Method, system and device for predicting hoisting of balance beam of crane

Abstract

The invention discloses a method, a system and a device for predicting hoisting of a balance beam of a crane. The method comprises the following steps: acquiring operation data of the balance beam in an operation space; establishing a measurement range model according to the position data; calculating an adjustment value of each lifting hook according to the inclination angle data and the spatial position data; according to the data of the horizontal distance between the balance beam and the surrounding space object, judging and determining a safety area of the operation of the balance beam; calculating the stress and strain of the balance beam by using the structural parameters of the balance beam; calculating an attitude angle in the lifting operation process by using acceleration data of the lifting operation of the balance beam; and analyzing the calculated data by using a fusion algorithm to obtain a balance beam hoisting operation model, a monitoring index and a prediction result. The system comprises modules corresponding to the method, the device comprises an ultrasonic distance measuring sensor, an inclinometer, an acceleration sensor, a processing module and a display device, the operation efficiency can be guaranteed, and the operation safety can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cranes, and in particular to a method, system and device for predicting the hoisting of a crane balance beam. Background Art

[0002] The balance beam, located at the interface between the lifting equipment and the hoisted object, serves as a bridge connecting the overhead crane and the rotor, playing a crucial role in rotor hoisting operations. Its primary functions include: ensuring the rotor remains balanced and stable during the hoisting process, preventing damage to the rotor due to tilting or rotation, which is crucial for ensuring the safety of the hoisting operation and the integrity of the equipment; reducing the direct pressure and friction of the sling on the rotor surface to prevent damage to the equipment; shortening the sling length and lowering the lifting height of the movable pulley, making the hoisting operation more convenient and safer; and rationally distributing or balancing the load at each lifting point. In the case of multiple cranes lifting, the balance beam rationally distributes or balances the load at each lifting point, ensuring the stability and safety of the hoisting operation.

[0003] Balance beams are widely used in the existing technology. The specifications of balance beams vary depending on the application scenarios of the balance beams. For example, a power station is currently equipped with a set of rotor hanging balance beams. The balance beam itself weighs about 160t and has dimensions of 17000mm×5346mm×3200mm. It is an overweight, overlong and overwide component. The roads in the work area where the power station is located are steep and have sharp bends. The transportation cost is high and the safety risk is greater. The transportation, installation and maintenance costs are high. Once a failure occurs, it will seriously affect the lifting operation and project progress. During maintenance, it is necessary to frequently transfer between the left and right bank factories, which seriously wastes manpower, material and financial resources. At the same time, it is inevitable that when the operator performs the lifting operation, the operation efficiency will be low due to force majeure factors such as weather and environment, which reduces the safety of the lifting operation and increases the degree of damage to the balance beam and the lifted materials, thereby making the safety of the balance beam and the materials lifted by the balance beam not guaranteed. Summary of the Invention

[0004] In order to solve some or all of the technical problems existing in the above-mentioned prior art, the present invention provides a crane balance beam hoisting prediction method, system and device, which can ensure operation efficiency and improve operation safety.

[0005] The technical solutions of the present invention are as follows:

[0006] Firstly, a crane balance beam hoisting prediction method is provided, including:

[0007] Acquiring operation data of the balance beam in the operation space, the operation data including inclination data of the balance beam, spatial position data, acceleration data of the balance beam hoisting operation, and horizontal distance data between the balance beam and surrounding space objects;

[0008] Establishing a measurement range model based on the acquired position data, wherein the measurement range model includes a posture model and a non-posture model;

[0009] According to the acquired balance beam inclination data and spatial position data, the adjustment value of each hook is calculated using trigonometric functions;

[0010] Based on the acquired horizontal distance data between the balance beam and surrounding objects, the safe area for the balance beam operation is judged and determined;

[0011] The stress and strain of the balance beam are calculated using the structural parameters of the balance beam to obtain the corresponding stress and strain values of the balance beam;

[0012] The acceleration data of the balance beam lifting operation is used to calculate the attitude angle of the balance beam during the lifting operation to obtain the attitude angle data of the balance beam lifting operation;

[0013] A fusion algorithm is used to analyze the adjustment value of the hook, the calculated stress and strain values of the balance beam, and the attitude angle data of the balance beam lifting operation to obtain the three-dimensional environmental model, lifting parameters, dynamic monitoring indicators and predicted operation results of the balance beam lifting operation.

[0014] Furthermore, in the above-mentioned crane balance beam hoisting prediction method, the posture-free model includes:

[0015]

[0016] Where i = 1, 2, 3, 4, represents the four corners of the balance beam; j = 1, 2, 3, represents the three directions (x, y, z) of each sensor group; x ij 、y ij and z ij Respectively represent the fixed position of the sensor on the balance beam; d max Indicates the maximum ranging range of the sensor; Represents set operations;

[0017] The posture model includes:

[0018]

[0019] Where a represents the semi-axis length of the ellipsoid in the x-axis direction, a=d max cosθ y ,θ y It represents the roll angle of the balance beam during the lifting process, b represents the semi-axis length of the ellipsoid in the y-axis direction, b = d max cosθ x ,θ x It represents the pitch angle of the balance beam during the lifting process, c represents the semi-axis length of the ellipsoid in the z-axis direction, c = dmax cosθ z ,θ z Indicates the yaw angle of the balance beam during the lifting process, x i ' represents the x coordinate of the sensor when there is posture, y i ' represents the sensor y coordinate when there is posture, z i ′ represents the z coordinate of the sensor when there is posture.

[0020] Furthermore, in the above-mentioned crane balance beam hoisting prediction method, based on the acquired balance beam inclination data and spatial position data, the adjustment value of each hook is calculated using trigonometric functions and determined by the following formula:

[0021] Δh i =d i tanθ i ;

[0022] Where Δh i Indicates the adjustment height of the ith hook of the balance beam; d i represents the distance value corresponding to the direction of the i-th hook; θ i It represents the inclination value of the balance beam measured by the i-th inclination measuring device installed on the balance beam.

