Rust removal robot control method and system

Abstract

The present disclosure provides a rust removal robot control method and system, which belongs to the field of robot control technology. The method includes: based on the visual sensor of the rust removal robot, monitoring the target area to obtain first rust data; based on the first rust data, determining the target rust state of the target area; and controlling the rust removal robot based on the target rust state of the target area. The rust removal robot control method and system provided by the present disclosure obtain the first rust data of the target area through the visual sensor and determine the target rust state, which can achieve accurate judgment of the rust situation and improve accuracy. According to different target rust states, the rust removal robot is controlled in a targeted manner to avoid excessive or insufficient rust removal, thereby improving the rust removal efficiency, ensuring the rust removal quality, and adapting to different application scenarios and needs.

Description

Technical Field

[0001] The present disclosure belongs to the field of robot control technology, and more specifically, relates to a rust removal robot control method and system. Background Art

[0002] In many fields, such as industrial production and infrastructure construction, metal equipment or components are susceptible to oxidative corrosion and rust due to long-term exposure to the natural environment. Rust not only affects the appearance of the equipment but, more seriously, reduces its structural strength, service life, and performance, potentially leading to safety accidents and significant economic losses. Rust removal methods mainly rely on workers using handheld rust removal tools such as sandpaper and electric grinding wheels to polish the rusted areas. However, this method has many drawbacks. First, manual rust removal is inefficient and requires a significant amount of manpower and time for large areas of rust. Second, rust removal work environments are often harsh, with hazards such as dust and noise, and long-term work can seriously damage workers' health. With the continuous advancement of technology, robotic technology has been gradually applied to rust removal to address the problems of manual rust removal. However, existing rust removal robots have shortcomings in control and are unable to accurately and efficiently perform rust removal operations based on the rust status of different areas, resulting in poor rust removal results and significant waste of resources. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a rust removal robot control method and system to solve the problem that existing rust removal robots cannot accurately perform efficient rust removal operations according to the rust state.

[0004] A first aspect of the embodiments of the present disclosure provides a rust removal robot control method, comprising:

[0005] Based on the visual sensor of the rust removal robot, the target area is monitored to obtain the first rust data;

[0006] Processing the first corrosion data based on a first random forest model to obtain a plurality of recognition results of difference, and determining a target corrosion state of the target area based on a comparison result of the plurality of recognition results of difference with the first difference;

[0007] The rust removal robot is controlled based on the target corrosion state of the target area.

[0008] A second aspect of the embodiments of the present disclosure provides a rust removal robot control system, comprising:

[0009] A detection module is used to monitor the target area based on the visual sensor of the rust removal robot to obtain first rust data;

[0010] a state determination module, configured to process the first corrosion data based on a first random forest model to obtain a plurality of difference degrees of recognition results, and determine a target corrosion state of the target area based on a comparison result of the difference degrees of the plurality of recognition results with the first difference degree;

[0011] A control module is used to control the rust removal robot based on the target corrosion state of the target area.

[0012] The beneficial effects of the rust removal robot control method and system provided by the disclosed embodiments are as follows: The present invention uses a visual sensor to obtain first rust data of a target area and utilizes advanced image processing and machine learning techniques to determine the target rust state, enabling precise judgment of the rust condition and improving accuracy. The rust removal robot is controlled specifically based on the target rust state, avoiding over- or under-removal. This improves rust removal efficiency, ensures removal quality, and adapts to different application scenarios and needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0014] Figure 1 A schematic flow chart of a rust removal robot control method according to an embodiment of the present disclosure;

[0015] Figure 2 This is a structural block diagram of a rust removal robot control system provided in one embodiment of the present disclosure;

[0016] Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0018] To make the purpose, technical solutions and advantages of this disclosure clearer, Figure 1-3 The following description will be given through specific examples.

[0019] Please refer to Figure 1 , Figure 1 This is a flow chart of a rust removal robot control method provided in one embodiment of the present disclosure, the method comprising:

[0020] S101: Based on the visual sensor of the rust removal robot, the target area is monitored to obtain first rust data.

