House jacking system based on multiple electric screw jacks

Abstract

The invention relates to the technical field of building construction equipment, and discloses a house jacking system based on multiple electric screw jacks, comprising: an electric screw jack module for jacking a house; the sensor network module is used for collecting jack states and environment data in real time; the central control module is used for calculating a synchronization error based on sensor data and adjusting the jacking speed through a PID (Proportion Integration Differentiation) algorithm; the intelligent prediction and adjustment module predicts load distribution by using machine learning and dynamically adjusts control parameters; and the fault-tolerant and exception handling module monitors exceptions and automatically adjusts system operation to ensure safety and stability. According to the method, the technical scheme of intelligent prediction and dynamic parameter adjustment is adopted, accurate pre-judgment and real-time response to load fluctuation in the house jacking process are achieved, and by adjusting control parameters in advance, the system can remarkably reduce synchronous errors and improve the coordination of jacking operation.

Description

Technical Field

[0001] This invention relates to the field of building construction equipment technology, specifically to a roof lifting system based on multiple electric screw jacks. Background Technology

[0002] With the acceleration of urbanization, many old buildings and infrastructures need to be reinforced or upgraded to meet requirements such as safety and functional upgrades. Roof lifting technology, as an important construction method, can lift the entire building without demolishing the structure, avoiding the high costs and long cycles of traditional demolition and reconstruction.

[0003] In existing technologies, multiple jacks are used to synchronously lift buildings through certain control strategies, improving the automation level and ease of operation in construction. Some systems utilize feedback control to effectively monitor load changes during the lifting process, ensuring balanced force within a certain range and reducing the complexity of manual operation. Simultaneously, some solutions integrate basic data acquisition devices to achieve real-time monitoring of the construction status, improving the visual management capabilities of the lifting work. However, these technical solutions still have shortcomings. Specifically, the synchronous control response speed is not timely enough, making it difficult to accurately predict load changes, leading to the gradual accumulation of errors during the lifting process and affecting overall coordination. Furthermore, the lack of effective anomaly detection and emergency response mechanisms prevents rapid handling of jack jamming or local foundation anomalies, posing significant safety hazards. Moreover, existing systems rely heavily on fixed settings for adjusting load and control parameters, lacking dynamic adaptive capabilities and failing to fully utilize historical data for intelligent optimization. Therefore, those skilled in the art propose a roof lifting system based on multiple electric screw jacks to address these problems. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a roof lifting system based on multiple electric screw jacks, which solves the problems of large lifting synchronization error, slow abnormal response, and lack of intelligent adjustment in existing technologies.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a roof lifting system based on multiple electric screw jacks, comprising: Electric screw jack module, used to lift the roof by electrically driving the screw lifting rod of the jack; The sensor network module is used to collect the working status and environmental data of each jack in real time and transmit them to the central control module; the central control module is used to calculate the synchronization error based on the sensor network data and adjust the lifting speed of each jack based on the PID control algorithm. The intelligent prediction and adjustment module is used to predict future load distribution through machine learning and dynamically adjust PID control parameters; the fault tolerance and anomaly handling module is used to monitor anomalies during the lifting process and automatically adjust the system operating status to ensure the safety and stability of the system. The electric screw jack module is connected to the sensor network module. The real-time data from the sensor network module is transmitted to the central control module through a communication interface. The central control module is connected to the intelligent prediction and adjustment module and the fault tolerance and anomaly handling module. The intelligent prediction and adjustment module provides load prediction information to the central control module. The fault tolerance and anomaly handling module monitors anomalies during the lifting process in real time and cooperates with the central control module to adjust the system operation.

[0006] Preferably, the electric screw jack module includes: Multiple electric screw jacks, each driven by an electric motor, lift the screw rod precisely to raise the house.

[0007] Preferably, the sensor network module includes: Displacement sensors are used to measure the lifting height of each jack; Load sensors are used to measure the load-bearing capacity of each jack; The foundation settlement sensor is used to monitor the deformation of the foundation and transmit the data to the central control module in real time. Environmental sensors are used to monitor environmental factors such as temperature, humidity, and wind speed in real time.

[0008] Preferably, the central control module includes: The data receiving unit is used to receive real-time data from various sensors; The synchronization control unit is used to calculate the global synchronization error and adjust the parameters of the PID controller according to the error to adjust the lifting speed of each jack. An error correction unit is used to dynamically adjust the tolerance range of synchronization error based on environmental data; The control output unit is used to output adjusted control signals to regulate the working status of the electric drive motor of each jack.

