Dual-vehicle crane cooperative lifting monitoring method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application disclose a double-vehicle crane cooperative lifting monitoring method and device, electronic equipment and storage medium, the method comprising: collecting component parameters and operation environment parameters of hoisting operation, constructing a reference data model and setting action expectation value and safety control threshold; state acquisition devices are arranged on two car cranes, real-time acquisition of multi-source data is realized, and the multi-source data is transmitted to a central control system to realize double-car synchronous sensing; the multi-source data is processed based on a data fusion algorithm, a dynamic attitude model and a cooperative operation state model are constructed; the state prediction correction value in the model is compared with the reference data model in real time, the deviation is judged and a safety warning is triggered; a feedback control instruction is generated, the hoisting operation is dynamically adjusted, the cooperative deviation is corrected, and the coordinated control of the hoisting attitude is realized. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of double-vehicle crane cooperative lifting operation are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automobile crane cooperative lifting technology, and particularly relates to a double-automobile-crane cooperative lifting monitoring method and device, an electronic device and a storage medium. BACKGROUND

[0002] In offshore bridge construction, especially in hoisting operations with large span and large load, double-automobile-crane cooperative operation is often used. Due to the complex offshore construction environment, affected by natural factors such as wind and waves, tides, etc., the existing double-crane cooperative operation monitoring system has problems such as single data, insufficient fusion, and feedback lag, which makes it difficult to accurately monitor the dynamic changes in the hoisting process in real time, and further affects the construction safety and efficiency. Therefore, a technology based on multi-source data fusion and real-time feedback control is needed to improve the safety and coordination of the operation. SUMMARY

[0003] The embodiments of the present application provide a double-automobile-crane cooperative lifting monitoring method and device, an electronic device and a storage medium, to solve the technical problems of lack of effective real-time monitoring and control mechanism in the cooperative lifting operation of double-automobile-crane, which may cause deviation, insufficient stability, and safety hazards in the operation process.

[0004] In a first aspect, the present application provides a double-automobile-crane cooperative lifting monitoring method, comprising:

[0005] S1, collecting component parameters and operation environment parameters required for hoisting operation, and setting action expectation value and safety control threshold of hoisting operation based on the component parameters and the operation environment parameters, and then constructing a baseline data model for subsequent state judgment and control logic;

[0006] S2, arranging state acquisition devices on two automobile cranes, and constructing a state perception network with a lifting point as the core, for real-time acquisition of multi-source data composed of beam body posture, lifting point height difference, hoisting load and environmental disturbance data in the hoisting process, and transmitting the multi-source data to a central control system to realize synchronous perception of the running state of the double-automobile-crane;

[0007] S3, processing and correcting the multi-source data transmitted to the central control system based on a data fusion algorithm, and constructing a dynamic posture model and a cooperative running state model corresponding to the current operation;

[0008] S4, comparing the relevant components of the state prediction correction value extracted from the dynamic posture model and the cooperative running state model with the action expectation value and the safety control threshold set in the baseline data model in real time, judging whether there is deviation in the current operation state, and triggering the corresponding safety warning mechanism according to the deviation level;

[0009] ​S5. Based on the deviation judgment result, generate a feedback control instruction and send it to the execution mechanism of the target truck crane to dynamically adjust the hoisting operation behavior, correct the coordination deviation, and achieve coordinated control of the hoisting attitude.

[0010] Further, the data fusion algorithm in step S3 includes the following processes:

[0011] Data prediction stage: Based on the state prediction correction value and control input at the previous moment, predict the state and state error covariance at the current moment. The prediction process is implemented through the following formula:

[0012] ;

[0013] ;

[0014] Among them, represents the state prediction value at time k, represents the state prediction correction value at time , represents the state transition matrix at time k, represents the control input matrix at time k, represents the control input at time k, represents the prediction error covariance matrix at time k, indicating the magnitude and uncertainty of the estimation error of the current state in the prediction stage, represents the state error covariance matrix at time , indicating the state uncertainty at the previous moment, represents the process noise covariance matrix at time k, describing the process noise and uncertainty existing in the system, is the transpose of the state transition matrix, used for covariance propagation to maintain the symmetry of the prediction covariance;

[0015] Data correction stage: Based on the multi-source data, correct the predicted state to obtain the updated state estimate. The formula is as follows:

[0016] ;

[0017] ;

[0018] Among them, is the Kalman gain, used to control the weighting of prediction and measurement;

[0019] is the measurement matrix, used to map the system state to observable quantities;

[0020] is the transpose of the measurement matrix;

[0021] To measure the noise covariance matrix;

[0022] A measurement data vector constructed based on the multi-source data;

[0023] For a moment The state prediction correction value.

[0024] Furthermore, the dynamic attitude model is based on time... State prediction correction value The attitude estimation components are used to construct the dynamic attitude model, which describes the dynamic behavior of the hoisting component, using the following formula:

[0025] ;

[0026] in, This indicates the attitude of the hoisted component at time k. Indicates time of attitude estimation components, This represents the control input signal at time k. Indicates at time External disturbances.

[0027] Furthermore, the cooperative operation state model is based on time. State prediction correction value The cooperative operating state model, constructed with the lifting point position estimation components, describes the load difference and lifting point position difference in dual-vehicle lifting, using the following formula:

[0028] ;

[0029] ;

[0030] in, Indicates the difference in lifting load; and Representing time respectively State prediction correction value Estimated load components of the first and second truck cranes; and Representing time respectively State prediction correction value Estimated components of the lifting point positions of the first and second truck cranes; This indicates the difference in the position of the lifting points between the two vehicles.

