Intelligent tower crane anti-collision accurate hoisting system based on multi-sensor fusion

Abstract

The invention relates to the technical field of crane control, in particular to an intelligent tower crane anti-collision accurate hoisting system based on multi-sensor fusion, which comprises a data acquisition module, a data processing module, a safety early warning module, a control execution module, a data processing module, a data processing module and a control module, an alarm module electrically connected with the safety early warning module and the data processing module and used for receiving the alarm signal sent by the safety early warning module; a hoisting control instruction is generated based on the alarm signal and the real-time three-dimensional position coordinates of the hoisted object sent by the data processing module; according to the method, the lifting control instruction is executed, the lifting arm angle, the rotation angle and the lifting height of the tower crane are adjusted, accurate lifting and anti-collision control are achieved, and the safety, accuracy and efficiency of the tower crane are comprehensively improved through organic combination of advanced technologies such as multi-sensor fusion, an intelligent algorithm and self-adaptive control.

Description

Technical Field

[0001] The present invention relates to the technical field of crane control, and in particular to an intelligent tower crane anti-collision precision lifting system based on multi-sensor fusion. Background Art

[0002] With the rapid development of modern construction industry, tower cranes, as important construction equipment, are receiving increasing attention for their safety and operating efficiency. Traditional tower crane operations mainly rely on the operator's experience and visual judgment, and often face safety hazards and efficiency bottlenecks in complex construction environments. In recent years, although some intelligent crane control systems have been gradually applied, there are still many problems.

[0003] At present, common intelligent crane systems on the market usually use a single sensor for environmental perception and positioning. For example, some systems rely only on laser rangefinders to measure the distance between the hoisted object and obstacles, or only use angle sensors to monitor the pitch angle of the boom. Although this single sensor solution improves the safety of operation to a certain extent, its reliability and accuracy are often difficult to meet the requirements in complex and changing construction environments. Especially in severe weather conditions, the failure of a single sensor may cause the entire system to fail, causing serious safety hazards.

[0004] On the other hand, although some existing multi-sensor systems have improved their perception capabilities, they often lack effective data fusion mechanisms. This results in the inability to fully coordinate the data between the sensors and to maximize the advantages of the multi-sensor system. For example, although some systems use laser ranging and visual positioning at the same time, the data of the two are processed independently and cannot be corrected or supplemented with each other, making it difficult to cope with complex construction environments.

[0005] In addition, existing crane control systems also have deficiencies in anti-collision strategies. Most systems use simple distance threshold judgments to trigger alarms, lacking consideration of the dynamic characteristics of the load. This approach often fails to respond in a timely manner when dealing with high-speed moving loads and cannot effectively prevent collision accidents. At the same time, the alarm system is often too simple to provide operators with sufficiently detailed risk information, affecting the accuracy of operational decisions.

[0006] Existing systems also face challenges in terms of lifting accuracy. Traditional open-loop control methods are difficult to cope with external interference such as wind and swinging of the hoisted objects, resulting in large positioning errors during the lifting process. Although some systems have introduced closed-loop control, the control effect still needs to be improved due to the lack of in-depth modeling of the dynamic characteristics of the crane. This not only affects the operating efficiency, but also increases the risk of collision. Summary of the invention

[0007] In view of the above problems, the present invention proposes an intelligent tower crane anti-collision precision lifting system based on multi-sensor fusion. The system realizes comprehensive perception and accurate modeling of the crane working environment through multi-sensor data fusion technology. At the same time, the system adopts advanced anti-collision algorithms and adaptive control strategies to greatly improve the safety and accuracy of lifting.

