Intelligent building hoisting and obstacle avoidance system

Abstract

The invention discloses an intelligent building hoisting and obstacle avoidance system which comprises a building information model (BIM) technology, a 3D scanning technology, a photogrammetry method, a sensor and a monitoring system. Through the BIM technology, the actual hoisting process can be simulated on a computer, which is beneficial to optimizing the construction scheme, reducing resource waste and accelerating the construction progress; scanning nearby physical structures by using a 3D technology, and constructing a virtual model at the same time; the photogrammetry method is a technology for reconstructing a three-dimensional model of an object by taking pictures of the object from multiple angles and then analyzing the pictures by using software, and the 3D scanning technology and the photogrammetry method supplement each other. The algorithm regulation and control unit is used for controlling the mechanical arm to accurately avoid the obstacle through an intelligent algorithm, and when the obstacle cannot be avoided, speed reduction is conducted by reducing kinetic energy, and the length of the telescopic arm is changed; the safety and efficiency of the hoisting operation are effectively improved, and particularly, powerful technical support is provided for the hoisting operation in the aspects of obstacle avoidance and speed reduction.

Description

Technical Field

[0001] The present invention relates to the field of intelligent obstacle avoidance and hoisting, and in particular to an intelligent building hoisting and obstacle avoidance system. Background Art

[0002] In the modern industrial field, hoisting operations are widely used in many scenarios such as construction, port loading and unloading, and heavy machinery manufacturing. The safety and efficiency of the operation are crucial. Traditional hoisting operations face many challenges in risk control and efficiency improvement.

[0003] On the one hand, there is a lack of accurate and efficient means for risk assessment. In the past, it was difficult to accurately calculate the probability of collision during the lifting process, and usually relied on empirical judgment, which failed to fully utilize the complex spatial information of the construction site. For example, in a complex construction site, there are a large number of building structures, equipment, and personnel flow. Traditional methods are difficult to accurately predict the possibility of collision between the robot arm and these obstacles, resulting in frequent safety hazards.

[0004] On the other hand, in terms of energy consumption and efficiency, traditional lifting operations cannot effectively balance the kinetic energy consumption and operating speed of the robotic arm. The operation of the robotic arm often fails to be intelligently optimized according to the actual working conditions, and lacks a scientific basis for the coordinated control of quality and speed, resulting in excessive energy consumption and low operating efficiency.

[0005] With the continuous development of science and technology, the emergence of technologies such as BIM (Building Information Modeling) technology, 3D scanning technology and photogrammetry has provided new opportunities to solve these problems. However, there is currently no complete system to effectively integrate these technologies and apply them to the optimization of lifting operations. An innovative method is urgently needed to fill this technological gap and minimize the risks of lifting operations and maximize efficiency. Summary of the invention

[0006] Purpose of the invention: To solve the problems mentioned in the background technology, the present invention discloses an intelligent building hoisting and obstacle avoidance system, which obtains detailed 3D information of the target building through the combination of BIM (Building Information Modeling) technology, 3D scanning technology and photogrammetry, and gives the best solution between reducing kinetic energy and changing the length of the telescopic arm through the GOOSE algorithm, thereby minimizing the risk of hoisting operations and improving operating efficiency.

[0007] Technical solution:

[0008] The present invention discloses an intelligent building hoisting and obstacle avoidance system, the system comprising an engineering crane, a sensor and a monitoring system and an algorithm control module;

[0009] The engineering crane comprises a lifting mechanism, and the lifting mechanism comprises a main arm and a secondary arm which can be retracted by power, and is used to control the lifting operation range and optimize the load distribution of the lifting operation;

[0010] The sensor and monitoring system integrates BIM technology, 3D scanning technology and photogrammetry to obtain target building data and establish a detailed 3D model;

[0011] The algorithm control module integrates the GOOSE optimization algorithm, which provides the best solution between reducing kinetic energy and changing the length of the telescopic arm, thereby minimizing the risk of hoisting operations and improving operation efficiency;

[0012] The system operation process is as follows:

[0013] The sensor and monitoring system obtains the target building data through 3D scanning technology and photogrammetry, and sends the detailed 3D model information to the algorithm control module. The algorithm control module outputs the best telescopic arm solution to the lifting mechanism to assist the lifting operation.