[0021] In this embodiment, the visual sensor uses a high-resolution camera capable of capturing the target area from all directions and angles, acquiring image data containing information such as the target area's surface color and texture. Initial processing of the image data, such as grayscaling and noise reduction, yields first corrosion data. The first corrosion data, obtained by the visual sensor through monitoring the target area, includes information such as the corrosion area, corrosion severity, and corrosion texture. The target area refers to the monitored object, such as the steel beams of a large steel bridge or the metal casing of industrial equipment.

[0022] In addition, the visual sensor can be a fixed-position visual sensor that can monitor the rust status in a preset area (i.e., a target area). In addition to being installed on the rust removal robot body, the installation location also includes other locations that are conducive to data collection.

[0023] S102: Processing the first corrosion data based on the first random forest model to obtain differences of multiple recognition results, and determining a target corrosion state of the target area based on a comparison result of the differences of the multiple recognition results with the first difference.

[0024] In this embodiment, the first random forest model is trained based on a large amount of corrosion sample data and has powerful pattern recognition and data analysis capabilities. The first corrosion data is processed in parallel by multiple decision trees, and each decision tree classifies and judges the first corrosion data according to its own algorithm rules, thereby generating multiple recognition results. These recognition results are not completely consistent and there are differences to a certain extent. The difference between these recognition results is calculated, and the difference is compared with the pre-set first difference. If the difference between multiple recognition results is less than the first difference, it indicates that the model's judgment on the corrosion state of the target area is relatively concentrated and stable, and it can be determined that the target area is in a relatively single and clear target corrosion state; on the contrary, if the difference is greater than the first difference, it means that there are large differences in the recognition results, and further analysis or other means are needed to determine the target corrosion state. The first difference can be set based on experience or the requirements of the user.

[0025] S103: Controlling the rust removal robot based on the target rust state of the target area.

[0026] In this embodiment, different control strategies or control methods can be set according to the target rust state. For example, if the target rust state is light rust, and only a thin layer of rust has formed on the surface of the object, the laser power output by the robot is controlled to be low and the scanning speed is fast. Or the high-precision polishing device carried by the robot is controlled to operate at a lower speed to polish the rusted surface. At the same time, the matching vacuum equipment is controlled to suck away the rust chips generated by polishing. If the target rust state is moderate rust, the rust layer has a certain thickness, posing a potential threat to the structure of the object. At this time, the laser power output by the robot is controlled to be medium and the scanning speed is fast. If the target rust state is severe rust, the rust layer is thick and may have penetrated deep into the object, seriously affecting the structural strength. At this time, the laser power output by the robot is controlled to be adjusted to the highest level, so that the laser continuously bombards the rusted area with high-frequency, high-energy pulses, and the scanning speed is slow.

[0027] As can be seen from the above, the present invention uses a visual sensor to obtain the first rust data of the target area and uses advanced image processing and machine learning techniques to determine the target rust state. This allows for precise judgment of the rust condition and improves accuracy. Based on the different target rust states, the rust removal robot is controlled in a targeted manner, avoiding over- or under-removal. This improves rust removal efficiency, ensures quality, and adapts to different application scenarios and needs.

[0028] In one embodiment of the present disclosure, determining a target rust state of a target area based on a comparison result of the difference between a plurality of recognition results and a first difference includes: in response to a difference between a plurality of recognition results being greater than or equal to the first difference, monitoring the target area based on an ultrasonic sensor built into the rust removal robot to obtain second rust data; determining target rust data based on the first rust data and the second rust data; in response to a difference between a plurality of recognition results being less than the first difference, using the first rust data as the target rust data; and determining a target rust state of the target area based on the target rust data.

[0029] Specifically, given that the target area can be curved and have curvature, while some vision sensors have a certain degree of depth perception, they may not be able to accurately measure the thickness of the rust layer solely relying on them. Rust hidden in gaps, holes, or depressions cannot be fully detected, resulting in an incomplete assessment of the rust status of the target area. For example, when inspecting mechanical parts with complex structures, the internal rust layer may not be accurately perceived by the vision sensor.

