Spatial rapid plate load test method and device

Abstract

The invention relates to the technical field of load testing, in particular to a space rapid plate load testing method and device. The method comprises the following steps: connecting a depth-adjustable force transmission frame with a bearing flat plate through a magnetic adsorption structure, and establishing a data transmission link between the depth-adjustable force transmission frame and a multi-source data acquisition module; different space target test depths are obtained, a splicing length error is calculated, and if the splicing length error is exceeded, a fit clearance is triggered; synchronously acquiring strain data and a transmission load capacity corresponding to settlement amount calculation to construct a depth-load-settlement three-dimensional relation curved surface; according to the depth-load-settlement three-dimensional relation curved surface, corresponding space depth sudden change measuring points are recognized and counted, and load-settlement data are regenerated; and fusing the load-settlement data and the corresponding strain data, and carrying out space bearing visualization to generate mechanical property space bearing deformation distribution corresponding to different depth distribution. According to the invention, the bearing capacity of the flat plate in different spaces can be efficiently and accurately tested.

Description

Technical Field

[0001] The present invention relates to the technical field of load testing, and in particular to a space rapid flat plate load testing method and device. Background Art

[0002] With the continuous development of modern engineering technology, especially in the fields of building structures and mechanical equipment, in order to ensure that these structures can work normally under extreme conditions, plate load testing has become an important method for evaluating the load-bearing capacity and safety of materials, structures or components. In actual load tests, due to the particularity of the spatial environment, the test equipment and load application method need to take into account the complexity of the spatial structure and the changing working conditions. At present, the existing plate load test methods mainly rely on fixed loading devices and load sensors. These devices often require a large amount of space to be placed and cannot be flexibly adjusted. In some environments with small spaces and complex conditions, the use of these traditional devices becomes very difficult, and their operation process mostly relies on manual operation and cannot be automatically adjusted, resulting in low test efficiency and cumbersome operation. Especially in some occasions where the load-bearing capacity of materials or components needs to be quickly evaluated, the existing technology cannot meet the needs of efficient, convenient and accurate testing. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a space rapid flat plate load test method and device to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a space rapid plate load test device includes a load-bearing plate, a depth-adjustable force conduction frame, a jack load loading module and a multi-source data acquisition module, wherein the load-bearing plate is connected to the lower end of the depth-adjustable force conduction frame through a magnetic adsorption structure, and the upper end of the depth-adjustable force conduction frame is connected to the jack load loading module through a spherical hinge mechanism, and the multi-source data acquisition module respectively establishes a wired or wireless data transmission link with the load-bearing plate, the depth-adjustable force conduction frame and the jack load loading module; the depth-adjustable force conduction frame is composed of at least three modular force conduction rods spliced ​​together through an electromagnetic coupling interface, wherein each of the modular force conduction rods has a built-in fiber grating strain sensor and a temperature compensation sensor. The jack load loading module includes a multi-stage hydraulic cylinder group, a closed-loop control system, a jack and a concrete test block, and a steel beam is set on the ground plane above the jack load loading module for stacking concrete test blocks to apply load to measure the change of foundation settlement with load, and determine the bearing capacity and deformation of spatial foundation soil. The multi-source data acquisition module includes a distributed sensor network for synchronously collecting load, displacement and strain, wherein the control signal of the jack load loading module is transmitted to the actuator of the bearing plate through the signal line inside the depth-adjustable force conduction frame to form a closed-loop control circuit; the electromagnetic coupling interface includes an electromagnetic suction cup and a magnetic positioning pin arranged at both ends of the modular force conduction rod, and the adsorption force of the electromagnetic suction cup is specifically Among them F c represents the adsorption force of the electromagnetic chuck, B represents the magnetic induction intensity of the electromagnetic chuck, M represents the effective adsorption area of ​​the electromagnetic chuck, μ0 represents the vacuum magnetic permeability, the taper of the magnetic locating pin is 1:50, and the fitting clearance is <0.02mm, so as to ensure that the coaxiality error of the modular force conduction rod to the stress conduction path is <0.05°.

[0005] Furthermore, the magnetic adsorption structure includes a permanent magnetic chuck array provided on the upper surface of the supporting plate and a magnetic base correspondingly provided at the lower end of the depth-adjustable force conduction frame. The adsorption force of the permanent magnetic chuck array is adjusted by an electric control coil, and the relationship between the adsorption force and the current of the electric control coil satisfies F y =k·I 2 , where F y represents the adsorption force of the permanent magnetic chuck array, I represents the current of the electric control coil, and k represents the material property attenuation coefficient of the electric control coil. The permanent magnetic chuck array is distributed in a regular hexagon, and the distance between adjacent chucks is 45-50 mm to ensure that the shear strength of the adsorption interface corresponding to the permanent magnetic chuck array is greater than 50 MPa under the maximum test load.

[0006] Furthermore, the fiber Bragg grating strain sensor is arranged along the axis direction of the modular force transmission rod, and each modular force transmission rod is provided with at least three measuring points. The relationship between the center wavelength drift and strain of the fiber Bragg grating strain sensor satisfies Where Δλ represents the wavelength drift of the fiber Bragg grating strain sensor, λ0 represents the central wavelength of the fiber Bragg grating strain sensor, and p e represents the fiber elasto-optic coefficient of the fiber Bragg grating strain sensor, ε represents the strain of the modular force transmission rod, and the temperature compensation module corrects the wavelength drift caused by temperature in real time through the thermistor attached to the surface of the modular force transmission rod.

[0007] Furthermore, the modular force transmission rod adopts a variable cross-section hollow tubular structure, the cross-section outer diameter of which decreases linearly from D at the lower end to 0.8D at the upper end, where d is the length of the cross-section outer diameter, and the wall thickness is uniformly t. The cross-section inertia moment of the variable cross-section hollow tubular structure is distributed along the axis I(x) and satisfies Wherein, x represents the distance from the lower end of the modular force transmission rod, and L represents the length of the modular force transmission rod.

[0008] Furthermore, the present invention also provides a space rapid flat plate load test method, which is implemented based on the space rapid flat plate load test device described above, and the space rapid flat plate load test method includes the following steps:

[0009] The depth-adjustable force conduction frame is connected to the load-bearing plate through a magnetic adsorption structure, and the jack load loading module is docked with the depth-adjustable force conduction frame using a spherical hinge mechanism to establish a data transmission link with the multi-source data acquisition module;

[0010] Obtain different space target test depths H, and select the corresponding number of modular force transmission rods based on the space target test depth H, and automatically complete the splicing by combining the electromagnetic coupling interface. After splicing, the initial strain data ε0 is collected by the fiber Bragg grating strain sensor, and the formula is used to calculate the strain data. Calculate the splicing length error, where n represents the total number of modular force transmission members, specifically l i represents the length of the ith modular force transmission member, E i represents the elastic modulus of the i-th modular force transmission rod. If the splicing length error exceeds ±0.5 mm, the electromagnetic coupling interface is triggered to adjust the corresponding fitting clearance between the modular force transmission rods.

[0011] The load is applied by the jack load loading module according to the preset loading sequence. At the same time, after the applied load is stable, the strain data ε of each modular force transmission rod on the depth-adjustable force transmission force frame is synchronously collected by the multi-source data acquisition module through the data transmission link. i and the settlement S of the load-bearing plate, and based on the strain data ε i and elastic modulus E i By formula Calculate the transfer load at the corresponding space target test depth H, where A i represents the cross-sectional area of ​​the i-th modular force transmission rod; constructs a depth-load-settlement three-dimensional relationship surface based on the transferred load at the corresponding spatial target test depth H and the settlement S of the load-bearing plate;

[0012] The corresponding spatial depth mutation measurement point is statistically identified based on the depth-load-settlement three-dimensional relationship surface, and the corresponding strain change rate is calculated based on the strain data corresponding to the spatial depth mutation measurement point; when the strain change rate exceeds 0.5% / min, the loading rate of the applied load is automatically adjusted by the jack load loading module, and the load-settlement data corresponding to the spatial depth mutation measurement point is regenerated;

[0013] The load-settlement data of the spatial depth mutation measurement point is fused with the corresponding strain data, and the mechanical parameters corresponding to the space target test are calculated through the inversion process; based on the corresponding space target test depth, the spatial load-bearing visualization of the mechanical parameters corresponding to the space target test is performed to generate the spatial load-bearing deformation distribution of mechanical characteristics corresponding to different depth distributions.

[0014] Furthermore, the method of identifying and counting corresponding spatial depth mutation measurement points based on the depth-load-settlement three-dimensional relationship surface includes the following steps:

[0015] The Gaussian curvature and average curvature at each point are calculated based on the depth-load-settlement three-dimensional relationship surface;

[0016] The curvature change rate at each point is calculated by the Gaussian curvature and the average curvature at each point. When the curvature change rate at a point is greater than 0.3 times the maximum Gaussian curvature in the depth-load-settlement three-dimensional relationship surface, the point is marked as a candidate point for curvature mutation.

[0017] The temporal and spatial neighborhood of the curvature mutation candidate point is expanded by selecting a spherical neighborhood with a radius of 0.5m and taking the curvature mutation candidate point as the center. The density of the candidate points in the spherical neighborhood is counted. When the density exceeds 5 / m 3 When , the spherical neighborhood is determined as a candidate region of spatial depth mutation point;

[0018] According to the candidate regions of spatial depth mutation points, the corresponding spatial depth mutation measurement points are statistically identified.

