Construction robot operation quality online evaluation and closed-loop feedback control method

Abstract

This invention relates to the field of robotics, specifically to a method for online evaluation and closed-loop feedback control of construction robot operation quality. The method includes the following steps: operation data acquisition; operation quality evaluation, constructing a local continuous operation unit centered on the current sampling point, extracting the local quality deviation of the current sampling point and the connection deviation between the current sampling point and adjacent operation areas, jointly determining the local quality deviation and the connection deviation, and generating real-time operation quality evaluation results and deviation data for the construction robot; feedback control command generation; and closed-loop feedback dynamic execution. This invention, by constructing a local continuous operation unit centered on the current sampling point, extracting the local quality deviation and the connection deviation between adjacent operation areas, and jointly determining the two types of deviations, can consider both the local quality of the operation and the connection status of adjacent operation areas in the operation quality evaluation, thus improving the completeness of the operation quality evaluation.

Description

Technical Field

[0001] This invention relates to the field of robotics, and more specifically, to a method for online evaluation and closed-loop feedback control of the operational quality of construction robots. Background Technology

[0002] Construction robots are widely used in the field of automated construction. The evaluation of work quality and parameter control are the key to ensuring construction results. The industry continues to promote the research and development of robot work quality assessment and construction control technology to improve the accuracy and continuity of construction operations.

[0003] In the existing technology, relevant patents have been researched in the field of robot operation quality assessment and construction control. For example, invention patent CN202510262253.4 discloses a service robot operation quality assessment method and related equipment based on knowledge graphs. This scheme quantifies operation assessment indicators, relies on knowledge graphs to conduct multi-level analysis to obtain a comprehensive score, and infers the reasons for quality decline and provides optimization suggestions when the score is low, thereby improving the reliability of operation quality assessment. Another example is invention patent CN202510850019.3, which discloses a construction robot construction assessment and control method, device, and electronic equipment. This scheme locates local abnormal flow areas based on the viscosity and speed distribution of plaster, compensates for the movement trajectory of the plastering robot arm, and then detects the flatness by the plaster layer thickness. After meeting the standard, the operation area is switched, improving plastering efficiency and wall flatness.

[0004] The aforementioned existing technologies all have limitations: they only perform single-dimensional detection and evaluation of work quality, without comprehensively considering the local quality of the work and the connection effect between adjacent work areas—CN202510262253.4 only conducts single-dimensional quantitative analysis and optimization reasoning on service robot work indicators, without involving the evaluation of the connection effect between work areas; CN202510850019.3 only performs anomaly compensation and flatness detection on local areas of plastering work, without paying attention to the connection status between adjacent work areas; therefore, neither can achieve a comprehensive evaluation of work quality. In view of this, we propose an online assessment and closed-loop feedback control method for construction robot work quality. Summary of the Invention

[0005] The purpose of this invention is to provide an online evaluation and closed-loop feedback control method for the operation quality of construction robots, so as to solve the problem mentioned in the background art that the prior art only performs single-dimensional detection and evaluation of operation quality, without comprehensively considering the local quality of the operation and the connection effect between adjacent operation areas.

[0006] To address the aforementioned technical problems, this invention provides a method for online evaluation and closed-loop feedback control of construction robot operation quality, comprising the following steps: S1. Operation Data Acquisition: Real-time acquisition of the operation parameter data, position and posture data and work surface detection data of the construction robot during the operation process, and preprocessing of the operation parameter data, position and posture data and work surface detection data to obtain preprocessed operation parameter data, position and posture data and work surface detection data; S2. Operation Quality Assessment: Based on the preprocessed operation parameter data, position and posture data and work surface detection data, a local continuous operation unit is constructed with the current sampling point as the center. The local quality deviation of the current sampling point and the connection deviation between the current sampling point and the adjacent operation area are extracted. The local quality deviation and the connection deviation are jointly judged to generate the real-time operation quality assessment results and deviation data of the construction robot. S3. Feedback control command generation: Based on deviation data and combined with construction design benchmark data and preset control rules, generate control commands to correct the operation parameters of the construction robot. S4. Closed-loop feedback dynamic execution: Based on the control command for correcting the operation parameters of the construction robot, the operation parameters of the construction robot are dynamically adjusted, and after the dynamic adjustment is completed, the operation data acquisition, operation quality assessment and feedback control command generation are continued to be executed to achieve closed-loop feedback control of the operation quality of the construction robot.

[0007] As a further improvement to this technical solution, the work data acquisition in step S1 includes the following steps: S11. Organize the real-time collected operating parameter data, position and attitude data and work surface detection data to establish a one-to-one correspondence between the operating parameter data, position and attitude data and work surface detection data. S12. Perform anomaly identification on the completed and sorted operation parameter data, position and attitude data and work surface detection data, and remove operation parameter data, position and attitude data and work surface detection data that do not meet the preset range. S13. Perform continuity correction processing on the operating parameter data, position and attitude data and work surface detection data after removing abnormal data to form continuous operating parameter data, position and attitude data and work surface detection data. S14. After completing the continuous correction process, the operating parameter data, position and attitude data and the work surface detection data are processed in a unified manner to obtain the preprocessed operating parameter data, position and attitude data and work surface detection data.

[0008] As a further improvement to this technical solution, the work quality assessment in step S2 includes the following steps: S21. Based on the preprocessed operating parameter data, position and attitude data and work surface detection data, construct a local continuous work unit with the current sampling point as the center. S22. Based on local continuous operation units, extract the local quality deviation of the current sampling point; S23. Based on local continuous operation units, extract the connection deviation between the current sampling point and adjacent operation areas; S24. Jointly determine the local quality deviation of the current sampling point and the connection deviation between the current sampling point and the adjacent work area to generate the real-time operation quality assessment results and deviation data of the construction robot.

