Self-installation method and system of heavy cargo cableway support based on intelligent control

Abstract

The application discloses a self-installation method and system of heavy freight cableway support based on intelligent control, and relates to the technical field of intelligent regulation and control construction, and comprises the following steps: determining the support base position and planning the installation path based on three-dimensional terrain modeling and stress analysis; under the constraint of the installation path, arranging a temporary traction system and setting an initial tension; based on the temporary traction system, performing self-installation of the support module and self-adaptive adjustment of the tension to form a stress-balanced support system; and installing the bearing cable and the traction cable on the stress-balanced support system and performing performance verification. The application is used to solve the problems of low installation precision, poor efficiency and high safety risk of the cableway support under complex terrain.

Description

Technical Field

[0001] This invention relates to the field of intelligent control construction technology, and more specifically, to a self-installation method and system for heavy-duty freight cableway supports based on intelligent control. Background Technology

[0002] Heavy-duty freight cableways are crucial equipment for transporting goods in complex terrains (such as deep mountains and canyons), and the installation of their support structures is a primary challenge and high-risk aspect of the construction process. To improve installation efficiency in challenging environments and minimize environmental disturbance while ensuring construction safety, engineers urgently need a safe, efficient, and adaptable support structure installation solution. However, traditional cableway support installation methods still have many limitations.

[0003] Traditional methods heavily rely on large lifting equipment (such as helicopters and large cranes) and extensive manual labor. In steep slopes or areas inaccessible to transport machinery, the use of large equipment is severely limited, or even completely impractical. Furthermore, the positioning and assembly of supports depend heavily on the experience of surveyors and installers. In complex terrain, not only is surveying and setting out inefficient, but manually determined installation paths and lifting schemes also carry high safety risks, easily leading to accidents due to human error.

[0004] In existing technologies, some solutions attempt installation through segmented hoisting or the use of simple traction devices, but these methods often lack systematic stress analysis and precise control. Before the entire support system is formed, the temporary structure is in a state of unstable stress, and the installation process is difficult to dynamically adjust according to real-time working conditions. This may lead to excessive internal forces or deformation of the support modules during hoisting, or even the risk of overturning. In addition, the installation of the load-bearing cables and traction cables needs to be carried out on an established stable support system. If the installation accuracy and structural stability of the support itself are not up to standard, it will directly affect the tensioning quality of the subsequent cable system and the operational safety of the entire cableway.

[0005] Especially when facing complex construction environments with large undulations, numerous obstacles, and limited access routes, the uncertainties at each stage are amplified, making it difficult for traditional methods to balance installation accuracy, construction safety, and operational efficiency. Therefore, there is an urgent need in this field for an intelligent self-installation technology solution that can autonomously sense the environment, intelligently plan paths, and achieve automatic docking and tension adjustment of support modules under controlled conditions. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a self-installation method and system for heavy-duty freight cableway supports based on intelligent control. Through the self-installation of support modules and tension adaptive adjustment technology, a support system with balanced force is formed to solve the problems of low installation accuracy, poor efficiency, and high safety risks of cableway supports in complex terrain.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The self-installation method for heavy-duty freight cableway supports based on intelligent control includes the following steps: determining the location of the support base and planning the installation path based on three-dimensional terrain modeling and stress analysis; deploying a temporary traction system and setting the initial tension under the constraints of the installation path; performing self-installation of the support modules and adaptive tension adjustment based on the temporary traction system to form a support system with balanced stress; and installing the load-bearing cable and traction cable on the support system with balanced stress and verifying its performance.

[0009] In a preferred embodiment, the steps for determining the location of the support base and planning the installation path based on three-dimensional terrain modeling and stress analysis are as follows: constructing a graphical model in the area containing the location of the support base, calculating the path length, slope, and obstacle factor; determining the weights of the path length, slope, and obstacle factor using the analytic hierarchy process (AHP), and constructing a path cost function; and solving the installation path using the shortest path algorithm based on the path cost function.

