A calibration platform, calibration method and cable force correction method for electromagnetic elastic cable force sensor considering the torsion effect of wire rope

Abstract

The present invention discloses a calibration platform, calibration method and cable force correction method of an electromagnetic elastic cable force sensor considering the torsion effect of a steel wire rope, and relates to the technical field of cable force monitoring of steel wire rope slings in bridge engineering and calibration of electromagnetic elastic cable force sensors. The calibration platform comprises: a static loading system, a temperature control box, an electromagnetic elastic cable force sensor, a steel wire rope test cable and a steel wire rope torsion fixing device. The steel wire rope torsion fixing device can be used to apply different rotation angles to the steel wire rope test cable, and complete the calibration experiment of the electromagnetic elastic cable force sensor under different degrees of torsion deformation of the steel wire rope test cable. The calibration method and cable force correction method provided by the present invention can realize the cable force correction of the electromagnetic elastic cable force sensor considering the torsion effect of the steel wire rope sling, and improve the applicability of the electromagnetic elastic cable force sensor.

Description

Technical Field

[0001] The invention relates to the technical field of monitoring the cable force of a steel wire rope sling in a bridge project and calibrating an electromagnetic elastic cable force sensor, and in particular to a calibration platform, a calibration method and a cable force correction method for an electromagnetic elastic cable force sensor taking into account the torsion effect of the steel wire rope. Background Art

[0002] As the key component connecting the main cable and stiffening beam of a suspension bridge, the sling is also one of the most fragile and important components in the suspension bridge structure system. The stress state of the sling directly affects the safe operation of the bridge. How to accurately and quickly measure the sling force has become an engineering problem that needs to be solved urgently.

[0003] Traditional cable force measurement methods generally evaluate cable force indirectly through changes in physical parameters of the structure, such as strain and vibration frequency. The strain method is simple, but can only obtain relative values. The vibration frequency method is usually limited by the boundary conditions, length, mass and cross-sectional area of ​​the measured cable; for short slings, its measurement accuracy needs to be improved.

[0004] The electromagnetic elastic cable tension sensor can realize the long-term and accurate monitoring of the absolute cable tension of ferromagnetic components such as bridge suspension cables, cables, and prestressed tendons (beams), thereby providing reliable analytical data for the safety assessment of bridge structures. However, due to the special structural form of the wire rope suspension cable, the outer strands of steel wire are arranged in a spiral shape. In addition to the axial deformation, it will also produce additional torsional deformation that cannot be ignored during the tension process, and as the axial tension increases, the torsional deformation also increases. According to the magnetoelastic effect of ferromagnetic components, different degrees of torsional deformation will change the magnetic characteristic quantity (such as magnetic flux density) of the wire rope, thereby affecting the output value of the electromagnetic elastic cable tension sensor. Affected by the torsional effect of the wire rope, the calibration curves of the electromagnetic elastic cable tension sensor in the loading and unloading stages cannot overlap; if the polynomial method is used to calculate the calibration formula, the test error of the electromagnetic elastic cable tension sensor is too large to meet the actual engineering needs. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide an electromagnetic elastic cable tension sensor calibration platform, calibration method and cable tension correction method that take into account the torsional effect of the wire rope, so as to complete the calibration of the electromagnetic elastic cable tension sensor under different degrees of torsional deformation of the wire rope, and realize the cable tension correction based on the electromagnetic elastic cable tension sensor.