[0009] As a further improvement to this technical solution, step S22, extracting the local quality deviation of the current sampling point, includes the following steps: S211. Extract the preprocessed operating parameter data, position and attitude data, and work surface detection data corresponding to the current sampling point, and record them as the operating parameter data vector of the current sampling point. Current sampling point position and attitude data vector The detection data vector of the current sampling point working surface ; S212. Extract the preprocessed running parameter data, position and attitude data, and work surface detection data that are continuously corresponding to the current sampling point within the local continuous operation unit, and determine the running parameter reference vector respectively. Position and attitude reference vector With the working surface detection reference vector ; S213, Data vector of running parameters based on the current sampling point With running parameter reference vector Calculate the deviation value of operating parameters Based on the current sampling point position and attitude data vector With position and attitude reference vector Calculate position and attitude deviation values Based on the detection data vector of the current sampling point work surface With the working surface detection reference vector Calculate the inspection deviation value of the working surface ; S214, Based on operating parameter deviation values Position and attitude deviation values Deviation value from the work surface Construct the local quality deviation of the current sampling point : ; in: The weighting coefficient represents the deviation of operating parameters and is used to reflect the degree of influence of operating parameters on local quality deviations; The weighting coefficient represents the position and attitude deviation, used to reflect the degree of influence of position and attitude changes on local quality deviations; The weighting coefficient represents the deviation of the work surface inspection, which is used to reflect the degree of influence of the work result status on the local quality deviation; ,and , , .

[0010] As a further improvement to this technical solution, step S23, extracting the connection deviation between the current sampling point and the adjacent working area, includes the following steps: S215. Determine the boundary of the work area corresponding to the current sampling point and the boundary of the work area adjacent to the work area corresponding to the current sampling point. Extract the work surface detection data at the corresponding points of the boundary along the work area boundary corresponding to the current sampling point and the adjacent work area boundary according to the same boundary sampling order. Record them as the work surface detection data on the boundary of the work area corresponding to the current sampling point. Inspection data of the working surface at the boundary of the adjacent working area ; S216. Based on the detection data of the work surface on the boundary of the work area corresponding to the current sampling point. Inspection data of the working surface at the boundary of the adjacent working area Calculate the connection deviation components at corresponding boundary points And based on the connection deviation components of each boundary point Determine the boundary difference value between the current sampling point and the adjacent work area. ; S217. For sampling points within a local continuous work unit where adjacent work areas exist, determine the boundary difference reference value. ; S218. Based on the boundary difference value between the current sampling point and adjacent work areas. and boundary difference reference value Extract the connection deviation between the current sampling point and the adjacent work area. : ; in: This is a function for calculating the connection deviation between the boundary difference value and the boundary difference reference value.

[0011] As a further improvement to this technical solution, step S24, which involves joint judgment and generation of real-time operation quality assessment results and deviation data for the construction robot, includes the following steps: S219. Obtain the local quality deviation of the current sampling point. and the connection deviation between the current sampling point and the adjacent work area. ; S220, Local quality deviation based on the current sampling point and the connection deviation between the current sampling point and the adjacent work area. Determine the deviation combination state of the current sampling point. ; S221, Deviation combination state based on the current sampling point Determine the combination state of the deviation from the current sampling point. Corresponding joint judgment weight coefficient And the local quality deviation of the current sampling point. and the connection deviation between the current sampling point and the adjacent work area. Perform a joint determination to obtain a joint determination value. : ; in ; S222, Based on joint decision value Generate real-time operation quality assessment results and deviation data for the construction robot, whereby the deviation data includes local quality deviations at the current sampling point. Connection deviation between the current sampling point and adjacent work areas The current sampling point's deviation combination state With joint judgment value .

[0012] As a further improvement to this technical solution, in step S242, the local quality deviation of the current sampling point is considered. and the connection deviation between the current sampling point and the adjacent work area. Determine the deviation combination state of the current sampling point. Includes the following steps: S220.1, Based on a preset local quality deviation threshold Local quality deviation at the current sampling point The system identifies whether the local quality deviation exceeds a preset threshold, thus obtaining the local quality deviation identification result for the current sampling point. ; S220.2, Based on a preset connection deviation threshold The connection deviation between the current sampling point and the adjacent work area The system identifies whether the connection deviation exceeds a preset threshold, thus obtaining the connection deviation identification result between the current sampling point and the adjacent work area. ; S220.3 Identify the local quality deviation results of the current sampling point. Identification results of the connection deviation between the current sampling point and the adjacent work area By performing corresponding combinations, the deviation combination state of the current sampling point is obtained. .

[0013] As a further improvement to this technical solution, in step S3, the generation of feedback control commands includes the following steps: S31. Obtain deviation data and extract the local quality deviation of the current sampling point, the connection deviation between the current sampling point and the adjacent work area, the deviation combination status of the current sampling point and the joint judgment value from the deviation data; S32. Obtain the construction design benchmark data corresponding to the current sampling point, and determine the target operation parameters corresponding to the current sampling point based on the construction design benchmark data; S33. Based on the local quality deviation of the current sampling point, the connection deviation between the current sampling point and the adjacent work area, the deviation combination state of the current sampling point, the joint judgment value, and the target work parameters, determine the work parameter correction amount corresponding to the current sampling point. S34. Based on the operation parameter correction amount and preset control rules, generate operation parameter correction control instructions for the construction robot.

[0014] As a further improvement to this technical solution, step S33, determining the correction amount of the operation parameters corresponding to the current sampling point, includes the following steps: S33.1. Based on the deviation combination state of the current sampling point, determine the operation parameters to be corrected corresponding to the current sampling point; S33.2. Based on the local quality deviation of the current sampling point, the connection deviation between the current sampling point and the adjacent work area, and the joint judgment value, determine the deviation correction direction corresponding to the work parameter to be corrected; S33.3. Based on the target operation parameters and the deviation correction direction, determine the parameter offset corresponding to the operation parameters to be corrected; S33.4. Based on the parameter offset, determine the correction amount of the operation parameters corresponding to the current sampling point.

