Single-leg crane operation control system and method based on big data analysis

Abstract

The invention relates to the technical field of crane control, in particular to a single-leg crane operation control system and method based on big data analysis, and the method specifically comprises the following steps: calculating to obtain a real-time stability index of goods; establishing a dynamic torque fluctuation coupling model of the equipment end of the single-leg crane, inputting the operation state data of the single-leg crane in the operation process into the dynamic torque fluctuation coupling model, and outputting a comprehensive torque fluctuation intensity index of the single-leg crane; and inputting the cargo real-time stability index and the comprehensive moment fluctuation intensity index into a hoisting strategy decision analysis model of the single-leg crane, evaluating the real-time operation energy efficiency of the single-leg crane, and outputting a motion control strategy for the single-leg crane under the current working condition. The problems that in the prior art, the special-shaped hoisting torque is unbalanced and the stability is poor in the operation process of the single-leg crane are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of crane control, and is a single-leg crane operation control system and method based on big data analysis. Background Art

[0002] In the fields of heavy equipment manufacturing and logistics, semi-gantry single-leg cranes (single-leg support structures) are widely used due to their strong site adaptability. However, their asymmetric support characteristics, that is, one foot is fixedly connected to the wall and the other foot supports on the ground, pose severe challenges when lifting special-shaped goods. The existing control methods mainly rely on static mechanical models and local feedback mechanisms, and there are the following technical bottlenecks: the dynamic balance of torque is insufficient. When lifting special-shaped goods, asymmetric loads are caused by the offset of the center of mass and the swinging inertia, resulting in a sharp increase in the bending moment gradient of the wall foot and a sudden change in the slip of the ground foot. The existing control methods lack real-time modeling of the torque coupling effect of the two feet and are difficult to suppress the risks of stress concentration (exceeding the strain limit of the wall foot) and out-of-control slip (exceeding the displacement threshold of the ground foot) caused by torque fluctuations; At the same time, single-leg cranes also have the problem of response lag due to multi-source disturbances, such as environmental factors (such as wind speed changes, ground settlement) and load dynamics (swinging of special-shaped goods, sudden change in the tension of the lifting rope), etc. The control strategy based on fixed weight coefficients cannot dynamically balance structural safety and operation stability; In addition, in the existing control methods for single-leg cranes, energy consumption and performance are often statically separated, and independent optimization objectives (such as minimizing torque or maximizing stability) are mostly used. It is impossible to perform dynamic predictive control and ignores the non-linear relationship between energy consumption and performance, resulting in frequent overshoot or emergency braking of the hydraulic system under high-energy consumption conditions, which exacerbates mechanical fatigue. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to propose a single-leg crane operation control system and method based on big data analysis for the problems of unbalanced torque and poor stability during the operation of single-leg cranes in the prior art.

[0004] In order to achieve the above object, the technical solution of the single-leg crane operation control method based on big data analysis of the present invention includes the following steps: Obtain the standard operation efficiency of the single-leg crane during historical operation; periodically collect the operation status data of the single-leg crane during real-time operation; record the lifting cargo data and operation environment data of the single-leg crane in real time; Import the lifting cargo data, operation environment data and operation status data of the single-leg crane into the cargo-end stability evaluation strategy of the single-leg crane, and calculate and obtain the real-time stability index of the cargo; Establish a dynamic torque fluctuation coupling model for the equipment end of a single-leg crane, input the operation state data during the operation of the single-leg crane into the dynamic torque fluctuation coupling model, and output the comprehensive torque fluctuation intensity index of the single-leg crane; Input the real-time cargo stability index and the comprehensive torque fluctuation intensity index into the hoisting strategy decision analysis model of the single-leg crane, evaluate the real-time operation energy efficiency of the single-leg crane, and output the motion control strategy for the single-leg crane under the current working conditions.

[0005] Specifically, the standard operation efficiency is specifically: the average operation speed of the single-leg crane when it involves this section of the target hoisting path during historical operations , specifically: S11: Determine the location and hoisting distance L of the target hoisting path of the cargo to be hoisted; S12: Extract the historical hoisting operation data of several times involving this section of the target hoisting path before the single-leg crane starts to hoist the current special-shaped cargo. The historical hoisting operation data includes: the time required for several hoisting operations to pass through this section of the target hoisting path, the maximum change value of the boom angle during several hoisting operations , the of the maximum main hoisting rope tension and the minimum main hoisting rope tension during several hoisting operations ; S13: Calculate and obtain the average operation speed .

