λ1>θ and λ2>θ, then determine the wind field complexity σ corresponding to the building module based on λ1 and λ2; θ is a preset volatility threshold;

[0008] Q400, based on σ and the preset wind field complexity and basic deceleration ratio mapping table QR, determines the basic deceleration ratio τ corresponding to σ; where QR includes several rows, each row corresponding to a set of wind field complexity and basic deceleration ratio;

[0009] Q500, based on η, adjust τ to obtain the target deceleration ratio ε;

[0010] Q600, based on ε, reduce the lifting speed of the lifting system.

[0011] According to another aspect of this application, a non-transitory computer-readable storage medium is also provided, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the control method of the above-mentioned large module lifting system.

[0012] According to another aspect of this application, an electronic device is also provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0013] The present invention has at least the following beneficial effects:

[0014] The control method of the large-scale modular lifting system of this invention accurately identifies the wind field complexity σ by calculating and simulating the overall force fluctuation rate λ1 and direction fluctuation rate λ2. This avoids safety risks such as module tilting and jack overload caused by insufficient deceleration during sudden wind load changes, while also preventing efficiency losses caused by excessive deceleration when the wind field is stable. Based on the mapping table QR between wind field complexity and foundation deceleration ratio, the foundation deceleration ratio τ is determined, and further adjusted by combining the similarity η between QA and QB to obtain the target deceleration ratio ε. This allows the deceleration strategy to simultaneously correlate the degree of anomaly with wind field characteristics, ensuring effective control over moderate anomalies while flexibly adapting to real-time working conditions, achieving a precise balance between safety control and construction efficiency. By quantifying the correlation between wind field complexity, anomaly degree, and deceleration ratio, the subjective error of empirical deceleration is avoided, providing a scientific and controllable basis for speed adjustment for lifting thousand-ton-level building modules, significantly reducing construction risks under dynamic wind loads. This achieves reasonable deceleration of the lifting system, thereby achieving a precise balance between safety control and construction efficiency. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1This is a side view of the lifting system provided in an embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of the top plan view of the lifting system provided in an embodiment of the present invention;

[0018] Figure 3 A flowchart of a control method for a large module lifting system provided in an embodiment of the present invention;

[0019] Symbol explanation:

[0020] 100. Lifting system; 110. Hydraulic jack. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that, based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Furthermore, this device and / or practice the method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0023] Example 1:

[0024] In this embodiment, the scenario addressed is the lifting process of a large building module, such as... Figure 1 and 2 As shown, the lifting system 100 includes several hydraulic jacks 110, which lift the building module to a preset height.

[0025] The main procedures for jacking operations are as follows:

[0026] Step 1: Installation, debugging, testing, and inspection of lifting fixtures and equipment.

[0027] Step 2: Slide the module above the pit and use a lifting tool to lift the module.

[0028] Step 3: Remove the sliding fixture.

[0029] Step 4: Use the lifting fixture to lower the module.

[0030] Step 5: Module placement and welding.

[0031] Step Six: Dismantle the lifting fixtures and equipment.

[0032] This embodiment provides a method for determining an anomaly in a lifting system, which may include the following steps:

[0033] S100, during the jacking process of the jacking system, at preset intervals, the working data of each hydraulic jack corresponding to each detection moment within the target historical time period T is acquired; the working data includes the pressure difference, displacement difference and oil temperature of the hydraulic jack at the corresponding detection moment; the pressure difference is obtained based on the working pressure and balance pressure of the hydraulic jack at the corresponding detection moment; the displacement difference is obtained based on the displacement of the hydraulic jack at adjacent detection moments; the end time of T is the current time.

[0034] First, a preset duration (e.g., 10 seconds) is set. During the operation of the lifting system, data acquisition is triggered at intervals of this duration. For each hydraulic jack, at each detection moment within the target historical time period T (e.g., the past minute ending at the current acquisition moment), the working pressure (the pressure provided by the hydraulic jack controlled by the control system) and the equilibrium pressure (the pressure of the hydraulic jack that keeps the building module stationary in windless conditions) are acquired. The difference between the two is the pressure difference at that moment. Displacement values ​​at adjacent detection moments are recorded using displacement sensors, and the displacement difference is obtained by subtracting the displacement of the previous moment from the displacement of the next moment. Simultaneously, the oil temperature at the corresponding detection moment is acquired using an oil temperature sensor. These data are then categorized and stored according to the detection moment and the jack number.

[0035] By collecting pressure difference, displacement difference, and oil temperature data at fixed intervals over historical time periods, the real-time operating status changes of hydraulic jacks can be dynamically captured. Pressure difference reflects the jack's force balance, displacement difference reflects lifting synchronization, and oil temperature reflects the hydraulic system's heat dissipation and oil performance stability. The accumulation of historical data provides a time-series reference for subsequent analysis, avoiding the influence of random data from a single moment, and providing reliable basic data for anomaly detection in the hydraulic system.

[0036] Furthermore, step S100 may include the following steps:

[0037] S110, acquire the working pressure, displacement, and oil temperature of the target hydraulic jack at each detection time within the target historical time period T, to obtain the initial data set sequence A = (A1, A2, ..., A...). i A n ); where A i A represents the initial data set corresponding to the target hydraulic jack at the i-th detection time. i = (A i，1 A i，2 A i，3A i，1 A i，2 and A i，3 The values ​​are, in order, the working pressure, displacement, and oil temperature of the target hydraulic jack at the i-th detection moment; the target hydraulic jack can be any hydraulic jack.

[0038] For any hydraulic jack in the lifting system (i.e., the target hydraulic jack), within the target historical time period T, the working pressure (A) is collected by a pressure sensor at preset detection intervals (consistent with the overall detection frequency, such as once every 10 seconds). i,1 ), displacement sensor collects displacement (A) i,2 ), oil temperature sensor collects oil temperature (A) i,3 The three parameters at each detection time are combined into an initial data set A. i (For example, the data set A3 for the third detection time is (15MPa, 25mm, 45℃)), and arranged in chronological order to form sequence A. Data storage is associated with the jack number, detection time, and parameter type to ensure traceability.

[0039] By collecting working pressure, displacement, and oil temperature data in a structured manner and forming a time-series sequence, the original working state of the target hydraulic jack over a historical period is fully preserved. Working pressure directly reflects the jack's output force, displacement reflects changes in lifting height, and oil temperature reflects the physical performance of the hydraulic system. This "parameter + time-series" storage method provides complete raw data support for subsequent calculations of pressure and displacement differences, avoiding calculation errors of derived indicators due to missing parameters. Furthermore, the data acquisition logic for a single jack can be extended to all jacks, ensuring data consistency.