[0015] As a further improvement to this technical solution, the closed-loop feedback dynamic execution in step S4 includes the following steps: S41. Obtain the operation parameter correction control command for the construction robot, and parse the operation parameters to be adjusted and the parameter adjustment amount corresponding to the operation parameter correction control command for the construction robot. S42. Based on the control command for correcting the operation parameters of the construction robot, the operation parameters of the construction robot to be adjusted are dynamically adjusted to obtain the adjusted operation parameters of the construction robot. S43. Control the construction robot to continue performing operations based on the adjusted operation parameters of the construction robot; S44. After completing the dynamic adjustment, continue to execute the operation data collection, operation quality assessment and feedback control instruction generation to continuously update the adjusted construction robot operation parameters. S45. Based on the continuously updated control instructions for correcting the operation parameters of the construction robot, the system cyclically executes the dynamic adjustment of the operation parameters of the construction robot, the acquisition of operation data, the evaluation of operation quality and the generation of feedback control instructions, until a closed-loop feedback control of the operation quality of the construction robot is achieved.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a local continuous operation unit centered on the current sampling point, extracts the local quality deviation of the operation and the connection deviation of adjacent operation areas, and jointly judges the two types of deviations. It can take into account both the local quality of the operation and the connection status of adjacent operation areas in the operation quality assessment, thereby improving the completeness of the operation quality assessment. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the steps of the online evaluation and closed-loop feedback control method for the operation quality of construction robots in this invention; Figure 2 This is a schematic diagram of the steps involved in the job quality assessment method of this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figures 1-2 As shown, this embodiment provides a method for online evaluation and closed-loop feedback control of the operation quality of a construction robot, including the following steps: S1. Operation Data Acquisition: Real-time acquisition of the construction robot's operating parameters, position and posture data, and work surface detection data during operation. Preprocessing of the operating parameters, position and posture data, and work surface detection data yields preprocessed operating parameters, position and posture data, and work surface detection data. S1 includes the following steps for operation data acquisition: Specifically, S11, the real-time collected operating parameter data, position and attitude data and work surface detection data are organized to establish a one-to-one correspondence between the operating parameter data, position and attitude data and work surface detection data; specifically, during the operation of the construction robot, each data source outputs data according to its own sampling period. In order to ensure that different data can correspond to the same working state, time identification information is added to various types of data and organized based on the time identification.

[0020] When processing the data, a unified sampling time series is used as the reference series. The running parameter data, position and attitude data and the work surface detection data are matched in chronological order. When the sampling times of different data are not completely consistent, the data closest to the target sampling time is taken as the data corresponding to that sampling point, or adjacent data are interpolated to obtain the data value corresponding to the target sampling time.

[0021] Through the above processing, each sampling point corresponds to a unique set of operating parameter data, position and attitude data, and work surface detection data, thus forming a data group with a consistent structure.

[0022] Specifically, S12, anomaly identification is performed on the processed operating parameter data, position and attitude data, and work surface detection data, and any operating parameter data, position and attitude data, and work surface detection data that do not meet the preset range are removed; as follows: For the operational parameter data, the allowable range of each parameter is set according to the construction process. When the data of a certain operational parameter exceeds the corresponding range, the sampling point is marked as abnormal data. For position and attitude data, the system determines whether the position or attitude changes between adjacent sampling points are continuous. If there are obvious abrupt changes or changes that do not conform to the motion law, the data is judged as abnormal. For the detection data of the work surface, by judging the continuity of the detection data and the changes between adjacent sampling points, if there are obvious abnormal changes or missing data, it is judged as abnormal data.

[0023] For data identified as anomalous, the sampling point can be removed or marked as invalid data to avoid the anomalous data affecting subsequent processing.

[0024] Specifically, S13, the operation parameter data, position and attitude data and work surface detection data after removing abnormal data are subjected to continuity correction processing to form continuous operation parameter data, position and attitude data and work surface detection data; specifically, when the data of a certain sampling point is removed or marked as invalid, the sampling point can be compensated according to the data of its adjacent sampling points to restore the continuity of the data sequence; the compensation processing can be carried out by interpolation based on adjacent sampling points.

[0025] If multiple consecutive sampling points are abnormal or missing, the interval can be corrected as a whole by combining the valid data before and after to keep the data changes continuous.

[0026] Through the above continuity correction process, the operating parameter data, position and attitude data and work surface detection data are all made into a continuous data sequence, providing a data foundation for the subsequent construction of local continuous work units.

[0027] Specifically, in step S14, the operational parameter data, position and attitude data, and work surface detection data after continuous correction are processed in a unified manner to obtain preprocessed operational parameter data, position and attitude data, and work surface detection data. Specifically, different types of data are organized according to a unified data format to ensure consistency in data structure; simultaneously, data with different dimensions are processed uniformly as needed to make all types of data numerically comparable.

[0028] In addition, the processed operating parameter data, position and attitude data and work surface detection data are combined according to the sampling point order to form a standardized data sequence for use in subsequent work quality assessment steps.

[0029] Therefore, through the processing of steps S11-S14 above, various types of data are made to correspond to each other in time, remain continuous in sequence, and have a unified data structure, so that they can be directly used for subsequent work quality assessment steps.

[0030] S2. Operation Quality Assessment: Based on preprocessed operating parameter data, position and posture data, and work surface detection data, a local continuous operation unit is constructed centered on the current sampling point. Local quality deviations at the current sampling point and connection deviations between the current sampling point and adjacent work areas are extracted. A joint judgment is made on the local quality deviations and connection deviations to generate real-time operation quality assessment results and deviation data for the construction robot. S2 includes the following steps: In this step, to avoid the evaluation results being susceptible to instantaneous fluctuations due to quality judgment based on only a single sampling point, multiple sampling points around the current sampling point and corresponding to it are used as the analysis object to construct a local continuous operation unit, so as to characterize the actual continuous operation state of the construction robot near the current sampling position.