[0023] Furthermore, in the above-mentioned crane balance beam hoisting prediction method, based on the acquired horizontal distance data between the balance beam and the surrounding space objects, the safe area for the balance beam operation is judged and determined by the following formula:

[0024]

[0025] Among them, Signal represents the signal result of judging and determining the safe area of the balance beam operation, d represents the measured horizontal distance between the balance beam and the surrounding objects during the lifting operation, and d safe Indicates the horizontal safety distance between the balance beam and surrounding objects during lifting operations;

[0026] The above formula indicates that when the measured horizontal distance between the balance beam and surrounding objects during the lifting operation is greater than the horizontal safety distance, the alarm signal is displayed as 0, triggering an alarm reminder to the lifting operator;

[0027] When the measured horizontal distance between the balance beam and surrounding objects during the lifting operation is less than or equal to the horizontal safety distance, the alarm signal is displayed as 1, no alarm reminder is triggered, and the lifting operation is carried out normally.

[0028] Furthermore, in the above-mentioned crane balance beam hoisting prediction method, the structural parameters of the balance beam are used to calculate the stress and strain of the balance beam to obtain the stress value and strain value corresponding to the balance beam, wherein:

[0029] The stress value is calculated by the following formula:

[0030]

[0031] Wherein, σ represents the stress value, F represents the load during the lifting process of the balance beam; A represents the cross-sectional area of the balance beam during the lifting process;

[0032] The strain value is calculated by the following formula:

[0033]

[0034] Where ∈ represents the strain of the balance beam, and E represents the elastic modulus of the balance beam material.

[0035] Furthermore, in the above-mentioned crane balance beam hoisting prediction method, the acceleration data of the balance beam hoisting operation is used to calculate the attitude angle of the balance beam during the hoisting operation, and the attitude angle data of the balance beam hoisting operation is obtained by calculation and determination using the following formula:

[0036]

[0037]

[0038] Among them, θ x represents the x-direction attitude angle during the lifting operation of the balance beam, arcsin(·) represents the inverse sine function operation, and a x represents the lifting acceleration in the x direction during the lifting process of the balance beam, g represents the acceleration due to gravity, and θ y Indicates the y-direction attitude angle of the balance beam during lifting operation, a y It represents the lifting acceleration in the y direction during the lifting process of the balance beam.

[0039] Secondly, a crane balance beam hoisting prediction system is provided, including:

[0040] a parameter acquisition module, the parameter acquisition module being used to acquire operating data of the balance beam in the operating space, the operating data including inclination data of the balance beam, spatial position data, acceleration data of the balance beam hoisting operation, and horizontal distance data between the balance beam and surrounding space objects;

[0041] A model building module, the model building module is used to build a measurement range model according to the acquired position data, the measurement range model including a posture model and a non-gesture model;

[0042] An adjustment value calculation module, configured to calculate an adjustment value of each hook using trigonometric functions based on the acquired balance beam inclination data and spatial position data;

[0043] A safety zone calculation module, configured to determine a safety zone for the balance beam operation based on the acquired horizontal distance data between the balance beam and surrounding objects;

[0044] A stress and strain calculation module is used to calculate the stress and strain of the balance beam using the structural parameters of the balance beam to obtain the stress value and strain value corresponding to the balance beam;

[0045] an attitude angle calculation module, wherein the attitude angle calculation module is used to calculate the attitude angle during the balance beam lifting operation using the acceleration data of the balance beam lifting operation to obtain attitude angle data of the balance beam lifting operation;

[0046] An analysis and prediction module is used to use a fusion algorithm to analyze the adjustment value of the hook, the calculated stress and strain values of the balance beam, and the posture angle data of the balance beam lifting operation, to obtain a three-dimensional environmental model, lifting parameters, dynamic monitoring indicators and predicted operation results of the balance beam lifting operation.

[0047] In a third aspect, a crane balance beam hoisting prediction device is provided that applies the above-mentioned crane balance beam hoisting prediction method, comprising:

[0048] An ultrasonic distance measuring sensor, which is fixedly mounted on the balance beam and is used to detect the position data of the balance beam in the working space;

[0049] An inclinometer is provided on the balance beam and is used to detect the tilt angle of the balance beam during operation;

[0050] An acceleration sensor is fixedly mounted on the balance beam and is used to detect the acceleration of the balance beam during lifting.

[0051] a processing module, the processing module being connected to the ultrasonic ranging sensor, the inclinometer, and the accelerometer, respectively, and being used to receive data detected by the ultrasonic ranging sensor, the inclinometer, and the accelerometer, respectively; the processing module being provided with a graphics processing model and a fusion algorithm or fusion algorithm module for prediction; after the processing module receives the data detected by the ultrasonic ranging sensor, the inclinometer, and the accelerometer, the processing module calls the graphics processing module so that the graphics processing module establishes and generates a three-dimensional model of the operation of the balance beam and its surrounding operating environment, and calls the fusion algorithm or fusion algorithm module for prediction to perform lifting collision prediction on the balance beam, and display an early warning when a collision is predicted;

[0052] A display device is connected to the processing module and is arranged in an operating room where the balancing beam is operated. The display device is used to display a three-dimensional model image of the balancing beam operation and its surrounding operating environment generated by the graphics processing module. When a collision occurs or is predicted during the operation of the balancing beam, the display device uses different colors to provide a warning display of the degree of collision.

[0053] Furthermore, in the above-mentioned crane balance beam hoisting prediction device, the processing module includes:

[0054] a sensing unit, configured to receive the ultrasonic ranging sensor data, the inclinometer data, and the acceleration sensor data;

[0055] a processing unit connected to the sensing unit, configured to perform comprehensive image processing on the data acquired by the sensing unit, and to establish and generate a three-dimensional model of the operation of the balance beam and its surrounding operating environment;

[0056] a calculation unit connected to the processing unit, the calculation unit pre-stored with the ultrasonic ranging sensor data, the inclinometer data, and threshold data for detecting safe operation of the balance beam by the acceleration sensor, the calculation unit being configured to compare the acquired data with the pre-stored safety threshold data and issue an early warning based on the comparison result;

[0057] A data storage and analysis unit is connected to the computing unit and the perception unit. A fusion algorithm is provided in the data storage and analysis unit. The data storage and analysis unit is used to predict the lifting operation of the balance beam according to the data received by the perception unit using the fusion algorithm, and transmit the prediction result to the computing unit. If the prediction result is a collision, the data storage and analysis unit controls the computing unit to issue an alarm. If the prediction result is normal, the balance beam operates normally.