[0031] Furthermore, the process of determining whether there is a deviation in the current operation status in step S4 includes the following steps:

[0032] Predicted correction value from the state Extracting state components , as well as ,in, For a moment The attitude estimation component represents the spatial attitude of the hoisting component; For a moment The load estimation component represents the lifting load carried by the truck crane; For a moment The estimated component of the lifting point position represents the position of the connection point between the sling and the component in three-dimensional space;

[0033] Will , as well as The expected value of the corresponding action set in the benchmark data model , as well as Compare the results and calculate the deviation:

[0034] ;

[0035] ;

[0036] ;

[0037] in, It represents the deviation of the attitude estimation component and measures whether the spatial angle of the hoisted component is consistent with the expected value; It represents the deviation of the load estimation component, measuring the difference between the actual load carried by the truck crane and the expected load; It represents the deviation of the estimated component of the lifting point position, and measures whether the lifting point position meets the set expected position;

[0038] Will , as well as Each operation is compared with its corresponding safety control threshold to determine whether the current operation status is within a safe range. and the corresponding attitude safety control threshold Compare; and the corresponding load safety control threshold Compare; Safety control thresholds for corresponding lifting point positions Compare;

[0039] Based on the comparison results, determine whether the current operating status exceeds the safe range.

[0040] Furthermore, the deviation judgment result is used to trigger a multi-level safety early warning mechanism, which includes:

[0041] when Exceeding the set attitude safety control threshold However, if the mandatory intervention threshold is not reached, a posture warning is generated, and the operator is alerted to pay attention through visual or audible and visual alarms.

[0042] when Exceeding the set load safety control threshold However, if the mandatory intervention threshold is not reached, a load warning is generated, and the operator is alerted to the situation via visual or audible alarm.

[0043] when Exceeding the set safety control threshold for the lifting point position However, if the mandatory intervention threshold is not reached, a warning of the hoisting point location is generated, and the operator is alerted to pay attention through visual or audible and visual alarms.

[0044] when , as well as If any one of these exceeds the set serious deviation threshold, a mandatory alarm will be triggered, and the current operation status will be marked as a serious risk level, entering the control intervention preparation stage.

[0045] Furthermore, the control intervention includes the following steps:

[0046] The central control system is based on the current system state estimate. and , as well as It assesses the current operational status and generates control commands;

[0047] The control commands are sent to the control actuators of the target mobile crane to achieve coordinated action correction and reduce [damage / loss]. , as well as This restores the coordination and stability of the lifting operation posture;

[0048] If the deviation continues to increase during the control intervention process, the control commands will be gradually strengthened to ensure that the hoisting operation can be restored to a safe range as soon as possible.

[0049] Secondly, the present invention provides a dual-truck crane collaborative lifting monitoring device, comprising:

[0050] The module is used to collect component parameters and working environment parameters required for hoisting operations, and to set the expected values ​​of hoisting operations and safety control thresholds based on the component parameters and working environment parameters, thereby constructing a benchmark data model for subsequent state judgment and control logic;

[0051] The data acquisition module is used to deploy status acquisition devices on two mobile cranes to build a status perception network with the lifting point as the core. It is used to collect multi-source data in real time, which consists of beam posture, lifting point height difference, lifting load and environmental disturbance data during the lifting process, and transmit the multi-source data to the central control system to realize synchronous perception of the operating status of the two cranes.

[0052] The processing module is used to process and correct the multi-source data transmitted to the central control system based on the data fusion algorithm, and to construct the dynamic attitude model and collaborative operation status model corresponding to the current operation.

[0053] The extraction module is used to extract the state prediction correction values ​​from the dynamic attitude model and the cooperative operation state model. The relevant components are compared in real time with the expected action value and safety control threshold set in the benchmark data model to determine whether there is a deviation in the current operation status, and trigger the corresponding safety warning mechanism according to the deviation level.

[0054] The judgment module is used to generate feedback control commands based on the deviation judgment results and send them to the target truck crane actuator to dynamically adjust the lifting operation behavior, correct the coordination deviation, and achieve coordinated control of the lifting posture.

[0055] Thirdly, the present invention provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0056] The memory stores computer-executed instructions;

[0057] When the processor executes the computer execution instructions stored in the memory, it is used to implement the dual-truck crane collaborative lifting monitoring method of the first aspect of the invention.

[0058] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the dual-truck crane collaborative lifting monitoring method of the first aspect of the invention.

[0059] This invention provides a method, device, electronic equipment, and storage medium for monitoring the coordinated lifting of two truck cranes. The method involves comprehensively collecting component and environmental parameters before lifting operations, setting expected action values ​​and safety thresholds, and constructing a benchmark data model to provide a reference standard for subsequent state judgment. Specifically, during the lifting process, state acquisition devices are deployed on both truck cranes to form a state perception network centered on the lifting points. This network can acquire multi-source data such as beam posture, lifting point height difference, lifting load, and environmental disturbances in real time and transmit it to the central control system, achieving synchronous perception of the operating status of both cranes. The central control system processes and corrects the multi-source data based on a data fusion algorithm, constructing a dynamic posture model and a coordinated operating state model corresponding to the current operation. The corrected state estimate is compared with the benchmark data model in real time, accurately judging operational deviations and triggering a graded early warning mechanism. Furthermore, the central control system automatically generates feedback control commands based on the deviation level and sends them to the truck crane actuators to dynamically correct the lifting operation, achieving coordinated control of the two truck cranes. Therefore, this method can realize real-time monitoring, intelligent judgment and closed-loop control of dual truck cranes in complex construction environments, which not only significantly improves the safety and coordination of the operation process, but also improves construction efficiency and reliability. It is particularly suitable for hoisting operations of large-span and heavy-load projects such as offshore bridges. Attached Figure Description

[0060] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0061] Figure 1 This is a flowchart illustrating the dual-truck crane collaborative lifting monitoring method provided in Embodiment 1 of the present invention;

[0062] Figure 2 This is a schematic diagram of the structure of the dual truck crane collaborative lifting monitoring device provided in Embodiment 2 of the present invention;

[0063] Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0064] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0065] To facilitate a clear description of the technical solutions in the embodiments of the present invention, some terms and technologies involved in the embodiments of the present invention will be briefly introduced below:

[0066] Component parameters and operating environment parameters: Static parameters and dynamic environmental variables involved in hoisting operations. Component parameters include beam dimensions, weight, center of gravity position, and hoisting point distribution. Operating environment parameters include marine environmental data such as wind speed, wave height, tidal cycle, and visibility.

