A planning method, system and equipment for hoisting and moving devices

Abstract

This invention provides a method, system, and equipment for planning hoisting mobile devices, belonging to the field of intelligent optimization. It calculates the anti-tilting redundancy index for each hoisting mobile device at an ideal positioning distance, derives a movement cost coefficient from the anti-tilting redundancy index, and selects the hoisting mobile device with the highest hoisting efficiency by combining the anti-tilting redundancy index, movement cost coefficient, and movement distance. For the selected hoisting mobile device, based on the direction of the line connecting the hoisting point coordinates and the initial position of the hoisting mobile device, a unit vector between the hoisting point coordinates and the initial position of the hoisting mobile device is calculated. Combined with the working radius data of the hoisting mobile device and the unit vector, an output ideal positioning position is generated for the selected hoisting mobile device. This organically combines the equipment's load-bearing capacity, stability, and movement cost, achieving globally optimal decision-making and ensuring that the selected crane is both safe and efficient in complex hoisting scenarios.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control optimization, and specifically relates to a planning method, system and equipment for hoisting and moving devices. Background Technology

[0002] In traditional lifting operation planning, crane selection and positioning strategies are typically undertaken by experienced engineers based on past experience, site surveys, and subjective judgment. This method relies on personal experience and lacks quantitative scientific calculations, leading to suboptimal selections and potentially causing safety hazards or reduced construction efficiency. Some optimization methods may screen cranes based on basic static parameters such as maximum lifting capacity and working radius, but neglect key factors like the crane's anti-tipping capability, relocation costs, and operating environment, potentially resulting in safety hazards or operational inconveniences with the selected equipment. Some intelligent optimization algorithms may select cranes and positions based on the shortest path principle, but these methods often only consider the shortest travel path or lowest energy consumption, failing to consider factors such as crane stability, lifting load, and anti-tipping capability, potentially leading to selected cranes that do not meet actual operational requirements.

[0003] Traditional methods typically only consider the crane's rated load and working radius, without establishing a precise match between the crane's anti-overturning moment and the lifting task. This may result in insufficient stability of the selected crane under extreme working conditions, posing a risk of overturning. Patent document CN113378455A describes an intelligent optimization method for the lifting process of prefabricated building components. Although it uses the displacement value during frame lifting and the frame speed at the end of lifting as objective functions and calls a genetic algorithm to optimize the solution, it lacks a scientific assessment of anti-overturning capability.

[0004] Existing technologies typically neglect the movement path of the crane from its initial position to the work site, and the resulting time and energy consumption. The selected crane may need to travel long distances, leading to reduced construction efficiency and increased fuel costs. For example, a multi-crane lifting optimization method described in patent document CN110069882A, while employing wireless positioning tags to monitor the location of the lifted object and thus optimizing the multi-crane lifting method, fails to consider equipment movement costs and lacks a quantitative comprehensive evaluation system. Existing technical solutions lack a unified optimization objective and cannot comprehensively consider multiple factors such as safety, stability, and movement costs, resulting in the selected solution failing to achieve global optimum. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and equipment for planning hoisting and moving devices, so as to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for planning a hoisting and moving device is provided, the method comprising the following steps:

[0007] Within the hoisting site to be automatically planned, there are multiple hoisting mobile devices and multiple hoisting tasks. A unified plane coordinate system is established, and the initial position data, overturning moment data, and maximum working radius data of each hoisting mobile device are input. The weight of the hoisting object and the coordinate data of the hoisting point corresponding to each hoisting task are also input.

[0008] By obtaining the weight of the hoisted object, the coordinates of the hoisting point and the moving distance between the initial position of the hoisting mobile device, and the overturning moment of the hoisting mobile device, the overturning redundancy index of each hoisting mobile device under the ideal station distance is calculated. The moving cost coefficient is obtained from the overturning redundancy index. The hoisting mobile device with the highest hoisting efficiency is selected by combining the overturning redundancy index, the moving cost coefficient and the moving distance.

[0009] For a selected hoisting mobile device, the unit vector between the hoisting point coordinates and the initial position of the hoisting mobile device is calculated based on the direction of the line connecting the hoisting point coordinates and the initial position of the hoisting mobile device. Combined with the working radius data of the hoisting mobile device and the unit vector, the ideal station position is generated for the selected hoisting mobile device.

[0010] Furthermore, the hoisting and moving device is a device with hoisting and moving functions, specifically including a crane.

[0011] Furthermore, the value of the anti-overturning moment of the hoisting and moving device is obtained by multiplying the value of the horizontal distance from the center of gravity of the hoisting and moving device to the overturning boundary and the value of the effective counterweight of the hoisting and moving device.

[0012] Furthermore, the working radius data of each hoisting and moving device includes the value of its maximum working radius and the value of its minimum working radius.

[0013] Furthermore, the method for selecting the hoisting mobile device with the highest hoisting efficiency by combining the anti-tilting redundancy index, the moving cost coefficient, and the moving distance is as follows:

[0014] For each hoisting point coordinate, obtain the movement distance between the initial position of each hoisting moving device and that hoisting point coordinate;

[0015] The ideal distance to the coordinates of the hoisting point is the maximum value among the minimum working radii of its various hoisting and moving devices;

[0016] The overturning redundancy index of each hoisting mobile device is obtained by dividing the value of the overturning moment of each hoisting mobile device by the product of the weight of the hoisting object and the ideal standing distance.

