Intelligent inverted trestle control method and system based on hydraulic technology

Abstract

The present application relates to the field of trestle control technology, and provides an intelligent inverted arch trestle control method and system based on hydraulic technology. The method includes: deploying the trestle, obtaining support point distribution information; obtaining a pouring task, and deploying sensors based on the task and distribution information; activating sensors to collect data when the task is executed, establishing a data set and analyzing dynamic and static loads; setting transient pressure and speed tracking sensors at preset nodes, performing time series acquisition, and generating vehicle dynamic load prediction data; configuring a hydraulic adjustment strategy based on the prediction data, load analysis results, and deployment information to achieve intelligent control of the trestle. The present application solves the technical problem that it is difficult to monitor and accurately adjust the support points of traditional inverted arch trestles in real time during concrete pouring and vehicle passage, resulting in untimely response to dynamic and static load changes and low stability of the trestle, and achieves the effect of ensuring the safety and stability of the trestle structure and improving construction efficiency and safety by adjusting the hydraulic support force of each support point.

Description

Technical Field

[0001] The present application relates to the technical field of trestle control, and in particular to a control method and system for an intelligent inverted trestle based on hydraulic technology. Background Art

[0002] With the vigorous development of infrastructure construction and the continuous advancement of construction technology, the performance and stability of intelligent inverted arch trestles, as important construction equipment in large-scale projects such as modern tunnels and bridges, are directly related to construction efficiency and safety. However, with the increasing scale of projects and the complexity of construction processes, the control and management difficulty of intelligent inverted arch trestles has increased significantly when they are subjected to complex loads such as concrete pouring and heavy vehicle traffic. The application of traditional hydraulic control technology in intelligent inverted arch trestles is often limited by problems such as insufficient control accuracy, delayed response, and poor system stability. These problems not only affect the self-adjustment ability of the trestles under extreme working conditions, but may also cause damage to the supporting structure, thereby affecting the smooth progress of the entire construction process and the safety of personnel. Summary of the invention

[0003] This application provides an intelligent inverted arch trestle control method and system based on hydraulic technology, aiming to solve the technical problems that traditional inverted arch trestle is difficult to monitor in real time and accurately adjust the support points during concrete pouring and vehicle passing, resulting in untimely response to dynamic and static load changes and low trestle stability.

[0004] In view of the above problems, the present application provides an intelligent inverted arch trestle control method and system based on hydraulic technology.

[0005] The first aspect disclosed in the present application provides a control method for an intelligent inverted arch trestle based on hydraulic technology, the method comprising: deploying an intelligent inverted arch trestle and obtaining deployment information, the deployment information including distribution information of trestle support points; obtaining a concrete pouring task of the intelligent inverted arch trestle, and deploying load sensors, vibration sensors, and mass sensors based on the concrete pouring task and the distribution information of the trestle support points; when the concrete pouring task begins to be executed, activating the load sensors, vibration sensors, and mass sensors to collect data and establish a data set; performing dynamic and static load analysis based on the data set to generate dynamic and static load analysis results; setting a transient pressure sensor at a preset distance node of the intelligent inverted arch trestle, and synchronously setting a speed tracking sensor, performing time series acquisition based on the transient pressure sensor and the speed tracking sensor, generating vehicle dynamic load prediction data based on the time series acquisition results, and the transient pressure sensor is set in a front section leading to the intelligent inverted arch trestle; configuring a hydraulic adjustment strategy for each support point based on the vehicle dynamic load prediction data, the dynamic and static load analysis results, and the deployment information, and controlling the intelligent inverted arch trestle with the hydraulic adjustment strategy.

[0006] Another aspect disclosed in the present application provides an intelligent inverted arch trestle control system based on hydraulic technology, the system comprising: a deployment information acquisition module, the deployment information acquisition module is used to deploy the intelligent inverted arch trestle and acquire deployment information, the deployment information including the distribution information of the trestle support points; a sensor deployment module, the sensor deployment module is used to acquire the concrete pouring task of the intelligent inverted arch trestle, and deploy load sensors, vibration sensors, and mass sensors based on the concrete pouring task and the distribution information of the trestle support points; a data acquisition module, the data acquisition module is used to activate the load sensors, vibration sensors, and mass sensors for data acquisition and establish a data set when the concrete pouring task starts to be executed; and a dynamic and static load analysis module. The analysis module is used to perform dynamic and static load analysis according to the data set and generate dynamic and static load analysis results; the time series acquisition module is used to set a transient pressure sensor at a preset distance node of the intelligent inverted arch trestle, and synchronously set a speed tracking sensor, perform time series acquisition according to the transient pressure sensor and the speed tracking sensor, and generate vehicle dynamic load prediction data based on the time series acquisition result, and the transient pressure sensor is set in the front section leading to the intelligent inverted arch trestle; the intelligent inverted arch trestle control module is used to configure the hydraulic adjustment strategy of each support point according to the vehicle dynamic load prediction data, the dynamic and static load analysis results, and the deployment information, and control the intelligent inverted arch trestle with the hydraulic adjustment strategy.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The above-mentioned intelligent inverted arch trestle control method based on hydraulic technology first deploys the trestle and determines the distribution of its support points. Then, according to the concrete pouring task that the trestle will undertake, sensors, including load, vibration and mass sensors, are installed at key positions to capture various data during the construction process in real time. When the concrete pouring starts, these sensors are activated to collect data and form a data set. By analyzing these data sets, the performance of the trestle under static and dynamic loads, that is, dynamic and static load analysis, is understood. In order to further improve the safety and efficiency of the trestle, transient pressure sensors and speed tracking sensors are set on the road in front of the trestle. These sensors can capture the transient pressure and speed information generated by passing vehicles, and predict the dynamic loads generated by vehicles on the trestle based on time series analysis. Subsequently, a set of hydraulic adjustment strategies is formulated in combination with the vehicle dynamic load prediction, dynamic and static load analysis results, and the deployment information of the trestle. This strategy aims to adapt to and alleviate the impact of different loads on the trestle by intelligently controlling the hydraulic devices at each support point of the trestle, thereby ensuring the stability and safety of the trestle.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 The figure is a flow chart of a method for controlling an intelligent inverted trestle based on hydraulic technology in one embodiment.

