Intelligent building method and device based on robot construction

Abstract

The invention discloses an intelligent building method and device based on robot construction. According to the method, the target function is constructed according to the current position matrix, the sensor data matrix, the energy consumption and the path smoothness, and the optimized robot position matrix is determined based on the target function, so that the robot path and operation parameters are dynamically adjusted, and the construction efficiency and quality are improved. In addition, in the maintenance stage, the self-adaptive multi-dimensional health state evaluation model is adopted for long-term monitoring and evaluation of the building, structural health problems are recognized, the maintenance robot is automatically dispatched for repair, the structural safety of the building is improved, the service life of the building is prolonged, and the later maintenance cost is reduced.

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] At present, building information modeling (BIM), robotics and sensor technologies have been widely used in the field of construction. BIM technology is used to create and manage digital models of construction projects, providing comprehensive geometric information, material information and construction progress information. Robotic technology is gradually applied on construction sites to complete high-risk and repetitive tasks such as handling, welding, and concrete spraying. Sensor technology is used to monitor the environmental parameters and construction progress of the construction site in real time to ensure the safety and effectiveness of the construction process. In addition, data analysis and optimization algorithms have also been introduced to analyze construction data, optimize construction plans, and improve construction 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, lacking system integration and coordination, resulting in information islands and the inability to achieve intelligent management of the entire process. Secondly, most of the existing optimization algorithms only consider a single factor, such as construction path or resource allocation, lack comprehensive optimization of multiple factors, and are difficult to cope with complex construction environments and changing construction requirements. Finally, during the maintenance stage of buildings, traditional monitoring and maintenance methods often rely on manual inspections and passive maintenance, making it difficult to detect and solve 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 buildings. Therefore, the final construction efficiency and construction 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 construction quality.

[0005] In a first aspect, the present application provides an intelligent construction method based on robot construction, comprising: Constructing an objective function according to 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 maintenance according to the maintenance status.

[0006] Optionally, the energy consumption is expressed in the following formula: ; ; 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 1st 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 1st to Robots at time The Z coordinate at the moment; n is the total number of robots.

[0007] Optionally, the path smoothness is expressed in the following formula: ; ; 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 ; is the current position matrix; It is from 1st to A robot in The X coordinate of the moment; It is from 1st 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.

[0008] Optionally, the objective function is expressed in the following formula: ; ; ;

[0009] 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 received at each moment is different; , , 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 The 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 the cth sensor at each moment; The nth robot is The data received from the 1st to the cth sensor at each 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.

[0010] Optionally, the constraint condition of the objective function is expressed in the following formula: ; ; ; 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 The 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 the cth sensor at each moment; The nth robot is The data received from the 1st to the cth sensor at each moment; c is the number of configured sensors for each robot.

[0011] Optionally, the health status assessment model for determining 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 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 as; It's time The monitoring data weight matrix at each moment.

[0012] Optionally, during the construction of the health status assessment model, the monitoring data weight matrix is ​​updated in the form expressed by the following formula: ; 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.

[0013] In a second aspect, the present application provides an intelligent construction device based on robot construction, comprising: A first determination module is used to construct an objective function according to a current position matrix, a sensor data matrix, an energy consumption and a 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; The second determination module is used to determine a structural health indicator matrix after the control construction, and determine a maintenance status of the robot based on the structural health indicator matrix to control the robot maintenance according to the maintenance status.

[0014] 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 as described above.

[0015] 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; The memory is used to store programs; The processor is used to execute the program in the memory, so that the execution device executes the above method.

[0016] In the method disclosed in this application, an objective function is constructed based on the current position matrix, sensor data matrix, energy consumption and path smoothness, and the optimized robot position matrix is ​​determined based on this objective function to dynamically adjust the robot path and operation parameters to improve construction efficiency and quality. In addition, an adaptive multi-dimensional health status assessment model is used in the maintenance phase to monitor and evaluate the building over a long period of time, identify structural health problems and automatically dispatch maintenance robots for repair, thereby improving the structural safety and service life of the building and reducing the cost of later maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The technical solution and other beneficial effects of the present application will be made apparent by describing in detail the specific implementation methods of the present application in conjunction with the accompanying drawings.

[0018] Figure 1 An operational flow chart of an intelligent construction method based on robot construction provided by an exemplary embodiment is shown.

[0019] Figure 2A block diagram of a smart construction device based on robot construction provided by an exemplary embodiment is shown.

[0020] Figure 3 A block diagram of the execution device provided by an exemplary embodiment is shown. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below. 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 here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any of their variations 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 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 change 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. There may be other division methods when it is implemented in actual applications. For example, multiple units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection 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. In addition, 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 in multiple circuit units, and some or all of the units may be selected according to actual needs to achieve the purpose of the present application.