[0040] S120, based on A, determine the pressure difference ΔP corresponding to the target hydraulic jack at the i-th detection moment. i and displacement difference ΔD i ; where ΔP i =A i，1 -PA;ΔD i =A i，2 -A i-1，2 PA is the equilibrium pressure corresponding to the target hydraulic jack; A 0，2 =0.

[0041] For pressure difference ΔP i The working pressure A obtained based on S110 i,1 Subtract the equilibrium pressure PA of the target hydraulic jack (PA is the preset theoretical equilibrium pressure). For the displacement difference ΔD i Take the displacement A at the current detection time. i,2 Displacement A from the previous moment i-1,2The difference, where the displacement A is the value before the first detection time (i=1). 0,2 Set it to 0 (i.e., ΔD1=A) 1,2 -0=A 1,2 ), and in subsequent moments, press ΔD i =A i,2 -A i-1,2 Calculate (e.g., A) 2,2 =30mm, A 1,2 When the diameter is 25mm, ΔD2 = 5mm. The calculation results are stored in association with the detection time and jack number, forming a pressure difference sequence (ΔP1, ΔP2, ..., ΔP...). n ) and displacement difference sequence (ΔD1, ΔD2, ..., ΔD n ).

[0042] Pressure difference ΔP i By comparing with the equilibrium pressure, the deviation between the actual force on the jack and the theoretical equilibrium state was quantified (positive deviation indicates overpressure, negative deviation indicates underpressure), which can directly reflect whether the jack is within the reasonable force range; displacement difference ΔD i By using the difference in displacement between adjacent moments, the change in jacking speed (such as ΔD) is dynamically captured. i A sudden increase may indicate that the jack is lifting too quickly, reflecting the jack's motion synchronization. Combining these two aspects, key state indicators of the jack are extracted from the two core dimensions of "force balance" and "motion synchronization," providing a more intuitive reflection of abnormal characteristics (such as ΔP) compared to the original parameters. i A sustained increase may indicate uneven load distribution, ΔD i Severe fluctuations may reflect synchronization failures, providing valuable derived features for subsequent overall anomaly detection.

[0043] S200: Obtain the wind speed and direction at each preset position on the lifted building module at each detection time.

[0044] Wind speed and wind direction sensors are installed at key locations (such as the four corners of the top and the midpoint of the side) of the lifted building module. The sensors collect real-time wind speed and wind direction (such as the angle value with due north as 0°) at the corresponding locations based on the detection time that is the same as the hydraulic data, and store the data in conjunction with the detection time and location information.

[0045] Targeted collection of wind speed and direction data on building modules can accurately reflect the effect of external wind loads on the building modules; differences in wind speed and direction at different locations will lead to uneven stress distribution on the modules. These data are the core basis for subsequent analysis of the impact of the environment on the overall stress, making up for the shortcomings of traditional detection that ignore environmental factors, and providing environmental data support for a comprehensive assessment of the stress state.

[0046] S300 integrates the working data of each hydraulic jack at each detection moment with the wind speed and direction at each preset position to obtain the working data matrix of the lifting system within T. The working data matrix includes several rows and several columns. Each row includes data from different detection moments in the same dimension, and each column includes the number of different dimensions at the same detection moment. The hydraulic jacks and working data types corresponding to different dimensions are not completely the same.

[0047] In this embodiment, a working data matrix is ​​constructed using "dimension" and "detection time" as dual axes. Pressure difference, displacement difference, oil temperature (dimensions are distinguished by jack), and wind speed and direction at each preset location are regarded as different data dimensions (such as "pressure difference of jack 1", "displacement difference of jack 1", "displacement difference of jack 2", "wind speed at location A", etc.). Data of the same dimension at different detection times are arranged in chronological order as a row of the matrix, and all dimension data at the same detection time are arranged in a preset order as a column of the matrix, forming a structured data set.

[0048] By integrating multi-dimensional and multi-temporal data through matrix, scattered hydraulic and environmental parameters are transformed into ordered structured data. This not only preserves the temporal variation characteristics of data in the same dimension (row data) but also intuitively presents the comprehensive state at a certain moment (column data). This provides a standardized format for subsequent model input and data correlation analysis, avoids analytical errors caused by data chaos, and improves data processing efficiency.

[0049] S400: Input the working data matrix into the preset overall stress prediction model of the building module to obtain the predicted overall stress data QA of the building module.

[0050] The preset overall stress prediction model for building modules is a pre-trained machine learning model. The working data matrix obtained from S300 is input into the model, and the model outputs the predicted overall stress data QA, which includes the magnitude and direction of the stress on the building modules, by learning the correlation between hydraulic parameters and building stress.

[0051] By analyzing the integrated data using a pre-set model, the stress on building modules can be predicted based on the actual working state of the hydraulic system. The prediction results integrate the operating characteristics of hydraulic equipment and historical experience, and can accurately reflect the "current stress state that the hydraulic system should have", providing a benchmark reference based on actual operating data for anomaly judgment.

[0052] Furthermore, step S400 may include the following steps:

[0053] S410, input the working data matrix into the preset GAN neural network model to obtain the predicted overall force magnitude and predicted overall force direction of the building module at each detection time, thereby obtaining the predicted overall force magnitude sequence XL1 and the predicted overall force direction sequence XL2; where XL1 = (XL 1，1 XL 1，2 , ..., XL 1，i , ..., XL 1，n ), i=1, 2,...,n; XL2=(XL 2，1 XL 2，2 , ..., XL 2，i , ..., XL 2，n ); XL 1，i Let XL be the predicted overall force magnitude of the building module at the i-th detection time. 2，i Let n be the predicted overall force direction of the building module at the i-th detection time, and n be the number of detection times within T.

[0054] In this embodiment, the preset GAN neural network model includes two sub-networks: a generator and a discriminator. The discriminator is used during the training phase of the model. It takes the predicted force data output by the generator and the actual force data during the historical normal jacking process as input. By comparing the differences in the distribution of the two, the parameters of the generator are optimized in reverse until the predicted data output by the generator is highly close to the distribution of the real data, thus completing the training of the model.

[0055] The generator has learned a stable mapping relationship between the input data (working data matrix) and the output data (overall force), and has the ability to independently complete the prediction task. Simply input the working data matrix into the trained generator, and it can directly output the predicted magnitude and direction of the overall force, thus forming XL1 and XL2.