[0008] The present invention proposes an intelligent tower crane anti-collision precision lifting system based on multi-sensor fusion, comprising:

[0009] Data acquisition module for:

[0010] The distance information between the environment and the suspended object is collected by using a laser sensor installed on the top of the tower crane body and a laser distance measuring sensor installed on the top of the suspended object;

[0011] The angle measuring unit installed on the tower crane boom is used to obtain the swing angle and tangent value of the boom relative to the horizontal plane in real time;

[0012] The lifting length of the lifting rope is obtained in real time through the encoder set at the bottom of the tower crane body;

[0013] A data processing module is electrically connected to the data acquisition module and is used to:

[0014] Receiving distance information, angle information and lifting length information sent by the data acquisition module;

[0015] Based on the distance information, angle information and lifting length information, calculate the real-time three-dimensional position coordinates of the hanging object;

[0016] Dynamically determine the maximum safe angle value of the current boom according to the comparison result of the absolute value of the tangent value of the swing angle and the preset reference maximum angle value;

[0017] A safety warning module is electrically connected to the data processing module and is used to:

[0018] Receiving the real-time three-dimensional position coordinates of the suspended object and the current maximum safe angle value of the boom sent by the data processing module;

[0019] Calculate the multi-level safety warning threshold based on the real-time three-dimensional position coordinates of the suspended object and the current maximum safety angle value of the boom;

[0020] When a potential collision risk is detected, an alarm signal of the corresponding level is triggered;

[0021] A control execution module is electrically connected to the safety warning module and the data processing module, and is used to:

[0022] Receiving an alarm signal sent by the safety warning module;

[0023] Generate a lifting control instruction based on the alarm signal and the real-time three-dimensional position coordinates of the hanging object sent by the data processing module;

[0024] The lifting control instructions are executed to adjust the boom angle, slewing angle and lifting height of the tower crane to achieve precise lifting and anti-collision control.

[0025] Preferably, the data processing module is further used for:

[0026] Comparing the lifting length with a preset reference length reference value;

[0027] When the lifting length is greater than the reference length, the lifting control is triggered;

[0028] When the ratio of the lifting length divided by the reference length reference value is greater than a preset length limit trigger value, a length limit warning signal is generated.

[0029] Preferably, the safety warning module includes:

[0030] A side alarm unit, used to trigger the first and second level distance alarms according to the safety distance threshold in the horizontal direction;

[0031] A top alarm unit, used to trigger a vertical distance alarm according to a safety distance threshold in the vertical direction;

[0032] The early warning calculation unit is used to dynamically calculate the safety distance thresholds of different levels.

[0033] Preferably, the early warning calculation unit is further used for:

[0034] When the absolute value of the swing angle tangent is greater than the reference maximum angle value, the maximum safety angle value of the current boom is used as a calculation parameter;

[0035] Calculate the first-level distance warning threshold by using the reference distance formula, combining the calculation parameters and the real-time distance value;

[0036] When the real-time distance value is greater than the first-level distance warning threshold, the compensation distance value is calculated;

[0037] Based on the compensation distance value and the current tangent swing angle value of the boom, a secondary distance warning threshold is calculated using a reference distance formula.

[0038] Preferably, the early warning calculation unit is further used for:

[0039] Set safety extension threshold;

[0040] When the real-time distance value is equal to the safety extension threshold, a safety distance compensation value is calculated;

[0041] The safety distance threshold is calculated by using the reference distance formula in combination with the safety distance compensation value and the real-time distance value.

[0042] Preferably, it further comprises a human-computer interaction module, which is electrically connected to the control execution module and is used for:

[0043] Display the real-time data collected by each sensor;

[0044] Visualize the relative position relationship between the load and the crane;

[0045] Receive control instructions from the operator and transmit them to the control execution module;

[0046] Real-time display of warning information and safety status at all levels.

[0047] Preferably, the data processing module further comprises a data fusion unit, which is used to:

[0048] Temporal synchronization and spatial registration of data from different sensors;

[0049] Use Kalman filter algorithm to fuse multi-source data to improve the accuracy of position estimation;

[0050] Based on the fused data, a real-time 3D model of the crane’s working environment is constructed.

[0051] Preferably, the control execution module further includes an adaptive control unit, configured to:

[0052] Establish a crane dynamics model based on historical lifting data;

[0053] Dynamically adjust the parameters of the control algorithm according to the real-time operating parameters;

[0054] Actively suppress the swing of the suspended object and improve the stability of lifting.

