Intelligent lifting platform and lifting control method thereof

Abstract

The invention is suitable for the technical field of lifting platforms, and provides an intelligent lifting platform and a lifting control method thereof.The intelligent lifting platform comprises a frame, a balance weight structure, a bearing box, a driving mechanism and a rope tensioning structure, the frame is used for providing installation space for all devices, the bearing box is arranged in the middle of the frame, and the bearing box is slidably connected with the frame; the balance weight structure is arranged below the bearing box, the driving mechanism is arranged at the top of the frame and comprises a driving motor, the rope tensioning structure comprises a guide part and a safety rope controller, the controller is arranged on one side of the rope tensioning structure, the guide part is an intelligent rope wheel guide device, and the safety rope wheel is arranged on the safety rope controller. Through non-contact visual monitoring and closed-loop control, the transverse offset of the safety rope is controlled within a safety threshold value, under the working condition that loads are suddenly increased, the offset peak value is small, the stable state can be rapidly recovered, and the rope body abrasion and falling risk situations caused by hysteresis of traditional mechanical limiting are reduced.

Description

Technical Field

[0001] This invention relates to the field of lifting platform technology, and more specifically, to an intelligent lifting platform and its lifting control method. Background Technology

[0002] As a type of lifting equipment widely used in construction, cargo handling, and high-altitude operations, the operational safety of lifting platforms is directly related to the safety of people's lives and property. As a core safety component of lifting platforms, safety ropes undertake key functions such as fall prevention and limit protection. Their operational stability and reliability are important prerequisites for ensuring the safe operation of lifting platforms.

[0003] Currently, the industry generally adopts the traditional mechanical limit control method for controlling the lateral deviation of safety ropes. This control method mainly uses mechanical structures such as mechanical blocks and limit switches at preset deviation limit positions on the running trajectory of the safety rope. When the safety rope laterally deviates and touches the mechanical limit component, the corresponding limit alarm or shutdown protection action is triggered.

[0004] However, the mechanical limit switch of the traditional control method relies on physical contact triggering, and the response has an inherent lag. Often, the protection mechanism can only be activated after the safety rope has already deviated significantly and worn to a certain extent. It cannot achieve early warning and timely correction of lateral deviation. This paper proposes an intelligent lifting platform and its lifting control method to improve the existing problems. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an intelligent lifting platform and its lifting control method.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent lifting platform, comprising: A frame that provides installation space for each device.

[0007] A support box is located in the middle of the frame and is slidably connected to the frame.

[0008] A counterweight structure is provided below the load-bearing box.

[0009] A drive mechanism, which is located at the top of the frame, includes a drive motor.

[0010] The tensioned rope structure includes a guide and a safety rope.

[0011] A controller is located on one side of the tension rope structure.

[0012] The guide component is an intelligent rope wheel guide device, which includes a visual monitoring unit for acquiring images containing the safety rope and preset laser markers.

[0013] The controller is an intelligent control unit, and the controller is connected to the visual monitoring unit, the load sensor located at the bottom of the carrier box, and the drive motor.

[0014] The present invention is further configured such that: the intelligent control unit has a built-in image processing module and a control algorithm, and is configured to perform the following functions: Based on the continuous image frames acquired by the visual monitoring unit, the lateral offset of the safety rope is calculated using an image analysis algorithm.

[0015] The running speed of the safety rope is calculated based on the movement trajectory and time of preset laser markers in continuous image frames.

[0016] Based on lateral offset and running speed data, combined with load sensor data, the output torque of the drive motor is dynamically adjusted through a rope deflection adaptive fuzzy PID compensation algorithm to control the safety rope offset within a preset threshold.

[0017] The invention is further configured such that: the drive motor is located on the top of the frame, the drive motor is used to provide power to the load-bearing box, the output end of the drive motor is connected to an output wheel, a main steel wire rope is sleeved on the outer side of the output wheel, a connecting wheel is provided below the output wheel, and the main steel wire rope is wound around the output wheel, the roller on the top of the counterweight structure, and the connecting wheel.