[0017] The values ​​of the anti-tilting redundancy index of each hoisting and moving device are normalized, and the variance of the normalized values ​​of the anti-tilting redundancy index of each hoisting and moving device is used as the moving cost coefficient.

[0018] Using the anti-tilting redundancy index of each hoisting mobile device as the numerator and the sum of the product of the mobile cost coefficient and the mobile distance plus one as the denominator, the mobile hoisting efficiency of each hoisting mobile device is calculated, and the hoisting mobile device with the highest mobile hoisting efficiency value is selected for the hoisting task.

[0019] The method described in this invention calculates an anti-tipping redundancy index to quantitatively match the crane's anti-tipping moment with the weight of the lifted object and the operating radius. This ensures that the selected crane not only meets the rated load requirements but also provides sufficient anti-tipping redundancy, guaranteeing operational safety. The anti-tipping redundancy index dynamically adapts to different lifting tasks, avoiding misjudgments caused by traditional rules of thumb and improving the reliability of lifting operations.

[0020] Traditional methods typically ignore the movement cost of the crane from its initial position to the work site, while this method optimizes this cost through a movement cost coefficient. This ensures that the selected crane not only has high safety but also a short movement path, low energy consumption, and higher construction efficiency. This method can dynamically adjust the movement cost weight to adapt to different working conditions. In particular, in complex construction sites where movement costs are high, the method prioritizes cranes with shorter movement distances; while in flat construction sites, the weight of the anti-tilting redundancy index can be appropriately increased to ensure operational safety.

[0021] Traditional methods lack uniformity in calculating parameters for different cranes, including anti-overturning moment and lifting radius, which may lead to incomparable calculation results between different devices. However, this invention normalizes the anti-overturning redundancy index, making the mathematical distribution of the anti-overturning redundancy index between different cranes statistically comparable in terms of the mathematical characteristics of movement cost, thus making the mathematical statistical results more stable and reliable.

[0022] This invention calculates the mobile lifting efficiency, an index that comprehensively considers the safety represented by the anti-tilting redundancy index and the impact of travel distance, ensuring that the selected crane achieves the optimal balance between safety and efficiency. This mobile lifting efficiency is used to score all candidate cranes, resulting in the final selected crane possessing the best overall performance, avoiding suboptimal choices caused by considering only single factors such as shortest path or maximum load capacity.

[0023] The technical method of this invention not only overcomes the problems of traditional hoisting operations, such as reliance on experience, lack of quantitative judgment, and insufficient real-time dynamic optimization capabilities in crane selection and positioning decisions, but also solves the technical challenge of balancing safety and efficiency in hoisting operations. In traditional working conditions, engineers often rely on past experience and simple rules to determine which crane is most suitable for a particular hoisting task, often only considering the equipment's rated load and working radius, while ignoring the crane's anti-tipping capability, the cost required to move the equipment from its initial position to the work site, and the impact of complex environmental factors. This not only easily leads to inappropriate crane selection, increasing the risk of tipping over or equipment damage during hoisting operations, but also affects construction efficiency and increases costs due to the long equipment movement distance.

[0024] This invention constructs a mathematical model to calculate the ratio of the crane's anti-overturning moment to the load moment generated by the lifting task, deriving the anti-overturning redundancy index described in this invention. It then incorporates the actual travel distance of the crane from its initial position to the task point, introducing the travel cost, to calculate the mobile lifting benefits described in this invention. This method quantifies equipment safety redundancy and travel costs, and ensures comparability between different pieces of equipment through normalization processing, thereby scientifically selecting the most suitable crane for performing the lifting task and determining its optimal position.

[0025] Furthermore, the method for generating the ideal station position for the selected hoisting mobile device by combining the working radius data of the hoisting mobile device with the unit vector is as follows:

[0026] For each selected hoisting mobile device, obtain the coordinates of its corresponding hoisting task, obtain the initial position of the selected hoisting mobile device, calculate the unit vector obtained by subtracting the initial position of the selected hoisting mobile device from the coordinates of its corresponding hoisting task, let the value of the ideal station distance be the value of the minimum working radius of the selected hoisting mobile device, and let the coordinates of the ideal station position be the sum of the product of the coordinates of its corresponding hoisting task, the value of its ideal station distance, and the unit vector.

[0027] Furthermore, the coordinates of the ideal station position are the difference between the coordinates of the corresponding hoisting task minus the product of the ideal station distance and the unit vector.

[0028] Furthermore, the specific value of the moving cost coefficient is set to 0.1.

[0029] This invention also provides a hoisting mobile device planning system, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the hoisting mobile device planning method. The hoisting mobile device planning system can run on computing devices such as desktop computers, laptops, handheld computers, and cloud data centers. The runnable system may include, but is not limited to, processors, memory, and server clusters. The processor executes the computer program within the following system units:

[0030] The input unit is used to input the initial position data, anti-overturning moment data, and maximum working radius data of each hoisting and moving device, as well as the weight of the hoisted object and the coordinate data of the hoisting point for each hoisting task.

