Supporting axial force servo intelligent optimization control system based on numerical control pump station

Abstract

The application discloses a support axial force servo intelligent optimization control system based on a numerical control pump station, relates to the technical field of intelligent control of pump stations, and comprises a data acquisition module, a numerical control pump station module and the like. The data acquisition module is arranged between a foundation pit support structure and a steel support, and is used for collecting axial force data of each support, lateral displacement data of the support structure and temperature data of the steel support in real time. The numerical control pump station module is connected with hydraulic jacks arranged at the end of each steel support through a hydraulic pipeline, and is used for independently applying or unloading axial prestress to each support in response to a control instruction. The application realizes global intelligent collaborative and accurate regulation and control of axial forces of multiple supports of a foundation pit by constructing a multi-objective optimization algorithm with the minimum deformation of the support structure and the equalization of the axial forces of the supports as targets, combining a temperature advanced active compensation mechanism and a support coupling and decoupling strategy, and significantly improves the deformation control effect of the support structure and the stress equalization of the support system.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for pumping stations, specifically to a support shaft force servo intelligent optimization control system based on a CNC pumping station. Background Technology

[0002] In the construction of deep foundation pits, steel support systems are a key means of controlling the deformation of the retaining structure and ensuring the safety and stability of the pit. As urban underground space development expands and deepens, the excavation depth of foundation pits continues to increase, placing increasingly higher demands on the precision and response speed of the support axial force control. Steel support axial force servo systems, which utilize hydraulic jacks at the support ends in conjunction with CNC pump stations to achieve real-time application and adjustment of axial force, have become the mainstream technical solution for controlling the lateral deformation of deep foundation pit retaining structures.

[0003] However, existing steel-supported axial force servo systems still have many shortcomings in practical applications. These shortcomings are detailed as follows: Traditional servo systems employ relatively simple axial force control strategies, typically relying on preset design axial force values ​​for open-loop or simple closed-loop control. They lack the ability to comprehensively analyze and make intelligent decisions based on multi-source information such as the real-time deformation status of the foundation pit retaining structure, the axial force distribution of adjacent supports, and the spatiotemporal effects of the soil, making it difficult to achieve global optimization and control of axial force.

[0004] Secondly, existing systems often employ a fixed-step, graded loading method during axial force application, failing to dynamically adjust the loading rate and amount based on the actual stress state of the support and the response characteristics of the enclosure structure. This easily leads to axial force overshoot or underloading, affecting the stress balance of the support system. Thirdly, when there are large diurnal temperature differences, the steel supports undergo expansion and contraction due to temperature changes, resulting in significant axial force loss. While existing systems can detect axial force decline through monitoring, they lack proactive prediction and anticipatory control mechanisms based on temperature compensation.

[0005] Furthermore, the axial forces of different supports are mutually coupled. Adjusting the axial force of a certain support will affect the stress state of its adjacent supports. However, the existing system adopts an independent control strategy for each support and fails to establish a collaborative optimization model between supports.

[0006] In summary, the steel support axial force servo system cannot fully leverage its active control advantages in actual engineering projects. The deformation control effect of the retaining structure is highly dependent on manual intervention and experience-based judgment, which not only increases the difficulty of construction management but also, to some extent, restricts the further improvement of the safety and construction efficiency of foundation pit engineering. Summary of the Invention

[0007] The purpose of this invention is to provide a support shaft force servo intelligent optimization control system based on a CNC pump station to address the aforementioned shortcomings in the prior art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a support shaft force servo intelligent optimization control system based on a CNC pump station, comprising: The data acquisition module is located between the foundation pit retaining structure and the steel support, and is used to collect axial force data of each support, lateral displacement data of the retaining structure, and temperature data of the steel support in real time. The CNC pump station module is connected to hydraulic jacks installed at the ends of each steel support via hydraulic pipelines. It is used to independently apply or unload axial prestress to each support in response to control commands. The central control module, which is coupled to the data acquisition module and the CNC pump station module respectively, is configured as follows: Based on the axial force data, displacement data, and temperature data collected in real time by the data acquisition module, a current state feature vector of the foundation pit support system is constructed. Based on the current state feature vector, a multi-objective optimization algorithm is used to calculate the optimal target axial force value of each support. The multi-objective optimization algorithm takes minimizing the lateral displacement of the retaining structure and equalizing the axial force distribution of each support as its optimization objectives. Based on the difference between the optimal target axial force value and the current actual axial force value, axial force control commands corresponding to each support are generated, and the control commands are sent to the CNC pump station module for execution.

