Intelligent construction method and device based on robot construction

Abstract

This application discloses a method and apparatus for intelligent construction based on robotic construction. This method constructs an objective function based on the current position matrix, sensor data matrix, energy consumption, and path smoothness. Based on this objective function, an optimized robot position matrix is determined to dynamically adjust the robot path and operating parameters, improving construction efficiency and quality. Furthermore, during the maintenance phase, an adaptive multidimensional health status assessment model is used to monitor and evaluate buildings over the long term, identifying structural health issues and automatically dispatching maintenance robots for repairs. This improves the building's structural safety and service life, while reducing subsequent maintenance costs.

Description

Technical Field

[0001] The present application relates to the technical field of construction engineering, and in particular to intelligent construction methods and devices based on robot construction. Background Art

[0002] Building Information Modeling (BIM), robotics, and sensor technologies are now widely used in the construction industry. BIM technology is used to create and manage digital models of construction projects, providing comprehensive information on geometry, materials, and construction progress. Robotics is increasingly being used on construction sites to perform high-risk and repetitive tasks such as lifting, welding, and concrete spraying. Sensor technology is used to monitor environmental parameters and construction progress in real time, ensuring the safety and effectiveness of the construction process. Furthermore, data analysis and optimization algorithms are being introduced to analyze construction data, optimize construction plans, and improve efficiency and quality.

[0003] Although existing technologies have made significant progress in the field of building construction, some problems still exist. First, the application of various technologies is often isolated and lacks system integration and coordination, resulting in information islands and the inability to achieve intelligent management of the entire process. Second, most existing optimization algorithms only consider a single factor, such as construction paths or resource allocation, and lack comprehensive optimization of multiple factors, making it difficult to cope with complex construction environments and changing construction requirements. Finally, during the maintenance phase of a building, traditional monitoring and maintenance methods often rely on manual inspections and passive maintenance, making it difficult to detect and resolve structural health problems in a timely manner, resulting in high maintenance costs and low efficiency, and unable to guarantee the long-term safety and service life of the building. Therefore, the ultimate construction efficiency and quality are affected. Summary of the Invention

[0004] In view of this, the present application provides an intelligent construction method and device based on robot construction, which can improve construction efficiency and quality.

[0005] In a first aspect, the present application provides an intelligent construction method based on robotic construction, comprising:

[0006] Constructing an objective function based on a current position matrix, a sensor data matrix, energy consumption, and path smoothness, and determining an optimized robot position matrix based on the objective function, so as to control construction based on the optimized robot position matrix;

[0007] After the control construction is completed, a structural health index matrix is determined, and a maintenance status of the robot is determined based on the structural health index matrix, so as to control the robot for maintenance according to the maintenance status.

[0008] Optionally, the energy consumption is expressed in the following formula:

[0009] ;

[0010] ;

[0011] In the above formula, No. A robot in Energy consumption at each moment; is the energy consumption coefficient; is the position matrix No. OK; It is robots at a time Moment and time Euclidean distance of position change at any moment; is the current position matrix; It is from 1 to A robot in X coordinate of the moment; It is from 1 to A robot in Y coordinate of the moment; It is from 1 to robots at a time The Z coordinate at the moment; n is the total number of robots.

[0012] Optionally, the path smoothness is expressed in the following formula:

[0013] ;

[0014] ;

[0015] In the above formula, It is A robot in The path smoothness at each moment; It is The robot's 3D position vector exist The first-order partial derivative at time t; is the current position matrix; It is from 1 to A robot in X coordinate of the moment; It is from 1 to A robot in Y coordinate of the moment; It is from 1 to robots at a time The Z coordinate at the moment; n is the total number of robots.

[0016] Optionally, the objective function is expressed in the following formula:

[0017] ;

[0018] ;

[0019] ;

[0020]

[0021] In the above formula, is the objective function; It is The robot from Sensors at time The data received at all times; It is The robot from Sensors at time and The data difference received at each moment; 、 、 is the weight coefficient; n is the total number of robots; c is the number of sensors configured for each robot; is the current position matrix; It is from 1 to A robot in X coordinate of the moment; It is from 1 to A robot in Y coordinate of the moment; It is from 1 to robots at a time The Z coordinate at the moment; n is the total number of robots; yes The data matrix of all sensors at all times, It is the first robot in Data received from the 1st to cth sensors at each moment; The nth robot is The data received from the 1st to the cth sensor at the moment; c is the number of sensors configured for each robot; No. A robot in Energy consumption at each moment; is the energy consumption coefficient; is the current position matrix No. OK; It is robots at a time Moment and time Euclidean distance of position change at any moment.