[0056] In this embodiment, XL1 represents the dynamic change trend of the resultant force on the building module under the combined action of the hydraulic jack's working state (pressure difference, displacement difference, oil temperature) and external wind load (wind speed, wind direction). The increase or decrease of values ​​in the sequence reflects the fluctuation of the force at different times. For example, a continuously increasing value in XL1 may mean that the load borne by the building module is gradually increasing. XL2 represents the temporal change law of the overall force direction of the building module. The direction is usually represented by an angle (such as the angle with the horizontal direction or the azimuth angle). The change of angle in the vector reflects the deflection of the force direction over time. For example, a sudden large shift in the angle in XL2 may mean a sudden change in the direction of the external wind load or an imbalance in the hydraulic jack's force, causing the overall force direction to deviate from the expected direction.

[0057] Together, they constitute the complete temporal characteristics of the building module's "force magnitude + direction". They not only retain the force state information at a single moment, but also reflect the dynamic evolution of the force through temporal arrangement. This provides a direct and structured basis for subsequent similarity comparison with the simulated overall force data QB (which also contains temporal characteristics of magnitude and direction). It is the core feature carrier for judging whether the lifting system is abnormal.

[0058] A pre-defined GAN neural network model can be trained in the following way:

[0059] I. Training Data Preparation

[0060] Input data acquisition: Collect a working data matrix consistent with the S300 steps during the historical normal jacking process, including the pressure difference, displacement difference, oil temperature (distinguished by dimension) of each hydraulic jack, and the wind speed and wind direction at the preset position of the building module, to ensure that the data covers different working conditions (such as different jacking stages and different wind speed conditions).

[0061] Label data collection: Synchronously record the actual overall force data of the building module at each detection time (obtained by force sensors installed at key parts of the module, including the magnitude and direction of the force) as the "real labels" for training.

[0062] Data preprocessing: Normalize the input data and label data (e.g., scale pressure difference, wind speed, etc. to the [0,1] range), remove outliers (e.g., jump data caused by sensor failure), and divide them into training set and validation set in an 8:2 ratio.

[0063] II. Model Structure Design

[0064] The generator employs a multi-layer neural network structure. The input is a working data matrix (with the same dimensions as the S300 output). It is processed through convolutional layers (extracting temporal features) and fully connected layers (mapping features to the force space). The output predicts the overall magnitude and direction of the force, thus simulating the prediction process of the S410.

[0065] Discriminator: The input is either "predicted force data output by the generator" or "real label data". It combines the working data matrix at the corresponding time point (as an auxiliary input to enhance the discrimination basis) and outputs a binary classification result ("0" represents generated data and "1" represents real data) through a fully connected layer and activation function (such as LeakyReLU) to determine the authenticity of the force data.

[0066] III. Implementation of the Training Process

[0067] Initialize parameters: Randomly initialize the weights of the generator and discriminator (e.g., using a Gaussian distribution), and set the hyperparameters (learning rate 0.0002, batch size 32, number of iterations 5000).

[0068] Alternately train the discriminator and generator:

[0069] Training the discriminator: With the generator parameters fixed, the working data matrix of the training set is input into the generator to obtain "fake samples". These samples are then mixed with "real labeled samples" and input into the discriminator. The discrimination error is calculated using the cross-entropy loss function, and the discriminator parameters are optimized through backpropagation. The goal is to improve the ability to distinguish between real and fake samples.

[0070] Training the generator: With the discriminator parameters fixed, the working data matrix is ​​input into the generator to obtain "fake samples". After inputting into the discriminator, the generator parameters are optimized through a loss function (including the discriminator's misclassification loss of fake samples and the error loss between fake samples and real labels). The goal is to make the generated fake samples as close as possible to the real labels and to be misclassified as "real" by the discriminator.

[0071] Validation and tuning: Every 100 iterations, evaluate the model performance using the validation set; if the average error (MAE) between the generator output and the real label is lower than the preset threshold (e.g., 5%), and the discriminator's accuracy in identifying real and fake samples is close to 50% (it cannot effectively distinguish between them, indicating that the generated data has approximated the real distribution), then stop training.

[0072] S500 simulates the building module based on the wind speed and direction at each preset position on the building module at each detection time, and obtains the simulated overall force data QB of the building module.

[0073] Furthermore, step S500 may include the following steps:

[0074] S510, establish the simulation model corresponding to the building module.

[0075] Based on the design drawings of the lifted building modules (including parameters such as structural dimensions, component connection methods, and material types), a three-dimensional simulation model is constructed using finite element simulation software (such as ANSYS and ABAQUS).

[0076] S520, based on the wind speed and direction at each preset position on the building module at each detection time, simulate the simulation model corresponding to the building module to obtain the simulated overall force magnitude and simulated overall force direction of the building module at each detection time, and then obtain the simulated overall force magnitude sequence XL3 and the simulated overall force direction sequence XL4; where XL3 = (XL 3，1 XL 3，2 , ..., XL 3，i, ..., XL 3，n ); XL4 = (XL 4，1 XL 4，2 , ..., XL 4，i , ..., XL 4，n ); XL 3，i XL represents the simulated overall force on the building module at the i-th detection time. 4，i The simulated overall force direction of the building module at the i-th detection time.

[0077] The wind speed and direction data acquired by S200 at each preset location are correlated to the simulation model according to the detection time; a time-varying wind load (wind speed converted to wind pressure, formula P=0.5ρ) is applied to the model surface at the corresponding location. k v 2 , where ρ k (where v is the air density and v is the wind speed; wind direction determines the direction of wind pressure).

[0078] The transient dynamic analysis method is used to perform iterative calculations at time intervals (e.g., 10 seconds / step) to solve for the overall force state of the model at each time step; the magnitude of the resultant force at the model's center of mass (i.e., the simulated overall force magnitude XL) is extracted. 3,i ) and the direction of the resultant force (i.e., the simulated overall force direction XL) 4,i (represented by the angle between the horizontal and vertical directions).

[0079] XL at all detection times 3,i Arranged in chronological order, the simulated overall force magnitude sequence XL3 (e.g., XL3 = (4800kN, 5000kN, ..., 4900kN)) is formed, and similarly, the simulated overall force direction sequence XL4 (e.g., XL4 = (31°, 33°, ..., 30°)) is formed, ensuring a one-to-one correspondence with XL1 and XL2 of S410 in the time dimension.

[0080] By converting real-time environmental data into dynamic wind loads and inputting them into the simulation model, the independent effects of wind loads on building modules can be quantified. The XL3 and XL4 sequences fully record the stress change trends when only affected by the environment, and their physical meaning is "theoretical stress state excluding hydraulic system interference." Compared with XL1 and XL2 output by the GAN model, this sequence provides a benchmark reference purely based on environmental factors. The comparison between the two can effectively isolate environmental interference, accurately locate the stress deviation caused by hydraulic system anomalies, and provide objective and quantifiable environmental dimension benchmark data for S600 similarity judgment.