[0009] Preferably, the central control module includes a preset temperature-axial force compensation model, which is used to calculate the estimated value of axial force loss caused by temperature change based on the steel support temperature data and its rate of change collected by the data acquisition module. When generating the axial force adjustment command, the central control module adds the estimated axial force loss to the optimal target axial force value to proactively compensate for temperature changes.

[0010] Preferably, the temperature-axial force compensation model is as follows: ΔF = α × E × A × ΔT; Where ΔF is the estimated axial force loss, α is the linear expansion coefficient of the steel support material, E is the elastic modulus of the steel support material, A is the cross-sectional area of ​​the steel support, and ΔT is the temperature change within the preset time window; When the rate of change of ambient temperature exceeds a preset threshold, the central control module activates the active compensation mode and sends a pre-addition / pre-unloading command to the CNC pump station module before the temperature change occurs.

[0011] Preferably, the multi-objective optimization algorithm employs particle swarm optimization, and the fitness function of the particle swarm optimization algorithm is: fitness=w i ×Σ(δ i / δ max )²+w2×Σ(F i -F avg ) 2 ; Where, δ i Let δ be the lateral displacement value of the retaining structure at the i-th monitoring point. max F represents the maximum allowable lateral displacement of the enclosure structure. i F represents the actual axial force value of the i-th support. avg The average value of all supporting axial forces is given by w1 and w2, which are the weighting coefficients for the displacement and equilibrium terms, respectively. The central control module iteratively optimizes the combination of support axial forces that minimizes the fitness function value, and uses this combination as the optimal target axial force value.

[0012] Preferably, the data acquisition module includes: Axial force sensors are installed between the end of each steel support and the hydraulic jack to collect the actual axial force value of each support. Displacement sensors are installed at preset monitoring points on the foundation pit retaining structure to collect lateral displacement values ​​of the retaining structure. Temperature sensors are installed on the surface of each steel support to collect real-time temperature data of the steel support. The axial force sensor, displacement sensor, and temperature sensor are coupled to the central control module via wired or wireless communication methods, respectively.

[0013] Preferably, the data acquisition module further includes strain sensors disposed on the surface of each steel support for acquiring real-time strain data of the steel support; The central control module performs online calibration of the axial force sensor based on the comparison results of the strain data and the axial force data, and generates a sensor fault alarm signal when the deviation between the strain data and the axial force data exceeds a preset threshold.

[0014] Preferably, the CNC pump station module includes multiple distributed CNC pump station units, each of which controls one or more adjacent hydraulic jacks supported by steel. Each of the CNC pump station units is equipped with an independent PLC controller and a wireless communication module. Each of the CNC pump station units interacts with the central control module through the wireless communication module. The central control module sends independent axial force control commands to each of the CNC pump station units.

[0015] Preferably, each of the CNC pump station units is equipped with a frequency converter and a pressure closed-loop control circuit. The frequency converter is used to adjust the output flow of the hydraulic pump according to the shaft force control command, and the pressure closed-loop control circuit is used to feed back the actual output pressure of the hydraulic jack to the PLC controller. The PLC controller adjusts the output frequency of the variable frequency drive in real time using a PID control algorithm based on the deviation between the target pressure value in the shaft force control command and the actual output pressure, until the actual output pressure converges to the target pressure value.

[0016] Preferably, the central control module further includes: The inter-support coupling relationship identification unit is used to establish an axial force transfer function model between adjacent supports based on historical axial force control data. When the central control module issues an axial force control command to any support, the inter-support coupling relationship identification unit calculates the estimated impact of the control on the axial force of adjacent supports based on the axial force transfer function model, and inputs the estimated impact as a constraint into the multi-objective optimization algorithm to eliminate axial force coupling interference between supports in the global optimization. The graded early warning unit is pre-set with an upper limit alarm value for axial force, a lower limit alarm value for axial force, a daily rate of change alarm value for displacement, and a cumulative change alarm value for displacement. When any monitoring data reaches the corresponding alarm value, the graded early warning unit generates an alarm signal of the corresponding level and automatically triggers at least one of the following operations: Pause the current axial force adjustment operation and maintain the current axial force status of each support; Send a pressure-maintaining command to the CNC pump station module to lock the current output pressure of each hydraulic jack; Alarm information is generated and pushed to the preset monitoring terminal via the communication network.

