A method and system for monitoring and early warning of double crane lifting operation

Abstract

The application provides a kind of double-machine lifting operation monitoring and early warning method and system, method includes the following steps: real-time acquisition from two hoisting equipment operating state data, and time correlation is carried out to the data, to establish unified data processing timing reference, based on each hoisting equipment time correlation after operating state data, data fusion is solved after each hoisting equipment spatial posture information, the spatial posture information of two hoisting equipment is mapped to same operation coordinate system, and combined with the operation parameter of hoisting equipment, constructs system spatial posture model, based on spatial posture information and system spatial posture model, real-time calculation risk index indicating operation safety, the risk index is compared with corresponding preset safety threshold in real time, when risk index exceeds safety threshold, trigger early warning and generate corresponding early warning information.The method realizes real-time monitoring, accurate risk assessment and timely early warning to double-machine operating state and sling posture.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of safety monitoring of engineering machinery, and particularly relates to a monitoring and early warning method and system for double-machine lifting operation. BACKGROUND

[0002] Large crawler cranes play an important role in infrastructure construction such as energy, chemical industry, bridges and large venues. With the development of engineering structures towards large-scale and overweight, a single crawler crane is difficult to meet the lifting requirements, and at this time, double-machine lifting is needed to jointly bear the load by two crawler cranes to complete the hoisting and positioning of giant components.

[0003] However, while double-machine lifting improves the lifting capacity, it also significantly increases the complexity and safety risk of the operation. The entire lifting system constitutes a dynamic, strongly coupled time-varying system. During the operation, any slight movement of the crane, uneven ground settlement, wind disturbance or slight changes in load distribution may cause instability of the spatial posture of the sling and the sling system. This instability mainly manifests in two forms: one is the angular deflection in the plane formed by the boom, sling and sling, which leads to unbalanced load distribution between the two cranes, and in severe cases, may cause single crane overload; the other is the lateral swing or torsion outside the plane, which produces a large lateral force, easily leading to sling unhooking, sling structure damage, and even catastrophic accidents such as crawler crane overturning.

[0004] During the actual operation of double-machine lifting, the monitoring of the spatial posture of the sling and the sling is usually dependent on the visual observation and experience judgment of the operator and the ground command personnel. This method has significant limitations and risks: first, the perception accuracy is insufficient, the human eye cannot accurately identify and quantify the slight angular deviation of the sling in space, and cannot timely warn at the early stage of safety hazards; second, the environmental dependence is high, the observation effect is easily disturbed by factors such as light intensity, weather conditions, and line of sight obstruction, especially at night or in adverse weather conditions, the reliability decreases significantly; third, human factors have a great influence, personnel are prone to visual fatigue and distraction after a long time of operation, and there is a risk of misjudgment and omission. In addition, the existing technology usually monitors a single device, lacks a mechanism for synchronously collecting, real-time fusing and collaborative analysis of the posture data of two crawler cranes, and cannot effectively evaluate and judge the dynamic stability and load balance state of the entire lifting system from the overall system level. SUMMARY

[0005] The present application provides a monitoring and early warning method and system for double-machine lifting operation, which realizes real-time monitoring, accurate risk assessment and timely early warning of the running state of the double machines and the posture of the sling, and ensures the safe performance of the double-machine lifting operation.

[0006] The technical solution of the present application is as follows:

[0007] A method for monitoring and early warning of dual-machine lifting operations includes the following steps:

[0008] S1: Real-time acquisition of operating status data from two lifting devices, and time correlation of the data to establish a unified data processing timing benchmark;

[0009] S2: Based on the time-correlated operating status data of each lifting device, the spatial attitude information of each lifting device is calculated after data fusion;

[0010] S3: Map the spatial attitude information of the two lifting devices to the same working coordinate system, and combine the working parameters of the lifting devices to construct a system spatial attitude model that includes the two lifting devices and the hoisting components;

[0011] S4: Based on the spatial attitude information and the system spatial attitude model, calculate the risk indicators that characterize the safety of the operation in real time;

[0012] S5: The risk indicators are compared with the corresponding preset safety thresholds in real time. When the risk indicators exceed the safety thresholds, an early warning is triggered and corresponding early warning information is generated.

