Synchronous control method of integrated intelligent jacking platform in well tower construction

Abstract

This invention relates to the field of building construction technology, specifically disclosing a synchronous control method for an integrated intelligent jacking platform in well tower construction. The method includes digital twin construction, multi-source information sensing, intelligent synchronous control strategy calculation, hydraulic execution and status feedback, cloud-edge collaboration and remote monitoring, and safety early warning and emergency response. This solution achieves accurate mapping of the platform's state by constructing a multi-source information sensing and fusion model based on digital twins; it overcomes the impact of uneven load and external disturbances on synchronization accuracy by employing a fuzzy adaptive PID and cross-coupling synchronous control strategy; and it combines edge computing and cloud platform collaboration to achieve millisecond-level response to control commands and full lifecycle data traceability, establishing a hierarchical early warning and proactive safety intervention mechanism, significantly improving the intelligence and unmanned operation level of well tower construction.

Description

Technical Field

[0001] This invention relates to the field of building construction technology, specifically to a synchronous control method for an integrated intelligent jacking platform in well tower construction. Background Technology

[0002] Integrated intelligent jacking platforms typically consist of a support system, a jacking system, a steel platform system, and a formwork system, achieving overall platform lifting through the collective action of hydraulic cylinders. However, in practical applications of well tower construction, existing synchronous control methods for jacking platforms still face numerous challenges. First, the cross-section of well tower structures varies greatly, resulting in extremely uneven load distribution on the platform during jacking, leading to asynchronous displacement at each jacking point. This can easily cause platform deformation, tilting, or even jamming, severely impacting construction quality and safety. Second, traditional control strategies are mostly based on single displacement closed-loop control, lacking sufficient dynamic response capability to external disturbances and load changes, making it difficult to guarantee synchronization accuracy. Furthermore, existing monitoring systems are relatively independent, lacking fusion analysis and real-time early warning mechanisms for multi-source heterogeneous data such as displacement, pressure, and tilt angle. This fails to provide comprehensive decision-making basis for the control system, and the level of intelligence in platform status assessment and emergency intervention is low, making it difficult to effectively and promptly control risks in the event of abnormal conditions. Summary of the Invention

[0003] To address the above issues and overcome the shortcomings of existing technologies, this invention provides a synchronous control method for an integrated intelligent jacking platform during well tower construction. Addressing the problem of extremely uneven load distribution on the platform during jacking, this solution constructs a multi-source information perception and fusion model based on digital twins to achieve precise mapping of the platform's state. To address the insufficient dynamic response capability of traditional control strategies to external disturbances and load changes, this solution employs a fuzzy adaptive PID and cross-coupling synchronous control strategy to overcome the impact of uneven load and external disturbances on synchronization accuracy. Furthermore, addressing the limitations of existing monitoring systems in providing comprehensive decision-making support and the low level of intelligence in platform state assessment and emergency intervention, making timely and effective risk control difficult, this solution combines edge computing and cloud platform collaboration to achieve millisecond-level response to control commands and full lifecycle data traceability, establishing a tiered early warning and proactive safety intervention mechanism, significantly improving the intelligence and unmanned operation level of well tower construction.

[0004] The technical solution adopted in this invention is as follows: This invention provides a synchronous control method for an integrated intelligent jacking platform in well tower construction, the method comprising the following steps:

[0005] Step S1: Digital twin construction. Based on the mechanical structure, hydraulic system and construction environment information of the jacking platform, construct geometric model, physical model and behavioral model. Deploy heterogeneous sensors to collect displacement data, pressure data, tilt angle data and micro-strain data in real time during the jacking process of the well tower construction, and establish a virtual-real synchronized digital twin.

[0006] Step S2: Multi-source information perception, preprocessing and feature extraction of real-time sensor data, data fusion using Kalman filtering algorithm, and outputting the posterior state estimation vector of the lifting platform.