[0081] S600, if the similarity between QA and QB is greater than the first preset similarity threshold, then the lifting system is determined to be normal; otherwise, the lifting system is determined to be abnormal.

[0082] Furthermore, step S600 may include the following steps:

[0083] S610, obtain the first similarity η1 between XL1 and XL3 = 1 / (1+(∑) n i=1 (XL) 1，i / MAX (XL1) - XL 3，i / MAX (XL3)) 2 ) 1 / 2 ); where MAX() is the preset function for finding the maximum value.

[0084] Normalization eliminates the comparison bias caused by the difference in absolute numerical magnitude between XL1 and XL3 (e.g., the overall value of XL1 is around 5000kN, and that of XL3 is around 4800kN; after normalization, both are mapped to the [0,1] interval), making the difference calculation more comparable. A similarity formula based on a variant of Euclidean distance quantifies the difference into a value within the [0,1] interval (the smaller the difference, the closer η1 is to 1), intuitively reflecting the degree of agreement in force magnitude and providing a standardized magnitude indicator for subsequent comprehensive judgment.

[0085] S620, obtain the second similarity η2 between XL2 and XL4 = 1 / (1+(∑) n i=1 (XL) 2，i / MAX (XL2) - XL 4，i / MAX (XL4)) 2 ) 1 / 2 ).

[0086] Direction, as an angular quantity, may vary in range depending on the scenario (e.g., 0°-90° or 0°-180°). Normalization ensures the comparability of different direction sequences. Employing the same calculation logic as for size similarity guarantees methodological uniformity while separately quantifying the degree of directional agreement. Directional deviation significantly impacts the stability of building modules (e.g., off-center loading leading to tilting). The introduction of η² overcomes the limitations of relying solely on size judgment, adding a crucial directional dimension for anomaly detection.

[0087] S630, based on η1 and η2, determine the similarity η between QA and QB: η = α1 × η1 + α2 × η2; where α1 is the preset weight for the similarity of the overall force magnitude, and α2 is the preset weight for the similarity of the overall force direction; α1 + α2 = 1.

[0088] Based on preset weights α1 (e.g., 0.6) and α2 (e.g., 0.4, α1+α2=1), η1 and η2 are weighted and fused.

[0089] In this embodiment, α1 and α2 can be determined in the following way:

[0090] If the building module is a high center of gravity structure (such as a slender tower or a high-altitude cantilever module), directional deviations can easily lead to overturning risks (such as eccentric loading caused by lateral forces). In this case, directional similarity is more important, and α2 can be set to a range of 0.6 to 0.7, and α1 to 0.3 to 0.4. If the module is a low center of gravity, large mass structure (such as a heavy foundation module), the magnitude deviation of the force is more likely to exceed the equipment's bearing capacity limit (such as jack overload). In this case, magnitude similarity has a higher weight, and α1 can be set to a range of 0.6 to 0.7, and α2 to 0.3 to 0.4.

[0091] Alternatively, based on dynamic adjustments during the jacking phase, the specific steps include:

[0092] Obtain the current stage of the ascent.

[0093] Based on the current stage of the jacking process and the preset jacking stage and weight mapping table, determine α1 and α2 corresponding to the current jacking stage.

[0094] For example: If the current lifting stage is the initial lifting stage (the module has just left the ground and is not yet stable):

[0095] Orientation deviation can easily cause the module to tilt or even collide with the surrounding structure. Therefore, directional stability should be given priority. We can set α2=0.55 and α1=0.45.

[0096] If the current lifting phase is a high-level lifting phase (the module is approaching the target height):

[0097] Deviations in the magnitude of the force may cause synchronous failure of the jacks (such as the collapse of a jack due to overload). Therefore, the weighting of magnitude needs to be increased. We can set α1=0.55 and α2=0.45.

[0098] Using the above method, the values ​​of α1 and α2 can be accurately matched with the actual risk points, avoiding the "one-size-fits-all" weight setting that leads to insufficient sensitivity in anomaly detection, and ultimately improving the system's ability to identify key risks.

[0099] S640, if η > η', then the lifting system is determined to be normal; otherwise, the lifting system is determined to be abnormal; η' is the first preset similarity threshold.

[0100] Compare the similarity η with the first preset threshold η' (e.g., 0.7, calibrated using historical normal operating data): If η>η', it indicates that the overall consistency between the predicted force (QA) and the simulated force (QB) is high, the hydraulic system and the environmental action are well matched, and no abnormality is judged; if η≤η', it indicates that the deviation between the two exceeds the reasonable range, there is an abnormality in the hydraulic system (e.g., jack synchronization failure causing deviation in force magnitude, or off-center load causing abnormal direction), and it is judged as abnormal.

[0101] Threshold judgment provides a clear standard for anomaly detection, avoiding subjective judgment errors. The threshold η' is set based on historical data, ensuring the engineering rationality of the judgment; when η exceeds the threshold, the system can be considered to be in a normal force balance state; otherwise, an anomaly is warned in time, providing an actionable decision basis for the safety control of the jacking system and effectively reducing the structural risks caused by force deviations.

[0102] Furthermore, after step S600, the method further includes the following steps:

[0103] S700, if the similarity between QA and QB is less than or equal to the first preset similarity threshold and greater than the second preset similarity threshold, then reduce the lifting speed of the lifting system.

[0104] When step S600 determines that the similarity η between QA and QB satisfies "second preset similarity threshold < η ≤ first preset similarity threshold" (e.g., first threshold = 0.9, second threshold = 0.6, η is between 0.6 and 0.9), the system triggers the lifting speed adjustment mechanism.

[0105] Specifically, the lifting speed of the jacks can be reduced by controlling the flow rate of the hydraulic pump or the opening of the valve in the lifting system; for example, from the original speed (e.g., 5 mm / min) to a preset low speed (e.g., 3 mm / min), or dynamically adjusted according to the difference between η and the threshold (the larger the deviation, the higher the speed reduction ratio, such as a 30% speed reduction when the deviation reaches the upper limit of the threshold range, and a 10% speed reduction when it approaches the lower limit). At the same time, the data changes after the speed reduction are monitored in real time to ensure a smooth transition in speed adjustment.