[0010] In a preferred embodiment, determining the weights of path length, slope, and obstacle factors using the analytic hierarchy process (AHP) specifically involves: constructing a pairwise comparison matrix based on the principle of prioritizing construction safety; normalizing the pairwise comparison matrix and calculating the average value of each row to obtain a weight vector; performing a consistency check by calculating the consistency index and consistency ratio of the weight vector; if the check passes, determining the final weights; if the check fails, adjusting the pairwise comparison matrix and repeating the weight vector calculation until the check passes; the principle of prioritizing safety means that the importance of obstacle factors is higher than that of slope, and slope is higher than that of path length.

[0011] In a preferred embodiment, the step of setting up a temporary traction system and establishing an initial tension under installation path constraints specifically involves: setting up temporary anchor points and establishing a temporary traction system under installation path constraints; performing a force analysis on the temporary traction system to calculate the traction force requirements for transportation and hoisting; and setting the initial tension of the temporary traction system according to the traction force requirements.

[0012] In a preferred embodiment, the step of performing self-installation and adaptive tension adjustment of the support module based on the temporary traction system to form a force-balanced support system involves the following steps: docking the support module based on the temporary traction system and obtaining the attitude and force state of the support module through multi-source sensors; performing force balance constraint calculation based on the attitude and force state of the support module to determine the target tension vector; performing PID control based on the target tension vector to obtain the actual tension state; and performing tension safety verification and adjustment on the actual tension state to achieve tension convergence and attitude stability of each traction rope, thus forming a force-balanced support system.

[0013] In a preferred embodiment, the step of calculating the force balance constraint based on the posture and force state of the support module to determine the target tension vector includes: establishing a force balance equation for multiple traction ropes based on the posture and force state of the support module to obtain force balance constraints; modifying the force balance constraints under the principle of minimum deviation, using prior tension as the initial value and combining it with a weight matrix to obtain a force balance optimization model; introducing the constraint conditions of upper and lower tension limits into the force balance optimization model to obtain a force constraint optimization problem; and solving the force constraint optimization problem using a quadratic programming method to obtain the target tension vector.

[0014] In a preferred embodiment, the step of obtaining the actual tension state by performing PID control based on the target tension vector includes: inputting the target tension vector into the PID controller to obtain a control error signal;

[0015] An online tuning objective function is constructed based on the control error signal; based on the objective function, an optimization algorithm is used to tune the gain parameter of the PID controller online to obtain the PID control parameters; the tension of the winch is adjusted based on the PID control parameters, and the system status is continuously monitored. When the actual tension stabilizes and converges to the tolerance range of the target tension, the actual tension at this time is output as the actual tension state.

[0016] The self-installation system for heavy-duty freight cableway supports based on intelligent control includes: a site and path planning module, used to determine the location of the support base and plan the installation path based on 3D terrain modeling and stress analysis; a temporary traction system construction module, used to deploy the temporary traction system and set the initial tension under the constraints of the installation path; a support attitude adaptive control module, used to perform self-installation and tension adaptive adjustment of the support module based on the temporary traction system to form a support system with balanced stress; and a cable system installation and verification module, used to install the load-bearing cable and traction cable on the support system with balanced stress and perform performance verification.

[0017] A self-installation device for a heavy-duty freight cableway support based on intelligent control includes a memory and a processor: the memory is used to store a program; the processor is used to execute the program to implement the various steps of the self-installation method for the heavy-duty freight cableway support based on intelligent control.

[0018] A readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the various steps of a self-installation method for a heavy-duty freight cableway support based on intelligent control.

[0019] The technical effects and advantages of the self-installation method and system for heavy-duty freight cableway supports based on intelligent control of this invention are as follows:

[0020] This invention accurately determines the location of the support base and plans the installation path through three-dimensional terrain modeling and stress analysis. Based on the path constraints, a temporary traction system with initial tension is deployed. Combined with the self-installation and tension adaptive adjustment of the support modules, a support system with balanced stress is quickly formed in complex terrain. Then, the load-bearing cable and traction cable are installed on this stable support system and their performance is verified, enhancing the reliability, accuracy, and efficiency of the entire cableway support installation process. Through this series of interconnected intelligent steps, it helps reduce the reliance on large hoisting equipment and a large amount of manpower, effectively solving the traditional problems of difficult erection, high safety risks, and difficulty in guaranteeing installation accuracy of heavy freight cableway supports in rugged mountainous environments. Attached Figure Description