[0006] Specifically, periodically collect the operation state data of the single-leg crane during real-time operation; record the hoisting cargo data and operation environment data of the single-leg crane in real time; Preset the interval of the data unit time, and periodically collect the foot operation data, main beam operation data, hoisting speed data, boom angle data and hoisting rope state data of the single-leg crane during operation according to the data unit time; The hoisting rope state data includes: the real-time tension magnitude of the main hoisting rope and the change rate of the main hoisting rope tension.

[0007] Specifically, the cargo end stability evaluation strategy includes: S21: Calculate the environmental interference coefficient of the operation in the data unit time according to the operation environment data of the single-leg crane operation in the data unit time ; Specifically: ; Among them, are the wind speed influence factor and the wind direction influence factor respectively, and both are greater than or equal to 0; It is the difference between the real-time wind speed on the largest windward side of the goods in a data unit time and the wind speed on the largest windward side of the goods when they are not lifted on the ground; It is the difference between the real-time wind direction on the largest windward side of the goods in a data unit time and the wind direction on the largest windward side of the goods when they are not lifted on the ground; It is the maximum fluctuating wind speed and maximum fluctuating wind direction that a single-leg crane can withstand during upward lifting operation; It is the offset distance of the real-time center of gravity position of the goods in a data unit time compared to the center of gravity position of the goods when they are not lifted on the ground; It is the maximum center of gravity offset distance generated by wind speed fluctuation during the upward lifting operation of a single-leg crane; It is the maximum center of gravity offset distance generated by wind direction fluctuation during the upward lifting operation of a single-leg crane; S22: Calculate the self-control response coefficient of the single-leg crane in a data unit time according to the lifting speed data and boom angle data of the crane in the data unit time ; Specifically: ; Among them, are respectively the lifting speed influence coefficient and boom angle influence coefficient of the single-leg crane, and both are greater than or equal to 0; is the lifting speed of the single-leg crane in a data unit time; is the change value of the boom angle of the single-leg crane in a data unit time; S23: Calculate the wire rope force fluctuation coefficient of the single-leg crane in a data unit time according to the real-time tension and tension change rate of the main hoisting wire rope ; Specifically: ; Among them, are respectively the main hoisting wire rope tension influence coefficient and main hoisting wire rope tension change rate influence coefficient, and both are greater than or equal to 0, is the magnitude of the real-time tension of the main hoisting wire rope in a data unit time, is the change rate of the main hoisting wire rope tension in a data unit time.

[0008] Specifically, the stability evaluation strategy for the goods end further includes: S24: Calculate the real-time stability index of the hoisted goods per unit time of the data according to S21 - S23 , and the real-time stability index of the hoisted goods Calculation strategy, specifically: ; Among them, are all proportionality coefficients, , and are all greater than 0.

[0009] Specifically, the dynamic moment fluctuation coupling model includes: S31: Uniformly arrange distributed strain gauges along the length direction of the wall foot of the single-leg crane to measure the bending moment at each point in real time and the bending moment gradient of the wall foot , obtain the dynamic bending moment distribution data of the wall foot of the single-leg crane during hoisting, and calculate the wall foot bending moment fluctuation component according to the dynamic bending moment distribution data ; Preferably, the calculation strategy of the wall foot bending moment fluctuation component is: ; Among them, is the effective bearing length of the wall foot of the single-leg crane; is the reference bending moment; S32: Measure the real-time slip amount of the ground foot of the single-leg crane through a laser displacement sensor , and calculate the ground foot slip speed , and obtain the horizontal reaction force by using the ground foot pressure sensor , calculate and obtain the ground foot hydraulic disturbance component ; Preferably, the calculation strategy of the ground foot hydraulic disturbance component is: ; Among them, is the track friction coefficient of the ground foot of the single-leg crane; is the critical slip speed of the ground foot of the single-leg crane; is the hydraulic system response time constant of the single-leg crane; is the dynamic change of the hydraulic support pressure over time; It should be noted that reflects the direct influence of the ground foot of the single-leg crane on the moment fluctuation during the hoisting of special-shaped goods; It should be noted that is the hydraulic pressure volatility Perform exponential filter integration, which can reflect the torque fluctuation caused by the hysteresis effect of the hydraulic system of a single-leg crane; S33: Input the wall-foot moment gradient and the ground-foot slip velocity into the biped dynamic coupling correction strategy to calculate and obtain the biped dynamic coupling correction factor MI; Preferably, the biped dynamic coupling correction strategy is specifically: ; Wherein, is the baseline torque of the wall-foot of the single-leg crane; is the rated supporting force of the ground-foot of the single-leg crane; is the hydraulic characteristic velocity of the hydraulic system of the single-leg crane; Specifically, the dynamic torque fluctuation coupling model further includes: According to S31 - S33, calculate and obtain the comprehensive torque fluctuation intensity index, and the calculation strategy of the comprehensive torque fluctuation intensity index is: ; Wherein, are all energy distribution weights, .

