Method for predicting mechanical property of boom of automobile crane

Abstract

The invention relates to the technical field of arm support performance prediction, and discloses a method for predicting the mechanical property of an automobile crane arm support. In order to solve the problems of high cost, long period and difficulty in testing part of working conditions in a traditional test performance prediction method, the invention provides a prediction method based on a model test in order to predict the mechanical property of an automobile crane boom. A nonlinear correction model and an error optimization model are constructed through a displacement equivalence principle and a partially similar scale model based on the consistency of dynamic characteristics of a prototype and based on the partially similar scale model conforming to a dynamic similarity principle in combination with statics and dynamic simulation analysis; feedback correction and iterative optimization are carried out on a prediction performance curve obtained through a test bed service test through the nonlinear correction model and the error optimization model, so that an optimized prediction performance curve is obtained through the test bed service test of the partially similar reduced scale model; therefore, the prediction prototype realizes a more accurate mechanical property prediction effect.

Description

Technical Field

[0001] The invention belongs to the technical field of boom performance prediction, and in particular relates to a method for predicting the mechanical performance of a truck crane boom. Background Art

[0002] Truck cranes and various types of construction machinery are essential equipment in modern engineering, widely used in infrastructure construction, energy engineering, and other scenarios. As the core load-bearing structure of a crane, the boom system must withstand complex and variable load conditions. Accurately predicting its mechanical properties and service life is crucial for equipment safety and operational stability. Traditional performance evaluation methods rely primarily on full-scale testing, which involves obtaining force and deformation data on key structures through actual vehicle testing. However, full-scale testing often faces practical challenges such as high cost, high risk, and long cycles. This further limits the feasibility of testing, especially in extreme environments or special operating conditions.

[0003] Regarding the testing and inspection technology for truck cranes, Chinese patent application number 202210810860.6 discloses a mobile crane load-bearing performance test and inspection equipment. The disclosed load-bearing performance test and inspection equipment includes a positioner and a mobile inspection vehicle, and is used for mobile crane load stability tests. It uses laser detection, visual detection, cylinder control, and Mecanum wheel control technologies. During the mobile crane stability load test, the cylinder accurately applies the load force, accurately measures the ultimate load capacity of the mobile crane, and has high test efficiency. However, the test equipment and test methods provided by this patent have low test feasibility under multiple working conditions, and the performance test range is narrow, only testing the ultimate load-bearing performance.

[0004] Scaled testing technology provides an efficient testing method to solve the above problems. This method simulates the actual working conditions of the crane in a controlled laboratory environment by constructing a scaled-down test model, revealing the mechanical properties and dynamic response of key structures. Compared with full-scale tests, scaled tests have the advantages of low cost, short cycle time, and high repeatability. They can explore the performance of equipment under extreme working conditions under limited test conditions. In fact, scaled model tests have been successfully applied in spacecraft mechanical tests, large bridge wind tunnel tests, etc., providing important theoretical support for the optimization design and safety assessment of complex structural systems.

[0005] However, the boom of a truck crane exhibits multi-condition coupled load characteristics, making it difficult to predict its performance under different operating conditions using a simple scaled model. During scaled-scale testing, due to scale effects, the material mechanical properties, structural stiffness distribution, and dynamic response are difficult to fully replicate with the full-scale prototype. This leads to deviations in the load-bearing mechanisms between the model and the prototype, making fully similar scaled models difficult to directly apply to such complex mechanical structures. Summary of the Invention

[0006] In view of this, the present invention discloses a method for predicting the mechanical properties of a truck crane boom. Specifically, the method provides a scale similarity criterion and an error correction method for the crane boom, so as to construct a test model that can accurately reflect the performance of the prototype. The method provides a scientific basis for the performance prediction and safety assessment of engineering machinery structures, thereby solving the shortcomings of the related art.

[0007] To achieve the above purpose, the present invention discloses the following technical solutions:

[0008] In a first aspect, the present invention provides a method for predicting the mechanical properties of a truck crane boom, the method comprising:

[0009] Step S1: Based on the geometric length similarity ratio and the displacement equivalence principle, and in accordance with the transverse free vibration equation of the variable-section Euler beam and the dynamic similarity principle, obtain the dynamic parameter similarity ratio and the related parameter similarity ratio of the prototype of the boom to be predicted and the preliminary scaled model of the boom to be predicted, thereby constructing the preliminary scaled model according to the dynamic parameter similarity ratio and the related parameter similarity ratio;

[0010] Step S2: performing a dynamic simulation test on the preliminary scaled model and the boom prototype to be predicted, and obtaining dynamic parameters and related parameters of the preliminary scaled model that are consistent with the dynamic characteristics of the prototype, thereby constructing a partially similar scaled model;

[0011] Step S3: obtaining a nonlinear correction model based on the simulation performance curves of the boom to be predicted and the partially similar scale model; building a partially similar scale model test bench, and obtaining an error optimization model based on the manufacturing deviation between the partially similar scale model and the partially similar scale model test bench;

[0012] Step S4: obtaining a preliminary predicted performance curve of the boom to be predicted based on the results of the dynamic performance test and the static performance test of the partially similar scale model test bench, and correcting the preliminary predicted performance curve based on the nonlinear correction model and the error optimization model to obtain an optimized predicted performance curve of the boom to be predicted;

[0013] Step S5: According to the optimized prediction performance curve, the performance of the boom to be predicted under a certain working condition is obtained, thereby realizing the performance prediction of the boom to be predicted.

[0014] As a preferred embodiment of the above method, the dynamic parameters include natural frequency and stiffness, and the related parameters include geometric length, position of the variable amplitude hinge point, boom working elevation angle, beam section moment of inertia, material elastic modulus, material Poisson's ratio, beam deflection, boom stress, lifting load and working time.

[0015] As a preferred embodiment of the above method, the predicted performance curve includes load-deflection curve, load-stress-strain curve, natural frequency performance curve, mode performance curve, damping ratio performance curve and acceleration frequency response curve under different working conditions.

[0016] As a preferred embodiment of the above method, the dynamic performance test includes:

[0017] Dynamic characteristics test, including vibration modal test, used to analyze the natural frequency and vibration shape of the telescopic boom;

[0018] Dynamic load test, used to evaluate the response of the telescopic boom under dynamic load;

[0019] Impact test, used to test the performance, acceleration response, natural frequency and damping ratio of the telescopic boom under sudden impact load;

[0020] The static performance test includes:

[0021] Boom static load test, used to verify the bearing capacity of the telescopic boom under different loads;

[0022] Boom stiffness test to evaluate the stiffness characteristics of the telescopic boom;

[0023] Boom stability test, to check the stability of the cover plate, web plate and local components of the telescopic boom.