[0006] To achieve the above-mentioned purpose, the present invention provides an electromagnetic elastic cable force sensor calibration platform considering the torsion effect of the wire rope, comprising: a static loading system, a temperature control box, an electromagnetic elastic cable force sensor, a wire rope test rope and a wire rope torsion fixing device; the static loading system comprises: a static loading frame and a tensioning system, and the tensioning system comprises: a hydraulic jack, a standard dynamometer, a loading end anchor plate and a fixed end anchor plate;

[0007] The hydraulic jack, the standard dynamometer and the loading end anchor plate are placed concentrically on the top of the static loading frame from top to bottom, and the fixed end anchor plate is arranged at the bottom of the static loading frame;

[0008] The temperature control box is arranged inside the static loading frame; the electromagnetic elastic cable force sensor is arranged in the middle position of the steel wire rope test cable;

[0009] The steel wire rope test cable passes through the hydraulic jack, the standard dynamometer, the loading end anchor plate, the temperature control box and the fixed end anchor plate in sequence, and is fixed on the static loading frame;

[0010] The wire rope torsion fixing device is arranged on the wire rope test rope, and is used for adjusting the rotation angle of the wire rope test rope, so as to complete the setting of different working conditions of the wire rope test rope.

[0011] To achieve the above object, the present invention further provides a calibration method for an electromagnetic elastic cable force sensor considering the torsion effect of a steel wire rope, the calibration method being applied to the above-mentioned calibration platform for an electromagnetic elastic cable force sensor considering the torsion effect of a steel wire rope, the calibration method comprising:

[0012] The rotation angle of the anchor head at the loading end is adjusted by the wire rope rotation fixing device, so that the wire rope test cable is twisted to different working conditions; the working conditions include rotation working conditions and different fixed working conditions;

[0013] Under different working conditions, the temperature control box is used to adjust the calibration temperature of the electromagnetic elastic cable force sensor and the wire rope test cable until the calibration temperature and the magnetic characteristic quantity of the wire rope test cable are stable.

[0014] At the calibration temperature, the output signals of the electromagnetic elastic cable force sensor under various levels of calibration tension are collected, and the calibration formula and test error of the electromagnetic elastic cable force sensor under different working conditions are determined according to the output signals to complete the calibration of the electromagnetic elastic cable force sensor.

[0015] To achieve the above object, the present invention further provides a cable force correction method, comprising:

[0016] According to the above-mentioned electromagnetic elastic cable force sensor calibration method considering the torsion effect of the wire rope, the electromagnetic elastic cable force sensor is calibrated under all working conditions, and the output signal curve of the electromagnetic elastic cable force sensor is obtained;

[0017] Constructing a training data set; the training data set includes: polynomial fitting coefficients of the output signal curve, calibration temperature, rotation angle and cable force;

[0018] The BP neural network is trained by the training data set; the input of the BP neural network is the polynomial fitting coefficient of the output signal curve and the calibration temperature, and the output is the rotation angle and the cable force;

[0019] The trained BP neural network is used to predict the cable tension of the wire rope sling under different working conditions and complete the cable tension correction.

[0020] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0021] The present invention builds a calibration platform for an electromagnetic elastic cable force sensor combined with a wire rope torsion fixing device, which can apply different rotation angles to the wire rope test cable to complete the calibration experiment of the electromagnetic elastic cable force sensor under different degrees of torsion deformation of the wire rope test cable. In addition, the present invention uses the output signal of the electromagnetic elastic cable force sensor and the BP neural network algorithm to realize the cable force correction of the electromagnetic elastic cable force sensor considering the torsion effect of the wire rope sling, improves the applicability of the electromagnetic elastic cable force sensor, and provides reliable data support for the force assessment of the wire rope sling and the safe operation of the bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0023] Figure 1 It is a structural schematic diagram of the calibration platform of the electromagnetic elastic cable force sensor considering the torsion effect of the wire rope;

[0024] Figure 2 It is a schematic diagram of the wire rope test cable;

[0025] Figure 3 It is a structural schematic diagram of a wire rope torsion fixing device;

[0026] Figure 4 It is a schematic diagram of the twisting of the wire rope test cable to different fixed working conditions;

[0027] Figure 5 It is a schematic diagram of the working condition of the wire rope test cable from twisting to rotation;

[0028] Figure 6 is the force exerted on V by the steel wire rope under the condition of cable rotation EME (t) Schematic diagram of the influence; in the figure, the solid line is the loading process, the dotted line is the unloading process, different symbols represent different force values, and the same symbol represents the same force value (loading or unloading);