[0009] Preferably, the intelligent prediction and adjustment module includes: The load prediction unit is used to predict future load distribution based on historical jacking data and real-time sensor data using a machine learning model. The control parameter adjustment unit is used to dynamically adjust the proportional gain, integral gain, and derivative gain of the PID controller based on the load prediction results.

[0010] Preferably, the fault tolerance and exception handling module includes: The anomaly detection unit is used to monitor the synchronization error and foundation settlement status in real time. When the synchronization error exceeds the preset range or the foundation settlement exceeds the safety threshold, the system anomaly handling is triggered. An anomaly response unit is used to automatically adjust the system's operating status based on detected anomalies, including reducing the lifting speed or activating a load compensation mechanism.

[0011] Preferably, the synchronization control unit calculates the global synchronization error using the following formula: Where: E(t) is the global synchronization error of the system at time t; h i (t) represents the lifting height of the i-th jack at time t; n represents the total number of electric screw jacks; h j (t) represents the lifting height of the j-th jack at time t.

[0012] Preferably, the error correction unit adjusts the tolerance range of the synchronization error based on environmental monitoring data using the following formula: Where: ΔE is the synchronization error tolerance threshold under the current environment; ΔE0 is the initial synchronization error threshold under standard environmental conditions; T is the current ambient temperature; T0 is the reference ambient temperature; T max H represents the maximum permissible temperature deviation; H represents the current ambient humidity; H0 represents the reference ambient humidity; H max Maximum permissible humidity deviation; α: influence coefficient of temperature change on tolerance error; β: influence coefficient of humidity change on tolerance error.

[0013] Preferably, the load prediction unit uses the following formula to predict future load: in: Let F be the predicted load of the i-th jack at a future time t+Δt; i (tk) represents the historical load of the i-th jack at time tk in the past; w k γ is the weighting coefficient for the k-th historical time; m is the number of historical load samples participating in the prediction; γ is the influence coefficient of foundation settlement on load prediction; ΔS(t) is the foundation settlement at the current time; ε is the random disturbance or model noise term. The control parameter adjustment unit adjusts the PID control parameters based on the load prediction results: Where: K p K represents the proportional gain of the i-th jack; p0 λ is the base proportional gain; λ is the sensitivity coefficient of the proportional gain adjustment to load changes; Fmax This is the maximum load that the jack can withstand.

[0014] Preferably, the anomaly response unit adjusts the synchronization error threshold according to the following formula: Where: h target (t) represents the target lifting height set by the system; η is the synchronization error feedback adjustment coefficient; E(t) is the current synchronization error of the system; ΔE is the error tolerance range under the current environment; The abnormal response unit also includes a load compensation unit, which adjusts the load distribution of adjacent jacks when the foundation settlement exceeds the safety threshold using the following formula: F j (t)=F j (t)+μ·ΔS(j∈neighboring region); Wherein: F j (t) The load of the j-th jack located in the adjacent area at time t; μ is the foundation settlement load compensation coefficient; ΔS is the foundation settlement amount; j∈adjacent area indicates that the j-th jack is located around the area where settlement occurs and is used for compensation.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. This invention adopts an intelligent prediction and dynamic parameter adjustment technology to achieve accurate prediction and real-time response to load fluctuations during roof lifting. By adjusting control parameters in advance, the system can significantly reduce synchronization errors and improve the coordination of lifting operations. Compared with the existing technology that relies on single feedback control, this invention solves the problem of synchronization imbalance caused by control response lag.

[0016] 2. This invention designs a multi-level fault tolerance and anomaly handling mechanism, which can quickly identify synchronization errors and foundation anomalies, and automatically adjust the jacking speed and target height. This technical solution effectively prevents structural instability caused by sudden events. Compared with traditional systems that lack rapid emergency response capabilities, it fills the gap in safety assurance.

[0017] 3. This invention introduces a load compensation algorithm to automatically adjust the load imbalance caused by local foundation settlement. This not only enhances the system's adaptability to environmental changes but also reduces the frequency of manual intervention. Compared with existing solutions, it significantly improves the stability and continuity of the construction process.