[0007] Step S3: Intelligent synchronous control strategy calculation. Set the lifting process target at the edge control end. Based on the posterior state estimation vector of the lifting platform, adopt the cooperative control algorithm based on fuzzy adaptive PID, combined with the adjacent deviation coupling synchronous compensation strategy, calculate the composite control voltage of each lifting hydraulic cylinder, and convert it into control commands.

[0008] Step S4: Hydraulic execution and status feedback. Control commands are sent to the servo proportional valves of each hydraulic cylinder via the fieldbus to control the displacement and speed of the hydraulic cylinders in real time. Heterogeneous sensors synchronously collect the execution results and feed them back to the edge control terminal.

[0009] Step S5: Cloud-edge collaboration and remote monitoring. The edge control terminal uploads the posterior state estimation vector, control commands and execution results of the jacking platform to the cloud. The cloud uses the digital twin to simulate and backtrack the jacking process of the well tower construction, generates an optimized set of control parameters and sends it to the edge control terminal.

[0010] Step S6: Safety early warning and emergency linkage. The edge control terminal monitors the operating status of the lifting platform in real time and sets a graded early warning mechanism and risk threshold: when an operational risk is detected, the graded early warning mechanism is triggered; when the risk level reaches the risk threshold, it automatically switches to emergency mode and executes proactive safety intervention measures.

[0011] Furthermore, in step S1, the construction of the digital twin specifically includes the following steps:

[0012] Step S11: Geometric model construction. Use BIM software to build three-dimensional solid models of each component system of the lifting platform, including the support system, steel platform, formwork system, and hydraulic system, to represent the spatial position and assembly relationship of each component.

[0013] Step S12: Physical model construction. Based on multibody dynamics and hydraulic control theory, establish a rigid-flexible coupled dynamic model and an electro-hydraulic proportional control system model for the lifting platform, and define key parameters. The formulas used are as follows: ;

[0014] In the formula, Indicates the index of the hydraulic cylinder. Indicates the first The output force of each hydraulic cylinder , These represent the pressures in the rodless chamber and the rod chamber, respectively. , These represent the effective working areas of the rodless cavity and the rod cavity, respectively. Indicates the first The speed of each hydraulic cylinder Representation and speed Related frictional forces;

[0015] Step S13: Behavioral model construction, describing the dynamic behavior rules of the jacking platform at different construction stages, as well as the interaction mechanism with the environment. The construction stages include jacking, leg retraction, and formwork closing, and the environment includes wind load and concrete lateral pressure.

[0016] Step S14: Deploy heterogeneous sensors. Install magnetostrictive displacement sensors and pressure sensors at each lifting hydraulic cylinder to acquire displacement and pressure data of the lifting hydraulic cylinder; install dual-axis tilt sensors at the four corners and center of the steel platform to acquire longitudinal and lateral tilt data of the steel platform under construction load and during the lifting process; install fiber optic strain sensors on key load-bearing components to collect microscopic strain data of key load-bearing components under load, including support brackets and main beams.

[0017] Step S15: Twin data association, which associates and maps the geometric model, physical model, behavioral model and real-time data from the sensor to establish a virtual-real synchronized digital twin.

[0018] Furthermore, in step S2, the multi-source information sensing specifically includes the following steps:

[0019] Step S21: Edge data preprocessing. Moving average filtering technology is used to filter and denoise the raw data from each sensor, remove errors, and synchronize and align the timestamps to obtain preprocessed data. The formula used is as follows: ;

[0020] In the formula, Indicates time, This represents the offset of the historical sampled signal. express The filtered output value at time t. Indicates the first The signal sampling input value at time 10:00. Indicates the window length of the moving average filter;

[0021] Step S22: Based on the Kalman filter algorithm, the preprocessed data is fused. The preprocessed data is used as the observation value, the physical model is used as the state transition equation, and the Kalman filter algorithm is used to make the optimal estimate of the real state of the lifting platform. The state includes the displacement, velocity, force of each lifting point, the overall tilt angle of the platform and the stress of key load-bearing components. The posterior state estimation vector is output.