[0014] Furthermore, the engineering crane also includes a base support part and a vehicle operation part. The base support part includes a chassis that can be driven on the road and frame legs. The chassis also provides power for the vehicle operation through a central rotating body. The vehicle operation part includes a slewing mechanism composed of a slewing support, a slewing reducer, and a slewing hydraulic system that enables the vehicle to achieve a full 360-degree rotation, and a luffing mechanism composed of a luffing cylinder and a luffing hydraulic system.

[0015] Furthermore, the lifting mechanism also includes a winch reducer, a lifting wire rope and a lifting hydraulic system, as well as a turntable, a counterweight, an on-board operating room and an electrically controlled lighting system.

[0016] Furthermore, the GOOSE optimization algorithm provides the best solution between reducing kinetic energy and changing the length of the telescopic arm. The optimization process is as follows:

[0017] Initialize the population and evaluate its performance according to a predetermined objective function, the objective function formula is as follows:

[0018] The goal is to minimize the risk and improve the efficiency of lifting operations, which can be defined as:

[0019] f(x) Min =ω 1 *R+ω 2 *T+ω 3 *E

[0020] ω 1 +ω 2 +ω 3 =1

[0021] R=P collision *d safe

[0022]

[0023] Among them, f(x) Min is to minimize the risk of hoisting operation, R is the risk assessment index, T is the total time of the hoisting operation obstacle avoidance system, E is the energy consumption index, including the kinetic energy and operating energy consumption of the robot arm, ω 1 ,ω 2 ,ω 3 is the weight coefficient, which is used to balance the importance of risk, time and energy consumption; R is the risk assessment index, P collision is the collision probability, which is calculated by using BIM technology based on the relevant data provided by 3D scanning technology and photogrammetry. safe is the obstacle avoidance safety distance, E is the energy consumption index; m is the mass of the robot arm, v is the speed of the robot arm, T detect is the time to detect obstacles, T calculate is the time to calculate the obstacle avoidance path, T execute is the time to perform the obstacle avoidance operation;

[0024] Set constraints and bring the initial population into the objective function:

[0025] In the development phase, we will find the weight of the stone that the goose stores in its feet. The variable is randomly selected in each iteration and ranges from 5 to 25 kg:

[0026] S_W it =randi([5,25],1,1)=d safe

[0027] T_o_A_O it =rand(1,dim)

[0028] T_o_A_S it =rand(1,dim)

[0029] Among them, S_W it is the random stone weight, which is a random number between 5 and 25, i.e. the obstacle avoidance safety distance d of the lifting system safe 5-25dm; randi is a function used to generate a random integer, T_o_A_O it is the time to detect obstacles, dim is the dimension, T_o_A_S it It is the sum of the time to calculate the obstacle avoidance path and the time to perform the obstacle avoidance operation;

[0030] The obstacle avoidance safety distance of the lifting system is used as input, and the kinetic energy and the length of the telescopic arm are used as output. Finally, the risk of minimizing the lifting operation is obtained. It is judged whether the output values ​​are the same twice. If so, exit. Otherwise, repeat the above steps and finally output the risk of minimizing the lifting operation.

[0031] Furthermore, the objective function constraints are as follows:

[0032] Obstacle avoidance safety distance: 5≤d safe ≤25

[0033] Response time: T response ≤T max

[0034] Kinetic energy reduction: K new ≤K old

[0035] Telescopic arm length adjustment: L new ≥L min

[0036] Among them, d safe is the obstacle avoidance safety distance; T response is the response time of the obstacle avoidance system; T max is the maximum allowed response time; K new is the kinetic energy after deceleration; K old is the original kinetic energy; L new is the adjusted telescopic arm length; L min is the minimum allowable telescopic arm length.