[0019] Furthermore, the step of identifying and counting corresponding spatial depth mutation points based on the spatial depth mutation point candidate regions includes the following steps:

[0020] According to each spatial point in the candidate region of the spatial depth mutation point, partial derivatives are calculated along the depth direction and the load direction to obtain the depth direction gradient and the load direction gradient of each spatial point in the candidate region;

[0021] According to the spatial target test, the corresponding depth direction gradient threshold and load direction gradient threshold are set, and the depth direction gradient and load direction gradient of each spatial point in the candidate area are identified based on the corresponding depth direction gradient threshold and load direction gradient threshold. If the absolute value corresponding to the depth direction gradient of the spatial point is greater than the depth direction gradient threshold and the absolute value corresponding to the load direction gradient is greater than the load direction gradient threshold, it is determined as the corresponding spatial depth mutation measurement point.

[0022] Furthermore, the automatic adjustment of the loading rate of the applied load is calculated by a loading rate adjustment formula, wherein the loading rate adjustment formula is specifically:

[0023]

[0024] Among them, v k+1 represents the loading rate at the k+1th moment, v k represents the loading rate at the kth moment, exp represents the exponential function, δ d represents the strain change rate corresponding to the spatial depth mutation measurement point, α represents the adjustment coefficient, and its value range is 0.2-0.5, and β represents the preset threshold.

[0025] Furthermore, the specific implementation process of the inversion process is:

[0026] Obtain the load data, settlement data, and strain data corresponding to the sudden change measurement points at different spatial depths, and use the dynamic time warping algorithm to synchronize the timestamps of the corresponding load data, settlement data, and strain data to eliminate the time offset caused by the sampling frequency difference and ensure that the time synchronization error is less than 10ms, so as to obtain the time-synchronized load data, settlement data, and strain data;

[0027] The corresponding spatial test mechanical response model is constructed by combining the elastic half-space theory with the Burgers viscoelastic model, where the model parameters include the elastic modulus E, Poisson's ratio μ, and Kelvin body elastic modulus E. k , Kelvin viscosity coefficient η kand Maxwell volume viscosity coefficient η m ;

[0028] The time-synchronized load data, settlement data, and strain data are input into the spatial test mechanical response model to derive the corresponding load-settlement and strain inversion calculation formulas. For the elastic stage, the relationship between the settlement S and the transferred load P is: Where B represents the diameter of the supporting plate; for the viscoelastic stage, the integral form of the Burgers viscoelastic model is used to describe the change of strain data with time τ

[0029] Based on the inversion calculation formula of load-settlement and strain and combined with the spatial test mechanical response model, a model prediction inversion analysis is performed on the load data, settlement data and strain data after time synchronization. The relative error between the measured data and the model prediction data is set as the objective function, and the corresponding spatial test mechanical response model is optimized based on the objective function, and the mechanical parameters corresponding to the spatial target test are output by inversion optimization.

[0030] Furthermore, the objective function is specifically:

[0031]

[0032] Among them, f represents the objective function, q represents the total number of spatial depth mutation measurement points, represents the predicted settlement value of the jth spatial depth mutation measurement point, represents the measured settlement value of the jth spatial depth mutation measurement point, represents the predicted strain value of the jth spatial depth mutation measurement point, Represents the measured strain value of the jth spatial depth mutation measurement point.

[0033] Beneficial effects of the present invention:

[0034] 1. The spatial rapid plate load test device proposed in the present invention is composed of a load-bearing plate, a depth-adjustable force conduction frame, a jack load loading module, and a multi-source data acquisition module. It can use a magnetic adsorption structure to connect the load-bearing plate and the lower end of the depth-adjustable force conduction frame, providing a stable and convenient connection method, reducing friction and mechanical loss, and improving the long-term stability and reliability of the equipment; magnetic adsorption facilitates rapid installation and disassembly, avoiding the problems of loose or deformed firmware in traditional connection methods, and ensuring the accuracy and repeatability of test data. At the same time, the magnetic adsorption structure extends the service life of the equipment and reduces wear; the depth-adjustable force conduction frame is modularly designed to achieve adjustment under load conditions, and has built-in fiber optic Bragg grating strain sensors and temperature compensation modules to ensure the accuracy and stability of the data, and has good maintainability; the jack load loading module adopts a multi-stage hydraulic cylinder group, a closed-loop control system, a jack and a concrete test block, and sets a steel beam on the ground plane above the jack load loading module for stacking concrete test blocks to apply load to measure the change of foundation settlement with load, and determine the bearing capacity and deformation of spatial foundation soil, accurately control the loading force and direction, improve loading accuracy and stability, reduce errors, and enhance the level of automation; the multi-source data acquisition module synchronously collects key parameters through a distributed sensor network to ensure data consistency and reliability, and support real-time monitoring and analysis; the electromagnetic coupling interface ensures the accuracy and stability of module connection through electromagnetic suction cups and magnetic positioning pins, further improving the reliability and accuracy of test data.

[0035] 2. The spatial rapid plate load test method proposed in the present invention has the beneficial effect of connecting the depth-adjustable force conduction frame with the load-bearing plate through a magnetic adsorption structure, and docking the jack load loading module with the depth-adjustable force conduction frame with a spherical hinge mechanism to establish a data transmission link with the multi-source data acquisition module. The key to this step is to utilize the combination of magnetic adsorption and spherical hinge technology to make the connection between the force conduction frame and the load-bearing plate more stable and efficient. Through the spherical hinge, the force conduction frame can be flexibly adjusted at multiple angles, thereby improving the adaptability and stability of the system. In addition, the established data transmission link ensures that data between different modules are exchanged in a timely and accurate manner, providing a solid foundation for subsequent data acquisition and analysis. This technological innovation helps to greatly improve the overall adjustability and accuracy, ensure that effective load testing can be carried out under different spatial depths and test conditions, and further promote the efficiency and reliability of multi-source data acquisition. Secondly, by obtaining different test depths of spatial targets and selecting the corresponding number of modular force conduction rods based on the test depths, the splicing is automatically completed in combination with the electromagnetic coupling interface. This process improves the flexibility and accuracy of the test device. The initial strain data is collected by the fiber grating strain sensor, and the splicing length error is calculated using the formula to ensure the precise fit between each modular force conduction rod. The application of the fiber grating strain sensor can monitor and record the strain data in real time, accurately reflect the changes and fit between modules, and thus optimize the force conduction process. If the splicing length error exceeds ±0.5mm, the fitting gap of the electromagnetic coupling interface will be automatically adjusted, effectively avoiding the influence of splicing error on force conduction and improving the splicing accuracy. This step not only reduces the error of manual operation through the automated splicing process, but also improves the accuracy and stability of the entire device, thereby improving the reliability of the experimental data and providing a more accurate basis for subsequent load transfer tests. This can achieve automatic adjustment in some environments with small spaces and complex conditions. The load is also applied according to the preset loading sequence through the jack load loading module. At the same time, the strain data of each modular force transmission rod and the settlement of the load-bearing plate are synchronously collected through the multi-source data acquisition module, providing sufficient data support for subsequent mechanical analysis. The combination of strain data and elastic modulus makes the calculation of the transferred load more accurate, ensuring that effective load evaluation can be performed at different spatial target test depths. In addition, based on the relationship between strain data and settlement, a three-dimensional depth-load-settlement relationship surface is constructed. The construction of this surface helps to comprehensively analyze and understand the mechanical properties of the test object, which not only can accurately capture the load and settlement characteristics at different depths, but also provide visualization and scientific basis for subsequent depth load optimization, settlement monitoring, etc., thereby improving the operability and accuracy of the test.Then, the depth-load-settlement three-dimensional relationship surface is used to identify and count the spatial depth mutation measurement points, and the loading rate is adjusted according to the strain change rate of the measurement point to ensure precise control of the entire loading process. When the strain change rate exceeds 0.5% / min, the loading rate is automatically adjusted. This design can effectively prevent data fluctuations or system errors caused by excessively fast loading rates. By automatically adjusting the loading rate, it is ensured that each spatial depth test can be carried out under the most suitable conditions, thereby optimizing the test effect. The innovation of this step lies in the ability to monitor and adjust the loading rate in real time, providing extremely high real-time feedback capabilities, which not only improves the data accuracy during the test process, but also meets the test needs of efficiency, convenience and accuracy, and responds to sudden strain changes in a timely manner, preventing abnormal situations during the test process. Finally, by fusing load-settlement data with strain data and calculating mechanical parameters using the inversion process, spatial load-deformation distribution diagrams of mechanical characteristics at different depth distributions are generated. This process achieves comprehensive coupling of depth and load, and utilizes multi-dimensional data fusion technology to enhance the depth and breadth of mechanical analysis. The mechanical parameters obtained through inversion calculation not only provide accurate physical parameter support for deep target testing, but also can clearly present the load-deformation characteristics at different spatial depths, providing effective visual data support for subsequent engineering design and theoretical research, thereby improving test efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0037] Figure 1 This is a schematic flow chart of the steps of the space rapid flat plate load test method of the present invention;

[0038] Figure 2 This is a schematic flow chart of the detailed steps for identifying and counting corresponding spatial depth mutation measurement points based on the depth-load-settlement three-dimensional relationship surface according to the present invention. DETAILED DESCRIPTION

[0039] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0040] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0041] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0042] To achieve this, please refer to Figures 1 to 2 The load test in the present invention is a field test in which loads are applied step by step to the foundation through a load-bearing plate of a certain area on site, and the change of foundation settlement with load is measured to determine the bearing capacity and deformation characteristics of the foundation soil.

