Self-adaptive high-altitude machine control system and method

Abstract

The invention discloses a self-adaptive high-altitude machine control system and method, and relates to the field of intelligent sandblasting algorithm control, and the self-adaptive sandblasting robot control system provided by the invention can provide intelligent and highly-adaptive working mode judgment for a sandblasting robot through model analysis of current environmental data and robot states, and can improve the working efficiency of the sandblasting robot. Higher operation efficiency, energy efficiency and reliability are achieved, meanwhile, manual intervention is reduced, and the automation level is improved.

Description

Technical Field

[0001] The invention relates to the field of sandblasting intelligent algorithm control, and in particular to an adaptive high-altitude machine control system and method. Background Art

[0002] An industrial robot is a machine device that can automatically perform various industrial tasks. It is usually composed of a front-end mechanical structure and a back-end control system. Industrial robots perform precise and efficient operations on the production line, and can replace or assist manpower in completing repetitive, tedious or dangerous tasks. The application range of industrial robots is very wide, including automobile manufacturing, electronic product assembly, metal processing, logistics warehousing and other fields. They can perform various tasks such as welding, assembly, handling, spraying, grinding, quality inspection, etc. Through the Internet of Things technology and data analysis and processing, industrial robots can achieve highly flexible movements and precise operations, improve equipment intelligence, production efficiency, quality and safety.

[0003] In the control system of the high-altitude sandblasting robot, it is necessary to select the working mode, especially the switch between "sandblasting" and "re-sanding", which mainly depends on the operator's experience and judgment. The robot is usually programmed to perform predetermined tasks under specific conditions, including the intensity, angle and working area of ​​sandblasting. The operator needs to manually set these parameters before the operation starts, and decide whether the robot should perform sandblasting or re-sanding based on the estimated operation requirements. In actual operation, the operator needs to monitor the working environment and the status of the robot in real time to determine whether the working mode needs to be switched. For example, if the sandblasting effect is detected to be poor or the sandblasting of a specific area is completed, the operator will manually instruct the robot to switch to the re-sanding mode. This method relies on the operator's understanding of the working environment and the mastery of the robot's performance.

[0004] Existing high-altitude sandblasting robots rely too much on manual judgment in selecting working modes, and have limitations in terms of high efficiency and high adaptability. In order to improve working efficiency and adaptability, it is very necessary to introduce a more intelligent working mode selection mechanism. Summary of the invention

[0005] The present invention provides an adaptive high-altitude machine control system and method, which aims to assist in decision-making through intelligent algorithms and select the working mode that best suits the current conditions.

[0006] Among them, an adaptive high-altitude machine control system includes:

[0007] A data collection module is used to collect climate status data, machine status data and material property data, and standardize the data;

[0008] A model training module is used to train the GBM model based on the standardized climate state data, machine state data and material property data as features;

[0009] A model prediction module, used to predict the working mode of the robot according to the output result of the GBM model, wherein the working mode includes a sandblasting mode and a sand return mode;

[0010] A decision output module is used to select the working mode of the robot according to the prediction results output by the model; wherein the model training module specifically includes:

[0011] A model initialization unit is used to set an initial prediction value, which is an average value or a constant of the target variable; and to set an initial model: F0(x) = average(y);

[0012] The residual calculation unit is used to calculate the residual between the current model and the actual target value;

[0013] The decision tree fitting unit is used to fit a new decision tree and predict the current residual;

[0014] Model update unit, used to update the current model and integrate the new decision tree output;

[0015] The model iteration unit is used to repeatedly build new trees and update the model until a predetermined number of iterations is reached or the model performance is optimal.

[0016] Furthermore, in the residual calculation unit, the calculation process of calculating the residual between the current model and the actual target value is:

[0017]

[0018] Among them, the r ti represents the residual of the i-th data point in the t-th iteration, and the L(y i ,F(x i )) represents the loss function, which is used to measure the prediction model F(x i ) and the actual target value y i The difference between the i represents the actual target value of the ith data point, the F(x i ) represents the predicted value of the model at the i-th data point, and the F t-1 (x) represents the model after the t-1th round of iteration. represents partial derivative.

[0019] Furthermore, in the decision tree fitting unit, a new decision tree is fitted to predict the current residual fitting process as follows: decision tree h t (x) By learning the dataset Perform residual fitting, where N represents the total number of data points in the data set.

[0020] Furthermore, the model updating unit updates the current model and integrates the new decision tree output as follows: t (x) = F t-1 (x)+α·h t (x) is updated, where α represents the learning rate.