[0022] Optionally, the constraint condition of the objective function is expressed in the following formula:

[0023] ;

[0024] ;

[0025] ;

[0026] In the above formula, is the Lagrangian function; is the Lagrange multiplier; is the number of constraints; is the constraint function; is the current position matrix; It is from 1 to A robot in X coordinate of the moment; It is from 1 to A robot in Y coordinate of the moment; It is from 1 to robots at a time The Z coordinate at the moment; n is the total number of robots; yes The data matrix of all sensors at all times, It is the first robot in Data received from the 1st to cth sensors at each moment; The nth robot is The data received from the 1st to the cth sensor at the moment; c is the number of configured sensors for each robot.

[0027] Optionally, the health status assessment model for determining the structural health indicator matrix is expressed as follows:

[0028] ;

[0029] In the above formula, yes Structural health indicator matrix of all monitoring points at all times; yes Structural health indicator matrix of all monitoring points at all times; is the adjustment parameter; represents the inverse of the monitoring data matrix; The initial matrix and The initial matrix is the same; It's time The monitoring data weight matrix at each moment.

[0030] Optionally, during the construction of the health status assessment model, the monitoring data weight matrix is updated using the following formula:

[0031] ;

[0032] In the above formula, is the learning rate; represents exponential operation, yes Structural health indicator matrix of all monitoring points at all times; yes Structural health indicator matrix of all monitoring points at all times; It's time The monitoring data weight matrix at each moment.

[0033] In a second aspect, the present application provides an intelligent construction device based on robot construction, comprising:

[0034] a first determination module configured to construct an objective function based on a current position matrix, a sensor data matrix, energy consumption, and path smoothness, and determine an optimized robot position matrix based on the objective function, so as to control construction based on the optimized robot position matrix;

[0035] The second determining module is used to determine a structural health index matrix after the control construction, and determine a maintenance status of the robot based on the structural health index matrix, so as to control the robot to be maintained according to the maintenance status.

[0036] In a third aspect, the present application provides a computer-readable storage medium comprising a program, which, when executed on a computer, enables the computer to execute the method described above.

[0037] In a fourth aspect, the present application provides an execution device, comprising a processor and a memory, wherein the processor is coupled to the memory;

[0038] The memory is used to store programs;

[0039] The processor is configured to execute the program in the memory, so that the execution device executes the above method.

[0040] The method disclosed in this application constructs an objective function based on the current position matrix, sensor data matrix, energy consumption, and path smoothness. Based on this objective function, an optimized robot position matrix is determined to dynamically adjust the robot path and operating parameters, improving construction efficiency and quality. Furthermore, during the maintenance phase, an adaptive multidimensional health status assessment model is used to monitor and evaluate buildings over the long term, identifying structural health issues and automatically dispatching maintenance robots for repairs. This improves the building's structural safety and service life, while reducing subsequent maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The following detailed description of the specific embodiments of the present application in conjunction with the accompanying drawings will make the technical solutions and other beneficial effects of the present application apparent.

[0042] Figure 1 An operational flow chart of an intelligent construction method based on robotic construction provided by an exemplary embodiment is shown.

[0043] Figure 2 A block diagram of a smart construction device based on robot construction provided by an exemplary embodiment is shown.

[0044] Figure 3 A block diagram of an execution device provided by an exemplary embodiment is shown. DETAILED DESCRIPTION

[0045] To make the above-mentioned objects, features, and advantages of the present application more clearly understood, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings. The following description sets forth many specific details to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar improvements without violating the scope of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0046] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can be changed in the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved. The division of units in this application is a logical division. In actual application, there may be other division methods. For example, multiple units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, and the indirect coupling or communication connection between units can be electrical or other similar forms, which are not limited in this application. Moreover, the units or sub-units described as separate components may or may not be physically separated, may or may not be physical units, or may be distributed into multiple circuit units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this application.

[0047] Please refer to Figure 1 ,in Figure 1 A flowchart of an intelligent construction method based on robot construction provided by an exemplary embodiment is shown. The method is implemented through steps 102-104.