[0037] Furthermore, the obstacle avoidance response time is calculated as follows:

[0038]

[0039] Among them, T_T is the total time required to propagate and reach individual geese in the group during the entire iteration; T_A is the average time required, that is, the obstacle avoidance response time; in order to protect and wake up the geese in the team, these equations should be calculated:

[0040]

[0041] Where F_F_S is the speed of free fall; P collision The collision probability is calculated by BIM technology based on the relevant data provided by 3D scanning technology and photogrammetry; the value of the variable pro is randomly selected from the range of [0,1]; considering that the value of the variable pro is greater than 0.2 and S_W it Greater than or equal to 12;

[0042] D_S_T it =S_S*T_o_A_S it

[0043] Among them, D_S_T it is the distance that sound travels, S_S is the speed of sound in air, which is 343.2 meters per second; T_o_A_Sit is the time it takes for the sound to travel;

[0044] D_G it =0.5*D_S_T it

[0045] D_Git is the distance between the guard goose and another goose that is resting or eating; D_S_Tit is the distance the sound travels;

[0046] X (it+1) =F_F_S+D_G it *T_A^ 2

[0047]

[0048] Among them, X it is the optimal individual position; F_F_S is the speed of the falling object, that is, the speed of the robot arm; if S_W it If it is less than 12 and pro is less than or equal to 0.2, then find a new X, T_o_a_O it is the time it will take to reach the object.

[0049] Furthermore, the search step size is optimized and improved, so that a new X is found. The improved formula is as follows:

[0050] H=κ×e -(β×loop) / Max_it

[0051] X (it+1) =F_F_S*H*D_G it *T_A^ 2 *Coe

[0052] Among them, κ is the step size control factor, β is the exponential control factor, loop is the current number of iterations, Max_it is the maximum number of iterations; Coe is a random variable, its value is between 0 and 1;

[0053] In the exploration phase, if one of the geese wakes up and starts screaming to protect all the individuals in the group, the exploration phase begins. If the collision probability P collision If the value of is less than 0.5, the following equation applies:

[0054] X (it+1) =randn(1,dim)*(M_T*alpha)+Best_pos

[0055]

[0056] Among them, dim is the number of problem dimensions; Best_pos is the best position found in the search area; the value of the variable alpha ranges from 2 to 0, and this value decreases significantly with each iteration in the loop, that is, the kinetic energy of the robot gradually decreases until it stops; loop is the current number of iterations, and Max_it is the maximum number of iterations allowed by the algorithm.

[0057] Beneficial effects:

[0058] 1. The present invention uses BIM technology, 3D scanning technology and photogrammetry to accurately obtain target building data, and combines scientifically set obstacle avoidance safety distances and strict constraints to further improve operational safety, reduce equipment damage, casualties and economic losses caused by accidents, and provide reliable safety guarantees for hoisting operations.

[0059] 2. The present invention is based on the calculation and optimization of energy consumption indicators related to the mass and speed of the robot arm, so that the robot arm can intelligently adjust the kinetic energy and operating energy consumption under different working conditions. While ensuring the quality of operation, it significantly reduces energy consumption, improves energy utilization efficiency, and reduces enterprise operating costs.

[0060] 3. The present invention adopts a modular control method and integrates the GOOSE optimization algorithm based on the algorithm control unit to provide the best solution for hoisting operations, which makes it easier to update and optimize the subsequent control algorithm, improve the accuracy and efficiency of hoisting operations, and thus enhance the competitiveness of enterprises in the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a structural schematic diagram of the present invention;

[0062] Figure 2 It is a flow chart of the goose optimization algorithm of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0064] like Figure 1-Figure 2 As shown, the present invention discloses an intelligent building hoisting and obstacle avoidance system, including an engineering crane, a sensor and a monitoring system, and an algorithm control module;

[0065] The construction crane includes a lifting mechanism, which includes a main arm and a secondary arm that can be retracted and powered, and is used to control the lifting operation range and optimize the load distribution of the lifting operation;

[0066] The construction crane is composed of a base support part and a vehicle operation part. The base support part includes a chassis that can be driven on the road and load-bearing structures such as frame legs. At the same time, the chassis also provides power for the vehicle operation through the central rotating body. The vehicle operation part includes a slewing mechanism consisting of a slewing support, a slewing reducer, and a slewing hydraulic system that enables the vehicle to achieve a 360-degree full rotation, a luffing mechanism consisting of a luffing cylinder and a luffing hydraulic system, a lifting mechanism consisting of a winch reducer, a lifting wire rope, and a lifting hydraulic system, as well as a powered retractable main arm, auxiliary arm, turntable, counterweight, vehicle operation room, electrical control or lighting system, etc.