[0021] Furthermore, the climate status data at least includes temperature, humidity and wind speed, the machine status data at least includes sandblasting intensity and sandblasting speed, and the material property data at least includes sandblasting type and particle size.

[0022] An adaptive high-altitude machine control method is implemented based on any of the above-mentioned adaptive high-altitude machine control systems, comprising the following steps:

[0023] S1. Collect climate status data, machine status data and material property data, and standardize the data;

[0024] S2. Perform GBM model training based on the standardized climate state data, machine state data and material property data as features;

[0025] S3. According to the output result of the GBM model, the working mode of the robot is predicted, wherein the working mode includes a sandblasting mode and a sand return mode;

[0026] S4. Select the robot's working mode according to the prediction results output by the model;

[0027] Wherein, the step S3 specifically includes the following sub-steps:

[0028] S301. Set an initial prediction value, which is the average value or constant of the target variable; and set the initial model: F0(x) = average(y);

[0029] S302. Calculate the residual between the current model and the actual target value;

[0030] S303. Fit a new decision tree and predict the current residual;

[0031] S304. Update the current model and integrate the new decision tree output;

[0032] S305. Repeat building new trees and updating the model until a predetermined number of iterations is reached or the model performance is optimal.

[0033] Furthermore, in step S302, the calculation process of calculating the residual between the current model and the actual target value is:

[0034]

[0035] Among them, the r ti represents the residual of the i-th data point in the t-th iteration, and the L(y i ,F(x i )) represents the loss function, which is used to measure the prediction model F(x i ) and the actual target value y i The difference between the i represents the actual target value of the ith data point, the F(x i ) represents the predicted value of the model at the i-th data point, and the F t-1 (x) represents the model after the t-1th round of iteration. represents partial derivative.

[0036] Furthermore, in step S303, a new decision tree is fitted to predict the current residual. The fitting process is: decision tree h t (x) By learning the dataset Perform residual fitting, where N represents the total number of data points in the data set.

[0037] Furthermore, in step S304, the current model is updated and the update process for integrating the new decision tree output is as follows: t (x) = F t-1 (x)+α·h t (x) is updated, where α represents the learning rate.

[0038] Furthermore, the step S1 specifically includes the following sub-steps:

[0039] S101. Collecting climate status data, machine status data and material attribute data, wherein the climate status data includes at least temperature, humidity and wind speed, the machine status data includes at least sandblasting intensity and sandblasting speed, and the material attribute data includes at least sandblasting type and particle size;

[0040] S102. Standardize parameters of individual parameters in the climate state data, machine state data, and material property data, respectively;

[0041] S103. Perform weighted average on the standardized climate state data, machine state data and material property data respectively to obtain a comprehensive climate state index, a comprehensive machine state index and a comprehensive material property index, and use the comprehensive climate state index, the comprehensive machine state index and the comprehensive material property index as input features of the model.

[0042] The beneficial effects of the invention are:

[0043] (1) The adaptive sandblasting robot control system proposed in the present invention analyzes the current environmental data and robot status through the GBM model, which can provide the sandblasting robot with intelligent and highly adaptive working mode judgment, achieve higher operating efficiency, energy efficiency and reliability, while reducing manual intervention and improving the level of automation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A method flow chart of an adaptive high-altitude machine control method provided by an embodiment of the present invention;

[0045] Figure 2 It is a structural schematic diagram of a terminal device for adaptively controlling an aerial machine proposed in an embodiment of the present invention;

[0046] Figure 3 It is a structural schematic diagram of a product of an adaptive aerial machine control system proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention. It should be noted that relational terms such as the terms "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0050] Moreover, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus. In the absence of more restrictions, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0051] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0052] An adaptive high-altitude machine control system, comprising:

[0053] A data collection module is used to collect climate status data, machine status data and material property data, and standardize the data;

[0054] A model training module is used to train the GBM model based on the standardized climate state data, machine state data and material property data as features;

[0055] A model prediction module, used to predict the working mode of the robot according to the output result of the GBM model, wherein the working mode includes a sandblasting mode and a sand return mode;

[0056] A decision output module is used to select the working mode of the robot according to the prediction results output by the model; wherein the model training module specifically includes:

[0057] A model initialization unit is used to set an initial prediction value, which is an average value or a constant of the target variable; and to set an initial model: F0(x) = average(y);

[0058] The residual calculation unit is used to calculate the residual between the current model and the actual target value;

[0059] Decision tree fitting unit, used to fit a new decision tree and predict the current residual;

[0060] Model update unit, used to update the current model and integrate the new decision tree output;

[0061] The model iteration unit is used to repeatedly build new trees and update the model until a predetermined number of iterations is reached or the model performance is optimal.