[0043] The present invention provides a space rapid plate load test device, comprising a load-bearing plate, a depth-adjustable force conduction frame, a jack load loading module and a multi-source data acquisition module, wherein the load-bearing plate is connected to the lower end of the depth-adjustable force conduction frame through a magnetic adsorption structure, and the upper end of the depth-adjustable force conduction frame is connected to the jack load loading module through a spherical hinge mechanism, and the multi-source data acquisition module respectively establishes a wired or wireless data transmission link with the load-bearing plate, the depth-adjustable force conduction frame and the jack load loading module; the depth-adjustable force conduction frame is composed of at least three modular force conduction rods spliced ​​together through an electromagnetic coupling interface, wherein each of the modular force conduction rods has a built-in fiber grating strain sensor and a temperature compensation Module, the jack load loading module includes a multi-stage hydraulic cylinder group, a closed-loop control system, a jack and a concrete test block, and a steel beam is set on the ground plane above the jack load loading module for stacking concrete test blocks to apply load to measure the change of foundation settlement with load, and determine the bearing capacity and deformation of spatial foundation soil, the multi-source data acquisition module includes a distributed sensor network for synchronously collecting load, displacement and strain, wherein the control signal of the jack load loading module is transmitted to the actuator of the bearing plate through the signal line inside the depth-adjustable force conduction frame to form a closed-loop control loop; the electromagnetic coupling interface includes an electromagnetic suction cup and a magnetic positioning pin arranged at both ends of the modular force conduction rod, and the adsorption force of the electromagnetic suction cup is specifically Among them F c represents the adsorption force of the electromagnetic chuck, B represents the magnetic induction intensity of the electromagnetic chuck, M represents the effective adsorption area of ​​the electromagnetic chuck, μ0 represents the vacuum magnetic permeability, the taper of the magnetic locating pin is 1:50, and the fitting clearance is <0.02mm, so as to ensure that the coaxiality error of the modular force conduction rod to the stress conduction path is <0.05°.

[0044] In an embodiment of the present invention, in the construction of a space rapid flat plate load test device, a magnetic adsorption structure is adopted for the connection between the load-bearing plate and the depth-adjustable force conduction frame. The load-bearing plate is made of high-strength aluminum alloy with a size of 1200mm×800mm×50mm. 20 electromagnetic adsorption units are evenly distributed on its surface. Each unit has a diameter of 50mm and a height of 20mm. An excitation coil is arranged inside. The same number of magnetic adsorption plates are installed at the corresponding position of the lower end of the depth-adjustable force conduction frame. The material is pure iron with high magnetic permeability and a thickness of 15mm. When performing the connection operation, the depth-adjustable force conduction frame is first aligned with the top of the load-bearing plate, and the positioning pin is used for preliminary positioning to ensure that the center deviation of the two does not exceed 0.5mm. Then, the electromagnetic adsorption unit on the load-bearing plate is energized. According to the formula Among them F crepresents the adsorption force of the electromagnetic chuck, B represents the magnetic induction intensity of the electromagnetic chuck, M represents the effective adsorption area of ​​the electromagnetic chuck, μ0 represents the vacuum magnetic permeability, when the magnetic induction intensity B of the electromagnetic chuck is set to 1.2T, the effective adsorption area M is 0.002m 2 , vacuum magnetic permeability μ0=4π×10 -7 N / A 2, it can be calculated that the adsorption force of each electromagnetic adsorption unit is about 11465N. The 20 units work together to generate a strong adsorption force, which firmly adsorbs the depth-adjustable force conduction frame on the load-bearing plate to ensure the stability and reliability of the connection during the test; the depth-adjustable force conduction frame is composed of 5 modular force conduction rods, each of which is a hollow cylindrical structure with an outer diameter of 80mm, an inner diameter of 60mm, and a length of 500mm. The material is titanium alloy TC4. Electromagnetic coupling interfaces are set at both ends of the rod, one end is an electromagnetic suction cup, and the other end is a magnetic positioning pin. The electromagnetic suction cup is wound with 2000 turns of enameled wire and an external DC power supply. The magnetic induction intensity is controlled by adjusting the current, and the magnetic positioning The taper of the pin head is strictly processed to 1:50, the surface roughness Ra≤0.8μm, and the clearance with the pin hole of the adjacent rod is controlled within 0.015mm. When splicing, the first rod is placed vertically, and the second rod is lifted by a crane, so that the magnetic positioning pin is aligned with the pin hole of the first rod. Insert it under the initial action of gravity and magnetic force. When the insertion depth reaches 80% of the pin length, the electromagnetic suction cup is energized to generate adsorption force to connect the two rods tightly. The splicing of the five rods is completed by analogy. Due to the high-precision cooperation of the magnetic positioning pin, the coaxiality error of the stress conduction path of the entire force conduction frame is controlled within 0.03°, meeting the high-precision requirements of the test. Each modular force conduction rod is internally along the axis. Two fiber Bragg grating strain sensors with a diameter of 0.1mm are buried in the linear direction, and semiconductor temperature sensors are installed for real-time temperature compensation to ensure the accuracy of strain measurement; the upper end of the depth-adjustable force conduction frame is connected to the jack load loading module through a spherical hinge mechanism. The spherical hinge mechanism consists of a ball head and a ball socket. The ball head has a diameter of 60mm and an inner diameter of 62mm. The surface is hard chrome-plated and the hardness reaches HV800. The ball head is installed on the top of the force conduction frame, and the ball socket is fixed to the connecting seat of the jack load loading module. When connecting, align the ball head with the ball socket, and apply a pressure of 500N through the hydraulic auxiliary device to make the ball head completely embedded in the ball socket, forming a flexible and stable connection, allowing the force conduction frame to be connected in one direction. The jack's load-loading module swings within a certain range to accommodate different loading direction requirements. The first-stage cylinder has an inner diameter of 100mm, the second-stage cylinder has an inner diameter of 80mm, and the third-stage cylinder has an inner diameter of 60mm. Their strokes are 200mm, 150mm, and 100mm, respectively. The closed-loop control system, centered around a PLC controller, monitors the pressure within the hydraulic cylinder in real time via a pressure sensor (accuracy 0.1% FS) and a displacement sensor (resolution 0.01mm) measures piston stroke. When the load plate needs to be loaded, the PLC controller adjusts the proportional valve to control the oil flow and pressure in the hydraulic cylinder according to the preset loading program. The load is then transferred to the load plate via a spherical hinge mechanism and a depth-adjustable force transmission frame. Simultaneously, the signal line within the force transmission frame transmits the control signal to the actuator of the load plate. The actuator adjusts the fixed state of the load plate or the operation of the auxiliary device based on the signal, forming a closed-loop control loop.The multi-source data acquisition module builds a distributed sensor network to realize synchronous data acquisition. Four high-precision load sensors (range 0-500kN, accuracy 0.2% FS) are arranged on the load-bearing plate, respectively located at the four corners of the load-bearing plate, to measure the load on the load-bearing plate; a fiber Bragg grating strain sensor is installed in the middle of each modular force conduction rod of the depth-adjustable force conduction frame to monitor the strain of the rod in real time; at the same time, a laser displacement sensor (range 0-500mm, resolution 0.005mm) is installed below the steel beam on the jack load loading module to measure the loading displacement of the load-bearing plate, where the corresponding steel beam is also used to stack concrete test blocks to apply load to measure the foundation settlement with load. changes, and determine the bearing capacity and deformation of the spatial foundation soil; all sensors are connected to the data acquisition instrument through shielded cables or wireless transmission modules. The data acquisition instrument has a built-in high-precision A / D converter and the sampling frequency is set to 1000Hz. The data acquisition instrument pre-processes the collected load, displacement, strain and other data, including filtering, denoising and normalization, and then transmits them to the computer through the Ethernet interface for storage and analysis. For example, in a loading test, when the hydraulic cylinder is loaded at a speed of 5mm / min, the data acquisition module synchronously collects data from each sensor, and the computer draws the load-displacement curve and strain-time curve in real time, providing accurate data support for test data analysis and result evaluation.

[0045] Furthermore, the magnetic adsorption structure includes a permanent magnetic chuck array provided on the upper surface of the supporting plate and a magnetic base correspondingly provided at the lower end of the depth-adjustable force conduction frame. The adsorption force of the permanent magnetic chuck array is adjusted by an electric control coil, and the relationship between the adsorption force and the current of the electric control coil satisfies F y =k·I 2 , where F y represents the adsorption force of the permanent magnetic chuck array, I represents the current of the electric control coil, and k represents the material property attenuation coefficient of the electric control coil. The permanent magnetic chuck array is distributed in a regular hexagon, and the distance between adjacent chucks is 45-50 mm to ensure that the shear strength of the adsorption interface corresponding to the permanent magnetic chuck array is greater than 50 MPa under the maximum test load.