[0012] Figure 2 The present invention is an architecture diagram of a control system for an intelligent inverted trestle based on hydraulic technology in one embodiment.

[0013] Explanation of the reference numerals: deployment information acquisition module 1, sensor layout module 2, data acquisition module 3, dynamic and static load analysis module 4, time series acquisition module 5, intelligent inverted arch trestle control module 6. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide an intelligent inverted arch trestle control method and system based on hydraulic technology to solve the technical problems that it is difficult to monitor and accurately adjust the support points of traditional inverted arch trestle in real time during concrete pouring and vehicle passing, resulting in untimely response to dynamic and static load changes and low trestle stability.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0017] Embodiment 1, as Figure 1As shown, the present application provides a control method for an intelligent inverted trestle based on hydraulic technology, the method comprising:

[0018] The intelligent inverted arch trestle is deployed, and deployment information is obtained, wherein the deployment information includes distribution information of trestle support points.

[0019] In the embodiment of the present application, after the intelligent inverted arch trestle is deployed, the system terminal obtains and records the deployment information of the trestle, and this deployment information includes the distribution information of the trestle support points. Through the distribution information of the trestle support points, the system terminal can understand which positions under the trestle are provided with support structures, and how these support points are distributed under the entire trestle. This information is crucial for subsequent adjustment and control, and they directly affect the stability and carrying capacity of the trestle.

[0020] The concrete pouring task of the intelligent inverted arch trestle is obtained, and load sensors, vibration sensors, and mass sensors are arranged based on the concrete pouring task and the distribution information of the trestle support points.

[0021] In one embodiment, when preparing for the concrete pouring task of the intelligent inverted arch trestle, the system terminal first clarifies the specific requirements of the task. Subsequently, according to the requirements of the concrete pouring task and the distribution information of the trestle support points obtained, a series of sensors are planned and deployed. These sensors include load sensors, vibration sensors, quality sensors, etc. The load sensor is used to monitor the forces on each support point of the trestle when it is subjected to concrete pouring to ensure that they do not exceed the designed bearing capacity. The vibration sensor is used to detect whether the trestle has abnormal vibration during the pouring process, which helps to timely discover and prevent potential structural problems. The quality sensor is used to monitor the quality of the poured concrete to ensure that it meets the construction standards. When laying out these sensors, the system terminal is reasonably arranged according to the characteristics of the concrete pouring task and the distribution of the trestle support points to ensure that the key parameters of the trestle during the pouring process can be fully and accurately monitored. In this way, by collecting and analyzing the data of these sensors in real time, the performance and stability of the trestle can be understood, providing a basis for subsequent control decisions.

[0022] When the concrete pouring task begins, the load sensor, vibration sensor, and mass sensor are activated to collect data and establish a data set.

[0023] In one embodiment, when the concrete pouring work begins, in order to ensure the quality and efficiency of the construction, the system terminal activates the deployed load, vibration, quality and other sensors to capture and record key data in real time. The load sensor monitors the force or weight changes applied during the concrete pouring process, the vibration sensor monitors the vibration of the concrete during the pouring and vibration process, and the quality sensor reflects the quality of the concrete by monitoring its density, strength and other parameters to ensure that the poured concrete meets the design requirements. The system terminal organizes the returned monitoring data to construct a data set for the concrete pouring process. This data set provides reference information for subsequent dynamic and static load analysis.

[0024] Performing dynamic and static load analysis according to the data set to generate dynamic and static load analysis results.