[0058] Furthermore, in the above-mentioned crane balance beam hoisting prediction device, the processing module includes an industrial computer.

[0059] The main advantages of the technical solution of the present invention are as follows:

[0060] The crane balance beam hoisting prediction method, system, and device of the present invention acquire operating data of the balance beam in an operating space, establish a measurement range model based on the position data in the acquired operating data, calculate the adjustment value of each hook based on the acquired balance beam inclination angle data and spatial position data, judge and determine the safe area for the balance beam operation based on the horizontal distance data between the balance beam and surrounding objects in the acquired operating data, calculate the stress and strain values of the balance beam based on the balance beam structure data in the operating data, and calculate the attitude angle of the balance beam during the hoisting operation based on the acceleration data of the balance beam hoisting operation in the operating data. A fusion algorithm is used to analyze the adjustment value of the hook, the calculated stress and strain values of the balance beam, and the attitude angle data of the balance beam lifting operation to obtain a final three-dimensional model and prediction results. The strength of the balance beam itself and the trajectory of the balance beam operation process are predicted from two aspects: the parameter data of the balance beam itself and the data detected and obtained during the operation. This ensures the safety of the balance beam lifting operation, improves the operation efficiency, avoids the time-consuming and labor-intensive problems caused by damage, and affects the progress of the project. At the same time, the present invention also ensures the safety of the balance beam lifting materials during the lifting process, as well as the safety of equipment and operators in the operating environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0062] Figure 1 A schematic flow chart of a crane balance beam hoisting prediction method provided by one embodiment of the present invention;

[0063] Figure 2 A schematic diagram of the structure of a crane balance beam hoisting prediction system provided by one embodiment of the present invention;

[0064] Figure 3 A schematic structural diagram of a crane balance beam hoisting prediction device provided in one embodiment of the present invention.

[0065] Description of reference numerals:

[0066] 10. Parameter acquisition module; 20. Model building module; 30. Adjustment value calculation module; 40. Safety area calculation module; 50. Stress and strain calculation module; 60. Attitude angle calculation module; 70. Analysis and prediction module;

[0067] 100. Ultrasonic ranging sensor; 200. Inclinometer; 300. Accelerometer; 400. Processing module; 500. Display device. DETAILED DESCRIPTION

[0068] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0069] The following is combined with Figure 1 -Attached Figure 3 , describes in detail the technical solution provided by the embodiments of the present invention.

[0070] As attached Figure 1 As shown, an embodiment of the present invention provides a crane balance beam hoisting prediction method, which includes steps S1 to S7:

[0071] Step S1: Acquire the operation data of the balance beam in the operation space, the operation data including the inclination data of the balance beam, the spatial position data, the acceleration data of the balance beam lifting operation, and the horizontal distance data between the balance beam and the surrounding space objects;

[0072] Specifically, balance beam operational data includes the load sensed by the hoisted object, the stress and deformation of the balance beam itself, its kinematic state during the hoisting process, and its perception of the surrounding environment. The system, supplemented by wireless communication and visualization technology, ensures that the load information, stress and deformation of the balance beam itself, rotor spatial posture information, and distance information collected during the rotor hoisting process are promptly and intuitively transmitted to the rotor hoisting operator, assisting in the rotor hoisting operation and improving rotor hoisting safety and efficiency. Furthermore, the relevant information stored during the rotor hoisting process provides data support for hoisting process optimization, balance beam fault diagnosis, and optimal balance beam design.

[0073] In some optional implementations of this embodiment, the above-mentioned operation data is obtained through devices such as sensing equipment and detectors.

[0074] As an example, the inclination data of the balance beam is obtained through an inclinometer, the spatial position data of the balance beam during operation and the horizontal distance data between the balance beam and surrounding space objects are obtained through an ultrasonic ranging sensor, and the acceleration data of the balance beam lifting operation is obtained through an acceleration sensor.

[0075] In order to obtain the above-mentioned desired data more accurately, the number of inclinometers is set to multiple, and multiple inclinometers are set on the front and rear sides of the upper surface of the balance beam to monitor the posture changes during the operation of the balance beam, and send the detected posture data to the preset position through the hub.

[0076] In some optional implementations of this embodiment, the number of inclinometers is set to two, and the two inclinometers are respectively located on the front and rear sides of the upper surface of the balance beam to monitor the posture changes of the balance beam during operation.

[0077] In some optional implementations of this embodiment, the hub includes a 485 hub.

[0078] In order to better and more comprehensively detect the spatial position data of the balance beam during operation and the horizontal distance data between the balance beam and the surrounding objects, multiple ultrasonic ranging sensors are included. The multiple ultrasonic ranging sensors are divided into multiple groups and distributed on the four corners of the lower surface of the balance beam to display the real-time position of the balance beam, and the real-time position data of the balance beam collected by multiple groups of ultrasonic ranging sensors are sent to a preset position through a hub.

[0079] In some optional implementations of this embodiment, the number of ultrasonic ranging sensors may be set to 12, with each 12 ultrasonic ranging sensors forming a group of 3 distributed at the four corners of the lower surface of the balance beam to display the real-time position of the balance beam.

[0080] This allows the ultrasonic ranging sensor to measure the distance from that point to the outside of the balance beam in the X, Y, and Z directions. By deploying 12 ultrasonic ranging sensors and combining them with real-time monitoring video data, a comprehensive perception of the balance beam's spatial distance and surrounding environment is achieved, ensuring the safety and accuracy of the lifting operation.

[0081] In order to better detect the operating status of the balance beam, detection cameras and their corresponding display devices and transmission equipment can be set inside and outside the operating environment of the balance beam, and the number of detection cameras can be set to multiple. Multiple detection cameras are used to capture real-time images of the balance beam operation, and the captured images are connected to the switch via a network cable and transmitted to the preset device through the switch for display.