[0024] Accordingly, the simulation test includes dynamic simulation test and static simulation test, among which:

[0025] The dynamics simulation test includes:

[0026] Dynamic characteristics test, including vibration modal test, used to analyze the natural frequency and vibration shape of the telescopic boom;

[0027] Dynamic load test, used to evaluate the response of the telescopic boom under dynamic load;

[0028] Impact test, used to test the performance, acceleration response, natural frequency and damping ratio of the telescopic boom under sudden impact load;

[0029] The statics simulation test includes:

[0030] Boom static load test, used to verify the bearing capacity of the telescopic boom under different loads;

[0031] Boom stiffness test to evaluate the stiffness characteristics of the telescopic boom;

[0032] Boom stability test, to check the stability of the cover plate, web plate and local components of the telescopic boom.

[0033] As a preferred embodiment of the above method, the step of obtaining a nonlinear correction model based on simulation performance curves of the boom to be predicted and a partially similar scale model includes:

[0034] Conducting static simulation tests under multiple working conditions, collecting maximum deformation, upper cover plate, and lower base plate maximum stress values ​​of the boom prototype to be predicted and some similar scaled models under multiple working conditions in the static simulation tests, and obtaining load-stress-strain curves and load-deflection curves under multiple corresponding working conditions;

[0035] The nonlinear correction model is obtained by using a curve fitting method according to the slope change results of the load-stress-strain curves and the load-deflection curves under the multiple corresponding working conditions.

[0036] As a preferred embodiment of the above method, the nonlinear correction model is:

[0037] The slope of the load-stress-strain curve obtained by the test results of some similar scale model test benches is corrected by the following function to obtain the corrected load-stress-strain curve of the boom to be predicted:

[0038] Where a1 is the plastic deformation coefficient of the material, a2 is the geometric defect correction coefficient, L is the arm extension length, k σ is the slope correction function of the load-deflection curve, k σ-P 、k σ-M are the slopes of the load-stress-strain curves obtained by simulating the prototype of the boom to be predicted and some similar models;

[0039] The slope of the load-deflection curve obtained by correcting the test results of some similar scale model test benches is used to obtain the corrected load-deflection curve of the boom to be predicted:

[0040] Where b1 is the connection flexibility correction factor, b2 is the load distribution correction factor, L is the arm extension length, k y is the slope correction function of the load-deflection curve, k y-P 、k y-M are the slopes of the load-deflection curves obtained by simulating the prototype of the boom to be predicted and some similar models, respectively.

[0041] As a preferred embodiment of the above method, obtaining an error optimization model according to manufacturing deviations between the partially similar scale model and the partially similar scale model test bench includes:

[0042] Obtaining physical parameters of the partially similar scale model and the partially similar scale model test bench, wherein the physical parameters include material density p, elastic modulus E, mass M, dimensional parameter L, stiffness and distribution thereof;

[0043] Calculate the relative errors of the physical parameters of the partially similar scale model and the partially similar scale model test bench;

[0044] Based on the error coefficient of the relative error, an error optimization model is established through multiple regression analysis.

[0045] As a preferred embodiment of the above method, the method further comprises the following steps:

[0046] A preset error value is used, and a root mean square error is obtained based on the actual service data of the boom to be predicted and the corresponding data predicted by the optimized prediction performance curve. If the root mean square error is greater than the preset error value, the nonlinear correction model of the partial similarity scale model is iteratively adjusted until the root mean square error is no greater than the preset error value, thereby obtaining a re-optimized prediction performance curve of the boom to be predicted, and realizing performance prediction of the boom to be predicted based on the re-optimized prediction performance curve.

[0047] On the other hand, the present invention also provides a device for predicting the mechanical properties of a truck crane boom, the device comprising:

[0048] Scaled model construction unit: Based on the geometric length similarity ratio and displacement equivalence principle, and in accordance with the lateral free vibration equation of the variable-section Euler beam and the dynamic similarity principle, the dynamic parameter similarity ratio and the related parameter similarity ratio of the boom prototype to be predicted and the preliminary scaled model of the boom to be predicted are obtained, and a preliminary scaled model is constructed according to the dynamic parameter similarity ratio and the related parameter similarity ratio, and a dynamic simulation test is performed on the preliminary scaled model and the boom prototype to be predicted to obtain the dynamic parameters and related parameters of the preliminary scaled model that are consistent with the dynamic characteristics of the prototype, thereby constructing a partially similar scaled model;

[0049] Correction and optimization unit: obtaining a nonlinear correction model based on the static simulation test results of the partially similar scale model and the prototype to be predicted; obtaining an error optimization model based on the manufacturing deviation of the partially similar scale model and the partially similar scale model test bench; obtaining a preliminary prediction performance curve of the boom to be predicted based on the dynamic performance test and static performance test results of the partially similar scale model test bench, and correcting the preliminary prediction performance curve based on the nonlinear correction model and the error optimization model, thereby obtaining an optimized prediction performance curve of the boom to be predicted;

[0050] Output unit: outputs the performance of the boom to be predicted under certain working conditions according to the optimized prediction performance curve.

[0051] As a preferred embodiment of the above-mentioned device, the partially similar scale model test bench comprises: a tensile testing mechanism 2 for providing a load to the boom 1, and a vibration test bench 4 for installing the boom 1, wherein:

[0052] The tension loading mechanism 2 includes a base 201, a slide rail 2011 is provided on the base 201, a loading seat 202 is slidably connected to the slide rail 2011, and the loading seat 202 is connected to the slide rail 2011 via a slider 2021. A loading hydraulic cylinder 2022 is provided on the loading seat 202, and the traction rope 105 on the boom 1 is connected to the loading hydraulic cylinder 2022, and the load is applied through the loading hydraulic cylinder 2022;

[0053] The vibration test platform 4 includes a platform 401, a vibration platform 402 is provided on the platform 401, and guide posts 403 are provided at the four corners of the vibration platform 402. The guide posts 403 are fixedly provided on the platform 401, and the vibration platform 402 is slidably connected to the guide posts 403. The arm 1 is installed on the vibration platform 402;

[0054] A drive shaft 405 is provided on the platform 401, and the drive shaft 405 is located below the vibration table 402. An eccentric wheel 406 is provided below the vibration table 402, and the eccentric wheel 406 is in contact with the drive shaft 405. The rotation of the drive shaft 405 drives the eccentric wheel 406 to rotate, and the eccentric wheel 406 drives the vibration table 402 to vibrate during the rotation. A mounting frame 4061 is provided on the lower surface of the vibration table 402, and the eccentric wheel 406 is rotatably set on the mounting frame 4061, and an axle seat 4052 for mounting the drive shaft 405 is provided on the platform 401.