[0025] In one embodiment, the data set obtained by the system terminal contains various load information borne by the trestle during the concrete pouring process, including static loads and dynamic loads. In order to understand the specific dynamic and static loads, the system terminal performs dynamic and static load analysis on this data set. The dynamic and static load analysis understands the inherent characteristics of the trestle and the material characteristics involved by segmenting and calling the data in the data set, and calculates the vibration dynamic load through a pre-constructed dynamic load formula. After the dynamic and static load analysis, the system terminal generates a dynamic and static load analysis result. This dynamic and static load analysis result shows the size and distribution of the various loads borne by the trestle during the concrete pouring process. Through this information, the performance of the trestle can be understood more accurately, providing support for the subsequent inverted arch trestle control.

[0026] Further, the present application provides the method of performing dynamic and static load analysis according to the data set to generate dynamic and static load analysis results, further comprising:

[0027] The data set is segmented in time series, and the load sensor data of the concrete pouring equipment that is not placed in the intelligent inverted arch trestle is called to construct the weight of the trestle itself; the independent measurement data of the concrete pouring equipment in the data set is called to generate the equipment weight; the first static load is configured according to the weight of the trestle itself and the equipment weight; the data set is parsed to obtain the concrete volume data and the steel bar weight data, and the second static load is configured based on the concrete volume data and the steel bar weight data; the dynamic and static load analysis is completed with the first static load and the second static load, and the dynamic and static load analysis results are generated.

[0028] Preferably, the system terminal parses the collected data set, obtains the timestamp of each data in the data set, and segments the data set according to these timestamps, extracts the data recorded by the load sensor in the data set that does not place the concrete pouring equipment into the intelligent inverted arch trestle, and constructs the self-weight of the trestle. According to the segmentation result, the independent measurement data about the concrete pouring equipment in the data set is called, and the weight of the equipment is directly calculated. This step is the key to understanding the impact of the equipment on the overall load. Subsequently, based on the self-weight of the trestle and the weight of the equipment obtained before, a first static load is combined. This static load represents the pressure of the trestle and the equipment itself on the supporting structure when there is no concrete pouring operation. By parsing the concrete volume data and the steel bar weight data in the data set, the total weight of concrete and steel bars in the pouring process is calculated, and the second static load is formed, that is, the maximum static pressure on the supporting structure when the concrete pouring operation is carried out. Afterwards, the first static load and the second static load are obtained to perform dynamic and static load analysis. During this analysis process, the system terminal uses the pre-built dynamic load calculation formula to calculate the total vibration dynamic load of the intelligent inverted arch trestle based on the first static load and the second static load, and adds this total vibration dynamic load to the dynamic and static load analysis results. This dynamic and static load analysis result is crucial for evaluating the stability and safety of the supporting structure.

[0029] Further, the present application provides the method of completing the static and dynamic load analysis using the first static load and the second static load to generate the static and dynamic load analysis results, including:

[0030] Based on the data set, the vibration frequency and vibration acceleration are obtained, and the structural unit division is performed according to the structure of the intelligent inverted arch trestle to establish the structural unit division result; the mass of the structural unit division result is obtained by the first static load and the second static load; the vibration dynamic load is calculated by the formula as follows: ; .

[0031] Optionally, the system terminal extracts key information about vibration frequency and vibration acceleration from the data set, which is essential for understanding the dynamic response and stability of the intelligent inverted arch trestle structure. The vibration frequency reflects how fast the structure vibrates when it is subjected to external excitation, while the vibration acceleration reveals the severity of the vibration change. Subsequently, the system terminal divides the structural units according to the specific structural characteristics of the intelligent inverted arch trestle to obtain the structural unit division results. For example, the main body of the trestle is divided into multiple beam units, longitudinal beam units, and supporting structure units. This step is the basis of structural analysis. By decomposing the complex overall structure into several relatively simple units, it is more convenient to apply the dynamic load calculation formula for subsequent analysis. Each structural unit has specific properties such as mass, stiffness, and damping. After the structural unit division is completed, the system terminal uses the first static load and the second static load to evaluate the mass of these units. The effect of these static loads on the structure is static. By applying these two different static loads, the mass response of the structural unit division results under different working conditions can be obtained, which is of great significance for evaluating the bearing capacity and stability of the structure. Afterwards, the system terminal synchronizes the obtained vibration frequency, vibration acceleration and mass distribution of the structural unit division results into the dynamic load calculation formula to calculate the total vibration dynamic load of the intelligent inverted arch trestle. The dynamic load calculation formula is as follows: ;in, Representation in time The instantaneous force caused by vibration. , , Respectively, in time exist , , The force caused by vibration in the direction. Characterizes the dynamic load amplification factor, which depends on the frequency ratio and damping ratio, reflects the degree of vibration amplification on the load, and is calculated by the following formula: ; Here, is the frequency ratio, given by The calculation shows that it reflects the relationship between the frequency of vibration and the frequency of the structure itself. is the vibration frequency, which is obtained from the data set. is the natural frequency of the structure, that is, the natural vibration frequency of the structure without external interference. The calculation formula is . The stiffness of the structural element division result represents the ability of the structure to resist deformation. is the damping ratio, which indicates the ratio of energy dissipated during vibration. arrive between. The quality of the results of the structural unit partitioning. Characterizes the vibration acceleration at time t, which is the intensity of the vibration at that moment. is the total vibration dynamic load, which is the composite vibration load in all directions. The system terminal can ensure that the intelligent inverted arch trestle maintains structural stability during the concrete pouring process by accurately calculating the vibration load and taking it as part of the static load analysis results.