[0067] Sensors and monitoring systems integrate BIM technology, 3D scanning technology and photogrammetry to obtain target building data and build detailed 3D models;

[0068] The algorithm control module integrates the GOOSE optimization algorithm, which provides the best solution between reducing kinetic energy and changing the length of the telescopic arm, minimizing the risk of lifting operations and improving operation efficiency.

[0069] The system operation process is as follows:

[0070] Sensors and monitoring systems obtain target building data through 3D scanning technology and photogrammetry. Photogrammetry takes photos of objects from multiple angles and then uses software to analyze these photos to reconstruct the three-dimensional model of the object. In the intelligent building hoisting and obstacle avoidance system, photogrammetry can be used for accurate three-dimensional modeling of the on-site environment and provide basic data for BIM technology. BIM technology creates a detailed 3D model on the computer based on the relevant data provided by 3D scanning technology and photogrammetry. This model contains all the information of the building structure, including size, material, weight, etc. Through this model, engineers can simulate the entire hoisting process in a virtual environment and predict possible problems and challenges, such as collisions, unbalanced loads, etc. This simulation helps to discover and solve potential problems before actual construction, thereby reducing errors and rework in construction.

[0071] The sensor and monitoring system sends detailed 3D model information to the algorithm control module, which outputs the best telescopic arm solution to the lifting mechanism to assist the lifting operation. The GOOSE optimization algorithm gives the best solution between reducing kinetic energy and changing the length of the telescopic arm. The specific optimization process is as follows:

[0072] Initialize the population:

[0073] At the beginning, a number of geese are randomly generated and each goose is randomly placed in the space;

[0074] The performance is evaluated according to a predetermined objective function, and the objective function formula is as follows:

[0075] The goal is to minimize the risk and improve the efficiency of lifting operations, which can be defined as:

[0076] f(x) Min =ω 1 *R+ω 2 *T+ω 3 *E

[0077] ω 1 +ω 2 +ω 3 =1

[0078] R=P collision *d safe

[0079]

[0080] T=T detect +T calculate +T execute

[0081] Among them, f(x) Min is to minimize the risk of hoisting operation; R is the risk assessment index; T is the total time of the obstacle avoidance system for hoisting operation; E is the energy consumption index, including the kinetic energy and operating energy consumption of the robot arm; ω 1 ,ω 2 ,ω 3 is the weight coefficient used to balance the importance of risk, time and energy consumption; R is the risk assessment index; P collision is the collision probability, which is calculated by BIM technology based on the relevant data provided by 3D scanning technology and photogrammetry; d safe is the obstacle avoidance safety distance; E is the energy consumption index; m is the mass of the robot arm; v is the speed of the robot arm; T detect is the time to detect obstacles, T calculate is the time to calculate the obstacle avoidance path, T execute is the time to perform the obstacle avoidance operation;

[0082] The constraints are as follows:

[0083] ①Obstacle avoidance safety distance: 5≤d safe ≤25

[0084] ②Response time: T response ≤T max

[0085] ③ Kinetic energy reduction: K new ≤Kold

[0086] ④ Telescopic arm length adjustment: L new ≥L min

[0087] Among them, d safe is the obstacle avoidance safety distance; T response is the response time of the obstacle avoidance system; T max is the maximum allowed response time; K new is the kinetic energy after deceleration; K old is the original kinetic energy; L new is the adjusted telescopic arm length; L min is the minimum permissible telescopic arm length;

[0088] Substitute the initial population into the objective function:

[0089] During the development phase, we will find the weight of the stone that the goose stores in its feet. This variable is randomly chosen in each iteration and ranges from 5 to 25 kg:

[0090] S_W it =randi([5,25],1,1)=d safe (1)