[0031] In specific implementation, after completing step S1, the preprocessed operating parameter data, position and attitude data, and work surface detection data are arranged according to the sampling order. Each sampling point corresponds to a set of operating parameter data, position and attitude data, and work surface detection data. For any current sampling point, taking the current sampling point as the center, consecutive sampling points adjacent to it are selected along the sampling order, and combined with the data of the current sampling point itself, to form a local continuous work unit corresponding to the current sampling point. That is to say, the local continuous work unit includes not only the data of the current sampling point, but also the data of several sampling points consecutively corresponding to it in front of and behind the current sampling point, so that the local continuous work unit can reflect the overall state of a continuous work process near the current sampling point.

[0032] "Continuous correspondence" means that the selected adjacent sampling points are continuous in sampling order and belong to adjacent positions in the same continuous operation process during the actual operation of the construction robot. In other words, the sampling points in a local continuous operation unit are not simply spliced ​​together according to their sequence numbers, but are required to be continuous in time, continuous in spatial position changes, and consistent with the current operation trajectory of the construction robot. Sampling points that do not meet the continuous operation relationship are not included in the local continuous operation unit corresponding to the current sampling point.

[0033] In the specific construction process, the number of sampling points for a local continuous operation unit can be pre-set according to the sampling density, continuity of operation, and subsequent deviation extraction requirements during the construction robot's operation. In specific implementation, the window size of the local continuous operation unit adopts the conventional local analysis window in this field. Preferably, 2-5 consecutive sampling points are selected forward and 2-5 consecutive sampling points are selected backward along the sampling sequence, centered on the current sampling point. In this embodiment, it is preferred to select 3 consecutive sampling points forward and 3 consecutive sampling points backward, meaning the local continuous operation unit contains 7 consecutive sampling points (the first 3 consecutive sampling points + the current sampling point + the last 3 consecutive sampling points), corresponding to an effective number of sampling points N=7 within the local continuous operation unit. Generally, selecting several consecutive sampling points before and after the current sampling point is sufficient to meet the local analysis requirements. When the current sampling point is located at the beginning or end of a continuous operation segment, resulting in insufficient consecutive sampling points to select before or after it, the local continuous operation unit is constructed using the actual number of available consecutive sampling points.

[0034] Furthermore, when constructing local continuous work units, it is also necessary to confirm the continuity between adjacent sampling points by combining position and attitude data. If there is a significant discontinuity in the position or attitude changes between adjacent sampling points, or if the corresponding work surface detection data indicates that the current sampling point and adjacent sampling points do not belong to the same continuous work area, then this discontinuity point is regarded as the boundary of the local continuous work unit, and sampling points will not be selected across this boundary. The local continuous work unit constructed in this way can more realistically reflect the continuous construction status of the local area where the current sampling point is located, avoiding the mixing of sampling points from different work stages, different work areas, or discontinuous work processes into the same analysis unit.

[0035] The above method yields a local continuous work unit centered on the current sampling point. Subsequently, based on this local continuous work unit, the local quality deviation of the current sampling point and the connection deviation between the current sampling point and adjacent work areas are extracted. Since the local continuous work unit includes continuous work information near the current sampling point, it can more accurately reflect the local work quality status of the current sampling point compared to judging solely based on the data of the current sampling point itself, and provides a basis for subsequent work quality assessment.

[0036] Specifically, extracting the local quality deviation of the current sampling point includes the following steps: S211. Extract the preprocessed operating parameter data, position and attitude data, and work surface detection data corresponding to the current sampling point, and record them as the operating parameter data vector of the current sampling point. Current sampling point position and attitude data vector The detection data vector of the current sampling point working surface ; In this step, for any current sampling point From the data sequence preprocessed in step S1, the data group corresponding to the sampling point is directly read, and the different types of data are represented in vector form, which facilitates subsequent unified calculation.

[0037] Specifically, it is expressed as follows: ; In the formula: This represents the data vector of operating parameters for the current sampling point; Indicates the first Each operating parameter component; This indicates the number of dimensions of the runtime parameters.

[0038] ; In the formula: This represents the attitude data vector of the current sampling point position; Indicates the first A position or attitude component; Indicates the number of dimensions in the position and orientation data.

[0039] ; In the formula: This represents the detection data vector of the current sampling point's work surface; Indicates the first Each detection data component; This indicates the number of dimensions in the work area detection data.

[0040] Each of the above data vectors can be composed of multiple components of the corresponding type. For example, the operating parameter data vector can contain multiple operating parameter components such as speed and pressure, the position and attitude data vector can contain position coordinates and attitude angle components, and the working surface detection data vector can contain detection values ​​such as working surface height or thickness.

[0041] S212. Extract the preprocessed running parameter data, position and attitude data, and work surface detection data that are continuously corresponding to the current sampling point within the local continuous operation unit, and determine the running parameter reference vector respectively. Position and attitude reference vector With the working surface detection reference vector ; In this step, the reference vector is used to characterize the overall operational level of the local continuous work unit where the current sampling point is located. Specifically, in the local continuous work unit (defined in this embodiment as...) Within the scope of this function, aggregate calculations are performed on the data corresponding to each sampling point.

[0042] The runtime parameter reference vector is: ; In the formula: This represents the reference vector for runtime parameters; Indicates the number of valid sampling points within a local continuous operation unit; Indicates the first unit within a local continuous operation unit The running parameter data vector of each sampling point.

[0043] The position and attitude reference vector is: ; In the formula: Represents the position and attitude reference vector; Indicates the first unit within a local continuous operation unit The position and orientation data vector of each sampling point.

[0044] The reference vector for surface detection is: ; In the formula: Represents the reference vector for surface detection; Indicates the first unit within a local continuous operation unit A vector of working surface detection data for each sampling point.

[0045] By using the above method, the reference vector is directly derived from the local continuous operation unit, thereby ensuring that it can reflect the continuous operation status near the current sampling point.