[0032] A transient pressure sensor is set at a preset distance node of the intelligent inverted arch trestle, and a speed tracking sensor is set synchronously. Time series acquisition is performed according to the transient pressure sensor and the speed tracking sensor, and vehicle dynamic load prediction data is generated based on the time series acquisition result. The transient pressure sensor is set on the front section leading to the intelligent inverted arch trestle.

[0033] In one embodiment, on the road section before the vehicle is about to enter the intelligent inverted arch trestle, the system terminal sets a sensor capable of measuring instantaneous pressure, i.e., a transient pressure sensor, at a preset distance node. These transient pressure sensors can capture the short and strong pressure changes generated when the vehicle tire contacts the road surface, which is closely related to factors such as the vehicle's weight, speed, and tire condition. In addition, a speed tracking sensor is also set to monitor and record the speed of the vehicle passing through the road section in real time. Speed ​​information is one of the key parameters for understanding the dynamic load characteristics of the vehicle, because it directly affects the impact force and duration of the vehicle on the road surface. Through the above two sensors, the system terminal continuously and synchronously collects the transient pressure and speed data generated when the vehicle passes. These data are recorded in chronological order to form time series data, which provides a rich source of information for subsequent analysis. Based on the collected time series data, the system terminal uses the pre-constructed speed dynamic load formula to predict the dynamic load and generate vehicle dynamic load prediction data. These prediction data can not only help evaluate the performance of the intelligent inverted arch trestle when it is subjected to vehicle dynamic loads, but also provide an important basis for the control of the intelligent inverted arch trestle.

[0034] Furthermore, the present application provides the method of generating vehicle dynamic load prediction data based on time series acquisition results, including:

[0035] Based on the time series acquisition result, the instantaneous weight of a single wheel is read, and the gross weight of the vehicle is calculated according to the instantaneous weight, wherein the gross weight of the vehicle carries a speed identifier; the speed extreme value is determined, and a speed dynamic load formula is established with the gross weight of the vehicle corresponding to the speed extreme value; and vehicle dynamic load prediction data is generated according to the speed dynamic load formula.

[0036] Preferably, the system terminal reads the instantaneous weight value of each wheel of the vehicle from the time series acquisition results. These values ​​represent the pressure generated when the wheel contacts the road surface at a specific time point and speed. Based on these instantaneous weight data, the system terminal estimates the total weight of the vehicle, that is, the instantaneous weight data of each wheel of the vehicle is added, and the calculated total vehicle weight is marked with the timestamp corresponding to the instantaneous weight data, so that the system terminal can understand the weight of the vehicle at a certain speed. Subsequently, the maximum and minimum speed values ​​are identified from the collected data. These two speed extremes are important reference points for establishing the speed dynamic load formula. The speed dynamic load formula is as follows: ;in, Characterize any speed The dynamic load under load is the total load borne by the vehicle at a given speed. It represents the maximum value among the speed extremes and is the maximum speed of the vehicle when it is running. It represents the minimum value among the speed extremes and is the lowest speed when the vehicle is running. For speed The gross vehicle weight is determined as follows: For speed The system terminal uses this speed dynamic load formula to calculate based on the obtained vehicle gross weight and the marked instantaneous speed to generate the dynamic load data of the vehicle at different speeds. This is of great significance for configuring the hydraulic adjustment strategy of each support point.

[0037] The hydraulic adjustment strategy of each support point is configured according to the vehicle dynamic load prediction data, dynamic and static load analysis results, and deployment information, and the intelligent inverted arch trestle is controlled by the hydraulic adjustment strategy.

[0038] In one embodiment, after obtaining the vehicle dynamic load prediction data, dynamic and static load analysis results, and deployment information, the system terminal matches the vehicle dynamic load prediction data and dynamic and static load analysis results with the trestle support point distribution information in the deployment information, and matches the vehicle dynamic load prediction data and the data in the dynamic and static load analysis results with the trestle support points one by one. Subsequently, the system terminal inputs the matched data into the pressure prediction model, and the pressure prediction model analyzes and calculates the vehicle dynamic load prediction data and dynamic and static load analysis results corresponding to each trestle support point through the learned correspondence, and then combines the calculation results with the configured hydraulic buffer zone to establish a hydraulic adjustment strategy for each support point. These strategies are intended to dynamically adjust the load-bearing capacity and stability of the trestle support points by accurately controlling the pressure, flow and other parameters of the hydraulic buffer zone to adapt to the complex load changes generated when different vehicles pass through.