[0010] Specifically, the hoisting strategy decision-making analysis model includes: S51: Construct the effectiveness function of the single-leg crane , specifically: ; Wherein, is the real-time energy consumption of the hydraulic system of the single-leg crane, is the average working energy consumption of the hydraulic system of the single-leg crane; is the energy consumption penalty coefficient of the single-leg crane; S52: Real-time record the comprehensive torque fluctuation ratio and the cargo stability index , and input them into the fuzzy adaptive weight regulator, and dynamically adjust and through the output of the fuzzy adaptive weight regulator, and synchronously and adaptively correct ; S53: Based on the updated weight parameters, execute in a rolling manner with a period of 10 ms by the MPC controller, including: According to the current effectiveness function of the single-leg crane and the set prediction constraint conditions, predict and solve the optimal control sequence of the single-leg crane for the next 3 steps ; is the increment of the wall footing compensation force, is the damping coefficient of the ground footing; It should be noted that, represents the adjustment range of the compensation force output by the wall footing hydraulic system relative to the rated value, reflecting the additional force that needs to be applied to balance the moment, and its range is limited to ±15%; It should be noted that according to the increment of the wall footing compensation force to increase or decrease the compensation force can actively offset the additional moment caused by the swing of the goods.

[0011] It should be noted that, represents the damping parameter of the ground footing hydraulic system, which determines the rigid or flexible response of the ground footing. Its adjustment speed is limited to ≤5% / ms. Limiting the adjustment speed can prevent hydraulic shock, avoid hydraulic fatigue of the single-leg crane, and avoid reducing the service life of the single-leg crane; It should be noted that according to the damping coefficient of the ground footing rapidly adjusting the ground footing stiffness can suppress slip and absorb vibration energy.

[0012] S54: According to the optimal control sequence of the single-leg crane obtained by prediction , convert the optimal control sequence into the control instructions of the single-leg crane, transmit them to the control system of the single-leg crane, and dynamically control the operation process of the single-leg crane.

[0013] In addition, the single-leg crane operation control system based on big data analysis of the present invention includes the following modules: Data acquisition module, cargo end stability evaluation module, equipment end torque coupling module, lifting strategy decision-making module, and control instruction execution module; The data acquisition module is used to obtain the standard operation efficiency of the single-leg crane during historical operation; periodically collect the operation status data of the single-leg crane during real-time operation; and record the lifting cargo data and operation environment data of the single-leg crane in real time; The cargo end stability evaluation module is used to import the lifting cargo data, operation environment data, and operation status data of the single-leg crane into the cargo end stability evaluation strategy of the single-leg crane, and calculate and obtain the real-time cargo stability index; The equipment end torque coupling module is used to establish a dynamic torque fluctuation coupling model at the equipment end of the single-leg crane, input the operation status data during the operation process of the single-leg crane into the dynamic torque fluctuation coupling model, and output the comprehensive torque fluctuation intensity index of the single-leg crane; The lifting strategy decision-making module is used to input the real-time stability index of the goods and the comprehensive torque fluctuation intensity index into the lifting strategy decision-making analysis model of the single-leg crane, evaluate the real-time operation energy efficiency of the single-leg crane, and output the motion control strategy for the single-leg crane under the current working conditions; The control instruction execution module is used to dynamically control the operation process of the single-leg crane.

[0014] Compared with the prior art, the technical effects of the present invention are as follows: 1) The present invention suppresses the sway of the goods and the structural imbalance through environmental interference compensation, optimized hoisting rope force, and biped torque coupling correction, significantly improving the operation stability and safety of the crane; 2) The present invention also balances the load dynamics and energy consumption, reduces the loss of the hydraulic system, enhances the energy efficiency and equipment life based on the adaptive adjustment of the effectiveness function and the energy consumption penalty coefficient; 3) The present invention optimizes the path speed using historical data, combines MPC to real-time roll and optimize the control instruction, and shortens the response time; In summary, the present invention realizes the collaborative optimization of the structure-load, effectively solving the problems of high-frequency torque fluctuation and slip disturbance in the hoisting of special-shaped goods by the single-leg crane. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them: Figure 1 is a schematic flow chart of the operation control method of the single-leg crane based on big data analysis of the present invention; Figure 2 is a schematic flow chart of the operation of the data acquisition module of the present invention; Figure 3 is a schematic flow chart of obtaining the real-time stability index of the goods of the present invention; Figure 4 is a schematic flow chart of obtaining the total amount and torque fluctuation intensity index of the present invention; Figure 5 is a schematic diagram of the dynamic control of the single-leg crane of the present invention; Figure 6 is a schematic structural diagram of the operation control system of the single-leg crane based on big data analysis of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0017] Secondly, as used herein, "an embodiment" or "embodiments" refer to specific features, structures, or characteristics that may be included in at least one implementation manner of the present invention. The appearances of "in an embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or selectively exclusive embodiments from other embodiments.