[0067] State acquisition device and state perception network: a multi-source sensor cluster and data transmission architecture deployed on two truck cranes to realize real-time synchronous acquisition of data such as beam posture, lifting point height difference, and load distribution. The specific components are: (1) Sensor types: IMU (Inertial Measurement Unit), GPS positioning module, tension sensor, laser rangefinder, anemometer, etc.; (2) Network architecture: with the lifting point as the core, the data is synchronously transmitted to the central control system through wired / wireless communication.

[0068] Data fusion algorithm: An algorithm module for spatiotemporal alignment, noise filtering, and state estimation of multi-source sensor data.

[0069] Safety early warning mechanism: A graded response strategy based on deviation levels, wherein the early warning mechanism includes: Level 1 deviation (minor) triggers an audible and visual alarm; Level 2 deviation (moderate) triggers automatic speed reduction; Level 3 deviation (severe) triggers emergency braking.

[0070] Feedback control commands: Control commands calculated by the central control system based on real-time state deviations are applied to the actuators of the target mobile crane. The feedback control commands are used to adjust any deviations that occur during the lifting process, ensuring that the mobile crane can quickly correct its attitude and synchronization during operation. For example, if an excessive load or a deviation in the lifting point height is detected during execution, the system will automatically generate feedback commands to adjust the position of the winch or boom of the mobile crane, ensuring that the coordinated lifting task can be carried out smoothly.

[0071] Dynamic attitude model: This model describes the dynamic behavior of lifting components (such as the crane beam and lifting points) by combining time variations and physical constraints. It reflects the attitude changes of each component during the lifting process. For example, during the lifting process, the attitude of the crane beam will change due to the lifting load and wind force. By establishing a dynamic attitude model, these changes can be predicted in advance, avoiding potential safety hazards.

[0072] State prediction correction value: This value is a corrected state estimate calculated by data fusion algorithms such as Kalman filtering based on the state estimate at the previous moment and the current observation data. For example, suppose that during prediction, the state estimate believes that the truck crane is in a certain position, but due to the influence of environmental factors such as wind speed, the observation data shows that the truck crane has actually deviated from the predicted position. By comparing, the obtained correction value can update the prediction and ensure a more accurate state judgment.

[0073] Collaborative Operation Status Model: This model describes the synchronicity and coordination of two truck cranes during collaborative operations. It reflects information such as the relative position and load distribution of the two truck cranes in the lifting task. The collaborative operation status model focuses on the collaborative working state of the two truck cranes, analyzes the dynamic relationship between the two truck cranes, and ensures that the two vehicles can operate synchronously during the operation. For example, in the collaborative operation of two truck cranes, if the load of one truck crane deviates or the boom angle changes, the collaborative operation status model can detect and adjust it in time to avoid operational errors.

[0074] Multi-source data: Measurement data from different sensors or devices, including but not limited to beam attitude, lifting point height difference, lifting load and environmental disturbance data. Multi-source data provides a more comprehensive monitoring and control capability for lifting operations by fusing information from different sensors. For example, beam attitude data may come from IMU (Inertial Measurement Unit), lifting point height difference may be obtained by laser rangefinder, lifting load is measured by tension sensor, and environmental disturbances (such as wind speed and wave height) come from meteorological sensors, etc.

[0075] In engineering projects such as offshore bridges and ultra-large industrial components, precise hoisting of heavy, long-span components is frequently required. Especially in complex offshore construction environments, two truck cranes are often used in tandem to complete large-span hoisting tasks, meeting the dual requirements of lifting capacity and lifting point distribution. However, due to problems such as slow dynamic response of the hoisting system, uneven stress distribution, and difficulty in coordinated control, this type of operation faces high risks and technical challenges.

[0076] Existing collaborative hoisting operation solutions mostly rely on manual experience or limited local monitoring methods, and generally have the following shortcomings:

[0077] Limited data acquisition dimensions: Existing monitoring systems mainly rely on the status feedback of a single sensor or a limited number of parts (such as the main boom), which cannot fully perceive the overall posture changes of the beam, the changes in the height difference of the lifting points, and the load distribution during the hoisting process.

[0078] Insufficient data fusion: Data collected by different sensors is usually distributed across multiple systems, lacking a unified data processing platform and fusion algorithm, resulting in ineffective information integration and difficulty in forming a unified hoisting cognitive model.

[0079] Response control lag: Existing systems mainly rely on manual intervention or preset logic for attitude correction, and cannot dynamically generate control commands based on real-time state deviations during hoisting, resulting in adjustment lag and increased operational instability.

[0080] Safety risks are difficult to predict in advance: Because it is impossible to judge the level of deviation and its impact on the overall hoisting stability in real time, serious errors are likely to occur and cannot be intervened in time, posing significant safety hazards.

[0081] Especially in offshore construction scenarios, there are many uncontrollable environmental disturbances (such as wind, waves, and tidal changes), which amplifies the uncertainty of construction and hoisting.

[0082] In summary, there is an urgent need for a technical solution that can sense the lifting status in real time, dynamically determine the deviation level, and perform closed-loop control based on multi-source data to ensure the synchronization, stability, and safety of collaborative lifting operations with two truck cranes.

[0083] Based on this, embodiments of the present invention provide a method, device, electronic device, and storage medium for monitoring the coordinated lifting of two truck cranes, in order to solve the aforementioned technical problems.

[0084] Example 1

[0085] Figure 1 This is a flowchart illustrating the dual-truck crane collaborative lifting monitoring method provided in Embodiment 1 of the present invention, as shown below. Figure 1 As shown, the method includes:

[0086] S1. Collect the component parameters and working environment parameters required for the hoisting operation, and set the expected values ​​of the hoisting operation and the safety control threshold based on the component parameters and working environment parameters, and then build a benchmark data model for subsequent state judgment and control logic.

[0087] Specifically, before the lifting operation begins, the necessary component parameters and operational environment parameters are collected. Component parameters include the dimensions, weight, center of gravity, and lifting point distribution of the object being lifted. Operational environment parameters include marine environmental data such as wind speed, tides, and wave height. These collected component and operational environment parameters form the foundation of the entire lifting operation, helping the system to preset expected action values ​​and safety control thresholds, providing a benchmark data model for subsequent state judgment and control. Based on these parameters, the expected goals of the lifting operation can be reasonably set, ensuring the system operates within a safe range. In particular, the accurate collection of operational environment and component information provides strong support for subsequent state prediction and control, reducing reliance on manual intervention and improving the system's adaptability to complex environments.