[0021] Figure 1 A schematic diagram of the self-installation method for a heavy-duty freight cableway support based on intelligent control, provided in an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of the self-installation system structure of a heavy-duty freight cableway support based on intelligent control, provided for an embodiment of the present invention;

[0023] Figure 3 A structural block diagram of an exemplary electronic device provided for implementing embodiments of the present disclosure;

[0024] Figure 4 This is a schematic diagram of an exemplary storage medium that can be used to implement embodiments of the present disclosure, as provided in the embodiments of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1, Figure 1 The present invention provides a self-installation method for a heavy-duty freight cableway support based on intelligent control, comprising the following steps:

[0027] S1, based on 3D terrain modeling and stress analysis, determines the location of the support base and plans the installation path.

[0028] In this embodiment, the construction area is first scanned using a drone equipped with lidar or photogrammetry equipment to obtain point cloud data containing elevation and ground feature information. The point cloud data is then processed to form a digital elevation model (DEM), expressed as follows:

[0029]

[0030] in, For geographic coordinates, This corresponds to the elevation value. The terrain slope can be further calculated using the DEM. The formula is as follows:

[0031]

[0032] and curvature :

[0033]

[0034] This is used to identify plateaus or depressions. Combined with an image segmentation algorithm, it identifies the distribution of obstacles, forming an obstacle grid Obs(x,y). This yields the input dataset. This lays the foundation for the selection of base site locations and route planning.

[0035] Screening candidate points that meet the criteria on the DEM Slope less than the threshold Obstacle value is 0 and curvature does not exceed Preferred 10° is acceptable. 0.07m can be taken. -1 The stress on the candidate points is checked, and the total design load of the support is set. for:

[0036]

[0037] in For self-respect, For variable loads, For wind load components, These are partial factors. Outrigger reaction force. for:

[0038]

[0039] in, For the number of outriggers, The distribution coefficient for the i-th leg is determined by the geometric arrangement and stiffness ratio; in the absence of prior knowledge, iterative correction is performed after equal division. The foundation bearing capacity check conditions are then calculated.

[0040]

[0041] in, For the i-th foundation, the compressive stress is... The base area is The allowable bearing capacity of the foundation at the candidate point is given by survey or empirical value of similar strata.

[0042] If the above conditions are met, the site is retained as a qualified foundation point. For each qualified foundation point, its bearing capacity characteristic value is calculated based on geological survey data. and the design pressure transmitted to the foundation by the support structure. Comparison. When When it is determined that the foundation at this location can be used directly, the minimum foundation area is determined by the formula:

[0043]

[0044] The calculation shows that, among which This refers to the vertical load borne by the support base. If Then based on the difference in bearing capacity Select appropriate foundation treatment measures: When When this is the case, surface replacement is preferred; when When necessary, dynamic compaction or drainage consolidation should be used; when In such cases, enlarged foundations or pile foundations are used.

[0045] The above calculation results are output in the form of a data table, including the coordinates of the base point, the corresponding minimum base area, and the recommended foundation treatment measures, which are attached to the output results as design auxiliary information.

[0046] Furthermore, a graph model is constructed within the feasible region. ,node As a feasible point, edge Connect adjacent nodes. Each edge e∈ The attributes include length ,slope Obstacles To unify the units, we normalize it, as shown in the following expression:

[0047]

[0048] in, To allow for a maximum slope, if Then it is determined to be an infeasible edge. The maximum reference value for slope within the path search area is preferred. and The angles were set to 19.3° and 26.6° respectively.

[0049] It should be noted that in the constructed graph model In, node set A feasible point is a discrete point in the 3D terrain model that, after screening, can still serve as a path node. A feasible point must meet the following conditions: First, it must be spatially within the reach of construction machinery, traction systems, or drones, and not fall into restricted areas or inside obstacles; second, the terrain slope should be controlled within allowable limits (e.g., preferably no more than 30°) to ensure the path's engineering feasibility; third, a connection that meets obstacle constraints can be established between this point and key locations such as the transportation starting point and support base, thus forming an edge set. The results obtained through the above constraints This can provide a reliable set of candidate nodes for subsequent path planning and optimization.