[0091] T_o_A_O it =rand(1,dim) (2)

[0092] T_o_A_S it =rand(1,dim) (3)

[0093] Among them, S_W it is the random stone weight, which is a random number between 5 and 25, i.e. the obstacle avoidance safety distance d of the lifting system safe 5-25dm; randi is a function used to generate a random integer. The parameters of this function usually specify the range of random numbers. randi([5,25],1,1) means to generate a random integer between 5 and 25 (including 5 and 25), and this function will return a 1x1 matrix, that is, a single random integer; T_o_A_O it is the time it takes for the stone to reach the ground when it falls, that is, the time it takes to detect an obstacle; dim is the dimension; T_o_A_S it It is the sum of the time to find the object hitting the ground and the sound transmitted to the individual geese in the flock, that is, the time to calculate the obstacle avoidance path and the time to perform the obstacle avoidance operation;

[0094] Find the total time required for the sound to propagate throughout the iterations and reach the individual geese in the flock and obtain the average time required, i.e. the obstacle avoidance response time calculation:

[0095]

[0096]

[0097] Among them, T_T is the total time required to propagate and reach individual geese in the group during the entire iteration; T_A is the average time required, that is, the obstacle avoidance response time; in order to protect and wake up the geese in the team, these equations should be calculated:

[0098]

[0099] Where F_F_S is the speed of free fall; P collision The collision probability is calculated by BIM technology based on the relevant data provided by 3D scanning technology and photogrammetry; the value of the variable pro is randomly selected from the range of [0,1]; considering that the value of the variable pro is greater than 0.2 and S_W it Greater than or equal to 12;

[0100] D_S_T it =S_S*T_o_A_S it (7)

[0101] Among them, D_S_T it is the distance that sound travels, S_S is the speed of sound in air, which is 343.2 meters per second; T_o_A_S it is the time it takes for the sound to travel;

[0102] D_G it =0.5*D_S_T it (8)

[0103] D_Git is the distance between the guard goose and another goose that is resting or eating; D_S_Tit is the distance the sound travels;

[0104] X (it+1) =F_F_S+D_G it *T_A^ 2 (9)

[0105]

[0106] Among them, X it is the optimal individual position; F_F_S is the speed of the falling object, that is, the speed of the robot arm; if S_W it If it is less than 12 and pro is less than or equal to 0.2, then find a new X, T_o_a_Oit is the time it will take to reach the object;

[0107] The search step size is optimized and improved to find a new X. The improved formula is as follows:

[0108] H=κ×e -(β×loop) / Max_it (11)

[0109] X (it+1) =F_F_S*H*D_G it *T_A^ 2 *Coe (12)

[0110] Among them, κ is the step size control factor, β is the exponential control factor, loop is the current number of iterations, and Max_it is the maximum number of iterations; Coe is a random variable whose value is between 0 and 1; In the development stage, formulas 9 and 12 are used to discover new goose positions;

[0111] In the behavior of geese, if one of the geese wakes up and starts screaming to protect all the individuals in the group, the exploration phase begins. collision If the value of is less than 0.5, the following equation applies:

[0112] X (it+1) =randn(1,dim)*(M_T*alpha)+Best_pos (13)

[0113]

[0114] Where dim is the number of problem dimensions; Best_pos is the best position found in the search area; the variable alpha ranges from 2 to 0, and this value decreases significantly with each iteration in the loop, which means that the kinetic energy of the robot gradually decreases until it stops; loop is the current number of iterations, and Max_it is the maximum number of iterations allowed by the algorithm.

[0115] The obstacle avoidance safety distance of the hoisting system is used as input, and the kinetic energy and the length of the telescopic arm are used as output. Finally, the risk of minimizing the hoisting operation is obtained, and it is judged whether the output values ​​are the same twice. If so, it exits, otherwise it repeats the above steps, and finally outputs the risk of minimizing the hoisting operation.