[0055] It can be seen from the above technical solutions that, compared with the prior art, the present invention has the following technical effects:

[0056] (1) Aiming at predicting the mechanical properties of automobile crane booms, the present invention aims to solve the problems of high cost, long cycle and difficulty in testing some working conditions in traditional test performance prediction methods. The present invention provides a prediction method based on model testing, which adopts the displacement equivalence principle and a partially similar scale model based on the dynamic characteristics consistent with the prototype. Based on the partially similar scale model that conforms to the dynamic similarity principle, a nonlinear correction model and an error optimization model are constructed in combination with statics and dynamics simulation analysis. Then, the predicted performance curve obtained through the test bench service test is fed back, corrected and iteratively optimized through the nonlinear correction model and error optimization model, so that the prototype is predicted more accurately through the test bench service test of the partially similar scale model.

[0057] (2) Different from traditional full-scale tests or single numerical simulation tests, the prediction method provided by the present invention integrates the advantages of scale ratio derivation, simulation analysis and test data, and proposes a comprehensive test method that integrates scale ratio derivation with nonlinear correction rules and error correction. This makes the prediction method of the present invention particularly suitable for predicting the performance of crane booms in the presence of large deformation and material yield state. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 The present invention is a flowchart of a method for predicting the mechanical properties of a truck crane boom provided by an exemplary embodiment.

[0059] Figure 2 This is the acceleration frequency response prediction curve and the prototype actual acceleration frequency response curve.

[0060] Figure 3 It is a block diagram of a device for predicting the mechanical properties of a truck crane boom provided by an exemplary embodiment.

[0061] Figure 4 It is a structural schematic diagram of a device for conducting actual service tests of a boom provided by an exemplary embodiment.

[0062] Figure 5 It is a structural diagram of the vibration test bench.

[0063] Figure 6 It is a structural diagram of the eccentric wheel and the drive shaft.

[0064] Figure 7 It is a structural diagram of the tension loading mechanism.

[0065] Figure 8 It is a structural diagram of the boom.

[0066] The accompanying drawings are marked as: boom 1, boom base 101, counterweight 1011, inclination sensor 102, displacement sensor 103, accelerometer 104, traction rope 105, arm section 106, strain gauge 107, variable amplitude cylinder 108, basic arm 109, tension loading mechanism 2, base 201, slide rail 2011, loading seat 202, slider 2021, loading hydraulic cylinder 2022, pulley seat 3, pulley 301, vibration test bench 4, platform 401, vibration table 402, spring 4021, connecting piece 4022, guide column 403, drive motor 404, driving pulley 4041, synchronous belt 4042, drive shaft 405, driven pulley 4051, shaft seat 4052, eccentric wheel 406, mounting bracket 4061. DETAILED DESCRIPTION

[0067] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible implementations consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of one or more embodiments of the present invention, as detailed in the appended claims.

[0068] It should be noted that in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in the present invention. In some other embodiments, the method may include more or fewer steps than those described in the present invention. In addition, a single step described in the present invention may be broken down into multiple steps for description in other embodiments, and multiple steps described in the present invention may be combined into a single step for description in other embodiments.

[0069] To further illustrate the present invention, the following examples are provided:

[0070] For typical structures, full-scale simulations can be used to predict their performance. However, when it comes to predicting the mechanical properties of truck crane booms, full-scale simulation tests are often used to predict their mechanical properties, and the prediction results obtained are often inaccurate. Specifically, this inaccuracy stems from the large-scale characteristics of the truck crane boom, that is, the length dimension is much larger than the other two dimensions, which causes the boom to have significant nonlinear deformation when subjected to force. During the nonlinear deformation process, the deformation and force of the boom do not follow Hooke's law, and are usually manifested as nonlinear force and displacement behavior. Due to the emergence of this nonlinear behavior, it is difficult to reflect the actual force or other states of the crane boom through simulation in simulation software to achieve the prediction of the boom's mechanical properties.

[0071] Traditional full-scale tests or single numerical simulation tests are limited to simple structures, and the accuracy of predicting the performance of large-scale truck crane booms using traditional simulation tests is low.

[0072] In order to solve the deficiencies in the related art, the present invention proposes a method for predicting the mechanical properties of a truck crane boom based on a model test.

[0073] Figure 1 This is a flow chart of a method for predicting the mechanical properties of a truck crane boom provided by an exemplary embodiment. Figure 1 As shown, the method may include the following steps S1 to S5:

[0074] Step S1: Based on the geometric length similarity ratio and the displacement equivalence principle, and in accordance with the lateral free vibration equation of the variable-section Euler beam and the dynamic similarity principle, obtain the dynamic parameter similarity ratio and the related parameter similarity ratio of the prototype of the boom to be predicted and the preliminary scaled model of the boom to be predicted, thereby constructing the preliminary scaled model according to the dynamic parameter similarity ratio and the related parameter similarity ratio.

[0075] Specifically, the dynamic parameters include natural frequency and stiffness, and the related parameters include geometric length, position of the variable amplitude hinge point, boom working elevation angle, beam section inertia moment, material elastic modulus, material Poisson's ratio, beam deflection, boom stress, lifting load and working time.

[0076] In general, based on the theorem of dimensional analysis and equation analysis, the similarity ratios of dynamic parameters and related parameters between the prototype and the scaled model are derived as shown in Table 1. In Table 1, the similarity ratio is represented by the letter C, and the similarity ratio of the corresponding parameter is represented by the letter C plus a subscript, and the subscript represents the corresponding parameter. For example, the similarity ratio of the elastic modulus E of the prototype and the scaled model is C E = Geometric length similarity ratio C L =λ, according to the actual length of the crane boom prototype and the site setting.

[0077] Table 1

[0078]

[0079]

[0080] In this embodiment, the crane boom structure is regarded as a variable-section Euler beam. Based on its lateral free vibration equation and the principle of dynamic similarity, the similarity ratio of the dynamic parameters and related parameters of the boom prototype to be predicted and the scaled model of the boom to be predicted is derived. Then, the dynamic parameters and related parameters of the scaled model are obtained based on the similarity ratio and the corresponding parameters of the boom prototype to be predicted (which are known). Thus, a preliminary scaled model is constructed based on the parameters of elastic modulus E, material Poisson's ratio μ, length dimension L, and hinge point position X.

[0081] The lateral free vibration equation is as follows:

[0082]

[0083] Where, is the coordinate along the boom axis, and is the lateral displacement of the boom.