[0106] Beneficial effects: This step adopts a gradient response strategy for "moderate deviations," avoiding efficiency losses caused by direct shutdowns for minor deviations and reducing the dynamic load on the system by slowing down. With reduced lifting speed, the force changes on the hydraulic jacks are smoother, and the additional forces generated by inertia or wind loads on the building modules are reduced, providing a buffer time for the system to autonomously correct deviations (such as hydraulic synchronization compensation) or for manual troubleshooting of potential problems (such as sensor drift or minor synchronization errors). Simultaneously, the graded speed reduction mechanism embodies the principle of "risk adaptation," keeping moderate risks within a manageable range and preventing deviations from escalating into serious anomalies (such as η falling below the second threshold) during the lifting process. This minimizes the impact on the construction period while ensuring construction safety, achieving a dynamic balance between safety and efficiency.

[0107] In this embodiment, by integrating working data such as pressure difference, displacement difference, and oil temperature of the hydraulic jacks, as well as environmental data such as wind speed and direction of the building module, a working data matrix is ​​constructed. This matrix is ​​then compared with the stress data obtained from the overall stress prediction model of the building module and the simulation. This allows for a comprehensive consideration of the impact of the lifting system's own working state and external environmental loads on the stress of the building module. Anomaly detection is achieved through the similarity judgment of the two-dimensional stress data, effectively solving the problem of insufficient detection accuracy caused by the single data dimension or neglect of environmental factors in existing methods. This improves the reliability of anomaly detection in thousand-ton lifting systems and provides a strong guarantee for the safety and stability of the lifting process.

[0108] Example 2:

[0109] The following will refer to Figure 3 The flowchart shown illustrates the control method for a large module lifting system, introducing a control method for such a system.

[0110] Based on the above embodiment one, the control method of the large module lifting system may include the following steps:

[0111] Q100: Obtain the predicted overall stress data QA and simulated overall stress data QB of the jacked building module within the target historical time period T. QA is obtained through the working data of the jacking system within T, and QB is obtained by simulating the three-dimensional model of the building module based on the wind force and wind direction experienced by the building module within T. QB includes the simulated overall stress magnitude sequence XL3 and the simulated overall stress direction sequence XL4 of the building module within T.

[0112] In this embodiment, based on the method in Embodiment 1, the predicted overall stress data QA and simulated overall stress data QB of the lifted building module within the target historical time period T can be obtained, which will not be elaborated here.

[0113] Q200, if the similarity η between QA and QB is less than or equal to the first preset similarity threshold and greater than the second preset similarity threshold, then determine the fluctuation rate λ1 of the simulated overall force magnitude of the building module within T according to XL3, and determine the fluctuation rate λ2 of the simulated overall force direction of the building module within T according to XL4.

[0114] First, calculate the similarity η between QA and QB (the calculation method is the same as steps S610-S630 in Example 1). When η satisfies "second preset similarity threshold < η ≤ first preset similarity threshold" (e.g., 0.6 < η ≤ 0.9), start the volatility calculation.

[0115] Volatility calculations are triggered only in moderate anomaly ranges, avoiding invalid calculations during minor or severe anomalies and improving efficiency. λ1 and λ2 quantify the severity of stress fluctuations caused by wind loads; λ1 reflects the stability of wind magnitude, and λ2 reflects the stability of wind direction, providing quantifiable indicators for judging the complexity of wind fields and compensating for the shortcomings of traditional methods that only consider static wind data.

[0116] Furthermore, λ1 is obtained through the following steps:

[0117] Q210, get XL3 = (XL 3，1 XL 3，2 , ..., XL 3，i , ..., XL 3，n ), i = 1, 2, ..., n; where XL 3，i Let n be the simulated overall force magnitude of the building module at the i-th detection time, and n be the number of detection times within T.

[0118] Define the total number of detection times within the target historical time period T as n (e.g., if T=10 minutes, and detection is performed once every 10 seconds, then n=60), extract the simulated overall force magnitude of the building module at each detection time, and arrange them in chronological order to form sequence XL3.

[0119] The structured sequence format fully preserves the temporal variation characteristics of the simulated force magnitude within T, with each element precisely correlated to the detection time, providing a continuous and traceable raw data foundation for subsequent volatility analysis. The clearly defined n value ensures consistent data length, avoiding volatility calculation deviations caused by differences in sample size, and providing a unified benchmark for comparing volatility across different time periods.

[0120] Q220, Based on XL3, determine the initial simulation overall force fluctuation rate ω1 = (1 / n) × ∑ n i=1 (XL 3，i -((1 / n)×∑ n i=1 XL 3，i )) 2 .

[0121] First, calculate the mean μ1 of the XL3 sequence (i.e., the arithmetic mean of the simulated force magnitudes at all detection times); then calculate the mean μ1 of each XL3 sequence. 3,i Squared deviation from the mean μ1 (XL) 3,i -μ1)²; Finally, the initial volatility is calculated using the formula ω1, which is essentially the variance of the XL3 sequence (a statistic reflecting the degree of data dispersion). For example, if the mean of XL3 is 5000kN and the sum of squared deviations at each time point is 120000, then ω1 = 120000 / 60 = 2000 (kN²).

[0122] The variance formula directly quantifies the dispersion of the simulated force magnitude. The accumulation of squared deviations amplifies the influence of extreme values, making ω1 more sensitive to significant fluctuations. This allows for the accurate capture of force jumps caused by sudden changes in wind load (such as XL caused by gusts). 3,i (A sharp increase). Compared with standard deviation, variance does not require square root calculation, making it simpler to calculate, and it retains the fluctuation information of the original magnitude, providing a clear numerical basis for subsequent normalization.

[0123] Q230, based on ω1 and the preset maximum simulated overall force fluctuation rate ω max Determine λ1 = ω1 / ω max .

[0124] Preset maximum simulated overall force fluctuation rate ω max (Its value can be determined through historical data, such as selecting the maximum variance of the XL3 sequence during all past jacking processes; or through theoretical derivation, such as the upper limit of stress fluctuation of building modules under extreme wind loads). Divide ω1 obtained from Q220 by ω max The normalized volatility λ1 = ω1 / ω is obtained. max This strictly limits the range of λ1 to the interval [0,1] (e.g., ω1=2000, ω max =5000, then λ1=0.4).

[0125] Normalization eliminates the impact of differences in force magnitude under different jacking scenarios (e.g., the absolute value of XL3 differs greatly between heavy and light modules, but λ1 can be uniformly mapped to the same interval), making λ1 comparable across operating conditions. Constraining λ1 within the [0,1] interval facilitates setting a uniform fluctuation threshold θ (e.g., 0.15), simplifying the judgment logic of "whether it is a complex wind field," and improving the practicality of the volatility index in engineering practice. Meanwhile, ω max The introduction of λ1 provides a clear upper limit reference for the degree of volatility, making the physical meaning of λ1 more intuitive (e.g., λ1=0.6 means that the current volatility has reached 60% of the historical maximum volatility).