[0018] The present invention is further configured such that: the tensioning structure includes a guide wheel, an auxiliary wheel is provided below the guide wheel, a safety rope is wound around the outer side of the guide wheel and the auxiliary wheel, the carrying box is inserted through the middle of the guide member, and the guide member is installed on the side wall of the carrying box.

[0019] The guide includes a main body, a guide rail is provided in the middle of the main body, a limiting part is provided on one side of the main body, the limiting part is integrally formed with the main body, the limiting part has a chamfer on the side near the guide rail, and the safety rope passes through the middle of the guide rail and the limiting part.

[0020] The visual monitoring unit includes a miniature industrial camera, which is located on top of the limiting part, and the optical axis of the miniature industrial camera is perpendicular to the running direction of the safety rope.

[0021] The safety rope surface has multiple laser marking points at fixed intervals along its length, and these laser marking points are formed through a laser surface modification process.

[0022] The present invention is further configured such that the image processing module is configured to perform the following steps to calculate the lateral offset: S1, the image processing module performs filtering and grayscale processing on the acquired image.

[0023] S2. The image processing module uses an edge detection algorithm to identify the left and right boundaries of the safety rope in the image.

[0024] S3. The image processing module calculates the pixel coordinates of the center points of the left and right boundaries and compares them with the preset ideal path coordinates to obtain the pixel-level offset.

[0025] S4. The image processing module converts the pixel-level offset into the actual physical offset based on the camera calibration parameters.

[0026] The present invention is further configured such that the image processing module is configured to perform the following steps to calculate the running speed: P1. The image processing module identifies and tracks the position of the laser marker point in a continuous image frame sequence.

[0027] P2, the image processing module records the pixel displacement of the same laser marker point in two adjacent frames and the corresponding frame interval time.

[0028] P3, the image processing module converts pixel displacement into actual physical displacement based on the camera calibration parameters and calculates instantaneous velocity.

[0029] P4, the image processing module filters multiple consecutive instantaneous velocities to obtain smooth speed output values.

[0030] The present invention is further configured such that: the intelligent control unit also incorporates a multi-objective optimization decision-making algorithm, which is configured to dynamically decide and switch between high-efficiency mode, standard mode, and energy-saving mode based on operating speed, load sensor data, and target travel distance, wherein: The high-efficiency mode is suitable for working conditions where the load rate is less than 30% and the stroke is greater than three times the height of the load box, and the operating speed is 110% of the rated speed.

[0031] The standard mode is suitable for operating conditions with a load rate between 30% and 80%, and operates at the rated speed.

[0032] The energy-saving mode is suitable for operating conditions with a load rate >80%, and uses 80% of the rated acceleration for smooth acceleration.

[0033] The present invention is further configured such that: the intelligent control unit also has a built-in fault prediction algorithm, the fault prediction algorithm being configured as follows: Establish a digital model that includes guide wear, safety rope surface condition, and laser marker attenuation.

[0034] The health status of key components is assessed based on long-term time-series data of lateral offset, spectrum of operating speed fluctuations, and confidence level of laser marker identification.

[0035] When the evaluation indicators exceed the preset threshold, a predictive maintenance warning is generated.

[0036] A lifting control method for an intelligent lifting platform, using the intelligent lifting platform as described above, includes the following steps: S101. Continuously acquire images of the safety rope exit section using a miniature industrial camera. Acquire real-time load values ​​using a load sensor.

[0037] S102. Process the image and calculate the lateral offset and running speed of the safety rope in real time.

[0038] S103. Input the lateral offset, running speed and real-time load value into the rope deflection adaptive fuzzy PID compensation algorithm, calculate and output the torque compensation command for the drive mechanism in real time, and form a closed-loop control.

[0039] 104. Based on the operating speed, real-time load value and target height information, select the operating mode with the best overall energy efficiency through a multi-objective optimization decision-making algorithm.

[0040] 105. Continuously record the time-series data of the lateral offset calculated in step S102 and the identification features of the marker points, input them into the fault prediction algorithm, perform trend analysis on the wear of the guide rail and the status of the safety rope, and generate a maintenance warning before the performance degrades to the safety threshold.

[0041] The present invention is further configured such that, in step S102, the calculation of the running speed is specifically as follows: The center coordinates of the laser marker points in each frame of the image are located using an image recognition algorithm.