[0022] Please refer to Figure 1 ,in Figure 1A 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.

[0023] In step 102, an objective function is constructed according to 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.

[0024] It is understandable that, before step 102, intelligent construction technology can also 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.

[0025] Here, the construction plan can be obtained by: obtaining the original construction data, which includes building information model (BIM) data, construction site environment data, construction tasks and requirements, robot and equipment data, and sensor and monitoring equipment data. The original construction data is analyzed and optimized using intelligent construction technology to generate a construction plan. Intelligent construction technology is an existing technology and will not be described in detail here.

[0026] According to the construction plan, the corresponding number and type of robots are allocated, such as handling robots, welding robots, and concrete spraying robots. During the construction process, sensor technology is used to monitor the construction process in real time, and the sensor data is analyzed through the objective function to adjust the robot path and operation parameters in real time to ensure the construction quality.

[0027] The objective function of the present 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: Step 1021, establish a robot position matrix, the robot position matrix represents all robots in The three-dimensional coordinates of the time: (1) 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 The 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.

[0028] Step 1022, define the sensor data matrix, which represents the sensor data matrix. Data at the moment: (2) 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 the cth sensor at each moment; The nth robot is The data received from the 1st to the cth sensor at each moment; c is the number of configured sensors for each robot.

[0029] Step 1023, define an energy consumption model definition, which represents the energy consumption during the robot movement and operation: (3) 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 The location at the moment; Yes, yes Robots at a time Moment and time The Euclidean distance of the position change at a certain moment. The meanings of other parameters involved in formula (3) refer to formula (2).

[0030] Step 1024, define a path smoothness model, and the path smoothness formula is as follows: (4) 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.

[0031] Step 1024, determine the objective function according to the above formulas (1)-(4), as follows: (5) 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 received at each moment is different; , , are weight coefficients, which control the balance between position change, sensor data change, energy consumption and path smoothness; n is the total number of robots; c is the number of configured sensors for each robot.

[0032] As a demonstration, the Lagrange multiplier method can be introduced to construct the constraints of the objective function. The constraints are as follows: (6) 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 a 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.

[0033] 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: (7) In formula (7), is the Lagrangian function For variables The partial derivative of is the Lagrangian function For variables The partial derivative of .

[0034] Solving the above equation, we get and The value of , and then get the optimized robot position matrix update formula: (8) 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 The gradient of It is a constraint right gradient.

[0035] In step 104, 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 to be maintained according to the maintenance status.

[0036] The optimized robot position matrix will be obtained The data is sent to each robot, and the robot adjusts the movement path and working position to continue to perform the task. After the construction is completed, it enters the maintenance phase, and sensors are installed at the monitoring points to monitor the building and obtain monitoring data. The monitoring points are specified according to the specific implementation scenarios. The sensors include stress sensors, strain sensors, vibration sensors and temperature sensors. The monitoring data specifically monitors the stress, strain, vibration and temperature data of the building.

[0037] An adaptive multidimensional health status assessment model is introduced to monitor the structural problems of buildings, dispatch maintenance robots for repairs, and use the adaptive multidimensional health status assessment model to build a structural health indicator matrix. The monitoring data weight matrix is ​​dynamically adjusted to achieve real-time assessment and accurate prediction of the building's structural health indicators, and to identify potential structural problems in a timely manner. The structural health indicator is an element in the health indicator matrix. The structural health indicator is a numerical value. The size of the numerical value indicates the structural health status of the monitoring point. The larger the numerical value, the better the health status.

[0038] The construction process of the adaptive multidimensional health status assessment model is as follows: Step 1041, defined in Structural health indicator matrix at this moment: (9) In formula (9), yes The structural health index matrix of all monitoring points at all times, 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.

[0039] Step 1042, defined in Monitoring data matrix at all times: (10) 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 The time from the first sensor to the Monitoring data of sensors; yes Moment Monitoring points in The time from the first sensor to the Monitoring data of sensors; is the number of sensors configured at each monitoring point.

[0040] Step 1043, define the monitoring data weight matrix: (11) In formula (11), It's time The monitoring data weight matrix at each moment, the initial matrix is ​​a full-1 matrix or is specifically set according to the importance of the monitoring data in the specific implementation scenario; The first monitoring point is The time from the first sensor to the The monitoring data weight of each sensor; It is Monitoring points in The time from the first sensor to the The monitoring data weight of each sensor.

[0041] Step 1044, construct a health status assessment model: (12) 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.