[0082] In some optional implementations of this embodiment, the number of the above-mentioned detection cameras can be set to 4, and the 4 monitoring cameras capture the real-time operation picture of the balance beam, are connected to the switch via a POE network cable, and are connected to the display device through the switch to display the monitoring picture.

[0083] In this way, it is possible to realize remote audio and video monitoring of the balance beam lifting, as well as monitor the key components such as the balance beam and its lifting materials, and provide visual image information for subsequent remote predictive diagnosis.

[0084] Furthermore, the camera may be a full-color camera to ensure that color images can still be presented in low-light environments, making it easier for operators to view.

[0085] Furthermore, in order to ensure the imaging quality of the camera, a camera bracket can be installed at the camera installation position, and a shock-absorbing pad can be provided at the bottom of the camera bracket, and the camera is installed on the camera bracket. In this way, the imaging quality of the camera can be guaranteed under severe vibration conditions.

[0086] Furthermore, the camera can be mounted on the camera bracket by welding or fixed on the camera bracket by thread and bolt connection.

[0087] Considering the harsh outdoor working environment, in order to avoid damage to the camera, the camera is sealed and installed in an explosion-proof shell.

[0088] It should be noted that no matter how many cameras there are, it is important to note that all the spliced images of the installed cameras can clearly capture the balance beam and all the images from the start to the end of lifting materials. This can ensure that the operator can assist in lifting according to the images transmitted, thereby ensuring the safety and efficiency of the lifting operation.

[0089] Step S2: establishing a measurement range model based on the acquired position data, wherein the measurement range model includes a posture model and a non-posture model;

[0090] Specifically, the attitude-free measurement range in the overall measurement range model for the balance beam includes: In the absence of attitude changes, the sensor's measurement range can be represented as a sphere centered at the sensor position and with the range as its radius. The overall measurement range is the union of all sensor spheres. Therefore, the attitude-free model includes:

[0091]

[0092] Where i = 1, 2, 3, 4, represents the four corners of the balance beam; j = 1, 2, 3, represents the three directions (x, y, z) of each sensor group; x ij 、y ij and z ij Respectively represent the fixed position of the sensor on the balance beam; d max Indicates the maximum ranging range of the sensor; Represents set operations;

[0093] It should be noted that this model is applicable to static conditions and the measurement range is symmetrical.

[0094] The measurement range affected by attitude includes: the pitch angle θ of the balance beam during the lifting process x , the rolling angle θ of the balance beam during the lifting process y And the yaw angle θ of the balance beam during the lifting process zWhen the dynamic changes of , the sensor's measurement direction and range will change. In this case, the sensor's measurement range needs to be corrected using the rotation matrix R.

[0095] The rotation matrix R is defined as:

[0096]

[0097] Measurement range matrix modeling:

[0098] Assume that the initial position matrix of the four groups of sensors is:

[0099]

[0100] Where P represents the initial position matrix of the four groups of sensors, x1, y1, and z1 represent the coordinates of the initial position of the first group of sensors, x2, y2, and z2 represent the coordinates of the initial position of the second group of sensors, x3, y3, and z3 represent the coordinates of the initial position of the third group of sensors, and x4, y4, and z4 represent the coordinates of the initial position of the fourth group of sensors;

[0101] The three-axis measurement range vector of each of the four sensor groups mentioned above is:

[0102]

[0103] Among them, D i Represents the overall measurement range set, d x,i Indicates the sensor's x-direction measurement range, d y,i Indicates the sensor's y-direction measurement range, d z,i Indicates the sensor's z-direction measurement range, and i indicates the sensor number;

[0104] Combined with the rotation matrix R, the corrected measurement position of each set of sensors is:

[0105]

[0106] Among them, P i ′ represents the corrected position set, x i 、y i and z i Represent the original position coordinates, t x , t y and t z Respectively represent the overall translation of the balance beam in the X, Y, and Z directions;

[0107] Therefore, the overall measurement range equation is:

[0108] Combined with attitude correction, the overall measurement range is the union of four ellipsoid ranges:

[0109]

[0110] Where: a represents the semi-axis length of the ellipsoid in the x-axis direction, a=d max cosθ y , b represents the semi-axis length of the ellipsoid in the y-axis direction, b = d max cosθ x , c represents the semi-axis length of the ellipsoid in the z-axis direction, c = d max cosθ z , x i ' represents the x coordinate of the sensor when there is posture, y i ' represents the sensor y coordinate when there is posture, z i ′ represents the z coordinate of the sensor when there is posture.

[0111] The model can dynamically adjust the measurement range and reflect in real time the impact of the attitude angle on the sensor measurement range during the lifting process.

[0112] Step S3: Calculating the adjustment value of each hook using trigonometric functions based on the acquired balance beam inclination data and spatial position data;

[0113] The balance beam's inclination angles are divided into two directions: X and Y. Two inclinometers located in the center of the beam monitor in real time. When the balance beam is hoisted onto the rotor, ultrasonic sensors at the four corners of the beam measure the relative distances from the rotor to the four corners of the beam.

[0114] Combining the inclinometer and distance sensor data, the adjustment value of each hook is calculated using trigonometric functions and determined by the following formula:

[0115] Δh i =d i tanθ i ;

[0116] Where Δh i Indicates the adjustment height of the ith hook of the balance beam; d i represents the distance value corresponding to the direction of the i-th hook; θ i It represents the inclination value of the balance beam measured by the i-th inclination measuring device installed on the balance beam.

[0117] Step S4: judging and determining the safe area for the balance beam operation based on the acquired horizontal distance data between the balance beam and surrounding objects;

[0118] The eight sensors in the X and Y directions of the balance beam measure the horizontal distance between the balance beam and the surrounding objects in real time. The detection range of the sensor is 200-4000mm. When the measured distance is less than the safety distance d safe When an error occurs, the operator is reminded through a multi-level alarm mechanism.