[0048] In step 102, an objective function is constructed based on the current position matrix, the sensor data matrix, the energy consumption and the path smoothness, and an optimized robot position matrix is determined based on the objective function to control the construction based on the optimized robot position matrix.

[0049] It is understandable that, before step 102, intelligent construction technology can be used to analyze the original construction data to obtain a construction plan, and the construction plan is input into the construction robot so that the construction robot can perform the construction.

[0050] Here, the construction plan can be obtained by acquiring raw construction data, including Building Information Model (BIM) data, construction site environment data, construction tasks and requirements, robot and equipment data, and sensor and monitoring equipment data. This raw construction data is analyzed and optimized using intelligent construction technology to generate a construction plan. Intelligent construction technology is currently available and will not be discussed in detail here.

[0051] The appropriate number and type of robots, such as handling robots, welding robots, and concrete spraying robots, are allocated based on the construction plan. During construction, sensor technology is used to monitor the progress in real time. Sensor data is analyzed using objective functions, allowing for real-time adjustments to robot paths and operating parameters to ensure construction quality.

[0052] The objective function of this application may be a nonlinear objective function. The specific process of determining the optimized robot position matrix based on the objective function can be demonstrated as follows:

[0053] Step 1021: Create a robot position matrix. The robot position matrix represents the position of all robots. The three-dimensional coordinates of the moment:

[0054] (1)

[0055] In formula (1), is the current position matrix The position matrix of all robots at the moment; It is from 1 to A robot in X coordinate of the moment; It is from 1 to A robot in Y coordinate of the moment; It is from 1 to robots at a time The Z coordinate at the moment; n is the total number of robots.

[0056] Step 1022, define the sensor data matrix, which represents the sensor data matrix. Data at this moment:

[0057] (2)

[0058] In formula (2), yes The data matrix of all sensors at all times, It is the first robot in Data received from the 1st to cth sensors at each moment; The nth robot is The data received from the 1st to the cth sensor at the moment; c is the number of configured sensors for each robot.

[0059] Step 1023, define an energy consumption model to represent the energy consumption during the robot's movement and operation:

[0060] (3)

[0061] In formula (3), No. A robot in Energy consumption at each moment; is the energy consumption coefficient, the specific value of which is obtained through experimental measurement or data provided by the equipment supplier based on the robot model, work efficiency and actual operating environment; is the position matrix No. row, indicating the A robot in Position at the moment; Yes, yes robots at a time Moment and time The Euclidean distance of the moment position change, the meanings of other parameters involved in formula (3) refer to formula (2).

[0062] Step 1024: define a path smoothness model. The path smoothness formula is as follows:

[0063] (4)

[0064] In formula (4), It is A robot in The path smoothness at each moment; It is The robot's 3D position vector exist The first-order partial derivative at time t reflects the path smoothness through the value of the partial derivative.

[0065] In step 1024, the objective function is determined according to the above formulas (1)-(4), as follows:

[0066] (5)

[0067] In formula (5), is the objective function, which represents the goal of the overall adjustment; It is The robot from Sensors at time The data received at all times; It is The robot from Sensors at time and The data difference received at each moment; 、 、 are weight coefficients that control the balance between position change, sensor data change, energy consumption, and path smoothness; n is the total number of robots; and c is the number of configured sensors for each robot.

[0068] As a demonstration, the Lagrange multiplier method can be introduced to construct the constraints of the objective function. The constraints are as follows:

[0069] (6)

[0070] In formula (6), is the Lagrangian function, which represents the objective function and a combination of constraints; is the Lagrange multiplier, introduced as an auxiliary variable, reflecting the The constraints on the objective function the extent of the impact; is the number of constraints; Is the constraint function, used to constrain Robot position matrix at each moment and sensor data matrix Must meet the Constraints are defined based on specific usage scenarios.

[0071] Based on the constraints of the above Lagrangian form, solve the Lagrangian equation and Lagrangian function Respectively and Find the partial derivative and set it to zero, the equation is as follows:

[0072] (7)

[0073] In formula (7), is the Lagrangian function For variables The partial derivative of is the Lagrangian function For variables The partial derivative of .

[0074] Solving the above equation, we get and The value of , and then the optimized robot position matrix update formula is obtained:

[0075] (8)

[0076] In formula (8), is the optimized robot position matrix, is the learning rate, which controls the update step size. The specific value is determined by experiments or tuning methods; Is the objective function J gradient; It is a constraint right gradient.