[0046] In the embodiment of the present invention, the construction of the magnetic adsorption structure strictly follows the design standards in the space rapid flat plate load test device. The upper surface of the load-bearing plate is machined to accurately mill out regular hexagonal array grooves for installing the permanent magnetic chuck. The permanent magnetic chuck consists of a neodymium iron boron permanent magnet and an electric control coil wound thereon. The permanent magnet has a diameter of 30 mm and a height of 20 mm. The electric control coil is evenly wound with 800 turns of enameled wire with a diameter of 0.5 mm. Through precision machining, a depth-adjustable force conduction force frame is made at the lower end. The magnetic base is made of DT4E electrical pure iron. The surface flatness error of the base is controlled within 0.01mm to ensure close fit with the permanent magnetic chuck. The permanent magnetic chuck array is distributed in a regular hexagon with a spacing of 48mm on the carrier plate. A total of 18 chucks are arranged. During the installation process, a high-precision laser positioning instrument is used to calibrate the position of each chuck to ensure that the array distribution accuracy is within the range of ±0.2mm. When connecting, align the magnetic base of the force transmission frame with the permanent magnetic chuck array of the carrier plate and use the auxiliary positioning pins for preliminary fixation. Subsequently, the electric control coil is powered by a DC power supply. According to the formula F y =k·I 2 , where F y represents the adsorption force of the permanent magnetic suction cup array, I represents the current of the electric control coil, k represents the material property attenuation coefficient of the electric control coil (for example, k=50N / A). When the current I is set to 10A, a single permanent magnetic suction cup generates an adsorption force of 5000N, and 18 suction cups work together to form a total adsorption force of 90000N. After actual testing, under the maximum test load condition, the shear strength of the adsorption interface reaches 55MPa, far exceeding the design requirement of 50MPa, effectively ensuring the stability of the connection between the load-bearing plate and the force conduction frame, and preventing relative displacement during loading.

[0047] Furthermore, the fiber Bragg grating strain sensor is arranged along the axis direction of the modular force transmission rod, and each modular force transmission rod is provided with at least three measuring points. The relationship between the center wavelength drift and strain of the fiber Bragg grating strain sensor satisfies Where Δλ represents the wavelength drift of the fiber Bragg grating strain sensor, λ0 represents the central wavelength of the fiber Bragg grating strain sensor, and p e represents the fiber elasto-optic coefficient of the fiber Bragg grating strain sensor, ε represents the strain of the modular force transmission rod, and the temperature compensation module corrects the wavelength drift caused by temperature in real time through the thermistor attached to the surface of the modular force transmission rod.

[0048] In the embodiment of the present invention, the arrangement and calibration of the fiber Bragg grating strain sensor strictly follow the standard process for strain monitoring of the modular force transmission rod. For each modular force transmission rod with a length of 500mm, three fiber Bragg grating strain sensors are precisely installed in the axial direction using a special fixture. The sensor installation positions are respectively located 100mm, 250mm, and 400mm above the lower end of the rod. Epoxy resin glue is used for pasting, and the thickness of the paste is controlled between 0.1-0.2mm to ensure that the sensor is tightly combined with the rod without affecting the mechanical properties of the rod. The central wavelength of the fiber Bragg grating strain sensor is 1550nm, and the optical fiber elastic coefficient p is 0. e =0.22. After the installation is completed, the sensor is initially calibrated and the central wavelength in the unstressed state is recorded. When the rod is subjected to external force and produces strain, according to the formula If a rod generates a strain of 1000με, the wavelength drift can be calculated. The temperature compensation module uses a negative temperature coefficient thermistor (NTC) to achieve real-time correction. A 10kΩ NTC thermistor is attached near each fiber Bragg grating strain sensor. The temperature change is converted into a voltage signal through a bridge circuit. When the ambient temperature changes, the NTC resistance value changes. After circuit calculation, the wavelength drift caused by temperature is reversely compensated to ensure that the strain measurement accuracy is controlled within ±10με, providing reliable protection for the accuracy of the test data.

[0049] Furthermore, the modular force transmission rod adopts a variable cross-section hollow tubular structure, the outer diameter of which decreases linearly from D at the lower end to 0.8D at the upper end, where D is the length of the outer diameter of the cross section, and the wall thickness is uniformly t. The cross-sectional inertia moment of the variable cross-section hollow tubular structure is distributed along the axis I(x) and satisfies Wherein, x represents the distance from the lower end of the modular force transmission rod, and L represents the length of the modular force transmission rod.

[0050] In the embodiment of the present invention, a variable-section hollow tubular structure is manufactured by a modular force-conducting rod using a CNC machining center. The outer diameter of the lower end of the rod is set to 80 mm, and it decreases linearly upward at a constant slope. The outer diameter is reduced to 64 mm (i.e., 0.8 times the outer diameter of the lower end) at 400 mm from the lower end, and the wall thickness is uniformly maintained at 10 mm. During the machining process, a five-axis CNC system is used to accurately control the tool path, and an outer diameter dimension is detected every 1 mm of machining to ensure that the outer diameter dimension error is controlled within 0.1 mm. According to the formula (where D is the length of the outer diameter of the cross-section, the uniform wall thickness is t, x represents the distance from the lower end of the modular force transmission rod, and L represents the length of the modular force transmission rod). Through this variable cross-section design, the rod can reduce its own weight by 15% while meeting the strength requirements. When subjected to bending loads, the stress distribution is more uniform and the maximum stress value is reduced by 20%, effectively improving the mechanical properties of the force transmission rod and the overall efficiency of the test device.

[0051] Furthermore, the present invention also provides a space rapid flat plate load test method, which is implemented based on the space rapid flat plate load test device as described above. In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic flow chart of the steps of the space rapid plate load test method of the present invention. In this example, the space rapid plate load test method includes the following steps:

[0052] S10: Connecting the depth-adjustable force conduction frame to the load-bearing plate through a magnetic adsorption structure, and docking the jack load loading module with the depth-adjustable force conduction frame using a spherical hinge mechanism to establish a data transmission link with the multi-source data acquisition module;

[0053] In the embodiment of the present invention, during the construction of the space rapid flat plate load test device, the components are first connected and the data link is established. The bearing plate is made of high-strength aluminum alloy, and the surface is processed with regular hexagonal array grooves for installing permanent magnetic suction cups. Each permanent magnetic suction cup has a diameter of 50mm and a height of 20mm. It has a built-in excitation coil. The spacing between adjacent suction cups is ensured to be 48mm through precise positioning. The depth is adjustable. The magnetic base is installed at the lower end of the force conduction frame. The material is high magnetic permeability pure iron, the thickness is 15mm, and the surface flatness error is controlled within 0.01mm. The force conduction frame is hoisted above the bearing plate, and the laser positioning system is used to ensure that the center deviation between the two does not exceed 0.5mm. After the auxiliary positioning pin is initially fixed, a 10A current is passed through the electric control coil of the permanent magnetic suction cup. According to the formula F y =k·I 2 , where F yRepresents the adsorption force of the permanent magnetic suction cup array, I represents the current of the electric control coil, k represents the material characteristic attenuation coefficient of the electric control coil (for example, k = 50N / A), which will generate 5000N adsorption force. The 18 suction cups work together to form a total adsorption force of 90000N, achieving a firm connection. The jack load loading module is docked with the force transmission frame through a spherical hinge mechanism. The ball head of the spherical hinge has a diameter of 60mm, the inner diameter of the ball socket is 62mm, and the surface is hard chrome plated. A hydraulic device is used to apply a pressure of 500N to fully embed the ball head into the ball socket, forming a flexible connection that can rotate 360°, and the angle deviation is controlled. Within ±0.1°, the multi-source data acquisition module includes a distributed sensor network and a data acquisition instrument. Load sensors (range 500kN, accuracy 0.1% FS) are installed at the four corners of the load-bearing plate, fiber Bragg grating strain sensors are arranged on each rod of the force conduction frame, and displacement sensors (resolution 0.01mm) are installed on the jack load loading module. All sensors are connected to the data acquisition instrument through shielded cables or wireless transmission modules. The data acquisition instrument uses a 16-bit A / D converter with a sampling frequency set to 1000Hz. A data transmission link is established with the industrial computer through the Ethernet interface to ensure real-time and accurate data transmission.

[0054] S20: Obtain different space target test depths H, and select a corresponding number of modular force transmission rods based on the space target test depth H and automatically complete the splicing in combination with the electromagnetic coupling interface. After splicing, the initial strain data ε0 is collected through the fiber Bragg grating strain sensor, and the formula is used Calculate the splicing length error, where n represents the total number of modular force transmission members, specifically l i represents the length of the ith modular force transmission member, E i represents the elastic modulus of the i-th modular force transmission rod. If the splicing length error exceeds ±0.5 mm, the electromagnetic coupling interface is triggered to adjust the corresponding fitting clearance between the modular force transmission rods.

[0055] In the embodiment of the present invention, assuming that the space target test depth H is 2300 mm and the standard length L of each modular force transmission rod is 500 mm, the number of rods to be spliced ​​is The rods are grabbed by a robotic arm, and the visual positioning system is used to ensure that the deviation between the magnetic positioning pin and the pin hole does not exceed 0.05mm. When the insertion depth reaches 80% of the pin length, the electromagnetic suction cup is energized to generate adsorption force, and the adjacent rods are tightly connected through the electromagnetic coupling interface. After the splicing is completed, the fiber Bragg grating strain sensor in each rod collects the initial strain data ε0. Assuming that the initial strains of the 1st to 5th rods are 20με, 25με, 18με, 22με, and 21με respectively, the elastic modulus E i Both are 110GPa, length l i Both are 500mm, according to the formula The calculated splicing length error ΔL = 0.523 mm. If this error exceeds the ±0.5 mm threshold, the electromagnetic coupling interface adjustment is automatically triggered. By fine-tuning the electromagnetic chuck current, the adjacent rods produce a ±0.01 mm displacement. The strain data is then re-collected and the error is calculated until the error is controlled within ±0.5 mm.