[0013] Furthermore, in the monitoring and early warning method for dual-machine lifting operations, the time correlation in step S1 includes: adding a timestamp with the same timing to the operating status data packets from each lifting device, and aligning all data to a virtual timeline established based on the timestamps; and / or,

[0014] The operational status data includes raw sensor data collected by an inertial measurement unit installed at the lifting equipment's spreader.

[0015] Furthermore, in the aforementioned monitoring and early warning method for dual-machine lifting operations, the original sensor data includes at least three-axis acceleration data, three-axis angular velocity data, and three-axis magnetic field strength data.

[0016] Furthermore, in the monitoring and early warning method for dual-machine lifting operations, step S2, which involves calculating the spatial attitude information of each lifting device after data fusion, includes the following steps:

[0017] S21: Perform timestamp alignment and filtering preprocessing on the operating status data of each lifting device to obtain preprocessed data;

[0018] S22: Based on the preprocessed data, the extended Kalman filter algorithm is used to perform data fusion and calculate the quaternion representing the boom attitude;

[0019] S23: convert the quaternion into an Euler angle form comprising a pitch angle, a roll angle and a yaw angle as the spatial attitude information.

[0020] Further, in the double-machine lifting operation monitoring and early warning method, the filtering preprocessing in step S21 comprises: adopting a first-order low-pass filter to denoise the original sensing data, and compensating soft magnetic and hard magnetic interference of the three-axis magnetic field intensity data based on a pre-calibrated compensation matrix; and / or,

[0021] The system state vector of the extended Kalman filtering algorithm comprises an attitude quaternion and a three-axis gyroscope zero bias vector, and the estimated values of the attitude quaternion and the three-axis gyroscope zero bias vector are updated online by iteratively performing a prediction step and an update step; and / or,

[0022] The pitch angle corresponds to the inclination angle of the lifting tool in the operation plane composed of the double-machine lifting arm and the lifting cable, and is used to evaluate the synchronization of load distribution and lifting action; and / or,

[0023] The roll angle corresponds to the inclination angle of the lifting tool in the direction perpendicular to the operation plane, and is used as a core evaluation index of system overturning risk; and / or,

[0024] The yaw angle corresponds to the rotation angle of the lifting tool in the horizontal plane, and is used as an auxiliary judgment index for identifying the twisting of the lifting cable or the swinging of the load.

[0025] Further, in the double-machine lifting operation monitoring and early warning method, the operation parameters at least comprise the luffing length of each hoisting device and the current height of the lifting hook from the ground; and / or,

[0026] The step S3 comprises:

[0027] According to the spatial attitude information and the operation parameters of the two hoisting devices, the three-dimensional coordinates of the respective lifting hooks in the same operation coordinate system are calculated, and a system spatial attitude model comprising the two hoisting devices and the lifting member is constructed.

[0028] Further, in the double-machine lifting operation monitoring and early warning method, the risk index in step S4 comprises:

[0029] A single-machine attitude index, the single-machine attitude index comprising the following three items: the absolute value of the pitch angle, the absolute value of the roll angle, and the change rate of the yaw angle;

[0030] A double-machine cooperation index, the double-machine cooperation index comprising the following two items: the absolute value of the difference between the pitch angles of the two hoisting devices, and the absolute value of the difference between the roll angles of the two hoisting devices.

[0031] A double-machine lifting operation monitoring and early warning system, comprising:

[0032] a data acquisition and synchronization module for acquiring real-time operation state data from two hoisting devices and time correlating the data to establish a unified data processing timing reference;

[0033] a pose solving module connected with the data acquisition and synchronization module for solving spatial pose information of each hoisting device based on the time-correlated operation state data of each hoisting device after data fusion;

[0034] a central processing module connected with the pose solving module for mapping the spatial pose information of the two hoisting devices to the same job coordinate system and combining with the job parameters of the hoisting devices to construct a system spatial pose model containing the two hoisting devices and the hoisting member; the central processing module is further used for real-time calculation of a risk index representing job safety based on the system spatial pose model;

[0035] a warning judgment and output module connected with the central processing module for real-time comparison of the risk index with a corresponding preset safety threshold, and triggering a warning and generating corresponding warning information when the risk index exceeds the safety threshold.

[0036] Further, in the double-machine lifting operation monitoring and warning system, the data acquisition and synchronization module comprises:

[0037] a spreader pose sensing unit arranged on a spreader of the hoisting device for acquiring the operation state data;

[0038] a data communication unit connected with the spreader pose sensing unit for transmitting the operation state data and performing the time correlation;

[0039] The pose solving module, the central processing module and the warning judgment and output module jointly constitute a central monitoring system.