[0017] Preferably, the central control module further includes a data display and interaction unit, which includes a display terminal and an input device located in the monitoring room; the display terminal is used to display in real time the current axial force value, target axial force value, axial force change curve, enclosure structure displacement change curve, and the operating status of each sensor for each support; the input device is used for operators to manually input axial force setpoints, switch between automatic control mode and manual control mode, and confirm or deactivate alarm signals.

[0018] In the above technical solution, the support shaft force servo intelligent optimization control system based on CNC pump station provided by the present invention has the following beneficial effects: 1. This invention constructs a multi-objective optimization algorithm through a central control module, with the dual objectives of minimizing the lateral displacement of the retaining structure and balancing the axial force distribution of each support. This enables global intelligent optimization and control of the axial force of each support in the foundation pit support system. It comprehensively considers the overall deformation state of the foundation pit retaining structure and the stress balance of the support system, solving for the optimal axial force distribution scheme from a global perspective. This effectively avoids the problem of stress imbalance between adjacent supports or increased local deformation of the retaining structure caused by local axial force adjustments, significantly improving the overall stability and safety of the foundation pit support system.

[0019] 2. This invention, by setting up a temperature-axial force compensation model, utilizes physical parameters such as the linear expansion coefficient, elastic modulus, and cross-sectional area of ​​the steel support material, combined with real-time acquired temperature data and its rate of change, to achieve quantitative prediction and proactive compensation for axial force loss caused by temperature changes. Compared to existing systems that can only passively add axial force after it occurs, this invention can generate pre-addition or pre-unloading commands before temperature changes occur, effectively eliminating the time lag between temperature changes and axial force compensation. This significantly improves the accuracy and responsiveness of axial force control, avoiding the problem of repeated loss of support prestress due to large diurnal temperature differences, which affects the deformation control effect of the enclosure structure.

[0020] 3. This invention establishes an inter-support coupling relationship identification unit, utilizes historical control data to build an axial force transfer function model between adjacent supports, and incorporates the estimated coupling effect as a constraint into a multi-objective optimization algorithm, effectively decoupling the axial force coupling interference between supports. It comprehensively considers the engineering reality of the mutual influence of forces among supports in a multi-support system for deep foundation pits, ensuring that axial force control decisions are no longer limited to isolated optimization of a single support, but are based on the collaborative optimization of the stress state of the entire support system. This fundamentally solves the technical problem in traditional systems where adjustments to the axial force of a certain support cause abnormal stress in adjacent supports, further improving the intelligence level and engineering adaptability of axial force servo control. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0022] Figure 1 An architecture diagram provided for an embodiment of the present invention; Figure 2 This is a schematic diagram of the user interface provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a first embodiment of the data acquisition module provided in this invention. Figure 4 This is a schematic diagram of a second implementation structure of the data acquisition module provided in an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0024] Please see Figure 1-4 This invention provides a technical solution: a support shaft force servo intelligent optimization control system based on a CNC pump station, comprising: The data acquisition module is located between the foundation pit retaining structure and the steel support, and is used to collect axial force data of each support, lateral displacement data of the retaining structure, and temperature data of the steel support in real time. In the specific implementation process, combined with Figure 1 , Figure 3 and Figure 4 As shown, the real-time acquisition of data from each support channel is achieved through the following methods: Axial force sensors are installed between the end of each steel support and the hydraulic jack to collect the actual axial force value of each support. Displacement sensors are installed at preset monitoring points on the foundation pit retaining structure to collect lateral displacement values ​​of the retaining structure. Temperature sensors are installed on the surface of each steel support to collect real-time temperature data of the steel support. The axial force sensor, displacement sensor, and temperature sensor are coupled to the central control module via wired or wireless communication methods.

[0025] The CNC pump station module is connected to hydraulic jacks installed at the ends of each steel support via hydraulic pipelines. It is used to independently apply or unload axial prestress to each support in response to control commands.