[0039] Furthermore, the present application provides the hydraulic adjustment strategy for configuring each trestle support point according to the vehicle dynamic load prediction data, dynamic and static load analysis results, and deployment information, and also includes:

[0040] A pressure prediction model is configured, and the vehicle dynamic load prediction data and dynamic and static load analysis results are input into the pressure prediction model to establish an ideal hydraulic pressure value for each trestle support point; a hydraulic buffer zone is configured, and the hydraulic buffer zone includes a buffer control unit and a buffer response unit, and the buffer response unit is configured with a hydraulic cylinder and an accumulator; a hydraulic adjustment strategy is established according to the ideal hydraulic pressure value and the hydraulic buffer zone.

[0041] Preferably, the system terminal collects a large amount of historical data from the historical log to form sample vehicle dynamic load prediction data and sample dynamic and static load analysis results, and then identifies the ideal hydraulic pressure value of the sample vehicle dynamic load prediction data and sample dynamic and static load analysis results based on the design requirements, material properties and safety standards of the trestle, combined with professional knowledge, to form a sample ideal hydraulic pressure value. Subsequently, a neural network is used as the basic framework of the model, and these sample data are used for training. Taking the multi-layer perceptron (MLP) as an example, the system terminal determines the number of neurons in the input layer, hidden layer (may have multiple layers) and output layer, as well as the activation function used between the hidden layers, such as ReLU, Sigmoid or Tanh, etc., according to the complexity of the problem and the characteristics of the data, and then uses random numbers to initialize the weights and biases in the MLP model, and divides the sample vehicle dynamic load prediction data, sample dynamic and static load analysis results and sample ideal hydraulic pressure values ​​into training sets and test sets. After that, for each sample in the training set, the vehicle dynamic load prediction data and the sample dynamic and static load analysis results are forward propagated through the MLP model to obtain the predicted ideal hydraulic pressure value, and the mean square error (MSE) is used to calculate the loss value of the predicted ideal hydraulic pressure value and the sample ideal hydraulic pressure value. According to the gradient of the loss function, the gradient of each parameter in the model is calculated by the back propagation algorithm. This involves the application of the chain rule, starting from the output layer, and calculating the gradient of each parameter backward layer by layer. Then, the optimization algorithm Adam is used to update the parameters of the model according to the calculated gradient. This involves multiplying the gradient by the learning rate and subtracting this product from the current parameter value to obtain the new parameter value. The above process is repeated until the preset maximum number of iterations is reached. When the training stops, the system terminal uses the test set to evaluate the performance of the MLP model, calculate the accuracy, and adjust the model according to the calculation results, such as increasing or decreasing the number of hidden layers, changing the activation function, etc., to improve the performance of the model. Finally, the system terminal outputs the trained MLP model as a pressure prediction model for the subsequent acquisition of the ideal hydraulic pressure value.

[0042] When the pressure prediction model is configured, the system terminal inputs the vehicle dynamic load prediction data and dynamic and static load analysis results of each trestle support point into the pressure prediction model. The pressure prediction model generates the ideal hydraulic pressure value of each trestle support point by analyzing and calculating the input data. These ideal hydraulic pressure values ​​are crucial to ensure the stability and safety of the trestle. Subsequently, in order to cope with the difference between the actual pressure and the ideal pressure, the system terminal configures a hydraulic buffer for each trestle support point. This buffer consists of two parts, namely the buffer control unit and the buffer response unit. The buffer control unit is responsible for monitoring and calculating the difference between the actual pressure and the ideal pressure, and issuing control instructions accordingly. The buffer response unit includes a hydraulic cylinder and an accumulator, which act according to the control instructions to adjust the pressure of the hydraulic system to approach or reach the ideal value. Based on the ideal hydraulic pressure value and the configuration of the hydraulic buffer, the system terminal determines the hydraulic adjustment strategy. The hydraulic adjustment strategy will clarify under what circumstances the buffer response unit needs to be activated, how the hydraulic cylinder and accumulator should work together, and how to compensate for the real-time pressure difference by adjusting their output to ensure that the hydraulic pressure of the trestle support point is always kept within a safe and stable range.

[0043] Furthermore, the present application provides a control buffer response unit action, further comprising:

[0044] The buffer control unit receives real-time pressure data and generates fuzzy intensity feedback based on the real-time pressure data; and controls the action of the buffer response unit based on the fuzzy intensity feedback.

[0045] Optionally, the system terminal receives real-time updated pressure data through the buffer control unit, which reflects the current pressure state and has high real-time and continuity. Subsequently, the system terminal defines a series of fuzzy sets to describe possible pressure states and intensity levels. These fuzzy sets are determined based on expert experience and actual needs, such as low pressure, medium pressure and high pressure, and a membership function is defined for each fuzzy set, which maps the precise real-time pressure data to a fuzzy value in the interval [0,1], indicating the degree to which the data belongs to the corresponding fuzzy set. The shape and parameters of the membership function are adjusted and optimized according to the actual situation. The real-time pressure data is then input into the membership function to calculate its membership value to each fuzzy set. These membership values ​​form the basis of fuzzy intensity feedback. Afterwards, a fuzzy control rule base is established based on expert experience and actual needs. These rules describe how the buffer response unit should act or adjust its parameters under different fuzzy states. The rule base is usually expressed in the form of "if-then", such as "if the pressure is high, increase the buffer intensity". The system terminal matches and infers the fuzzy intensity feedback of real-time pressure data with the rules in the rule base. Through fuzzy logic operations (such as taking the larger, taking the smaller, algebraic operations, etc.), specific instructions for controlling the action of the buffer response unit are obtained, and the basic fuzzy intensity feedback is supplemented to form fuzzy intensity feedback. Then, the system terminal combines the obtained fuzzy intensity feedback with the real-time pressure difference compensation to control the action of the buffer response unit and respond to the change of pressure, thereby achieving a more stable and reliable bearing capacity of the intelligent inverted arch trestle.