[0084] Furthermore, according to the lateral free vibration equation and the principle of dynamic similarity, the natural frequency similarity ratio is obtained. Stiffness similarity ratio In this embodiment, the natural frequency similarity ratio Stiffness similarity ratio The scaled model can be used to simulate and predict the mechanical behavior characteristics of the boom prototype more accurately.

[0085] Step S2: performing dynamic simulation tests on the preliminary scaled model and the boom prototype to be predicted, and obtaining dynamic parameters and related parameters of the preliminary scaled model that are consistent with the dynamic characteristics of the prototype, thereby constructing a partially similar scaled model.

[0086] The simulation test includes dynamic simulation test and static simulation test, among which:

[0087] The dynamics simulation test includes:

[0088] Dynamic characteristics test, including vibration modal test, used to analyze the natural frequency and vibration shape of the telescopic boom;

[0089] Dynamic load test, used to evaluate the response of the telescopic boom under dynamic load;

[0090] Impact test, used to test the performance, acceleration response, natural frequency and damping ratio of the telescopic boom under sudden impact load;

[0091] The statics simulation test includes:

[0092] Boom static load test, used to verify the bearing capacity of the telescopic boom under different loads;

[0093] Boom stiffness test to evaluate the stiffness characteristics of the telescopic boom;

[0094] Boom stability test, to check the stability of the cover plate, web plate and local components of the telescopic boom.

[0095] In one embodiment, λ is set to λ=10 based on the size of the boom prototype to be predicted, the actual test site size, and the test equipment specifications. Then, a dynamic simulation test is performed based on λ and the lateral free vibration equation of the variable-section Euler beam, the principle of dynamic similarity, and the conditions based on displacement equivalence and consistent dynamic characteristics. The dynamic parameters and the similarity ratio of related parameters of the partially similar scale model to be constructed in step S1 are derived and calculated, thereby realizing the construction of a preliminary scale model.

[0096] In another embodiment, based on the preliminary scaled model with λ=10, finite element simulation tests of the prototype and the preliminary scaled model are established and the same boundary conditions are set. Modal analysis and dynamic response analysis are performed on the preliminary scaled model to ensure that the first six modal vibration shapes of the scaled model and the prototype are similar. The MAC modal confidence matrix is ​​used for evaluation to ensure that the first six natural frequencies meet partial similarity and that the dynamic characteristics of the scaled model and the prototype are consistent. The preliminary scaled model is optimized to construct a partially similar scaled model.

[0097] Table 2 shows the parameters and similarity ratios of the partially similar scaled model constructed in this embodiment according to the displacement equivalence principle and the condition of consistent dynamic characteristics. In Table 2, the similarity ratio is represented by the letter C, and the corresponding parameter similarity ratio is represented by the letter C plus a superscript, and the superscript represents the corresponding parameter. The partially similar scaled model can be constructed based on the similarity ratios of the elastic modulus E, the material Poisson's ratio μ, the length dimension L, and the hinge position X in Table 2 and the corresponding parameters of the boom prototype to be predicted (which are known). Among them, the length and width dimensions of the cross section of the partially similar model can be adjusted according to actual needs, as long as the displacement equivalence principle and the condition of consistent dynamic characteristics are met.

[0098] Table 2

[0099]

[0100] Step S3: performing dynamic simulation tests and static simulation tests on the partially similar scale model, and obtaining simulation performance curves of the boom prototype to be predicted and the partially similar scale model based on the simulation test results of the boom prototype to be predicted and the partially similar scale model; obtaining a nonlinear correction model based on the simulation performance curves of the boom prototype to be predicted and the partially similar scale model; then building a partially similar scale model test bench, and obtaining an error optimization model based on the manufacturing deviation between the partially similar scale model and the partially similar scale model test bench.

[0101] In response to practical needs, the present invention aims to predict the deflection, stress, and strain of a truck crane boom under different operating conditions, as well as the natural frequency performance, mode performance, and damping ratio under different operating conditions. This invention improves existing prediction methods and provides the present invention. Therefore, the simulation performance curves provided in step S3 include load-deflection curves, load-stress-strain curves, natural frequency performance curves, mode performance curves, damping ratio performance curves, and acceleration frequency response curves under different operating conditions. These predicted performance curves, including simulation performance curves of the boom prototype to be predicted and a partially similar scaled model, are obtained through simulation analysis and simulation tests of different operating conditions of the boom prototype to be predicted and the partially similar scaled model constructed in step S2.

[0102] In the finite element simulation test of the prototype and some similar scale models, according to the working conditions of the crane boom, including the boom angle θ P Value range [θ P-min ,θ P-max ]、Load capacity P P The value range of [P P-min ,P P-max ], and telescopic length L P The value range of [L P-min ,L P-max ], and the amplitude angle of the scale model derived based on the partial similarity scale ratio Load and telescopic length The corresponding derivation value range is selected at equal intervals within the value range, and the response characteristics of the prototype and scaled model under different working conditions are simulated. In order to establish a nonlinear correction model that is closer to the prototype, the simulation test is carried out by selecting several values ​​at equal intervals within the value range, and the response characteristics of the prototype and scaled model under different working conditions are simulated. By selecting the test variables in an evenly dispersed manner, the simulation results are guaranteed to better reflect the real situation.

[0103] In one embodiment, obtaining a nonlinear correction model based on simulation performance curves of a prototype to be predicted and a partially similar scaled model includes:

[0104] Conducting static simulation tests under multiple working conditions, collecting maximum deformation, upper cover plate, and lower base plate maximum stress values ​​of the boom prototype to be predicted and some similar scaled models under multiple working conditions in the static simulation tests, and obtaining load-stress-strain curves and load-deflection curves under multiple corresponding working conditions;

[0105] The nonlinear correction model is obtained by using a curve fitting method according to the slope change results of the load-stress-strain curves and the load-deflection curves under the multiple corresponding working conditions.

[0106] The nonlinear correction model is:

[0107] The slope of the load-stress-strain curve obtained by the test results of some similar scale model test benches is corrected by the following function to obtain the corrected load-stress-strain curve of the boom to be predicted:

[0108] Where a1 is the plastic deformation coefficient of the material, a2 is the geometric defect correction coefficient, L is the arm extension length, k σ is the slope correction function of the load-deflection curve, k σ-P 、k σ-M are the slopes of the load-stress-strain curves obtained by simulating the prototype of the boom to be predicted and some similar models;

[0109] The slope of the load-deflection curve obtained by correcting the test results of some similar scale model test benches is used to obtain the corrected load-deflection curve of the boom to be predicted:

[0110] Where b1 is the connection flexibility correction factor, b2 is the load distribution correction factor, L is the arm extension length, k y is the slope correction function of the load-deflection curve, k y-P 、k y-M are the slopes of the load-deflection curves obtained by simulating the prototype of the boom to be predicted and some similar models, respectively.