[0040] Further, in the double-machine lifting operation monitoring and warning system, the spreader pose sensing unit comprises a base, an inertial measurement unit and a packaging shell, the base is used for fixing and mounting the packaging shell on the spreader, the inertial measurement unit is fixedly mounted in the packaging shell, and the inertial measurement unit comprises a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer; and / or,

[0041] The data communication unit is in communication connection with the central monitoring system and the spreader pose sensing unit, the data communication unit comprises a data transmission unit and a transmission antenna, the data transmission unit is arranged in the packaging shell, the transmission antenna is arranged on the outside of the packaging shell, and the transmission antenna is in communication connection with the data transmission unit; and / or,

[0042] The data communication unit is provided with a time synchronization function based on a network time protocol to perform the time association.

[0043] The beneficial effects of the present application are as follows:

[0044] The double-machine lifting operation monitoring and early warning method of the present application realizes real-time monitoring of the operating state of the double machines and the attitude of the lifting device, accurate risk assessment and timely early warning, and guarantees the safe performance of the double-machine lifting operation through data acquisition and time association, spatial attitude information solving, system spatial attitude model construction, risk index calculation and early warning determination and output.

[0045] The double-machine lifting operation monitoring and early warning method also builds a system spatial attitude model containing the double machines and the lifting components, and combines single-machine attitude indexes and double-machine coordination indexes to perform risk assessment, which not only considers the safety of single-machine operation, but also pays attention to the coordination of double-machine action, and comprehensively covers the main safety risk points of double-machine lifting operation. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is a flowchart of the double-machine lifting operation monitoring and early warning method of the present application;

[0047] Figure 2 is a layout schematic diagram of an inertial measurement unit of the double-machine lifting operation monitoring and early warning method of the present application;

[0048] Figure 3 is a data transmission schematic diagram of the inertial measurement unit of the double-machine lifting operation monitoring and early warning method of the present application.

[0049] In the figure: 1, lifting device; 2, inertial measurement unit; 3, sling. DETAILED DESCRIPTION

[0050] The present application will be further described in detail below in combination with the drawings and specific embodiments. The advantages and features of the present application will be more apparent according to the following description and claims. It should be noted that the drawings are all greatly simplified and use non-precise proportions, and are only used to facilitate and clarify the purpose of assisting in the description of the embodiments of the present application.

[0051] As shown in Figure 1 The present embodiment provides a double-machine lifting operation monitoring and early warning method, which comprises the following steps: S1-S5.

[0052] S1: Real-time acquisition of operating state data from two hoisting devices, and time association of the data to establish a unified data processing time sequence reference. The hoisting device can be preferably a crawler crane.

[0053] The time correlation includes adding the same time stamp of time service to the operation state data packet from each hoisting device, and aligning all data on a virtual time axis established based on the time stamp, to ensure the time sequence consistency of data processing.

[0054] The operation state data includes raw sensor data collected by an inertial measurement unit 2 (IMU) arranged at the hoist 1 of the hoisting device, which can be preferably a nine-axis inertial measurement unit 2. The raw sensor data at least includes three-axis acceleration data, three-axis angular velocity data, and three-axis magnetic field intensity data.

[0055] Specifically, the inertial measurement unit 2 is fixedly installed at the outside of the hoist 1 and the distribution plane (i.e. Y-Z plane) of the sling 3 of each hoisting device (as shown in Figure 2 The inertial measurement unit 2 collects real-time three-axis acceleration data ( ), three-axis angular velocity data ( ), and three-axis magnetic field intensity data ( ). At the same time, to ensure the comparability of the data of the two machines and establish a unified time sequence reference, the time synchronization function (such as the network time protocol NTP) integrated by the data communication unit is realized. The specific process is as follows: when the inertial measurement unit 2 (IMU) collects a group of data, the data communication unit initiates a time query to the central monitoring system to obtain an accurate coordinated universal time (UTC) time stamp t i . Then, the time stamp t i and the collected data are packaged into a data packet , and are wirelessly sent to the central monitoring system (as shown in Figure 3 ). The data communication unit includes a data transmission unit and a transmission antenna.