[0030] The ultrasonic sensor transmits ultrasonic waves to the target area and receives the reflected ultrasonic signals. Based on the propagation characteristics and reflection conditions of ultrasonic waves in different media (such as rust layers and metal bodies), it analyzes and obtains secondary corrosion data. The secondary corrosion data can reflect information such as the thickness of the rust layer, the internal structure, and whether there are hidden rust areas. It supplements and verifies the primary corrosion data to better complete the monitoring of the target area.

[0031] As can be seen from the above, the present disclosure processes the first corrosion data using the first random forest model, fully leveraging the ensemble learning advantages of the random forest algorithm and combining the outputs of multiple decision trees to effectively reduce the recognition errors that may be introduced by a single decision tree. When the differences between the multiple recognition results are significant, the ultrasonic sensor is activated for supplementary monitoring, enabling this embodiment to automatically respond to special monitoring environments, such as the internal structure of the target area, thereby improving the accuracy and robustness of monitoring.

[0032] In this embodiment, determining the target corrosion data based on the first corrosion data and the second corrosion data includes: performing feature fusion on the first corrosion data and the second corrosion data, and performing weighted calculation to obtain the target corrosion data.

[0033] Specifically, first corrosion data from a visual sensor and second corrosion data from an ultrasonic sensor are received, key features (such as corrosion area, corrosion depth, and rust spot distribution density) are extracted, and different weights are assigned to the first corrosion data and the second corrosion data.

[0034] In this embodiment, the first corrosion data and the second corrosion data are subjected to feature fusion and weighted calculation to obtain target corrosion data, including:

[0035] determining a first weight adjustment step size based on the curvature of the target area;

[0036] Alternatively, the first weight adjustment step is determined based on the standard deviation difference rate between the first corrosion data and the second corrosion data;

[0037] Adjusting the first weight reference value based on the first weight adjustment step to obtain a first weight; adjusting the second weight reference value based on the first weight adjustment step to obtain a second weight;

[0038] Performing weighted calculation based on the first weight, the second weight, the first corrosion data, and the second corrosion data to obtain target corrosion data;

[0039] The first weight is a weight corresponding to the first corrosion data, the second weight is a weight corresponding to the second corrosion data, and the adjustment directions of the first weight reference value and the second weight reference value are different.

[0040] Specifically, the first weight adjustment step is determined based on the curvature of the target area: the curvature of the target area plays an important role in reflecting the complexity of the surface morphology of the area. The larger the curvature, the more drastic the surface changes in the area. For the fusion of the first corrosion data and the second corrosion data, we can determine the first weight adjustment step based on a certain mapping relationship between the curvature and the weight adjustment step. For example, a correspondence table between the curvature range and the weight adjustment step can be set in advance. After obtaining the curvature value of the target area, the corresponding first weight adjustment step is determined by searching the correspondence table. Alternatively, the curvature value is used as input and the first weight adjustment step is calculated by a specific algorithm.

[0041] The curvature of the target area is obtained through laser scanning, and if the curvature is high (C>C1), the ultrasonic weight step size is increased (because visual perception is easily distorted on curved surfaces). The formula for calculating the first weight adjustment step size is Δw = k × (C-C1), where k is the proportional coefficient, Δw is the first weight adjustment step size, and C1 is the preset curvature threshold.

[0042] Alternatively, the first weight adjustment step size can be determined based on the standard deviation difference rate between the first and second rust data: the standard deviation reflects the degree of data dispersion. Calculate the standard deviation of the first rust data and the standard deviation of the second rust data, then calculate their standard deviation difference rate. The first weight adjustment step size is determined based on the relationship between the standard deviation difference rate and the first weight adjustment step size. For example, when the standard deviation difference rate is large, it indicates that the degree of dispersion of the first and second rust data is significantly different. In this case, the first weight adjustment step size can be appropriately increased to better balance the weight distribution of the two types of data during the fusion process. Conversely, when the standard deviation difference rate is small, the first weight adjustment step size can be reduced. Similarly, we can pre-set a correspondence table between the standard deviation difference rate range and the weight adjustment step size, or adjust the first weight adjustment step size based on the comparison result of the difference rate calculated by the formula and the standard deviation threshold.