[0018] 4. This invention integrates fault self-checking and anomaly recording functions within the system, supporting long-term data accumulation and model optimization. This technology enables the system to continuously improve itself, reducing operation and maintenance costs. Compared with previous solutions that relied on manual inspection, it significantly improves management efficiency and equipment lifespan. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the sensor network module architecture of the present invention; Figure 3 This is a schematic diagram of the central control module architecture of the present invention; Figure 4 This is a schematic diagram of the intelligent prediction and adjustment module architecture of the present invention; Figure 5 This is a schematic diagram of the fault tolerance and exception handling module architecture of the present invention. Detailed Implementation

[0020] The technical solutions in 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.

[0021] Please see the appendix Figure 1 -Appendix Figure 5 This invention provides a roof lifting system based on multiple electric screw jacks, comprising: Electric screw jack module, used to lift the roof by electrically driving the screw lifting rod of the jack; Specifically, in the overall roof lifting system of this invention, the electric screw jack module, as the direct actuator of the system, undertakes the tasks of vertical support and displacement adjustment of the building structure. The design and performance of this module directly determine the accuracy and stability of the lifting operation. During system operation, the electric screw jack module and the sensor network module maintain real-time cooperation, complete fine displacement adjustment with the help of control signals, and ensure consistency with the synchronization commands set by the central control module.

[0022] In practical applications, the nonlinear response of building structures, the uneven distribution of loads, and the complexity of foundation conditions all place higher demands on the response characteristics of jack modules. Therefore, this module not only needs to have basic mechanical support functions, but also needs to take into account the smoothness of motion response, the sensitivity of micro-adjustment, and the flexibility of structural layout.

[0023] In this embodiment, the electric screw jack module includes several electric screw jacks that are distributed in a specific manner.

[0024] Each jack is equipped with an independent drive unit. Generally, the drive unit is preferably a servo motor or a stepper motor, which is used to control the rotation of the screw pair, thereby realizing lifting and displacement.

[0025] An electric screw pair consists of a lead screw and a nut, and is usually made of high-strength alloy steel, which has good load resistance and fatigue resistance.

[0026] Specifically, the lifting stroke of the screw jack is determined by both the motor's rotation angle and the screw pitch, and the lifting displacement can be expressed as: Where: h i (t) represents the lifting height of the i-th jack at time t (unit: mm); P represents the screw pitch (unit: mm); θ i (t) represents the cumulative rotation angle of the motor at time t (unit: rad).

[0027] In one possible implementation, the motor employs closed-loop control, using an encoder to detect the rotation angle and then calculate the displacement in real time.

[0028] In some embodiments, each jack is equipped with an integrated load sensor and displacement sensor, which are used to monitor the load-bearing status and lifting status, respectively.

[0029] To accommodate irregular building layouts, electric jacks are typically arranged in a ring, rectangular array, or other symmetrical configurations, with their specific locations determined by the building's center of gravity and structural form.

[0030] As an alternative, the jack base adopts an adjustable support structure, and its grounding part is designed with a spherical universal joint to compensate for small angle errors and prevent lateral shear caused by slight tilting of the building structure.

[0031] In actual construction, the distribution density of jacks can be optimized according to the load analysis results, so that the working load of each jack is controlled within the design range, usually not exceeding 70% of its maximum bearing capacity.

[0032] To improve adjustment resolution, the lead screw drive structure adopts a fine pitch scheme, generally between 2mm and 5mm. Combined with motor microstepping drive technology, sub-millimeter level lifting accuracy can be achieved.

[0033] In a typical application scenario, such as the overall lifting of a three-story brick-concrete old building, the system is equipped with 16 jacks, each with a maximum load capacity of 20 tons, and the total lifting stroke is controlled within 100mm. At this time, by reasonably setting the motor control parameters, the entire structure can be lifted smoothly at a speed of 0.5mm per second.

[0034] In this embodiment, all jacks are powered by a centralized power supply and are equipped with overcurrent and overvoltage protection mechanisms to ensure timely power outage and shutdown in case of unexpected load changes.

[0035] In some embodiments, for ease of maintenance and transportation, each jack adopts a modular design, consisting of three parts: a drive end, a transmission end, and a sensing end, which are connected by a quick-connect structure to improve installation and replacement efficiency.

[0036] Before the lifting begins, the jack module is preloaded by sensors, gradually and evenly applying support force to prevent structural damage caused by instantaneous force.

[0037] To prevent uneven deformation caused by jamming of a single jack, the system has a redundant layout, that is, a small number of spare jacks are deployed, which will automatically connect to the working circuit when necessary.

[0038] As an extension of the present invention, the electric screw jack module can also be equipped with a temperature compensation device to adjust the lubrication status in real time in low-temperature environments and prevent the screw from jamming.