[0026] The central control module, which is coupled to the data acquisition module and the CNC pump station module respectively, is configured as follows: Based on the axial force data, displacement data, and temperature data collected in real time by the data acquisition module, a current state feature vector of the foundation pit support system is constructed. Based on the current state feature vector, a multi-objective optimization algorithm is used to calculate the optimal target axial force value of each support. The multi-objective optimization algorithm aims to minimize the lateral displacement of the retaining structure and equalize the axial force distribution of each support. Based on the difference between the optimal target axial force value and the current actual axial force value, axial force control commands are generated for each support, and the control commands are sent to the CNC pump station module for execution.

[0027] Furthermore, as a further embodiment of the present invention, the central control module includes a preset temperature-axial force compensation model, which is used to calculate the estimated value of axial force loss caused by temperature change based on the steel support temperature data and its rate of change collected by the data acquisition module. When generating axial force control commands, the central control module adds the estimated axial force loss to the optimal target axial force value to proactively compensate for temperature changes.

[0028] The temperature-axial force compensation model in the above embodiments is as follows: ΔF = α × E × A × ΔT; Where ΔF is the estimated axial force loss, α is the linear expansion coefficient of the steel support material, E is the elastic modulus of the steel support material, A is the cross-sectional area of ​​the steel support, and ΔT is the temperature change within the preset time window; When the rate of change of ambient temperature exceeds a preset threshold, the central control module activates the active compensation mode and sends a pre-addition / pre-unloading command to the CNC pump station module before the temperature change occurs.

[0029] It should be noted that the multi-objective optimization algorithm described above uses particle swarm optimization, and the fitness function of particle swarm optimization is: fitness=w i ×Σ(δ i / δ max )²+w2×Σ(F i -F avg ) 2 ; Where, δ i Let δ be the lateral displacement value of the retaining structure at the i-th monitoring point. max F represents the maximum allowable lateral displacement of the enclosure structure. i F represents the actual axial force value of the i-th support. avg The average value of all supporting axial forces is given by w1 and w2, which are the weighting coefficients for the displacement and equilibrium terms, respectively. The central control module iteratively optimizes the combination of support axial forces that minimizes the fitness function value, and uses this combination as the optimal target axial force value. Example 2

[0030] Based on the above embodiment 1, the CNC pump station module provided in this example includes multiple distributed CNC pump station units, each of which controls one or more adjacent steel-supported hydraulic jacks. Each CNC pump station unit is equipped with an independent PLC controller and wireless communication module. Each CNC pump station unit interacts with the central control module through the wireless communication module, and the central control module sends independent axial force adjustment commands to each CNC pump station unit.

[0031] Furthermore, each CNC pump station unit is equipped with a built-in frequency converter and a pressure closed-loop control circuit. The frequency converter is used to adjust the output flow of the hydraulic pump according to the shaft force control command, and the pressure closed-loop control circuit is used to feed back the actual output pressure of the hydraulic jack to the PLC controller. The PLC controller adjusts the output frequency of the variable frequency drive in real time using a PID control algorithm based on the deviation between the target pressure value in the shaft force control command and the actual output pressure, until the actual output pressure converges to the target pressure value.

[0032] Specifically, based on the distribution of the foundation pit support system and on-site construction conditions, each CNC pump station unit is configured to control a hydraulic jack at the end of one steel support. Alternatively, depending on the axial force coupling relationship and control precision requirements of adjacent supports, it can be configured to control hydraulic jacks for multiple adjacent steel supports, thus forming a flexible networking mode of "one pump controlling one jack" or "one pump controlling multiple jacks". Each CNC pump station unit is independently equipped with a PLC controller and a wireless communication module. Each CNC pump station unit establishes a point-to-point data interaction link with the central control module through its own wireless communication module. The central control module sends independent axial force control commands to each CNC pump station unit based on the calculation results of the multi-objective optimization algorithm, thereby realizing differentiated and precise setting of the target axial force for each support.