[0055] Preferably, the system further comprises a fault diagnosis module, which is electrically connected to the data acquisition module and the data processing module and is used to:

[0056] Real-time monitoring of the working status of each sensor;

[0057] Detect potential sensor failures based on consistency analysis of sensor data;

[0058] When a sensor anomaly is detected, redundant sensors are started or data fusion strategies are adjusted to ensure system reliability.

[0059] Preferably, it further comprises a trajectory planning module, which is electrically connected to the data processing module and the control execution module and is used for:

[0060] Generate the optimal lifting trajectory based on the 3D model of the working environment and task requirements;

[0061] Consider the anti-collision constraints when multiple cranes work together to achieve group intelligent scheduling;

[0062] When an obstacle is detected, the trajectory is replanned in real time to ensure the safety and efficiency of the lifting process.

[0063] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0064] The system of the present invention has shown significant advantages in practical applications. First, multi-sensor fusion technology greatly improves the system's environmental perception and positioning accuracy. For example, in a typical high-rise building construction site, the system can simultaneously use laser ranging, visual positioning and GPS data to achieve centimeter-level positioning accuracy for hanging objects, and maintain stable performance even in complex obstacle environments. This high-precision positioning lays a solid foundation for subsequent anti-collision control and precise lifting.

[0065] Secondly, the multi-level warning mechanism and dynamic safety distance calculation method of the present invention significantly improve the anti-collision capability of the system. In a simulation test, when the hanging object approaches the obstacle at a speed of 2m / s, the system can trigger the first-level alarm 5 seconds in advance and the second-level alarm 3 seconds in advance, leaving sufficient time for the operator's response and the system's automatic intervention. This meticulous hierarchical warning not only improves safety, but also reduces unnecessary downtime and optimizes construction efficiency.

[0066] Furthermore, the adaptive control strategy of the present invention effectively solves the shortcomings of the traditional system in dealing with external interference. Under a working condition with a wind speed of 8m / s, the system controls the swing amplitude of the hanging object within 10cm by adjusting the control parameters in real time, which is significantly improved compared with the 50cm swing amplitude of the traditional system. This not only improves the accuracy of the lifting, but also greatly reduces the risk of collision.

[0067] Finally, the intelligent trajectory planning function of the present invention provides strong support for multi-machine collaborative operations. In a large construction project involving three tower cranes, the system successfully coordinated the work rhythm of multiple machines, increasing the operating efficiency by about 30% while completely avoiding the risk of collision between machines.

[0068] In general, the present invention comprehensively improves the safety, accuracy and efficiency of tower cranes through the organic combination of advanced technologies such as multi-sensor fusion, intelligent algorithms and adaptive control. This not only brings technological innovation to the construction industry, but also provides valuable reference for the intelligent control of other large-scale mechanical equipment. With the application and optimization of the system in more projects, it is expected to further promote the intelligent and safe development of the construction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a logic block diagram of the entire system of the present invention.

[0070] Figure 2 It is a logic block diagram of the data acquisition module of the present invention.

[0071] Figure 3 It is a logic block diagram of the data processing module of the present invention.

[0072] Figure 4 It is a logic block diagram of the safety warning module of the present invention.

[0073] Figure 5 It is a logic block diagram of the control execution module of the present invention. DETAILED DESCRIPTION

[0074] like Figure 1-5 As shown, the intelligent tower crane anti-collision precision lifting system based on multi-sensor fusion provided by the present invention includes a data acquisition module 1, a data processing module 2, a safety warning module 3 and a control execution module 4.

[0075] The data acquisition module 1 is used to obtain real-time data of the tower crane and its surrounding environment. Specifically, the data acquisition module 1 includes a laser sensor 11 arranged on the top of the tower crane body and a laser distance sensor 12 arranged on the top of the hanging object, which are used to collect distance information of the environment and the hanging object. Preferably, the laser sensor 11 can adopt a laser radar, whose scanning frequency can reach 20Hz, the measurement accuracy is better than ±2cm, and the effective measurement distance can reach 100m. In addition, the data acquisition module 1 also includes an angle measuring unit 13 arranged on the boom of the tower crane, which is used to obtain the swing angle and tangent value of the boom relative to the horizontal plane in real time. In one embodiment of the present invention, the angle measuring unit 13 can adopt a high-precision inclination sensor, and its measurement accuracy can reach 0.01°. The data acquisition module 1 also includes an encoder 14 arranged at the bottom of the tower crane body, which is used to obtain the lifting length of the lifting rope in real time.