[0039] In some special working conditions, such as damp underground environments, a sealed waterproof cover and drainage structure can be added to the outside of the jack to improve the service life of the equipment.

[0040] The sensor network module is used to collect the working status and environmental data of each jack in real time and transmit them to the central control module. Specifically, the sensor network module, as a key link in information perception and feedback, undertakes multiple functions such as status detection, environmental monitoring, and data transmission. Located between the electric screw jack module and the central control module, this module forms the front-end data layer in the system's closed-loop control chain. Its main task is to continuously collect multi-dimensional information related to the lifting process and reliably and efficiently transmit this data to the control center, providing necessary data for synchronous control, predictive regulation, and safety protection.

[0041] Typically, the sensor network module needs to cover the entire jacking area, with sensing units deployed at all working nodes of the electric screw jacks to construct a complete data sensing network. The types of signals collected include, but are not limited to, structural displacement, single-point load, foundation settlement, and ambient temperature and humidity. The data acquisition frequency of this module is usually no less than 1Hz, and some key parameters can be updated in milliseconds.

[0042] In this embodiment, the sensor network module includes multiple physical sensors, a data acquisition unit, and a communication interface circuit.

[0043] Displacement sensors are used to measure the lifting displacement of each jack in real time; linear displacement sensors or grating encoders are preferred. These sensors are installed at critical connection points between the jacks and the building structure, and their output signal accuracy can reach ±0.2mm. The measured height data constitutes the direct input of control variables in the system.

[0044] Load sensors are used to detect the actual load-bearing weight of jacks and are typically installed on top of the jack or inside the bearing base. The sensor output is an analog voltage signal or digital quantized data, reflecting local load changes in the building. The sensor range is usually set above 20 tons, with a full-scale accuracy of less than 1%. This signal provides the initial basis for subsequent load distribution prediction and control parameter adjustment.

[0045] Foundation settlement sensors are used to monitor the response state of the foundation, and typically employ laser rangefinders, hydrostatic levels, or fiber optic sensors. The sensors are installed beside the jack foundation to record the relative displacement between the building and the ground. This displacement is denoted as foundation settlement, ΔS(t), and is defined as follows: ΔS(t)=S ref -S cur (t); Where: ΔS(t) represents the ground settlement at the current moment, in mm; S ref S represents the reference height in the initial state. cur (t) represents the current measured foundation height at time t.

[0046] By comparing and analyzing the changing trend of ΔS(t), abnormal risks such as foundation softening and localized loss can be assessed, providing a basis for fault-tolerant response.

[0047] In one possible implementation, the environmental monitoring sensors include a temperature sensor, a humidity sensor, and an anemometer. These sensors are located around the periphery of the lifting structure and are used to collect environmental parameters at the construction site. Temperature is denoted as T, humidity as H, and wind speed as V.

[0048] Alternatively, all sensor nodes connect to the local data acquisition unit via a CAN bus or RS485 interface. Data is packaged and uploaded to the central control module via an industrial-grade control gateway. The communication interface supports redundancy verification and disconnection detection, automatically retransmitting data in the event of a link failure.

[0049] In some embodiments, the system also integrates a time synchronization mechanism into the sensor network module, such as using a GPS clock source or the IEEE 1588 protocol, to ensure that all sampled data has a unified timestamp, which facilitates subsequent calculation and processing.

[0050] Specifically, each group of sensors is configured with a unique address code. When the system starts up, it automatically identifies the currently available sensor units and dynamically constructs the sensor network topology.

[0051] During the lifting operation, sensor data is updated every 0.5 seconds, and a sliding filter is used to eliminate interference. All raw data and calculation results can be cached locally and periodically written to the system log file for later backtracking or diagnosis.

[0052] The central control module is used to calculate the synchronization error based on sensor network data and adjust the lifting speed of each jack based on the PID control algorithm. Specifically, the central control module plays a crucial role in coordinating and executing the system, serving as the control hub of the entire roof lifting system. Its main function is to synchronize and coordinate the jacks based on real-time data transmitted from the sensor network module, and to perform control tasks such as error calculation, PID parameter control, and feedback adjustment. This module directly controls the lifting action of the electric screw jack module; therefore, the stability of its processing logic and the accuracy of its calculation model directly affect the overall response quality and synchronization capability of the system.