[0056] In this step S1, the same time stamp of time service is used to realize the time correlation of the operation state data of the two machines, establish a unified data processing time sequence reference, and solve the evaluation error problem caused by the time sequence difference in the prior art, thereby improving the reliability of data processing.

[0057] S2: Based on the operation state data of each hoisting device after time correlation, the spatial attitude information of each hoisting device is calculated after data fusion.

[0058] After the central monitoring system receives the data, step S2 is performed, which specifically includes the following sub-steps: S21-S23.

[0059] S21: Time stamp alignment and filtering preprocessing are performed on the operation state data of each hoisting device to obtain preprocessed data.

[0060] First, time synchronization processing is performed. The central monitoring system takes the system startup time t sAs the zero point to establish a virtual timeline, will receive the data packet with time stamp t i Convert to virtual timeline: , , The converted timestamp. Set a fixed solution cycle (eg according to the data communication unit output frequency set), then the solution time , , Is a positive integer greater than or equal to 1. At any solution time , the received data packet Linear interpolation alignment:

[0061]

[0062] Where, And The two converted time stamps adjacent to each other, meet , And The data packet corresponding to the timestamp And , The time Aligned data packet. This process provides a strict time synchronization of the subsequent fusion algorithm double machine data stream .

[0063] Secondly, filtering and noise reduction. A first-order low-pass filter is used to filter the synchronized original sensor data independently to retain low-frequency signals (usually 0-15Hz) reflecting the attitude changes of the spreader 1, and to filter out high-frequency noise. The filtering formula is:

[0064]

[0065] Where, Any sensor data in the current time (I.e. Nine data of a certain one), Filter output, The filter coefficient is calculated by the set cutoff frequency (10-15Hz can be taken) and solution period : This process independently preprocesses three-axis acceleration data, three-axis angular velocity data and three-axis magnetic field intensity data.

[0066] Then the three-axis magnetometer compensation. To eliminate the interference of the crawler crane's own steel structure on the geomagnetic field, initial calibration is required. In the calibration phase, slowly rotate the spreader 1 and collect at least 5 minutes of three-axis magnetometer data , tri-axial magnetometer data , in microtesla . The data without disturbance should be distributed on a sphere, and become an ellipsoid after the hard and soft magnetic disturbance, and its mathematical model is:

[0067]

[0068] wherein, , is the hard magnetic disturbance vector, is a symmetric positive definite matrix. The tri-axial magnetometer data is fitted by the least square method to solve and .

[0069] During the double-machine lifting operation, the tri-axial magnetic field intensity data filtered in real time is compensated:

[0070]

[0071] wherein, is the tri-axial magnetic field intensity data collected and filtered in real time, is the tri-axial magnetic field intensity data compensated and corrected, is a lower triangular matrix, and satisfies .

[0072] Finally, the zero offset is initialized. In the static state of the lifting tool 1, the tri-axial gyroscope data is collected for at least 5 minutes, and the average value of the tri-axial gyroscope data is taken as the initial zero offset value of the tri-axial gyroscope .

[0073] S22: based on the preprocessed data, an extended Kalman filter (EKF) algorithm is used for data fusion to obtain a quaternion representing the attitude of the boom.

[0074] Specifically, the extended Kalman filter (EKF) algorithm is used to high-precision fuse the preprocessed acceleration, angular velocity and magnetic field data. The system state vector is defined as wherein, the first four items are unit quaternions representing the attitude, and the last three items are the tri-axial gyroscope zero offset vectors to be estimated.

[0075] The extended Kalman filter (EKF) algorithm iteratively performs the following two steps:

[0076] Prediction step: based on the optimal attitude quaternion estimation q k-1 and the zero offset estimation b ω,k-1 of the last moment, the angular velocity ω k =ω m,k-b ω,k The integral is performed to predict the attitude quaternion q r and the bias b ω,r at the current time (assuming the bias changes slowly). Meanwhile, the prediction error covariance matrix is updated.

[0077] Update step: the predicted values are corrected using the observed values from the three-axis accelerometer and the three-axis magnetometer.

[0078] First, based on the predicted attitude q r , the theoretical gravity vector and the geomagnetic field vector are calculated. is the gravity reference vector, is the reference vector determined according to the local geomagnetic information (m N is the north component, and m D is the earth component).

[0079] Then, the Kalman gain K is calculated , where H is the observation Jacobian matrix, and R is the observation noise covariance matrix.