[0029] Figure 7 is the force exerted on V by the wire rope test cable when it is twisted to the fixed working condition C2 EME (t) Schematic diagram of the influence; in the figure, the solid line is the loading process, the dotted line is the unloading process, different symbols represent different force values, and the same symbol represents the same force value (loading or unloading);

[0030] Figure 8 When the cable force is 300kN, the fixed working condition has an effect on V EME Schematic diagram of the impact of (t);

[0031] Fig. 9 It is a schematic diagram of the structure of the BP neural network;

[0032] Fig.10 It is the training flow chart of BP neural network;

[0033] Fig.11 It is a flowchart of optimizing BP neural network using GA algorithm;

[0034] Fig.12 It is a schematic diagram of the prediction results of the cable force using the BP neural network under the condition of cable rotation in the wire rope test;

[0035] In the figure: 1-static loading frame, 101-top crossbeam of loading frame, 102-bottom crossbeam of loading frame, 103-column, 104-joining nut, 2-hydraulic jack, 3-standard dynamometer, 4-electromagnetic elastic cable force sensor, 5-temperature control box, 6-wire rope test cable, 601-cable body, 602-loading end connecting rod, 603-loading end nut, 604-loading end anchor head, 605-loading end anchor head, 606-fixed end nut, 607-fixed end connecting rod, -7 wire rope torsion fixing device, 701-fixing rod, 702-fixing device frame, 703-angle fixing baffle, 801-loading end anchor pad, 802-fixed end anchor pad. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] The purpose of the present invention is to provide an electromagnetic elastic cable tension sensor calibration platform, calibration method and cable tension correction method that take into account the torsional effect of the wire rope, so as to complete the calibration of the electromagnetic elastic cable tension sensor under different degrees of torsional deformation of the wire rope and realize the cable tension correction based on the electromagnetic elastic cable tension sensor.

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] Embodiment 1

[0040] This embodiment provides a calibration platform for electromagnetic elastic cable force sensors that takes into account the torsion effect of wire ropes. Figure 1 As shown, the calibration platform includes: a static loading system, a temperature control box 5, an electromagnetic elastic cable force sensor 4, a wire rope test cable 6 and a wire rope torsion fixing device 7. The static loading system includes: a static loading frame 1 and a tensioning system, and the tensioning system includes: a hydraulic jack 2, a standard dynamometer 3, a loading end anchor plate 801 and a fixed end anchor plate 802. The hydraulic jack 2 is used to apply tension to the wire rope test cable 6, and the cable force value is read through the standard dynamometer 3.

[0041] Furthermore, if Figure 1 As shown, the static loading frame 1 includes four columns 103, a top crossbeam 101 of the loading frame, a bottom crossbeam 102 of the loading frame, and a splicing nut 104. The columns 103, the top crossbeam 101 of the loading frame, and the bottom crossbeam 102 of the loading frame are spliced ​​and fixed by the splicing nut 104. The center of the top crossbeam 101 of the loading frame and the center of the bottom crossbeam 102 of the loading frame are both provided with a circular hole with a diameter of 400 mm for the wire rope test cable 6 to pass through. The hydraulic jack 2, the standard dynamometer 3, and the loading end anchor plate 801 are concentrically placed on the top of the top crossbeam 101 of the loading frame from top to bottom, and the fixed end anchor plate 802 is arranged at the bottom of the bottom crossbeam 102 of the loading frame.

[0042] Furthermore, if Figure 1 As shown, the wire rope test cable 6 is installed in the static loading frame 1, including Figure 2 The cable body 601, loading end anchor head 604, fixed end anchor head 605, loading end connecting rod 602, fixed end connecting rod 607, loading end connecting rod nut 603, and fixed end connecting rod nut 606 are shown. The loading end connecting rod 602 passes through the loading end connecting rod nut 603, hydraulic jack 2, standard dynamometer 3, loading end anchor pad 801, and the round hole of the top cross beam 101 of the loading frame in sequence, and is connected to one end of the loading end anchor head 604, the other end of the loading end anchor head 604 is connected to one end of the cable body 601, the other end of the cable body 601 is connected to one end of the fixed end anchor head 605, the other end of the fixed end anchor head 605 is connected to one end of the fixed end connecting rod 607, the other end of the fixed end connecting rod 607 passes through the bottom cross beam 102 of the loading frame and the fixed end anchor pad 802 in sequence, and the steel wire rope test cable 6 is fixed through the fixed end connecting rod nut 606.