[0043] The formula for calculating the difference rate is:

[0044] R=

[0045] in, σ 1 is the standard deviation of the first corrosion data, σ 2 is the standard deviation of the second corrosion data; Δw=g( R ), g is a function expression determined according to actual conditions.

[0046] In this embodiment, the first weight reference value is adjusted based on the first weight adjustment step to obtain the first weight; the second weight reference value is adjusted based on the first weight adjustment step to obtain the second weight. It should be noted here that the adjustment directions of the first weight reference value and the second weight reference value are different. For example, assuming that the first weight reference value is w1, the second weight reference value is w2, and w1+w2=1. When the first weight adjustment step Δw is a positive number, the first weight w r1 =w1+Δw, the second weight w r2 =w2−Δw; when the first weight adjustment step Δw is negative, the first weight w r1 =w1+Δw, the second weight w r2 =w2−Δw. This adjustment ensures that the sum of the first weight and the second weight is always 1, and also reflects the reasonable adjustment of the weights of the two data according to different conditions.

[0047] Based on the first weight, the second weight, the first corrosion data and the second corrosion data, a weighted calculation is performed to obtain the target corrosion data. Assuming that the first corrosion data is D1 and the second corrosion data is D2, the target corrosion data D=w r1 ×D1+w r2 ×D2. By using this weighted calculation method, the first corrosion data and the second corrosion data are feature-fused to obtain target corrosion data that can more accurately reflect the corrosion situation of the target area.

[0048] In this embodiment, determining the target corrosion state of the target area based on the target corrosion data includes:

[0049] Perform feature extraction on target corrosion data to obtain corrosion feature data;

[0050] Calculate the target similarity between the corrosion feature data and each corrosion state in the corrosion state library;

[0051] A target corrosion state is selected from the corrosion state library based on target similarity.

[0052] In this embodiment, feature extraction is performed on the target rust data to obtain information such as rust area, rust depth, and rust spot distribution density. The target similarity between the rust feature data and each rust state in the rust state library is calculated. The rust state library is pre-constructed and stores a large number of known rust states and their corresponding standard rust features. The extracted rust feature data is then compared with the standard features of the rust states in the rust state library. When calculating similarity, a variety of suitable algorithms can be used, such as the cosine similarity algorithm, which measures the degree of similarity by calculating the cosine of the angle between two vectors (here, the rust feature data vector and the standard rust feature vector); or the Euclidean distance algorithm, which calculates the straight-line distance between two feature vectors in multidimensional space to obtain similarity.

[0053] In this embodiment, a target corrosion state is selected from a corrosion state library based on target similarity. After obtaining the target similarities between the corrosion feature data and each corrosion state, these similarity values ​​are compared to identify the corrosion state with the highest similarity. This corrosion state with the highest similarity becomes the target corrosion state for the target area determined based on the target corrosion data. For example, if the calculated similarity between the corrosion feature data and the "moderate corrosion" state in the corrosion state library is the highest, the target corrosion state for the target area is determined to be "moderate corrosion."

[0054] In this embodiment, a target corrosion state of a target area is determined based on target corrosion data, including: performing feature extraction on the target corrosion data to obtain texture complexity features, oxide layer thickness features, and porosity distribution features; determining a first corrosion state based on matching the texture complexity features with a corrosion state map; performing a first correction on the first corrosion state based on the oxide layer thickness features to obtain a second corrosion state; and performing a second correction on the second corrosion state based on the porosity distribution features to obtain a target corrosion state of the target area.