[0033] Within the internal structure of each CNC pump station unit, a variable frequency drive (VFD) and a pressure closed-loop control loop are further integrated. The VFD is electrically connected to the drive motor of the hydraulic pump and is used to adjust the output flow of the hydraulic pump according to the frequency commands issued by the PLC controller, thereby controlling the oil inlet rate and output pressure of the hydraulic jack. The pressure closed-loop control loop consists of a pressure sensor and a signal conditioning circuit installed in the hydraulic jack's oil circuit. The pressure sensor collects the actual output pressure of the hydraulic jack in real time and feeds it back to the analog input port of the PLC controller. When the PLC controller executes the axial force control command, it compares the target pressure value contained in the command with the actual output pressure value fed back by the pressure closed-loop control loop, calculates the deviation between the two, and then performs calculations based on this deviation using a PID (Proportional-Integral-Derivative) control algorithm to dynamically adjust the output frequency of the VFD, thereby changing the speed and output flow of the hydraulic pump, so that the actual output pressure of the hydraulic jack gradually approaches and eventually converges to the target pressure value. Throughout the adjustment process, the proportional, integral, and derivative coefficients of the PID control algorithm can be tuned according to the response characteristics and control accuracy requirements of the hydraulic system to ensure that the axial force regulation process has both rapid response and steady-state accuracy. Through the combination of distributed independent control and local closed-loop regulation, this system can simultaneously achieve high-precision independent execution and coordinated regulation of each supporting axial force within the global optimization framework of the central control module. Example 3

[0034] Based on the above embodiment 1, the data acquisition module in this embodiment includes strain sensors disposed on the surface of each steel support, for acquiring real-time strain data of the steel support; The central control module performs online calibration of the axial force sensor based on the comparison results of strain data and axial force data, and generates a sensor fault alarm signal when the deviation between strain data and axial force data exceeds a preset threshold.

[0035] Specifically, the central control module calculates the theoretical axial force value Fc based on strain inversion using the formula Fc = E × A × ε (where ε is the average strain value after temperature compensation correction) based on the elastic modulus E and cross-sectional area A of the steel support. Since the strain sensors are directly attached to the surface of the steel pipe, their response is unaffected by hydraulic system pressure fluctuations and the friction of the jack's sealing ring; therefore, Fc can serve as a third-party reference independent of the hydraulic system. During the calculation process, the central control module prioritizes the average of multiple strain sensor readings and discards data that deviates from the average by more than three standard deviations to improve the reliability and robustness of the strain axial force reference value.

[0036] After obtaining the theoretical axial force value Fc, the central control module compares it in real time with the measured value Fs currently output by the axial force sensor, and calculates the relative deviation η = |Fc - Fs| / Fc × 100%. Considering that both the strain sensor and the axial force sensor have inherent measurement uncertainties, the central control module presets a normal deviation threshold. and fault alarm threshold .when When the system determines that the axial force sensor is in normal working condition, the central control module automatically records the deviation value and uses the least squares method to dynamically fit the correction coefficient k=Fc / Fs of the axial force sensor. This correction coefficient is written as a soft calibration factor into the calibration register of the axial force measurement channel, so that the subsequent axial force reading is Fadjusted=k×Fs, thereby realizing real-time online calibration of the zero drift and sensitivity change of the axial force sensor.

[0037] when At this point, the central control module determines that the axial force sensor has a significant measurement deviation. However, since the strain sensor may also produce some error due to local stress concentration in the steel support or the influence of surface deposits, the system does not immediately trigger a fault alarm. Instead, it initiates a continuous observation mechanism. Specifically, in this state, the central control module extends the observation window to 10 consecutive samples (i.e., within 10 seconds). If η remains higher than [a certain value] during this period, [further action is taken]. But lower If the deviation value is stable rather than fluctuating drastically, the system will send an "Axial force sensor calibration abnormality" warning to the monitoring terminal and recommend manual verification. If the deviation fluctuates drastically and shows no convergence trend during this period, a level 2 warning will be triggered.