[0076] The data processing module 2 is electrically connected to the data acquisition module 1, and is used to receive and process various information from the data acquisition module 1. Specifically, the data processing module 2 receives the distance information, angle information, and lifting length information sent by the data acquisition module 1. Based on this information, the data processing module 2 calculates the real-time three-dimensional position coordinates of the hanging object. The coordinate calculation method adopted by the present invention is as follows:

[0077] Assume that the rotation center of the tower crane is the coordinate origin, the direction of the boom is the positive direction of the x-axis, the horizontal direction perpendicular to the boom is the positive direction of the y-axis, and the vertical upward direction is the positive direction of the z-axis. Then the three-dimensional coordinates (x, y, z) of the hoisted object can be calculated by the following formula:

[0078]

[0079] Wherein, L is the distance measured by the laser ranging sensor 12, θ is the pitch angle of the boom, is the rotation angle of the boom, and H is the height of the crane.

[0080] The data processing module 2 also dynamically determines the maximum safe angle value of the current boom according to the comparison result between the absolute value of the tangent value of the swing angle obtained by the angle measuring unit 13 and the preset reference maximum angle value. Preferably, the reference maximum angle value can be set to 75°, which takes into account the safe working range of most tower cranes. When the absolute value of the tangent value of the actual swing angle is less than tan75°≈3.73, the system considers the current angle to be safe. Otherwise, the system will use the actual angle as the current maximum safe angle value to ensure operational safety.

[0081] The safety warning module 3 is electrically connected to the data processing module 2, and is used to receive the real-time three-dimensional position coordinates of the hanging object and the current maximum safety angle value of the boom sent by the data processing module 2. Based on these data, the safety warning module 3 calculates the multi-level safety warning threshold. The multi-level warning mechanism adopted by the present invention includes a first-level warning, a second-level warning and an emergency warning.

[0082] The calculation formula for the first-level warning threshold D1 is:

[0083] D1=k1·L·cosθ,

[0084] Among them, k1 is the safety factor, which is usually taken as 1.2; L is the length of the boom; θ is the maximum safe angle value of the current boom.

[0085] The calculation formula for the second-level warning threshold D2 is:

[0086] D2=k2·L·cosθ,

[0087] Where k2 is the safety factor, which is usually set to 1.1. The calculation formula for the emergency warning threshold D3 is:

[0088] D3=k3·L·cosθ,

[0089] Among them, k3 is the safety factor, which is usually taken as 1.05.

[0090] When a potential collision risk is detected, the safety warning module 3 will trigger an alarm signal of the corresponding level. For example, when the distance between the hanging object and the obstacle is less than D1 but greater than D2, a first-level alarm is triggered; when the distance is less than D2 but greater than D3, a second-level alarm is triggered; when the distance is less than D3, an emergency alarm is triggered.

[0091] The control execution module 4 is electrically connected to the safety warning module 3 and the data processing module 2, and is used to receive the alarm signal sent by the safety warning module 3. Based on the alarm signal and the real-time three-dimensional position coordinates of the hanging object sent by the data processing module 2, the control execution module 4 generates a hoisting control instruction. These instructions are used to adjust the boom angle, slewing angle and lifting height of the tower crane, so as to achieve precise hoisting and anti-collision control.

[0092] In a preferred embodiment of the present invention, the data processing module 2 has an additional function. It compares the lifting length with a preset reference length reference value. When the lifting length is greater than the reference length reference value, the lifting control is triggered. This ensures that the suspended object is lifted to a sufficient height before starting to move horizontally, avoiding collisions that may be caused by the suspended object moving at a low position.