[0080] Finally, the residual between the observed vector (a m is the filtered acceleration, and m c is the compensated magnetic field) and the predicted observation vector is used to update the state vector and the error covariance:

[0081]

[0082]

[0083] The updated bias b ω,k is also obtained. This process realizes the online joint estimation and compensation of attitude and bias, effectively suppressing the drift of the three-axis gyroscope. The quaternion part in x k is normalized to obtain the optimal attitude quaternion q k .

[0084] S23: Convert the quaternion into Euler angle form containing pitch angle, roll angle, and yaw angle as the spatial attitude information.

[0085] Convert the optimal estimation quaternion q k output by the extended Kalman filter (EKF) into more intuitive Euler angles, and the conversion formula is as follows:

[0086] Pitch angle ​

[0087] Roll angle

[0088] Yaw angle

[0089] wherein, is the two-argument arctangent function, is the arcsine function.

[0090] The plane (y-z plane) defined by the boom, sling 3 and spreader 1 is defined as “in-plane”, and the plane (x-z plane) perpendicular to this plane and the ground is defined as “out-of-plane”. Thus:

[0091] Pitch angle reflects the fore-aft tilt of the spreader 1 in-plane, and is directly related to the load distribution balance between the two hoisting devices and the synchronization of the lifting / descending action.

[0092] Roll angle reflects the side-to-side tilt of the spreader 1 out-of-plane, and is one of the most dangerous working conditions leading to lateral instability and even overturning.

[0093] Yaw angle reflects the rotation of the spreader 1 in the horizontal plane, and its stable value is usually not used as a direct early warning criterion, but its rate of change If it changes suddenly, it indicates that the sling 3 may be entangled or the load is undergoing uncontrolled horizontal rotation.

[0094] This step S2, based on the multi-dimensional raw sensor data collected by the inertial measurement unit 2, combines filter preprocessing and extended Kalman filter algorithm for data fusion, which can accurately calculate the pitch angle, roll angle and yaw angle of the spreader 1 and other spatial attitude information, and provide accurate quantitative basis for risk assessment.

[0095] S3: Map the spatial attitude information of the two hoisting devices to the same operation coordinate system, and combine the operation parameters of the hoisting devices to construct a system spatial attitude model including the two hoisting devices and the hoisting component.

[0096] The operation parameters at least include the luffing length and the current hook height of each hoisting device.

[0097] The specific component process is: according to the spatial attitude information and operation parameters of the two hoisting devices, the three-dimensional coordinates of the respective hooks in the same operation coordinate system are calculated, and a system spatial attitude model including the two hoisting devices and the hoisting component is constructed.

[0098] This step S3: by combining the spatial posture information with the operation parameters to construct the system spatial posture model, lays the foundation for future integration of more complex system-level stability analysis (such as anti-overturning stability coefficient calculation), and improves the expansibility and intelligent level of the system.

[0099] S4: based on the spatial posture information and the system spatial posture model, real-time calculation of risk indicators representing operation safety.

[0100] Based on the directly usable spatial posture information obtained in step S2, real-time calculation of single-machine posture indicators and double-machine coordination indicators.

[0101] The single-machine posture indicators include the absolute value of the pitch angle , the absolute value of the roll angle , and the change rate of the yaw angle .

[0102] The double-machine coordination indicators include the absolute value of the difference between the pitch angles of the two hoisting devices , and the absolute value of the difference between the roll angles of the two hoisting devices .

[0103] This step S4 covers single-machine posture abnormalities (such as over-limit tilting, rapid rotation), double-machine coordination abnormalities (such as excessive angle difference), and other dimensions, achieving comprehensive safety monitoring from the local to the system.

[0104] S5: real-time comparison of the risk indicators with corresponding preset safety thresholds, and triggering an early warning and generating corresponding early warning information when the risk indicators exceed the safety thresholds.

[0105] Real-time comparison of the single-machine posture indicators and double-machine coordination indicators calculated in step S4 with the preset safety thresholds. When any indicator exceeds its threshold, the system immediately triggers a corresponding level of early warning (such as audible and visual alarm, screen prompt, remote notification).

[0106] The early warning thresholds can be set according to engineering specifications and field experience. For example, in a typical construction engineering scenario, the following thresholds can be used:

[0107] Single-machine posture thresholds: in-plane tilt early warning threshold = 5°; out-of-plane tilt early warning threshold = 3°; yaw angle change rate early warning threshold = 8° / s.