[0126] Furthermore, λ2 is obtained through the following steps:

[0127] Q240, get XL4 = (XL 4，1 XL 4，2 , ..., XL 4，i , ..., XL 4，n ); among them, XL 4，i The simulated overall force direction of the building module at the i-th detection time.

[0128] Extract the simulated overall force direction corresponding to n detection times within the target historical time period T, and arrange them in chronological order to form sequence XL4. Wherein, XL...4,i The direction angle (in degrees, usually 0°-360°, such as 0° representing due north and 90° representing due east) is obtained through simulation at the i-th detection time. For example, XL4 = (30°, 35°, 28°, ..., 32°). The sequence strictly corresponds to the time-series detection data within T, and completely preserves the trajectory of the direction change over time.

[0129] By storing directional data in a structured time-series format, each angle value is precisely correlated with the detection time, providing a continuous and traceable original basis for subsequent analysis of dynamic fluctuations in direction. Unlike the magnitude of force, direction is a periodic angular quantity (e.g., the actual deviation between 350° and 10° is 20°). Serialized storage facilitates subsequent targeted processing of the periodic characteristics of angles and avoids calculation errors caused by data format chaos.

[0130] Q250 uses a preset sliding window to process XL4. Each time the sliding window is slid, the maximum angle difference between any two simulated overall force directions within the sliding window is obtained.

[0131] A preset sliding window (e.g., window size for 5 detection times, sliding step size for 1 time time) is used to process XL4. After each slide, the angle difference between all pairs of directions within the sliding window is calculated, and the maximum value is taken as the "extreme fluctuation value" of the sliding window. The angle difference calculation needs to consider periodicity: if the two angles are α and β, then the angle difference is min(|α-β|, 360°-|α-β|) (e.g., the angle difference between 350° and 10° is 20°, not 340°). For example, if the directions within the sliding window are (30°, 45°, 20°, 50°, 35°), and the maximum value of the maximum angle difference between any two pairs of angles is 30° (the difference between 50° and 20°), then the maximum angle difference of the sliding window is 30°.

[0132] The sliding window approach focuses on directional changes within a short period of time through a localized window, avoiding the "averaging" effect of overall statistics on drastic local fluctuations (e.g., if there are sudden directional changes at three consecutive moments but the overall mean is close, the sliding window can capture this fluctuation). The selection of the maximum angle difference highlights extreme fluctuations within the window, making it more sensitive to sudden changes in wind direction (e.g., gusts causing a sudden deflection of more than 20°), accurately reflecting the key impact of "sudden changes in wind direction" on the stability of building modules in wind loads.

[0133] Q260, get the number of sliding windows NUM1 whose maximum angle difference is greater than the preset angle difference threshold.

[0134] A preset angle difference threshold (e.g., 20°, set based on the building module's resistance to eccentric loads; directional fluctuations exceeding this value may affect stability) is used to count the number of windows with a "maximum angle difference > threshold" across all sliding windows, denoted as NUM1. For example, if the total number of slides is 58, and 15 windows have a maximum angle difference exceeding 20°, then NUM1 = 15.

[0135] By using threshold screening, the number of "significant fluctuation windows" was quantified, directly linking the actual impact of directional fluctuations on system safety. Only fluctuations exceeding the safety threshold were counted, avoiding interference from small angle changes (such as ±5°) on the indicators. This made the subsequent calculated volatility more in line with engineering safety requirements and improved the practicality of the indicators.

[0136] Q270, based on NUM1 and the number of times the sliding window slides NUM2, determine λ2 = NUM1 / NUM2.

[0137] Calculate the total number of sliding windows NUM2 (if the window size is k, then NUM2 = n - k + 1, such as when n = 60 and k = 5, NUM2 = 56). Obtain the directional fluctuation rate through the formula λ2 = NUM1 / NUM2, which has a value range of [0, 1] (such as when NUM1 = 15 and NUM2 = 50, then λ2 = 0.3).

[0138] The proportional calculation normalizes λ2 to the [0,1] interval, aligning it with the range of λ1, facilitating a unified threshold judgment for "λ1 > θ and λ2 > θ" (e.g., using θ = 0.15 for both). This indicator intuitively reflects the "proportion of windows with significant directional fluctuations." The larger λ2 is, the higher the frequency of drastic wind direction changes within T, indicating a more complex wind field. Compared to traditional variance (which is insensitive to angular periodicity), this method is more suitable for the characteristics of directional data, significantly improving the accuracy of directional fluctuation quantification.

[0139] Q300, if λ1>θ and λ2>θ, then the wind field complexity σ corresponding to the building module is determined according to λ1 and λ2; θ is the preset volatility threshold.

[0140] Furthermore, σ = ρ1 × λ1 + ρ2 × λ2; where ρ1 is the preset weight of the fluctuation rate of the overall simulated force magnitude, and ρ2 is the preset weight of the fluctuation rate of the overall simulated force direction; ρ1 + ρ2 = 1.

[0141] In this embodiment, ρ1 and ρ2 are dynamically adjusted according to the characteristics of the building module and the construction scenario. For example, for a high center of gravity module (where directional fluctuations are more likely to cause overturning), ρ1=0.4 and ρ2=0.6 are set; for a low center of gravity heavy module (where size fluctuations are more likely to cause overload), ρ1=0.6 and ρ2=0.4 are set.

[0142] By adjusting ρ1 and ρ2, the calculation of σ can specifically reflect the core risks in different scenarios. When directional fluctuations have a greater impact on module stability (such as high-altitude jacking), increasing ρ2 can make σ highlight the contribution of directional fluctuations. When magnitude fluctuations are the main risks (such as heavy-load jacking), increasing ρ1 can strengthen the impact of magnitude fluctuations, thus solving the problem that fixed weights cannot adapt to diverse scenarios.

[0143] The weights are directly related to the "actual contribution of magnitude / direction fluctuations to wind field complexity", so that the value of σ can not only reflect the intensity of fluctuations, but also the "risk weight" of fluctuations. For example, under the same λ1 and λ2, σ is larger when the direction is emphasized, which provides a more accurate quantitative basis for determining the subsequent basic deceleration ratio.

[0144] By using the condition that "both volatility exceeds the threshold," complex wind fields that are "unstable in both magnitude and direction" are accurately screened out, avoiding misjudgments caused by single-dimensional fluctuations (such as wind speed fluctuations but stable wind direction). The weighted calculation of σ integrates the contributions of magnitude and direction fluctuations, making the quantification of wind field complexity more in line with the actual impact on building modules, and providing a core basis for determining the subsequent deceleration ratio.