[0046] S213, Data vector of running parameters based on the current sampling point With running parameter reference vector Calculate the deviation value of operating parameters Based on the current sampling point position and attitude data vector With position and attitude reference vector Calculate position and attitude deviation values Based on the detection data vector of the current sampling point work surface With the working surface detection reference vector Calculate the inspection deviation value of the working surface ; In this step, the deviation value is calculated using a vector difference metric, specifically: Operating parameter deviation value Position and attitude deviation values Deviation value from the work surface The calculation is as follows: ; ; ; The three deviation values ​​mentioned above reflect the degree of deviation of the current sampling point in different dimensions.

[0047] S214, Based on operating parameter deviation values Position and attitude deviation values Deviation value from the work surface Construct the local quality deviation of the current sampling point : ; in: The weighting coefficient represents the deviation of operating parameters and is used to reflect the degree of influence of operating parameters on local quality deviations; The weighting coefficient represents the position and attitude deviation, used to reflect the degree of influence of position and attitude changes on local quality deviations; The weighting coefficient represents the deviation of the work surface inspection, which is used to reflect the degree of influence of the work result status on the local quality deviation; ,and , , .

[0048] In practical implementation, the weighting coefficient It can be set according to the characteristics of the construction process, for example: When the quality of the work is mainly affected by the forming effect of the work surface, the efficiency can be appropriately increased. The value of ; When the job quality places high demands on the robot's operational stability, the efficiency can be appropriately increased. The value of ; When high requirements are placed on path accuracy or attitude control, the accuracy can be appropriately increased. The value of .

[0049] In addition, the weighting coefficients can remain unchanged or be adjusted according to actual needs at different construction stages or in different work areas.

[0050] Furthermore, in order to illustrate local quality deviations The method for determining this will be explained below with a specific example.

[0051] For example, during the continuous operation of a construction robot, for the current sampling point The following is calculated through step S213: Operating parameter deviation value ; Position and attitude deviation value ; Deviation value of work surface inspection .

[0052] Based on the construction process requirements, the weighting coefficients are set as follows: ; Substitute the above parameters into the local quality deviation calculation formula: ; The results show that the deviation of the work surface detection accounts for a relatively high proportion of the local quality deviation at the current sampling point and has a more significant impact on the final result, indicating that the quality deviation at the current sampling point is mainly reflected in the work result.

[0053] Specifically, extracting the connection deviation between the current sampling point and adjacent work areas includes the following steps: S215. Determine the boundary of the work area corresponding to the current sampling point and the boundary of the work area adjacent to the work area corresponding to the current sampling point. Extract the work surface detection data at the corresponding points of the boundary along the work area boundary corresponding to the current sampling point and the adjacent work area boundary according to the same boundary sampling order. Record them as the work surface detection data on the boundary of the work area corresponding to the current sampling point. Inspection data of the working surface at the boundary of the adjacent working area ; Specifically, for the current sampling point First, the work area to which the device belongs is determined based on its position and attitude data, and then the boundary of the work area is further determined. At the same time, according to the construction path or work division rules, the work areas adjacent to the device are determined, and the corresponding boundaries of the adjacent work areas are extracted.

[0054] After determining the two side boundaries, the detection data of the working surface on the boundary is extracted along the boundary direction according to a unified sampling order (e.g., according to the path advancement direction or the boundary curve order), and a one-to-one correspondence is established. In this way, the sampling points on the current working area boundary and the adjacent working area boundary form corresponding point pairs in spatial location.

[0055] Therefore, we obtain the following respectively: ; ; In the formula: Indicates the first [unit] on the current region boundary. Detection data from each sampling point; Indicates the first corresponding region on the boundary of the adjacent region. Detection data from each sampling point; Indicates the number of boundary sampling points.

[0056] S216. Based on the detection data of the work surface on the boundary of the work area corresponding to the current sampling point. Inspection data of the working surface at the boundary of the adjacent working area Calculate the connection deviation components at corresponding boundary points And based on the connection deviation components of each boundary point Determine the boundary difference value between the current sampling point and the adjacent work area. ; In this step, for each set of boundary points, the difference is calculated: ; In the formula: Indicates the first The connection deviation components of each boundary point.

[0057] After obtaining the deviation components of all corresponding boundary points, a global characterization is performed to obtain the boundary difference value: ; In the formula: This indicates the boundary difference value between the current sampling point and the adjacent work area; Indicates the number of boundary sampling points.

[0058] S217. For sampling points within a local continuous work unit where adjacent work areas exist, determine the boundary difference reference value. ; In this step, the boundary difference reference value is used to characterize the normal level of boundary connection within a local continuous operation range. Specifically, within a local continuous operation unit, sampling points with adjacent operation areas are selected, and their corresponding boundary difference values ​​are statistically processed to obtain the reference value: ; In the formula: This represents the boundary difference reference value corresponding to the current sampling point; Indicates the first unit within a local continuous operation unit The boundary difference value of sampling points that meet the adjacent region conditions; This indicates the number of sampling points included in the statistics.

[0059] This method allows the boundary difference reference to be derived from the current work process itself, thereby reflecting the actual level of connection in local continuous construction.

[0060] S218. Based on the boundary difference value between the current sampling point and adjacent work areas. and boundary difference reference value Extract the connection deviation between the current sampling point and the adjacent work area. The connection deviation represents the degree of deviation of the current boundary state from the local normal connection level. The general calculation form of the connection deviation is: ; in: This is a function for calculating the connection deviation between the boundary difference value and the boundary difference reference value, used to quantify the degree of deviation between the current boundary state and the local normal connection level.

[0061] In one optional embodiment, the connection deviation is calculated using a normalized difference form, specifically as follows: ; In the formula: This indicates the connection deviation at the current sampling point, reflecting the degree of deviation of the current boundary state from the normal connection level under local continuous operation conditions; This represents a very small positive number used to avoid a denominator of zero.

[0062] The above method enables the connection deviation to reflect the degree of deviation of the current boundary state from the normal connection level under local continuous operation conditions.