[0042] In the process of building the health status assessment model, the following formula is used to dynamically adjust the monitoring data weight matrix: (13) In formula (13), is the learning rate, which controls the update step size of the monitoring data weight matrix and is set according to the specific application scenario; Represents exponential operation.

[0043] It is understandable that in the actual demonstrable operation, "controlling robot maintenance according to the maintenance status" in step 104 may be: setting health indicator thresholds according to specific implementation scenarios; 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.

[0044] 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 re-evaluate the structural health status of the entire building and continue monitoring using sensors.

[0045] Please refer to Figure 2 , which shows a block diagram of the intelligent construction device based on robot construction provided by the present application. The device 200 includes: A first determination module 202 is used to construct an objective function according to 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 based on the optimized robot position matrix; The second determination module 204 is used to determine a structural health indicator matrix after the control construction, and determine a maintenance status of the robot based on the structural health indicator matrix to control the robot maintenance according to the maintenance status.

[0046] Since the above methods have been discussed in detail, the specific implementation of the above modules will not be repeated here.

[0047] Next, an execution device provided by an embodiment of the present application is introduced. Figure 3 , Figure 3 This is a schematic diagram of a structure of an execution device provided in an 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 1The functions of the execution device in the corresponding embodiment. Specifically, the execution device 300 includes: a receiver 301, a transmitter 302, a processor 303 and a memory 304 (wherein the number of the processor 303 in the execution device 300 can be one or more, Figure 3 In the example of FIG. 3 , 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.

[0048] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 303. A portion of the memory 304 may also include a non-volatile random access memory (NVRAM). The memory 304 stores processor and operation instructions, executable modules or data structures, or subsets thereof, or extended sets thereof, wherein the operation instructions may include various operation instructions for implementing various operations.

[0049] The processor 303 controls the operation of the execution device. In a specific application, the various components of the execution device are coupled together through a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus, and a status signal bus, etc. However, for the sake of clarity, various buses are referred to as bus systems in the figure.

[0050] The method disclosed in the above embodiment 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. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 303. The above processor 303 can be a general processor, a digital signal processor (digital signal processing, DSP), a microprocessor or a microcontroller, and can further include an application specific integrated circuit (application specific integrated circuit, ASIC), a field programmable gate array (field-programmable gate array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, 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 processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 304, and the processor 303 reads the information in the memory 304 and completes the steps of the above method in combination with its hardware.

[0051] The receiver 301 can be used to receive input digital or character information and generate signal input related to the relevant settings and function control of the execution device. The transmitter 302 can be used to output digital or character information through the first interface; the transmitter 302 can also be used to send instructions to the disk group through the first interface to modify the data in the disk group; the transmitter 302 can also include a display device such as a display screen.

[0052] 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.

[0053] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. An intelligent construction method based on robot construction, characterized in that: include: Constructing an objective function according to 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 maintenance according to the maintenance status.

2. The method according to claim 1, characterized in that The energy consumption is expressed in the following formula: ； ； 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 The 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.

3. The method according to claim 1, characterized in that: The path smoothness is expressed in the following formula: ； ； 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 ; is the current position matrix, It is from 1 to A robot in The X coordinate of the moment; It is from 1 to A robot in Y coordinate of the moment; It is from 1st to Robots at a time The Z coordinate at the moment; n is the total number of robots.

4. The method according to claim 1, characterized in that: The objective function is expressed in the following formula: ； ； ； 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 received at each moment is different; , , 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 1st to A robot in The X coordinate of the moment; It is from 1st 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 Data matrix of all sensors at all times; It is the first robot in Data received from the 1st to the cth sensor at each moment; The nth robot is The data received from the 1st to the cth sensor at each 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 position matrix No. OK; It is Robots at a time Moment and time Euclidean distance of position change at any moment.

5. The method according to claim 1, characterized in that The constraint condition of the objective function is expressed in the following formula: ； ； ； 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 1st to A robot in The X coordinate of the moment; It is from 1st to A robot in Y coordinate of the moment; It is from 1st 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 the cth sensor at each moment; The nth robot is The data received from the 1st to the cth sensor at each moment; c is the number of configured sensors for each robot.

6. The method according to claim 1, characterized in that 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 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 as; It's time The monitoring data weight matrix at each moment.

7. The method according to claim 6, characterized in that In the process of constructing the health status assessment model, the monitoring data weight matrix is ​​updated in the form expressed by the following formula: ； 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.

8. An intelligent construction device based on robot construction, characterized in that: include: A first determination module is used to construct an objective function according to a current position matrix, a sensor data matrix, an energy consumption and a 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; The second determination module is used to determine a structural health indicator matrix after the control construction, and determine a maintenance status of the robot based on the structural health indicator matrix to control the robot maintenance according to the maintenance status.

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

10. 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 8.

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