[0022] Furthermore, in step S3, the intelligent synchronization control strategy calculation specifically includes the following steps:

[0023] Step S31: Set the lifting process objectives, including the target lifting height, the maximum allowable synchronization error, and the lifting speed curve;

[0024] Step S32: Displacement closed-loop control. Calculate the deviation and rate of change between the displacement component in the posterior state estimation vector of the lifting platform and the theoretical target displacement, and input it to the displacement controller. The displacement controller integrates a fuzzy inference mechanism and a PID controller. The fuzzy inference mechanism is used to tune the parameters of the PID controller. The PID controller uses the tuned parameters to calculate the basic flow control voltage of each hydraulic cylinder.

[0025] Step S33: Coupling and Synchronization Compensation. Using a synchronization compensation controller, calculate the displacement deviation between the current hydraulic cylinder and its adjacent hydraulic cylinders, which is used as the synchronization compensation term for the displacement controller. The formula used is as follows: ;

[0026] In the formula, Indicates coupling error. Indicates the first Position tracking error of each hydraulic cylinder , They represent the first Position tracking error between two adjacent hydraulic cylinders;

[0027] Step S34: Control command generation. The output of the displacement controller and the output of the synchronization compensation controller are linearly superimposed to generate a composite control voltage. The formula used is as follows: ;

[0028] In the formula, Indicates composite control voltage. , , These are the parameters of the PID controller after tuning. This represents the synchronous compensation gain coefficient.

[0029] Furthermore, in step S5, the cloud-edge collaboration and remote monitoring specifically includes the following steps:

[0030] Step S51: Edge data aggregation and uploading. The edge control terminal compresses and encrypts the posterior state estimation vector, control commands and execution results of the lifting platform, and uploads them to the cloud periodically.

[0031] Step S52: Cloud-based digital twin simulation. The cloud receives real-time data and drives the digital twin to perform synchronous mapping. It predicts and simulates the state of the lifting platform at future moments. Based on the simulation results and the lifting process objectives, the genetic algorithm is used to optimize the parameters of the fuzzy inference mechanism and PID controller and the synchronous compensation gain coefficient offline, generating an optimized set of control parameters.

[0032] Step S53: Parameter distribution and model update. The cloud distributes the optimized control parameter set to the edge control terminal and updates the local control strategy of the edge control terminal.

[0033] The beneficial effects achieved by the present invention using the above solution are as follows:

[0034] (1) To address the problem of extremely uneven load distribution on the platform during the lifting process, this solution constructs a multi-source information perception and fusion model based on digital twins to achieve accurate mapping of the platform's state.

[0035] (2) In view of the problem that traditional control strategies are not dynamic enough to respond to external disturbances and load changes, this scheme adopts a fuzzy adaptive PID and cross-coupled synchronization control strategy to overcome the impact of uneven load and external disturbances on synchronization accuracy.

[0036] (3) In view of the problem that the existing monitoring system cannot provide comprehensive decision-making basis and the platform status assessment and emergency intervention methods are not intelligent enough, making it difficult to control risks in a timely and effective manner, this solution combines edge computing and cloud platform collaboration to achieve millisecond-level response of control commands and full life cycle data traceability, establish a hierarchical early warning and proactive safety intervention mechanism, and significantly improve the level of intelligence and unmanned operation of well tower construction. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating the synchronous control method of an integrated intelligent jacking platform in well tower construction proposed in this invention.

[0038] Figure 2 This is a flowchart illustrating step S3.