[0043] Furthermore, the electromagnetic elastic cable tension sensor 4 is installed in the middle position of the wire rope test cable 6, that is, on the cable body 601, and the cable body 601 is placed in the temperature control box 5. The temperature control box 5 is used to achieve temperature regulation of the electromagnetic elastic cable tension sensor 4 and the wire rope test cable 6.

[0044] Furthermore, if Figure 1 and Figure 3 As shown, the wire rope torsion fixing device 7 is installed at the loading end anchor head 604, and the setting of working conditions of different torsion degrees of the wire rope test cable 6 is completed by adjusting the rotation angle of the loading end anchor head 604. Figure 3 As shown, the wire rope torsion fixing device 7 includes two fixing rods 701, a fixing device frame 702, and five groups of angle fixing baffles 703; the fixing device frame 703 is fixed on four columns 103, and the angle fixing baffles 703 are welded on the fixing device frame 702 according to the designed position. One end of the fixing rod 701 is welded in parallel to the loading end anchor head 604, and the other end is installed on the angle fixing baffle 703, thereby realizing the setting of the initial rotation angle of the wire rope test rope 6 and limiting its rotation during the tension loading and unloading process.

[0045] The installation position of the wire rope torsion fixing device 7 includes but is not limited to the loading end anchor head 604, but needs to be reasonably selected according to the calibration platform and the size of the test rope; the connection method of the fixing rod 701 includes but is not limited to welding, and clamping connection or other connection forms can also be selected. Its core purpose is to apply different degrees of torsional deformation to the wire rope test rope 6.

[0046] The calibration platform of the electromagnetic elastic cable force sensor combined with the wire rope torsion fixing device constructed in this embodiment can impose different rotation angles on the wire rope test cable to complete the calibration experiment of the electromagnetic elastic cable force sensor under different degrees of torsional deformation of the wire rope test cable.

[0047] Embodiment 2

[0048] Based on the electromagnetic elastic cable force sensor calibration platform considering the torsion effect of the wire rope provided in the first embodiment, this embodiment provides an electromagnetic elastic cable force sensor calibration method considering the torsion effect of the wire rope, the method comprising:

[0049] S101: The rotation angle of the anchor head at the loading end is adjusted by the wire rope rotation fixing device to twist the wire rope test cable to different working conditions; the working conditions include rotation conditions and different fixed conditions, such as Figure 4-Figure 5 shown.

[0050] S102: Under different working conditions, the calibration temperature of the electromagnetic elastic cable force sensor and the wire rope test cable is regulated by a temperature control box until the calibration temperature and the magnetic characteristic quantity of the wire rope test cable are both stable.

[0051] S103: Under the calibration temperature, the output signals of the electromagnetic elastic cable tension sensor under various calibration tensions are collected, and the calibration formula and test error of the electromagnetic elastic cable tension sensor under different working conditions are determined according to the output signals to complete the calibration of the electromagnetic elastic cable tension sensor.

[0052] The specific process is as follows:

[0053] (1) The wire rope test cable 6 with the electromagnetic elastic cable force sensor 4 is hoisted into the static loading frame 1, and the tensioning system, the temperature control box 5 and the wire rope torsion fixing device 7 are installed in sequence.

[0054] (2) The wire rope torsion fixing device 7 adjusts the rotation angle of the loading end anchor head 604 to make the wire rope test rope 6 to the fixed working condition C1, and uses the temperature control box to regulate the calibration temperature of the electromagnetic elastic cable force sensor 4 and the wire rope test rope 6 until the temperature and the magnetic characteristic quantity of the wire rope test rope 6 are stable. At this time, the calibration temperature is T1.