[0055] Specifically, the rust state atlas is a pre-constructed reference atlas that includes the correspondence between different texture complexity features and rust states. The extracted texture complexity features are compared one by one with each condition in the atlas. Using a matching algorithm (such as a pattern recognition algorithm), the rust state that best matches the current texture complexity features is found, and the first rust state is determined. Oxide layer thickness is an important indicator of rust severity, and different oxide layer thicknesses often correspond to different stages of rust development. The extracted oxide layer thickness feature data is compared with the theoretical oxide layer thickness range corresponding to the first rust state. If the actual oxide layer thickness exceeds the theoretical oxide layer thickness range, the first rust state is adjusted based on the deviation. If the actual oxide layer thickness exceeds the theoretical range, the first rust state is adjusted to a higher level based on the deviation. Conversely, if the actual oxide layer thickness is thinner, the rust state is adjusted to a lower level, resulting in the second rust state. The extracted porosity distribution feature data is compared and analyzed with the theoretical porosity distribution range corresponding to the second rust state. Based on the difference in porosity distribution, the second corrosion state is adjusted again. If the porosity is too large or the unevenness of the pore distribution exceeds the theoretical range, it indicates that there are special conditions in the corrosion state, and further corrections are needed to the second corrosion state to finally obtain the target corrosion state of the target area.

[0056] The extracted texture complexity features, oxide layer thickness features and porosity distribution features are quantified. Assuming that F T is the quantized value of texture complexity feature, F O is the characteristic quantitative value of the oxide layer thickness, FP is the quantitative value of porosity distribution characteristics; M T The mapping function represents the matching relationship between the texture complexity feature and the rust state map. The rust states in the rust state map include: S1, S2, ..., S n , the first corrosion state S is obtained by matching the function first , the formula is: S first =M T (F T ), M T It is a classification function based on pattern recognition or machine learning. For example, a support vector machine (SVM) model can be trained, the texture complexity feature quantization value FT is input, and the corresponding rust state category is output.

[0057] Correct the first corrosion state and quantify the thickness of the oxide layer according to the characteristic value F O For the first rust state S first Adjust to get the second rust state S second. The formula is: S second =C O (S first , F O ), where C O is the first correction function.

[0058] Assume T O1 and T O2 are two threshold values ​​of oxide thickness, and T O1 <T O2 , the first correction rule is as follows:

[0059] F O <T O1 , then S second =S first −ΔS O ,

[0060] T O1 ≤F O <T O2 , then S second =S first

[0061] F O ≥T O2 , then S second =S first +ΔS O

[0062] Where, ΔS O is the first calibration step.

[0063] According to the porosity distribution characteristic quantitative value F P For the second corrosion state Ssecond Perform correction to obtain the final target corrosion state S target The formula is as follows: target =C P (S second , F P ), C P is the second correction function.

[0064] Set the threshold T of porosity distribution characteristics P1 and T P2 , and T P1 <T P2 , the second correction rule is as follows:

[0065] F P <T P1 , then S target =S second −ΔS P

[0066] T P1 ≤F P <T P2 , then S target =S second

[0067] F P ≥T P2 , then S target =S second +ΔS P

[0068] Where, ΔS P is the second correction step length, which is a positive integer.

[0069] In this embodiment, based on the rust state of the target area, controlling the rust removal robot includes:

[0070] Based on the target corrosion state of the target area, a target control strategy corresponding to the target corrosion state is selected from a target strategy library, where a plurality of control strategies are stored.

[0071] The laser power output by the rust removal robot is controlled based on the target control strategy.

[0072] The control strategies in the target strategy library are preliminarily sorted from high to low based on target similarity to obtain a first sorted list. An initial dynamic evaluation matrix is ​​then established, with the rows representing the control strategies in the strategy library and the columns representing strategy evaluation indicators across multiple dimensions. Each control strategy in the first sorted list is evaluated based on the initial dynamic evaluation matrix, and the evaluation results for each dimension are added to the dynamic evaluation matrix to obtain a target dynamic evaluation matrix. A comprehensive evaluation score is calculated for each control strategy in the first sorted list based on the target dynamic evaluation matrix, and the control strategy with the highest comprehensive evaluation score is selected as the target control strategy for the target corrosion state. The control strategy includes a table of basic laser power, first scanning speed, and material correction factors.