[0053] Generally, the central control module needs to have efficient data processing capabilities and highly reliable control output interfaces to ensure that control commands are issued in real time and to avoid control abnormalities caused by time delays, error accumulation or external interference.

[0054] In this embodiment, the central control module includes a data receiving unit, a synchronization control unit, an error correction unit, and a control output unit.

[0055] The data receiving unit is used to receive multi-source data from the sensor network module, including the real-time lifting displacement h of each jack. i (t), Real-time load F i The received data includes ground settlement ΔS(t), environmental parameters T and H, etc., and are timestamped to ensure time consistency in control calculations.

[0056] Specifically, the synchronization control unit calculates the global synchronization error based on the real-time lifting data of the jacks. This error is used to determine the current height consistency among the jacks, and its calculation formula is as follows: Where: E(t) is the global synchronization error of the system at time t (unit: mm). 2 );h i (t) represents the lifting height of the i-th jack at time t (unit: mm); n represents the total number of electric screw jacks; h j (t) represents the lifting height of the j-th jack at time t.

[0057] This formula reflects the current synchronization level of the system by calculating the sum of squares of the deviations of each jack from the average height. The smaller the value, the more precise the synchronization control. The synchronization control unit adjusts the PID control parameters in real time based on this error, dynamically generating the control output for each jack.

[0058] The error correction unit is used to automatically adjust the synchronization error tolerance threshold ΔE under the influence of environmental interference factors. Its calculation relationship is expressed as follows: Where: ΔE represents the error tolerance threshold under the current conditions, in mm. 2 ΔE0 is the initial synchronization error threshold under standard environmental conditions; T is the current ambient temperature (°C); T0 is the reference ambient temperature (°C); T max H represents the maximum permissible temperature deviation (°C); H represents the current ambient humidity (%RH); H0 represents the reference ambient humidity (%RH); H max The maximum allowable humidity deviation (%RH); α and β are the adjustment coefficients for the error tolerance threshold of ambient temperature and humidity, respectively.

[0059] In one possible implementation, the error correction unit reduces the probability of system malfunctions caused by adverse environmental factors such as high temperature and humidity by automatically adjusting ΔE.

[0060] The control output unit calculates the target action command for each jack based on the control quantity output by the synchronous control unit and the error tolerance dynamic boundary provided by the error correction unit, and sends the control signal to the corresponding motor controller through the industrial control interface (such as CANopen, Modbus or IO port).

[0061] In some embodiments, the central control module is configured with an embedded computing platform, such as an ARM architecture control board or an industrial-grade x86 platform, running an RTOS or Linux system, and equipped with a high-precision timer and a real-time task scheduling mechanism.

[0062] To improve robustness, a moving average filter and an anomaly detection mechanism are also set in the system control logic to prevent instantaneous abnormal data from affecting the overall control stability.

[0063] As an alternative, the central control module incorporates a buffer mechanism and a redundant instruction queue. When a brief interruption in the communication link is detected, the system can maintain its original control rhythm within the buffer period, preventing sudden stops during the lifting process.

[0064] During the lifting process, the central control module analyzes the error growth trend and can generate early warning signals to notify operators through preset alarm ports or remote interfaces.

[0065] In some embodiments, the central control module also has built-in self-testing logic to automatically perform functional verification during system startup, parameter loading, and sensor initialization phases, including data integrity verification, communication status check, and control path response test.

[0066] In addition, the central control module continuously refreshes the sensor status and reads back the control command results through a timed polling mechanism, thereby achieving tight coupling between control and feedback.

[0067] The intelligent prediction and adjustment module is used to predict future load distribution and dynamically adjust PID control parameters through machine learning. Specifically, the intelligent prediction and adjustment module plays a crucial role in feedforward control and adaptive optimization. This module primarily uses data collected by the sensor network and historical operational information to predict future load changes in real time, and dynamically adjusts control parameters accordingly to compensate for the shortcomings of traditional PID control due to response lag. Compared to methods relying solely on error feedback, the intelligent prediction module can intervene in the adjustment logic earlier and intervene in the synchronization control process in advance, thereby improving the overall lifting coordination.

[0068] In general, during the lifting process of a building structure, the load distribution is not constant, but fluctuates continuously with the lifting height, structural deformation, and foundation response. To avoid control oscillations or synchronization imbalances caused by lag in load changes, this module introduces a data-driven prediction mechanism to achieve dynamic self-adjustment of controller parameters.

[0069] In this embodiment, the intelligent prediction and adjustment module includes two core units: a load prediction unit and a control parameter adjustment unit.