[0063] Specifically, the joint judgment and generation of real-time operation quality assessment results and deviation data for construction robots includes the following steps: S219. Obtain the local quality deviation of the current sampling point. and the connection deviation between the current sampling point and the adjacent work area. ; In this step, local quality deviation The connection deviation is calculated in step S22. Calculated by step S23, these two deviations reflect the operational quality status of the current sampling point from different perspectives. The local quality deviation mainly reflects the deviation of the current sampling point's own operational status, while the connection deviation reflects the continuity of the current sampling point at the boundary of the region.

[0064] Therefore, in this step, the two deviations mentioned above are used as input data for joint judgment, providing a basis for a comprehensive evaluation of the overall quality status of the current sampling point.

[0065] S220, Local quality deviation based on the current sampling point and the connection deviation between the current sampling point and the adjacent work area. Determine the deviation combination state of the current sampling point. Among them, the local quality deviation based on the current sampling point and the connection deviation between the current sampling point and the adjacent work area. Determine the deviation combination state of the current sampling point. Includes the following steps: S220.1, Based on a preset local quality deviation threshold Local quality deviation at the current sampling point The system identifies whether the local quality deviation exceeds a preset threshold, thus obtaining the local quality deviation identification result for the current sampling point. ; In this step, the preset local quality deviation threshold is first determined. The local quality deviation of the current sampling point is judged to identify whether there is any abnormality in the local operation quality of the sampling point.

[0066] Specifically, it is expressed as follows: ; when If the threshold is exceeded, the current sampling point is considered to have a significant deviation in the local operation state and is recorded as an abnormal state; otherwise, the sampling point is considered to be within the normal range in terms of local quality.

[0067] S220.2, Based on a preset connection deviation threshold The connection deviation between the current sampling point and the adjacent work area The system identifies whether the connection deviation exceeds a preset threshold, thus obtaining the connection deviation identification result between the current sampling point and the adjacent work area. ; In this step, based on the preset connection deviation threshold... For connection deviation Make a judgment to identify the continuity of the current sampling point at the junction of the work area.

[0068] Specifically, it is expressed as follows: ; when If the threshold is exceeded, it is considered that the current sampling point has obvious discontinuity or mismatch at the junction of regions; otherwise, it is considered that its junction status is normal.

[0069] S220.3 Identify the local quality deviation results of the current sampling point. Identification results of the connection deviation between the current sampling point and the adjacent work area By performing corresponding combinations, the deviation combination state of the current sampling point is obtained. .

[0070] In this step, the results of local quality deviation identification are obtained respectively. Connection Deviation Identification Results Then, the two are combined to obtain the deviation combination state of the current sampling point: ; This combination method allows us to categorize the quality status of the current sampling point into different types, for example: (0,0): Local quality and connection status are both normal; (1,0): There is a local quality deviation, but the connection is normal; (0,1): Local quality is normal, but there are problems with the connection; (1,1): There are deviations in both local quality and connection.

[0071] By introducing deviation combination states, subsequent processing can be tailored to different types of quality problems, rather than simply performing a uniform evaluation.

[0072] S221, Deviation combination state based on the current sampling point Determine the combination state of the deviation from the current sampling point. Corresponding joint judgment weight coefficient And the local quality deviation of the current sampling point. and the connection deviation between the current sampling point and the adjacent work area. Perform a joint determination to obtain a joint determination value. Different weighting coefficients can be preset according to different combinations of deviations. This is used to adjust the proportion of local quality deviations and connection deviations in the overall evaluation. For example, when connection issues are more critical, the proportion can be reduced. The value of is chosen to make the connection deviation account for a larger proportion of the result. The calculation formula is as follows: ; in ; By employing the above methods, the joint judgment value can be dynamically adjusted according to different deviation states, thereby more accurately reflecting the overall operational quality of the current sampling point.

[0073] Furthermore, in order to illustrate the joint decision value The method for determining this will be explained below with a specific example.

[0074] For example, during the continuous operation of a construction robot, for the current sampling point The calculation obtained through the aforementioned steps is as follows: The local quality deviation formed by the combined operating parameters is: ; The connection deviation between the current sampling point and the adjacent work area is: ; Based on the system's preset judgment conditions, the following settings are made: ; Deviation identification for the current sampling point: ,get ; ,get ; Therefore, the current deviation combination state of the sampling point can be determined as follows: ; In this embodiment, for the deviation combination state (1,1)(1,1)(1,1), the preset joint judgment weight coefficient is: ; Substitute the above parameters into the joint determination formula: ; get: ; The results show that the impact of the connection deviation is more significant in the overall quality assessment of the current sampling point, indicating that the main quality problem of this sampling point comes from the connection status between adjacent work areas.

[0075] S222, Based on joint decision value Generate real-time operation quality assessment results and deviation data for the construction robot, whereby the deviation data includes local quality deviations at the current sampling point. Connection deviation between the current sampling point and adjacent work areas The current sampling point's deviation combination state With joint judgment value .

[0076] In this step, the joint determination value will be... As part of the overall quality evaluation results for the current sampling points, the following data will also be output as deviation data: Local quality deviation ; Connection deviation ; Deviation combination state ; Joint judgment value .

[0077] By using the above methods, not only can a single quality assessment result be obtained, but also various deviation information and their status can be retained, providing a more comprehensive basis for decision-making in subsequent feedback control steps.

[0078] S3. Feedback Control Command Generation: Based on deviation data and combined with construction design benchmark data and preset control rules, generate control commands to correct the operation parameters of the construction robot; S3, feedback control command generation includes the following steps: Specifically, in step S31, the deviation data is acquired, and the local quality deviation of the current sampling point, the connection deviation between the current sampling point and the adjacent work area, the deviation combination state of the current sampling point, and the joint judgment value are extracted from the deviation data. Specifically, after completing step S24, the complete deviation data of the current sampling point has been obtained, including the local quality deviation. Connection deviation Deviation combination state and joint judgment value The above data reflect the current operational quality status of the sampling points from different perspectives.

[0079] Among them, local quality deviation This mainly reflects the deviation of the current sampling point's own operational status and the connection deviation. Reflects the continuity and deviation combination status between the current sampling point and adjacent work areas. Characterizes the type of quality problem at the current sampling point, while the joint judgment value This is used to comprehensively describe the overall degree of deviation.