[0056] S30: Apply the load according to the preset loading sequence through the jack load loading module, and after the applied load is stable, use the multi-source data acquisition module through the data transmission link to synchronously collect the strain data ε of each modular force conduction rod on the depth-adjustable force conduction force frame i and the settlement S of the load-bearing plate, and based on the strain data ε i and elastic modulus E i By formula Calculate the transfer load at the corresponding space target test depth H, where A i represents the cross-sectional area of ​​the i-th modular force transmission rod; constructs a depth-load-settlement three-dimensional relationship surface based on the transferred load at the corresponding spatial target test depth H and the settlement S of the load-bearing plate;

[0057] In the embodiment of the present invention, the load is applied by the jack load loading module according to the preset loading sequence. The loading sequence is set to 0-50kN at a loading rate of 10kN / min, 50-100kN at a loading rate of 15kN / min, and 100-150kN at a loading rate of 20kN / min. Each load level is maintained for 5 minutes. When the load fluctuation does not exceed ±1%, the load is considered stable. After the load is stable, the multi-source data acquisition module synchronously collects data. Assuming that at a depth of H = 2300mm, the strain data ε of each rod i They are 100με, 120με, 110με, 130με, and 125με respectively, and the cross-sectional area of ​​the rod is A. i Both are 2198mm 2 , elastic modulus E i Both are 110GPa. According to the formula The corresponding transferred load calculated at the test depth H of the space target is approximately 121.9 kN. At the same time, the settlement S of the load-bearing plate is measured by the displacement sensor to be 4.2 mm. By changing the test depth H (such as 1800 mm, 2800 mm) and the load p (such as 80 kN, 160 kN), the above process is repeated to obtain multiple sets of depth-load-settlement data points. These discrete data points are then fitted into a continuous three-dimensional depth-load-settlement relationship surface using a three-dimensional interpolation algorithm. The surface accuracy is controlled within ±0.1 mm to ensure that the settlement characteristics under different depths and load conditions can be accurately reflected.

[0058] S40: identifying and counting corresponding spatial depth mutation measurement points based on the depth-load-settlement three-dimensional relationship surface, and calculating corresponding strain change rates based on the strain data corresponding to the spatial depth mutation measurement points; when the strain change rate exceeds 0.5% / min, automatically adjusting the loading rate of the applied load through the jack load loading module, and regenerating the load-settlement data corresponding to the spatial depth mutation measurement points;

[0059] In the embodiment of the present invention, after obtaining the depth-load-settlement three-dimensional relationship surface, the curvature analysis algorithm is used to identify the spatial depth mutation measurement points, the surface is meshed, the mesh size is set to 10mm×10mm, and the Gaussian curvature at each mesh node is calculated. When the absolute value of the Gaussian curvature of a node exceeds 0.05mm, the Gaussian curvature of the node is calculated. -1 For example, at a depth of 2100mm and a load of 130kN, the Gaussian curvature of the grid node is 0.065mm. -1 , is identified as a mutation measurement point, and the strain data corresponding to the mutation measurement point is extracted. The strain change rate is calculated through time series analysis. Within 1 minute, the strain data is collected at a sampling frequency of 10 Hz, and a total of 60 data points are obtained. Assuming that the initial strain is 150 με and changes to 152.25 με after 1 minute, the strain change rate is: (152.25-150) / 150×100%÷1min=1.5% / min. This value exceeds the threshold of 0.5% / min, triggering the automatic adjustment of the jack load loading module, that is, the jack load loading module starts the loading rate adjustment mechanism, setting the adjustment coefficient α=0.3, the preset threshold β=0.5% / min, and the current loading rate v k =15kN / min, adjust the formula according to the loading rate The calculation results are 8.235 kN / min. The jack load loading module adjusts the loading rate from 15 kN / min to 8.235 kN / min and continues to apply the load at the new loading rate. At the same time, the load-settlement data of the spatial depth mutation measuring point during the subsequent loading process are recollected, and the settlement amount is recorded every time the 2 kN increase is increased to ensure that the data collection accuracy is within ±0.02 mm.

[0060] S50: The load-settlement data of the spatial depth mutation measurement point are integrated with the corresponding strain data, and the mechanical parameters corresponding to the space target test are calculated through an inversion process; the spatial load-bearing visualization of the mechanical parameters corresponding to the space target test is performed based on the corresponding space target test depth to generate the spatial load-bearing deformation distribution of the mechanical characteristics corresponding to different depth distributions.

[0061] In an embodiment of the present invention, the load-settlement data and strain data of the mutation measuring point are fused to establish a three-dimensional coordinate system with depth as the Z axis, load as the X axis, and settlement as the Y axis. The strain data is mapped to a color attribute. For example, a strain of 150 με corresponds to blue, and a strain of 200 με corresponds to red. The continuous change of the strain value is represented by the gradient color. The mechanical parameters are calculated by the inversion algorithm. Based on the theory of elastic mechanics, a load-settlement-strain relationship model is established: Where μ is the Poisson's ratio and B represents the diameter of the load-bearing plate. The least squares method was used to fit the measured data, and the iterative calculations were performed until the error was less than 1%. For example, at the sudden change measurement point at a depth of 2100 mm, 10 iterative calculations yielded an elastic modulus E = 115 GPa and a Poisson's ratio μ = 0.32. Based on the spatial target test depth, the spatial load-bearing visualization of the mechanical parameters was performed. The test space was divided into 5 mm × 5 mm × 5 mm cubic units using voxelization, and each unit was assigned a mechanical parameter value corresponding to the depth. A ray casting algorithm was used to render the three-dimensional scene. The elastic modulus was represented by color saturation, and the deformation was represented by the degree of mesh distortion. For example, high elastic modulus areas were displayed as bright red, and low elastic modulus areas were displayed as dim blue. In areas with deformation exceeding 5 mm, the mesh distortion was increased by 20%. This ultimately generated a realistic spatial load-bearing deformation distribution of mechanical properties with a spatial resolution of 5 mm, ensuring that even subtle changes in mechanical properties could be clearly visualized.

[0062] Further, such as Figure 2 As shown, the method of identifying and counting corresponding spatial depth mutation measurement points based on the depth-load-settlement three-dimensional relationship surface includes the following steps:

[0063] S401: Calculate the Gaussian curvature and the average curvature at each point based on the depth-load-settlement three-dimensional relationship surface;

[0064] In an embodiment of the present invention, after obtaining the depth-load-settlement three-dimensional relationship surface, the curvature of the points on the surface is calculated, and the three-dimensional relationship surface is discretized into a grid with a spacing of 10mm×10mm×10mm. Each grid intersection is a calculation point, and the entire surface forms a total of 100×100×80 calculation points. The finite difference method is used to calculate the Gaussian curvature and the average curvature at each point. For a certain calculation point, by obtaining the depth, load, and settlement data of the six adjacent points around it (front and back, left and right, and up and down directions), a local quadratic surface fitting equation is constructed. For example, at a calculation point at a depth of 2000mm, a load of 100kN, and a settlement of 3mm, the quadratic surface equation z=ax is fitted using the data of its surrounding points (such as adjacent points at a depth of 1990mm, a load of 90kN, and a settlement of 2.5mm). 2 +bxy+cy 2+dx+ey+f, according to the differential geometry formula, we can get a system of equations containing 6 equations: Solve the equations by the least squares method to obtain the values ​​of coefficients a, b, c, d, e, and f, thereby determining the local quadratic surface fitting equation. Next, calculate the Gaussian curvature K and mean curvature C according to the differential geometry formula. First, calculate the partial derivative, the first partial derivative z x =2ax+by+d,z y =bx+2cy+e; quadratic partial derivative z xx =2a,z xy =b,z yy =2c, calculate the coefficients of the first fundamental form: Coefficients of the second fundamental form: Then, the Gaussian curvature is calculated according to the formula Mean curvature Substituting the coefficient values ​​calculated previously into the corresponding formulas, we can obtain the Gaussian curvature and mean curvature values ​​of the calculation point. By performing the above steps on all calculation points on the entire surface, we can obtain the curvature data corresponding to each point, laying the foundation for the subsequent identification of spatial depth mutation measurement points through curvature changes.

[0065] S402: Calculate the curvature change rate at each point using the Gaussian curvature and the average curvature at each point. When the curvature change rate at the point is greater than 0.3 times the maximum Gaussian curvature in the depth-load-settlement three-dimensional relationship surface, mark the point as a candidate point for curvature mutation.

[0066] In the embodiment of the present invention, the curvature change rate at each point is calculated based on the previously calculated Gaussian curvature and average curvature of each point. For each calculated point, 9 points around it (3×3×1 neighborhood range) are selected with the point as the center, and the difference ΔK between the Gaussian curvature of the point and each point in the neighborhood is calculated. i The difference between the mean curvature and the i , take the maximum absolute value of the Gaussian curvature difference max|ΔK i The maximum absolute value of the difference between | and the mean curvature max|ΔC i For example, the Gaussian curvature at a certain point is 0.02mm -1 The maximum Gaussian curvature in its neighborhood is 0.03 mm. -1 , minimum 0.01mm -1 , then the Gaussian curvature change at this point is 0.02mm -1 ; The average curvature at this point is 0.015mm -1 The maximum average curvature in the neighborhood is 0.02mm -1 , minimum 0.01mm -1, the average curvature change is 0.01mm -1 , the Gaussian curvature change and the mean curvature change are weighted and summed according to weights 0.6 and 0.4, and the comprehensive curvature change ΔQ = 0.6 × max | ΔK is obtained. i |+0.4×max|ΔC i |, then divide the comprehensive curvature change by the time interval (assuming that the data collection interval is 1 minute each time) to obtain the curvature change rate of the point. At the same time, find the maximum Gaussian curvature in the entire depth-load-settlement three-dimensional relationship surface, assuming it is 0.08mm -1 , when the curvature change rate of a point is greater than 0.3 times the maximum Gaussian curvature (ie greater than 0.024mm -1 / min), the point is marked as a candidate point of curvature mutation. For example, the curvature change rate of a point is calculated to be 0.026mm. -1 / min, exceeding the threshold, is marked as a candidate point for curvature mutation, and in this way, points with spatial depth mutation are screened out.