[0088] S2. Deploy status acquisition devices on the two truck cranes to build a status perception network with the lifting point as the core. This network is used to collect multi-source data in real time, including beam posture, lifting point height difference, lifting load and environmental disturbance data during the lifting process. The multi-source data is then transmitted to the central control system to achieve synchronous perception of the operating status of the two cranes.

[0089] Specifically, through the state-aware network, the central control system can obtain real-time data during the hoisting process, including changes in the beam's posture, height differences between lifting points, load distribution, and changes in the external environment (such as wind speed and wave height). This multi-source data provides accurate information for subsequent model building and control command generation. By combining multiple sensors, comprehensive perception of the entire hoisting operation process can be achieved, ensuring that the operational status of each truck crane can be monitored in real time, thus improving operational transparency and response speed.

[0090] S3. Based on the data fusion algorithm, process and correct the multi-source data transmitted to the central control system to construct the dynamic attitude model and collaborative operation status model corresponding to the current operation.

[0091] Specifically, the data fusion algorithm performs a weighted average of data from different sensors (such as IMU, GPS, tension sensors, etc.) to correct noise and errors in the original data, resulting in a more accurate dynamic attitude model and collaborative operation state model. These models can reflect the real-time dynamic behavior of each component during hoisting, providing a basis for subsequent deviation judgment and feedback control. Through data fusion, not only can the errors that may exist in a single sensor be eliminated, but the system's adaptability to complex dynamic environments can also be improved, enhancing the stability and safety of the operation.

[0092] S4. Extract the state prediction correction values ​​from the dynamic attitude model and the cooperative operation state model. The relevant components are compared in real time with the expected values ​​of actions and safety control thresholds set in the benchmark data model to determine whether there is a deviation in the current operation status, and trigger the corresponding safety warning mechanism according to the deviation level.

[0093] Specifically, by comparing the predicted state with the expected value, the system can detect possible deviations (such as uncoordinated posture, unbalanced load, etc.) during the hoisting process in real time. When the deviation exceeds the preset safety control threshold, the system will automatically trigger the early warning mechanism to promptly remind the operator and take necessary corrective measures. That is, through real-time monitoring and comparison, the system can react when the deviation first appears, which significantly improves the safety and real-time response capability of hoisting operations and avoids the possibility of accidents.

[0094] S5. Based on the deviation judgment result, generate feedback control commands and send them to the target truck crane actuator to dynamically adjust the lifting operation behavior, correct the coordination deviation, and achieve coordinated control of the lifting posture.

[0095] Specifically, upon detecting a deviation, the system automatically generates control commands to instruct the target truck crane to adjust its operation (such as adjusting the lifting point position and controlling the crane boom angle) to ensure the coordination and synchronization of the lifting operation. This allows for real-time correction of deviations, maintaining synchronized operation of the two truck cranes and preventing further amplification of errors during operation. Through dynamic adjustment and real-time feedback, the system ensures posture coordination during collaborative operation of the two truck cranes, thereby improving the accuracy, stability, and efficiency of the lifting operation. This method significantly reduces the need for manual intervention, increases the level of automation, and enhances the overall safety of the operation.

[0096] This embodiment provides a method, device, electronic equipment, and storage medium for monitoring the coordinated lifting of two truck cranes. This method effectively solves the problems of low real-time monitoring accuracy, insufficient data fusion, delayed feedback control, and untimely safety warnings in existing technologies for coordinated operation of two truck cranes. Specifically, it has the following significant beneficial effects:

[0097] 1. Improve operational safety: By collecting multi-source data in real time (including beam posture, lifting point height difference, lifting load and environmental disturbance, etc.) and comparing the data with preset safety control thresholds, the system can automatically trigger a safety warning mechanism when deviations occur, effectively avoiding dangerous operations and accident risks during the lifting process.

[0098] 2. Enhanced operational stability: Data fusion algorithms are used to process and correct various sensor data, constructing dynamic attitude models and collaborative operation status models. This accurately reflects the real-time status of the two truck cranes during the lifting process, significantly improving the stability of the lifting operation. Especially in complex environments, it ensures precise control of the operation process.

[0099] 3. Improved operational efficiency: The system automatically generates feedback control commands and adjusts hoisting operation behavior in real time, effectively correcting coordination deviations, reducing the need for manual intervention, and shortening adjustment response time, thereby improving operational efficiency and automation level.

[0100] 4. Optimize dual-vehicle collaborative operation: By constructing a state perception network centered on the lifting point, the state changes of the two truck cranes are monitored in real time and synchronized, avoiding the coordination problems caused by the lack of information of a single vehicle in traditional collaborative operations. This enables the two truck cranes to complete complex lifting tasks more efficiently and safely.

[0101] 5. Adaptable to complex operating environments: This method is particularly suitable for complex operating environments such as offshore bridges and deep-water engineering projects. It can effectively cope with the impact of environmental disturbances, such as wind speed changes and tidal fluctuations, thereby ensuring that high precision and high stability of operations can still be maintained in dynamic environments.

[0102] In some implementations, the data fusion algorithm efficiently fuses data from different sensors through a data prediction phase and a data correction phase, thereby improving the accuracy of system state estimation. Specifically, the algorithm brings significant benefits in the following aspects:

[0103] (1) In the data prediction stage, based on the state prediction correction value, control input, and process noise covariance matrix of the previous moment, the system state and state error covariance at the current moment are accurately predicted. This process can comprehensively consider the dynamic characteristics of the system and the control input, and fully reduce the prediction error caused by external disturbances (such as environmental changes such as wind speed and tides). Specifically, through reasonable state transition matrix and control input matrix, the system can more accurately estimate the state at each moment, especially in dynamic environments (such as offshore lifting operations), which can effectively reduce the error caused by external factors.