[0070] The load prediction unit, based on historical load data sequences and current foundation settlement information, uses a weighted regression model or machine learning algorithm to construct a prediction function and output the expected load of each jack in the near future.

[0071] The prediction model is described by the following formula: in: F represents the predicted load (in kN) of the i-th jack at a future time t+Δt; i (tk) represents the historical load (in kN) of the i-th jack at time tk in the past; w k is the weighting coefficient for the k-th historical moment; m is the number of historical load samples participating in the prediction; γ is the influence coefficient of foundation settlement on load prediction; ΔS(t) is the foundation settlement at the current moment; ∈ is the random disturbance or model noise term.

[0072] In one possible implementation, the weighting coefficient wk By setting data correlation or time decay factors, recent load data are given higher weight in the prediction results.

[0073] The control parameter adjustment unit dynamically adjusts the PID controller parameters corresponding to each jack based on the predicted load results. In particular, the proportional gain K... p Adjustments can help the system increase its response speed in advance before the load increases.

[0074] The proportional gain correction formula is as follows: Where: K p K represents the proportional gain of the i-th jack; p0 λ is the base proportional gain; λ is the sensitivity coefficient of the proportional gain adjustment to load changes; F max This is the maximum load that the jack can withstand (unit: kN).

[0075] Generally, when a significant increase in predicted load is expected, the controller raises K in advance. p The system adjusts its parameters to improve response sensitivity; when the predicted load decreases or stabilizes, it automatically returns to the baseline parameters to maintain stable system operation.

[0076] In some embodiments, a lightweight neural network model is embedded within the intelligent prediction and adjustment module to continuously learn from historical job data and prediction bias, and automatically update the weight coefficients w. k And the parameter λ, to improve long-term prediction accuracy.

[0077] Specifically, the initial training phase of the prediction model can be completed using a similar working condition dataset, followed by online edge learning after deployment. This mechanism requires no external computing platform support, as it runs entirely within the edge processing unit, keeping latency within an acceptable range.

[0078] Alternatively, the control parameter adjustment unit can also simultaneously affect the integral gain K of the PID controller. i With differential gain K d Its adjustment rules can be dynamically optimized based on the root mean square error (RMSE).

[0079] In practical applications, such as when there are significant quality differences in the local structure of a building, the load response fluctuates drastically. In such cases, the intelligent prediction and adjustment module can significantly reduce error response time, enabling the system to maintain a high degree of coordination.

[0080] To ensure the stability of control parameter adjustment, the module also integrates an adjustment limiter and a fine-tuning step mechanism to avoid parameter jumps from causing excitation to the system.

[0081] During the lifting process, the system periodically executes prediction and adjustment logic every 0.5 seconds; the range of control parameter changes can be set with threshold boundaries to prevent control divergence due to prediction errors.

[0082] The fault tolerance and anomaly handling module is used to monitor anomalies during the jacking process and automatically adjust the system's operating status to ensure the system's safety and stability.

[0083] Specifically, during the roof lifting process, unforeseen events are inevitable, such as jack jamming, localized foundation subsidence, and control signal failure. Failure to respond promptly to these anomalies can lead to serious structural imbalances or even construction accidents. Therefore, in this invention, a fault-tolerance and anomaly handling module serves as a safety defense mechanism for the lifting system, constructing a response closed loop with self-diagnostic and self-adjusting capabilities. This module continuously monitors synchronization errors, foundation changes, and sudden load changes, and automatically initiates anomaly handling procedures when set thresholds are reached, thereby coordinating control strategies to ensure the continuity and stability of system operation.

[0084] Under normal circumstances, this module obtains real-time status information through a data interaction interface with the control module and independently runs fault diagnosis logic and emergency adjustment strategies. Its response time should not exceed half of the main control system's control cycle to meet rapid response requirements.

[0085] In this embodiment, the fault tolerance and anomaly handling module mainly includes an anomaly detection unit, an anomaly response unit, and a load compensation unit.

[0086] The anomaly detection unit is used to monitor key indicators in the system, such as synchronization error and foundation settlement, and to determine whether there are any abnormalities.

[0087] Specifically, the module first uses the real-time synchronization error E(t) as the criterion and compares it with the current tolerance threshold ΔE. Once the following relationship is satisfied: E(t) > ΔE; This is considered a synchronization anomaly trigger condition.