[0050] Furthermore, define the comprehensive cost function:

[0051]

[0052] In a preferred embodiment, the weight coefficients of each index in the path cost function are... This was obtained through the Analytic Hierarchy Process (AHP). Specifically, the objective of the path cost function was first determined to be to achieve the optimal transportation path while ensuring safety. The indicator system included path length. ,slope With barrier factors Based on the principle of "safety first" in the construction of heavy-duty cableways in mountainous areas, the importance of obstacle factors is determined to be higher than that of slope, and slope is higher than length. Based on this principle, a pairwise comparison matrix is ​​constructed:

[0053]

[0054] The row and column order of the matrix is ​​as follows: , , After obtaining the pairwise comparison matrix, the columns of the matrix are normalized, and the average value of each row is calculated to obtain an approximate principal eigenvector. This approximate eigenvector is then normalized again to obtain the weight coefficients. ,in Corresponding length, Corresponding slope, Corresponding obstacles.

[0055] After obtaining the pairwise comparison matrix, a consistency check is still needed to ensure the rationality of the judgment matrix. Specifically:

[0056] Calculate matrix With weight vector The product of these two vectors yields a vector. Then calculate the ratio of each component to its corresponding weight. And take their average value to obtain an approximate value of the largest eigenvalue of the matrix. Based on this, a consistency index is calculated. :

[0057]

[0058] in Let be the order of the matrix, in this embodiment... Then calculate the consistency ratio using the following formula. :

[0059]

[0060] in, As a random consistency indicator, when hour If the calculation result satisfies If the pairwise comparison matrix is ​​found to have acceptable consistency, then it is determined that the pairwise comparison matrix has acceptable consistency. In this embodiment, the following is obtained through calculation: Approaching 3, finally This indicates that the matrix judgment is reasonable.

[0061] Based on the aforementioned comprehensive cost function, shortest path optimization algorithms such as Dijkstra's algorithm or A* (A* Star) are used to solve the problem of finding the path from the material distribution point. To the base point The optimal path is expressed as follows:

[0062]

[0063] After completing the foundation stress verification and installation path planning, the output of this step includes not only the optimal path itself, but also auxiliary results directly related to construction. Specifically:

[0064] The shortest path algorithm generates a sequence of nodes that forms a continuous polyline on the 3D terrain model. This polyline is the installation path from the material distribution point to the target base and can be directly used for on-site transportation and hoisting operations.

[0065] Each node on the path has specific spatial coordinates. By outputting these node coordinates, precise positioning and layout of the construction site can be achieved, ensuring that the deployment of guide ropes, the setting of temporary anchor points, and the placement of equipment all have clear geographical references.

[0066] During the path search process, due to the hard constraints on the extreme values ​​of slope and the distribution of obstacles, some edges are judged as infeasible and given infinite cost during calculation. These infeasible edges are listed separately in the algorithm results to form an "infeasible edge list", which is used for construction safety tips and alternative scheme design.

[0067] Therefore, the final result of this step consists of three parts: "installation path polyline, node coordinate list, and infeasible edge list". These three parts are used as input conditions to be passed to subsequent steps for the deployment of the temporary traction system of the support and the initial tension configuration.

[0068] Finally, based on the base points and installation path, a modular assembly sequence for the support frame was determined: base construction -> leg erection -> beam hoisting -> saddle and cable pulley installation. The feasibility of the assembly sequence was verified through 3D simulation, checking whether the lifting trajectory and equipment envelope interfered with obstacles, and whether the lifting load met the equipment's rated operating condition curve. If not, the path or assembly sequence was adjusted retrospectively. The final output included a base coordinate table, an installation path set, and the assembly sequence, serving as input conditions for the temporary traction system deployment in the next step.

[0069] In summary, this step achieved integrated design from data acquisition, foundation site selection, path planning to assembly sequence. DEM modeling and stress analysis ensured foundation safety, weighted path planning guaranteed safe and efficient transportation and hoisting, and assembly simulation avoided construction interference and rework. The final deliverables are provided in a parametric, coordinate-based, and directly executable format, laying the foundation for subsequent construction phases.

[0070] S2, under the constraints of the installation path, set up a temporary traction system and set the initial tension.

[0071] In this embodiment, after the installation path is determined After the assembly sequence, select nodes along the path that have stable foundations, gentle slopes, and are not located in obstacle areas as temporary anchor points. Preferably, the slope should be less than... Simultaneously, the ground surface must possess good anchoring conditions. Ground anchors or expansion bolts are installed at the anchor points, and a continuous traction channel is formed using steel wire ropes or high-strength fiber ropes. The traction channel covers the base location and component lifting points, ensuring that each module in the assembly sequence can be smoothly transported to the target location.