[0055] (3) Complete the application of each level of calibration tension and collect the output signal V of the electromagnetic elastic cable tension sensor 4 under each level of calibration tension EME (t), the output value X of the electromagnetic elastic cable force sensor 4 is obtained by signal demodulation EME , and then obtain the calibration formula and test error of the electromagnetic elastic cable tension sensor 4 under the fixed working condition C1. The calculation formula is:

[0056]

[0057]

[0058] in, is the tension applied to the i-th calibration point, Calculate the cable force for the electromagnetic elastic cable force sensor at the i-th calibration point. The calculation formula is:

[0059]

[0060] Wherein, d0, d1, d2 and d3 are cubic polynomial fitting coefficients of the calibration curve of the electromagnetic elastic cable force sensor 4.

[0061] (4) At the calibration temperature T1, calibration experiments are carried out on the electromagnetic elastic cable tension sensor 4 in sequence with the torsion conditions C2, C3, ..., Cn and the rotation condition Cn+1 without additional torsion restriction, and the corresponding data processing is completed.

[0062] (5) The temperature of the temperature control box is adjusted to T2, T3, ..., Tm in sequence, and the calibration experiments of the electromagnetic elastic cable force sensor under the working conditions of C1, C2..., Cn+1 are completed respectively, and corresponding data processing is performed.

[0063] S106: Complete all calibration experiments and summarize the calibration formulas and test errors of the electromagnetic elastic cable tension sensor 4 under various working conditions.

[0064] This implementation can obtain the influence of the torsional deformation of the wire rope on the test results of the electromagnetic elastic cable force sensor.

[0065] Embodiment 3

[0066] Based on the first and second embodiments, this embodiment provides a cable force correction method, including:

[0067] S201: complete the calibration of the electromagnetic elastic cable force sensor 4 under all working conditions according to the calibration method provided in the second embodiment, and obtain the output signal V of the electromagnetic elastic cable force sensor 4 EME (t) curve, such as Figure 6-Figure 8 shown.

[0068] Using V EME The polynomial fitting coefficients of (t) represent the characteristics of each signal curve. The fitting formula is:

[0069] V EME (t) = a0 + a1t + a2t 2 +a3t 3

[0070] Where a0, a1, a2 and a3 are V EME (t) The cubic polynomial fitting coefficients of the fitting curve.

[0071] V EME (t) The polynomial degree of the curve needs to be determined according to V EME The goodness of fit and training effect of (t) are determined.

[0072] S202: constructing a training data set; the training data set includes: polynomial fitting coefficients of the output signal curve, calibration temperature, rotation angle and cable force.

[0073] S203: training a BP neural network through a training data set; the input of the BP neural network is the polynomial fitting coefficient of the output signal curve and the calibration temperature, and the output is the rotation angle and the cable force.

[0074] Input the dataset to Fig. 9 In the BP neural network shown in the figure, the training and testing process of the BP neural network is as follows Fig.10As shown, the data set is normalized so that the data values ​​of the training set, validation set, and test set are all kept within [0,1]. The data sets under all fixed working conditions are used as the training set and validation set of the BP neural network, and the K-fold cross-validation algorithm is used to obtain the optimal data set and the number of hidden layer nodes. For K-fold cross-validation, the data sets under all fixed working conditions are randomly divided into K parts, K-1 parts of the data are used as the training set, and the other 1 part of the data is used as the validation set to increase the number of training samples and improve the generalization performance of the BP neural network. In each cycle, the prediction results under different numbers of hidden layer nodes are tested, and then the number of hidden layer nodes is determined. The training performance evaluation indicators of the BP neural network are the cable force prediction error and the correlation coefficient between the predicted values ​​of the rotation angle and cable force and the target value. The BP neural network is tested using the optimal data set and the optimal number of hidden layer nodes to achieve the prediction of the rotation angle and cable force under the rotation condition. In order to improve the prediction accuracy of the BP neural network, the use Fig.11 The GA algorithm shown optimizes the weights and biases of the BP neural network. Specifically, the optimal data set in the above BP neural network is substituted into the GA algorithm, and the population is initialized, the weights and biases of the BP neural network are encoded, and the fitness function is determined in turn; then the encoded data is subjected to the steps of "selection", "crossover", "mutation", and "iteration of new population" to complete the population data update. When the algorithm terminates the debugging, the weights and biases of the BP neural network optimized by the GA algorithm are output, and finally the GABP neural network is tested to obtain the predicted optimal solution. Through continuous training and testing, the cable force and rotation angle output by the neural network meet the accuracy requirements. In addition to being obtained through calibration experiments, the test set data in the BP neural network can also be obtained using measured signals from actual projects.