[0073] In this embodiment, the laser power output by the rust removal robot is controlled based on a target control strategy, including: determining a basic laser power based on the target rust state; and adjusting the basic laser power based on the material type of the target area to obtain the laser power of the rust removal robot. Common material types include steel, aluminum alloy, copper alloy, etc. The basic laser power is preliminarily determined based on the target rust state, and is a basic laser power calculated or preset without considering the material. The basic laser power is the final laser power determined and used to control the robot to actually perform the rust removal task. It is obtained after adjusting the basic laser power with comprehensive consideration of the material type of the target grasped object, and can ensure that the robot can safely and stably remove rust from the target area.

[0074] In this embodiment, a first scanning speed of the rust removal robot is determined based on a target control strategy; the first scanning speed is corrected based on the laser power output by the rust removal robot to obtain the scanning speed of the rust removal robot. If the laser power output by the rust removal robot is greater than a first preset threshold, the first scanning speed is corrected based on the difference between the first scanning speed of the rust removal robot and the first preset threshold to obtain the scanning speed of the rust removal robot; if the laser power output by the rust removal robot is less than or equal to the first preset threshold, the first scanning speed is corrected based on a mapping table to obtain the scanning speed of the rust removal robot; the mapping table is a mapping table of laser power and scanning speed.

[0075] When the rust removal robot outputs a high laser power, it indicates that the current laser energy is strong and may cause damage to the target area. At this point, the difference between the laser power and a first preset threshold is calculated, and the first scanning speed is corrected based on this difference. The larger the difference, the more the laser power exceeds the normal range, and the magnitude of the correction will also increase accordingly, typically by increasing the scanning speed to reduce potential damage to the material during the rust removal process. When the laser power is less than or equal to the first preset threshold, the first scanning speed is corrected using a first mapping table. The first preset threshold is used to distinguish between conventional power adjustment and over-limit protection mode. The first mapping table pre-establishes the relationship between laser power and corresponding scanning speed. Through extensive experimental testing, rust removal operations were performed on various rust samples under different laser power conditions, and the corresponding optimal scanning speeds were recorded, thereby summarizing and summarizing the mapping pattern between the two. The current laser power is then used to find the corresponding scanning speed correction value from the first mapping table, thereby determining the appropriate scanning speed for the rust removal robot, ensuring that the rust removal robot can complete the grasping task at the optimal speed under different laser power conditions. It can be concluded from the above that this embodiment ensures the accuracy and safety of the rust removal robot operation by dynamically adjusting the scanning speed to adapt to different laser powers.

[0076] Specifically, when P final >P th , the difference calculation formula is: ΔP=P final −P th , the speed correction formula is: v final =v1−Y×ΔP, where P final is the current laser power, P th is the first preset threshold, ΔP is the difference, Y is the power-speed attenuation coefficient, v final is the scanning speed, v1 is the current scanning speed. final ≤P th , select the speed correction coefficient α in the first mapping table according to the current laser power, and the speed correction formula is: v final =v1×α.

[0077] Table 1 First mapping table

[0078]

[0079] Where P is the laser power.

[0080] Corresponding to the rust removal robot control method of the above embodiment, Figure 2 This is a structural block diagram of a rust removal robot control system provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2The rust removal robot control system 20 includes: a detection module 21, a state determination module 22 and a control module 23.

[0081] Among them, the detection module 21 is used to monitor the target area based on the visual sensor of the rust removal robot to obtain first rust data; the state determination module 22 is used to process the first rust data based on the first random forest model to obtain the difference of multiple recognition results, and determine the target rust state of the target area based on the comparison result of the difference of the multiple recognition results and the first difference; the control module 23 is used to control the rust removal robot based on the target rust state of the target area.