[0072] Perform a force analysis on the traction system. Assume the mass of the component is... The gravity acting on it is G, and the slope is... The traction requirement along the path is:

[0073]

[0074] in, For rolling or sliding friction resistance, For safety margin adjustments, the maximum load should be selected. The initial tension of the wire rope It should be no less than 1.2 times the maximum traction force required to ensure sufficient margin under construction disturbances and dynamic impacts.

[0075] After the initial tension is set, the stress on each section of the rope is monitored by a tension sensor, and the stress condition is judged using a verification formula. If the tension in a certain section exceeds 70% of the material's allowable ultimate strength, it is necessary to redistribute the tension by adjusting the anchor point position or the tensioner parameters. The verification formula is:

[0076]

[0077] in, This represents the actual stress of the rope. To measure the tension, Let the cross-sectional area of ​​the rope be . The allowable stress of the material is preferably 0.5 to 0.6 times the breaking strength of the wire rope.

[0078] This step, by deploying a temporary traction system within the constraints of the installation path and setting a reasonable initial tension, ensures the balance and safety of the traction force during the transport and hoisting of the support structure. On one hand, the reasonable setting of anchor points along the path to form a continuous traction channel allows components to smoothly reach the base position even in complex terrain environments, avoiding installation interruptions due to path limitations. On the other hand, by introducing a safety factor into the initial tension and combining it with a real-time tension monitoring and adjustment mechanism, risks such as wire rope overload breakage and lateral swaying during component hoisting are effectively prevented. Therefore, this step not only improves the stability and safety redundancy of the temporary traction system but also provides reliable physical conditions and force guarantees for the subsequent self-installation and adaptive tension adjustment of the support modules.

[0079] S3, based on a temporary traction system, performs self-installation of the support module and adaptive tension adjustment to form a support system with balanced force.

[0080] In this embodiment, a temporary traction system has been deployed along the installation path and an initial tension has been set in step S2. Based on this, the support modules are automatically lifted and connected according to the assembly sequence. Specifically:

[0081] The module is lifted from the transport point to the target position via a traction rope-guide pulley-winch system, and docking with the base or preceding module is completed using positioning pins and quick-connect fittings. During the lifting process, the control unit determines whether the module has entered the quasi-static balance range based on fused state variables. Automatic locking and fine-tuning are triggered at certain times;

[0082] in, To directly correlate the real-time tension of the traction rope with this module, Let be the angle between the rope and the vertical direction. For module weight, To allow for deviation, the preferred option is... .

[0083] Furthermore, the module's attitude and stress state are obtained through multi-source sensor fusion. Specifically, to suppress noise and jitter from individual sensors, tension, displacement, acceleration, and tilt sensors are deployed at the lifting and anchor points. Weighted Kalman filtering is then used for sensor fusion to obtain a smooth state estimate. ,in For location, For speed, The attitude angle vector is used, and the fusion weights are updated online based on the covariance of each measurement noise.

[0084] To achieve coordinated force distribution across multiple traction circuits, the target tension vector... It is determined by combining force balance constraints and the principle of minimum deviation. Let there be a total of [number] traction ropes participating in sharing the weight. Root, definition Let be the angle between the k-th rope and the vertical direction. Let be the azimuth angle of the rope relative to the x-axis in the horizontal plane. The force equilibrium constraints are as follows:

[0085]

[0086] in Let this be the total vertical load currently borne by this group of ropes. The above three equations are denoted as... ,in column k is , The target tension vector is obtained by solving the following optimization problem using quadratic programming. The optimization problem consists of a force equilibrium optimization model and constraints of upper and lower tension limits:

[0087]

[0088] in, For the prior tension, the initial value is preferably taken from the solution of the previous time step or obtained by geometrical division, and at the same time , As a weight matrix, it is preferable to assign higher weights to branches closer to the hanging point. and These represent the upper and lower bounds of the tension in the k-th rope, respectively. To ensure clarity of definition, the above constraints are defined as follows:

[0089]

[0090] in The allowable stress of the rope material is preferably taken as a fraction of the tensile strength of the steel wire rope; Let be the effective cross-sectional area of ​​the k-th rope;

[0091]

[0092] in , To maintain the geometric stability of the rope path, the preferred coefficient is... When only equality constraints are involved, Lagrange multipliers can also be used to give closed-form solutions:

[0093]

[0094] When inequality constraints exist, the active set or interior point method is preferred for solving the problem.