[0038] when Upon detection, the central control module immediately determines that the sensing system has a serious anomaly. At this point, the central control module further executes a fault source identification subroutine: comparing the consistency of readings from multiple strain sensors on the same steel support. If the strain sensor readings are consistent while the axial force sensor reading deviates significantly, the fault source is determined to be the axial force sensor or its signal transmission link (such as a damaged pressure transmitter, hydraulic line leakage, or internal leakage in the jack). The central control module then generates an "axial force sensor fault" alarm signal, automatically freezes the current axial force control operation of the support, and sends a pressure-holding command to the CNC pump station module to lock the jack pressure, preventing safety risks caused by erroneous control due to sensor inaccuracy. Conversely, if the dispersion between strain sensor readings increases significantly, it is determined that there may be a local failure of the strain sensor or local buckling deformation of the steel support, and the system generates corresponding structural warning information. Example 4

[0039] Based on the above embodiment one, the central control module includes: The inter-support coupling relationship identification unit is used to establish an axial force transfer function model between adjacent supports based on historical axial force control data. When the central control module issues an axial force control command to any support, the inter-support coupling relationship identification unit calculates the estimated impact of the control on the axial force of adjacent supports based on the axial force transfer function model, and inputs the estimated impact as a constraint into a multi-objective optimization algorithm to eliminate axial force coupling interference between supports in global optimization. The graded early warning unit has preset alarm values ​​for upper limit of axial force, lower limit of axial force, daily rate of displacement change, and cumulative displacement change. When any monitoring data reaches the corresponding alarm value, the graded early warning unit generates an alarm signal of the corresponding level and automatically triggers at least one of the following operations: Pause the current axial force adjustment operation and maintain the current axial force status of each support; Send a pressure-maintaining command to the CNC pump station module to lock the current output pressure of each hydraulic jack; Alarm information is generated and pushed to the preset monitoring terminal via the communication network.

[0040] The data display and interaction unit includes a display terminal and input devices located in the monitoring room. The display terminal is used to display the current axial force value, target axial force value, axial force change curve, enclosure structure displacement change curve, and operating status of each sensor in real time. The input devices are used for operators to manually input axial force setpoints, switch between automatic and manual control modes, and confirm or deactivate alarm signals.

[0041] Specifically, the inter-support coupling relationship identification unit relies on the historical data storage and analysis unit of the central control module to obtain the axial force time history data of each support during each axial force adjustment process. When the axial force of the i-th support occurs... When changes occur, the system simultaneously records the axial force response of the supports at the (i-1)th and (i+1)th tracks. and By employing a systematic identification method and utilizing the least squares method to fit multiple sets of historical control data, a transfer function model of the following form is established: ; ; in and The coupling coefficient is... and This is a constant term. After the model is established, it is stored in the memory of the central control module and continuously updated online through newly generated data during subsequent control processes to reflect the changes in coupling relationships under different excavation depths and different combinations of support passes.

[0042] Secondly, this unit is activated synchronously when the central control module issues an axial force adjustment command to any support. The unit adjusts according to the support number to be adjusted and its target adjustment amount. By calling the corresponding transfer function model, the estimated impact of the control operation on the axial force of adjacent supports can be quickly calculated. and .

[0043] Finally, these estimated impact values ​​are transformed into constraints input to a multi-objective optimization algorithm. Specifically, during the iterative solution process of the particle swarm optimization algorithm, each candidate axial force combination must satisfy the following constraints: ; ; ; ; in, This represents the maximum allowable adjustment for a single support. This refers to the range of axial force deviation for a single support. This represents the upper limit of the coupling influence between adjacent supports. Candidate solutions that do not meet the above constraints are given extremely high penalty values ​​in the fitness calculation, and are thus automatically excluded by the optimization algorithm. Through this mechanism, the unit incorporates the local influence of single-point control into the global optimization perspective, so that the optimal target axial force values ​​of each support in the final output have theoretically been pre-resolved by the coupling influence, achieving effective decoupling of axial force coupling interference between supports.

[0044] The graded early warning unit is the core module for real-time monitoring and graded response of the safety status of the foundation pit. Its implementation involves four stages: alarm value preset, real-time comparison, grade determination, and automatic response.

[0045] First, during the system initialization phase, the tiered early warning unit pre-sets various alarm thresholds in its internal memory according to the design documents. Specifically, these include: for Φ609 steel supports (t=16mm), the upper limit alarm value for axial force is 2500kN, and the lower limit is 1000kN; for Φ800 steel supports (t=20mm), the upper limit alarm value for axial force is 5000kN, and the lower limit is 2000kN; the daily displacement change rate alarm value is set to 2mm / d; and the cumulative displacement change alarm value is determined based on the allowable lateral displacement of the retaining structure in the design documents. These thresholds can be adjusted within the range specified in the design documents according to the actual engineering situation.