[0042] Marker point matching and tracking are performed between consecutive frames.

[0043] The instantaneous linear velocity of the safety rope is calculated based on the pixel displacement, frame rate, and spatial calibration coefficient of the matched marker point between adjacent frames.

[0044] In summary, this application includes at least one of the following beneficial technical effects: (1) By using non-contact visual monitoring and closed-loop control, the lateral offset of the safety rope is controlled within the safety threshold. Under sudden load conditions, the offset peak is small and can quickly recover to a steady state, reducing the occurrence of rope wear and fall risk caused by the lag of traditional mechanical limit.

[0045] (2) The multi-objective optimization decision algorithm dynamically switches modes according to working conditions, which improves efficiency under light-load long-stroke working conditions and reduces energy consumption under heavy-load working conditions, taking into account both work efficiency and energy-saving needs.

[0046] (3) Fault prediction algorithms can identify problems such as guide rail wear and safety rope aging in advance, reducing unplanned downtime and lowering maintenance costs. Attached Figure Description

[0047] Figure 1 This is a structural schematic diagram of an intelligent lifting platform according to the present invention.

[0048] Figure 2 for Figure 1 Isometric side view.

[0049] Figure 3 This is a schematic diagram of the guide component in this invention.

[0050] Figure 4 This is a control logic block diagram of the adaptive fuzzy PID compensation algorithm in this invention.

[0051] Figure 5 This is a flowchart illustrating a lifting control method for an intelligent lifting platform according to the present invention.

[0052] Figure labeling: 1. Frame; 2. Carrier box; 3. Counterweight structure; 4. Drive mechanism; 41. Drive motor; 42. Output wheel; 43. Connecting wheel; 5. Tensioning rope structure; 51. Guide wheel; 52. Safety rope; 53. Guide component; 531. Main body; 532. Guide rail; 533. Limiting part; 534. Miniature industrial camera; 54. Auxiliary wheel; 6. Controller. Detailed Implementation

[0053] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0054] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0055] Please see Figures 1-5 The present invention provides the following technical solutions: Example 1, see Figure 1 An intelligent lifting platform, comprising: Frame 1 provides installation space for each device.

[0056] The carrier box 2 is located in the middle of the frame 1 and is slidably connected to the frame 1.

[0057] Counterweight structure 3 is located below the load-bearing box 2.

[0058] See Figure 2 The drive mechanism 4 is located on the top of the frame 1 and includes a drive motor 41.

[0059] See Figure 2 The tension rope structure 5 includes a guide 53 and a safety rope 52.

[0060] Controller 6 is located on one side of the tension rope structure 5.

[0061] See Figure 2 The drive motor 41 is located on the top of the frame 1. The drive motor 41 is used to provide power to the load box 2. The output end of the drive motor 41 is connected to the output wheel 42. The main steel wire rope is sleeved on the outside of the output wheel 42. The connecting wheel 43 is located below the output wheel 42. The main steel wire rope is wound around the output wheel 42, the roller on the top of the counterweight structure 3 and the connecting wheel 43.

[0062] See Figure 2 The tensioning structure 5 includes a guide wheel 51, an auxiliary wheel 54 is provided below the guide wheel 51, a safety rope 52 is wound around the outside of the guide wheel 51 and the auxiliary wheel 54, and the carrying box 2 is inserted through the middle of the guide member 53, which is installed on the side wall of the carrying box 2.

[0063] See Figure 3 The guide member 53 includes a main body 531, a guide rail 532 is provided in the middle of the main body 531, a limiting part 533 is provided on one side of the main body 531, the limiting part 533 is integrally formed with the main body 531, the limiting part 533 has a chamfer on the side of the limiting part 533 near the guide rail 532, and the safety rope 52 passes through the middle of the guide rail 532 and the limiting part 533.

[0064] The frame 1 is a steel structure, the load-bearing box 2 is slidably connected to the frame 1 via a slide rail, the drive mechanism 4 drives the main steel wire rope to drive the system to run, and the safety rope 52 of the tensioning structure 5, as part of the safety system, passes through the intelligent rope wheel guide device installed on the side wall of the load-bearing box 2.