[0062] Furthermore, in the residual calculation unit, the calculation process of calculating the residual between the current model and the actual target value is:

[0063]

[0064] Among them, the r ti represents the residual of the i-th data point in the t-th iteration, and the L(y i ,F(x i )) represents the loss function, which is used to measure the prediction model F(x i ) and the actual target value y i The difference between the i represents the actual target value of the ith data point, the F(x i ) represents the predicted value of the model at the i-th data point, and the F t-1 (x) represents the model after the t-1th round of iteration. represents partial derivative.

[0065] Furthermore, in the decision tree fitting unit, a new decision tree is fitted to predict the current residual fitting process as follows: decision tree h t (x) By learning the dataset Perform residual fitting, where N represents the total number of data points in the data set.

[0066] Furthermore, the model updating unit updates the current model and integrates the new decision tree output as follows: t (x) = F t-1 (x)+α·h t (x) is updated, where α represents the learning rate.

[0067] Furthermore, the climate status data includes at least temperature, humidity and wind speed, the machine status data includes at least sandblasting intensity and sandblasting speed, and the material property data includes at least sandblasting type and particle size. It should be noted that the parameters in the data combination can be replaced and modified according to actual conditions, and the type of parameter only indicates the degree of influence of the parameter on the work of the sandblasting robot.

[0068] Furthermore, for the above embodiment, a possible output result is proposed, which is as follows:

[0069] The GBM model outputs a probability score between 0 and 1, indicating the possibility of the "sandblasting" mode. A threshold (such as 0.3) is set to determine the working mode it is suitable for, namely:

[0070] P 喷砂 =GBM (comprehensive climate state index, comprehensive machine state index, comprehensive machine state index, comprehensive material properties);

[0071] The decision logic for its working mode is: when P 喷砂 ≥0.3, then select the sandblasting mode. 喷砂It should be noted that the above thresholds are only for illustration, and the setting range of the thresholds can be selected and modified according to the output type of the GBM model or the type of task used.

[0072] As a further preferred solution of the above embodiment, an adaptive high-altitude machine control method is proposed. The method is implemented based on any of the above adaptive high-altitude machine control systems.

[0073] like Figure 1 , including the following steps:

[0074] S1. Collect climate status data, machine status data and material property data, and standardize the data;

[0075] S2. Perform GBM model training based on the standardized climate state data, machine state data and material property data as features;

[0076] S3. According to the output result of the GBM model, the working mode of the robot is predicted, wherein the working mode includes a sandblasting mode and a sand return mode;

[0077] S4. Select the robot's working mode according to the prediction results output by the model;

[0078] Wherein, the step S3 specifically includes the following sub-steps:

[0079] S301. Set an initial prediction value, which is the average value or constant of the target variable; and set the initial model: F0(x) = average(y);

[0080] S302. Calculate the residual between the current model and the actual target value;

[0081] S303. Fit a new decision tree and predict the current residual;

[0082] S304. Update the current model and integrate the new decision tree output;

[0083] S305. Repeat building new trees and updating the model until a predetermined number of iterations is reached or the model performance is optimal.

[0084] Furthermore, in step S302, the calculation process of calculating the residual between the current model and the actual target value is:

[0085]

[0086] Among them, the r ti represents the residual of the i-th data point in the t-th iteration, and the L(y i ,F(x i)) represents the loss function, which is used to measure the prediction model F(x i ) and the actual target value y i The difference between the i represents the actual target value of the ith data point, the F(x i ) represents the predicted value of the model at the i-th data point, and the F t-1 (x) represents the model after the t-1th round of iteration. represents partial derivative.

[0087] Furthermore, in step S303, a new decision tree is fitted to predict the current residual. The fitting process is: decision tree h t (x) By learning the dataset Perform residual fitting, where N represents the total number of data points in the data set.

[0088] Furthermore, in step S304, the current model is updated and the update process for integrating the new decision tree output is as follows: t (x) = F t-1 (x)+α·h t (x) is updated, where α represents the learning rate.