[0116] The invention discloses an intelligent building hoisting and obstacle avoidance system, comprising building information model (BIM) technology, 3D scanning technology, photogrammetry, sensors and monitoring systems; through BIM technology, the actual hoisting process can be simulated on a computer, which is helpful to optimize the construction plan, reduce resource waste and speed up the construction progress; 3D technology is used to scan nearby physical structures and build virtual models at the same time; photogrammetry is a technology that reconstructs the three-dimensional model of the object by taking photos of the object from multiple angles and then using software to analyze the photos, and 3D scanning technology and photogrammetry complement each other; an algorithm control unit uses an intelligent algorithm to control a mechanical arm to accurately avoid obstacles, and when the obstacle cannot be avoided, the speed is reduced by reducing kinetic energy and the length of the telescopic arm is changed; the invention provides an intelligent building hoisting and obstacle avoidance system solution, which can effectively improve the safety and efficiency of hoisting operations, especially in terms of obstacle avoidance and speed reduction, and provides strong technical support for hoisting operations.

[0117] The above description of the embodiments enables professionals and technicians in the field to implement or use the present invention. Various modifications to the embodiments will be apparent to professionals and technicians. The general principles of the present invention can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention should not be limited to the embodiments shown herein, but should cover the widest range consistent with the principles and novel features disclosed in the present invention.

Claims

1. An intelligent building hoisting and obstacle avoidance system, characterized in that: The system includes an engineering crane, a sensor and monitoring system, and an algorithm control module; The construction crane comprises a lifting mechanism, and the lifting mechanism comprises a main arm and a secondary arm which can be retracted by power, and is used to control the lifting operation range and optimize the load distribution of the lifting operation; The sensor and monitoring system integrates BIM technology, 3D scanning technology and photogrammetry to obtain target building data and establish a detailed 3D model; The algorithm control module integrates the GOOSE optimization algorithm, which provides the best solution between reducing kinetic energy and changing the length of the telescopic arm, thereby minimizing the risk of hoisting operations and improving operation efficiency; The system operation process is as follows: The sensor and monitoring system obtains the target building data through 3D scanning technology and photogrammetry, and sends the detailed 3D model information to the algorithm control module. The algorithm control module outputs the best telescopic arm solution to the lifting mechanism to assist the lifting operation.

2. The intelligent building hoisting and obstacle avoidance system according to claim 1, characterized in that: The engineering crane also includes a base support part and a vehicle operation part. The base support part includes a chassis that can be driven on the road and frame legs. The chassis also provides power for the vehicle operation through a central rotating body. The vehicle operation part includes a slewing mechanism consisting of a slewing support, a slewing reducer, and a slewing hydraulic system that enables the vehicle to achieve a full 360-degree rotation, and a luffing mechanism consisting of a luffing cylinder and a luffing hydraulic system.

3. The intelligent building hoisting and obstacle avoidance system according to claim 1, characterized in that: The lifting mechanism also includes a winch reducer, a lifting wire rope and a lifting hydraulic system, as well as a turntable, a counterweight, an on-board operating room and an electrically controlled lighting system.

4. The intelligent building hoisting and obstacle avoidance system according to claim 1, characterized in that: The GOOSE optimization algorithm provides the best solution between reducing kinetic energy and changing the length of the telescopic arm. The optimization process is as follows: Initialize the population and evaluate its performance according to a predetermined objective function, the objective function formula is as follows: The goal is to minimize the risk and improve the efficiency of lifting operations, which can be defined as: f(x) Min =ω1*R+ω2*T+ω3*E ω1+ω2+ω3=1 R=P collision *d safe Among them, f(x) Min is to minimize the risk of hoisting operation, R is the risk assessment index, T is the total time of the obstacle avoidance system for hoisting operation, E is the energy consumption index, including the kinetic energy of the robot arm and the operating energy consumption, ω1, ω2, ω3 are weight coefficients used to balance the importance of risk, time and energy consumption; R is the risk assessment index, P collision is the collision probability, which is calculated by using BIM technology based on the relevant data provided by 3D scanning technology and photogrammetry. safe is the obstacle avoidance safety distance, E is the energy consumption index; m is the mass of the robot arm, v is the speed of the robot arm, T detect is the time to detect obstacles, T calculate is the time to calculate the obstacle avoidance path, T execute is the time to perform the obstacle avoidance operation; Set constraints and bring the initial population into the objective function: The development phase will find the weight of the stone that the goose stores in its feet. The variable is randomly selected in each iteration and ranges from 5 to 25 kg: S_W it =randi([5,25],1,1)=d safe T_o_A_O it =rand(1,dim) T_o_A_S it =rand(1,dim) Among them, S_W it is the random stone weight, which is a random number between 5 and 25, i.e. the obstacle avoidance safety distance d of the lifting system safe 5-25dm; randi is a function used to generate a random integer, T_o_A_O it is the time to detect obstacles, dim is the dimension, T_o_A_S it It is the sum of the time to calculate the obstacle avoidance path and the time to perform the obstacle avoidance operation; The obstacle avoidance safety distance of the lifting system is used as input, and the kinetic energy and the length of the telescopic arm are used as output. Finally, the risk of minimizing the lifting operation is obtained. It is judged whether the output values ​​are the same twice. If so, exit. Otherwise, repeat the above steps and finally output the risk of minimizing the lifting operation.