[0145] Q400 determines the basic deceleration ratio τ corresponding to σ based on σ and the preset wind field complexity and basic deceleration ratio mapping table QR; where QR includes several rows, each row corresponding to a set of wind field complexity and basic deceleration ratio.

[0146] A pre-defined wind field complexity and basic deceleration ratio mapping table QR is used, where each row contains an interval of σ (e.g., [0.2, 0.3)) and the corresponding basic deceleration ratio τ (e.g., 20%, meaning the speed is reduced to 80% of the original speed). QR is established based on historical simulation data: by simulating the safe deceleration requirements under different σ values, it is determined that the larger σ is, the higher τ is (e.g., τ = 30% when σ = 0.3). Matching σ obtained from Q300 to the corresponding interval in QR yields τ.

[0147] QR directly transforms the abstract complexity of wind fields into an executable basic deceleration ratio, enabling rapid correlation between complex wind fields and deceleration strategies. Preset values ​​based on historical data ensure the engineering rationality of τ, avoiding the subjectivity of ad-hoc decisions and providing a scientific benchmark for subsequent adjustments.

[0148] Q500, based on η, adjust τ to obtain the target deceleration ratio ε.

[0149] Furthermore, the Q500 may include the following steps:

[0150] Q510, obtain the first preset similarity threshold YA1 and the second preset similarity threshold YA2.

[0151] Furthermore, YA1 ranges from 0.9 to 0.95, and YA2 ranges from 0.5 to 0.6.

[0152] The system retrieves a first preset similarity threshold YA1 (e.g., 0.9) and a second preset similarity threshold YA2 (e.g., 0.5) from its preset parameters. These two thresholds constitute the boundary of the moderate anomaly range (i.e., YA2 < η ≤ YA1). For example, YA1 corresponds to the "acceptable maximum deviation threshold", and YA2 corresponds to the "minimum deviation threshold for initiating enhanced deceleration". The difference between the two (YA1 - YA2) is the quantification range of the anomaly degree (e.g., 0.2).

[0153] The clearly defined threshold boundaries provide a reference coordinate system for subsequent adjustments. YA1 and YA2 not only limit the trigger range of Q500, but also serve as a reference for the relative position of η within the interval, enabling the subsequent adjustment formula to accurately map the relationship between the degree of abnormality and the deceleration magnitude, thus avoiding adjustment errors caused by threshold ambiguity.

[0154] Q520, based on η, YA1, and YA2, determine ε = τ × [1 - (η - YA2) × (1 - W min ) / (YA1-YA2)]; among them, W min The preset minimum adjustment ratio, 0 < W min <1.

[0155] Furthermore, W min The range is from 0.4 to 0.6.

[0156] Based on YA1, YA2 obtained from Q510 and the preset minimum adjustment ratio W min (e.g., 0.5, meaning the deceleration ratio is not less than 50% of the base value), the target deceleration ratio ε is calculated using the formula: ε=τ×[1-(η-YA2)×(1-W min ) / (YA1-YA2)];

[0157] Formula Explanation: (η-YA2) / (YA1-YA2) represents the relative position of η within the medium anomaly range, with a value range of [0,1]. When η=YA2, the value is 0 (maximum deviation); when η=YA1, the value is 1 (minimum deviation).

[0158] (1-W) min This indicates the maximum adjustment range of the deceleration ratio (e.g., W). min When =0.5, 1-W min =0.5, which is 50% of the maximum adjustable base deceleration ratio.

[0159] Overall logic: the closer η is to YA2 (the larger the deviation), the closer (η-YA2) / (YA1-YA2) is to 0, the closer the adjustment term is to 0, and the closer ε is to τ×1 (i.e., the base reduction ratio τ); the closer η is to YA1 (the smaller the deviation), the closer the adjustment term is to (1-W min The closer ε is to τ×W min (i.e., minimum deceleration ratio).

[0160] By using linear calculations of relative position, ε is continuously and smoothly adjusted as η changes (e.g., when η increases from 0.51 to 0.69, ε linearly decreases from 20% to 10%), avoiding the impact of step-by-step adjustments on the lifting system and ensuring the stability of the module under stress. min The setting ensures that even when the deviation is minimal (η≈YA1), the deceleration ratio is not less than τ×W. min (e.g., 10%), preserving basic safety redundancy; while the maximum deceleration ratio does not exceed τ, avoiding excessive deceleration from affecting the construction period, thus achieving a precise match between "risk level" and "intervention intensity". W min It can be flexibly configured according to engineering needs (e.g., set to 0.6 for precision module lifting, prioritizing safety; set to 0.4 for conventional modules, taking efficiency into account), so that the deceleration strategy can adapt to the priority of different construction scenarios.

[0161] By introducing the influence of the degree of anomaly on the deceleration ratio by η, ε can simultaneously reflect the wind field complexity (τ) and the actual deviation of the system (η). The larger the deviation, the greater the deceleration amplitude. This avoids the potential safety loopholes that may exist in deceleration based solely on the wind field and achieves dual precise control of "wind field characteristics + degree of anomaly".

[0162] Q600, based on ε, reduce the lifting speed of the lifting system.

[0163] Furthermore, step Q600 includes the following steps:

[0164] Q610, obtain the current lifting speed V of the lifting system. now .

[0165] The current lifting speed V is obtained through the speed sensor of the lifting system (such as a displacement sensor installed on the piston rod of the hydraulic jack, which calculates the real-time speed in combination with the time difference) or the real-time data recording of the control system. now (Units such as mm / min). For example, if the sensor detects that the jack's lifting displacement is 5 mm in the past 10 seconds, then V now = (5mm / 10s) × 60s / min = 30mm / min. The acquisition process requires simultaneous recording of the detection time to ensure V... now It matches the ε calculated by Q500 in the time dimension (i.e., based on the state adjustment at the same moment).

[0166] Get V in real timenow It provides a precise benchmark value for speed adjustment, avoiding the adjustment lag caused by using historical average speeds (e.g., if the system has spontaneously decelerated due to load changes, adjusting to the old speed will result in errors). The immediacy of speed data ensures that the deceleration strategy can quickly respond to the current operating conditions, laying a data foundation for subsequent accurate adjustments.

[0167] Q620, V now Adjusted to V now ×(1-ε)

[0168] Based on the target deceleration ratio ε calculated using Q520, and through the formula V new =V now Calculate the adjusted jacking speed using ×(1-ε), V new This is the adjusted speed. For example, V now =30mm / min, ε=15%, then V new =30×(1-15%)=25.5mm / min. The system will V new This is converted into a control signal, which, by adjusting the output flow of the hydraulic pump (e.g., reducing the pump speed) or the opening of the hydraulic valve, changes the lifting speed of the jack from V... now Smooth transition to V new (The transition process can be achieved through PID control to achieve shock-free switching).