[0046] Furthermore, the present application provides the method of establishing a hydraulic pressure adjustment strategy according to the ideal hydraulic pressure value and the hydraulic buffer zone, further comprising:

[0047] Establish point associations for various trestle support points; generate pressure difference constraints based on the point associations; and perform real-time pressure difference compensation for various trestle support points based on the pressure difference constraints.

[0048] Optionally, the system terminal first understands the spatial position relationship between the trestle support points based on the design data of the inverted arch trestle, and regards these support points as nodes in the network, and uses their relative position, structural strength and other factors to establish an interrelated point association model. Subsequently, based on this point association model, the factors that may cause the pressure difference between the trestle support points are identified, such as changes in the external environment, uneven internal load distribution, etc., and the allowable pressure difference threshold between each support point is set according to the design specifications, safety standards and historical operation experience of the trestle. This threshold should be able to ensure that the trestle will not be damaged due to excessive pressure difference during normal operation. Then, the pressure difference threshold is used as a constraint condition, combined with the point association model, to generate a pressure difference constraint for the trestle support point. These constraints will guide the subsequent real-time pressure difference compensation work. After obtaining the pressure difference constraint, the system terminal performs real-time pressure difference compensation, that is, the absolute difference between the real-time monitored pressure value and the ideal hydraulic pressure value is calculated to determine whether the difference is within the pressure difference constraint. When it is monitored that the pressure of one or some supporting points is not within the pressure difference constraint, the system terminal adjusts the buffer response unit of the supporting points involved in combination with fuzzy strength feedback to restore or maintain the pressure balance between the supporting points and ensure the overall stability and safety of the trestle.

[0049] In summary, the embodiments of the present application have at least the following technical effects:

[0050] The embodiment of the present application first obtains the support point distribution information, and uses load sensors, vibration sensors and mass sensors to collect data during the concrete pouring task. These data are segmented and analyzed in time series to construct the first static load of the trestle's own weight and equipment weight and the second static load based on concrete and steel bars to complete the dynamic and static load analysis. At the same time, a transient pressure sensor and a speed tracking sensor are set in the front section of the trestle to consider the impact of the vehicle speed on the dynamic load and predict the vehicle dynamic load. Based on the vehicle dynamic load prediction data, dynamic and static load analysis results and deployment information, the hydraulic adjustment strategy of each support point is configured, including establishing a pressure prediction model to determine the ideal hydraulic pressure value, and configuring a hydraulic buffer to implement the adjustment. In addition, the fuzzy intensity feedback control buffer response unit is used to optimize the hydraulic adjustment effect. In the whole process, by establishing the point association of the trestle support points and generating pressure difference constraints, real-time pressure difference compensation is achieved to ensure the stable operation of the trestle. These technical effects jointly solve the technical problems of traditional inverted arch trestles, which are difficult to monitor and accurately adjust the support points in real time during concrete pouring and vehicle passage, resulting in untimely response to changes in dynamic and static loads and low stability of the trestles. By adjusting the hydraulic support force of each support point, the safety and stability of the trestles structure can be ensured, and the construction efficiency and safety can be improved.

[0051] Embodiment 2 is based on the same inventive concept as the intelligent inverted trestle control method based on hydraulic technology in the above embodiment. Figure 2As shown, the present application provides an intelligent inverted arch trestle control system based on hydraulic technology, and the system includes: a deployment information acquisition module 1: the deployment information acquisition module 1 is used to deploy the intelligent inverted arch trestle and obtain deployment information, and the deployment information includes the distribution information of the trestle support points; a sensor layout module 2: the sensor layout module 2 is used to obtain the concrete pouring task of the intelligent inverted arch trestle, and to layout load sensors, vibration sensors, and mass sensors based on the concrete pouring task and the distribution information of the trestle support points; a data acquisition module 3: the data acquisition module 3 is used to activate the load sensors, vibration sensors, and mass sensors for data acquisition and establish a data set when the concrete pouring task starts to be executed; a dynamic and static load analysis module 4: the dynamic and static load analysis module Block 4 is used to perform dynamic and static load analysis according to the data set and generate dynamic and static load analysis results; time series acquisition module 5: the time series acquisition module 5 is used to set a transient pressure sensor at a preset distance node of the intelligent inverted arch trestle, and synchronously set a speed tracking sensor, perform time series acquisition according to the transient pressure sensor and the speed tracking sensor, and generate vehicle dynamic load prediction data based on the time series acquisition results. The transient pressure sensor is set in the front section leading to the intelligent inverted arch trestle; intelligent inverted arch trestle control module 6: the intelligent inverted arch trestle control module 6 is used to configure the hydraulic adjustment strategy of each support point according to the vehicle dynamic load prediction data, the dynamic and static load analysis results, and the deployment information, and control the intelligent inverted arch trestle with the hydraulic adjustment strategy.