[0119] Specifically, based on the acquired horizontal distance data between the balance beam and the surrounding space objects, the safe area for the balance beam operation is judged and determined by the following formula:

[0120]

[0121] Among them, Signal represents the signal result of judging and determining the safe area of the balance beam operation, d represents the measured horizontal distance between the balance beam and the surrounding objects during the lifting operation, and d safe Indicates the horizontal safety distance between the balance beam and surrounding objects during lifting operations;

[0122] The above formula indicates that when the measured horizontal distance between the balance beam and surrounding objects during the lifting operation is greater than the horizontal safety distance, the alarm signal is displayed as 0, triggering an alarm reminder to the lifting operator;

[0123] When the measured horizontal distance between the balance beam and surrounding objects during the lifting operation is less than or equal to the horizontal safety distance, the alarm signal is displayed as 1, no alarm reminder is triggered, and the lifting operation is carried out normally.

[0124] As an example, for example:

[0125] Low risk (distance close to the threshold): The interface displays a yellow warning to prompt the operator to pay attention.

[0126] Medium risk (distance reaches the threshold): The interface displays an orange warning and a warning tone is emitted.

[0127] High risk (distance below the threshold): The interface displays a red warning, accompanied by a high-frequency alarm tone and vibration reminder.

[0128] Furthermore, to further enhance the accuracy of these data, the sensor data can be converted into 3D images, generating a real-time 3D model of the balance beam and its surroundings on the user interface. The operator can visually assess the distance to obstacles and the degree of risk through warning signals of varying colors and shapes, allowing them to adjust their operations and further ensure the safety of lifting operations using the balance beam.

[0129] Step S5: Calculating the stress and strain of the balance beam using the structural parameters of the balance beam to obtain the stress value and strain value corresponding to the balance beam;

[0130] During the lifting process, the balance beam may deform due to long-term use, excessive load, or external environmental influences. The system simulates and monitors the deformation of the balance beam using finite element analysis. During the modeling process, the system uses the structural parameters of the balance beam to perform stress and strain analysis and dynamically calculates the deformation using the following formula, where:

[0131] The stress value is calculated using the following formula:

[0132]

[0133] Wherein, σ represents the stress value, F represents the load during the lifting process of the balance beam; A represents the cross-sectional area of the balance beam during the lifting process;

[0134] The strain value is calculated using the following formula:

[0135]

[0136] Where ∈ represents the strain of the balance beam, and E represents the elastic modulus of the balance beam material.

[0137] Specifically, the structural parameters of the balance beam include but are not limited to: material properties, geometric shape, and connection point distribution data.

[0138] By monitoring stress and strain distribution in real time, the system can predict potential deformation of the balance beam and analyze trends based on historical data. When deformation reaches a warning threshold, the operator is immediately prompted to adjust lifting parameters, ensuring safe balance beam lifting operations.

[0139] Step S6: Calculating the attitude angle of the balance beam during the lifting operation using the acceleration data of the balance beam lifting operation to obtain attitude angle data of the balance beam lifting operation;

[0140] At the same time, the system uses the inclinometer and acceleration sensor to collaboratively monitor the attitude angle changes of the balance beam (such as pitch, roll, and yaw). The system uses the acceleration data of the balance beam lifting operation to calculate the attitude angle during the balance beam lifting operation, and obtains the attitude angle data of the balance beam lifting operation. It is calculated and determined using the following formula:

[0141]

[0142] Among them, θ x represents the x-direction attitude angle during the lifting operation of the balance beam, arcsin(·) represents the inverse sine function operation, and a x represents the lifting acceleration in the x direction during the lifting process of the balance beam, g represents the acceleration due to gravity, and θ y Indicates the y-direction attitude angle of the balance beam during lifting operation, a y This represents the y-axis acceleration of the balance beam during lifting. By adjusting the attitude angle data in real time, the system can dynamically optimize the measurement range, further improving the stability and safety of the lifting operation.

[0143] Step S7: Use the fusion algorithm to analyze the adjustment value of the hook, the calculated stress and strain values of the balance beam, and the attitude angle data of the balance beam lifting operation to obtain the three-dimensional environmental model, lifting parameters, dynamic monitoring indicators and predicted operation results of the balance beam lifting operation.

[0144] In some optional implementations of this embodiment, the fusion algorithm includes Kalman filtering, Bayesian reasoning, machine learning and linear regression algorithms, etc., which make predictions based on the acquired data to obtain a three-dimensional environmental model, lifting parameters, dynamic monitoring indicators and predicted operation results of the balance beam lifting operation.

[0145] However, it should be noted that the above-mentioned fusion algorithm is determined according to actual needs. The number and type of algorithms and the calculation method of the fusion algorithm are all existing commonly used prediction algorithm models. When obtaining the prediction results, it is only necessary to input the obtained data into the algorithm model, or the algorithm model calls the corresponding data, and make predictions and warnings through data comparison, threshold comparison, or based on straight line or curve fitting and trend judgment.

[0146] Therefore, the crane balance beam hoisting prediction method of the present invention obtains the operation data of the balance beam in the operation space, establishes a measurement range model according to the position data in the obtained operation data, and calculates the adjustment value of each hook according to the obtained balance beam inclination angle data and spatial position data; judges and determines the safe area of the balance beam operation according to the horizontal distance data between the balance beam and the surrounding space objects in the obtained operation data; calculates the stress and strain values of the balance beam according to the balance beam structure data in the operation data; calculates the attitude angle of the balance beam during the hoisting operation according to the acceleration data of the balance beam hoisting operation in the operation data; and utilizes A fusion algorithm is used to analyze the adjustment value of the hook, the calculated stress and strain values of the balance beam, and the attitude angle data of the balance beam lifting operation to obtain a final three-dimensional model and prediction results. The strength of the balance beam itself and the trajectory of the balance beam operation process are predicted from two aspects: the parameter data of the balance beam itself and the data detected and obtained during the operation. This ensures the safety of the balance beam lifting operation, improves the operating efficiency, avoids the time-consuming and labor-intensive problems caused by damage, and affects the progress of the project. At the same time, the present invention also ensures the safety of the balance beam lifting materials during the lifting process, as well as the safety of equipment and operators in the operating environment.