[0039] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0040] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0041] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0042] Example 1, see Figure 1 The present invention provides a synchronous control method for an integrated intelligent jacking platform in well tower construction, the method comprising the following steps:

[0043] Step S1: Digital twin construction. Based on the mechanical structure, hydraulic system and construction environment information of the jacking platform, construct geometric model, physical model and behavioral model. Deploy heterogeneous sensors to collect displacement data, pressure data, tilt angle data and micro-strain data in real time during the jacking process of the well tower construction, and establish a virtual-real synchronized digital twin.

[0044] Step S2: Multi-source information perception, preprocessing and feature extraction of real-time sensor data, data fusion using Kalman filtering algorithm, and outputting the posterior state estimation vector of the lifting platform.

[0045] Step S3: Intelligent synchronous control strategy calculation. Set the lifting process target at the edge control end. Based on the posterior state estimation vector of the lifting platform, adopt the cooperative control algorithm based on fuzzy adaptive PID, combined with the adjacent deviation coupling synchronous compensation strategy, calculate the composite control voltage of each lifting hydraulic cylinder, and convert it into control commands.

[0046] Step S4: Hydraulic execution and status feedback. Control commands are sent to the servo proportional valves of each hydraulic cylinder via the fieldbus to control the displacement and speed of the hydraulic cylinders in real time. Heterogeneous sensors synchronously collect the execution results and feed them back to the edge control terminal.

[0047] Step S5: Cloud-edge collaboration and remote monitoring. The edge control terminal uploads the posterior state estimation vector, control commands and execution results of the jacking platform to the cloud. The cloud uses the digital twin to simulate and backtrack the jacking process of the well tower construction, generates an optimized set of control parameters and sends it to the edge control terminal.

[0048] Step S6: Safety early warning and emergency linkage. The edge control terminal monitors the operating status of the lifting platform in real time and sets a graded early warning mechanism and risk threshold: when an operational risk is detected, the graded early warning mechanism is triggered; when the risk level reaches the risk threshold, it automatically switches to emergency mode and executes proactive safety intervention measures.

[0049] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, the construction of the digital twin specifically includes the following steps:

[0050] Step S11: Geometric model construction. Use BIM software to build three-dimensional solid models of each component system of the lifting platform, including the support system, steel platform, formwork system, and hydraulic system, to represent the spatial position and assembly relationship of each component.

[0051] Step S12: Physical model construction. Based on multibody dynamics and hydraulic control theory, establish a rigid-flexible coupled dynamic model and an electro-hydraulic proportional control system model for the lifting platform. Define key parameters, including hydraulic cylinder diameter, rod diameter, proportional valve flow-pressure characteristics, structural stiffness, and damping coefficient. The formulas used are as follows: ;

[0052] In the formula, Indicates the index of the hydraulic cylinder. Indicates the first The output force of each hydraulic cylinder , These represent the pressures in the rodless chamber and the rod chamber, respectively. , These represent the effective working areas of the rodless cavity and the rod cavity, respectively. Indicates the first The speed of each hydraulic cylinder Representation and speed Related frictional forces;

[0053] Step S13: Behavioral model construction, describing the dynamic behavior rules of the jacking platform at different construction stages, as well as the interaction mechanism with the environment. The construction stages include jacking, leg retraction, and formwork closing, and the environment includes wind load and concrete lateral pressure.

[0054] Step S14: Deploy heterogeneous sensors. Install magnetostrictive displacement sensors and pressure sensors at each lifting hydraulic cylinder to acquire displacement and pressure data of the lifting hydraulic cylinder; install dual-axis tilt sensors at the four corners and center of the steel platform to acquire longitudinal and lateral tilt data of the steel platform under construction load and during the lifting process; install fiber optic strain sensors on key load-bearing components to collect microscopic strain data of key load-bearing components under load, including support brackets and main beams.

[0055] Step S15: Twin data association, which associates and maps the geometric model, physical model, behavioral model and real-time data from the sensor to establish a virtual-real synchronized digital twin.

[0056] By performing the aforementioned operations, this solution addresses the problem of extremely uneven load distribution on the platform during the lifting process by constructing a multi-source information perception and fusion model based on digital twins to achieve accurate mapping of the platform's state.