[0095] To make the actual tension Smooth tracking The target tension vector is input into the PID controller to obtain control commands for the change in winch speed / rope length. The winch speed / rope length change loop is tuned online using PID control based on particle swarm optimization (PSO). The control error signal is defined as a weighted combination of tension error and attitude error, as shown in the following formula:

[0096]

[0097] in, The tension error weights for each branch can be preferably set according to the force-sharing ratio, i.e. , Let the reference tension of the k-th rope be under force equilibrium. The attitude error weight is preferably set to a fixed value of 0.2, or it can be calculated using the dimensional balancing method. , The nominal tension error can be taken as 5% of the reference tension. The value can be taken as the radian value with an allowable attitude deviation of 2°. The error signal is given by the assembly geometry to indicate the desired orientation. As input to the PID controller, it is used to generate control commands for adjusting the speed or rope length of the winch, thereby adjusting the tension of each traction rope to achieve the desired actual tension. convergence to .

[0098] The objective function for online tuning is a discrete weighted performance index, as shown in the following formula:

[0099]

[0100] in, For the sampling period, the preferred method is... N is the number of sampling steps. As an incremental penalty factor, the preferred option is... The particle position of PSO is the PID gain to be tuned. Its preferred value range , , Group size is preferred at 25, with inertia weighting. Optimal learning factor: 0.7 Particle velocity and position are updated according to the following formula:

[0101]

[0102] in and These represent the particle velocity and position in the (q+1)th iteration. , For the individual's optimal, For the group optimal, The iteration terminates when the fitness improvement between two consecutive iterations is less than 1. Or the number of iterations reaches 50. (Tuned) Used in the winch control circuit, to right The tracking is fast and without significant overshoot, while suppressing attitude sway.

[0103] To ensure safety redundancy, tension safety verification and adjustment are always performed in parallel with the control closed loop. Real-time stress is calculated using the following formula:

[0104]

[0105] and the allowable stress of the material Comparison, Selection ,in This represents the tensile strength of the wire rope. When... Maintain current control when constraints are met; when Or reach the warning threshold, preferred At that time, the tension of the branch is first reduced by adjusting the winch command, and then the new boundary conditions are applied. and Resolve If this still fails to meet the requirements, then the anchor point position can be fine-tuned, i.e., the position can be changed. Thus change the matrix Or temporarily change the assembly sequence to distribute the load.

[0106] After the aforementioned cyclical control process of "sensor fusion—tension reference calculation—PSO-PID dynamic adjustment—verification—redistribution—rechecking," the actual tension vectors of all traction ropes converge to the target tension reference value range, and the stress in each branch satisfies the requirements. Furthermore, the overall posture deviation of the support structure is controlled within the allowable range, preferably less than 2°. In this state, geometric locking and mechanical self-balancing are achieved between the various modules of the support structure, the force path is clear, and the sharing ratio of the main and secondary force branches is stable, forming a self-stable structural unit. This structural unit can be called a "force-balanced support system," which can maintain balance independently without relying on external temporary supports, laying the foundation for further loading and performance verification of the cableway's operating load.

[0107] This step introduces multi-source sensor fusion during the support module installation process, combining tension, displacement, acceleration, and tilt angle signals. Weighted Kalman filtering is then used to achieve high-precision estimation of state variables. This step accurately acquires the module's attitude and stress conditions, avoiding control distortion caused by single sensor errors. Simultaneously, based on force balance constraints and the target tension vector obtained through quadratic programming, a reasonable force distribution is provided for each traction branch. In the control loop, a PID algorithm based on particle swarm optimization is used for online gain tuning. Through a clearly defined error function and performance indicators, rapid convergence and tension tracking of the winch control are achieved, ensuring that the hoisting speed and tension distribution remain stable under dynamic conditions. Through these technical means, this step effectively solves the problems of delay and error accumulation caused by traditional manual synchronization based on experience. It achieves force balance and attitude stability during the self-installation of multiple modules, significantly shortens the support erection and leveling time, and improves the safety and efficiency of installation operations in complex terrain conditions.