[0077] In step 104 , after the control construction is completed, a structural health index matrix is determined, and a maintenance status of the robot is determined based on the structural health index matrix, so as to control the robot to be maintained according to the maintenance status.

[0078] The optimized robot position matrix will be obtained The data is sent to each robot, which then adjusts its movement path and work position to continue its task. After construction is completed, the maintenance phase begins. Sensors are installed at monitoring points to monitor the building and obtain monitoring data. Monitoring points are specified based on the specific implementation scenario. Sensors include stress sensors, strain sensors, vibration sensors, and temperature sensors. The monitoring data specifically monitors the building's stress, strain, vibration, and temperature.

[0079] An adaptive multidimensional health status assessment model is introduced to monitor structural issues in buildings and dispatch maintenance robots for repairs. This model is used to construct a structural health indicator matrix and dynamically adjust the monitoring data weight matrix. This enables real-time assessment and accurate prediction of building structural health indicators, enabling timely identification of potential structural issues. A structural health indicator is an element in the health indicator matrix. A numerical value represents the structural health status of a monitoring point; a larger value indicates better health.

[0080] The construction process of the adaptive multidimensional health status assessment model is as follows:

[0081] Step 1041, defined in Structural health indicator matrix at this moment:

[0082] (9)

[0083] In formula (9), yes The structural health index matrix of all monitoring points at the moment, the initial matrix is a zero matrix; yes The structural health index of each monitoring point at the moment, subscript Indicates different monitoring point numbers; is the total number of monitoring points.

[0084] Step 1042, defined in Monitoring data matrix at all times:

[0085] (10)

[0086] In formula (10), yes The monitoring data matrix of all sensors at all times. The initial matrix is set by the monitoring data collected when the sensor is started. The first monitoring point is Time from the first sensor to the Monitoring data of sensors; yes Moment Monitoring points in Time from the first sensor to the Monitoring data of sensors; is the number of sensors configured at each monitoring point.

[0087] Step 1043, define the monitoring data weight matrix:

[0088] (11)

[0089] In formula (11), It's time The monitoring data weight matrix at each moment, the initial matrix is an all-one matrix or is set according to the importance of the monitoring data in the specific implementation scenario; The first monitoring point is Time from the first sensor to the The monitoring data weight of each sensor; It is Monitoring points in Time from the first sensor to the The monitoring data weight of each sensor.

[0090] Step 1044: Build a health status assessment model:

[0091] (12)

[0092] In formula (12), yes The structural health index matrix of all monitoring points at all times reflects the updated structural health indexes of all monitoring points; It is an adjustment parameter, which is set according to the specific usage scenario; represents the inverse of the monitoring data matrix; The initial matrix and The initial matrix is the same.

[0093] In the process of building the health status assessment model, the following formula is used to dynamically adjust the monitoring data weight matrix:

[0094] (13)

[0095] In formula (13), The learning rate controls the update step size of the monitoring data weight matrix and is set according to the specific application scenario; Represents exponential operation.

[0096] It is understandable that in the actual demonstrable operation of "controlling robot maintenance according to the maintenance status" in step 104, the health indicator threshold is set according to the specific implementation scenario, when the structure health indicator matrix When any structural health indicator is less than the health indicator threshold, the corresponding maintenance robot is dispatched to perform repair work.

[0097] After the repair is completed, the repair area is inspected using sensors, and the sensor monitoring data is updated to the monitoring data matrix. Based on the updated monitoring data matrix, the adaptive multidimensional health status assessment model is used to reassess the structural health status of the entire building and continue monitoring using sensors.

[0098] Please refer to Figure 2 , which shows a block diagram of the intelligent construction device based on robot construction provided by this application. The device 200 includes:

[0099] A first determination module 202 is configured to construct an objective function based on the current position matrix, the sensor data matrix, the energy consumption, and the path smoothness, and determine an optimized robot position matrix based on the objective function, so as to control the construction operation based on the optimized robot position matrix;

[0100] The second determining module 204 is configured to determine a structural health index matrix after the control construction, and determine a maintenance status of the robot based on the structural health index matrix, so as to control the robot to perform maintenance according to the maintenance status.

[0101] Since the above methods have been discussed in detail, the specific implementation of the above modules will not be repeated here.