[0108] Double-machine coordination thresholds: in-plane angle difference early warning threshold = 3°-5° (reflecting load distribution balance); out-of-plane angle difference early warning threshold = 2°-3° (reflecting lateral twisting degree, used to prevent hooking).

[0109] The step S5 realizes real-time calculation and threshold comparison of the risk index, can timely trigger early warning and generate early warning information, provides intuitive safety prompt for the operator, facilitates rapid adjustment measures, effectively reduces the probability of safety accidents, and improves the safety and stability of the double-machine lifting operation. The steps S4 and S5 not only consider the attitude safety of the single machine itself, but also evaluate the cooperative safety of the double-machine operation, so that early warning can be given in the risk germination stage (such as when the load is just beginning to be unevenly distributed, and the lifting tool 1 appears slight yaw), and truly preventive and systematic safety protection is realized.

[0110] The above method realizes real-time monitoring, accurate risk assessment and timely early warning of the double-machine operation state and the attitude of the lifting tool 1 through data acquisition and time correlation, spatial attitude information solving, system spatial attitude model construction, risk index calculation, and early warning judgment and output, thereby ensuring the safe performance of the double-machine lifting operation.

[0111] The embodiment also provides a double-machine lifting operation monitoring and early warning system, which comprises a data acquisition and synchronization module, an attitude solving module, a central processing module, and an early warning judgment and output module.

[0112] The data acquisition and synchronization module is used for acquiring the running state data from the two hoisting devices in real time, and performing time correlation on the data to establish a unified data processing time sequence reference. The data acquisition and synchronization module can comprise a lifting tool attitude sensing unit and a data communication unit. The data communication unit is provided with a time synchronization function based on a network time protocol to perform the time correlation.

[0113] The lifting tool attitude sensing unit is rigidly fixed on the lifting tool 1 of the hoisting device, and the preferred position is the outside of the lifting tool 1 and the distribution parallel plane (i.e. the y-z plane) of the sling 3, and is arranged as close to the geometric center as possible (as shown in the figure). Figure 2 The lifting tool attitude sensing unit comprises a base, an inertial measurement unit 2, and a packaging shell. The base is used for fixedly mounting the packaging shell on the lifting tool 1, and the inertial measurement unit 2 is fixedly mounted in the packaging shell. The inertial measurement unit 2 comprises a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer.

[0114] Specifically, the three-axis accelerometer is used for measuring the linear acceleration of three axial directions, including the gravity acceleration component, and the range is ±4g (g is the gravity acceleration), and the accuracy is ±3%. The three-axis gyroscope is used for measuring the angular velocity of the carrier rotating around three axial directions, and the range of the three-axis gyroscope is ±280° / s, and the zero offset stability is <0.1° / hr. The three-axis magnetometer is used for measuring the components of the earth's magnetic field in three axial directions.

[0115] A flat mounting base is welded or bonded to the selected mounting position, and the base is securely connected to the main body structure of the lifting appliance 1. A high-strength anti-loose bolt is used to fasten the inertial measurement unit 2 to the base, and a shock-absorbing pad is provided between the inertial measurement unit 2 and the base to filter out external high-frequency mechanical vibrations and improve the quality of the collected data. The packaging shell located outside the inertial measurement unit 2 is reliably connected to the base through a bolt, and the packaging shell has a protection level of ≥IP67, ensuring that the inertial measurement unit 2 can work normally in a temperature range of -30℃ to 85℃. The lifting appliance attitude sensing unit is integrated with a power supply module, and the power supply module is provided with a storage battery. When the storage battery is fully charged, it can ensure that the lifting appliance 1 attitude sensing unit works continuously for 24 hours.

[0116] The lifting appliance attitude sensing unit realizes long-term stable and accurate measurement of the full attitude information of the lifting appliance 1 in harsh industrial environments, significantly improving the safety and control accuracy of lifting operations.

[0117] The data communication unit is connected to the lifting appliance attitude sensing unit and is used to transmit the running state data and perform the time correlation. The data communication unit is in communication connection with the central monitoring system and the lifting appliance attitude sensing unit, and the data communication unit includes a data transmission unit and a transmission antenna. The data transmission unit is arranged in the packaging shell, and the transmission antenna is arranged on the outside of the packaging shell. The transmission antenna is in communication connection with the data transmission unit.