5. The intelligent building hoisting and obstacle avoidance system according to claim 4, characterized in that: The objective function constraints are as follows: Obstacle avoidance safety distance: 5≤d safe ≤25 Response time: T response ≤T max Kinetic energy reduction: K new ≤K old Telescopic arm length adjustment: L new ≥L min Among them, d safe is the obstacle avoidance safety distance; T response is the response time of the obstacle avoidance system; T max is the maximum allowed response time; K new is the kinetic energy after deceleration; K old is the original kinetic energy; L new is the adjusted telescopic arm length; L min is the minimum allowable telescopic arm length.

6. The intelligent building hoisting and obstacle avoidance system according to claim 5, characterized in that: The obstacle avoidance response time is calculated as follows: Among them, T_T is the total time required to propagate and reach individual geese in the group during the entire iteration; T_A is the average time required, that is, the obstacle avoidance response time; in order to protect and wake up the geese in the team, these equations should be calculated: Where F_F_S is the speed of free fall; P collision The collision probability is calculated by BIM technology based on the relevant data provided by 3D scanning technology and photogrammetry; the value of the variable pro is randomly selected from the range of [0,1]; considering that the value of the variable pro is greater than 0.2 and S_W it Greater than or equal to 12; D_S_T it =S_S*T_o_A_S it Among them, D_S_T it is the distance that sound travels, S_S is the speed of sound in air, which is 343.2 meters per second; T_o_A_S it is the time it takes for the sound to travel; D_G it =0.5*D_S_T it D_Git is the distance between the guard goose and another goose that is resting or eating; D_S_Tit is the distance the sound travels; X (it+1) =F_F_S+D_G it *T_A^ 2 Among them, X it is the optimal individual position; F_F_S is the speed of the falling object, that is, the speed of the robot arm; if S_W it If it is less than 12 and pro is less than or equal to 0.2, then find a new X, T_o_a_O it is the time it will take to reach the object.

7. The intelligent building hoisting and obstacle avoidance system according to claim 6, characterized in that: The search step size is optimized and improved to find a new X. The improved formula is as follows: H=κ×e -(β×loop) / Max_it X (it+1) =F_F_S*H*D_G it *T_A^ 2 *Coe Among them, κ is the step size control factor, β is the exponential control factor, loop is the current number of iterations, Max_it is the maximum number of iterations; Coe is a random variable, its value is between 0 and 1; In the exploration phase, if one of the geese wakes up and starts screaming to protect all the individuals in the group, the exploration phase begins. If the collision probability P collision If the value of is less than 0.5, the following equation applies: X (it+1) =randn(1,dim)*(M_T*alpha)+Best_pos Among them, dim is the number of problem dimensions; Best_pos is the best position found in the search area; the value of the variable alpha ranges from 2 to 0, and this value decreases significantly with each iteration in the loop, that is, the kinetic energy of the robot gradually decreases until it stops; loop is the current number of iterations, and Max_it is the maximum number of iterations allowed by the algorithm.

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