[0093] In addition, the data processing module 2 also sets a length limit trigger value. When the ratio of the lifting length divided by the reference value of the base length is greater than the length limit trigger value, the system will generate a length limit warning signal. This function can prevent the suspended object from being over-lifted, resulting in instability or exceeding the safe working range of the crane. For example, if the reference value of the base length is set to 10 meters and the length limit trigger value is set to 1.5, then when the lifting length exceeds 15 meters, the system will issue a warning.

[0094] The safety warning module 3 of the present invention includes a side alarm unit 31, a top alarm unit 32 and a warning calculation unit 33. The side alarm unit 31 triggers the first and second distance alarms according to the safety distance threshold in the horizontal direction. This is mainly used to prevent the collision of the hanging object with the surrounding buildings or other obstacles. The top alarm unit 32 triggers the vertical distance alarm according to the safety distance threshold in the vertical direction, which is mainly used to prevent the collision of the hanging object with the upper obstacles (such as the boom of other cranes).

[0095] The early warning calculation unit 33 is responsible for dynamically calculating the safety distance thresholds of different levels. This dynamic calculation method enables the system to flexibly adjust the safety threshold according to the current working state, thereby improving the adaptability and safety of the system. For example, when the boom is at a higher angle, the system will reduce the horizontal safety distance threshold accordingly, because the horizontal distance between the suspended object and the obstacle is originally smaller.

[0096] Through the above design, the intelligent tower crane anti-collision precision lifting system based on multi-sensor fusion of the present invention can fully perceive the working environment of the crane, accurately calculate the position of the hoisted object, dynamically evaluate the safety status, and make corresponding control decisions in time. This not only greatly improves the safety of crane operation, but also improves the accuracy and efficiency of lifting. In a preferred embodiment of the present invention, the function of the early warning calculation unit 33 is further refined. When the absolute value of the tangent value of the swing angle is greater than the reference maximum angle value, the early warning calculation unit 33 uses the maximum safety angle value of the current boom as a calculation parameter. This design enables the system to maintain effective safety warnings even in extreme situations.

[0097] The warning calculation unit 33 uses the reference distance formula, combined with the above calculation parameters and the real-time distance value, to calculate the first-level distance warning threshold. The reference distance formula is as follows:

[0098] D=k·L·cosθ,

[0099] Among them, D is the warning distance threshold, k is the safety factor, L is the boom length, and θ is the current maximum safety angle value of the boom.

[0100] Preferably, the safety factor k of the first-level distance warning can be set to 1.3. The selection of this value is based on a lot of practical experience, which can provide sufficient safety margin without excessively limiting the working efficiency of the crane. When the real-time distance value is greater than the first-level distance warning threshold, the warning calculation unit 33 will calculate the compensation distance value. The introduction of the compensation distance value enables the system to adjust the warning threshold more finely and improve the sensitivity of the system. The compensation distance value can be calculated by the following formula:

[0101] D comp =D real -D1,

[0102] Among them, D comp is the compensation distance value, D real is the real-time distance value, and D1 is the first-level distance warning threshold.

[0103] Based on the compensation distance value and the current tangent swing angle value of the boom, the warning calculation unit 33 calculates the secondary distance warning threshold using the reference distance formula. When calculating the secondary distance warning threshold, the safety factor k can be selected as 1.2, which is slightly smaller than the safety factor of the primary warning, to form a gradient warning mechanism.

[0104] The system of the present invention also introduces the concept of a safety extension threshold. In a specific embodiment, the early warning calculation unit 33 sets a safety extension threshold. When the real-time distance value is equal to the safety extension threshold, the system calculates a safety distance compensation value. The calculation formula of the safety distance compensation value is as follows:

[0105] D safe_comp=D safe -D real ,

[0106] Among them, D safe_comp is the safety distance compensation value, D safe is the safety extension threshold, D real The real-time distance value.

[0107] The early warning calculation unit 33 then uses the reference distance formula, combined with the safety distance compensation value and the real-time distance value, to calculate the safety distance threshold. This dynamically adjusted safety distance threshold enables the system to maintain optimal safety performance under different working conditions.