[0104] (2) In the data correction stage, Kalman gain is used to balance the weights of the predicted state and the multi-source data measurement values ​​to correct the state estimation. This process integrates data from various sources (such as beam posture, lifting point height difference, and lifting load) to ensure that the final state estimation is more accurate and reflects the current real state of the system. Specifically, through the weighting mechanism of Kalman filtering, the data provided by different sensors can complement each other, eliminating the errors and inaccuracies that may be caused by a single sensor. The system can correct the predicted state based on the real-time measurement values ​​to obtain a more accurate state estimation. Furthermore, even if the data of a certain sensor has a certain error, the data fusion algorithm can correct it through the data of other sensors to ensure that the final state estimation is as accurate as possible and avoid system failure due to individual data anomalies.

[0105] (3) The measurement noise covariance matrix in the data correction stage can accurately estimate the impact of sensor errors and environmental disturbances, ensuring the system's adaptability to different data sources. By combining the measurement matrix and Kalman gain, the system can automatically adjust the influence of measurement data on state estimation, improving its adaptability to environmental changes and sensor fluctuations.

[0106] (4) Through the dynamic attitude model after data fusion, the system can provide real-time feedback of correction instructions during operation and make dynamic adjustments according to the deviation level. This process takes all influencing factors into account, thereby enabling timely detection of coordination deviations and the implementation of corresponding control measures.

[0107] For example, in the data prediction stage: based on the state prediction correction value and control input from the previous time step, the current state and state error covariance are predicted. The prediction process is achieved through the following formula:

[0108] ;

[0109] ;

[0110] in, This represents the predicted state value at time k; Indicates at time State prediction correction value; This represents the state transition matrix at time k; This represents the control input matrix at time k; This represents the control input at time k; The prediction error covariance matrix at time k represents the magnitude and uncertainty of the estimation error of the current state at the current time during the prediction phase. Indicates at time The state error covariance matrix represents the state uncertainty at the previous moment; Let represent the process noise covariance matrix at time k, which describes the process noise and uncertainty present in the system; This is the transpose of the state transition matrix, used for covariance propagation to maintain the symmetry of the prediction covariance;

[0111] Data correction stage: The predicted state is corrected based on multi-source data to obtain the updated state estimate, as shown in the following formula:

[0112] ;

[0113] ;

[0114] in, Kalman gain is used to control the weighting of prediction and measurement;

[0115] This is a measurement matrix used to map the system state to observables;

[0116] This is the transpose of the measurement matrix;

[0117] To measure the noise covariance matrix;

[0118] A measurement data vector constructed from multi-source data;

[0119] For a moment The state prediction correction value.

[0120] For example, the prediction and correction stages of the above data fusion algorithm effectively improve the accuracy, stability and safety of the collaborative lifting operation of two truck cranes. The data prediction stage can ensure that the state estimation is always accurate in the dynamically changing lifting environment, while the data correction stage uses algorithms such as Kalman filtering to efficiently fuse multi-source data, ensuring that the system can operate stably under various complex environmental conditions and provide accurate feedback control commands.

[0121] In some implementations, the dynamic attitude model is based on time. State prediction correction value The attitude estimation components are used to construct the dynamic attitude model, which describes the dynamic behavior of the hoisting component, using the following formula:

[0122] ;

[0123] in, This indicates the attitude of the hoisted component at time k. Indicates time of attitude estimation components, This represents the control input signal at time k. Indicates at time External disturbances.

[0124] Specifically, the dynamic pose model combines time... The state prediction correction value, control input signal, and external disturbances accurately describe the dynamic behavior of the hoisted component during the hoisting operation. The dynamic attitude model, based on the attitude estimation component in the state prediction correction value, can track the attitude change of the component in real time and predict its dynamic response under complex working conditions. The dynamic attitude model can effectively combine the influence of control inputs (such as hoisting point adjustment, boom movement, etc.) and external disturbances (such as environmental factors such as wind speed and wave height) to provide an accurate estimate of the component's state, providing a precise foundation for subsequent deviation judgment and coordinated control.

[0125] Through the dynamic attitude model, the central control system can monitor the changes in the attitude of the components in real time during the hoisting process, ensuring that the dynamic estimation at each moment is as accurate as possible, and avoiding state deviations caused by sensor errors or environmental disturbances.

[0126] The benefits of dynamic attitude model are as follows: First, dynamic attitude model can significantly improve the system's prediction accuracy of component dynamic changes, enabling the system to grasp the precise position and attitude of components in real time; second, the model can effectively eliminate the influence of external interference factors (such as environmental disturbances) on state estimation, improving the system's adaptability and robustness to complex environments; finally, by combining with the cooperative operation state model, dynamic attitude model ensures the coordination and synchronization during the operation of two truck cranes, avoiding problems such as attitude imbalance or uneven load during operation, thereby improving the safety, stability and efficiency of the operation.

[0127] In some implementations, the cooperative operating state model is based on time. State prediction correction value The cooperative operating state model, constructed from the lifting point position estimation components, describes the load difference and lifting point position difference in dual-vehicle lifting, using the following formula:

[0128] ;

[0129] ;

[0130] in, Indicates the difference in lifting load; and Representing time respectively State prediction correction value Estimated load components of the first and second truck cranes; and Representing time respectively State prediction correction value Estimated components of the lifting point positions of the first and second truck cranes; This indicates the difference in the position of the lifting points between the two vehicles.

[0131] The collaborative operation state model is based on time. The state prediction correction values ​​are constructed from the components related to load and lifting point position, and are used to characterize the operational relationship between the two truck cranes during collaborative lifting. Specifically, the system decomposes the state prediction correction values ​​to obtain the time-related components of the two truck cranes. Load estimation components and and estimated components of the lifting point position and This allows us to calculate the load difference between the two vehicles. and the difference in lifting point position between the two vehicles By measuring the load difference and the difference in lifting point position, the force balance and spatial coordination of the two vehicles during collaborative operation can be fully reflected.