[0147] Second, as Figure 2 As shown, the present invention also provides a crane balance beam hoisting prediction system, which includes: a parameter acquisition module 10, a model building module 20, an adjustment value calculation module 30, a safety area calculation module 40, a stress and strain calculation module 50, an attitude angle calculation module 60 and an analysis and prediction module 70, wherein:

[0148] The parameter acquisition module 10 is used to obtain the operation data of the balance beam in the operation space, and the operation data includes the inclination data of the balance beam, the spatial position data, the acceleration data of the balance beam lifting operation, and the horizontal distance data between the balance beam and the surrounding space objects; the model establishment module 20 is used to establish a measurement range model based on the acquired position data, and the measurement range model includes a posture model and a non-posture model; the adjustment value calculation module 30 is used to calculate the adjustment value of each hook based on the acquired balance beam inclination data and spatial position data using trigonometric functions; the safety area calculation module 40 is used to calculate the safety of the balance beam operation based on the acquired horizontal distance data between the balance beam and the surrounding space objects. The area is judged and determined; the stress and strain calculation module 50 is used to calculate the stress and strain of the balance beam by using the structural parameters of the balance beam, and obtain the stress value and strain value corresponding to the balance beam; the attitude angle calculation module 60 is used to calculate the attitude angle of the balance beam during the lifting operation by using the acceleration data of the balance beam lifting operation, and obtain the attitude angle data of the balance beam lifting operation; the analysis and prediction module 70 is used to use the fusion algorithm to analyze the adjustment value of the hook, the calculated stress value and strain value of the balance beam, and the attitude angle data of the balance beam lifting operation, and obtain the three-dimensional environmental model, lifting parameters, dynamic monitoring indicators and predicted operation results of the balance beam lifting operation.

[0149] Thirdly, as Figure 3 As shown, the present invention also provides a crane balance beam hoisting prediction device using the above-mentioned three-dimensional distance measurement method for the balance beam hoisting operation of a hydropower station bridge crane. The device includes: an ultrasonic distance measurement sensor 100, an inclinometer 200, an acceleration sensor 300, a processing module 400 and a display device 500, wherein:

[0150] The ultrasonic distance measuring sensor 100 is fixedly mounted on the balance beam and is used to detect the position data of the balance beam in the working space; the inclinometer 200 is mounted on the balance beam and is used to detect the tilt angle of the balance beam during operation; the acceleration sensor 300 is fixedly mounted on the balance beam and is used to detect the running acceleration of the balance beam during the lifting process; the processing module 400 is respectively connected to the ultrasonic distance measuring sensor 100, the inclinometer 200 and the acceleration sensor 300, and is respectively used to receive the data detected by the ultrasonic distance measuring sensor 100, the inclinometer 200 and the acceleration sensor 300. A graphics processing model and a fusion algorithm or a fusion algorithm module for prediction are set on the processing module 400. After the processing module 400 receives the data from the ultrasonic distance measuring sensor 100 and the inclinometer 200, the fusion algorithm or the fusion algorithm module is generated. 00 and the acceleration sensor 300 respectively detect the data, the processing module 400 calls the graphics processing module 400, so that the graphics processing module 400 establishes and generates a three-dimensional model of the operation of the balance beam and its surrounding operating environment, and calls the fusion algorithm or fusion algorithm module for prediction, predicts the lifting collision of the balance beam, and displays an early warning when a collision is predicted; the display device 500 is connected to the processing module 400, and the display device 500 is set in the operation room where the balance beam is operated. The display device 500 is used to display the three-dimensional model image of the operation of the balance beam and its surrounding operating environment generated by the graphics processing module 400, and when a collision occurs or is predicted during the operation of the balance beam, the display device 500 uses different colors to display an early warning of the degree of collision.

[0151] In order to obtain the above-mentioned desired data more accurately, the number of inclinometers 200 is set to multiple, and multiple inclinometers 200 are set on the front and rear sides of the upper surface of the balance beam to monitor the posture changes during the operation of the balance beam, and send the detected posture data to the preset position through the hub.

[0152] As an example, the number of the inclinometers 200 is set to two, and the two inclinometers 200 are respectively located on the front and rear sides of the upper surface of the balance beam for monitoring the posture changes of the balance beam during operation.

[0153] In some optional implementations of this embodiment, the hub includes a 485 hub.

[0154] In order to better and more comprehensively detect the spatial position data of the balance beam during operation and the horizontal distance data between the balance beam and the surrounding objects, multiple ultrasonic ranging sensors are included. The multiple ultrasonic ranging sensors are divided into multiple groups and distributed on the four corners of the lower surface of the balance beam to display the real-time position of the balance beam, and the real-time position data of the balance beam collected by multiple groups of ultrasonic ranging sensors are sent to a preset position through a hub.

[0155] As an example, the number of ultrasonic ranging sensors may be set to 12, and the 12 ultrasonic ranging sensors are distributed in four corners of the lower surface of the balance beam in groups of three to display the real-time position of the balance beam.

[0156] This allows the ultrasonic ranging sensor 100 to measure the distance from that location to the outside of the balance beam in the X, Y, and Z directions. By deploying 12 ultrasonic ranging sensors 100 and combining them with real-time monitoring video data, a comprehensive understanding of the balance beam's spatial distance and surrounding environment is achieved, ensuring the safety and accuracy of the lifting operation.

[0157] In order to better detect the operating status of the balance beam, a detection camera and its corresponding display device 500 and transmission equipment can be set inside and outside the operating environment of the balance beam, and the number of detection cameras can be set to multiple. Multiple detection cameras are used to capture real-time images of the balance beam operation, and the captured images are connected to the switch via a network cable and transmitted to the preset device through the switch for display.

[0158] In some optional implementations of this embodiment, the number of the above-mentioned detection cameras can be set to 4, and the 4 monitoring cameras capture the real-time operation picture of the balance beam, are connected to the switch via a POE network cable, and are connected to the display device 500 through the switch to display the monitoring picture.