[0080] Finally, the aforementioned deviation data are extracted and used as input for subsequent control decisions.

[0081] Specifically, S32, acquire the construction design benchmark data corresponding to the current sampling point, and determine the target operation parameters corresponding to the current sampling point based on the construction design benchmark data; as follows: Construction design baseline data originates from construction plans or work path planning results and is used to describe the target operational state that the construction robot should achieve at each location. For example, construction design baseline data may include target operating speed, target material flow rate, target spraying thickness, or target compaction degree.

[0082] For the current sampling point, the corresponding target operation parameters are matched from the construction design benchmark data according to its spatial location or operation sequence index, and used as the standard operation status of the sampling point.

[0083] This approach ensures that subsequent parameter correction processes are based on construction design objectives, rather than blindly adjusting based solely on deviations.

[0084] Specifically, S33, based on the local quality deviation of the current sampling point, the connection deviation between the current sampling point and adjacent work areas, the deviation combination state of the current sampling point, the joint judgment value, and the target work parameters, determine the work parameter correction amount corresponding to the current sampling point; wherein, determining the work parameter correction amount corresponding to the current sampling point includes the following steps: S33.1. Based on the deviation combination state of the current sampling point, determine the operation parameters to be corrected corresponding to the current sampling point; In this step, based on the deviation combination state Determine the primary problem type at the current sampling point to identify the operational parameters that need priority adjustment. For example: when When this occurs, it indicates that there is a local quality deviation but the connection is normal, and the operating parameters can be adjusted first. when If this occurs, it indicates a problem with the connection, and path or attitude-related parameters should be adjusted first. when When this occurs, it indicates that there are problems with both local quality and connection; multiple parameters can be adjusted simultaneously. when At this time, you can maintain the current parameters or make fine adjustments.

[0085] By using the above methods, different quality problems can be addressed by corresponding control objects.

[0086] S33.2. Based on the local quality deviation of the current sampling point, the connection deviation between the current sampling point and the adjacent work area, and the joint judgment value, determine the deviation correction direction corresponding to the work parameter to be corrected; In this step, the correction direction is used to determine whether the parameter should be increased or decreased. For example: When the deviation of the working surface is large and manifests as insufficient thickness, the corresponding parameters should be adjusted to increase. When the boundary connection deviation is large and excessive overlap is observed, the corresponding parameters should be adjusted to decrease.

[0087] In one possible implementation, the correction direction can be expressed as: ; In the formula: Indicates the direction of correction; Represents a symbolic function; Indicates the current detection value; This represents the target job value.

[0088] S33.3. Based on the target operation parameters and the deviation correction direction, determine the parameter offset corresponding to the operation parameters to be corrected; In this step, the parameter offset and the joint decision value This correlation allows the offset magnitude to vary with the degree of deviation: ; In the formula: Indicates the parameter offset; Indicates the adjustment coefficient; Indicates the joint decision value; Indicates the direction of correction.

[0089] S33.4. Based on the parameter offset, determine the correction amount of the operation parameters corresponding to the current sampling point.

[0090] In this step, the offset is added to the target job parameters to obtain the corrected parameter values: ; In the formula: This indicates the corrected operation parameters; Indicates the target operation parameters.

[0091] Specifically, S34 generates control instructions for correcting the operation parameters of the construction robot based on the correction amount of the operation parameters and the preset control rules.

[0092] In this step, the corrected operating parameters are converted into control commands executable by the robot control system, such as speed adjustment commands, actuator control commands, or path correction commands, and constrained according to preset control rules, for example: Limit the range of parameter variation; Ensure continuous parameter changes; Avoid frequent fluctuations; Finally, control instructions for correcting the operation parameters of the construction robot are generated and used to drive the robot to perform parameter adjustments.

[0093] S4. Closed-Loop Feedback Dynamic Execution: Based on the control commands for correcting the operation parameters of the construction robot, the operation parameters of the construction robot are dynamically adjusted. After the dynamic adjustment is completed, the operation data acquisition, operation quality assessment, and feedback control command generation continue to be executed to achieve closed-loop feedback control of the construction robot's operation quality. S4, closed-loop feedback dynamic execution includes the following steps: Specifically, S41, obtain the construction robot operation parameter correction control command, and parse the operation parameters to be adjusted and the parameter adjustment amount corresponding to the construction robot operation parameter correction control command; In this step, the construction robot control system receives the operation parameter correction control command generated in step S3. This control command contains the type of operation parameter that needs to be adjusted at the current sampling point and the corresponding parameter correction amount.

[0094] Specifically, the control commands are parsed and the following are extracted: The types of operational parameters to be adjusted, such as operating speed, actuator output parameters, or path attitude parameters; The corresponding parameter adjustment amount is used to correct the change in the current operation status.

[0095] Through the above analysis process, the control commands are converted into parameter adjustment information that the robot control system can directly execute.

[0096] Specifically, S42, based on the construction robot operation parameter correction control command, dynamically adjust the operation parameters of the construction robot to be adjusted, and obtain the adjusted operation parameters of the construction robot; In this step, the current operating parameters of the construction robot are updated based on the operation parameters to be adjusted and their corresponding adjustment amounts obtained from S41.

[0097] Specifically, the current operation parameters are superimposed or corrected with the parameter adjustment amount to obtain new operation parameter values, which are then written into the robot control system so that the robot can execute according to the updated parameters in subsequent operations.

[0098] To ensure operational stability, parameter changes can be constrained according to preset control rules during parameter adjustment, for example: Limit the magnitude of each adjustment to avoid sudden parameter changes; Ensure the continuity of parameter changes; Constrain the range of parameter values ​​to avoid exceeding the device's allowable range.

[0099] By using the above methods, the adjustment of operating parameters can respond to changes in deviation without causing instability in the construction process.