[0031] The calculation unit is used to calculate the anti-overturning redundancy index of each hoisting mobile device at the ideal station distance by obtaining the weight of the hoisted object, the coordinates of the hoisting point and the moving distance between the initial position of the hoisting mobile device, and the anti-overturning moment of the hoisting mobile device. The moving cost coefficient is obtained from the anti-overturning redundancy index. The hoisting mobile device with the highest hoisting efficiency is selected by combining the anti-overturning redundancy index, the moving cost coefficient and the moving distance.

[0032] The output unit is used to calculate the unit vector between the coordinates of the lifting point and the initial position of the lifting mobile device for the selected lifting mobile device, based on the direction of the line connecting the coordinates of the lifting point and the initial position of the lifting mobile device. Combined with the working radius data of the lifting mobile device in the unit vector, the unit vector is used to generate the output ideal station position for the selected lifting mobile device.

[0033] Correspondingly, the present invention also provides an electronic device, a readable storage medium, and a computer program product:

[0034] An electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a hoisting mobile device planning method and the methods for each step therein.

[0035] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the hoisting mobile device planning method and the methods for each step therein.

[0036] A computer program product includes a computer program that, when executed by a processor, implements the hoisting mobile device planning method and the methods for each step therein.

[0037] The beneficial effects of this invention are as follows: This invention provides a method, system, and equipment for planning lifting mobile devices. By calculating the anti-tilting redundancy index of each lifting mobile device at the ideal station distance, a movement cost coefficient is obtained from the anti-tilting redundancy index. Combining the anti-tilting redundancy index, the movement cost coefficient, and the movement distance, the lifting mobile device with the highest lifting efficiency is selected. For the selected lifting mobile device, based on the direction of the line connecting the lifting point coordinates and the initial position of the lifting mobile device, a unit vector between the lifting point coordinates and the initial position of the lifting mobile device is calculated. Combining the working radius data of the lifting mobile device with the unit vector, an output ideal station position is generated for the selected lifting mobile device. This organically combines the equipment's load-bearing capacity, stability, and movement cost, achieving globally optimal decision-making and ensuring that the selected crane in complex lifting sites is both safe and efficient. Attached Figure Description

[0038] The above and other features of the present invention will become more apparent from the detailed description of the embodiments shown in conjunction with the accompanying drawings. In the accompanying drawings, the same reference numerals denote the same or similar elements. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort. In the drawings:

[0039] Figure 1 The diagram shown is a flowchart of a method for planning a hoisting and moving device.

[0040] Figure 2 The figure shown is a system structure diagram of a hoisting and moving device planning system. Detailed Implementation

[0041] The following will provide a clear and complete description of the concept, specific structure, and technical effects of the present invention in conjunction with the embodiments and accompanying drawings, so as to fully understand the purpose, solution, and effects of the present invention. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0042] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0043] like Figure 1 The diagram shown is a flowchart of a hoisting and moving device planning method according to the present invention. The following is a summary of the method. Figure 1 This paper describes a method, system, and equipment for planning a hoisting and moving device according to an embodiment of the present invention.

[0044] This invention proposes a planning method for hoisting and moving devices, the method specifically including the following steps:

[0045] Input the initial position data, anti-overturning moment data, and maximum working radius data of each hoisting and moving device; input the weight of the hoisting object and the coordinate data of the hoisting point for each hoisting task.

[0046] By obtaining the weight of the hoisted object, the coordinates of the hoisting point and the moving distance between the initial position of the hoisting mobile device, and the overturning moment of the hoisting mobile device, the overturning redundancy index of each hoisting mobile device under the ideal station distance is calculated. The moving cost coefficient is obtained from the overturning redundancy index. The hoisting mobile device with the highest hoisting efficiency is selected by combining the overturning redundancy index, the moving cost coefficient and the moving distance.

[0047] For a selected hoisting mobile device, the unit vector between the hoisting point coordinates and the initial position of the hoisting mobile device is calculated based on the direction of the line connecting the hoisting point coordinates and the initial position of the hoisting mobile device. Combined with the working radius data of the hoisting mobile device and the unit vector, the ideal station position is generated for the selected hoisting mobile device.

[0048] Furthermore, the hoisting and moving device is a device with hoisting and moving functions, specifically including a crane.

[0049] Furthermore, the value of the anti-overturning moment of the hoisting and moving device is obtained by multiplying the value of the horizontal distance from the center of gravity of the hoisting and moving device to the overturning boundary and the value of the effective counterweight of the hoisting and moving device.

[0050] Furthermore, the working radius data of each hoisting and moving device includes the value of its maximum working radius and the value of its minimum working radius.