[0052] Furthermore, the dynamic and static load analysis module 4 is also used to perform the following method:

[0053] The data set is segmented in time series, and the load sensor data of the concrete pouring equipment that is not placed in the intelligent inverted arch trestle is called to construct the weight of the trestle itself; the independent measurement data of the concrete pouring equipment in the data set is called to generate the equipment weight; the first static load is configured according to the weight of the trestle itself and the equipment weight; the data set is parsed to obtain the concrete volume data and the steel bar weight data, and the second static load is configured based on the concrete volume data and the steel bar weight data; the dynamic and static load analysis is completed with the first static load and the second static load, and the dynamic and static load analysis results are generated.

[0054] Furthermore, the dynamic and static load analysis module 4 is also used to perform the following method:

[0055] Based on the data set, the vibration frequency and vibration acceleration are obtained, and the structural unit division is performed according to the structure of the intelligent inverted arch trestle, and the structural unit division result is established; the mass of the structural unit division result is obtained by the first static load and the second static load; the vibration dynamic load is calculated by the formula as follows: ; ;in, Representation in time The instantaneous force caused by vibration, , , Respectively, in time exist , , The force caused by vibration in the direction, Characterizes the dynamic load amplification factor, ,in, is the frequency ratio, , is the vibration frequency, is the structural natural frequency, , is the stiffness of the structural element partitioning result, is the damping ratio, , The quality of the structural unit partitioning results, Characterize vibration acceleration, is the total vibration dynamic load.

[0056] Furthermore, the time series acquisition module 5 is also used to execute the following method:

[0057] Based on the time series acquisition result, the instantaneous weight of a single wheel is read, and the gross vehicle weight is calculated according to the instantaneous weight, wherein the gross vehicle weight carries a speed mark; the speed extreme value is determined, and the speed dynamic load formula is established with the gross vehicle weight corresponding to the speed extreme value, as follows: ;in, Characterize any speed Under dynamic load, Characterizes the maximum value among the velocity extremes, Characterizes the minimum value among the velocity extremes, For speed The gross vehicle weight is determined as follows: For speed The vehicle gross weight is measured under the speed; and the vehicle dynamic load prediction data is generated according to the speed dynamic load formula.

[0058] Furthermore, the intelligent inverted trestle control module 6 is also used to execute the following method:

[0059] A pressure prediction model is configured, and the vehicle dynamic load prediction data and dynamic and static load analysis results are input into the pressure prediction model to establish an ideal hydraulic pressure value for each trestle support point; a hydraulic buffer zone is configured, and the hydraulic buffer zone includes a buffer control unit and a buffer response unit, and the buffer response unit is configured with a hydraulic cylinder and an accumulator; a hydraulic adjustment strategy is established according to the ideal hydraulic pressure value and the hydraulic buffer zone.

[0060] Furthermore, the intelligent inverted trestle control module 6 is also used to execute the following method:

[0061] The buffer control unit receives real-time pressure data and generates fuzzy intensity feedback based on the real-time pressure data; and controls the action of the buffer response unit based on the fuzzy intensity feedback.

[0062] Furthermore, the intelligent inverted trestle control module 6 is also used to execute the following method:

[0063] Establish point associations for various trestle support points; generate pressure difference constraints based on the point associations; and perform real-time pressure difference compensation for various trestle support points based on the pressure difference constraints.