[0088] Where: E(t) represents the global synchronization error of the system at the current moment, in mm. 2 ΔE is the error tolerance threshold calculated by the error correction unit, in the same unit as above.

[0089] Meanwhile, if the ground settlement ΔS reported by the sensor exceeds the set safe settlement threshold ΔS lim This will also trigger the exception handling process simultaneously. The judgment logic is as follows: |ΔS(t)|>ΔS lim ; Where: ΔS(t) represents the current foundation settlement displacement, in mm; ΔS limThe maximum allowable settlement is set in advance.

[0090] In one possible implementation, the anomaly detection unit uses a sliding window to filter the raw data to avoid misjudgment caused by short-term signal mutations.

[0091] Upon confirmation of an abnormal event, the anomaly response unit immediately generates an adjustment command, first slowing down the jack's lifting speed, and then initiating the height target fine-tuning logic. This logic dynamically adjusts based on the ratio between the current synchronization error and the tolerance threshold. The adjustment formula is as follows: Where: h target (t) represents the target lifting height set by the system, in mm; η is the synchronization feedback adjustment coefficient, used to control the adjustment rate; E(t) is the current synchronization error; ΔE is the current tolerance error.

[0092] By adjusting the target height, the control system can reduce the movement pace in the short term, achieving flexible buffering and reducing the risk of error propagation.

[0093] As an option, the anomaly response unit can be pre-set with multiple anomaly levels, each corresponding to a different response strategy. For example, minor errors may only result in a decrease in execution speed, moderate errors may be accompanied by load shifting, and severe anomalies may trigger an emergency system shutdown.

[0094] The load compensation unit is used to automatically adjust the bearing capacity of adjacent jacks when uneven settlement occurs in the foundation, in order to offset the displacement differences caused by abnormal settlement. The adjustment is based on the following compensation formula: F j (t)=F j (t)+μ·ΔS(j∈neighboring region); Wherein: F j (t) is the adjusted load value of the j-th jack in the adjacent region at time t, in kN; μ is the compensation gain factor; ΔS is the ground settlement at the location of the anomaly, in mm; j∈adjacent region represents the set of jack numbers deployed adjacent to the anomaly point.

[0095] In practice, adjacent regions can be determined by adjacency matrix modeling based on the jack layout diagram.

[0096] In some embodiments, the fault-tolerant module also includes an exception event logging mechanism to archive the parameter status, response time, and executed instructions for each processing step. This data can then be used for later system upgrades or model optimization.

[0097] To enhance operational assurance, the module also integrates a fault self-checking mechanism. After each complete lifting cycle, the system automatically runs a self-checking program to perform a full-range scan of the control link integrity, sensor response status, and adjustment effect.

[0098] Generally, fault tolerance and exception handling modules should have higher priority than regular control commands. Once an abnormal state is entered, its control outputs will have priority interruption authority to ensure that system behavior can be controlled even under extreme conditions.

[0099] In one specific implementation, the module is connected to other control nodes via an independent high-speed bus to avoid response delays during communication congestion.

[0100] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, all equivalent changes made to the structure, shape, and principle of this application should be covered within the scope of protection of this application.

Claims

1. A house lifting system based on multiple electric screw jacks, characterized in that, The application relates to a building lifting system based on intelligent control and fault-tolerant technology. The application comprises: An electric screw jack module for lifting the building by driving the screw lifting rod with an electric drive jack; A sensor network module for real-time collection of the working state of each jack and environmental data and transmission to the central control module; A central control module for calculating synchronization errors according to the sensor network data and adjusting the lifting speed of each jack based on a PID control algorithm; An intelligent prediction and adjustment module for predicting future load distribution through machine learning and dynamically adjusting the PID control parameters; A fault-tolerant and abnormality processing module for monitoring abnormalities in the lifting process and automatically adjusting the system running state to ensure the safety and stability of the system.