[0159] In this way, it is possible to realize remote audio and video monitoring of the balance beam lifting, as well as monitor the key components such as the balance beam and its lifting materials, and provide visual image information for subsequent remote predictive diagnosis.

[0160] Furthermore, the camera may be a full-color camera to ensure that color images can still be presented in low-light environments, making it easier for operators to view.

[0161] Furthermore, in order to ensure the imaging quality of the camera, a camera bracket can be installed at the camera installation position, and a shock-absorbing pad can be provided at the bottom of the camera bracket, and the camera is installed on the camera bracket. In this way, the imaging quality of the camera can be guaranteed under severe vibration conditions.

[0162] Furthermore, the camera can be mounted on the camera bracket by welding or fixed on the camera bracket by thread and bolt connection.

[0163] Considering the harsh outdoor working environment, in order to avoid damage to the camera, the camera is sealed and installed in an explosion-proof shell.

[0164] It should be noted that no matter how many cameras there are, it is important to note that all the spliced images of the installed cameras can clearly capture the balance beam and all the images from the start to the end of lifting materials. This can ensure that the operator can assist in lifting according to the images transmitted, thereby ensuring the safety and efficiency of the lifting operation.

[0165] In some optional implementations of this embodiment, the processing module 400 includes: a sensing unit, a processing unit, a computing unit, and a data storage and analysis unit, wherein:

[0166] The sensing unit is used to receive data from the ultrasonic ranging sensor 100, the inclinometer 200 and the acceleration sensor 300; the processing unit is connected to the sensing unit, and the processing unit is used to perform comprehensive image processing on the data obtained by the sensing unit, and establish and generate a three-dimensional model of the operation of the balance beam and its surrounding operating environment; the computing unit is connected to the processing unit, and the computing unit pre-stores the ultrasonic ranging sensor 100 data, the inclinometer 200 data and the threshold data for detecting the safe operation of the balance beam by the acceleration sensor 300, and the computing unit is used to compare the acquired data with the pre-stored safety threshold data, and issue an early warning based on the comparison result; the data storage and analysis unit is connected to the computing unit and the sensing unit, and a fusion algorithm is provided in the data storage and analysis unit, and the data storage and analysis unit is used to predict the lifting operation of the balance beam based on the data received by the sensing unit using the fusion algorithm, and transmit the prediction result to the computing unit. If the prediction result is a collision, the data storage and analysis unit controls the computing unit to issue an alarm. If the prediction result is normal, the balance beam operates normally.

[0167] In some optional implementations of this embodiment, the processing module 400 includes an industrial computer.

[0168] Therefore, the present invention realizes all-round monitoring of the balance beam by integrating ultrasonic sensors, inclinometers 200 and accelerometers 300 in the system, and enhances the accuracy of monitoring through multi-source data fusion. Combined with finite element analysis and dynamic posture correction algorithm, the system can accurately predict the deformation trend and posture change of the balance beam, providing guarantees for the safety and reliability of the lifting operation. The graphical interface is combined with the multi-level alarm mechanism to provide intuitive and efficient operation support. The operator can obtain the lifting status and early warning information in real time, so as to complete the lifting task more safely and efficiently. The system supports historical data storage and analysis, generates optimization reports through big data mining technology, provides a scientific basis for equipment maintenance and lifting plan improvement, and realizes data-driven operation optimization.

[0169] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In addition, "front", "back", "left", "right", "upper" and "lower" in this document are all referenced to the placement states shown in the accompanying drawings.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A crane balance beam hoisting prediction method, characterized in that: include: Acquiring operation data of the balance beam in the operation space, the operation data including inclination data of the balance beam, spatial position data, acceleration data of the balance beam hoisting operation, and horizontal distance data between the balance beam and surrounding space objects; Establishing a measurement range model based on the acquired position data, wherein the measurement range model includes a posture model and a non-posture model; According to the acquired balance beam inclination data and spatial position data, the adjustment value of each hook is calculated using trigonometric functions; Based on the acquired horizontal distance data between the balance beam and surrounding objects, the safe area for the balance beam operation is judged and determined; The stress and strain of the balance beam are calculated using the structural parameters of the balance beam to obtain the corresponding stress and strain values of the balance beam; The acceleration data of the balance beam lifting operation is used to calculate the attitude angle of the balance beam during the lifting operation to obtain the attitude angle data of the balance beam lifting operation; A fusion algorithm is used to analyze the adjustment value of the hook, the calculated stress and strain values of the balance beam, and the attitude angle data of the balance beam lifting operation to obtain the three-dimensional environmental model, lifting parameters, dynamic monitoring indicators and predicted operation results of the balance beam lifting operation.

2. The crane balance beam hoisting prediction method according to claim 1, characterized in that: The pose-free model includes: Where i = 1, 2, 3, 4, represents the four corners of the balance beam; j = 1, 2, 3, represents the three directions (x, y, z) of each sensor group; x ij 、y ij and z ij Respectively represent the fixed position of the sensor on the balance beam; d max Indicates the maximum ranging range of the sensor; Represents set operations; The posture model includes: Where a represents the semi-axis length of the ellipsoid in the x-axis direction, a=d max cosθ y , b represents the semi-axis length of the ellipsoid in the y-axis direction, b = d max cosθ x , c represents the semi-axis length of the ellipsoid in the z-axis direction, c = d max cosθ z , x i ' represents the x coordinate of the sensor when there is posture, y i ' represents the sensor y coordinate when there is posture, z i ′ represents the z coordinate of the sensor when there is posture.

3. The crane balance beam hoisting prediction method according to claim 1, characterized in that: Based on the acquired balance beam inclination data and spatial position data, the adjustment value of each hook is calculated using trigonometric functions and determined by the following formula: Δh i =d i ·tanθ i ; Where Δh i Indicates the adjustment height of the ith hook of the balance beam; d i represents the distance value corresponding to the direction of the i-th hook; θ i It represents the inclination value of the balance beam measured by the i-th inclination measuring device installed on the balance beam.