[0082] In one embodiment of the present disclosure, the state determination module 22 is specifically used to: in response to the difference between multiple recognition results being greater than or equal to a first difference, monitor the target area based on the ultrasonic sensor built into the rust removal robot to obtain second rust data; determine target rust data based on the first rust data and the second rust data; in response to the difference between multiple recognition results being less than the first difference, use the first rust data as target rust data; and determine the target rust state of the target area based on the target rust data.

[0083] In one embodiment of the present disclosure, the state determination module 22 is specifically configured to:

[0084] The first corrosion data and the second corrosion data are subjected to feature fusion and weighted calculation to obtain target corrosion data.

[0085] In one embodiment of the present disclosure, the state determination module 22 is specifically configured to:

[0086] determining a first weight adjustment step size based on the curvature of the target area;

[0087] Alternatively, the first weight adjustment step is determined based on the standard deviation difference rate between the first corrosion data and the second corrosion data;

[0088] Adjusting the first weight reference value based on the first weight adjustment step to obtain a first weight; adjusting the second weight reference value based on the first weight adjustment step to obtain a second weight;

[0089] Performing weighted calculation based on the first weight, the second weight, the first corrosion data, and the second corrosion data to obtain target corrosion data;

[0090] The first weight is a weight corresponding to the first corrosion data, the second weight is a weight corresponding to the second corrosion data, and the adjustment directions of the first weight reference value and the second weight reference value are different.

[0091] In one embodiment of the present disclosure, the state determination module 22 is specifically configured to:

[0092] Feature extraction is performed on target corrosion data to obtain corrosion feature data; target similarity between the corrosion feature data and each corrosion state in the corrosion state library is calculated; and target corrosion state is selected from the corrosion state library based on the target similarity.

[0093] In one embodiment of the present disclosure, the control module 23 is specifically configured to:

[0094] Based on the target corrosion state of the target area, a target control strategy corresponding to the target corrosion state is selected from a target strategy library, where a variety of control strategies are stored; and based on the target control strategy, the laser power output by the rust removal robot is controlled.

[0095] In one embodiment of the present disclosure, the control module 23 is specifically configured to:

[0096] Based on the target rust state, the basic laser power is determined; based on the material type of the target area, the basic laser power is adjusted to obtain the laser power of the rust removal robot.

[0097] In one embodiment of the present disclosure, the control module 23 is specifically configured to:

[0098] The first scanning speed of the rust removal robot is determined based on the target control strategy; and the first scanning speed is corrected based on the laser power output by the rust removal robot to obtain the scanning speed of the rust removal robot.

[0099] In one embodiment of the present disclosure, the control module 23 is specifically configured to:

[0100] If the laser power output by the rust removal robot is greater than a first preset threshold, the first scanning speed is corrected based on the difference between the first scanning speed of the rust removal robot and the first preset threshold to obtain the scanning speed of the rust removal robot;

[0101] If the laser power output by the rust removal robot is less than or equal to the first preset threshold, the first scanning speed is corrected based on the mapping table to obtain the scanning speed of the rust removal robot; the mapping table is a mapping table of laser power and scanning speed.

[0102] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0103] It should be understood that in the embodiments of the present disclosure, the processor 301 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0104] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0105] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store device type information.

[0106] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the rust removal robot control method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0107] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0108] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0109] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0110] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components 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 an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0112] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present disclosure.