[0108] In order to further improve the human-machine interaction performance of the system, the system of the present invention also includes a human-machine interaction module 5. The human-machine interaction module 5 is electrically connected to the control execution module 4 and is used to display the real-time data collected by each sensor. These data include but are not limited to key parameters such as boom angle, hanging object position, and lifting rope length.

[0109] Preferably, the human-machine interaction module 5 uses a high-resolution touch screen with a resolution of not less than 1920x1080 and supports multi-touch to provide clear and intuitive information display and convenient operation experience. The human-machine interaction module 5 also visualizes the relative position relationship between the hanging object and the crane. This three-dimensional visualization greatly improves the operator's intuitive understanding of the current working status.

[0110] In addition, the human-machine interaction module 5 also receives the operator's control instructions and transmits them to the control execution module 4. The operator can input the control instructions through the touch screen or other input devices (such as joysticks, buttons, etc.). The human-machine interaction module 5 also displays various levels of warning information and safety status in real time to ensure that the operator can understand potential safety risks in a timely manner.

[0111] In another embodiment of the present invention, the data processing module 2 further includes a data fusion unit 21. The main function of the data fusion unit 21 is to perform time synchronization and spatial registration on data from different sensors. This step is crucial to ensure the consistency of multi-sensor data.

[0112] The data fusion unit 21 uses the Kalman filter algorithm to fuse the multi-source data, thereby improving the accuracy of position estimation. The core idea of ​​the Kalman filter algorithm is to obtain the optimal state estimation by recursively estimating the state equation and observation equation of the system. Its basic formula is as follows:

[0113] Prediction Steps:

[0114]

[0115] Update steps:

[0116]

[0117] in, is the state estimation, P is the estimation error covariance matrix, F is the state transfer matrix, Q is the process noise covariance matrix, K is the Kalman gain, H is the observation matrix, R is the observation noise covariance matrix, and z is the observation value.

[0118] In this way, the data fusion unit 21 can effectively reduce the measurement noise of each sensor and provide more accurate position information. Based on the fused data, the data fusion unit 21 also constructs a real-time three-dimensional model of the crane working environment. This three-dimensional model provides important basic data for subsequent collision prediction and path planning. In another preferred embodiment of the present invention, the control execution module 4 also includes an adaptive control unit 41. The introduction of the adaptive control unit 41 greatly improves the intelligence level and control accuracy of the system.

[0119] The adaptive control unit 41 first establishes a crane dynamics model based on historical lifting data. This model includes multiple aspects such as the crane's structural characteristics, load characteristics, and environmental factors. Preferably, the dynamics model can be expressed as:

[0120]

[0121] Among them, q is the generalized coordinate vector, M(q) is the inertia matrix, is the Coriolis force and centrifugal force matrix, G(q) is the gravity term, and τ is the generalized force vector.

[0122] The adaptive control unit 41 dynamically adjusts the parameters of the control algorithm according to the real-time operating parameters. This adaptive mechanism enables the system to maintain the best control effect under different working conditions. For example, when a change in wind speed is detected, the adaptive control unit 41 can adjust the parameters of the PID controller accordingly to offset the influence of wind force.

[0123] In addition, the adaptive control unit 41 also realizes active suppression of the swing of the suspended object, significantly improving the stability of the lifting operation. Its core algorithm is the anti-swing control based on the input shaping technology, and its control law can be expressed as:

[0124] u(t)=A1δ(t)+A2δ(tT / 2)+A3δ(tT),

[0125] Among them, u(t) is the control input, A i is the amplitude of the input shaper, T is the natural period of the system, and δ(t) is the Dirac function.

[0126] Through the carefully designed input shaper, the system can effectively suppress the swing of the load during acceleration and deceleration, greatly improving the accuracy and efficiency of lifting.

[0127] In order to further improve the reliability of the system, the present invention further comprises a fault diagnosis module 6. The fault diagnosis module 6 is electrically connected to the data acquisition module 1 and the data processing module 2, and is used to monitor the working status of each sensor in real time.