[0018] Embodiment 1: As Figure 1 shown, the single-leg crane operation control method based on big data analysis according to the embodiment of the present invention, as Figure 1 shown, includes the following specific steps: Obtain the standard operation efficiency of the single-leg crane during historical operations; periodically collect the operation status data of the single-leg crane during real-time operations; and record in real-time the lifting cargo data and operation environment data of the single-leg crane; The standard operation efficiency is specifically: the average operation speed of the single-leg crane when involving this section of the target lifting path during historical operations , specifically: S11: Determine the location and lifting distance L of the target lifting path of the cargo to be lifted; S12: Extract the historical lifting operation data involving this section of the target lifting path several times before the single-leg crane starts to lift the current special-shaped cargo. The historical lifting operation data includes: the time required for several lifting operations to pass through this section of the target lifting path, the maximum change value of the boom angle during several lifting operations , the maximum main hoist rope tension during several lifting operations and the minimum main hoist rope tension; S13: Calculate and obtain the average operation speed .

[0019] Exemplarily, in this embodiment, the calculation formula for the average operation speed is: ; where is the time required for the nth historical lifting operation to pass through this section of the target lifting path, and n is the serial number index of the historical lifting operation; As Figure 2 shown, periodically collect the operation status data of the single-leg crane during real-time operations; and record in real-time the lifting cargo data and operation environment data of the single-leg crane; Preset the interval of the data unit time, and periodically collect the foot operation data, main beam operation data, hoisting speed data, boom angle data, and sling state data during the operation of the single-leg crane according to the data unit time; The sling state data includes: the real-time tension magnitude of the main sling and the change rate of the main sling tension.

[0020] Import the lifting cargo data, operation environment data, and operation status data of the single-leg crane into the cargo-end stability evaluation strategy of the single-leg crane, and calculate and obtain the real-time stability index of the cargo; The cargo-end stability evaluation strategy includes: S21: Calculate the environmental interference coefficient of the operation in the j-th data unit time according to the operation environment data of the single-leg crane operation in the j-th data unit time ; Specifically: ; Among them, are the wind speed influence factor and the wind direction influence factor respectively, and both are greater than or equal to 0; is the difference between the real-time wind speed received by the largest windward surface of the cargo in the j-th data unit time and the wind speed received by the largest windward surface of the cargo when it is not lifted on the ground; is the difference between the real-time wind direction received by the largest windward surface of the cargo in the j-th data unit time and the wind direction received by the largest windward surface of the cargo when it is not lifted on the ground; is the maximum fluctuating wind speed and the maximum fluctuating wind direction that the single-leg crane can withstand during the upward lifting operation; is the offset distance of the real-time center of gravity position of the cargo in the j-th data unit time compared to the center of gravity position of the cargo when it is not lifted on the ground; is the maximum center of gravity offset distance generated by the wind speed fluctuation during the upward lifting operation of the single-leg crane; is the maximum center of gravity offset distance generated by the wind direction fluctuation during the upward lifting operation of the single-leg crane; S22: Calculate the self-control response coefficient of the single-leg crane in the j-th data unit time according to the hoisting speed data and boom angle data of the crane in the j-th data unit time ; Specifically: ; Among them, They are respectively the hoisting speed influence coefficient and the boom angle influence coefficient of the single-leg crane, and both are greater than or equal to 0; is the hoisting speed of the single-leg crane per unit time of the j-th data; is the boom angle change value of the single-leg crane per unit time of the j-th data; S23: Calculate the hoisting rope force fluctuation coefficient of the single-leg crane per unit time of the j-th data according to the real-time tension and the tension change rate of the main hoisting rope in the j-th data unit time ; Specifically: ; Among them, They are respectively the main hoisting rope tension influence coefficient and the main hoisting rope tension change rate influence coefficient, and both are greater than or equal to 0, is the magnitude of the real-time tension of the main hoisting rope in the j-th data unit time, is the change rate of the main hoisting rope tension in the j-th data unit time.

[0021] S24: As Figure 3 shown, calculate the real-time stability index of the lifted goods in the j-th data unit time according to S21 - S23 , and the real-time stability index of the lifted goods calculation strategy is specifically: ; Among them, are all proportionality coefficients, , and are all greater than 0.