[0113] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0114] The above are only specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in this disclosure, and such modifications or replacements should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A rust removal robot control method, characterized in that: include: Based on the visual sensor of the rust removal robot, the target area is monitored to obtain the first rust data; Processing the first corrosion data based on a first random forest model to obtain a plurality of recognition results having different degrees of difference, and in response to the difference of the plurality of recognition results being greater than or equal to the first difference, monitoring a target area using an ultrasonic sensor built into the rust removal robot to obtain second corrosion data; and determining target corrosion data based on the first corrosion data and the second corrosion data; In response to the difference between the plurality of recognition results being less than a first difference, taking the first corrosion data as target corrosion data; determining a target corrosion state of the target area based on the target corrosion data; Selecting a target control strategy corresponding to the target corrosion state from a target strategy library based on the target corrosion state, wherein the target strategy library stores a plurality of control strategies; Determining a basic laser power based on the target corrosion state; adjusting the basic laser power based on the material type of the target area to obtain the laser power of the rust removal robot; A first scanning speed of the rust removal robot is determined based on the target control strategy; the first scanning speed is corrected based on the laser power output by the rust removal robot to obtain the scanning speed of the rust removal robot; if the laser power output by the rust removal robot is greater than a first preset threshold, the first scanning speed is corrected based on the difference between the laser power output by the rust removal robot and the first preset threshold to obtain the scanning speed of the rust removal robot; if the laser power output by the rust removal robot is less than or equal to the first preset threshold, the first scanning speed is corrected based on a mapping table to obtain the scanning speed of the rust removal robot; the mapping table is a mapping table of laser power and scanning speed.

2. The rust removal robot control method according to claim 1, characterized in that: The determining target corrosion data based on the first corrosion data and the second corrosion data includes: The first corrosion data and the second corrosion data are subjected to feature fusion and weighted calculation to obtain target corrosion data.

3. The rust removal robot control method according to claim 2, characterized in that: The feature fusion of the first corrosion data and the second corrosion data and weighted calculation to obtain target corrosion data include: determining a first weight adjustment step size based on the curvature of the target area; Alternatively, the first weight adjustment step is determined based on the standard deviation difference rate between the first corrosion data and the second corrosion data; Adjusting a first weight reference value based on the first weight adjustment step to obtain a first weight; adjusting a second weight reference value based on the first weight adjustment step to obtain a second weight; Performing weighted calculation based on the first weight, the second weight, the first corrosion data, and the second corrosion data to obtain the target corrosion data; The first weight is a weight corresponding to the first corrosion data, the second weight is a weight corresponding to the second corrosion data, and the adjustment directions of the first weight reference value and the second weight reference value are different.

4. The rust removal robot control method according to claim 3, characterized in that: The determining the target corrosion state of the target area based on the target corrosion data includes: Performing feature extraction on the target corrosion data to obtain corrosion feature data; Calculating target similarity between the corrosion feature data and each corrosion state in the corrosion state library; The target corrosion state is selected from a corrosion state library based on the target similarity.

5. A rust removal robot control system, characterized in that: include: A detection module is used to monitor the target area based on the visual sensor of the rust removal robot to obtain first rust data; a state determination module configured to process the first corrosion data based on a first random forest model to obtain a plurality of recognition results having different degrees of difference; and in response to the plurality of recognition results having different degrees of difference being greater than or equal to the first difference, monitor a target area using an ultrasonic sensor built into the rust removal robot to obtain second corrosion data; and determine target corrosion data based on the first corrosion data and the second corrosion data; In response to the difference between the plurality of recognition results being less than a first difference, taking the first corrosion data as target corrosion data; determining a target corrosion state of the target area based on the target corrosion data; A control module, configured to select a target control strategy corresponding to the target corrosion state from a target strategy library based on the target corrosion state, wherein the target strategy library stores a plurality of control strategies; Determining a basic laser power based on the target corrosion state; adjusting the basic laser power based on the material type of the target area to obtain the laser power of the rust removal robot; A first scanning speed of the rust removal robot is determined based on the target control strategy; the first scanning speed is corrected based on the laser power output by the rust removal robot to obtain the scanning speed of the rust removal robot; if the laser power output by the rust removal robot is greater than a first preset threshold, the first scanning speed is corrected based on the difference between the laser power output by the rust removal robot and the first preset threshold to obtain the scanning speed of the rust removal robot; if the laser power output by the rust removal robot is less than or equal to the first preset threshold, the first scanning speed is corrected based on a mapping table to obtain the scanning speed of the rust removal robot; the mapping table is a mapping table of laser power and scanning speed.

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