[0128] Fault diagnosis module 6 detects potential sensor faults based on consistency analysis of sensor data. Its core algorithm is based on a data-driven anomaly detection method. Specifically, fault diagnosis module 6 uses principal component analysis (PCA) to build a data model under normal working conditions, and then uses T 2 Statistics and SPE statistics are used to detect anomalies.

[0129] T 2 The calculation formula of the statistic is:

[0130] T 2 =x T PΛ -1 P T x,

[0131] The calculation formula of SPE statistic is:

[0132] SPE=x T (I-PP T )x,

[0133] Where x is the observation vector, P is the principal component matrix, Λ is the eigenvalue matrix, and I is the unit matrix. When a sensor abnormality is detected, the fault diagnosis module 6 will start the redundant sensor or adjust the data fusion strategy to ensure the reliability of the system. For example, if a laser sensor failure is detected, the system may temporarily switch to a vision-based positioning method to maintain the normal operation of the system.

[0134] When a sensor anomaly is detected, the fault diagnosis module 6 will start redundant sensors or adjust the data fusion strategy to ensure the reliability of the system. For example, if a laser sensor failure is detected, the system may temporarily switch to a vision-based positioning method to maintain the normal operation of the system.

[0135] In another embodiment of the present invention, the system further comprises a trajectory planning module 7. The trajectory planning module 7 is electrically connected to the data processing module 2 and the control execution module 4, and is used to generate an optimal lifting trajectory.

[0136] The trajectory planning module 7 first generates an initial trajectory based on the three-dimensional model of the working environment and the task requirements using the rapidly expanding random tree (RRT*) algorithm. The core idea of ​​the RRT* algorithm is to randomly sample in the configuration space and gradually build a tree by connecting the nearest nodes, and finally find a feasible path from the starting point to the end point.

[0137] After the initial trajectory is generated, the trajectory planning module 7 will further optimize the trajectory. The optimization objective function can be expressed as:

[0138] J=w1J1+w2J2+w3J3,

[0139] Among them, J1 represents the path length, J2 represents the energy consumption, J3 represents the safety margin, and w i is the corresponding weight coefficient.

[0140] Preferably, the trajectory planning module 7 also considers the anti-collision constraints when multiple cranes work together to achieve group intelligent scheduling. This group intelligent scheduling algorithm is based on distributed optimization theory. Each crane acts as an intelligent agent and coordinates its own behavior through local information exchange to achieve the global optimal operation effect.

[0141] In addition, when an obstacle is detected, the trajectory planning module 7 can re-plan the trajectory in real time to ensure the safety and efficiency of the lifting process. This dynamic trajectory planning capability enables the system to flexibly respond to complex and changing construction environments, greatly improving the adaptability and robustness of the system.

[0142] Through the above design, the intelligent tower crane anti-collision precision lifting system based on multi-sensor fusion of the present invention not only realizes high-precision positioning and control, but also has advanced functions such as adaptive control, fault diagnosis and intelligent trajectory planning. The organic combination of these functions enables the system to achieve safe, efficient and accurate lifting operations in complex construction environments, greatly improving construction efficiency, reducing safety risks, and has broad application prospects.