[0118] Specifically, the data communication unit has a high MTBF (mean time between failures), and is integrated with a data transmission unit (such as a 4G DTU). The data transmission unit is packaged in the packaging shell, effectively shielding external electromagnetic interference. The output frequency of the data transmission unit is ≥30Hz. The transmission antenna is arranged on the outside of the packaging shell, and the transmission antenna is in communication connection with the data transmission unit, ensuring that the transmission delay of the data end-to-end transmitted by the data transmission unit is stably within 100ms, and the communication distance is ≥500m in the absence of shielding.

[0119] The data communication unit realizes high-frequency, low-delay, long-distance, and stable and reliable wireless transmission of the running state data of the lifting appliance 1, ensuring the real-time performance and data synchronization accuracy of the monitoring system.

[0120] The attitude solving module is connected to the data acquisition and synchronization module, and is used to solve the spatial attitude information of each hoisting equipment based on the running state data of each hoisting equipment after time correlation and data fusion.

[0121] The central processing module is connected with the attitude solving module, and is configured to map the spatial attitude information of the two hoisting devices to the same operation coordinate system, and construct a system spatial attitude model of the two hoisting devices and the hoisting component in combination with operation parameters of the hoisting devices; the central processing module is further configured to calculate a risk index representing operation safety in real time based on the system spatial attitude model. The risk index includes a single-machine attitude index and a double-machine cooperative index.

[0122] The early warning judgment and output module is connected with the central processing module, and is configured to compare the risk index with a corresponding preset safety threshold in real time, and trigger early warning and generate corresponding early warning information when the risk index exceeds the safety threshold.

[0123] The attitude solving module, the central processing module and the early warning judgment and output module jointly constitute a central monitoring system.

[0124] The central monitoring system solves the attitude of each lifting tool 1 through high-precision data fusion, and constructs a real-time system spatial attitude model of the hoisting component in a unified operation coordinate system, so as to accurately calculate a risk index reflecting overall operation safety, and finally realize active real-time monitoring and early warning of risks such as lifting collision and load out of control, thereby improving safety management of traditional lifting operation from relying on manual experience to intelligent active prevention and control level.

[0125] The above system realizes risk quantitative evaluation and threshold-exceeding active early warning of the whole lifting operation process by collecting, synchronizing and solving the attitude data of the double hoisting devices in real time, and constructing a system spatial attitude model of the hoisting component in a unified model, improves the safety, controllability and intelligent level of double-machine cooperative operation, solves the problem of real-time overturning early warning failure of the double crawler cranes in the lifting working condition, and effectively prevents serious accidents such as sling 3 unhooking, structure damage and even whole machine overturning caused by instability of the lifting tool 1.

[0126] The above description is only a description of the preferred embodiments of the present application, and does not limit the scope of the present application in any way. Any modification or modification of the present application by a person skilled in the art based on the above disclosure is within the protection scope of the claims.

Claims

1. A method for monitoring and early warning of double-crane lifting operation, characterized in that, The method comprises the following steps: S1: Real-time acquisition of running state data from two hoisting devices, and time correlation of the data to establish a unified data processing time sequence reference; S2: Based on the time-correlated running state data of each hoisting device, the spatial attitude information of each hoisting device is calculated after data fusion; S3: Map the spatial attitude information of the two hoisting devices to the same job coordinate system, and combine the job parameters of the hoisting devices to construct a system spatial attitude model containing the two hoisting devices and the hoisting member; S4: Based on the spatial attitude information and the system spatial attitude model, the risk index representing the job safety is calculated in real time; S5: Real-time comparison of the risk index with the corresponding preset safety threshold, when the risk index exceeds the safety threshold, triggering an early warning and generating corresponding early warning information.

2. The method of claim 1, wherein the method further comprises: The time correlation in step S1 includes: adding the same time stamp to the running state data packet from each hoisting device, and aligning all data to the virtual time axis established based on the time stamp; and / or, The running state data includes raw sensor data collected by the inertial measurement unit (2) arranged at the hoist (1) of the hoisting device.

3. The method of claim 2, wherein the method further comprises: The raw sensor data at least includes three-axis acceleration data, three-axis angular velocity data and three-axis magnetic field intensity data.