[0111] In one embodiment, obtaining the error optimization model according to the manufacturing deviation between the partially similar scale model and the partially similar scale model test bench includes:

[0112] Obtaining physical parameters of the partially similar scale model and the partially similar scale model test bench, wherein the physical parameters include material density p, elastic modulus E, mass M, dimensional parameter L, stiffness and distribution thereof;

[0113] Calculate the relative errors of the physical parameters of the partially similar scale model and the partially similar scale model test bench;

[0114] Based on the error coefficient of the relative error, an error optimization model is established through multiple regression analysis.

[0115] In one embodiment, the identified error sources include material density deviation, test bench mass deviation, geometric dimension deviation, structural stiffness and its distribution. Aiming at the errors caused by the above physical parameters, an error optimization model is constructed, including a stress optimization function, a deflection optimization function, and a natural frequency optimization function, where:

[0116] The stress optimization function is:

[0117]

[0118] The deflection optimization function is:

[0119]

[0120] The natural frequency optimization function is:

[0121]

[0122] In the error optimization model, ρ, E, and M are the material density, elastic modulus, and mass, respectively; A and L are the area and length of the cross section; J is the term related to the assembly error; and K J1 , K J2 , K J3 are the assembly error correction terms corresponding to stress, deflection and natural frequency, respectively. Subscript a represents the corresponding parameters of the test bench. Subscript a represents the corresponding parameters of the partial similarity model. ω1, ω2, ν1, ν2, ν3, ν4, μ1, μ2, μ3, μ4 are the error coefficients of the corresponding terms in the corresponding optimization functions, respectively.

[0123] Step S4: obtaining a preliminary predicted performance curve of the boom to be predicted based on the results of the dynamic performance test and the static performance test of the partially similar scale model test bench; and correcting the preliminary predicted performance curve based on the nonlinear correction model and the error optimization model to obtain an optimized predicted performance curve of the boom to be predicted.

[0124] The results of the dynamic and static performance tests on the partially similar scale model test rig are for a service test device built based on the partially similar scale model. Accordingly, the service tests conducted include the dynamic performance test, dynamic load test, impact test, boom static load test, boom stiffness test, and boom stability test.

[0125] Corresponding to the simulation test, the dynamic performance test includes:

[0126] Dynamic characteristics test, including vibration modal test, used to analyze the natural frequency and vibration shape of the telescopic boom;

[0127] Dynamic load test, used to evaluate the response of the telescopic boom under dynamic load;

[0128] Impact test, used to test the performance, acceleration response, natural frequency and damping ratio of the telescopic boom under sudden impact load;

[0129] The static performance test includes:

[0130] Boom static load test, used to verify the bearing capacity of the telescopic boom under different loads;

[0131] Boom stiffness test to evaluate the stiffness characteristics of the telescopic boom;

[0132] Boom stability test, to check the stability of the cover plate, web plate and local components of the telescopic boom.

[0133] Step S5: According to the optimized prediction performance curve, the performance of the boom to be predicted under a certain working condition is obtained, thereby realizing the performance prediction of the boom to be predicted.

[0134] According to the above-mentioned embodiment of the present invention, a prediction method based on model testing is provided. A partially similar scaled model is established based on the displacement equivalence principle and the consistency of dynamic characteristics with the prototype. Based on this partially similar scaled model that conforms to the dynamic similarity principle, a nonlinear correction model and an error optimization model are constructed in combination with static and dynamic simulation analysis. Then, the nonlinear correction model and the error optimization model are used to feedback correct and iteratively optimize the predicted performance curve obtained through the test bench service test. As a result, the test bench service test of the partially similar scaled model obtains an optimized predicted performance curve, thereby predicting that the prototype achieves a more accurate prediction of the mechanical properties.

[0135] In one embodiment, in order to further improve the mechanical properties of the boom, the method further includes the following steps:

[0136] A preset error value is used, and a root mean square error is obtained based on the actual service data of the boom to be predicted and the corresponding data predicted by the optimized prediction performance curve. If the root mean square error is greater than the preset error value, the nonlinear correction model of the partial similarity scale model is iteratively adjusted until the root mean square error is no greater than the preset error value, thereby obtaining a re-optimized prediction performance curve of the boom to be predicted, and realizing performance prediction of the boom to be predicted based on the re-optimized prediction performance curve.

[0137] Specifically, according to the actual application situation and the allowable error range, for example, according to the actual situation, the preset error value is 3%. In this embodiment, the actual vehicle service data of the boom to be predicted is collected and recorded, and then the root mean square error of the actual vehicle service data of the boom to be predicted and the corresponding data predicted by the optimized prediction performance curve are calculated. If the obtained root mean square error is greater than the preset error value, the nonlinear correction model of the partial similar scale model is iteratively adjusted until the root mean square error is no greater than the preset error value, and a new similar scale model is obtained. Finally, based on the test bench service test results of the new similar scale model, a new prediction performance curve of the boom to be predicted is obtained, and the performance prediction of the boom to be predicted is realized using the new prediction performance curve of the boom to be predicted.

[0138] The collected actual vehicle service data was compared with the corresponding data predicted by the optimized prediction performance curve and the corresponding data predicted by the full-scale model simulation, and the accuracy was calculated to verify the prediction effect of the method provided by the present invention. The results are shown in Table 3. In Table 3, the correlation coefficient is used as a measure of accuracy, with a value range of (-1, 1). The closer the correlation coefficient is to 1, the better the correlation, that is, the higher the accuracy. As can be seen from Table 3, the optimized prediction provided by the optimized prediction performance curve of the method of the present invention has a high correlation with the actual vehicle service and a high accuracy, while the prediction correlation of the full-scale simulation test is only about 0.8.

[0139] Table 3

[0140] Correlation coefficient Pearson correlation Spearman correlation Kendall correlation Simulation analysis and actual vehicle service 0.81141 0.90236 0.80824 Optimization prediction and actual vehicle service 0.92383 0.94241 0.93675 Improvement 13.85％ 4.44％ 15.90％

[0141] In addition, in order to verify the accuracy of the prediction effect of the method provided by the present invention, the amplitude to acceleration frequency response data of the actual prototype vehicle in service was collected and made into Figure 2 The response curve is shown and compared with the predicted curve of amplitude versus acceleration frequency response. Figure 2 In the figure, the curve formed by the model prediction data represents the performance prediction curve - acceleration frequency response curve provided by the method of the present invention, the curve formed by the prototype actual data represents the acceleration frequency response curve obtained by the collected prototype actual vehicle service, and the curve formed by the prototype simulation data represents the prediction curve - acceleration frequency response curve obtained by the full-scale simulation test. Figure 2 As shown, the prediction curve provided by the method of the present invention has a high degree of fitting, and it can be clearly seen that the prediction accuracy of the method of the present invention is significantly improved compared with the prediction accuracy of the full-scale simulation test.