[0022] Establish a dynamic torque fluctuation coupling model at the equipment end of the single-leg crane, input the operation state data during the operation of the single-leg crane into the dynamic torque fluctuation coupling model, and output the comprehensive torque fluctuation intensity index of the single-leg crane; The dynamic torque fluctuation coupling model includes: S31: Uniformly arrange distributed strain gauges along the length direction of the wall foot of the single-leg crane, and measure the bending moment at each point and the wall foot bending moment gradient in real time, obtain the dynamic bending moment distribution data of the wall foot of the single-leg crane during hoisting, and calculate the wall foot bending moment fluctuation component according to the dynamic bending moment distribution data ; In this embodiment, it should be noted that the swinging of special-shaped goods will cause high-frequency bending moment fluctuations of the wall foot; Preferably, the calculation strategy of the wall foot bending moment fluctuation component is: ; Among them, is the effective bearing length of the wall foot of the single-leg crane; is the reference bending moment; Preferably, in this embodiment, the reference bending moment is defined as the product of the cargo weight and the lifting distance; S32: Measure the real-time slip amount of the ground foot of the single-leg crane through a laser displacement sensor , and calculate the ground foot slip speed , and obtain the horizontal reaction force by using the ground foot pressure sensor , calculate and obtain the ground foot hydraulic disturbance component ; In this embodiment, it should be noted that there will be transient disturbances in the ground foot active leveling and slip compensation; Preferably, the calculation strategy of the ground foot hydraulic disturbance component is: ; Among them, is the track friction coefficient of the ground foot of the single-leg crane; is the critical slip speed of the ground foot of the single-leg crane; Exemplarily, in this embodiment, ; is the ground stiffness of the ground supported by the ground foot of the single-leg crane; is the critical slip amount of the ground foot of the single-leg crane, is the equivalent mass of the ground foot support system of the crane; is the hydraulic system response time constant of the single-leg crane; is the dynamic change of the hydraulic support pressure over time; It should be noted that reflects the direct influence of the ground foot of the single-leg crane on the moment fluctuation during the hoisting of special-shaped goods; is the exponential filter integration of the hydraulic pressure volatility , which can reflect the moment fluctuation caused by the hysteresis effect of the single-leg crane hydraulic system; S33: Import the wall foot moment gradient and the ground foot slip speed into the biped dynamic coupling correction strategy, and calculate and obtain the biped dynamic coupling correction factor MI; Preferably, the biped dynamic coupling correction strategy is specifically: ; Among them, is the baseline moment of the wall foot of the single-leg crane; is the rated support force of the ground foot of the single-leg crane; is the hydraulic characteristic speed of the hydraulic system of the single-leg crane; Exemplarily, in this embodiment, it should be noted that, represents the equivalent moment of slip driven by the bending moment gradient, reflecting the driving effect of the uneven bending moment distribution of the wall foot of the single-leg crane on the ground foot slip; Such as Figure 4 shown, according to S31-S33, the comprehensive moment fluctuation intensity index is calculated and obtained, and the calculation strategy of the comprehensive moment fluctuation intensity index is: ; Among them, are all energy distribution weights, .

[0023] Exemplarily, in this embodiment, is the proportion of bending energy, representing the proportion of the bending strain energy generated by the wall foot due to the swing of the goods or eccentric hoisting in the total energy; is the proportion of frictional energy, representing the proportion of the energy dissipated by the ground foot slip friction and the hydraulic system in the total energy; is to reflect the non-linear coupling effect between the wall foot and the ground foot that has not been and individually described; It should be noted that by quantifying the combined influence of the crane moment of bending and friction, when and are relatively small, is approximately equal to 1, indicating that the coupling effect of the wall foot and the ground foot is dominant. When and are relatively large, decreases, which can suppress the risk of repeated quantification of moment fluctuations.

[0024] The real-time stable index of the goods and the comprehensive moment fluctuation intensity index are input into the hoisting strategy decision analysis model of the single-leg crane to evaluate the real-time operation energy efficiency of the single-leg crane, and the motion control strategy for the single-leg crane under the current working conditions is output.

[0025] The hoisting strategy decision analysis model includes: S51: Construct the efficiency function of the single-leg crane , specifically: ; Among them, among them, is the real-time energy consumption of the single-leg crane hydraulic system, is the average working energy consumption of the single-leg crane hydraulic system; is the energy consumption penalty coefficient of the single-leg crane; Exemplarily, in this embodiment, it is defaulted to 0.2; Exemplarily, in this embodiment, a comparison schematic diagram of the real-time cargo stability index and the torque fluctuation intensity index is provided as shown in the following table:

[0026] S52: Real-time record the comprehensive torque fluctuation ratio and the cargo stability index , and input them into the fuzzy adaptive weight regulator, and dynamically adjust and through the output of the fuzzy adaptive weight regulator, and synchronously and adaptively correct ; Exemplarily, in this embodiment, it should be noted that reflects the structural mechanics performance of the single-leg crane equipment end, that is, the balance of the biped torque, characterizes the dynamic behavior of the cargo, that is, swing suppression, and constructs the effectiveness function of the single-leg crane characterizes the efficiency transfer efficiency of the single-leg crane that interacts between the structure and the load from the system level. When the cargo swings violently decreases, that is, the comprehensive torque fluctuation intensity index increases, and the overall effectiveness still decreases significantly, forcing the single-leg crane controller to intervene in the structure and load dynamics simultaneously.