[0046] Secondly, during system operation, the graded early warning unit continuously receives axial force data, displacement data, and temperature data from the data acquisition module at a set sampling period (preferably once every 10 minutes), and compares the real-time values ​​of each monitoring point with the corresponding preset alarm values ​​in real time.

[0047] Furthermore, the unit incorporates a three-tiered management level judgment logic. When all monitoring data are within the normal range and the deformation characteristic curve tends to converge, it is judged as a normal (green) state, and the system only performs routine monitoring and data recording. When deformation shows abnormal acceleration, the deformation characteristic curve shows no signs of convergence, and the daily average deformation rate difference is greater than 2 mm / d for two consecutive days, it is judged as a level two (yellow) warning state. When the above abnormal state continues to develop and the daily average deformation rate difference is greater than 2 mm / d for three consecutive days, it is judged as a level one (red) warning state.

[0048] Finally, based on the different levels of judgment results, the graded early warning unit automatically triggers corresponding response operations. In a yellow warning state, the unit generates alarm information and pushes it through the communication network (including wired network and 4G / 5G wireless network) to preset monitoring terminals (including monitoring room display terminals, management personnel's mobile phones, and on-site audible and visual alarms). Simultaneously, it suspends the currently executing axial force control operation, maintaining the current axial force state of each support unchanged, thus buying time for manual intervention and judgment. In a red warning state, in addition to executing the above alarm and information push, the unit automatically sends a pressure-holding command to the CNC pump station module, locking the current output pressure of each hydraulic jack through the hydraulic system's pressure-holding circuit to ensure that the support system will not lose pressure due to misoperation or system failure in extreme cases. Furthermore, when the foundation pit retaining structure experiences negative displacement at the support location or the concrete support is under tension, the unit also triggers an alarm process, promptly reporting to the construction unit for analysis by the design unit.

[0049] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A support shaft force servo intelligent optimization control system based on a CNC pump station, characterized in that, include: The data acquisition module is located between the foundation pit retaining structure and the steel support, and is used to collect axial force data of each support, lateral displacement data of the retaining structure, and temperature data of the steel support in real time. The CNC pump station module is connected to hydraulic jacks installed at the ends of each steel support via hydraulic pipelines. It is used to independently apply or unload axial prestress to each support in response to control commands. The central control module, which is coupled to the data acquisition module and the CNC pump station module respectively, is configured as follows: Based on the axial force data, displacement data, and temperature data collected in real time by the data acquisition module, a current state feature vector of the foundation pit support system is constructed. Based on the current state feature vector, a multi-objective optimization algorithm is used to calculate the optimal target axial force value of each support. The multi-objective optimization algorithm takes minimizing the lateral displacement of the retaining structure and equalizing the axial force distribution of each support as its optimization objectives. Based on the difference between the optimal target axial force value and the current actual axial force value, axial force control commands corresponding to each support are generated, and the control commands are sent to the CNC pump station module for execution.

2. The intelligent optimization control system for servo-driven support shaft force based on a CNC pump station according to claim 1, characterized in that, The central control module includes a preset temperature-axial force compensation model, which is used to calculate the estimated value of axial force loss caused by temperature change based on the steel support temperature data and its rate of change collected by the data acquisition module. When generating the axial force adjustment command, the central control module adds the estimated axial force loss to the optimal target axial force value to proactively compensate for temperature changes.

3. The intelligent optimization control system for support shaft force servo based on CNC pump station according to claim 2, characterized in that, The temperature-axial force compensation model is as follows: ΔF = α × E × A × ΔT; Where ΔF is the estimated axial force loss, α is the linear expansion coefficient of the steel support material, E is the elastic modulus of the steel support material, A is the cross-sectional area of ​​the steel support, and ΔT is the temperature change within the preset time window; When the rate of change of ambient temperature exceeds a preset threshold, the central control module activates the active compensation mode and sends a pre-addition / pre-unloading command to the CNC pump station module before the temperature change occurs.