[0051] Furthermore, the method for selecting the hoisting mobile device with the highest hoisting efficiency by combining the anti-tilting redundancy index, the moving cost coefficient, and the moving distance is as follows:

[0052] For each hoisting point coordinate, obtain the movement distance between the initial position of each hoisting moving device and that hoisting point coordinate;

[0053] The ideal distance to the coordinates of the hoisting point is the maximum value among the minimum working radii of its various hoisting and moving devices;

[0054] The overturning redundancy index of each hoisting mobile device is obtained by dividing the value of the overturning moment of each hoisting mobile device by the product of the weight of the hoisting object and the ideal standing distance.

[0055] The values ​​of the anti-tilting redundancy index of each hoisting and moving device are normalized, and the variance of the normalized values ​​of the anti-tilting redundancy index of each hoisting and moving device is used as the moving cost coefficient.

[0056] Using the anti-tilting redundancy index of each hoisting mobile device as the numerator and the sum of the product of the mobile cost coefficient and the mobile distance plus one as the denominator, the mobile hoisting efficiency of each hoisting mobile device is calculated, and the hoisting mobile device with the highest mobile hoisting efficiency value is selected for the hoisting task.

[0057] In traditional working conditions, engineers often rely on past experience and simple rules to determine which crane is best suited for a particular lifting task. They typically only consider the equipment's rated load and working radius, neglecting factors such as the crane's anti-tipping capability, the cost of moving the equipment from its initial position to the work site, and the impact of complex environmental factors. This not only easily leads to inappropriate crane selection, increasing the risk of tipping over or equipment damage during lifting operations, but also affects construction efficiency and increases costs due to the long distances the equipment needs to travel.

[0058] This invention constructs a mathematical model to calculate the ratio of the crane's anti-overturning moment to the load moment generated by the lifting task, deriving an anti-overturning redundancy index. This index is then incorporated into the actual travel distance of the crane from its initial position to the task point, introducing the travel cost, to calculate the mobile lifting benefits described in this invention. This method quantifies equipment safety redundancy and travel costs, and ensures comparability between different pieces of equipment through normalization, thereby scientifically selecting the most suitable crane for the lifting task and determining its optimal position.

[0059] Furthermore, the method for generating the ideal station position for the selected hoisting mobile device by combining the working radius data of the hoisting mobile device with the unit vector is as follows:

[0060] For each selected hoisting mobile device, obtain the coordinates of its corresponding hoisting task, obtain the initial position of the selected hoisting mobile device, calculate the unit vector obtained by subtracting the initial position of the selected hoisting mobile device from the coordinates of its corresponding hoisting task, let the value of the ideal station distance be the value of the minimum working radius of the selected hoisting mobile device, and let the coordinates of the ideal station position be the sum of the product of the coordinates of its corresponding hoisting task, the value of its ideal station distance, and the unit vector.

[0061] Furthermore, the coordinates of the ideal station position are the difference between the coordinates of the corresponding hoisting task minus the product of the ideal station distance and the unit vector.

[0062] Furthermore, the specific value of the moving cost coefficient is set to 0.1.

[0063] In the embodiments provided by this invention, within the selected area to be automatically planned, such as a hoisting construction site, there are several hoisting mobile devices. These hoisting mobile devices can be equipment with hoisting and moving functions, including cranes. The data for each hoisting mobile device includes its initial position coordinates, overturning moment value, and maximum working radius value. Each crane has its maximum working radius R and minimum working radius r, which are the maximum and minimum distances that the crane can reach when operating from a fixed position.

[0064] Within the hoisting site, there are several hoisting tasks, and the data for these hoisting tasks includes the mass of the hoisted object and the coordinates of the hoisting point.

[0065] In such a complex hoisting site, based on real-time physical data, the most suitable crane for a particular hoisting task is precisely selected, and the optimal position of that crane on site is calculated.

[0066] By obtaining the weight L of the hoisted object, the moving distance Dmove between the task point and the crane's initial position, and the crane's anti-tipping moment Mbase, the anti-tipping redundancy index S of each crane under the ideal working distance R is calculated. Combining the anti-tipping redundancy index S, the moving cost coefficient k, and the moving distance Dmove, the crane with the highest moving hoisting efficiency is determined.

[0067] For the selected crane, calculate the unit vector between the task point and the crane's initial position based on the direction of the line connecting the task point and the crane's initial position.

[0068] Calculate an ideal station location, which is usually a location near the task point that meets the minimum safe distance R.

[0069] Specifically, the coordinates of each hoisting task are used as task points to obtain the moving distance Dmove of each crane from its initial position to the task point;

[0070] Provided that each crane can be stationed around the task point, the ideal stationing distance is the maximum value among the minimum working radii of each crane, denoted as Rs. In some embodiments, the ideal stationing distance is Rs = 5m.

[0071] The anti-overturning moment of each crane is divided by the product of the weight L of the load to be lifted in the lifting task and the ideal standing distance Rs, and the anti-overturning redundancy index S of each crane is calculated.

[0072] The anti-tipping redundancy index is a measure of the anti-tipping capacity provided by a crane, including but not limited to the ratio between the anti-tipping moment generated by the base and the overturning moment generated by the lifting load at a safe distance. It measures the redundancy of the crane's anti-tipping capacity relative to load requirements during lifting operations. A higher value indicates a greater additional safety reserve, enabling the crane to perform lifting tasks more stably.