[0108] S4. On a support system with balanced stress, install the load-bearing cable and traction cable and verify their performance.

[0109] In this embodiment, based on the already formed, force-balanced support system in S3, the load-bearing cable and traction cable are installed and adjusted. Specifically:

[0110] Bearing cables are laid sequentially on the cable saddle at the top of the support structure. Both ends of the bearing cables are fixed to the anchor foundations in the mountainside, and prestress is gradually applied using a tensioning device. Assume the mid-span sag of the bearing cable is... The span is Then in terms of self-respect The theoretical horizontal component of the force under action is:

[0111]

[0112] in Let H be the self-weight of the cable per unit length, and H be the horizontal tension component at both ends of the cable. Preferably, by monitoring the mid-span sag in real time during installation and comparing it with the theoretical calculation value, the error between the actual installation tension and the design tension is controlled within ∓5%, thereby ensuring that the geometry of the cable meets the design requirements.

[0113] After tensioning and fixing the load-bearing cable, the traction cable is installed above or parallel to the load-bearing cable via a pulley system and connected to the drive unit. To verify the cableway's operational performance, a static load test is first conducted: equivalent load blocks are placed on the load-bearing cable, with the total load preferably not less than 1.25 times the rated carrying capacity, to check whether the deflection, displacement, and stress of the support frame and the load-bearing cable meet the design specifications. Secondly, a dynamic load test is conducted: the traction cable is used to drive the test run trolley back and forth along the load-bearing cable, testing the system's vibration acceleration, cableway tension fluctuations, and trolley attitude stability at rated speed.

[0114] During performance verification, strain gauges, displacement gauges, and accelerometers are used to simultaneously collect data, which is then processed and compared with design specifications. If the test results indicate that the maximum stress does not exceed the allowable stress of the material, the verification is successful. If the mid-span deflection does not exceed 1 / 300 of the span L, and all running smoothness indicators are within the allowable range, then the installation and commissioning of this step are deemed qualified.

[0115] This step, combining theoretical formulas with real-time monitoring, achieves precise tensioning and stable installation of the load-bearing and traction cables, ensuring the geometric shape and stress safety of the cableway system under complex loads and operating conditions. Through joint verification of static and dynamic load tests, it effectively solves the problems of relying on experience-based judgment and lacking system performance testing in traditional cableway construction, ensuring the long-term safety and reliability of the constructed cableway.

[0116] Example 2, Figure 2 The present invention provides a self-installation system for heavy-duty freight cableway supports based on intelligent control, comprising:

[0117] The site and path planning module is used to determine the location of the support base and plan the installation path based on 3D terrain modeling and stress analysis.

[0118] The temporary traction system construction module is used to deploy the temporary traction system and set the initial tension under installation path constraints.

[0119] The support posture adaptive control module is used to perform self-installation and tension adaptive adjustment of the support module based on the temporary traction system, so as to form a support system with balanced force.

[0120] The cable system installation and verification module is used to install load-bearing cables and traction cables on a support system with balanced stress and to verify their performance.

[0121] Example 3: A self-installation device for heavy-duty freight cableway supports based on intelligent control, such as... Figure 3 As shown, it includes a memory and a processor: the memory is used to store a program; the processor is used to execute the program to implement any of the embodiments in Example 1.

[0122] Since the self-installation method of the heavy-duty freight cableway support based on intelligent control described in this embodiment is the equipment used to implement the method in Embodiment 1 of this invention, those skilled in the art can understand the specific implementation and various variations of the electronic device in this embodiment based on the method described in Embodiment 1 of this application. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. Any equipment used by those skilled in the art to implement the method in this application embodiment falls within the scope of protection of this application.

[0123] Example 4: A readable storage medium having a computer program stored thereon, such as... Figure 4 As shown, when the computer program is executed by the processor, it implements any of the embodiments in Example 1.