[0027] Exemplarily, in this embodiment, the dynamic adjustment of and through the output of the fuzzy adaptive weight regulator, and the synchronous and adaptive correction of are specifically as follows: If it is detected that fluctuates violently (such as a sudden increase exceeding 30%) and is low (poor stability), then automatically increase and , and preferentially suppress the torque fluctuation of the single-leg crane; When > 0.8 (good stability), then reduce the energy consumption penalty coefficient of the crane to 0.1, focusing on energy-saving operation.

[0028] S53: Based on the updated weight parameters, the MPC controller executes in a rolling manner with a period of 10 ms, including: According to the effectiveness function of the current single-leg crane and the set prediction constraint conditions, predict and solve the optimal control sequence of the single-leg crane for the next 3 steps (i.e., 30 ms). ; Exemplarily, in this embodiment, the prediction constraint conditions are as follows: and the bending moment at each point of the wall foot of the single-leg crane The average value is less than or equal to ; is the increment of the wall foot compensation force, is the ground foot damping coefficient; It should be noted that represents the adjustment range of the compensation force output by the wall foot hydraulic system relative to the rated value, reflecting the additional force required to balance the torque, and its range is limited to ±15%; It should be noted that according to the increment of the wall foot compensation force to increase or decrease the compensation force can actively offset the additional torque caused by the swing of the goods.

[0029] It should be noted that represents the damping parameter of the ground foot hydraulic system, which determines the rigid or flexible response of the ground foot. The adjustment speed is limited to ≤5% / ms. Limiting the adjustment speed can prevent hydraulic shock, avoid hydraulic fatigue of the single-leg crane, and avoid reducing the service life of the single-leg crane; It should be noted that according to the ground foot damping coefficient quickly adjust the ground foot stiffness, which can suppress slippage and absorb vibration energy.

[0030] S54: According to the optimal control sequence of the single-leg crane obtained by prediction , convert the optimal control sequence into the control instruction of the single-leg crane, and transmit it to the control system of the single-leg crane to dynamically control the operation process of the single-leg crane.

[0031] Exemplarily, in this embodiment, as Figure 5 shown, converting the optimal control sequence into the control instruction of the single-leg crane includes: S541: Convert the increment of the wall foot compensation force into the target pressure of the hydraulic cylinder and the pose adjustment amount of the parallel structure of the single-leg crane; According to the target pressure of the hydraulic cylinder, increase the corresponding pressure of the wall foot, and at the same time, drive the parallel structure to move in the specified direction according to the pose adjustment amount of the parallel structure of the single-leg crane to adjust the pose of the contact plate of the wall foot of the single-leg crane; The conversion of the target pressure of the hydraulic cylinder is specifically: ; Among them, is the rated pressure, is the pressure conversion coefficient; Exemplarily, in this embodiment, , ; The conversion of the pose adjustment amount is specifically: Construct a dynamic motion model of the parallel structure, including: ; Among them, is the Jacobian matrix of the parallel structure of the single-leg crane, which is used to describe the relationship between the joint speed and the end-effector speed; is the maximum displacement of the single-axis compensation amount; S542: Convert the ground-foot damping coefficient into the hydraulic valve opening and the target speed of the servo motor of the single-leg crane; Reduce the hydraulic resistance of the hydraulic system according to the adjusted hydraulic valve opening, and at the same time drive the motor to rotate according to the target speed of the servo motor of the single-leg crane to compensate for the ground-foot movement; The hydraulic valve opening The conversion is specifically: ; is the nominal damping coefficient of the ground-foot hydraulic system; is the valve flow coefficient; The target speed of the servo motor The conversion is specifically: ; Among them, is the speed regulation coefficient; Exemplarily, in this embodiment, the control strategy further includes: performing closed-loop correction on the cumulative error through a PID controller to ensure that the actual deviation from the target value < 3%.