[0132] Based on the collaborative operation status model, the central control system can not only monitor in real time whether there is uneven load distribution or deviation of lifting point position between the two truck cranes during operation, but also quantitatively describe the magnitude of the deviation and provide feedback to the central control system to determine whether a synchronization correction command needs to be triggered.

[0133] The beneficial effects of the collaborative operation status model are as follows: First, it can accurately identify the differences in force and position during dual-crane hoisting, ensuring a more balanced load distribution and reducing the risk of single-crane overloading or uneven component stress. Second, by monitoring the difference in lifting point positions, it can effectively avoid beam tilting or rotation caused by asynchronous operations, improving the stability of the hoisting process. Third, it enables a quantitative expression of the collaborative state, making deviation judgment and control decisions more objective and faster, reducing the uncertainty brought about by human experience judgment. Finally, combined with the input of the dynamic attitude model, the collaborative operation status model can maintain high-precision monitoring and dynamic adjustment of the collaborative state of the two cranes in complex environments, thus significantly improving the safety, reliability, and efficiency of collaborative lifting operations of two truck cranes.

[0134] For example, the dynamic attitude model and the collaborative operation state model are closely linked, jointly supporting the efficiency and safety of collaborative lifting operations by two truck cranes. The dynamic attitude model is mainly responsible for describing the attitude changes of the lifted components in real time. By combining state prediction correction values, control input signals, and external disturbance factors, it accurately reflects the dynamic behavior of the lifted components. The collaborative operation state model further analyzes and evaluates the relative motion and collaborative working state of the two truck cranes, ensuring the synchronization and coordination between the two truck cranes.

[0135] Specifically, the component attitude data output by the dynamic attitude model provides the necessary input for the collaborative operation state model. This data includes the current position, attitude angle, and dynamic change trend of the hoisted component. Based on this information, combined with the control inputs of both cranes (such as lifting point adjustment, boom angle change, etc.) and the stress conditions in both workshops, the collaborative operation state model determines whether there is any deviation between the two cranes and decides whether collaborative correction is needed. In this way, the dynamic attitude model and the collaborative operation state model complement each other; the former ensures accurate feedback of the component status, while the latter ensures coordinated operation of the two cranes, achieving closed-loop control from local to overall.

[0136] In some implementations, the deviation judgment process in step S4 comprehensively assesses whether the current hoisting operation status is within a safe range by extracting and comparing key state components from the state prediction correction value. Specifically, the central control system first analyzes the state prediction correction value... Extract three types of state components: , as well as ,in, For a moment The attitude estimation component represents the spatial attitude of the hoisting component; For a moment The load estimation component represents the lifting load carried by the truck crane; For a moment The estimated component of the lifting point position represents the position of the connection point between the sling and the component in three-dimensional space;

[0137] After that, , as well as The expected value of the corresponding action set in the benchmark data model , as well as Compare the results and calculate the deviation:

[0138] ;

[0139] ;

[0140] ;

[0141] in, It represents the deviation of the attitude estimation component and measures whether the spatial angle of the hoisted component is consistent with the expected value; It represents the deviation of the load estimation component, measuring the difference between the actual load carried by the truck crane and the expected load; It represents the deviation of the estimated component of the lifting point position, and measures whether the lifting point position meets the set expected position;

[0142] Will , as well as Each operation is compared with its corresponding safety control threshold to determine whether the current operation status is within a safe range. and the corresponding attitude safety control threshold Compare; and the corresponding load safety control threshold Compare; Safety control thresholds for corresponding lifting point positions Compare;

[0143] Based on the comparison results, determine whether the current operating status exceeds the safe range.

[0144] Through the deviation judgment process, the central control system realizes a closed-loop judgment mechanism from single data acquisition to comprehensive status analysis, enabling comprehensive monitoring of key safety parameters during operation. Its beneficial effects are mainly reflected in the following aspects: First, by introducing deviation calculations for three types of estimated components—attitude, load, and lifting point position—the system can achieve multi-dimensional detection of the operational status, avoiding the one-sidedness caused by monitoring a single indicator; second, by combining deviations with benchmark expected values ​​and safety thresholds, the system can achieve quantitative and standardized status assessment, ensuring the scientific nature and repeatability of deviation judgments; finally, this process can promptly identify and trigger corresponding early warning and control commands when deviations first occur, significantly improving the sensitivity and response speed of deviation detection.

[0145] In some implementations, deviation assessment results are used to trigger a multi-level safety early warning mechanism, which improves the system's intelligent monitoring capabilities while ensuring operational safety. When the deviation exceeds a set safety control threshold but does not reach a mandatory intervention threshold, the system automatically generates a corresponding early warning signal and alerts the operator via visual or audible alarms, reminding them to pay attention to and adjust the current status. The specific early warning mechanism is as follows:

[0146] (1) Attitude warning: when Exceeding the set attitude safety control threshold However, if the mandatory intervention threshold is not reached, a posture warning is generated, and the operator is alerted through visual or audible alarms. The posture warning is used to remind the operator that the spatial angle of the currently hoisted component is close to or exceeds the safe range, and the operator needs to observe and adjust.

[0147] (2) Load warning: when Exceeding the set load safety control threshold However, if the mandatory intervention threshold is not reached, a load warning is generated, and the operator is alerted through visual or audible alarms. The load warning is used to remind the operator that the current load distribution of the mobile crane is uneven or there is a risk of overload, and measures need to be taken as soon as possible.

[0148] (3) Warning of lifting point position: when Exceeding the set safety control threshold for the lifting point position However, if the mandatory intervention threshold is not reached, a lifting point position warning is generated, and the operator is alerted through visual or audible alarms. The lifting point position warning is used to remind the operator that the current lifting point position deviation may affect the stability of the lifting operation and requires adjustment.

[0149] (4) Mandatory early warning: When , as well as If any one of these factors exceeds the set critical deviation threshold, a mandatory alarm is triggered, and the current operational status is marked as a critical risk level, entering the control intervention preparation phase. That is, the central control system triggers a mandatory alarm, marks the current operational status as a critical risk level, and enters the control intervention preparation phase. This phase signifies that the current operation is no longer safe, and the system will activate an automatic intervention mechanism to prevent accidents.