[0065] The guide rail 532 is equipped with a low-friction guide liner made of polytetrafluoroethylene. The entire inner wall of the guide rail 532 in contact with the safety rope 52 is equipped with a low-friction guide liner to reduce friction loss when the safety rope 52 is raised and lowered, while reducing operating noise, avoiding vibration of the safety rope due to friction, and improving the stability of rope deviation detection.

[0066] The guide wheel 51 and the auxiliary wheel 54 work together vertically, with the safety rope 52 wrapped around the outside to form a two-point support guide structure. The guide wheel 51 undertakes the steering and main support functions of the safety rope, while the auxiliary wheel 54 provides secondary limit for the lower rope segment. Together with the guide rail 532 and the limiting part 53 to form a channel-type guide, the dual constraints of wheel system pre-guidance and channel guidance are achieved, which reduces the adjustment pressure for the subsequent rope deviation control algorithm.

[0067] The limiting part 533 has a chamfer on the side near the guide rail 532, and the rounded contact reduces local stress concentration in the rope and lowers the risk of wire breakage and aging of the safety rope due to friction.

[0068] The guide component 53 is an intelligent rope wheel guide device. The guide component 53 includes a visual monitoring unit, which is used to acquire images containing the safety rope 52 and preset laser marker points.

[0069] See Figure 3 The visual monitoring unit includes a miniature industrial camera 534, which is located on top of the limiting part 533. The optical axis of the miniature industrial camera 534 is perpendicular to the running direction of the safety rope 52.

[0070] The surface of the safety rope 52 has multiple laser marking points set at fixed intervals along its length. The laser marking points are formed by a laser surface modification process.

[0071] The controller 6 is an intelligent control unit. The controller 6 is connected to the visual monitoring unit, the load sensor located at the bottom of the carrier box 2, and the drive motor 41.

[0072] The intelligent control unit has a built-in image processing module and control algorithm, and is configured to perform the following functions: Based on continuous image frames acquired by the visual monitoring unit, the lateral offset of the safety rope 52 is calculated using an image analysis algorithm.

[0073] The running speed of the safety rope 52 is calculated based on the movement trajectory and time of the preset laser marker points in continuous image frames.

[0074] Based on the lateral offset and running speed data, combined with the load sensor data, the output torque of the drive motor 41 is dynamically adjusted through the rope deflection adaptive fuzzy PID compensation algorithm to control the offset of the safety rope 52 within a preset threshold.

[0075] Among them, the controller 6 adopts the ALCDragon intelligent control board, whose built-in image processing module runs a proprietary algorithm. The load sensor is a pressure weighing sensor, and there are four sets of pressure weighing sensors. The four sets of sensors are respectively set at the bottom four corners of the bearing box 2. The load of the lifting platform may be eccentric. The four corner sensors can calculate the actual total load through data fusion to avoid the error of single-point detection.

[0076] The actual total load is calculated using a weighted average fusion algorithm based on the data collected from the four sets of sensors. The fusion formula for the four sets of sensors is as follows:

[0077] Wherein, F_total is the actual total load of the four sets of sensors, and F1, F2, F3, and F4 are the measured values ​​of the four corner sensors.

[0078] The image processing module is configured to perform the following steps to calculate the lateral offset: S1, the image processing module performs filtering and grayscale processing on the acquired image.

[0079] S2. The image processing module uses an edge detection algorithm to identify the left and right boundaries of the safety rope 52 in the image.

[0080] S3. The image processing module calculates the pixel coordinates of the center points of the left and right boundaries and compares them with the preset ideal path coordinates to obtain the pixel-level offset.

[0081] S4. The image processing module converts the pixel-level offset into the actual physical offset based on the camera calibration parameters.

[0082] The image processing module is configured to perform the following steps to calculate the running speed: P1, the image processing module identifies and tracks the position of laser markers in a continuous sequence of image frames.

[0083] P2, the image processing module records the pixel displacement of the same laser marker point in two adjacent frames and the corresponding frame interval time.

[0084] P3, the image processing module converts pixel displacement into actual physical displacement based on the camera calibration parameters and calculates instantaneous velocity.