[0067] S403: Expand the spatiotemporal neighborhood of the curvature mutation candidate point by selecting a spherical neighborhood with a radius of 0.5m and centering on the curvature mutation candidate point, and count the density of the candidate points in the spherical neighborhood. When the density exceeds 5 / m 3 When , the spherical neighborhood is determined as a candidate region of spatial depth mutation point;

[0068] In an embodiment of the present invention, a spatiotemporal neighborhood expansion analysis is performed on the marked curvature mutation candidate points, and a spherical neighborhood with a radius of 0.25m is constructed with each curvature mutation candidate point as the center. For example, for a curvature mutation candidate point located at a depth of 2100mm, a load of 110kN, and a settlement of 3.5mm, a spherical space range with a radius of 0.5m and the point as the center is established. The number of all curvature mutation candidate points in the spherical neighborhood is counted, and the candidate point density is calculated. The formula is: candidate point density = number of candidate points in the spherical neighborhood / spherical neighborhood volume. Assuming that there are 8 curvature mutation candidate points in a spherical neighborhood, according to the spherical volume formula The volume of the spherical neighborhood is approximately 0.0654m 3 , then the candidate point density is about 15.286 / m 3 , more than 5 / m 3 The spherical neighborhood is determined as a candidate region for spatial depth mutation points by using a judgment threshold. By traversing all candidate points for curvature mutation, the entire surface is analyzed and multiple candidate regions for spatial depth mutation points are determined.

[0069] S404: Counting corresponding spatial depth mutation measurement points based on the spatial depth mutation point candidate regions.

[0070] In an embodiment of the present invention, after determining the candidate region for the spatial depth mutation point, the partial derivatives of each spatial point in the region along the depth direction and the load direction are calculated, so that the spatial points in the candidate region are gridded according to the depth and load directions, and the grid spacing is set to 5mm×5kN. For each spatial point, its gradient along the depth direction and the load direction gradient are calculated, thereby obtaining the depth direction gradient and load direction gradient data of each spatial point in the entire candidate region. Then, according to the characteristics of the space target test, the depth direction gradient threshold is set to 0.008mm / mm, and the load direction gradient threshold is set to 0.003mm / kN. The depth direction gradient and load direction gradient of each spatial point in the candidate region are subjected to mutation point identification. For example, For the spatial point at a depth of 2100mm and a load of 110kN, its depth direction gradient is 0.01mm / mm, and the load direction gradient is 0.004mm / kN. Their absolute values ​​are calculated respectively, and these two absolute values ​​are compared with the corresponding directional gradient thresholds. Among them, since 0.01mm / mm>0.008mm / mm and 0.004mm / kN>0.003mm / kN, both conditions are met at the same time, so this point is determined as a spatial depth mutation measurement point. For all spatial points in the candidate area, they are judged according to the above method, and all spatial depth mutation measurement points in the candidate area are accurately identified, providing accurate data support for subsequent mechanical parameter analysis and spatial load visualization.

[0071] Furthermore, the step of identifying and counting corresponding spatial depth mutation points based on the spatial depth mutation point candidate regions includes the following steps:

[0072] According to each spatial point in the candidate region of the spatial depth mutation point, partial derivatives are calculated along the depth direction and the load direction to obtain the depth direction gradient and the load direction gradient of each spatial point in the candidate region;

[0073] In an embodiment of the present invention, after determining the candidate region of the spatial depth mutation point, the partial derivative of each spatial point in the region is calculated along the depth direction and the load direction, so that the spatial points in the candidate region are gridded according to the depth and load direction, and the grid spacing is set to 5mm×5kN. For each spatial point, the central difference method is used to calculate the partial derivative. For example, for a spatial point located at a depth of 2100mm and a load of 110kN, two adjacent points 5mm in front and behind are selected along the depth direction (depth 2095mm, load 110kN and depth 2105mm, load 110kN), and two adjacent points 5kN in the left and right are selected along the load direction (depth 2100mm, load 105kN and depth 2100mm, load 115kN). When calculating the depth gradient, the settlement data of the two adjacent points are obtained. Assuming that the settlement at the depth of 2095mm is 3.45mm and the settlement at the depth of 2105mm is 3.55mm, the depth gradient of the point is: When calculating the load direction gradient, obtain the settlement data of the corresponding adjacent points. Assuming that the settlement at a load of 105kN is 3.48mm and the settlement at a load of 115kN is 3.52mm, the load direction gradient at this point is: The above calculation process is repeated for each spatial point in the candidate area, and finally the depth direction gradient and load direction gradient data of each spatial point in the entire candidate area are obtained, which provides a basis for subsequent mutation point identification.

[0074] Preferably, the corresponding depth direction gradient threshold and load direction gradient threshold are set according to the space target test, and the depth direction gradient and load direction gradient of each spatial point in the candidate area are identified as mutation points based on the corresponding depth direction gradient threshold and load direction gradient threshold. If the absolute value corresponding to the depth direction gradient of the spatial point is greater than the depth direction gradient threshold and the absolute value corresponding to the load direction gradient is greater than the load direction gradient threshold, it is determined as the corresponding spatial depth mutation measurement point.

[0075] In the embodiment of the present invention, according to the characteristics of the space target test, the depth direction gradient threshold is set to 0.008mm / mm, and the load direction gradient threshold is set to 0.003mm / kN. The depth direction gradient and load direction gradient of each spatial point in the candidate area are subjected to mutation point identification. For example, for the spatial point at a depth of 2100mm and a load of 110kN, the depth direction gradient is 0.01mm / mm and the load direction gradient is 0.004mm / kN. The absolute values ​​are calculated respectively, and the two absolute values ​​are compared with the corresponding direction gradient thresholds respectively. Since 0.01mm / mm>0.008mm / mm and 0.004mm / kN>0.003mm / kN, which meets both conditions. Therefore, the point is determined as a spatial depth mutation measurement point. For all spatial points in the candidate area, the above method is used for judgment. For example, the depth direction gradient of a point is 0.007mm / mm, and the load direction gradient is 0.005mm / kN. Although the absolute value of the load direction gradient is greater than the threshold, the absolute value of the depth direction gradient is less than the threshold. It does not meet the condition of being greater than the threshold at the same time, so it is not determined as a mutation measurement point. Through this strict dual-threshold judgment method, all spatial depth mutation measurement points in the candidate area can be accurately identified, providing accurate data support for subsequent mechanical parameter analysis and spatial load visualization.

[0076] Furthermore, the automatic adjustment of the loading rate of the applied load is calculated by a loading rate adjustment formula, wherein the loading rate adjustment formula is specifically:

[0077]

[0078] Among them, v k+1 represents the loading rate at the k+1th moment, v k represents the loading rate at the kth moment, exp represents the exponential function, δ d represents the strain change rate corresponding to the spatial depth mutation measurement point, α represents the adjustment coefficient, and its value range is 0.2-0.5, and β represents the preset threshold.

[0079] The present invention obtains a loading rate adjustment formula by using a specific mathematical model and verifying that the loading rate of the applied load is automatically adjusted. The formula fully considers the loading rate v at the k+1th moment. k+1 , the loading rate v at the kth moment k , exponential function exp, the strain change rate δ corresponding to the spatial depth mutation measurement point d , adjustment coefficient α, its value range is 0.2-0.5, preset threshold β, according to the loading rate v at the k+1th moment k+1 The mutual correlation between the above parameters constitutes a functional relationship This formula dynamically adjusts the loading rate based on the real-time rate of strain change. By incorporating the strain rate as a feedback variable, the process achieves adaptive control of the applied load, ensuring that the loading rate can be promptly reduced in the event of large fluctuations to avoid potential material damage or experimental errors, which is crucial for safety in engineering practice. The design of the adjustment coefficient α and threshold β used in the formula ensures that the loading rate adjustment is sensitive to strain changes. When the strain rate exceeds 0.5% / min, the loading rate will drop significantly. This rapid response effectively prevents sudden strain changes caused by excessive loading, maintaining the stability and reliability of the test process. The loading rate adjustment formula uses an exponential decay, meaning that the loading rate adjustment decreases rapidly when the strain rate approaches the preset threshold β. This approach prevents drastic fluctuations while ensuring a smoother loading process. The exponential function processing mitigates large load fluctuations, making it suitable for many practical applications. In addition, the adjustment coefficient α can be selected in the range of 0.2-0.5, which provides flexibility and adjustability of the formula. Researchers can adjust this coefficient according to experimental requirements or material properties to optimize various strain response situations. This degree of freedom in the design makes this adjustment scheme widely applicable to different materials and loading conditions.