[0089] Furthermore, the step S1 specifically includes the following sub-steps:

[0090] S101. Collecting climate status data, machine status data and material attribute data, wherein the climate status data includes at least temperature, humidity and wind speed, the machine status data includes at least sandblasting intensity and sandblasting speed, and the material attribute data includes at least sandblasting type and particle size;

[0091] S102. Standardize the individual parameters in the climate state data, the machine state data, and the material property data. For example, a set of standardization processes is proposed, as follows:

[0092] For climate status data, calculate the average values ​​of temperature, humidity, and wind speed μ respectively. 温度 , μ 湿度 , μ 风速 and standard deviation σ 温度 , σ 湿度 , σ 风速 , the temperature, humidity and wind speed are standardized by the standardized formula, namely:

[0093]

[0094]

[0095]

[0096] Among them, the std represents the standardized value of the corresponding parameter

[0097] For the machine status data, calculate the average values ​​of the blasting intensity and blasting speed μ respectively. 强度 , μ 速度 and standard deviation σ 强度 , σ 速度 , the sandblasting intensity and sandblasting speed are standardized by the standardized formula, namely:

[0098]

[0099]

[0100] For material property standardization, the mean and standard deviation of the particle size are calculated and the particle size is standardized using the standardization formula, namely:

[0101]

[0102] The sandblasting type was standardized using one-hot coding;

[0103] S103. Perform weighted average on the standardized climate state data, machine state data and material attribute data respectively to obtain a comprehensive climate state index, a comprehensive machine state index and a comprehensive material attribute index. The comprehensive climate state index, the comprehensive machine state index and the comprehensive material attribute index are used as input features of the model. Exemplarily, a calculation process for weighted average is proposed, which is as follows:

[0104] Comprehensive climate state index = w1·temperature std +w2·Humidity std +w3 Wind speed std ;

[0105] Comprehensive machine condition index = w4·strength std +w5 Speed std ;

[0106] Comprehensive material property index = w6·particle size std +w7 Sandblasting Type std ;

[0107] Among them, w1, w2, w3, w4, w5, w6, and w7 represent the weight values ​​of the corresponding parameters. In addition, w1+w2+w3=1, w4+w5=1, and w6+w7=1 to meet the weight requirements. In addition, in the actual production process, other parameters can also be considered as features. The selection method is mainly based on the degree of influence on the working mode of the sandblasting robot. For example, three-dimensional data (x, y, and z coordinates for this solution) such as machine position can be added, which belongs to machine status data, and can also be used as a separate data type. The specific standardization process is: standardize each dimension separately, and convert data of different scales to a unified scale to facilitate the training and prediction of machine learning models, that is:

[0108] Calculate the mean value μ x , μ y , μ z and standard deviation σ x , σ y , σ z ;

[0109] Normalize each dimension separately:

[0110] As a further preferred embodiment, a terminal device for adaptive high-altitude machine control is proposed, such as Figure 2 The terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0111] The memory 210 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 211 and / or a cache memory 212 , and may further include a read-only memory (ROM) 213 .

[0112] The memory 210 also stores a computer program, which can be executed by the processor 220, so that the processor 220 executes any one of the above-mentioned adaptive aerial machine control methods in the embodiments of the present application, and its specific implementation is consistent with the implementation method and the technical effect achieved in the embodiments of the above-mentioned method, and some of the contents are not repeated. The memory 210 can also include a program / utility 214 having a set (at least one) of program modules 215, such program modules include but are not limited to: an operating system, one or more application programs, other program modules, and program data, each of these examples or some combination may include the implementation of a network environment.

[0113] Accordingly, the processor 220 may execute the above-mentioned computer programs, and may execute the program / utility 214 .

[0114] The bus 230 may be a bus representing one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0115] The terminal device 200 may also communicate with one or more external devices 240 such as keyboards, pointing devices, Bluetooth devices, etc., and may also communicate with one or more devices that can interact with the terminal device 200, and / or communicate with any device (such as a router, a modem, etc.) that enables the terminal device 200 to communicate with one or more other computing devices. Such communication may be performed through an input / output (I / O) interface 250. In addition, the terminal device 200 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 260. The network adapter 260 may communicate with other modules of the terminal device 200 through a bus 230. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0116] As a further preferred embodiment, a computer-readable storage medium for adaptive high-altitude machine control is proposed, on which instructions are stored, and when the instructions are executed by a processor, any of the above-mentioned adaptive high-altitude machine control methods is implemented. The specific implementation method is consistent with the implementation method and the technical effect achieved in the embodiment of the above-mentioned method, and some contents are not repeated here.