[0085] P4, the image processing module filters multiple consecutive instantaneous velocities to obtain smooth speed output values.

[0086] In practice, the workflow of the image processing module is as follows: The image processing module first performs image preprocessing, that is, the image processing module receives the 534 video stream from the miniature industrial camera and performs noise reduction and grayscale processing.

[0087] Next, the rope deflection calculation thread is executed.

[0088] The Canny operator is used to detect the left and right edges of the safety rope in the image. The left and right edge point sets are fitted with least squares straight lines to obtain the center line equation. The horizontal distance between the center line and the vertical baseline in the image coordinate system is calculated, which is the pixel-level rope deviation. The physical rope deviation is calculated based on the pre-calibrated spatial calibration coefficients (obtained by photographing a calibration plate of known size).

[0089] The relationship between physical rope deflection and pixel-level rope deflection is as follows:

[0090] Where Pz is the physical rope deviation, Pp is the pixel-level rope deviation, and q is the spatial calibration coefficient.

[0091] Finally, the speed calculation thread is executed.

[0092] In the preprocessed image, the laser marker area is located using template matching. In this embodiment, the template matching method can be implemented using normalized cross-correlation. Then, the center coordinates of the marker area are calculated. Finally, target tracking is performed between consecutive frames, and the displacement pixels of the center coordinates of the same coordinate point are recorded. After obtaining the displacement pixels, the instantaneous velocity can be calculated. Finally, the instantaneous velocity is smoothed by averaging filtering.

[0093]

[0094] Where v is the instantaneous velocity, q is the displacement pixel, f is the frame rate, and q is the spatial calibration coefficient.

[0095] In this embodiment, the target tracking method can be implemented using a Kalman filter, and the displacement pixels can be obtained after Kalman filtering. f is the frame rate. T is the frame interval between adjacent frames, the frame rate is a preset value of 120Hz, and q is the camera calibration parameter, which is obtained by photographing a calibration board of known size.

[0096] The intelligent control unit also incorporates a multi-objective optimization decision-making algorithm. This algorithm is configured to dynamically decide and switch between high-efficiency mode, standard mode, and energy-saving mode based on operating speed, load sensor data, and target travel distance. The high-efficiency mode is suitable for working conditions where the load rate is <30% and the stroke is > three times the height of the load box 2, and the operating speed is 110% of the rated speed.

[0097] The standard mode is suitable for operating conditions with a load rate between 30% and 80%, and operates at the rated speed.

[0098] The energy-saving mode is suitable for operating conditions with a load rate >80%, and uses 80% of the rated acceleration for smooth acceleration.

[0099] The algorithm runs within the intelligent control unit, specifically the ALCDragon intelligent control board, which has a built-in dedicated image processing module and control chip.

[0100] The rated speed is 1.0 m / s, the rated acceleration is 0.5 m / s², the operating speed in high-efficiency mode is 1.1 m / s (110% of the rated speed), and the acceleration in energy-saving mode is 0.4 m / s² (80% of the rated acceleration).

[0101] See Figure 4 The process of adapting the adaptive fuzzy PID compensation algorithm to the operating conditions is as follows: The adaptive fuzzy PID compensation algorithm dynamically adjusts the output torque of the drive motor 41 to stabilize the lateral offset of the safety rope 52 within a preset threshold (0±0.5mm), while adapting to different loads and operating speeds, taking into account both control accuracy and system stability.

[0102] The algorithm input data includes the lateral offset of the safety rope 52, the running speed of the safety rope 52, and the real-time load value of the carrying box 2.

[0103] The lateral offset and running speed of the safety rope 52 can be obtained through the above calculation process.

[0104] The core logic of the algorithm implementation is as follows: First, the input is fuzzified, converting the three input quantities into fuzzy linguistic variables. For example, the offset is divided into negative large, negative small, zero, positive small, and positive large; the load rate is divided into light load, medium load, and heavy load; and the speed is divided into low speed, medium speed, and high speed.

[0105] Pre-set control rules based on engineering experience: If the offset is large, the load is light, and the speed is high, the fuzzy output will significantly increase the torque, quickly correct the offset, and the system will have a high fault tolerance under light load.