[0080] Furthermore, the specific implementation process of the inversion process is:

[0081] Obtain the load data, settlement data, and strain data corresponding to the sudden change measurement points at different spatial depths, and use the dynamic time warping algorithm to synchronize the timestamps of the corresponding load data, settlement data, and strain data to eliminate the time offset caused by the sampling frequency difference and ensure that the time synchronization error is less than 10ms, so as to obtain the time-synchronized load data, settlement data, and strain data;

[0082] In an embodiment of the present invention, after completing the identification of the spatial depth mutation measuring point, the load data, settlement data and strain data corresponding to each measuring point are obtained. Assuming that a total of 10 spatial depth mutation measuring points are identified, the load data of each measuring point is collected by the jack load loading module at a frequency of 1 Hz, the settlement data is collected by the displacement sensors at the four corners of the supporting plate at a frequency of 2 Hz, and the strain data is collected by the fiber Bragg grating strain sensor at a frequency of 5 Hz. Since the sampling frequencies of the data are different, there is a time offset. A dynamic time warping algorithm is used for timestamp synchronization. Taking the first spatial depth mutation measuring point as an example, the load data, settlement data and strain data of the measuring point are respectively organized into time series arrays. The dynamic time warping algorithm finds the optimal time alignment path by calculating the similarity distance between different time series. The algorithm first constructs a distance matrix. The matrix elements represent the differences between data at different time points, such as the square of the difference between the load data value at a certain moment and the settlement data value at the corresponding moment. Then, a dynamic programming method is used. Starting from the upper left corner of the matrix, the cumulative distance is calculated to find a path with the minimum cumulative distance from the upper left corner to the lower right corner. This path is the basis for time alignment. After calculation, the timestamps of each data are adjusted to align them in time. The above operation is repeated for all 10 measuring points. Calibration is performed using high-precision time synchronization equipment to ensure that the time synchronization error between any two sets of data is less than 10ms. For example, the time difference between the load data and the strain data at the fifth measuring point before synchronization is 150ms. After synchronization, the time difference is reduced to 8ms. Accurate load data, settlement data, and strain data after time synchronization are obtained, providing a reliable basis for subsequent analysis.

[0083] Preferably, the corresponding spatial test mechanical response model is constructed by combining the elastic half-space theory with the Burgers viscoelastic model, wherein the model parameters include the elastic modulus E, Poisson's ratio μ, Kelvin body elastic modulus E k , Kelvin viscosity coefficient η k and Maxwell volume viscosity coefficient η m ;

[0084] In the embodiment of the present invention, a space test mechanical response model is constructed based on the elastic half-space theory combined with the Burgers viscoelastic model. The load-bearing plate adopts a circular aluminum plate with a diameter of B = 800 mm. According to the test material properties and actual working conditions, the model parameters are preliminarily set. The initial value of the elastic modulus E is set to 70 GPa (the common elastic modulus range of aluminum plates), the Poisson's ratio μ is set to 0.33, and the Kelvin body elastic modulus E is set to 0. k Assuming 50GPa, Kelvin viscosity coefficient η k Set to 1×10 8 Pa·s, Maxwell bulk viscosity coefficient η m Set to 2×109 Pa·s. During model construction, the interaction between the load-bearing plate and the test space was considered to be a circular load acting on an elastic half-space. For the viscoelastic component, the Burgers model consists of a Maxwell model and a Kelvin model in series, which respectively describe the material's transient elastic response, viscous flow characteristics, and delayed elastic response. Through the secondary development interface of the finite element analysis software, the elastic half-space theoretical formulas and the constitutive equations of the Burgers model were embedded in the calculation program to construct a complete spatial test mechanical response model. This model can comprehensively consider the mechanical behavior of the material in both the elastic and viscoelastic stages.

[0085] Preferably, the time-synchronized load data, settlement data, and strain data are input into the spatial test mechanical response model to derive the corresponding load-settlement and strain inversion calculation formulas, where for the elastic stage, the relationship between the settlement S and the transferred load P is: Where B represents the diameter of the supporting plate; for the viscoelastic stage, the integral form of the Burgers viscoelastic model is used to describe the change of strain data with time τ

[0086] In the embodiment of the present invention, by inputting the time-synchronized load data, settlement data, and strain data into the constructed spatial test mechanical response model, in the elastic stage, for the third spatial depth mutation measurement point, it is known that the transferred load P = 120kN, the bearing plate diameter B = 800mm, and the Poisson's ratio μ = 0.33, these data are substituted into the formula Assuming that the elastic modulus E takes the initial value of 70GPa set by the model, the calculated settlement S≈1.23mm. In the viscoelastic stage, taking the seventh spatial depth mutation measurement point as an example, at a certain time τ=60s, the transferred load P=150kN, according to the integral form of the Burgers viscoelastic model Substitute the initial model parameters E k =50GPa,η k =1×10 8 Pa·s,η m =2×10 9 Pa·s, E=70GPa, the calculated strain value ε(60)≈4.98×10 -6 By deriving these formulas, a quantitative relationship between load, settlement and strain is established, providing a theoretical basis for subsequent inverse analysis.

[0087] Preferably, based on the inversion calculation formula of load-settlement and strain and combined with the spatial test mechanical response model, the load data, settlement data and strain data after time synchronization are subjected to model prediction inversion analysis to set the relative error between the measured data and the model prediction data as the objective function, and optimize the corresponding spatial test mechanical response model based on the objective function, and inversely optimize and output the mechanical parameters corresponding to the space target test.

[0088] In the embodiment of the present invention, based on the derived load-settlement and strain inversion calculation formula, combined with the spatial test mechanical response model, the measured data of 10 spatial depth mutation measurement points after time synchronization are subjected to model prediction inversion analysis, and the relative error between the measured data and the model prediction data is set as the objective function. Where f represents the objective function, q represents the total number of spatial depth mutation measurement points, represents the predicted settlement value of the jth spatial depth mutation measurement point, represents the measured settlement value of the jth spatial depth mutation measurement point, represents the predicted strain value of the jth spatial depth mutation measurement point, Represents the measured strain value of the jth spatial depth mutation measurement point. Taking the second spatial depth mutation measurement point as an example, assuming that the model predicts the settlement value Measured settlement value Predicted strain value Measured strain value Substitute these values ​​into the objective function, perform the same calculation on all 10 measuring points and sum them up to get the current value of the objective function, and use the genetic algorithm to calculate the parameters of the spatial test mechanical response model (elastic modulus E, Poisson's ratio μ, E k ,η k ,η m ) is optimized. In each iteration, the parameter combination is selected, crossed and mutated according to the objective function value to generate a new parameter combination. After 500 iterative calculations, when the objective function value no longer changes significantly, the iteration is stopped. Finally, the inversion optimization outputs the mechanical parameters corresponding to the space target test, providing accurate parameters for the evaluation of space mechanical performance.

[0089] Furthermore, the objective function is specifically:

[0090]

[0091] Among them, f represents the objective function, q represents the total number of spatial depth mutation measurement points, represents the predicted settlement value of the jth spatial depth mutation measurement point, represents the measured settlement value of the jth spatial depth mutation measurement point, represents the predicted strain value of the jth spatial depth mutation measurement point, Represents the measured strain value of the jth spatial depth mutation measurement point.

[0092] The present invention obtains an objective function by using a specific mathematical model and verifying it, which is used to optimize the corresponding spatial test mechanical response model. The function fully considers the total number q of spatial depth mutation measurement points, the predicted settlement value of the jth spatial depth mutation measurement point, and the predicted settlement value of the jth spatial depth mutation measurement point. The measured settlement value at the jth spatial depth mutation measurement point The predicted strain value of the j-th spatial depth mutation measurement point The measured strain value of the jth spatial depth mutation measurement point According to the objective function f and the above parameters, a functional relationship is formed This objective function effectively evaluates the model's fitting performance by calculating the relative error between predicted and measured values. By minimizing the objective function, the optimization algorithm adjusts the model parameters to ensure that the predicted results are as close as possible to the observed data. This feedback mechanism ensures the model's applicability and accuracy under different conditions, thereby improving the reliability of the mechanical response model for spatial testing. The sum-of-squares form of the relative error increases the model's sensitivity to large errors. Large deviations significantly affect the value of the objective function, resulting in the model prioritizing data points with large prediction errors during the optimization process. This effect helps identify and correct model problems, improving the model's applicability. Furthermore, the objective function considers not only the errors in the settlement data but also the errors in the strain data. This multidimensional evaluation system enhances the model's comprehensiveness in describing complex physical phenomena, ensuring that conditions at different measurement points are appropriately reflected. Consequently, it can more comprehensively capture the response characteristics of a material or structure under load. In practical applications, the test environment and material properties will change over time. Through relative error feedback, the parameters in the model (such as elastic modulus, Poisson's ratio, etc.) can be adjusted in a timely manner to make the model better adapt to new measurement data. This dynamic update capability is of great significance for long-term monitoring and maintenance.

[0093] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced within the present invention.

[0094] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A space rapid flat plate load test device, characterized by: It includes a bearing plate, a depth-adjustable force conduction frame, a jack load loading module and a multi-source data acquisition module. The bearing plate is connected to the lower end of the depth-adjustable force conduction frame through a magnetic adsorption structure, and the upper end of the depth-adjustable force conduction frame is connected to the jack load loading module through a spherical hinge mechanism. The multi-source data acquisition module establishes a wired or wireless data transmission link with the bearing plate, the depth-adjustable force conduction frame and the jack load loading module respectively; the depth-adjustable force conduction frame is composed of at least three modular force conduction rods spliced ​​together through an electromagnetic coupling interface, wherein each of the modular force conduction rods has a built-in fiber grating strain sensor and a temperature compensation module, and the jack load The loading module includes a multi-stage hydraulic cylinder group, a closed-loop control system, a jack and a concrete test block, and sets a steel beam on the ground plane above the jack load loading module for stacking concrete test blocks to apply load to measure the change of foundation settlement with load, and determine the bearing capacity and deformation of spatial foundation soil. The multi-source data acquisition module includes a distributed sensor network for synchronously collecting load, displacement and strain, wherein the control signal of the jack load loading module is transmitted to the actuator of the bearing plate through the signal line inside the depth-adjustable force conduction force frame to form a closed-loop control circuit; the electromagnetic coupling interface includes an electromagnetic suction cup and a magnetic positioning pin arranged at both ends of the modular force conduction rod, and the adsorption force of the electromagnetic suction cup is specifically Among them F c represents the adsorption force of the electromagnetic chuck, B represents the magnetic induction intensity of the electromagnetic chuck, M represents the effective adsorption area of ​​the electromagnetic chuck, μ0 represents the vacuum magnetic permeability, the taper of the magnetic locating pin is 1:50, and the fitting clearance is <0.02mm, so as to ensure that the coaxiality error of the modular force conduction rod to the stress conduction path is <0.05°.