[0143] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Intelligent tower crane anti-collision precision lifting system based on multi-sensor fusion, characterized by: include: Data acquisition module for: The distance information between the environment and the suspended object is collected by using a laser sensor installed on the top of the tower crane body and a laser distance measuring sensor installed on the top of the suspended object; The angle measuring unit installed on the tower crane boom is used to obtain the swing angle and tangent value of the boom relative to the horizontal plane in real time; The lifting length of the lifting rope is obtained in real time through the encoder set at the bottom of the tower crane body; A data processing module is electrically connected to the data acquisition module and is used to: Receiving distance information, angle information and lifting length information sent by the data acquisition module; Based on the distance information, angle information and lifting length information, calculate the real-time three-dimensional position coordinates of the hanging object; Dynamically determine the maximum safe angle value of the current boom according to the comparison result of the absolute value of the tangent value of the swing angle and the preset reference maximum angle value; A safety warning module is electrically connected to the data processing module and is used to: Receiving the real-time three-dimensional position coordinates of the suspended object and the current maximum safe angle value of the boom sent by the data processing module; Calculate the multi-level safety warning threshold based on the real-time three-dimensional position coordinates of the suspended object and the current maximum safety angle value of the boom; When a potential collision risk is detected, an alarm signal of the corresponding level is triggered; A control execution module is electrically connected to the safety warning module and the data processing module, and is used to: Receiving an alarm signal sent by the safety warning module; Generate a lifting control instruction based on the alarm signal and the real-time three-dimensional position coordinates of the hanging object sent by the data processing module; The lifting control instructions are executed to adjust the boom angle, slewing angle and lifting height of the tower crane to achieve precise lifting and anti-collision control.

2. The system according to claim 1, characterized in that The data processing module is also used for: Comparing the lifting length with a preset reference length reference value; When the lifting length is greater than the reference length, the lifting control is triggered; When the ratio of the lifting length divided by the reference length reference value is greater than a preset length limit trigger value, a length limit warning signal is generated.

3. The system according to claim 1, characterized in that The safety warning module includes: A side alarm unit, used to trigger the first and second level distance alarms according to the safety distance threshold in the horizontal direction; A top alarm unit, used to trigger a vertical distance alarm according to a safety distance threshold in the vertical direction; The early warning calculation unit is used to dynamically calculate the safety distance thresholds of different levels.

4. The system according to claim 3, characterized in that The early warning calculation unit is also used for: When the absolute value of the swing angle tangent is greater than the reference maximum angle value, the maximum safety angle value of the current boom is used as a calculation parameter; Calculate the first-level distance warning threshold by using the reference distance formula, combining the calculation parameters and the real-time distance value; When the real-time distance value is greater than the first-level distance warning threshold, the compensation distance value is calculated; Based on the compensation distance value and the current tangent swing angle value of the boom, a secondary distance warning threshold is calculated using a reference distance formula.

5. The system according to claim 4, characterized in that The early warning calculation unit is also used for: Set safety extension threshold; When the real-time distance value is equal to the safety extension threshold, a safety distance compensation value is calculated; The safety distance threshold is calculated by using the reference distance formula in combination with the safety distance compensation value and the real-time distance value.

6. The system according to claim 1, characterized in that It also includes a human-computer interaction module, which is electrically connected to the control execution module and is used to: Display the real-time data collected by each sensor; Visualize the relative position relationship between the load and the crane; Receive control instructions from the operator and transmit them to the control execution module; Real-time display of warning information and safety status at all levels.

7. The system according to claim 1, characterized in that The data processing module also includes a data fusion unit, which is used to: Temporal synchronization and spatial registration of data from different sensors; Use Kalman filter algorithm to fuse multi-source data to improve the accuracy of position estimation; Based on the fused data, a real-time 3D model of the crane’s working environment is constructed.

8. The system according to claim 1, characterized in that The control execution module also includes an adaptive control unit, which is used to: Establish a crane dynamics model based on historical lifting data; Dynamically adjust the parameters of the control algorithm according to the real-time operating parameters; Actively suppress the swing of the suspended object and improve the stability of lifting.

9. The system according to claim 1, characterized in that It also includes a fault diagnosis module, which is electrically connected to the data acquisition module and the data processing module and is used to: Real-time monitoring of the working status of each sensor; Detect potential sensor failures based on consistency analysis of sensor data; When a sensor anomaly is detected, redundant sensors are started or data fusion strategies are adjusted to ensure system reliability.

10. The system according to claim 1, characterized in that It also includes a trajectory planning module, which is electrically connected to the data processing module and the control execution module and is used to: Generate the optimal lifting trajectory based on the 3D model of the working environment and task requirements; Consider the anti-collision constraints when multiple cranes work together to achieve group intelligent scheduling; when obstacles are detected, re-plan the trajectory in real time to ensure the safety and efficiency of the lifting process.

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