[0106] If the offset is small, the load is heavy, and the speed is low, the fuzzy output will slightly adjust the torque to avoid sudden torque changes under heavy load, which could cause system oscillation.

[0107] Convert fuzzy linguistic variables into specific PID parameter adjustment values, such as the adjustment value of the proportional coefficient Kp and the adjustment value of the integral coefficient Ki.

[0108] Based on the parameters provided by fuzzy control, the torque compensation command is calculated through the PID algorithm to stabilize the offset within the threshold.

[0109] The PID algorithm calculation formula is as follows:

[0110] Where e(t) represents the deviation between the real-time lateral offset (physical value) and the target value (0mm), Kp is the proportional coefficient, which is the immediate adjustment force in response to the offset, Ki is the integral coefficient, which eliminates steady-state error, and Kd is the derivative coefficient, which suppresses overshoot.

[0111] After the drive motor executes the torque compensation command, the vision monitoring unit collects new offset data in real time, re-inputs it into the algorithm, and forms a closed-loop control of acquisition, calculation, execution, and feedback.

[0112] The adaptive mechanism adapts to dynamic parameter adjustments under different operating conditions, dynamically optimizing PID parameters based on load rate and operating speed to avoid performance degradation of fixed parameters under complex operating conditions. The specific adaptation logic is as follows: Load rate adaptation: Under light load (<30%), increase Kp to enhance response speed, decrease Ki to avoid integral saturation, and adjust the torque step size to 0.05 N·m to quickly correct offset.

[0113] Under medium load (30%-80%) conditions, the default PID parameters are used to balance response and stability.

[0114] Under heavy load (>80%), reduce Kp and adjust the step size to 0.02 N·m, and increase Ki to eliminate steady-state offset under heavy load and avoid system oscillation caused by sudden torque changes.

[0115] Speed ​​adaptation: Under high-speed operation, the speed is 110% of the rated speed. Kd is reduced to avoid overshoot caused by high speed.

[0116] In low-speed operation, it operates in energy-saving mode, increasing Kd to enhance stability.

[0117] The intelligent control unit also has a built-in fault prediction algorithm, which is configured as follows: A digital model was established that includes the wear of the guide component 53, the surface condition of the safety rope 52, and the attenuation of the laser marker points.

[0118] The health status of key components is assessed based on long-term time-series data of lateral offset, spectrum of operating speed fluctuations, and confidence level of laser marker identification.

[0119] When the evaluation indicators exceed the preset threshold, a predictive maintenance warning is generated.

[0120] The intelligent control unit receives the physical offset and instantaneous speed, and combines the data from the load sensor to execute the lifting control method and the adaptive fuzzy PID compensation algorithm. Its control process includes rope deflection control, speed and efficiency management, and fault prediction.

[0121] The preset thresholds for the fault prediction algorithm include: the standard deviation of the lateral offset increases by ≥20% for one week, the confidence level of laser marker recognition is ≤85%, and the amplitude of the velocity fluctuation spectrum is ≥0.2m / s. When any two or more of these conditions are met, a maintenance warning is generated.

[0122] The rope deflection control process is as follows: The target position of rope deviation is set to 0±0.5mm. The fuzzy PID controller takes the physical pixels and their change rate as input and outputs the torque fine adjustment of the drive motor 41. In specific implementation, under the condition of sudden processing of 125% load, the peak value of rope deviation is suppressed to within 0.6mm and recovers to the steady-state range of ±0.3mm within 1 second.

[0123] The speed and efficiency management process is as follows: When the system detects a light load (<30%) long stroke, it automatically switches to high-efficiency mode, increasing the running speed from 1.0m / s to 1.1m / s and shortening the running time by about 9%. When the system is under heavy load (>80%), it switches to energy-saving mode and accelerates smoothly. In this mode, energy consumption is reduced by about 18%.

[0124] The fault prediction process is as follows: The system continuously records the standard deviation of the rope deviation and the contrast of the marker point image. When the standard deviation of the rope deviation increases by 20% and the contrast of the marker point decreases by 15% within a week, the prediction model determines that the guide rail 532 is worn and generates an early warning work order to suggest checking the guide component 53.