[0075] S204: Predicting the cable force of the wire rope sling under different working conditions through the trained BP neural network, and completing the cable force correction.

[0076] Fig.12 The GABP neural network is the prediction result of the cable tension of the wire rope rotation condition in this embodiment. The maximum relative error of the electromagnetic elastic cable tension sensor is reduced from 11.72% of the polynomial method to 5.24% of GABP, and the maximum absolute error is reduced from 21.75kN of the polynomial method to 14.67kN of GABP. This shows that the correction method can improve the test accuracy of the electromagnetic elastic cable tension sensor and can make it meet the error requirements of the actual cable tension detection of the wire rope sling.

[0077] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0078] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A calibration method for an electromagnetic elastic cable force sensor considering the torsion effect of a wire rope, characterized in that: The calibration method is applied to an electromagnetic elastic cable force sensor calibration platform considering the torsion effect of the wire rope, and the electromagnetic elastic cable force sensor calibration platform considering the torsion effect of the wire rope comprises: a static loading system, a temperature control box, an electromagnetic elastic cable force sensor, a wire rope test cable and a wire rope torsion fixing device; the static loading system comprises: a static loading frame and a tensioning system, and the tensioning system comprises: a hydraulic jack, a standard dynamometer, a loading end anchor plate and a fixed end anchor plate; The hydraulic jack, the standard dynamometer and the loading end anchor plate are placed concentrically on the top of the static loading frame from top to bottom, and the fixed end anchor plate is arranged at the bottom of the static loading frame; The temperature control box is arranged inside the static loading frame; the electromagnetic elastic cable force sensor is arranged in the middle position of the steel wire rope test cable; The steel wire rope test cable passes through the hydraulic jack, the standard dynamometer, the loading end anchor plate, the temperature control box and the fixed end anchor plate in sequence, and is fixed on the static loading frame; The wire rope torsion fixing device is arranged on the wire rope test rope, and is used to adjust the rotation angle of the wire rope test rope, so as to complete the setting of different working conditions of the wire rope test rope; The calibration method comprises: The rotation angle of the anchor head at the loading end is adjusted by the wire rope rotation fixing device, so that the wire rope test cable is twisted to different working conditions; the working conditions include rotation working conditions and different fixed working conditions; Under different working conditions, the temperature control box is used to adjust the calibration temperature of the electromagnetic elastic cable force sensor and the wire rope test cable until the calibration temperature and the magnetic characteristic quantity of the wire rope test cable are stable. At the calibration temperature, the output signals of the electromagnetic elastic cable force sensor under various levels of calibration tension are collected, and the calibration formula and test error of the electromagnetic elastic cable force sensor under different working conditions are determined according to the output signals to complete the calibration of the electromagnetic elastic cable force sensor.

2. The calibration method of electromagnetic elastic cable force sensor considering the torsion effect of wire rope according to claim 1 is characterized in that: The static loading frame includes: a column, a top crossbeam of the loading frame, a bottom crossbeam of the loading frame and a splicing nut, wherein the column, the top crossbeam of the loading frame and the bottom crossbeam of the loading frame are spliced ​​and fixed by the splicing nut; a circular hole is arranged at the center of the top crossbeam of the loading frame and the bottom crossbeam of the loading frame for the wire rope test cable to pass through; the hydraulic jack, the standard dynamometer and the loading end anchor plate are concentrically placed on the top of the top crossbeam of the loading frame from top to bottom, and the fixed end anchor plate is arranged at the bottom of the bottom crossbeam of the loading frame.