[0057] Example 3, see Figure 1 This embodiment is based on the above embodiment. In step S2, the multi-source information perception specifically includes the following steps:

[0058] Step S21: Edge data preprocessing. Moving average filtering technology is used to filter and denoise the raw data from each sensor, remove errors, and synchronize and align the timestamps to obtain preprocessed data. The formula used is as follows: ;

[0059] In the formula, Indicates time, This represents the offset of the historical sampled signal. express The filtered output value at time t. Indicates the first The signal sampling input value at time 10:00. Indicates the window length of the moving average filter;

[0060] Step S22: Based on the Kalman filter algorithm, the preprocessed data is fused. The preprocessed data is used as the observation value, and the physical model is used as the state transition equation. The Kalman filter algorithm is used to make the optimal estimate of the actual state of the lifting platform. The state includes the displacement, velocity, force of each lifting point, overall tilt angle of the platform, and stress of key load-bearing components. The posterior state estimation vector is output, including the following steps:

[0061] Step S231: State prediction. Calculate the state estimation vector of the lifting platform using the following formula: ;

[0062] In the formula, , They represent Time and The state estimation vector of the lifting platform at any given time. Here is the state transition matrix. For the control matrix, express Time-based control input;

[0063] Step S232: Covariance prediction, using the following formula: ;

[0064] In the formula, , They represent Time and The error covariance matrix at time t. The transpose of the state transition matrix. The process noise covariance matrix;

[0065] Step S233: Kalman gain, using the following formula: ;

[0066] In the formula, Indicates Kalman gain, , Let them represent the observation matrix and its transpose, respectively. Represents the measurement noise covariance matrix. Represents the inverse matrix;

[0067] Step S234: State update, using the following formula: ;

[0068] In the formula, express The posterior state estimation vector of the lifting platform at any given time. This represents the actual observation vector of the sensor;

[0069] Step S235: Covariance update, using the following formula: ;

[0070] In the formula, express The posterior estimate of the time error covariance matrix. It is an identity matrix.

[0071] Example 4, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S3, the intelligent synchronization control strategy calculation specifically includes the following steps:

[0072] Step S31: Set the lifting process objectives, including the target lifting height, the maximum allowable synchronization error, and the lifting speed curve;

[0073] Step S32: Displacement closed-loop control. Calculate the deviation and rate of change between the displacement component in the posterior state estimation vector of the lifting platform and the theoretical target displacement, and input it to the displacement controller. The displacement controller integrates a fuzzy inference mechanism and a PID controller. The fuzzy inference mechanism is used to tune the parameters of the PID controller. The PID controller uses the tuned parameters to calculate the basic flow control voltage of each hydraulic cylinder.

[0074] Step S33: Coupling and Synchronization Compensation. Using a synchronization compensation controller, calculate the displacement deviation between the current hydraulic cylinder and its adjacent hydraulic cylinders, which is used as the synchronization compensation term for the displacement controller. The formula used is as follows: ;

[0075] In the formula, Indicates coupling error. Indicates the first Position tracking error of each hydraulic cylinder , They represent the first Position tracking error between two adjacent hydraulic cylinders;

[0076] Step S34: Control command generation. The output of the displacement controller and the output of the synchronization compensation controller are linearly superimposed to generate a composite control voltage, which is then converted into a control command output. The formula used is as follows: ;

[0077] In the formula, Indicates composite control voltage. , , These are the parameters of the PID controller after tuning. This represents the synchronous compensation gain coefficient.

[0078] By performing the aforementioned operations, this solution addresses the problem of insufficient dynamic response capability of traditional control strategies to external disturbances and load changes. It adopts a fuzzy adaptive PID and cross-coupled synchronization control strategy to overcome the impact of uneven load and external disturbances on synchronization accuracy.