[0032] Embodiment 2: As Figure 6 shown, the single-leg crane operation control system based on big data analysis according to the embodiment of the present invention, as Figure 6 shown, includes the following modules: A data acquisition module, a cargo-end stability evaluation module, an equipment-end torque coupling module, a lifting strategy decision-making module, and a control instruction execution module; The data acquisition module is used to obtain the standard operation efficiency of the single-leg crane during the historical operation process; periodically collect the operation status data of the single-leg crane during the real-time operation process; and record the lifting cargo data and operation environment data of the single-leg crane in real time; The cargo - end stability evaluation module is used to import the hoisting cargo data, operation environment data, and operation status data of the single - leg crane into the cargo - end stability evaluation strategy of the single - leg crane, and calculate and obtain the real - time stability index of the cargo; The equipment - end torque coupling module is used to establish a dynamic torque fluctuation coupling model for the equipment end of the single - leg crane, input the operation status data during the operation of the single - leg crane into the dynamic torque fluctuation coupling model, and output the comprehensive torque fluctuation intensity index of the single - leg crane; The hoisting strategy decision - making module is used to input the real - time stability index of the cargo and the comprehensive torque fluctuation intensity index into the hoisting strategy decision - making analysis model of the single - leg crane, evaluate the real - time operation energy efficiency of the single - leg crane, and output the motion control strategy for the single - leg crane under the current working conditions; The control instruction execution module is used to dynamically control the operation process of the single - leg crane.

[0033] Embodiment Three: This embodiment provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory; The processor executes the above - mentioned operation control of the single - leg crane based on big - data analysis by calling the computer program stored in the memory.

[0034] This electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPU) and one or more memories. Among them, at least one computer program is stored in the memory, and this computer program is loaded and executed by the processor to implement the operation control of the single - leg crane based on big - data analysis provided by the above - mentioned method embodiment. This electronic device can also include other components for realizing the functions of the device. For example, this electronic device can also have components such as wired or wireless network interfaces and input - output interfaces for data input and output. This embodiment will not be elaborated here.

[0035] Embodiment Four: This embodiment proposes a computer - readable storage medium, on which a rewritable computer program is stored; When the computer program runs on a computer device, it enables the computer device to execute the above - mentioned operation control of the single - leg crane based on big - data analysis.

[0036] For example, a computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0037] It should be understood that determining B based on A does not mean determining B solely based on A, and B can also be determined based on A and / or other information.

[0038] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0039] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0040] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0041] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one way, and in actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0042] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0043] In addition, each functional unit in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0044] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

Claims

1. A single-leg crane operation control method based on big data analysis, characterized in that The method includes the following specific steps: Obtain the standard operation efficiency of the single-leg crane during its historical operation; periodically collect the operation status data of the single-leg crane during its real-time operation; record in real time the data of the goods lifted by the single-leg crane and the operation environment data; Import the data of the goods lifted by the single-leg crane, the operation environment data and the operation status data into the goods-end stability evaluation strategy of the single-leg crane, and calculate and obtain the real-time stability index of the goods; Establish a dynamic torque fluctuation coupling model for the equipment end of the single-leg crane, input the operation status data during the operation of the single-leg crane into the dynamic torque fluctuation coupling model, and output the comprehensive torque fluctuation intensity index of the single-leg crane; Input the real-time stability index of the goods and the comprehensive torque fluctuation intensity index into the lifting strategy decision analysis model of the single-leg crane, evaluate the real-time operation energy efficiency of the single-leg crane, and output the motion control strategy for the single-leg crane under the current working conditions.

2. The single-leg crane operation control method based on big data analysis according to claim 1, characterized in that The specific standard operation efficiency is: the average operation speed of the single-leg crane when it involves the target lifting path during the historical operation , specifically: S11: Determine the location and lifting distance L of the target lifting path of the goods to be lifted; S12: Extract the historical lifting operation data related to this section of the target lifting path several times before the single-leg crane starts to lift the current special-shaped goods. The historical lifting operation data includes: the time required for several lifting operations to pass through this section of the target lifting path, the maximum change value of the boom angle during several lifting operations, the maximum main hoisting rope tension and the minimum main hoisting rope tension during several lifting operations; S13: Calculate and obtain the average operation speed .

3. The single-leg crane operation control method based on big data analysis according to claim 2, wherein Periodically collect the operation status data of the single-leg crane during its real-time operation; record in real time the data of the goods lifted by the single-leg crane and the operation environment data; Preset the interval of the data unit time, and periodically collect the foot operation data, main beam operation data, hoisting speed data, boom angle data and hoisting rope status data during the operation of the single-leg crane according to the data unit time; The hoisting rope status data includes: the real-time tension magnitude of the main hoisting rope and the change rate of the main hoisting rope tension.