[0150] By setting up a multi-level safety early warning mechanism, operators can be alerted in stages based on the deviation judgment results, preventing the deviation from escalating into a safety accident. The multi-level safety early warning mechanism not only enhances the accurate monitoring of the operation status, but also effectively reduces the possibility of human error, further improving the safety, reliability and intelligence level of the system. Combined with visual and audible alarms, operators can be quickly alerted during the operation and take necessary control measures in a timely manner to ensure that the hoisting operation is completed smoothly within the safe range.

[0151] In some implementations, a control intervention mechanism is triggered when deviations exceed safe limits to dynamically correct the coordinated operation of the two truck cranes. Specifically, the central control system adjusts the system state based on current system state estimates. and , as well as The system assesses the current operational status and generates control commands. These commands are then sent to the control actuators of the target crane, driving them to perform corresponding actions. This achieves coordinated correction of the lifting posture, effectively reducing various deviations. , as well as This restores the coordination and stability of the hoisted components. Simultaneously, during control intervention, if the system detects that the deviation fails to decrease with the intervention or even continues to increase, the central control system will gradually strengthen control commands to ensure that the hoisting operation can return to a safe range in the shortest possible time, preventing further escalation of risks.

[0152] The beneficial effects of this control intervention mechanism are as follows: First, by introducing a dynamic control command generation method based on state estimation and deviation, the system can achieve precise and real-time intervention, ensuring the safety of hoisting operations. Second, the central control system incorporates deviations in attitude, load, and hoisting point position into a unified evaluation framework, achieving comprehensive judgment of multiple factors and avoiding the one-sidedness caused by single-factor control. Third, the design of graded enhanced control commands ensures the system's flexibility and gradualness in dealing with deviations of varying severity, reducing the adverse effects of over-control on operational efficiency and enabling rapid recovery to a safe state under severe deviation conditions. Finally, through the combination of automated intervention and collaborative correction by two truck cranes, the system can maintain high stability and reliability under complex working conditions, significantly improving the intelligence and safety assurance level of large-scale hoisting operations.

[0153] Example 2

[0154] Figure 2 This is a structural schematic diagram of the dual-truck crane collaborative lifting monitoring device 100 provided in Embodiment 2 of the present invention. Figure 2 As shown, the dual-truck crane collaborative lifting monitoring device 100 provided in Embodiment 2 of the present invention includes a construction module 110, a data acquisition module 120, a processing module 130, an extraction module 140, and a judgment module 150.

[0155] Among them, the construction module 110 is used to collect the component parameters and working environment parameters required for the hoisting operation, and set the expected value of the hoisting operation and the safety control threshold based on the component parameters and working environment parameters, thereby constructing a benchmark data model for subsequent state judgment and control logic;

[0156] The data acquisition module 120 is used to deploy status acquisition devices on two truck cranes to build a status perception network with the lifting point as the core. It is used to collect multi-source data consisting of beam posture, lifting point height difference, lifting load and environmental disturbance data during the lifting process in real time, and transmit the multi-source data to the central control system to realize synchronous perception of the operating status of the two cranes.

[0157] Processing module 130 is used to process and correct multi-source data transmitted to the central control system based on data fusion algorithm, and to construct dynamic attitude model and collaborative operation status model corresponding to the current operation.

[0158] Extraction module 140 is used to extract state prediction correction values ​​from the dynamic attitude model and the cooperative running state model. The relevant components are compared in real time with the expected values ​​of actions and safety control thresholds set in the benchmark data model to determine whether there is a deviation in the current operation status, and trigger the corresponding safety warning mechanism according to the deviation level.

[0159] The judgment module 150 is used to generate feedback control commands based on the deviation judgment results and send them to the target truck crane actuator to dynamically adjust the lifting operation behavior, correct the coordination deviation, and achieve coordinated control of the lifting posture.

[0160] Example 3

[0161] Figure 3 This is a structural diagram of an electronic device according to Embodiment 3 of the present invention. Figure 3 A block diagram is shown of an exemplary electronic device 12 suitable for implementing embodiments of the present invention. Figure 3 The electronic device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0162] like Figure 3 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0163] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0164] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0165] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as "hard disk drives"). Disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disk drives for reading and writing to removable non-volatile optical disks (e.g., CD-ROMs, DVD-ROMs, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0166] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0167] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the electronic device 12 / server / computer, and / or with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 3 As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0168] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the dual truck crane collaborative lifting monitoring method provided in the embodiments of the present invention.

[0169] Example 4

[0170] Embodiment 4 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the dual-truck crane collaborative lifting monitoring method provided in the above embodiments.