[0142] Please refer to Figure 3 , is a block diagram of a device for predicting the mechanical properties of a truck crane boom. To implement the technical solution of the present invention, the device may include:

[0143] Scaled model construction unit: Based on the geometric length similarity ratio and displacement equivalence principle, and in accordance with the lateral free vibration equation of the variable-section Euler beam and the dynamic similarity principle, the dynamic parameter similarity ratio and the related parameter similarity ratio of the boom prototype to be predicted and the preliminary scaled model of the boom to be predicted are obtained, and a preliminary scaled model is constructed according to the dynamic parameter similarity ratio and the related parameter similarity ratio, and a dynamic simulation test is performed on the preliminary scaled model and the boom prototype to be predicted to obtain the dynamic parameters and related parameters of the preliminary scaled model that are consistent with the dynamic characteristics of the prototype, thereby constructing a partially similar scaled model;

[0144] Correction and optimization unit: obtaining a nonlinear correction model based on the static simulation test results of the partially similar scale model and the prototype to be predicted; obtaining an error optimization model based on the manufacturing deviation of the partially similar scale model and the partially similar scale model test bench; obtaining a preliminary prediction performance curve of the boom to be predicted based on the dynamic performance test and static performance test results of the partially similar scale model test bench, and correcting the preliminary prediction performance curve based on the nonlinear correction model and the error optimization model, thereby obtaining an optimized prediction performance curve of the boom to be predicted;

[0145] Output unit: outputs the performance of the boom to be predicted under certain working conditions according to the optimized prediction performance curve.

[0146] In one embodiment, according to the size of the boom prototype to be predicted, the actual test site size, and the test equipment specifications, the geometric length similarity ratio is set to 1:10. Based on the geometric length similarity ratio and the displacement equivalence principle, and according to the lateral free vibration equation of the variable-section Euler beam and the principle of dynamic similarity, the dynamic parameters and related parameters of the preliminary scaled model that are consistent with the dynamic characteristics of the prototype are obtained, thereby constructing a partially similar scaled model. The dynamic parameters and related parameters of the partially similar scaled model are shown in Table 2.

[0147] In one embodiment, obtaining a nonlinear correction model based on simulation performance curves of a prototype to be predicted and a partially similar scaled model includes:

[0148] Conducting static simulation tests under multiple working conditions, collecting maximum deformation, upper cover plate, and lower base plate maximum stress values ​​of the boom prototype to be predicted and some similar scaled models under multiple working conditions in the static simulation tests, and obtaining load-stress-strain curves and load-deflection curves under multiple corresponding working conditions;

[0149] The nonlinear correction model is obtained by using a curve fitting method according to the slope change results of the load-stress-strain curves and the load-deflection curves under the multiple corresponding working conditions.

[0150] The nonlinear correction model is:

[0151] The slope of the load-stress-strain curve obtained by the test results of some similar scale model test benches is corrected by the following function to obtain the corrected load-stress-strain curve of the boom to be predicted:

[0152] Where a1 is the plastic deformation coefficient of the material, a2 is the geometric defect correction coefficient, L is the arm extension length, k σ is the slope correction function of the load-deflection curve, k σ-P 、k σ-M are the slopes of the load-stress-strain curves obtained by simulating the prototype of the boom to be predicted and some similar models;

[0153] The slope of the load-deflection curve obtained by correcting the test results of some similar scale model test benches is used to obtain the corrected load-deflection curve of the boom to be predicted:

[0154] Where b1 is the connection flexibility correction factor, b2 is the load distribution correction factor, L is the arm extension length, k y is the slope correction function of the load-deflection curve, k y-P 、k y-M are the slopes of the load-deflection curves obtained by simulating the prototype of the boom to be predicted and some similar models, respectively.

[0155] In one embodiment, obtaining an error optimization model based on manufacturing deviations between a partially similar scale model and a partially similar scale model test bench includes:

[0156] Obtaining physical parameters of the partially similar scale model and the partially similar scale model test bench, wherein the physical parameters include material density p, elastic modulus E, mass M, dimensional parameter L, stiffness and distribution thereof;

[0157] Calculate the relative errors of the physical parameters of the partially similar scale model and the partially similar scale model test bench;

[0158] Based on the error coefficient of the relative error, an error optimization model is established through multiple regression analysis.

[0159] The sources of manufacturing deviation include material density deviation, test bench mass deviation, geometric dimension deviation, structural stiffness and its distribution. In view of the errors caused by the above physical parameters, an error optimization model is constructed, including stress optimization function, deflection optimization function, and natural frequency optimization function, where:

[0160] The stress optimization function is:

[0161]

[0162] The deflection optimization function is:

[0163]

[0164] The natural frequency optimization function is:

[0165]

[0166] In the error optimization model, ρ, E, and M are the material density, elastic modulus, and mass, respectively; A and L are the area and length of the cross section; J is the term related to the assembly error; and K J1 , K J2 , K J3 are the assembly error correction terms corresponding to stress, deflection and natural frequency, respectively. Subscript a represents the corresponding parameters of the test bench. Subscript a represents the corresponding parameters of the partial similarity model. ω1, ω2, ν1, ν2, ν3, ν4, μ1, μ2, μ3, μ4 are the error coefficients of the corresponding terms in the corresponding optimization functions, respectively.

[0167] Please refer to Figure 4 , is a structural diagram of a device for conducting actual service tests of the boom, that is, a structural diagram of the partially similar scale model test bench, including: a tensile testing mechanism 2 for providing load to the boom 1, and a vibration test bench 4 for installing the boom 1. The corresponding loading device is installed only when the corresponding test is required. When the vibration test is not carried out, the boom 1 is not installed on the vibration test bench 4, but can be installed on an ordinary platform. When the vibration test is carried out, it is installed on the vibration test bench 4. At this time, no load is required, so there is no need to use the tensile loading mechanism 2.

[0168] like Figure 4-Figure 8 As shown, the tension loading mechanism 2 includes a base 201, a slide rail 2011 is provided on the base 201, a loading seat 202 is slidably connected to the slide rail 2011, the loading seat 202 is connected to the slide rail 2011 through a slider 2021, a loading hydraulic cylinder 2022 is provided on the loading seat 202, the traction rope 105 on the boom 1 is connected to the loading hydraulic cylinder 2022, and the load is applied through the loading hydraulic cylinder 2022.