2. A housing jacking-up system based on multiple electric screw jacks according to claim 1, characterized in that, The electric screw jack module is connected with the sensor network module, the real-time data of the sensor network module is transmitted to the central control module through a communication interface, the central control module is connected with the intelligent prediction and adjustment module and the fault-tolerant and abnormality processing module, the intelligent prediction and adjustment module provides load prediction information to the central control module, and the fault-tolerant and abnormality processing module monitors abnormalities in the lifting process in real time and cooperates with the central control module to adjust the system operation. The electric screw jack module comprises:

3. A housing jacking-up system based on multiple electric screw jacks according to claim 1, characterized in that, A plurality of electric screw jacks, each jack drives the screw lifting rod through an electric drive motor to accurately lift the building. The sensor network module comprises: A displacement sensor for measuring the lifting height of each jack; A load sensor for measuring the bearing capacity of each jack; A foundation settlement sensor for monitoring the deformation of the foundation and transmitting the data to the central control module in real time; 4. A housing jacking-up system based on multiple electric screw jacks according to claim 1, characterized in that, An environmental sensor for real-time monitoring of temperature, humidity and wind speed environmental factors. The central control module comprises: A data receiving unit for receiving real-time data from each sensor; A synchronization control unit for calculating global synchronization errors and adjusting the parameters of the PID controller according to the errors to adjust the lifting speed of each jack; An error correction unit for dynamically adjusting the synchronization error tolerance range according to environmental data; 5. A housing jacking system based on multiple electric screw jacks as claimed in claim 1, wherein, A control output unit for outputting the adjusted control signal to adjust the working state of the electric drive motor of each jack. The intelligent prediction and adjustment module comprises: A load prediction unit for predicting future load distribution using a machine learning model based on historical lifting data and real-time sensor data; 6. A housing jacking-up system based on multiple electric screw jacks according to claim 1, characterized in that, A control parameter adjustment unit for dynamically adjusting the proportional gain, integral gain and differential gain of the PID controller according to the load prediction result. The fault-tolerant and abnormality processing module comprises: An abnormality detection unit for real-time monitoring of synchronization errors and foundation settlement states, and triggering system abnormality processing when the synchronization error exceeds the preset range or the foundation settlement amount exceeds the safety threshold; 7. A housing jacking system based on multiple electric screw jacks as claimed in claim 4, wherein, An abnormality response unit for automatically adjusting the system running state according to the detected abnormality, including reducing the lifting speed or starting the load compensation mechanism. Wherein: E(t) is the global synchronization error of the system at time t; h i (t) is the jacking height of the i-th jack at time t; n is the total number of electric screw jacks; h j (t) is the jacking height of the j-th jack at time t.

8. A housing jacking-up system based on multiple electric screw jacks according to claim 4, characterized in that, The synchronization control unit calculates the global synchronization error through the following formula: wherein: ΔE is the synchronization error tolerance threshold in the current environment; ΔE0 is the initial synchronization error threshold in the standard environmental conditions; T is the current environmental temperature; T0 is the reference environmental temperature; T max is the maximum allowed temperature deviation; H is the current environmental humidity; H0 is the reference environmental humidity; H max is the maximum allowed humidity deviation; a is the influence coefficient of temperature variation on the tolerance error; β is the influence coefficient of humidity variation on the tolerance error.

9. A housing jacking-up system based on multiple electric screw jacks according to claim 5, characterized in that, The error correction unit adjusts the tolerance range of the synchronization error through the following formula according to the environmental monitoring data: wherein: F (t + Δt) is the predicted load of the ith jack at future time t + Δt; F i (t - k) is the historical load of the ith jack at past time t - k; w k is the weight coefficient of the kth historical time; m is the number of historical load samples participating in the prediction; γ is the influence coefficient of the foundation settlement on the load prediction; ΔS(t) is the foundation settlement amount at the current time; ∈ is a random disturbance or model noise term; The load prediction unit predicts future load using the following formula: The control parameter adjustment unit adjusts the PID control parameters according to the load prediction result: where: K p is the proportional gain of the ith jack; K p0 is the base proportional gain; λ is the sensitivity coefficient of the proportional gain adjustment to load changes; F max is the maximum load that the jack can withstand.

10. A housing jacking system based on multiple electric screw jacks as claimed in claim 6, wherein, The abnormal response unit adjusts the synchronization error threshold value according to the following formula: wherein: h target (t) is the target jacking height set for the system; η is the synchronization error feedback adjustment coefficient; E(t) is the current synchronization error of the system; ΔE is the error tolerance range under the current environment; The abnormal response unit further comprises a load compensation unit which adjusts the load distribution of the adjacent jacks when the local foundation settlement amount exceeds the safety threshold value according to the following formula: F j (t) = F j (t) + μ · ΔS (j ∈ neighboring regions) ; wherein: F j (t) the load of the jth jack in the adjacent area at time t; μ the ground settlement load compensation coefficient; ΔS the ground settlement amount; j ∈ adjacent area indicates that the jth jack is located around the area where settlement occurs, for compensation.

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