[0125] Example 2, see Figure 5 A lifting control method for an intelligent lifting platform, using the intelligent lifting platform described above, includes the following steps: S101. Images of the exit section of the safety rope 52 are continuously acquired using a miniature industrial camera 534. Real-time load values ​​are acquired using a load sensor.

[0126] S102. Process the image and calculate the lateral offset and running speed of the safety rope 52 in real time.

[0127] S103. The lateral offset, running speed and real-time load value are input into the rope deflection adaptive fuzzy PID compensation algorithm, which calculates and outputs torque compensation command to drive mechanism 4 in real time to form closed-loop control.

[0128] S104. Based on the operating speed, real-time load value and target altitude information, select the operating mode with the best overall energy efficiency through a multi-objective optimization decision algorithm.

[0129] S105. Continuously record the lateral offset time series data and marker point identification features calculated in step S102, input them into the fault prediction algorithm, perform trend analysis on the wear of guide rail 532 and the status of safety rope, and generate maintenance warning before the performance degrades to the safety threshold.

[0130] In step S102, the calculation of the running speed is specifically as follows: The center coordinates of the laser marker points in each frame of the image are located using an image recognition algorithm.

[0131] Marker point matching and tracking are performed between consecutive frames.

[0132] The instantaneous linear velocity of the safety rope 52 is calculated based on the pixel displacement, frame rate, and spatial calibration coefficient of the matched same marker point between adjacent frames.

[0133] Obviously, the embodiments described above are merely some, not all, embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

Claims

1. A smart lift platform, characterized by: The utility model relates to a kind of crane, including: Frame (1), the frame (1) is used to provide mounting space for each device; Carring box (2), the carring box (2) is arranged in the middle position of frame (1), and the carring box (2) is slidably connected with frame (1); Counterweight structure (3), the counterweight structure (3) is arranged below carring box (2); Driving mechanism (4), the driving mechanism (4) is arranged on the top of frame (1), and the driving mechanism (4) includes driving motor (41); Rope structure (5), the rope structure (5) includes guide piece (53) and safety rope (52); Controller (6), the controller (6) is arranged on one side of rope structure (5); Wherein, the guide piece (53) is intelligent rope wheel guide device, and the guide piece (53) includes vision monitoring unit, and the vision monitoring unit is used to collect image including safety rope (52) and preset laser mark point; The controller (6) is intelligent control unit, and the controller (6) is signal connected vision monitoring unit, load sensor arranged in the bottom of carring box (2) and driving motor (41).

2. The intelligent lifting platform according to claim 1, characterized in that: The image processing module and control algorithm built in the intelligent control unit are configured to perform the following functions: Based on the continuous image frame collected by the vision monitoring unit, the lateral offset of the safety rope (52) is calculated by image analysis algorithm; Based on the movement trajectory and time of the preset laser mark point in the continuous image frame, the running speed of the safety rope (52) is calculated; Based on the lateral offset and running speed data, combined with the load sensor data, the output torque of the driving motor (41) is dynamically adjusted by the rope offset adaptive fuzzy PID compensation algorithm to control the safety rope (52) offset within the preset threshold.

3. The intelligent lifting platform according to claim 1, wherein: The driving motor (41) is arranged on the top of frame (1), and the driving motor (41) is used to power carring box (2), and the output end of the driving motor (41) is connected with output wheel (42), the outer side of the output wheel (42) is sleeved with main steel wire rope, and the lower side of the output wheel (42) is provided with link wheel (43), and the main steel wire rope is wound on the output wheel (42), the roller on the top of counterweight structure (3) and link wheel (43).

4. The intelligent lifting platform of claim 1, wherein: The rope structure (5) includes guide wheel (51), and the lower side of the guide wheel (51) is provided with auxiliary wheel (54), and the outer side of the guide wheel (51) and auxiliary wheel (54) is wound with safety rope (52), and the carring box (2) is arranged in the middle of guide piece (53), and the guide piece (53) is installed on the side wall of carring box (2); Wherein, the guide piece (53) includes main body (531), the middle position of the main body (531) is provided with guide rail (532), one side of the main body (531) is provided with limiting portion (533), the limiting portion (533) is integrally formed with the main body (531), the inner side of the limiting portion (533) near the guide rail (532) is provided with chamfer, and the safety rope (52) is arranged through the middle position of guide rail (532) and limiting portion (533). The visual monitoring unit comprises a miniature industrial camera (534) arranged on the top of the limiting part (533), and the optical axis of the miniature industrial camera (534) is perpendicular to the running direction of the safety rope (52); A plurality of laser mark points are arranged on the surface of the safety rope (52) at fixed intervals along the length direction, and the laser mark points are formed by a laser surface modification process.