4. The method of claim 3, wherein the method further comprises: In step S2, the spatial attitude information of each hoisting device is calculated after data fusion, which comprises the following steps: S21: Time stamp alignment and filtering pretreatment of the running state data of each hoisting device to obtain pretreatment data; S22: Based on the pretreatment data, the extended Kalman filter algorithm is used for data fusion to calculate the quaternion representing the attitude of the boom; S23: Convert the quaternion into Euler angle form containing pitch angle, roll angle and yaw angle as the spatial attitude information.

5. The method of claim 4, wherein the method further comprises: The filtering pretreatment in step S21 includes: using a first-order low-pass filter to denoise the raw sensor data, and compensating the three-axis magnetic field intensity data based on the pre-calibrated compensation matrix for soft and hard magnetic interference; and / or, The system state vector of the extended Kalman filter algorithm includes attitude quaternion and three-axis gyroscope zero bias vector, and the estimated values of the attitude quaternion and three-axis gyroscope zero bias vector are updated online by iterative execution of prediction step and update step; and / or, The pitch angle corresponds to the inclination of the hoist (1) in the working plane composed of the double-machine boom and the sling (3), and is used to evaluate the synchronization of load distribution and lifting action; and / or, The roll angle corresponds to the inclination of the hoist (1) in the direction perpendicular to the working plane, and is used as the core evaluation index of system overturning risk; and / or, The yaw angle corresponds to the rotation angle of the hoist (1) in the horizontal plane, and is used as an auxiliary judgment index for identifying the twisting of the sling (3) or the swinging of the load.

6. The method of claim 1, wherein the method further comprises: The job parameters at least include the luffing length and the current hook height of each hoisting device; And / or, The step S3 comprises: According to the spatial posture information and the operation parameters of the two hoisting devices, three-dimensional coordinates of respective hooks in the same operation coordinate system are calculated, and a system spatial posture model including the two hoisting devices and the hoisting component is constructed.

7. The method of claim 4, wherein the method further comprises: The risk indicators in the step S4 include: a single-machine posture indicator, which includes the following three items: an absolute value of the pitch angle, an absolute value of the roll angle, and a change rate of the yaw angle; a double-machine cooperation indicator, which includes the following two items: an absolute value of a difference between the pitch angles of the two hoisting devices, and an absolute value of a difference between the roll angles of the two hoisting devices.

8. A monitoring and early warning system for double-crane lifting operations, characterized in that it comprises: The system includes: a data acquisition and synchronization module, which is configured to acquire running state data from the two hoisting devices in real time, and perform time correlation on the data to establish a unified data processing time sequence reference; a posture solving module, which is connected with the data acquisition and synchronization module, and is configured to solve spatial posture information of each hoisting device based on the running state data of each hoisting device after time correlation and data fusion; a central processing module, which is connected with the posture solving module, and is configured to map the spatial posture information of the two hoisting devices to the same operation coordinate system, and construct a system spatial posture model including the two hoisting devices and the hoisting component in combination with operation parameters of the hoisting devices; the central processing module is further configured to calculate risk indicators representing operation safety in real time based on the spatial posture information and the system spatial posture model; a pre-warning judgment and output module, which is connected with the central processing module, and is configured to compare the risk indicators with corresponding preset safety thresholds in real time, and trigger a pre-warning and generate corresponding pre-warning information when the risk indicators exceed the safety thresholds.

9. The dual-crane lifting operation monitoring and warning system of claim 8, wherein, The data acquisition and synchronization module includes: a sling posture sensing unit, which is arranged on a sling (1) of a hoisting device, and is configured to acquire the running state data; a data communication unit, which is connected with the sling posture sensing unit, and is configured to transmit the running state data and perform the time correlation. The posture solving module, the central processing module, and the pre-warning judgment and output module jointly constitute a central monitoring system.

10. The dual-crane lifting operation monitoring and warning system of claim 9, wherein, The sling posture sensing unit includes a base, an inertial measurement unit (2), and a packaging shell; the base is configured to fix and install the packaging shell on the sling (1); the inertial measurement unit (2) is fixedly installed in the packaging shell; the inertial measurement unit (2) includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer; and / or The data communication unit is in communication connection with the central monitoring system and the sling posture sensing unit; the data communication unit includes a data transmission unit and a transmission antenna; the data transmission unit is arranged in the packaging shell; the transmission antenna is arranged on the outside of the packaging shell; and the transmission antenna is in communication connection with the data transmission unit; and / or The data communication unit is provided with a time synchronization function based on a network time protocol to perform the time correlation.

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