[0064] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0066] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent inverted trestle control method based on hydraulic technology, characterized in that: The method comprises: Deploy the intelligent inverted arch trestle and obtain deployment information, wherein the deployment information includes distribution information of trestle support points; Acquire a concrete pouring task of the intelligent inverted arch trestle, and deploy load sensors, vibration sensors, and mass sensors based on the concrete pouring task and distribution information of the trestle support points; When the concrete pouring task begins, the load sensor, vibration sensor, and mass sensor are activated to collect data and establish a data set; Performing dynamic and static load analysis according to the data set to generate dynamic and static load analysis results; A transient pressure sensor is set at a preset distance node of the intelligent inverted arch trestle, and a speed tracking sensor is set synchronously, time series acquisition is performed according to the transient pressure sensor and the speed tracking sensor, and vehicle dynamic load prediction data is generated based on the time series acquisition result, and the transient pressure sensor is set at the front section leading to the intelligent inverted arch trestle; According to the vehicle dynamic load prediction data, dynamic and static load analysis results, and deployment information, a hydraulic adjustment strategy for each support point is configured, and the intelligent inverted trestle bridge is controlled by the hydraulic adjustment strategy; The step of performing dynamic and static load analysis according to the data set to generate dynamic and static load analysis results further includes: Performing time series segmentation on the data set, calling the load sensor data of the concrete pouring equipment that is not placed in the intelligent inverted arch trestle, and constructing the weight of the trestle itself; calling the independent measurement data of the concrete pouring equipment in the data set to generate the equipment weight; Configuring a first static load according to the weight of the trestle itself and the weight of the equipment; Parsing the data set to obtain concrete volume data and steel bar weight data, and configuring a second static load based on the concrete volume data and steel bar weight data; Performing static and dynamic load analysis using the first static load and the second static load to generate static and dynamic load analysis results; The step of performing static and dynamic load analysis using the first static load and the second static load to generate static and dynamic load analysis results further includes: Acquire the vibration frequency and vibration acceleration based on the data set, divide the structure unit according to the structure of the intelligent inverted arch trestle, and establish the structure unit division result; The quality of the structural unit division result is obtained by using the first static load and the second static load; The vibration dynamic load is calculated by the formula as follows: ； ； in, Representation in time The instantaneous force caused by vibration, , , Respectively expressed in time exist , , The force caused by vibration in the direction, Characterizes the dynamic load amplification factor, ,in, is the frequency ratio, , is the vibration frequency, is the structural natural frequency, , is the stiffness of the structural element partitioning result, is the damping ratio, , The quality of the structural unit partitioning results, Characterize vibration acceleration, is the total vibration dynamic load; Wherein, the generating of vehicle dynamic load prediction data based on the time series acquisition result further includes: Reading the instantaneous weight of a single wheel based on the time series acquisition result, and calculating the gross vehicle weight according to the instantaneous weight, wherein the gross vehicle weight is marked with a speed mark; Determine the speed extreme value and establish the speed dynamic load formula based on the vehicle gross weight corresponding to the speed extreme value, as follows: ； in, Characterize any speed Under dynamic load, Characterizes the maximum value among the velocity extremes, Characterizes the minimum value among the velocity extremes, For speed The gross vehicle weight is determined as follows: For speed The gross vehicle weight as determined below; The vehicle dynamic load prediction data is generated according to the speed dynamic load formula.

2. The intelligent inverted trestle control method based on hydraulic technology according to claim 1, characterized in that: The hydraulic adjustment strategy of each support point is configured according to the vehicle dynamic load prediction data, dynamic and static load analysis results, and deployment information, and further includes: A pressure prediction model is configured, and the vehicle dynamic load prediction data and dynamic and static load analysis results are input into the pressure prediction model to establish an ideal hydraulic pressure value for each trestle support point; A hydraulic buffer zone is configured, wherein the hydraulic buffer zone includes a buffer control unit and a buffer response unit, and the buffer response unit is configured with a hydraulic cylinder and an accumulator; A hydraulic pressure adjustment strategy is established according to the ideal hydraulic pressure value and the hydraulic buffer zone.

3. The intelligent inverted trestle control method based on hydraulic technology according to claim 2, characterized in that: The method further comprises: receiving real-time pressure data with the buffer control unit and generating fuzzy intensity feedback with the real-time pressure data; The buffer response unit action is controlled based on the blur strength feedback.

4. The intelligent inverted trestle control method based on hydraulic technology as claimed in claim 2, characterized in that: The establishing of the hydraulic pressure adjustment strategy according to the ideal hydraulic pressure value and the hydraulic buffer zone also includes: Establish point associations for each trestle support point; A pressure difference constraint is generated based on the point association, and real-time pressure difference compensation is performed on each trestle supporting point based on the pressure difference constraint.

5. Intelligent inverted trestle control system based on hydraulic technology, characterized in that: The steps for implementing the intelligent inverted trestle control method based on hydraulic technology as described in any one of claims 1 to 4 include: Deployment information acquisition module: deploys the intelligent inverted arch trestle and acquires deployment information, wherein the deployment information includes distribution information of trestle support points; Sensor deployment module: obtains the concrete pouring task of the intelligent inverted arch trestle, and deploys load sensors, vibration sensors, and mass sensors based on the concrete pouring task and the distribution information of the trestle support points; Data acquisition module: When the concrete pouring task starts, the load sensor, vibration sensor, and mass sensor are activated to collect data and establish a data set; Dynamic and static load analysis module: performs dynamic and static load analysis according to the data set and generates dynamic and static load analysis results; Time series acquisition module: a transient pressure sensor is set at a preset distance node of the intelligent inverted arch trestle, and a speed tracking sensor is set synchronously, time series acquisition is performed according to the transient pressure sensor and the speed tracking sensor, and vehicle dynamic load prediction data is generated based on the time series acquisition result, and the transient pressure sensor is set at the front section leading to the intelligent inverted arch trestle; Intelligent inverted arch trestle control module: configures the hydraulic adjustment strategy of each support point according to the vehicle dynamic load prediction data, dynamic and static load analysis results, and deployment information, and controls the intelligent inverted arch trestle with the hydraulic adjustment strategy.

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