4. The crane balance beam hoisting prediction method according to claim 1, characterized in that: Based on the acquired horizontal distance data between the balance beam and the surrounding objects, the safe area for the balance beam operation is judged and determined by the following formula: Among them, Signal represents the signal result of judging and determining the safe area of the balance beam operation, d represents the measured horizontal distance between the balance beam and the surrounding objects during the lifting operation, and d safe Indicates the horizontal safety distance between the balance beam and surrounding objects during lifting operations; The above formula indicates that when the measured horizontal distance between the balance beam and surrounding objects during the lifting operation is greater than the horizontal safety distance, the alarm signal is displayed as 0, triggering an alarm reminder to the lifting operator; When the measured horizontal distance between the balance beam and surrounding objects during the lifting operation is less than or equal to the horizontal safety distance, the alarm signal is displayed as 1, no alarm reminder is triggered, and the lifting operation is carried out normally.

5. The crane balance beam hoisting prediction method according to claim 1, characterized in that: The stress and strain of the balance beam are calculated using the structural parameters of the balance beam to obtain the corresponding stress and strain values of the balance beam, where: The stress value is calculated by the following formula: Wherein, σ represents the stress value, F represents the load during the lifting process of the balance beam; A represents the cross-sectional area of the balance beam during the lifting process; The strain value is calculated by the following formula: Where ∈ represents the strain of the balance beam, and E represents the elastic modulus of the balance beam material.

6. The crane balance beam hoisting prediction method according to claim 1, characterized in that: The acceleration data of the balance beam lifting operation is used to calculate the attitude angle of the balance beam during the lifting operation. The attitude angle data of the balance beam lifting operation is obtained and determined by the following formula: Among them, θ x represents the x-direction attitude angle during the lifting operation of the balance beam, arcsin(·) represents the inverse sine function operation, and a x represents the lifting acceleration in the x direction during the lifting process of the balance beam, g represents the acceleration due to gravity, and θ y Indicates the y-direction attitude angle of the balance beam during lifting operation, a y It represents the lifting acceleration in the y direction during the lifting process of the balance beam.

7. Crane balance beam lifting prediction system, characterized by: include: a parameter acquisition module, the parameter acquisition module being used to acquire operating data of the balance beam in the operating space, the operating data including inclination data of the balance beam, spatial position data, acceleration data of the balance beam hoisting operation, and horizontal distance data between the balance beam and surrounding space objects; A model building module, the model building module is used to build a measurement range model according to the acquired position data, the measurement range model including a posture model and a non-gesture model; An adjustment value calculation module, configured to calculate an adjustment value of each hook using trigonometric functions based on the acquired balance beam inclination data and spatial position data; A safety area calculation module, the safety area calculation module is used to judge and determine the safety area of the balance beam operation based on the acquired horizontal distance data between the balance beam and the surrounding space objects; A stress and strain calculation module is used to calculate the stress and strain of the balance beam using the structural parameters of the balance beam to obtain the stress value and strain value corresponding to the balance beam; an attitude angle calculation module, wherein the attitude angle calculation module is used to calculate the attitude angle during the balance beam lifting operation using the acceleration data of the balance beam lifting operation to obtain attitude angle data of the balance beam lifting operation; An analysis and prediction module is used to use a fusion algorithm to analyze the adjustment value of the hook, the calculated stress and strain values of the balance beam, and the posture angle data of the balance beam lifting operation, to obtain a three-dimensional environmental model, lifting parameters, dynamic monitoring indicators and predicted operation results of the balance beam lifting operation.

8. A crane balance beam hoisting prediction device using the crane balance beam hoisting prediction method according to any one of claims 1 to 6, characterized in that: include: An ultrasonic distance measuring sensor, which is fixedly mounted on the balance beam and is used to detect the position data of the balance beam in the working space; An inclinometer is provided on the balance beam and is used to detect the tilt angle of the balance beam during operation; An acceleration sensor is fixedly mounted on the balance beam and is used to detect the acceleration of the balance beam during lifting. a processing module, the processing module being connected to the ultrasonic ranging sensor, the inclinometer, and the accelerometer, respectively, and being used to receive data detected by the ultrasonic ranging sensor, the inclinometer, and the accelerometer, respectively; the processing module being provided with a graphics processing model and a fusion algorithm or fusion algorithm module for prediction; after the processing module receives the data detected by the ultrasonic ranging sensor, the inclinometer, and the accelerometer, the processing module calls the graphics processing module so that the graphics processing module establishes and generates a three-dimensional model of the operation of the balance beam and its surrounding operating environment, and calls the fusion algorithm or fusion algorithm module for prediction to perform lifting collision prediction on the balance beam, and display an early warning when a collision is predicted; A display device is connected to the processing module and is arranged in an operating room where the balancing beam is operated. The display device is used to display a three-dimensional model image of the balancing beam operation and its surrounding operating environment generated by the graphics processing module. When a collision occurs or is predicted during the operation of the balancing beam, the display device uses different colors to provide a warning display of the degree of collision.

9. The crane balance beam hoisting prediction device according to claim 8, characterized in that: The processing module includes: a sensing unit, configured to receive the ultrasonic ranging sensor data, the inclinometer data, and the acceleration sensor data; a processing unit connected to the sensing unit, configured to perform comprehensive image processing on the data acquired by the sensing unit, and to establish and generate a three-dimensional model of the operation of the balance beam and its surrounding operating environment; a calculation unit connected to the processing unit, the calculation unit pre-stored with the ultrasonic ranging sensor data, the inclinometer data, and threshold data for detecting safe operation of the balance beam by the acceleration sensor, the calculation unit being configured to compare the acquired data with the pre-stored safety threshold data and issue an early warning based on the comparison result; A data storage and analysis unit is connected to the computing unit and the perception unit. A fusion algorithm is provided in the data storage and analysis unit. The data storage and analysis unit is used to predict the lifting operation of the balance beam according to the data received by the perception unit using the fusion algorithm, and transmit the prediction result to the computing unit. If the prediction result is a collision, the data storage and analysis unit controls the computing unit to issue an alarm. If the prediction result is normal, the balance beam operates normally.

10. The crane balance beam hoisting prediction device according to claim 8, characterized in that: The processing module includes an industrial computer.

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