[0073] The anti-tilting redundancy index S of each crane is normalized, and the variance of the normalized anti-tilting redundancy index S of each crane is used as the moving cost coefficient k. In some embodiments, the moving cost coefficient can preferably be k=0.1.

[0074] Using the anti-tilting redundancy index S of each crane as the numerator, and the sum of 1 plus the product of the moving cost coefficient and the moving distance Dmove as the denominator, the moving lifting efficiency of each crane is calculated, and the crane with the highest moving lifting efficiency value is selected for the lifting task.

[0075] The mobile lifting benefit is calculated by measuring the crane's load-bearing capacity, anti-tilting redundancy (representing dynamic stability), and the cost of moving it from its initial position to the work site. This results in an index reflecting the degree of matching between the crane and the current lifting task. It not only reflects the matching degree between the crane and the lifting task but also emphasizes the overall benefits of moving the crane from its initial position to the work site. A higher value indicates that the equipment not only has redundancy and safety margins in load-bearing capacity and stability but also requires lower costs and time during movement, making it more suitable for the current lifting task.

[0076] For each selected crane, the coordinates of its corresponding lifting task are obtained as Ptask, and the coordinates of the initial position of the selected crane are obtained as Pcrane. The unit vector of the vector Vec obtained by subtracting Pcrane from Ptask is calculated as v. The ideal station position Pstation is calculated by multiplying the coordinates of Ptask, the value of r, and the vector v, where r is the minimum operating distance of the selected crane. In some embodiments, r can be the minimum safe distance, which is 5m here.

[0077] The ideal station position Pstation is calculated as follows: the coordinates of Pstation are equal to the coordinates of Ptask minus the value of r and the product of vector v.

[0078] The anti-overturning moment Mbase of the crane is generally determined through static calculations based on the crane's structural and load diagrams. In some embodiments, the horizontal distance d (m) from the center of gravity to the overturning boundary of each crane is obtained, and the effective counterweight of the crane is obtained as Wc (N). Then, the anti-overturning moment can be approximately calculated as Mbase = Wc × d. The horizontal distance d (m) from the center of gravity to the overturning boundary and the effective counterweight of the crane as Wc (N) are usually determined by the crane's design parameters, derived from the calculation of the crane's self-weight and distribution, the reaction force such as the moment generated by the counterweight block, and the friction between the base and the ground and the support area. These values ​​are generally provided by the manufacturer through detailed structural analysis.

[0079] In some embodiments, three mobile cranes A, B, and C on site are monitored, and their initial positions, anti-tipping capabilities, and maximum working radii are obtained. There are two lifting tasks, each with its own load mass and lifting point coordinates, and a Cartesian coordinate system is established based on this data. To ensure safety, a horizontal distance of at least m meters must be maintained during the lifting process; this is the minimum safe distance between the crane and the load. This serves as a constraint for automatic planning, initiating data calculations. Precise measuring equipment is provided on site, including but not limited to laser rangefinders, GPS, BIM models, and sensors necessary for data acquisition, storage, and transmission, for real-time collection of crane position, load weight, and environmental data.

[0080] In addition to using laser rangefinders and GPS to determine the initial positions of each crane on site, BIM model data can be used for auxiliary correction. A wireless sensor network is employed to collect real-time data on the weight of the hoisted object, including data from load cells, environmental parameters such as wind speed, temperature, and humidity, and the current status of the cranes, including boom angle, equipment vibration, and operating current. This data is transmitted to the central control platform in real time. Simultaneously, high-definition cameras and drones are deployed on-site to monitor the actual obstacles and terrain conditions between the cranes and the hoisting task, providing a more accurate environmental model for subsequent calculations.

[0081] Before data enters the automatic planning system, it needs to be validated. For example, it should be verified whether GPS data has abnormal drift and whether laser ranging data is consistent with the measurements in the BIM model. All data should be unified into a standard Cartesian coordinate system, such as a unified BIM coordinate system, and abnormal data should be filtered to ensure the accuracy and reliability of the data input into the model.

[0082] According to some embodiments, among multiple cranes, the anti-tilting redundancy index of each device when lifting the same load at the task site is calculated, and the most suitable device for the lifting task is determined by combining the movement cost of the crane from its initial position to the task site.

[0083] In one embodiment, for hoisting task 1, the known mass of the object to be hoisted is 2000 kg. This value can be obtained on-site using a weighing sensor or from the design documents. Multiplying the mass of 2000 kg by the gravitational acceleration 9.81 (m / s²), the weight of the object to be hoisted is approximately 19,620 N. The hoisting point coordinates for hoisting task 1 are (5,5); the hoisting point for hoisting task 2 is (18,-2). These data can be obtained directly from the BIM model or on-site survey data. For the three cranes, the on-site engineer obtained the following data from the equipment manuals: Crane A, initial position (0,0), overturning moment 300,000 N·m, maximum working radius 15 meters; Crane B, initial position (20,0), overturning moment 250,000 N·m, maximum working radius 12 meters; Crane C, initial position (10,10), overturning moment 350,000 N·m, maximum working radius 20 meters.