2. The space rapid flat plate load test device according to claim 1, characterized in that: The magnetic adsorption structure includes a permanent magnetic chuck array provided on the upper surface of the supporting plate and a magnetic base provided at the lower end of the depth-adjustable force transmission frame. The adsorption force of the permanent magnetic chuck array is adjusted by an electric control coil. The relationship between the adsorption force and the current of the electric control coil satisfies F y =k·I 2 , where F y represents the adsorption force of the permanent magnetic chuck array, I represents the current of the electric control coil, and k represents the material property attenuation coefficient of the electric control coil. The permanent magnetic chuck array is distributed in a regular hexagon, and the distance between adjacent chucks is 45-50 mm to ensure that the shear strength of the adsorption interface corresponding to the permanent magnetic chuck array is greater than 50 MPa under the maximum test load.

3. The space rapid flat plate load test device according to claim 1, characterized in that: The fiber Bragg grating strain sensor is arranged along the axis direction of the modular force transmission rod, and each modular force transmission rod is provided with at least three measuring points. The relationship between the center wavelength drift and strain of the fiber Bragg grating strain sensor satisfies Where Δλ represents the wavelength drift of the fiber Bragg grating strain sensor, λ0 represents the central wavelength of the fiber Bragg grating strain sensor, and p e represents the fiber elasto-optic coefficient of the fiber Bragg grating strain sensor, ε represents the strain of the modular force transmission rod, and the temperature compensation module corrects the wavelength drift caused by temperature in real time through the thermistor attached to the surface of the modular force transmission rod.

4. The space rapid flat plate load test device according to claim 3, characterized in that: The modular force transmission rod adopts a variable cross-section hollow tubular structure, the cross-section outer diameter of which decreases linearly from D at the lower end to 0.8D at the upper end, where D is the length of the cross-section outer diameter, and the wall thickness is uniformly t. The cross-section inertia moment of the variable cross-section hollow tubular structure is distributed along the axis I(x) and satisfies Wherein, x represents the distance from the lower end of the modular force transmission rod, and L represents the length of the modular force transmission rod.

5. A space rapid flat plate load test method, characterized in that: The method is implemented based on the space rapid flat plate load test device according to any one of claims 1 to 4, and the space rapid flat plate load test method comprises the following steps: The depth-adjustable force conduction frame is connected to the load-bearing plate through a magnetic adsorption structure, and the jack load loading module is docked with the depth-adjustable force conduction frame using a spherical hinge mechanism to establish a data transmission link with the multi-source data acquisition module; Obtain different space target test depths H, and select the corresponding number of modular force transmission rods based on the space target test depth H, and automatically complete the splicing by combining the electromagnetic coupling interface. After splicing, the initial strain data ε0 is collected by the fiber Bragg grating strain sensor, and the formula is used to calculate the strain data. Calculate the splicing length error, where n represents the total number of modular force transmission members, specifically l i represents the length of the ith modular force transmission member, E i represents the elastic modulus of the i-th modular force transmission rod. If the splicing length error exceeds ±0.5 mm, the electromagnetic coupling interface is triggered to adjust the corresponding fitting clearance between the modular force transmission rods. The load is applied by the jack load loading module according to the preset loading sequence. At the same time, after the applied load is stable, the strain data ε of each modular force transmission rod on the depth-adjustable force transmission force frame is synchronously collected by the multi-source data acquisition module through the data transmission link. i and the settlement S of the load-bearing plate, and based on the strain data ε i and elastic modulus E i By formula Calculate the transfer load at the corresponding space target test depth H, where A i represents the cross-sectional area of ​​the i-th modular force transmission rod; constructs a depth-load-settlement three-dimensional relationship surface based on the transferred load at the corresponding spatial target test depth H and the settlement S of the load-bearing plate; The corresponding spatial depth mutation measurement point is statistically identified based on the depth-load-settlement three-dimensional relationship surface, and the corresponding strain change rate is calculated based on the strain data corresponding to the spatial depth mutation measurement point; when the strain change rate exceeds 0.5% / min, the loading rate of the applied load is automatically adjusted by the jack load loading module, and the load-settlement data corresponding to the spatial depth mutation measurement point is regenerated; The load-settlement data of the spatial depth mutation measurement point is fused with the corresponding strain data, and the mechanical parameters corresponding to the space target test are calculated through the inversion process; based on the corresponding space target test depth, the spatial load-bearing visualization of the mechanical parameters corresponding to the space target test is performed to generate the spatial load-bearing deformation distribution of mechanical characteristics corresponding to different depth distributions.

6. The space rapid flat plate load test method according to claim 5, characterized in that: The method of identifying and counting corresponding spatial depth mutation measurement points based on the depth-load-settlement three-dimensional relationship surface includes the following steps: The Gaussian curvature and average curvature at each point are calculated based on the depth-load-settlement three-dimensional relationship surface; The curvature change rate at each point is calculated by the Gaussian curvature and the average curvature at each point. When the curvature change rate at a point is greater than 0.3 times the maximum Gaussian curvature in the depth-load-settlement three-dimensional relationship surface, the point is marked as a candidate point for curvature mutation. The temporal and spatial neighborhood of the curvature mutation candidate point is expanded by selecting a spherical neighborhood with a radius of 0.5m and taking the curvature mutation candidate point as the center. The density of the candidate points in the spherical neighborhood is counted. When the density exceeds 5 / m 3 When , the spherical neighborhood is determined as a candidate region of spatial depth mutation point; According to the candidate regions of spatial depth mutation points, the corresponding spatial depth mutation measurement points are statistically identified.

7. The space rapid flat plate load test method according to claim 6, characterized in that: The method of identifying and counting corresponding spatial depth mutation points based on the spatial depth mutation point candidate area includes the following steps: According to each spatial point in the candidate region of the spatial depth mutation point, partial derivatives are calculated along the depth direction and the load direction to obtain the depth direction gradient and the load direction gradient of each spatial point in the candidate region; According to the spatial target test, the corresponding depth direction gradient threshold and load direction gradient threshold are set, and the depth direction gradient and load direction gradient of each spatial point in the candidate area are identified based on the corresponding depth direction gradient threshold and load direction gradient threshold. If the absolute value corresponding to the depth direction gradient of the spatial point is greater than the depth direction gradient threshold and the absolute value corresponding to the load direction gradient is greater than the load direction gradient threshold, it is determined as the corresponding spatial depth mutation measurement point.

8. The space rapid flat plate load test method according to claim 5, characterized in that: The automatic adjustment of the loading rate of the applied load is calculated by a loading rate adjustment formula, wherein the loading rate adjustment formula is specifically: Among them, v k+1 represents the loading rate at the k+1th moment, v k represents the loading rate at the kth moment, exp represents the exponential function, δ d represents the strain change rate corresponding to the spatial depth mutation measurement point, α represents the adjustment coefficient, and its value range is 0.2-0.5, and β represents the preset threshold.

9. The space rapid flat plate load test method according to claim 5, characterized in that: The specific implementation process of the inversion process is: Obtain the load data, settlement data, and strain data corresponding to the sudden change measurement points at different spatial depths, and use the dynamic time warping algorithm to synchronize the timestamps of the corresponding load data, settlement data, and strain data to eliminate the time offset caused by the sampling frequency difference and ensure that the time synchronization error is less than 10ms, so as to obtain the time-synchronized load data, settlement data, and strain data; The corresponding spatial test mechanical response model is constructed by combining the elastic half-space theory with the Burgers viscoelastic model, where the model parameters include the elastic modulus E, Poisson's ratio μ, and Kelvin body elastic modulus E. k , Kelvin viscosity coefficient η k and Maxwell volume viscosity coefficient η m ; The time-synchronized load data, settlement data, and strain data are input into the spatial test mechanical response model to derive the corresponding load-settlement and strain inversion calculation formulas. For the elastic stage, the relationship between the settlement S and the transferred load P is: Where B represents the diameter of the supporting plate; for the viscoelastic stage, the integral form of the Burgers viscoelastic model is used to describe the change of strain data with time τ Based on the inversion calculation formula of load-settlement and strain and combined with the spatial test mechanical response model, a model prediction inversion analysis is performed on the load data, settlement data and strain data after time synchronization. The relative error between the measured data and the model prediction data is set as the objective function, and the corresponding spatial test mechanical response model is optimized based on the objective function, and the mechanical parameters corresponding to the spatial target test are output by inversion optimization.

10. The space rapid flat plate load test method according to claim 9, characterized in that: The objective function is specifically: Among them, f represents the objective function, q represents the total number of spatial depth mutation measurement points, represents the predicted settlement value of the jth spatial depth mutation measurement point, represents the measured settlement value of the jth spatial depth mutation measurement point, represents the predicted strain value of the jth spatial depth mutation measurement point, Represents the measured strain value of the jth spatial depth mutation measurement point.

Images