5. The intelligent lifting platform of claim 1, wherein: The image processing module is configured to perform the following steps to calculate the lateral offset: S1, the image processing module filters and performs grayscale processing on the collected image; S2, the image processing module identifies the left and right boundaries of the safety rope (52) in the image using an edge detection algorithm; S3, the image processing module calculates the pixel coordinates of the center points of the left and right boundaries, and compares them with the preset ideal path coordinates to obtain the pixel-level offset; S4, the image processing module converts the pixel-level offset into actual physical offset according to the camera calibration parameters.

6. The intelligent lifting platform of claim 1, wherein: The image processing module is configured to perform the following steps to calculate the running speed: P1, the image processing module identifies and tracks the position of the laser mark point in the sequence of consecutive image frames; P2, the image processing module records the pixel displacement of the same laser mark point in the adjacent two frames of images and the corresponding frame interval time; P3, the image processing module converts the pixel displacement into actual physical displacement according to the camera calibration parameters, and calculates the instantaneous speed; P4, the image processing module filters a plurality of instantaneous speeds to obtain a smooth speed output value.

7. The intelligent lifting platform of claim 1, wherein: The intelligent control unit further comprises a multi-objective optimization decision algorithm, which is configured to dynamically decide and switch between the high-efficiency mode, the standard mode and the energy-saving mode according to the running speed, the load sensor data and the target stroke, wherein: The high-efficiency mode is suitable for the working condition that the load rate is less than 30% and the stroke is greater than three times the height of the bearing box (2), and the running speed is 110% of the rated speed; The standard mode is suitable for the working condition that the load rate is between 30% and 80%, and runs at the rated speed; The energy-saving mode is suitable for the working condition that the load rate is greater than 80%, and uses 80% of the rated acceleration for gentle acceleration.

8. The intelligent lifting platform of claim 1, wherein: The intelligent control unit further comprises a fault prediction algorithm, which is configured to: establish a digital model including the wear of the guide (53), the surface state of the safety rope (52) and the attenuation of the laser mark point; based on the long-term time series data of the lateral offset, the running speed fluctuation spectrum and the laser mark point recognition confidence, evaluate the health status of the key components; when the evaluation index exceeds the preset threshold, a predictive maintenance warning is generated.

9. A method for controlling the lifting of an intelligent lifting platform, using an intelligent lifting platform as claimed in any one of claims 1-8, characterized in that: The method comprises the following steps: S101, continuously collecting images of the outlet section of the safety rope (52) by the miniature industrial camera (534); collecting real-time load values by the load sensor; S102, processing the images to calculate the lateral offset and running speed of the safety rope (52) in real time; S103, inputting the lateral offset, running speed and real-time load value into the rope offset adaptive fuzzy PID compensation algorithm to calculate and output the torque compensation instruction of the driving mechanism (4) in real time, forming a closed-loop control; S104, according to the running speed, real-time load value and target height information, the current comprehensive energy efficiency optimal operation mode is selected through multi-objective optimization decision algorithm; S105, the lateral offset time series data and the marker point identification features calculated in step S102 are continuously recorded and input into the fault prediction algorithm, the trend analysis of the guide rail (532) wear and the safety rope state is carried out, and the maintenance warning is generated before the performance degradation to the safety threshold.

10. The method of claim 9, wherein: In step S102, the calculation of the running speed is specifically: The center coordinates of the laser marker points in each frame of image are located through the image recognition algorithm; The marker points are matched and tracked between the continuous frames; According to the pixel displacement of the same marker point between adjacent frames, the frame rate and the space calibration coefficient, the instantaneous linear velocity of the safety rope (52) is calculated.

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