[0117] Figure 3The program product 300 provided in this embodiment for implementing the above method is shown, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not limited to this. In this embodiment, the readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The program product 300 can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0118] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium may also be any readable medium other than the readable storage medium, which may send, propagate, or transmit a program used by or in combination with an instruction execution system, an apparatus, or a device. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the operation of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device, partially on a remote computing device, or entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0119] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. An adaptive high-altitude machine control system, characterized in that: include: A data collection module is used to collect climate status data, machine status data and material property data, and standardize the data; A model training module is used to train the GBM model based on the standardized climate state data, machine state data and material property data as features; A model prediction module, used to predict the working mode of the robot according to the output result of the GBM model, wherein the working mode includes a sandblasting mode and a sand return mode; The decision output module is used to select the robot's working mode according to the prediction results output by the model; The model training module specifically includes: A model initialization unit is used to set an initial prediction value, which is an average value or a constant of the target variable; and to set an initial model: F0(x) = average(y); The residual calculation unit is used to calculate the residual between the current model and the actual target value; Decision tree fitting unit, used to fit a new decision tree and predict the current residual; Model update unit, used to update the current model and integrate the new decision tree output; The model iteration unit is used to repeatedly build new trees and update the model until a predetermined number of iterations is reached or the model performance is optimal.

2. An adaptive aerial machine control system as claimed in claim 1, characterized in that: In the residual calculation unit, the calculation process of the residual between the current model and the actual target value is: Among them, the r ti represents the residual of the i-th data point in the t-th iteration, and the L(y i ,F(x i )) represents the loss function, which is used to measure the prediction model F(x i ) and the actual target value y i The difference between the i represents the actual target value of the ith data point, the F(x i ) represents the predicted value of the model at the i-th data point, and the F t-1 (x) represents the model after the t-1th round of iteration. represents partial derivative.

3. An adaptive aerial machine control system as claimed in claim 1, characterized in that: In the decision tree fitting unit, a new decision tree is fitted to predict the current residual. The fitting process is: decision tree h t (x) By learning the dataset Perform residual fitting, where N represents the total number of data points in the data set.

4. An adaptive aerial machine control system as claimed in claim 1, characterized in that: In the model updating unit, the updating process of updating the current model and integrating the new decision tree output is as follows: t (x) = F t-1 (x)+α·h t (x) is updated, where α represents the learning rate.

5. The adaptive aerial machine control system according to claim 1, characterized in that: The climate status data at least include temperature, humidity and wind speed, the machine status data at least include sandblasting intensity and sandblasting speed, and the material property data at least include sandblasting type and particle size.

6. An adaptive high-altitude machine control method, the method is implemented based on an adaptive high-altitude machine control system according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1. Collect climate status data, machine status data and material property data, and standardize the data; S2. Perform GBM training based on the standardized climate state data, machine state data and material property data as features; S3. According to the output result of GBM, the working mode of the robot is predicted, and the working mode includes a sandblasting mode and a sand return mode; S4. Select the robot's working mode according to the prediction results output by the model; Wherein, the step S3 specifically includes the following sub-steps: S301. Set an initial prediction value, which is the average value or constant of the target variable; and set the initial model: F0(x) = average(y); S302. Calculate the residual between the current model and the actual target value; S303. Fit a new decision tree and predict the current residual; S304. Update and integrate the new decision tree output; S305. Repeat step S304 until a predetermined number of iterations is reached or the model performance is optimal.

7. An adaptive high altitude and control method thereof as claimed in claim 6, characterized in that: In step S302, the calculation process of calculating the residual between the current model and the actual target value is: Among them, the r ti represents the residual of the i-th data point in the t-th iteration, and the L(y i ,F(x i )) represents the loss function, which is used to measure the prediction model F(x i ) and the actual target value y i The difference between the i represents the actual target value of the ith data point, the F(x i ) represents the predicted value of the model at the i-th data point, and the F t-1 (x) represents the model after the t-1th round of iteration. represents partial derivative.

8. The adaptive aerial machine control method according to claim 6, characterized in that: In step S303, a new decision tree is fitted to predict the current residual. The fitting process is as follows: t (x) By learning the dataset Perform residual fitting, where N represents the total number of data points in the data set.

9. The adaptive aerial machine control method according to claim 6, characterized in that: In step S304, the current model is updated and the update process of integrating the new decision tree output is as follows: t (x) = F t-1 (x)+α·h t (x) is updated, where α represents the learning rate.

10. The adaptive aerial machine control method according to claim 6, characterized in that: The step S1 specifically includes the following sub-steps: S101. Collecting climate status data, machine status data and material attribute data, wherein the climate status data includes at least temperature, humidity and wind speed, the machine status data includes at least sandblasting intensity and sandblasting speed, and the material attribute data includes at least sandblasting type and particle size; S102. Standardize parameters of individual parameters in the climate state data, machine state data, and material property data, respectively; S103. Perform weighted average on the standardized climate state data, machine state data and material property data respectively to obtain a comprehensive climate state index, a comprehensive machine state index and a comprehensive material property index, and use the comprehensive climate state index, the comprehensive machine state index and the comprehensive material property index as input features of the model.

Images