4. The intelligent optimization control system for support shaft force servo based on CNC pump station according to claim 1, characterized in that, The multi-objective optimization algorithm employs particle swarm optimization, and the fitness function of the particle swarm optimization algorithm is: fitness=w i ×Σ(δ i / δ max )²+w2×Σ(F i -F avg ) 2 ; Where, δ i Let δ be the lateral displacement value of the retaining structure at the i-th monitoring point. max F represents the maximum allowable lateral displacement of the enclosure structure. i F represents the actual axial force value of the i-th support. avg The average value of all supporting axial forces is given by w1 and w2, which are the weighting coefficients for the displacement and equilibrium terms, respectively. The central control module iteratively optimizes and solves for the combination of support axial forces that minimizes the fitness function value, and uses this combination as the optimal target axial force value.

5. The intelligent optimization control system for support shaft force servo based on CNC pump station according to claim 1, characterized in that, The data acquisition module includes: Axial force sensors are installed between the end of each steel support and the hydraulic jack to collect the actual axial force value of each support. Displacement sensors are installed at preset monitoring points on the foundation pit retaining structure to collect lateral displacement values ​​of the retaining structure. Temperature sensors are installed on the surface of each steel support to collect real-time temperature data of the steel support. The axial force sensor, displacement sensor, and temperature sensor are coupled to the central control module via wired or wireless communication methods, respectively.

6. The intelligent optimization control system for support shaft force servo based on CNC pump station according to claim 5, characterized in that, The data acquisition module also includes strain sensors installed on the surface of each steel support for collecting real-time strain data of the steel support. The central control module performs online calibration of the axial force sensor based on the comparison results of the strain data and the axial force data, and generates a sensor fault alarm signal when the deviation between the strain data and the axial force data exceeds a preset threshold.

7. The intelligent optimization control system for support shaft force servo based on CNC pump station according to claim 1, characterized in that, The CNC pump station module includes multiple distributed CNC pump station units, each of which controls one or more adjacent steel-supported hydraulic jacks. Each of the CNC pump station units is equipped with an independent PLC controller and a wireless communication module. Each of the CNC pump station units interacts with the central control module through the wireless communication module. The central control module sends independent axial force control commands to each of the CNC pump station units.

8. The intelligent optimization control system for support shaft force servo based on CNC pump station according to claim 7, characterized in that, Each of the CNC pump station units is equipped with a frequency converter and a pressure closed-loop control circuit. The frequency converter is used to adjust the output flow of the hydraulic pump according to the shaft force control command, and the pressure closed-loop control circuit is used to feed back the actual output pressure of the hydraulic jack to the PLC controller. The PLC controller adjusts the output frequency of the variable frequency drive in real time using a PID control algorithm based on the deviation between the target pressure value in the shaft force control command and the actual output pressure, until the actual output pressure converges to the target pressure value.

9. The intelligent optimization control system for support shaft force servo based on CNC pump station according to claim 1, characterized in that, The central control module also includes: The inter-support coupling relationship identification unit is used to establish an axial force transfer function model between adjacent supports based on historical axial force control data. When the central control module issues an axial force control command to any support, the inter-support coupling relationship identification unit calculates the estimated impact of the control on the axial force of adjacent supports based on the axial force transfer function model, and inputs the estimated impact as a constraint into the multi-objective optimization algorithm to eliminate axial force coupling interference between supports in the global optimization. The graded early warning unit is pre-set with an upper limit alarm value for axial force, a lower limit alarm value for axial force, a daily rate of change alarm value for displacement, and a cumulative change alarm value for displacement. When any monitoring data reaches the corresponding alarm value, the graded early warning unit generates an alarm signal of the corresponding level and automatically triggers at least one of the following operations: Pause the current axial force adjustment operation and maintain the current axial force status of each support; Send a pressure-maintaining command to the CNC pump station module to lock the current output pressure of each hydraulic jack; Alarm information is generated and pushed to the preset monitoring terminal via the communication network.

10. The intelligent optimization control system for support shaft force servo based on CNC pump station according to claim 9, characterized in that, The central control module further includes a data display and interaction unit, which includes a display terminal and an input device located in the monitoring room. The display terminal is used to display the current axial force value, target axial force value, axial force change curve, enclosure structure displacement change curve, and operating status of each sensor in real time. The input device is used for operators to manually input axial force setpoints, switch between automatic and manual control modes, and confirm or deactivate alarm signals.

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