[0169] Furthermore, a pulley 301 is provided to cooperate with the tension loading mechanism 2 for changing the direction of the traction rope 105 , thereby facilitating the connection between the traction rope 105 and the loading hydraulic cylinder 2022 . The pulley 301 is a fixed pulley and is fixedly arranged on the pulley seat 3 .

[0170] The vibration test platform 4 includes a platform 401, on which a vibration table 402 is arranged. Guide columns 403 are respectively provided at the four corners of the vibration table 402. The guide columns 403 are fixedly arranged on the platform 401, and the vibration table 402 is slidably connected to the guide columns 403. The arm 1 is installed on the vibration table 402.

[0171] A drive shaft 405 is provided on the platform 401, and the drive shaft 405 is located below the vibration table 402. An eccentric wheel 406 is provided below the vibration table 402, and the eccentric wheel 406 is in contact with the drive shaft 405. The rotation of the drive shaft 405 drives the eccentric wheel 406 to rotate, and the eccentric wheel 406 drives the vibration table 402 to vibrate during the rotation. A mounting frame 4061 is provided on the lower surface of the vibration table 402, and the eccentric wheel 406 is rotatably set on the mounting frame 4061, and an axle seat 4052 for mounting the drive shaft 405 is provided on the platform 401.

[0172] A driving motor 404 is provided on the platform 401 to drive the driving shaft 405 to rotate. Specifically, the output end of the driving motor 404 is connected to a driving pulley 4041, and a driven pulley 4051 is provided at the end of the driving shaft 405. A synchronous belt 4042 is provided on the driven pulley 4051 and the driving pulley 4041. The synchronous belt 4042 drives the driven pulley 4051 and the driving pulley 4041 to rotate synchronously, thereby driving the driving shaft 405 to rotate.

[0173] In addition, a spring 4021 is provided under the vibration table 402, and the bottom end of the spring 4021 is connected to the platform 401, that is, a connecting piece 4022 is provided on the platform 401, and the bottom end of the spring 4021 is connected to the connecting piece 4022. The connecting piece 4022 is a cylindrical structure with an external thread formed on its outer wall. The connecting piece 4022 is threadedly connected to the platform 401. By rotating the connecting piece 4022, the tightness of the spring 4021 can be adjusted, and then the vibration amplitude of the vibration table 402 can be adjusted. Through the provided spring 4021, under the action of the elastic force of the spring 4021, the vibration time of the vibration table 402 is increased. At the same time, under the action of the spring 4021, the vibration table 402 can be buffered during the vibration process, thereby ensuring that the arm 1 will not tip over due to severe vibration during the vibration process.

[0174] A boom base 101 is provided at the bottom of the boom 1, and the boom base 101 is installed on the vibration table 402. A counterweight 1011 is provided on the boom base 101. The boom 1 includes a basic arm 109 and a plurality of arm sections 106 connected to the basic arm 109 and retractable. Accelerometers 104 are provided at both ends of the basic arm 109 and the top of the arm section 106. The natural frequency, vibration mode, damping ratio and other dynamic response parameters are measured by the accelerometer 104.

[0175] Strain gauges 107 are attached to the upper cover plate, lower base plate and other high-stress areas of the boom 1 to measure the local stress distribution and strain value; a displacement sensor 103 is set in the middle of the basic arm 109 to measure the deflection or deformation of the boom 1 and the deformation under the action of concentrated load; an inclination sensor 102 is installed in the middle of the basic arm 109 to measure and record the amplitude angle working condition of the boom 1 itself.

[0176] A luffing cylinder 108 is provided between the basic arm 109 and the boom base 101 for adjusting the inclination angle of the boom 1 .

[0177] The devices or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email transceiver, game console, tablet computer, wearable device, or any combination of these devices.

[0178] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0179] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0180] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0181] For the computer-readable medium (or computer-readable storage medium) as described above or in any other form, computer instructions may be stored thereon, which, when executed by a processor, implement one or more of the above-mentioned embodiments, thereby realizing the technical solution of the present invention.

[0182] The present invention further provides a computer program that, when executed by a processor, implements one or more of the aforementioned embodiments, thereby realizing the technical solution of the present invention. The computer program may be recorded on the aforementioned or any other form of computer-readable medium, and the present invention is not limited thereto.

[0183] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0184] The foregoing description describes specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0185] The terms used in one or more embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of the present invention. The singular forms "a", "an", "the" and "the" used in one or more embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0186] It should be understood that although the terms first, second, third, etc. may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of the present invention, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining."

[0187] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit one or more embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present invention shall be included in the scope of protection of one or more embodiments of the present invention.

Claims

1. A method for predicting the mechanical properties of a truck crane boom, characterized by: The method comprises: Step S1: Based on the geometric length similarity ratio and the displacement equivalence principle, and in accordance with the transverse free vibration equation of the variable-section Euler beam and the dynamic similarity principle, obtain the dynamic parameter similarity ratio and the related parameter similarity ratio of the prototype of the boom to be predicted and the preliminary scaled model of the boom to be predicted, thereby constructing the preliminary scaled model according to the dynamic parameter similarity ratio and the related parameter similarity ratio; Step S2: performing a dynamic simulation test on the preliminary scaled model and the boom prototype to be predicted, and obtaining dynamic parameters and related parameters of the preliminary scaled model that are consistent with the dynamic characteristics of the prototype, thereby constructing a partially similar scaled model; Step S3: obtaining a nonlinear correction model based on the simulation performance curves of the boom prototype to be predicted and the partially similar scale model; building a partially similar scale model test bench, and obtaining an error optimization model based on the manufacturing deviation between the partially similar scale model and the partially similar scale model test bench; Step S4: obtaining a preliminary predicted performance curve of the boom to be predicted based on the results of the dynamic performance test and the static performance test of the partially similar scale model test bench, and correcting the preliminary predicted performance curve based on the nonlinear correction model and the error optimization model to obtain an optimized predicted performance curve of the boom to be predicted; Step S5: According to the optimized prediction performance curve, the performance of the boom to be predicted under a certain working condition is obtained, thereby realizing the performance prediction of the boom to be predicted.

2. The method for predicting mechanical properties of a truck crane boom according to claim 1, characterized in that: The dynamic parameters include natural frequency and stiffness, and the related parameters include geometric length, position of the variable amplitude hinge point, boom working elevation angle, beam section inertia moment, material elastic modulus, material Poisson's ratio, beam deflection, boom stress, lifting load and working time.

3. The method for predicting mechanical properties of a truck crane boom according to claim 1, wherein: The predicted performance curves include load-deflection curves, load-stress-strain curves, natural frequency performance curves, mode shape performance curves, damping ratio performance curves and acceleration frequency response curves under different working conditions.