4. The method for controlling the operation of a single-leg crane based on big data analysis according to claim 3, characterized in that, The goods-end stability evaluation strategy includes: S21: Calculate the environmental interference coefficient during operation in the data unit time according to the operation environment data of the single-leg crane in the data unit time ; S22: Calculate the autonomous control response coefficient of the single-leg crane per unit time of data based on the hoisting speed data and boom angle data of the crane per unit time of data ; S23: Calculate the load fluctuation coefficient of the sling of the single-leg crane per unit time of data according to the real-time tension and the tension change rate of the main sling per unit time of data .

5. The method for controlling the operation of a single-leg crane based on big data analysis according to claim 4, wherein, The goods-end stability evaluation strategy also includes: S24: Calculate the real-time stability index of the lifted goods per unit time according to S21 - S23 , and the calculation strategy of the real-time stability index of the lifted goods is specifically as follows: ； Among them, are all proportionality coefficients, , and are all greater than 0.

6. The single-leg crane operation control method based on big data analysis according to claim 5, wherein The dynamic torque fluctuation coupling model includes: S31: Uniformly arrange distributed strain gauges along the length direction of the single-leg crane wall footing, measure the bending moment at each point and the bending moment gradient of the wall footing in real time, obtain the dynamic bending moment distribution data of the single-leg crane wall footing during the lifting process, and calculate the bending moment fluctuation component of the wall footing based on the dynamic bending moment distribution data ; S32: Measure the real-time slip amount of the single-leg crane's ground foot through a laser displacement sensor, calculate the ground foot slip speed, and use the ground foot pressure sensor to obtain the horizontal reaction force to calculate and obtain the ground foot hydraulic disturbance component ; S33: Import the wall-foot moment gradient and the ground-foot slip speed into the biped dynamic coupling correction strategy, and calculate and obtain the biped dynamic coupling correction factor MI.

7. The single-leg crane operation control method based on big data analysis according to claim 6, characterized in that, The dynamic torque fluctuation coupling model also includes: According to S31 - S33, calculate and obtain the comprehensive torque fluctuation intensity index, and the calculation strategy of the comprehensive torque fluctuation intensity index is as follows: ； Among them, are all energy distribution weights, .

8. The single-leg crane operation control method based on big data analysis according to claim 7, characterized in that The lifting strategy decision analysis model includes: S51: Construct the effectiveness function of the single-leg crane , specifically as follows: ； Among them, is the real-time energy consumption of the single-leg crane hydraulic system, is the average working energy consumption of the single-leg crane hydraulic system; is the energy consumption penalty coefficient of the single-leg crane; S52: Record the comprehensive torque fluctuation ratio and the cargo stability index in real time , and input them into the fuzzy adaptive weight regulator, and dynamically adjust them through the output of the fuzzy adaptive weight regulator and , and synchronously and adaptively correct ; S53: Based on the updated weight parameters, it is executed cyclically by the MPC controller at a period of 10 ms, including: according to the effectiveness function of the current single-leg crane and the set prediction constraint conditions, predicting and solving the optimal control sequence of the single-leg crane for the next 3 steps ; is the increment of the wall footing compensation force, is the ground footing damping coefficient; S54: Optimal control sequence of the single-leg crane obtained according to the prediction , convert the optimal control sequence into the control commands of the single-leg crane, transmit them to the control system of the single-leg crane, and dynamically control the operation process of the single-leg crane.

9. A single-leg crane operation control system based on big data analysis, which is used to implement the single-leg crane operation control method based on big data analysis as described in any one of claims 1-8, characterized in that, The system includes the following modules: Data acquisition module, goods-end stability evaluation module, equipment-end torque coupling module, lifting strategy decision module and control instruction execution module; The data acquisition module is used to obtain the standard operation efficiency of the single-leg crane during its historical operation; periodically collect the operation status data of the single-leg crane during its real-time operation; record in real time the data of the goods lifted by the single-leg crane and the operation environment data; The goods-end stability evaluation module is used to import the data of the goods lifted by the single-leg crane, the operation environment data and the operation status data into the goods-end stability evaluation strategy of the single-leg crane, and calculate and obtain the real-time stability index of the goods; The device - side torque coupling module is used to establish a dynamic torque fluctuation coupling model for the device side of the single - leg crane, input the operation state data during the operation of the single - leg crane into the dynamic torque fluctuation coupling model, and output the comprehensive torque fluctuation intensity index of the single - leg crane; The lifting strategy decision - making module is used to input the real - time stability index of the goods and the comprehensive torque fluctuation intensity index into the lifting strategy decision - making analysis model of the single - leg crane, evaluate the real - time operation energy efficiency of the single - leg crane, and output the motion control strategy for the single - leg crane under the current working conditions; The control instruction execution module is used to dynamically control the operation process of the single - leg crane.

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