[0084] Preferably, during hoisting operations, a safety distance of at least 5 meters must be maintained between the crane and the hoisting point, a value determined according to on-site safety regulations.

[0085] In one embodiment, a laser rangefinder or GPS system is used on-site to calculate the straight-line distance from each crane's initial position to the task point. For crane A, the distance from (0,0) to (5,5) is measured to be approximately 7.07 meters; that is, for crane B, the distance from (20,0) to (5,5) is approximately 15.81 meters; and for crane C, the distance from (10,10) to (5,5) is approximately 7.07 meters. These distances not only reflect the length the cranes need to move but also relate to the preparation time before operation and the energy consumption during the movement.

[0086] In some embodiments related to calculating the anti-tipping redundancy index, it is assumed that the crane can reach an ideal position at the task site during hoisting, with a horizontal distance of exactly 5 meters from the hoisting point. At this distance, the load moment generated by the hoisted object can be calculated by multiplying the weight of the hoisted object by 5 meters. Taking hoisting task 1 as an example, the load moment generated by the hoisted object is approximately: Load moment = 19,620 (N) × 5 (M) = 98,100 (N·M). Next, for each crane, the anti-tipping redundancy index is obtained by dividing its anti-tipping moment by this load moment. For crane A, 300,000 (N·m) divided by 98,100 (N·m) yields approximately 3.06; for crane B, 250,000 N·m divided by 98,100 N·m yields approximately 2.55; for crane C, 350,000 N·m divided by 98,100 N·m yields approximately 3.57. These calculations show that, for the same working distance, crane C has the highest anti-tilting redundancy index, followed by crane A, and crane B has the lowest.

[0087] In some embodiments, to reflect the additional cost of moving the crane from its initial position to the task site, this invention introduces a moving lifting benefit. The variance of the normalized value of the anti-tilting redundancy index S of each crane is used as the moving cost coefficient k. For convenience, k is preferably set to 0.1, meaning that the cost increases by 10% for every 1 meter moved. When calculating the moving lifting benefit of each crane, the anti-tilting redundancy index is used, divided by 1, and then k multiplied by the moving distance. For crane A, its moving distance is approximately 7.07 meters, so the moving lifting efficiency is 3.06 divided by (1 + 0.1 × 7.07) ≈ 3.06 divided by 1.707, resulting in approximately 1.79. For crane B, its moving distance is approximately 15.81 meters, and the moving lifting efficiency is 2.55 divided by (1 + 0.1 × 15.81) ≈ 2.55 divided by 2.581, resulting in approximately 0.99. For crane C, its moving distance is also approximately 7.07 meters, and the moving lifting efficiency is 3.57 divided by 1.707, resulting in approximately 2.09. Based on the numerical distribution of the moving lifting efficiency values, crane C has the highest moving lifting efficiency value, indicating that for lifting task 1, crane C is the most suitable choice to perform the lifting task.

[0088] In some embodiments, after selecting a suitable crane, the optimal position of the crane at the work site is determined so that it can ensure a safe distance from the lifting point, such as at least about 5 meters, while minimizing the load torque during the lifting process and ensuring the smooth movement of the lifted object.

[0089] In specific embodiments of some site location calculation methods, the precise coordinates of the task point and the initial position of the crane can be provided by on-site surveying or a BIM model. The vector from the crane's initial position to the task point can be calculated and normalized to obtain a directional unit vector. For example, for lifting task 1 and crane C, the task point coordinates are (5,5), and the initial position of crane C is (10,10), with a difference vector of (-5,-5); its length is approximately 7.07 meters, and the normalized unit vector is approximately (-0.707,-0.707). According to safety requirements, the distance between the task point and the crane is fixed at 5 meters. The site location can be selected in the direction extending 5 meters from the task point along the unit vector. Since the specific choice of which side to extend depends on the site environment, such as the presence of obstacles and terrain flatness, the site engineer can decide whether to extend in the forward or reverse direction based on the actual situation.

[0090] In a specific calculation example, for hoisting task 1, when the task point is located at (5,5), the initial position of crane C is (10,10). The difference vector between the two is (-5,-5), which, after normalization, is approximately (-0.707,-0.707). If the chosen position is 5 meters forward from the task point, the position coordinates are (5,5) plus (-0.707×5,-0.707×5), i.e., (5-3.54,5-3.54), approximately (1.46,1.46). Another option is to choose a position extending backward from the task point, i.e., (5,5) plus (+3.54,+3.54), approximately (8.54,8.54). The on-site engineer will determine the safest and less costly position based on factors such as terrain, obstacle distribution, and road conditions, ultimately determining the optimal position. Assuming that after comprehensive evaluation, (1.46,1.46) is chosen as the optimal position.

[0091] For hoisting task 2, the task point is located at (18, -2), and the suitable crane for this task is crane B, whose initial position is (20, 0). The difference vector (18-20, -2-0) = (-2, -2) is calculated, with a length of approximately 2.83 meters. After normalization, the unit vector is approximately (-0.707, -0.707). If a safety distance of 5 meters is used, extending 5 meters from the task point yields the station position (18, -2) plus (-0.707×5, -0.707×5), approximately (18-3.54, -2-3.54), or (14.46, -5.54). However, the site conditions will determine whether to choose this side or the other, ultimately determining the optimal station coordinates.