4. The method for predicting mechanical properties of a truck crane boom according to claim 1, wherein: The dynamic performance test includes: Dynamic characteristics test, including vibration modal test, used to analyze the natural frequency and vibration shape of the telescopic boom; Dynamic load test, used to evaluate the response of the telescopic boom under dynamic load; Impact test, used to test the performance, acceleration response, natural frequency and damping ratio of the telescopic boom under sudden impact load; The static performance test includes: Boom static load test, used to verify the bearing capacity of the telescopic boom under different loads; Boom stiffness test to evaluate the stiffness characteristics of the telescopic boom; Boom stability test, to check the stability of the cover plate, web plate and local components of the telescopic boom.

5. The method for predicting mechanical properties of a truck crane boom according to claim 1, characterized in that: The method of obtaining a nonlinear correction model based on the simulation performance curves of the boom prototype to be predicted and a partially similar scale model includes: Conducting static simulation tests under multiple working conditions, collecting maximum deformation, upper cover plate, and lower base plate maximum stress values ​​of the boom prototype to be predicted and some similar scaled models under multiple working conditions in the static simulation tests, and obtaining load-stress-strain curves and load-deflection curves under multiple corresponding working conditions; The nonlinear correction model is obtained by using a curve fitting method according to the slope change results of the load-stress-strain curves and the load-deflection curves under the multiple corresponding working conditions.

6. The method for predicting mechanical properties of a truck crane boom according to claim 5, characterized in that: The nonlinear correction model is: The slope of the load-stress-strain curve obtained by the test results of some similar scale model test benches is corrected by the following function to obtain the corrected load-stress-strain curve of the boom to be predicted: Where a1 is the plastic deformation coefficient of the material, a2 is the geometric defect correction coefficient, L is the arm extension length, k σ is the slope correction function of the load-deflection curve, k σ-P 、k σ-M are the slopes of the load-stress-strain curves obtained by simulating the prototype of the boom to be predicted and some similar models; The slope of the load-deflection curve obtained from the test results of the scaled model test bench is partially similar to the following function to obtain the modified load-deflection curve of the boom to be predicted: Where b1 is the connection flexibility correction factor, b2 is the load distribution correction factor, L is the arm extension length, k y is the slope correction function of the load-deflection curve, k y-P 、k y-M are the slopes of the load-deflection curves obtained by simulating the prototype of the boom to be predicted and some similar models, respectively.

7. The method for predicting mechanical properties of a truck crane boom according to claim 1, characterized in that: Obtaining an error optimization model according to manufacturing deviations between the partially similar scale model and the partially similar scale model test bench, including: Obtaining physical parameters of the partially similar scale model and the partially similar scale model test bench, wherein the physical parameters include material density p, elastic modulus E, mass M, dimensional parameter L, stiffness and distribution thereof; Calculate the relative errors of the physical parameters of the partially similar scale model and the partially similar scale model test bench; Based on the error coefficient of the relative error, an error optimization model is established through multiple regression analysis.

8. The method for predicting mechanical properties of a truck crane boom according to claim 1, wherein: The method further comprises the following steps: A preset error value is used, and a root mean square error is obtained based on the actual service data of the boom to be predicted and the corresponding data predicted by the optimized prediction performance curve. If the root mean square error is greater than the preset error value, the nonlinear correction model of the partial similarity scale model is iteratively adjusted until the root mean square error is no greater than the preset error value, thereby obtaining a re-optimized prediction performance curve of the boom to be predicted, and realizing performance prediction of the boom to be predicted based on the re-optimized prediction performance curve.

9. A device for predicting the mechanical properties of a truck crane boom, characterized by: The device comprises: Scaled model construction unit: Based on the geometric length similarity ratio and displacement equivalence principle, and in accordance with the lateral free vibration equation of the variable-section Euler beam and the dynamic similarity principle, the dynamic parameter similarity ratio and the related parameter similarity ratio of the boom prototype to be predicted and the preliminary scaled model of the boom to be predicted are obtained, and a preliminary scaled model is constructed according to the dynamic parameter similarity ratio and the related parameter similarity ratio, and a dynamic simulation test is performed on the preliminary scaled model and the boom prototype to be predicted to obtain the dynamic parameters and related parameters of the preliminary scaled model that are consistent with the dynamic characteristics of the prototype, thereby constructing a partially similar scaled model; Correction and optimization unit: obtaining a nonlinear correction model based on the static simulation test results of the partially similar scale model and the prototype to be predicted; obtaining an error optimization model based on the manufacturing deviation of the partially similar scale model and the partially similar scale model test bench; obtaining a preliminary prediction performance curve of the boom to be predicted based on the dynamic performance test and static performance test results of the partially similar scale model test bench, and correcting the preliminary prediction performance curve based on the nonlinear correction model and the error optimization model, thereby obtaining an optimized prediction performance curve of the boom to be predicted; Output unit: outputs the performance of the boom to be predicted under certain working conditions according to the optimized prediction performance curve.

10. The device for predicting mechanical properties of a truck crane boom according to claim 9, characterized in that: The partially similar scale model test bench includes: a tensile testing mechanism 2 for providing a load to the boom 1, and a vibration test bench 4 for installing the boom 1, wherein: The tension loading mechanism 2 includes a base 201, a slide rail 2011 is provided on the base 201, a loading seat 202 is slidably connected to the slide rail 2011, and the loading seat 202 is connected to the slide rail 2011 via a slider 2021. A loading hydraulic cylinder 2022 is provided on the loading seat 202, and the traction rope 105 on the boom 1 is connected to the loading hydraulic cylinder 2022, and the load is applied through the loading hydraulic cylinder 2022; The vibration test platform 4 includes a platform 401, a vibration platform 402 is provided on the platform 401, and guide posts 403 are provided at the four corners of the vibration platform 402. The guide posts 403 are fixedly provided on the platform 401, and the vibration platform 402 is slidably connected to the guide posts 403. The arm 1 is installed on the vibration platform 402; A drive shaft 405 is provided on the platform 401, and the drive shaft 405 is located below the vibration table 402. An eccentric wheel 406 is provided below the vibration table 402, and the eccentric wheel 406 is in contact with the drive shaft 405. The rotation of the drive shaft 405 drives the eccentric wheel 406 to rotate, and the eccentric wheel 406 drives the vibration table 402 to vibrate during the rotation. A mounting frame 4061 is provided on the lower surface of the vibration table 402, and the eccentric wheel 406 is rotatably set on the mounting frame 4061, and an axle seat 4052 for mounting the drive shaft 405 is provided on the platform 401.

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