3. The calibration method of electromagnetic elastic cable force sensor considering the torsion effect of wire rope according to claim 2 is characterized in that: The steel wire rope test cable comprises: A cable body, a loading end anchor head, a fixed end anchor head, a loading end connecting rod, a fixed end connecting rod, a loading end connecting rod nut and a fixed end connecting rod nut; the cable body is arranged in the temperature control box; the wire rope torsion fixing device is arranged at the loading end anchor head, and the electromagnetic elastic cable force sensor is arranged on the cable body; The loading end connecting rod nut is arranged on the top of the hydraulic jack. The loading end connecting rod passes through the loading end connecting rod nut, the hydraulic jack, the standard dynamometer, the loading end anchor plate and the round hole of the top cross beam of the loading frame in sequence, and is connected to one end of the loading end anchor head. The other end of the loading end anchor head is connected to one end of the cable body, and the other end of the cable body is connected to one end of the fixed end anchor head. The fixed end connecting rod passes through the fixed end connecting rod nut, the fixed end anchor plate and the round hole of the bottom cross beam of the loading frame in sequence, and is connected to the other end of the fixed end anchor head.

4. The calibration method of electromagnetic elastic cable force sensor considering the torsion effect of wire rope according to claim 3 is characterized in that: The wire rope torsion fixing device includes: a fixing rod, a fixing device frame, and an angle fixing baffle; the fixing device frame is fixed to the column, the angle fixing baffle is fixed to the fixing device frame, one end of the fixing rod is fixed parallel to the loading end anchor head, and the other end of the fixing rod is fixed to the angle fixing baffle.

5. The calibration method of electromagnetic elastic cable force sensor considering the torsion effect of wire rope according to claim 1 is characterized in that: The calibration formula of electromagnetic elastic cable force sensor is: Among them, T calc The cable force calculated by the electromagnetic elastic cable force sensor, d0, d1, d2 and d3 are the cubic polynomial fitting coefficients of the electromagnetic elastic cable force sensor calibration curve, X EME It is the output value of the electromagnetic elastic cable force sensor obtained by demodulating the output signal of the electromagnetic elastic cable force sensor.

6. The calibration method of electromagnetic elastic cable force sensor considering the torsion effect of wire rope according to claim 5 is characterized in that: The test error of the electromagnetic elastic cable force sensor is: Among them, RE EXP is the relative error of the electromagnetic elastic cable force sensor, AE EXP is the relative error of the electromagnetic elastic cable force sensor, is the tension applied to the i-th calibration point, is the cable force calculated by the electromagnetic elastic cable force sensor at the i-th calibration point.

7. A cable force correction method, characterized in that: include: According to the calibration method of the electromagnetic elastic cable force sensor considering the torsion effect of the wire rope according to any one of claims 1 to 6, the calibration of the electromagnetic elastic cable force sensor under all working conditions is completed, and the output signal curve of the electromagnetic elastic cable force sensor is obtained; Build a training dataset; The training data set includes: polynomial fitting coefficients of the output signal curve, calibration temperature, rotation angle and cable force; The BP neural network is trained by the training data set; the input of the BP neural network is the polynomial fitting coefficient of the output signal curve and the calibration temperature, and the output is the rotation angle and the cable force; The trained BP neural network is used to predict the cable tension of the wire rope sling under different working conditions and complete the cable tension correction.

8. The cable force correction method according to claim 7, characterized in that: During the training process of the BP neural network, a K-fold cross validation algorithm is used to obtain the optimal training set and the number of hidden layer nodes.

9. The cable force correction method according to claim 7, characterized in that: During the training process of the BP neural network, a GA algorithm is used to optimize the weights and biases of the BP neural network.

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