[0092] For crane selection, the initial position and straight-line distance between each crane and the task point can be determined using on-site measuring equipment. Then, the weight of the object to be lifted can be calculated based on its mass; for example, 2000 kg corresponds to approximately 19,620 Newtons. Assuming a working distance of m meters is maintained during lifting, the load torque generated at that distance can be calculated. The crane's anti-overturning moment data can also be used, for example, crane C has an anti-overturning moment of 350,000 (Newton-meters), to calculate the anti-overturning redundancy index, which is the anti-overturning moment divided by the load torque. Combining the crane's travel distance from the initial position to the task site and a preset coefficient k, a comprehensive score is calculated. The crane with the highest comprehensive score is selected as the optimal equipment for the lifting task; for example, crane C is selected for lifting task 1.

[0093] The direction vector between the task point and the crane's initial position can be calculated and normalized based on the coordinate difference between the two. Using a safe distance of m meters as a baseline, candidate positions are calculated by extending m meters forward or backward from the task point along this unit vector. The optimal position is then determined by considering factors such as on-site obstacles, terrain conditions, and movement costs. Note that since the crane's initial position is (10, 10), to reduce the movement distance while ensuring the load remains within the safe operating range, the position can be adjusted to the opposite side, m meters away from the task point. Another approach is to calculate the direction from the crane C's initial position to the task point and select a point m meters away from the task point along the extended direction of the line connecting the task point and the crane C's initial position. For example, for lifting task 1 and crane C, candidate positions (1.46, 1.46) or (8.54, 8.54) can be calculated, and the optimal solution is selected after on-site evaluation.

[0094] The specific embodiments described above detail how to select the most suitable crane and determine the optimal position based on actual on-site physical data by calculating the weight of the load, the travel distance, the load torque, and the crane's anti-overturning capability. This data-driven optimization process not only considers static parameters but also incorporates travel costs and the actual on-site environment, ensuring that the lifting operation achieves optimal safety and operational efficiency.

[0095] The hoisting mobile device planning system runs on any computing device, such as a desktop computer, laptop computer, handheld computer, or cloud data center. The computing device includes a processor, a memory, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps in the hoisting mobile device planning method. The runnable system may include, but is not limited to, a processor, a memory, and a server cluster.

[0096] An embodiment of the present invention provides a hoisting and moving device planning system, such as... Figure 2As shown, a hoisting mobile device planning system of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-described hoisting mobile device planning method embodiment. The processor executes the computer program in the following system units:

[0097] The input unit is used to input the initial position data, anti-overturning moment data, and maximum working radius data of each hoisting and moving device, as well as the weight of the hoisted object and the coordinate data of the hoisting point for each hoisting task.

[0098] The calculation unit is used to calculate the anti-overturning redundancy index of each hoisting mobile device at the ideal station distance by obtaining the weight of the hoisted object, the coordinates of the hoisting point and the moving distance between the initial position of the hoisting mobile device, and the anti-overturning moment of the hoisting mobile device. The moving cost coefficient is obtained from the anti-overturning redundancy index. The hoisting mobile device with the highest hoisting efficiency is selected by combining the anti-overturning redundancy index, the moving cost coefficient and the moving distance.

[0099] The output unit is used to calculate the unit vector between the coordinates of the lifting point and the initial position of the lifting mobile device for the selected lifting mobile device, based on the direction of the line connecting the coordinates of the lifting point and the initial position of the lifting mobile device. Combined with the working radius data of the lifting mobile device in the unit vector, the unit vector is used to generate the output ideal station position for the selected lifting mobile device.

[0100] In order to better unify the linear relationship and probabilistic connection between physical quantities with different units of measurement, dimensionless processing can be performed on different physical quantities.

[0101] Preferably, all undefined variables in this invention, if not explicitly defined, can be manually set thresholds.

[0102] The hoisting mobile device planning system described above can run on computing devices such as desktop computers, laptops, handheld computers, and cloud data centers. The hoisting mobile device planning system includes, but is not limited to, a processor and a memory. Those skilled in the art will understand that the examples described are merely illustrations of a hoisting mobile device planning method, system, and device, and do not constitute a limitation on such a method, system, and device. It may include more or fewer components, or combinations of certain components, or different components. For example, the hoisting mobile device planning system may also include input / output devices, network access devices, buses, etc.

[0103] The present invention also provides an electronic device, a readable storage medium, and a computer program product:

[0104] An electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a hoisting mobile device planning method and the methods for each step therein.

[0105] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the hoisting mobile device planning method and the methods for each step therein.

[0106] A computer program product includes a computer program that, when executed by a processor, implements the hoisting mobile device planning method and the methods for each step therein.

[0107] The term "electronic device" is intended to refer to various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also refer to various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0108] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0109] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0110] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0111] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0112] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0113] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0114] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete component gate circuits, transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the hoisting and moving device planning system, connecting various sub-areas of the entire hoisting and moving device planning system via various interfaces and lines.