[0124] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0125] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0126] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0128] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0129] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A self-installation method for heavy-duty freight cableway supports based on intelligent control, characterized in that, Includes the following steps: Based on 3D terrain modeling and stress analysis, the location of the support base is determined and the installation path is planned. Specifically, a graphical model is constructed in the area containing the location of the support base, and the path length, slope, and obstacle factor are calculated. The weights of the path length, slope, and obstacle factor are determined by the analytic hierarchy process (AHP), and a path cost function is constructed. Based on the path cost function, the shortest path algorithm is used to solve for the installation path. Under the constraints of the installation path, a temporary traction system is set up and the initial tension is set. Specifically, under the constraints of the installation path, temporary anchor points are set up and a temporary traction system is established; the force analysis of the temporary traction system is performed to calculate the traction force requirements for transportation and hoisting; and the initial tension of the temporary traction system is set according to the traction force requirements. Based on the temporary traction system, the self-installation and adaptive tension adjustment of the support module are implemented to form a support system with balanced force. Specifically, the support module is docked based on the temporary traction system, and the attitude and force state of the support module are obtained through multi-source sensors; force balance constraint calculation is performed based on the attitude and force state of the support module to determine the target tension vector; PID control is performed based on the target tension vector to obtain the actual tension state; the actual tension state is checked and adjusted for tension safety to achieve tension convergence and attitude stability of each traction rope, thus forming a support system with balanced force. On a support system with balanced stress, load-bearing cables and traction cables are installed and their performance is verified.

2. The self-installation method for a heavy-duty freight cableway support based on intelligent control according to claim 1, characterized in that, The determination of the weights of path length, slope, and obstacle factors using the analytic hierarchy process (AHP) is as follows: Based on the principle of prioritizing construction safety, a pairwise comparison matrix is ​​constructed. Normalize the pairwise comparison matrix and calculate the average value of each row to obtain the weight vector; Consistency is verified by calculating the consistency index and consistency ratio of the weight vector; If the test passes, the final weights are determined. If the test fails, adjust the comparison matrix and repeat the weight vector calculation until the test passes. The principle of prioritizing safety means that obstacle factors are more important than slope, and slope is more important than path length.

3. The self-installation method for a heavy-duty freight cableway support based on intelligent control according to claim 2, characterized in that, The step of calculating the force balance constraint based on the posture and force state of the support module to determine the target tension vector includes: Based on the posture and stress state of the support module, the force balance equation of the multiple traction ropes is established, and the force balance constraint is obtained. By applying the principle of minimum deviation to the force balance constraint, using the prior tension as the initial value and combining it with the weight matrix for correction, a force balance optimization model is obtained. By introducing the constraints of upper and lower tension limits into the force balance optimization model, a force-constrained optimization problem is obtained. The target tension vector is obtained by solving the stress constraint optimization problem using a quadratic programming method.

4. The self-installation method for a heavy-duty freight cableway support based on intelligent control according to claim 3, characterized in that, The PID control based on the target tension vector to obtain the actual tension state includes: The target tension vector is input into the PID controller to obtain the control error signal; Construct an online tuning objective function based on the control error signal; Based on the objective function, an optimization algorithm is used to tune the gain parameters of the PID controller online to obtain the PID control parameters; The tension of the winch is adjusted based on PID control parameters, and the system status is continuously monitored. When the actual tension stabilizes and converges to the tolerance range of the target tension, the actual tension at this time is output as the actual tension status.

5. A system using the self-installation method for a heavy-duty freight cableway support based on intelligent control as described in any one of claims 1-4, comprising: The site and path planning module is used to determine the location of the support base and plan the installation path based on 3D terrain modeling and stress analysis. The temporary traction system construction module is used to deploy the temporary traction system and set the initial tension under installation path constraints. The support posture adaptive control module is used to perform self-installation and tension adaptive adjustment of the support module based on the temporary traction system, so as to form a support system with balanced force. The cable system installation and verification module is used to install load-bearing cables and traction cables on a support system with balanced stress and to verify their performance.

6. A self-installation device for a heavy-duty freight cableway support based on intelligent control, characterized in that, Including memory and processor: The memory is used to store programs; The processor is configured to execute the program to implement the various steps of the self-installation method for the heavy-duty freight cableway support based on intelligent control as described in any one of claims 1-4.

7. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the self-installation method for the heavy-duty freight cableway support based on intelligent control as described in any one of claims 1-4.

Images