[0079] Example 5, see Figure 1 This embodiment is based on the above embodiment. In step S5, the cloud-edge collaboration and remote monitoring specifically includes the following steps:

[0080] Step S51: Edge data aggregation and uploading. The edge control terminal compresses and encrypts the posterior state estimation vector, control commands and execution results of the lifting platform, and uploads them to the cloud periodically.

[0081] Step S52: Cloud-based digital twin simulation. The cloud receives real-time data and drives the digital twin to perform synchronous mapping. It predicts and simulates the state of the lifting platform at future moments. Based on the simulation results and the lifting process objectives, the genetic algorithm is used to optimize the parameters of the fuzzy inference mechanism and PID controller and the synchronous compensation gain coefficient offline, generating an optimized set of control parameters.

[0082] Step S53: Parameter distribution and model update. The cloud distributes the optimized control parameter set to the edge control terminal to update the local control strategy.

[0083] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S6, the safety warning and emergency response linkage specifically includes the following steps:

[0084] Step S61: Set multi-level early warning thresholds, collect well tower construction specifications and safety requirements, and set four levels of early warning thresholds (blue, yellow, orange, and red) for displacement synchronization deviation, single-point overload, tilt angle, and stress extreme values.

[0085] Step S62: Real-time risk assessment, comparing the posterior state estimation vector of the lifting platform with the warning thresholds at all levels to comprehensively assess the current risk level;

[0086] Step S63: Active safety intervention. When the risk reaches the orange level, the lifting speed is automatically reduced; when the risk reaches the red level, an emergency stop command is immediately triggered, the hydraulic power source is cut off, all hydraulic valves are locked to maintain pressure, and an audible and visual alarm is issued. At the same time, a pop-up notification is displayed on the remote monitoring terminal.

[0087] By performing the aforementioned operations, this solution addresses the problems of existing monitoring systems failing to provide comprehensive decision-making support, having low levels of intelligence in platform status assessment and emergency intervention, and struggling to control risks in a timely and effective manner. It combines edge computing and cloud platform collaboration to achieve millisecond-level response to control commands and full lifecycle data traceability, establishes a tiered early warning and proactive safety intervention mechanism, and significantly improves the intelligence and unmanned operation level of well tower construction.

[0088] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0090] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A synchronous control method for an integrated intelligent jacking platform in well tower construction, characterized in that: The method includes the following steps: Step S1: Digital twin construction. Based on the mechanical structure, hydraulic system and construction environment information of the jacking platform, construct geometric model, physical model and behavioral model. Deploy heterogeneous sensors to collect displacement data, pressure data, tilt angle data and micro-strain data in real time during the jacking process of the well tower construction, and establish a virtual-real synchronized digital twin. Step S2: Multi-source information perception, preprocessing and feature extraction of real-time sensor data, data fusion using Kalman filtering algorithm, and outputting the posterior state estimation vector of the lifting platform. Step S3: Intelligent synchronous control strategy calculation. Set the lifting process target at the edge control end. Based on the posterior state estimation vector of the lifting platform, adopt the cooperative control algorithm based on fuzzy adaptive PID, combined with the adjacent deviation coupling synchronous compensation strategy, calculate the composite control voltage of each lifting hydraulic cylinder, and convert it into control commands. Step S4: Hydraulic execution and status feedback. Control commands are sent to the servo proportional valves of each hydraulic cylinder via the fieldbus to control the displacement and speed of the hydraulic cylinders in real time. Heterogeneous sensors synchronously collect the execution results and feed them back to the edge control terminal. Step S5: Cloud-edge collaboration and remote monitoring. The edge control terminal uploads the posterior state estimation vector, control commands and execution results of the jacking platform to the cloud. The cloud uses the digital twin to simulate and backtrack the jacking process of the well tower construction, generates an optimized set of control parameters and sends it to the edge control terminal. Step S6: Safety early warning and emergency linkage. The edge control terminal monitors the operating status of the lifting platform in real time and sets a graded early warning mechanism and risk threshold: when an operational risk is detected, the graded early warning mechanism is triggered; when the risk level reaches the risk threshold, it automatically switches to emergency mode and executes proactive safety intervention measures.