[0169] Hydraulic jacks are typically controlled using PID algorithms. Reducing the lifting speed increases the accuracy of PID calculations (for the same lifting distance, the number of calculations involved in the PID increases, improving the accuracy of PID control). The new speed is calculated directly based on the target deceleration ratio ε, ensuring that the deceleration magnitude strictly matches the risk level assessed by Q500. The greater the deviation and the more complex the wind field, the higher ε and V. new The lower the level, the more precise the "risk-intervention" response.

[0170] In this embodiment, by calculating the fluctuation rate λ1 of the overall force magnitude and the fluctuation rate λ2 of the direction, the wind field complexity σ is accurately identified, avoiding safety risks such as module tilting and jack overload caused by insufficient deceleration during sudden changes in wind load. Simultaneously, it prevents efficiency losses caused by excessive deceleration when the wind field is stable. Based on the mapping table QR between wind field complexity and the foundation deceleration ratio, the foundation deceleration ratio τ is determined, and further adjusted by combining the similarity η between QA and QB to obtain the target deceleration ratio ε. This allows the deceleration strategy to simultaneously correlate the degree of anomaly with wind field characteristics, ensuring effective control over moderate anomalies while flexibly adapting to real-time operating conditions, achieving a precise balance between safety control and construction efficiency. By quantifying the correlation between wind field complexity, anomaly degree, and deceleration ratio, the subjective errors of empirical deceleration are avoided, providing a scientific and controllable basis for speed adjustment for the lifting of thousand-ton-level building modules, significantly reducing construction risks under dynamic wind loads. This enables reasonable deceleration of the lifting system, thereby achieving a precise balance between safety control and construction efficiency.

[0171] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0172] Embodiments of the present invention also provide a non-transitory computer-readable storage medium that can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a method in the method embodiments, wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiments.

[0173] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0174] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0175] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0176] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0177] Embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0178] The electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0179] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including memory and processor).

[0180] The memory stores program code that can be executed by the processor, causing the processor to perform the steps in the various embodiments described in this specification.

[0181] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0182] The memory may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0183] A bus can represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus structures.

[0184] Electronic devices can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable user interaction with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be achieved through input / output (I / O) interfaces. Furthermore, electronic devices can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0185] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0186] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.

[0187] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention.

Claims

1. A control method for a large modular lifting system, characterized in that, The method includes the following steps: Q100: Obtain the predicted overall stress data QA and simulated overall stress data QB of the jacked building module within the target historical time period T. QA is obtained through the working data of the jacking system within T, and QB is obtained by simulating the three-dimensional model of the building module based on the wind force and wind direction experienced by the building module within T. QB includes the simulated overall stress magnitude sequence XL3 and the simulated overall stress direction sequence XL4 of the building module within T. Q200, if the similarity η between QA and QB is less than or equal to the first preset similarity threshold and greater than the second preset similarity threshold, then determine the fluctuation rate λ1 of the simulated overall force magnitude of the building module within T according to XL3, and determine the fluctuation rate λ2 of the simulated overall force direction of the building module within T according to XL4. Q300, if λ1>θ and λ2>θ, then determine the wind field complexity σ corresponding to the building module based on λ1 and λ2; θ is a preset volatility threshold; Q400, based on σ and the preset wind field complexity and basic deceleration ratio mapping table QR, determines the basic deceleration ratio τ corresponding to σ; where QR includes several rows, each row corresponding to a set of wind field complexity and basic deceleration ratio; Q500, based on η, adjust τ to obtain the target deceleration ratio ε; Q600, based on ε, reduce the lifting speed of the lifting system.

2. The control method for the large module lifting system according to claim 1, characterized in that, λ1 is obtained through the following steps: Q210, get XL3 = (XL 3，1 XL 3，2 , ..., XL 3，i , ..., XL 3，n ), i = 1, 2, ..., n; where XL 3，i Let n be the simulated overall force magnitude of the building module at the i-th detection time, and n be the number of detection times within T. Q220, Based on XL3, determine the initial simulation overall force fluctuation rate ω1 = (1 / n) × ∑ n i=1 (XL 3，i -((1 / n)×∑ n i=1 XL 3，i )) 2 ; Q230, based on ω1 and the preset maximum simulated overall force fluctuation rate ω max Determine λ1 = ω1 / ω max .

3. The control method for the large module lifting system according to claim 2, characterized in that, λ2 is obtained through the following steps: Q240, get XL4 = (XL 4，1 XL 4，2 , ..., XL 4，i , ..., XL 4，n ); among them, XL 4，i The simulated overall force direction of the building module at the i-th detection time; Q250, use a preset sliding window to perform sliding window processing on XL4. Each time the sliding window is slid, obtain the maximum angle difference between any two simulated overall force directions within the sliding window; Q260, obtain the number of sliding windows NUM1 whose maximum angle difference is greater than the preset angle difference threshold; Q270, based on NUM1 and the number of times the sliding window slides NUM2, determine λ2 = NUM1 / NUM2.

4. The control method for the large module lifting system according to claim 1, characterized in that, σ = ρ1 × λ1 + ρ2 × λ2; Wherein, ρ1 is the preset weight of the fluctuation rate of the overall simulated force magnitude, and ρ2 is the preset weight of the fluctuation rate of the overall simulated force direction; ρ1+ρ2=1.

5. The control method for the large module lifting system according to claim 1, characterized in that, Q500 includes the following steps: Q510, obtain the first preset similarity threshold YA1 and the second preset similarity threshold YA2; Q520, based on η, YA1, and YA2, determine ε = τ × [1 - (η - YA2) × (1 - W min ) / (YA1-YA2)]; among them, W min The preset minimum adjustment ratio, 0 < W min <1.

6. The control method for the large module lifting system according to claim 5, characterized in that, W min The range is 0.4 to 0.6; the range of YA1 is 0.9 to 0.95, and the range of YA2 is 0.5 to 0.

6.

7. The control method for the large module lifting system according to claim 1, characterized in that, Step Q600 includes the following steps: Q610, obtain the current lifting speed V of the lifting system. now ; Q620, V now Adjusted to V now ×(1-ε) 8. A non-transitory computer-readable storage medium, wherein the storage medium stores at least one instruction or at least one program segment, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the control method of the large module lifting system as described in any one of claims 1-7.

9. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 8.

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