[0115] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the hoisting mobile device planning method, system, and equipment by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0116] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0117] This invention provides a method, system, and equipment for planning hoisting mobile devices. By calculating the anti-tilting redundancy index of each hoisting mobile device at an ideal station distance, a movement cost coefficient is obtained from the anti-tilting redundancy index. Combining the anti-tilting redundancy index, the movement cost coefficient, and the movement distance, the hoisting mobile device with the highest hoisting efficiency is selected. For the selected hoisting mobile device, based on the direction of the line connecting the hoisting point coordinates and the initial position of the hoisting mobile device, a unit vector between the hoisting point coordinates and the initial position of the hoisting mobile device is calculated. Combining the working radius data of the hoisting mobile device with the unit vector, an output ideal station position is generated for the selected hoisting mobile device. This organically combines the equipment's load-bearing capacity, stability, and movement cost, achieving globally optimal decision-making and ensuring that the selected crane is both safe and efficient in complex hoisting scenarios.

[0118] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for planning hoisting mobile devices, wherein multiple hoisting mobile devices and multiple hoisting tasks exist within a hoisting site to be automatically planned, characterized in that, The method includes the following steps: Establish a unified plane coordinate system, input the initial position data, anti-overturning moment data and maximum working radius data of each hoisting and moving device, and input the weight of the hoisting object and the coordinate data of the hoisting point for each hoisting task; By obtaining the weight of the hoisted object, the coordinates of the hoisting point and the moving distance between the initial position of the hoisting mobile device, and the overturning moment of the hoisting mobile device, the overturning redundancy index of each hoisting mobile device under the ideal station distance is calculated. The moving cost coefficient is obtained from the overturning redundancy index. The hoisting mobile device with the highest hoisting efficiency is selected by combining the overturning redundancy index, the moving cost coefficient and the moving distance. For a selected hoisting mobile device, the unit vector between the hoisting point coordinates and the initial position of the hoisting mobile device is calculated based on the direction of the line connecting the hoisting point coordinates and the initial position of the hoisting mobile device. The working radius data of the hoisting mobile device is then combined with the unit vector to generate the ideal station position for the selected hoisting mobile device. The method for selecting the hoisting mobile device with the highest hoisting efficiency by combining the anti-tilting redundancy index, the moving cost coefficient, and the moving distance is as follows: For each hoisting point coordinate, obtain the movement distance between the initial position of each hoisting moving device and that hoisting point coordinate; The ideal distance to the coordinates of the hoisting point is the maximum value among the minimum working radii of its various hoisting and moving devices; The overturning redundancy index of each hoisting mobile device is obtained by dividing the value of the overturning moment of each hoisting mobile device by the product of the weight of the hoisting object and the ideal standing distance. The values ​​of the anti-tilting redundancy index of each hoisting and moving device are normalized, and the variance of the normalized values ​​of the anti-tilting redundancy index of each hoisting and moving device is used as the moving cost coefficient. Using the anti-tilting redundancy index of each hoisting mobile device as the numerator and the sum of the product of the mobile cost coefficient and the mobile distance plus one as the denominator, the mobile hoisting efficiency of each hoisting mobile device is calculated, and the hoisting mobile device with the highest mobile hoisting efficiency value is selected for the hoisting task. The method for generating the ideal station position for the selected hoisting mobile device by combining the working radius data of the hoisting mobile device with the unit vector is as follows: For each selected hoisting mobile device, obtain the coordinates of its corresponding hoisting task, obtain the initial position of the selected hoisting mobile device, calculate the unit vector obtained by subtracting the initial position of the selected hoisting mobile device from the coordinates of its corresponding hoisting task, let the value of the ideal station distance be the value of the minimum working radius of the selected hoisting mobile device, and let the coordinates of the ideal station position be the sum of the product of the coordinates of its corresponding hoisting task, the value of its ideal station distance, and the unit vector.

2. The hoisting and moving device planning method according to claim 1, characterized in that, in, The hoisting and moving device is a piece of equipment with hoisting and moving functions, specifically including a crane.

3. The method for planning a hoisting and moving device according to claim 1, characterized in that, in, The overturning moment of the hoisting and moving device is obtained by multiplying the horizontal distance from the center of gravity of the hoisting and moving device to the overturning boundary and the effective counterweight of the hoisting and moving device.

4. The hoisting and moving device planning method according to claim 3, characterized in that, in, The working radius data for each hoisting and moving device includes the value of its maximum working radius and the value of its minimum working radius.

5. The hoisting and moving device planning method according to claim 1, characterized in that, in, The coordinates of the ideal station position, or the difference between the coordinates of the corresponding hoisting task minus the product of the ideal station distance and the unit vector, are used to determine the ideal station position.

6. The method for planning a hoisting and moving device according to claim 1, characterized in that, in, Let the specific value of the moving cost coefficient be 0.

1.

7. A hoisting and moving device planning system, characterized in that, The hoisting mobile device planning system operates on any computing device, such as a desktop computer, a laptop computer, or a cloud data center. The computing device includes a processor, a memory, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps in the hoisting mobile device planning method as described in any one of claims 1 to 6.

8. An electronic device, comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.

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