[0171] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0172] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0173] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0174] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0175] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for monitoring the coordinated lifting of two truck cranes, characterized in that, include: S1. Collect the component parameters and working environment parameters required for the hoisting operation, and set the expected value of the hoisting operation and the safety control threshold based on the component parameters and the working environment parameters, thereby constructing a benchmark data model for subsequent state judgment and control logic; S2. Deploy status acquisition devices on the two truck cranes to construct a status perception network with the lifting point as the core. This network is used to collect multi-source data in real time, including beam posture, lifting point height difference, lifting load and environmental disturbance data during the lifting process. The multi-source data is then transmitted to the central control system to achieve synchronous perception of the operating status of the two trucks. S3. Based on the data fusion algorithm, process and correct the multi-source data transmitted to the central control system to construct the dynamic attitude model and collaborative operation status model corresponding to the current operation. S4. Combine the state prediction correction values ​​extracted from the dynamic attitude model and the cooperative operation state model. The relevant components are compared in real time with the expected action value and safety control threshold set in the benchmark data model to determine whether there is a deviation in the current operation status, and trigger the corresponding safety warning mechanism according to the deviation level. S5. Based on the deviation judgment result, generate feedback control commands and send them to the target truck crane actuator to dynamically adjust the lifting operation behavior, correct the coordination deviation, and achieve coordinated control of the lifting posture. The data fusion algorithm in step S3, Includes the following processes: Data prediction phase: Based on the state prediction correction value and control input from the previous time step, predict the current state and state error covariance. The prediction process is achieved through the following formula: ； ； in, This represents the predicted state value at time k. Indicates at time State prediction correction value The state transition matrix at time k is represented by... The control input matrix at time k is represented by... This represents the control input at time k. The prediction error covariance matrix at time k represents the magnitude and uncertainty of the estimation error of the current state at time k during the prediction phase. Indicates at time The state error covariance matrix represents the state uncertainty at the previous moment. This represents the process noise covariance matrix at time k, describing the process noise and uncertainties present in the system. This is the transpose of the state transition matrix, used for covariance propagation to maintain the symmetry of the prediction covariance; Data correction stage: Based on the multi-source data, the predicted state is corrected to obtain the updated state estimate, as shown in the following formula: ； ； in, Kalman gain is used to control the weighting of prediction and measurement; This is a measurement matrix used to map the system state to observables; This is the transpose of the measurement matrix; To measure the noise covariance matrix; A measurement data vector constructed based on the multi-source data; For a moment State prediction correction value; The dynamic attitude model is based on time. State prediction correction value The attitude estimation components are used to construct the dynamic attitude model, which describes the dynamic behavior of the hoisting component, using the following formula: ； in, This indicates the attitude of the hoisted component at time k. Indicates time of attitude estimation components, This represents the control input signal at time k. Indicates at time External disturbances; The collaborative operation state model is based on time. State prediction correction value The cooperative operating state model, constructed with the lifting point position estimation components, describes the load difference and lifting point position difference in dual-vehicle lifting, using the following formula: ； ； in, Indicates the difference in lifting load; and Representing time respectively State prediction correction value Estimated load components of the first and second truck cranes; and Representing time respectively State prediction correction value Estimated components of the lifting point positions of the first and second truck cranes; This indicates the difference in the position of the lifting points between the two vehicles.

2. The dual-truck crane collaborative lifting monitoring method according to claim 1, characterized in that, Step S4, determining whether there is a deviation in the current job status, includes the following process: Predicted correction value from the state Extracting state components , as well as ,in, For a moment The attitude estimation component represents the spatial attitude of the hoisting component; For a moment The load estimation component represents the lifting load carried by the truck crane; For a moment The estimated component of the lifting point position represents the position of the connection point between the sling and the component in three-dimensional space; Will , as well as The expected value of the corresponding action set in the benchmark data model , as well as Compare the results and calculate the deviation: ； ； ； in, It represents the deviation of the attitude estimation component and measures whether the spatial angle of the hoisted component is consistent with the expected value; It represents the deviation of the load estimation component, measuring the difference between the actual load carried by the truck crane and the expected load; It represents the deviation of the estimated component of the lifting point position, and measures whether the lifting point position meets the set expected position; Will , as well as Each operation is compared with its corresponding safety control threshold to determine whether the current operation status is within a safe range. and the corresponding attitude safety control threshold Compare; and the corresponding load safety control threshold Compare; Safety control thresholds for corresponding lifting point positions Compare; Based on the comparison results, determine whether the current operating status exceeds the safe range.

3. The dual-truck crane collaborative lifting monitoring method according to claim 1, characterized in that, The deviation judgment result is used to trigger a multi-level safety early warning mechanism, which includes: when Exceeding the set attitude safety control threshold However, if the mandatory intervention threshold is not reached, a posture warning is generated, and the operator is alerted to pay attention through visual or audible and visual alarms. when Exceeding the set load safety control threshold However, if the mandatory intervention threshold is not reached, a load warning is generated, and the operator is alerted to the situation via visual or audible alarm. when Exceeding the set safety control threshold for the lifting point position However, if the mandatory intervention threshold is not reached, a warning of the hoisting point location is generated, and the operator is alerted to pay attention through visual or audible and visual alarms. when , as well as If any one of these exceeds the set serious deviation threshold, a mandatory alarm will be triggered, and the current operation status will be marked as a serious risk level, entering the control intervention preparation stage.

4. The dual-truck crane collaborative lifting monitoring method according to claim 3, characterized in that, The control intervention includes the following steps: The central control system is based on the current system state estimate. and , as well as It assesses the current operational status and generates control commands; The control commands are sent to the control actuators of the target mobile crane to achieve coordinated action correction and reduce [damage / loss]. , as well as This restores the coordination and stability of the lifting operation posture; If the deviation continues to increase during the control intervention process, the control commands will be gradually strengthened to ensure that the hoisting operation can be restored to a safe range as soon as possible.

5. A dual-truck crane collaborative lifting monitoring device, used to implement the dual-truck crane collaborative lifting monitoring method as described in any one of claims 1-4, characterized in that, The dual-truck crane collaborative lifting monitoring device includes: The module is used to collect component parameters and working environment parameters required for hoisting operations, and to set the expected values ​​of hoisting operations and safety control thresholds based on the component parameters and working environment parameters, thereby constructing a benchmark data model for subsequent state judgment and control logic; The data acquisition module is used to deploy status acquisition devices on two mobile cranes to build a status perception network with the lifting point as the core. It is used to collect multi-source data in real time, which consists of beam posture, lifting point height difference, lifting load and environmental disturbance data during the lifting process, and transmit the multi-source data to the central control system to realize synchronous perception of the operating status of the two cranes. The processing module is used to process and correct the multi-source data transmitted to the central control system based on the data fusion algorithm, and to construct the dynamic attitude model and collaborative operation status model corresponding to the current operation. The extraction module is used to extract the state prediction correction values ​​from the dynamic attitude model and the cooperative operation state model. The relevant components are compared in real time with the expected action value and safety control threshold set in the benchmark data model to determine whether there is a deviation in the current operation status, and trigger the corresponding safety warning mechanism according to the deviation level. The judgment module is used to generate feedback control commands based on the deviation judgment results and send them to the target truck crane actuator to dynamically adjust the lifting operation behavior, correct the coordination deviation, and achieve coordinated control of the lifting posture.

6. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; When the processor executes the computer execution instructions stored in the memory, it is used to implement the dual truck crane collaborative lifting monitoring method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the dual-truck-crane collaborative lifting monitoring method as described in any one of claims 1 to 4.

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