[0102] Next, we will introduce an execution device provided by the embodiment of the present application. Figure 3 , Figure 3This is a schematic diagram of the structure of the execution device provided in the embodiment of the present application. The execution device 300 can be specifically manifested as an autonomous driving vehicle, a mobile phone, a tablet, a laptop computer, a desktop computer, a monitoring data processing device, etc., which is not limited here. Figure 1 The execution device 300 includes a receiver 301, a transmitter 302, a processor 303, and a memory 304 (the number of processors 303 in the execution device 300 can be one or more, Figure 3 (taking one processor as an example), the processor 303 may include an application processor 3031 and a communication processor 3032. In some embodiments of the present application, the receiver 301, the transmitter 302, the processor 303 and the memory 304 may be connected via a bus or other means.

[0103] Memory 304 may include read-only memory and random access memory, and provides instructions and data to processor 303. A portion of memory 304 may also include non-volatile random access memory (NVRAM). Memory 304 stores processor and operation instructions, executable modules, or data structures, or subsets or extended sets thereof. Operation instructions may include various operation instructions for implementing various operations.

[0104] Processor 303 controls the operation of the execution device. In specific applications, the various components of the execution device are coupled together via a bus system. In addition to a data bus, the bus system may also include a power bus, a control bus, and a status signal bus. However, for clarity, all of these buses are referred to as a bus system in the figure.

[0105] The methods disclosed in the above embodiments of the present application can be applied to the processor 303 or implemented by the processor 303. The processor 303 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by the hardware integrated logic circuit in the processor 303 or by instructions in the form of software. The above processor 303 can be a general-purpose processor, a digital signal processor (DSP), a microprocessor or a microcontroller, and can further include an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The processor 303 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 304, and processor 303 reads the information in memory 304 and performs the steps of the above method in conjunction with its hardware.

[0106] Receiver 301 can be used to receive input digital or character information and generate signal input related to executing device-related settings and function control. Transmitter 302 can be used to output digital or character information through the first interface. Transmitter 302 can also be used to send instructions to the disk pack through the first interface to modify data in the disk pack. Transmitter 302 can also include a display device such as a display screen.

[0107] In the embodiment of the present application, the processor 303 is used to execute Figure 1 The specific manner in which the application processor 3031 in the processor 303 performs the above steps is the same as that in the present application. Figure 1 The corresponding method embodiments are based on the same concept, and the technical effects they bring are the same as those in this application. Figure 1 The corresponding method embodiments are the same. For specific contents, please refer to the description in the method embodiments shown above in this application, which will not be repeated here.

[0108] The above is only a preferred specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed in this application should be covered by the scope of protection of the present application.

Claims

1. An intelligent construction method based on robot construction, characterized in that: include: Constructing an objective function based on a current position matrix, a sensor data matrix, energy consumption, and path smoothness, and determining an optimized robot position matrix based on the objective function, so as to control construction based on the optimized robot position matrix; After the control construction is completed, a structural health indicator matrix is determined, and a maintenance status of the robot is determined based on the structural health indicator matrix, so as to control the robot for maintenance according to the maintenance status; The health status assessment model used to determine the structural health indicator matrix is expressed in the following formula: In the above formula, yes Structural health indicator matrix of all monitoring points at all times; yes The structural health index matrix of all monitoring points at the moment; ζ is the adjustment parameter; represents the inverse of the monitoring data matrix; The initial matrix and The initial matrix is the same; It's time The weight matrix of monitoring data at each moment; During the construction of the health status assessment model, the monitoring data weight matrix is updated using the following formula: In the above formula, τ is the learning rate; exp represents the exponential operation, yes Structural health indicator matrix of all monitoring points at all times; yes Structural health indicator matrix of all monitoring points at all times; It's time The monitoring data weight matrix at each moment.

2. The method according to claim 1, characterized in that The energy consumption is expressed in the following formula: E i (t)=κ·||R i (t+1)-R i (t)||; In the above formula, E i (t) Energy consumption of the i-th robot at time t; κ is the energy consumption coefficient; R i (t) is the i-th row of the position matrix R(t); || R i (t+1)-R i (t)|| is the Euclidean distance between the position change of the i-th robot at time t+1 and time t; R(t) is the current position matrix; x1(t), x2(t),…, x n (t) is the X coordinate of the 1st to nth robot at time t; y1(t), y2(t),…,y n (t) is the Y coordinate of the 1st to nth robots at time t; z1(t), z2(t),…, z n (t) is the Z coordinate of the 1st to nth robots at time t; n is the total number of robots.