2. The synchronous control method for an integrated intelligent jacking platform in well tower construction according to claim 1, characterized in that: In step S1, the construction of the digital twin specifically includes the following steps: Step S11: Geometric model construction, using BIM software to create three-dimensional solid models of each component system of the lifting platform; Step S12: Physical model construction. Based on multibody dynamics and hydraulic control theory, establish a rigid-flexible coupling dynamic model and an electro-hydraulic proportional control system model for the lifting platform, and define key parameters. Step S13: Behavioral model construction, describing the dynamic behavior rules of the jacking platform at different construction stages, as well as its interaction mechanism with the environment; Step S14: Deploy heterogeneous sensors to acquire displacement and pressure data of the lifting hydraulic cylinder, longitudinal and lateral tilt angle data of the steel platform under construction load and during the lifting process, and microscopic strain data of key load-bearing components under load. Step S15: Twin data association, which associates and maps the geometric model, physical model, behavioral model and real-time data from the sensor to establish a virtual-real synchronized digital twin.

3. The synchronous control method for an integrated intelligent jacking platform in well tower construction according to claim 1, characterized in that: In step S2, the multi-source information sensing specifically includes the following steps: Step S21: Edge data preprocessing. The moving average filtering technique is used to filter and denoise the raw data of each sensor, remove errors, and synchronize and align the timestamps to obtain the preprocessed data. Step S22: Based on the Kalman filter algorithm, the preprocessed data is fused, the preprocessed data is used as the observation value, the physical model is used as the state transition equation, and the Kalman filter algorithm is used to make the optimal estimate of the true state of the lifting platform, and the posterior state estimation vector is output.

4. The synchronous control method for an integrated intelligent jacking platform in well tower construction according to claim 1, characterized in that: In step S3, the intelligent synchronization control strategy calculation specifically includes the following steps: Step S31: Set the lifting process objectives, including the target lifting height, the maximum allowable synchronization error, and the lifting speed curve; Step S32: Displacement closed-loop control. Calculate the deviation and rate of change between the displacement component in the posterior state estimation vector of the lifting platform and the theoretical target displacement, and input it to the displacement controller. The displacement controller integrates a fuzzy inference mechanism and a PID controller. The fuzzy inference mechanism is used to tune the parameters of the PID controller. The PID controller uses the tuned parameters to calculate the basic flow control voltage of each hydraulic cylinder. Step S33: Coupling and Synchronization Compensation. Using a synchronization compensation controller, calculate the displacement deviation between the current hydraulic cylinder and its adjacent hydraulic cylinders, and use it as the synchronization compensation item for the displacement controller. Step S34: Control command generation, which linearly superimposes the output of the displacement controller and the output of the synchronous compensation controller to generate a composite control voltage.

5. The synchronous control method for an integrated intelligent jacking platform in well tower construction according to claim 1, characterized in that: In step S5, the cloud-edge collaboration and remote monitoring specifically includes the following steps: Step S51: Edge data aggregation and uploading. The edge control terminal compresses and encrypts the posterior state estimation vector, control commands and execution results of the lifting platform, and uploads them to the cloud periodically. Step S52: Cloud-based digital twin simulation. The cloud receives real-time data and drives the digital twin to perform synchronous mapping. It predicts and simulates the state of the lifting platform at future moments. Based on the simulation results and the lifting process objectives, the genetic algorithm is used to optimize the parameters of the fuzzy inference mechanism and PID controller and the synchronous compensation gain coefficient offline, generating an optimized set of control parameters. Step S53: Parameter distribution and model update. The cloud distributes the optimized control parameter set to the edge control terminal and updates the local control strategy of the edge control terminal.

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