3. The method according to claim 1, characterized in that The path smoothness is expressed in the following formula: In the above formula, is the path smoothness of the i-th robot at time t; is the 3D position vector R of the i-th robot i (t) is the first-order partial derivative at time t; R(t) is the current position matrix, x1(t), x2(t),…, x n (t) is the X coordinate of the 1st to nth robot at time t; y1(t), y2(t),…,y n (t) is the Y coordinate of the 1st to nth robots at time t; z1(t), z2(t),…, z n (t) is the Z coordinate of the 1st to nth robots at time t; n is the total number of robots.

4. The method according to claim 1, wherein The objective function is expressed as the following formula: E i (t)=κ·||R i (t+1)-R i (t)|| In the above formula, J is the objective function; s ij (t) is the data received by the i-th robot from the j-th sensor at time t; s ij (t)-s ij (t-1) is the difference in data received by the i-th robot from the j-th sensor at time t and t-1; α, β, γ are weight coefficients; n is the total number of robots; c is the number of sensors configured for each robot; R(t) is the current position matrix; x1(t), x2(t),…, x n (t) is the X coordinate of the 1st to nth robot at time t; y1(t), y2(t),…,y n (t) is the Y coordinate of the 1st to nth robots at time t; z1(t), z2(t),…, z n (t) is the Z coordinate of the 1st to nth robots at time t; n is the total number of robots; S(t) is the data matrix of all sensors at time t; s 11 (t),s 12 (t),…,s 1c (t) is the data received by the first robot from the first to the cth sensors at time t; s n1 (t),s n2 (t),…,s nc (t) is the data received by the nth robot from the 1st to the cth sensors at time t; c is the number of sensors configured for each robot; E i (t) Energy consumption of the i-th robot at time t; κ is the energy consumption coefficient; R i (t) is the i-th row of the position matrix R(t); || R i (t+1)-R i (t)|| is the Euclidean distance between the position change of the i-th robot at time t+1 and time t.

5. The method according to claim 1, wherein The constraint conditions of the objective function are expressed in the following formula: In the above formula, is the Lagrangian function; λ v is the Lagrange multiplier; p is the number of constraints; g v (R(t), S(t)) is the constraint function; R(t) is the current position matrix; x1(t), x2(t),…, x n (t) is the X coordinate of the 1st to nth robot at time t; y1(t), y2(t),…,y n (t) is the Y coordinate of the 1st to nth robots at time t; z1(t), z2(t),…, z n (t) is the Z coordinate of the 1st to nth robots at time t; n is the total number of robots; S(t) is the data matrix of all sensors at time t, s 11 (t),s 12 (t),…,s 1c (t) is the data received by the first robot from the first to the cth sensors at time t; s n1 (t),s n2 (t),…,s nc (t) is the data received by the nth robot from the 1st to cth sensors at time t; c is the number of configured sensors of each robot.

6. An intelligent construction device based on robot construction, characterized in that: include: a first determination module configured to construct an objective function based on a current position matrix, a sensor data matrix, energy consumption, and path smoothness, and determine an optimized robot position matrix based on the objective function, so as to control construction based on the optimized robot position matrix; a second determining module, configured to determine a structural health index matrix after the control construction, and determine a maintenance status of the robot based on the structural health index matrix, so as to control the robot to be maintained according to the maintenance status; The health status assessment model used to determine the structural health indicator matrix is expressed in the following formula: In the above formula, yes Structural health indicator matrix of all monitoring points at all times; yes The structural health index matrix of all monitoring points at the moment; ζ is the adjustment parameter; represents the inverse of the monitoring data matrix; The initial matrix and The initial matrix is the same; It's time The weight matrix of monitoring data at each moment; During the construction of the health status assessment model, the monitoring data weight matrix is updated using the following formula: In the above formula, τ is the learning rate; exp represents the exponential operation, yes Structural health indicator matrix of all monitoring points at all times; yes Structural health indicator matrix of all monitoring points at all times; It's time The monitoring data weight matrix at each moment.

7. A computer-readable storage medium, characterized in that The invention comprises a program, which, when being run on a computer, causes the computer to execute the method according to any one of claims 1 to 5.

8. An execution device, characterized in that: comprising a processor and a memory, wherein the processor is coupled to the memory; The memory is used to store programs; The processor is configured to execute the program in the memory, so that the execution device executes the method according to any one of claims 1 to 5.

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