[0100] Specifically, S43, based on the adjusted operating parameters of the construction robot, control the construction robot to continue performing the operation; In this step, after updating its parameters, the construction robot continues to perform its current construction task according to the adjusted operating parameters. At this time, the motion state, output state, and work effect of the robot's actuators are all affected by the updated parameters, thereby achieving real-time correction of the current operating state.

[0101] This process allows the deviation correction results to be directly applied to the actual construction process, rather than just remaining at the evaluation level.

[0102] Specifically, S44, after completing the dynamic adjustment, continues to execute the operation data collection, operation quality assessment and feedback control instruction generation to continuously update the adjusted construction robot operation parameters; In this step, while the construction robot continues to work according to the adjusted parameters, the system re-enters the data acquisition process of step S1 to collect the current new work status in real time.

[0103] Then, based on the updated data, execute again: Job quality assessment (corresponding to S2); Joint determination (corresponding to S24); Control command generation (corresponding to S3).

[0104] This process allows the effects of each parameter adjustment to be monitored in real time and used for further adjustments, thus forming a continuous update mechanism.

[0105] Specifically, S45, based on the continuously updated control instructions for correcting the operation parameters of the construction robot, cyclically executes the dynamic adjustment of the operation parameters of the construction robot, the acquisition of operation data, the evaluation of operation quality and the generation of feedback control instructions, until a closed-loop feedback control of the operation quality of the construction robot is achieved.

[0106] In this step, processes S41 to S44 are executed cyclically to form a complete closed-loop control process. Specifically: In each cycle, the deviation is calculated based on the current operation status of the sampling point; Generate new control commands based on the deviation; Update the operation parameters; Collect data again and evaluate it.

[0107] As the cycle continues, the operating parameters of the construction robot gradually approach the target operating state, and the deviation in operating quality gradually decreases, thereby achieving dynamic optimization control of the construction process.

[0108] When the combined judgment value of multiple consecutive sampling points remains within the preset range, it can be considered that the current construction state has reached the stable quality requirements. At this time, the system can maintain the current parameters or enter the next operation stage.

[0109] Those skilled in the art will understand that the process of implementing all or part of the steps of the above embodiments can be carried out by hardware or by a program instructing the relevant hardware.

[0110] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for online evaluation and closed-loop feedback control of the operation quality of a construction robot, characterized in that, Includes the following steps: S1. Operation Data Acquisition: Real-time acquisition of the operation parameter data, position and posture data and work surface detection data of the construction robot during the operation process, and preprocessing of the operation parameter data, position and posture data and work surface detection data to obtain preprocessed operation parameter data, position and posture data and work surface detection data; S2. Operation Quality Assessment: Based on preprocessed operational parameter data, position and attitude data, and work surface detection data, a local continuous operation unit is constructed centered on the current sampling point. Local quality deviations at the current sampling point and connection deviations between the current sampling point and adjacent work areas are extracted. These local quality deviations and connection deviations are jointly assessed to generate real-time operation quality assessment results and deviation data for the construction robot. Among these: The local continuous operation unit refers to: taking the current sampling point as the center, selecting 2 to 5 continuous sampling points forward and backward along the sampling sequence, and the sampling points in the local continuous operation unit are continuous in terms of time and spatial position changes and are consistent with the operation trajectory currently executed by the construction robot; The local quality deviation refers to the weighted composite value of the degree of difference between the current sampling point's operating parameters, position and attitude, work surface detection data and the corresponding reference state within its local continuous work unit; The connection deviation refers to the degree of difference between the work surface detection data at the boundary between the current sampling point's work area and the adjacent work area. S3. Feedback control command generation: Based on deviation data and combined with construction design benchmark data and preset control rules, generate control commands to correct the operation parameters of the construction robot. S4. Closed-loop feedback dynamic execution: Based on the control command for correcting the operation parameters of the construction robot, the operation parameters of the construction robot are dynamically adjusted, and after the dynamic adjustment is completed, the operation data acquisition, operation quality assessment and feedback control command generation are continued to be executed to achieve closed-loop feedback control of the operation quality of the construction robot.

2. The online evaluation and closed-loop feedback control method for construction robot operation quality according to claim 1, characterized in that, In step S1, the work data collection includes the following steps: S11. Organize the real-time collected operating parameter data, position and attitude data and work surface detection data to establish a one-to-one correspondence between the operating parameter data, position and attitude data and work surface detection data. S12. Perform anomaly identification on the completed and sorted operation parameter data, position and attitude data and work surface detection data, and remove operation parameter data, position and attitude data and work surface detection data that do not meet the preset range. S13. Perform continuity correction processing on the operating parameter data, position and attitude data and work surface detection data after removing abnormal data to form continuous operating parameter data, position and attitude data and work surface detection data. S14. After completing the continuous correction process, the operating parameter data, position and attitude data and the work surface detection data are processed in a unified manner to obtain the preprocessed operating parameter data, position and attitude data and work surface detection data.

3. The online evaluation and closed-loop feedback control method for construction robot operation quality according to claim 1, characterized in that, In step S2, extracting the local quality deviation of the current sampling point includes the following steps: S211. Extract the preprocessed operating parameter data, position and attitude data, and work surface detection data corresponding to the current sampling point, and record them as the operating parameter data vector of the current sampling point. Current sampling point position and attitude data vector The detection data vector of the current sampling point working surface ; S212. Extract the preprocessed running parameter data, position and attitude data, and work surface detection data that are continuously corresponding to the current sampling point within the local continuous operation unit, and determine the running parameter reference vector respectively. Position and attitude reference vector With the working surface detection reference vector ; S213, Data vector of running parameters based on the current sampling point With running parameter reference vector Calculate the deviation value of operating parameters Based on the current sampling point position and attitude data vector With position and attitude reference vector Calculate position and attitude deviation values Based on the detection data vector of the current sampling point work surface With the working surface detection reference vector Calculate the inspection deviation value of the working surface ; S214, Based on operating parameter deviation values Position and attitude deviation values Deviation value from the work surface Construct the local quality deviation of the current sampling point : ； in: Weighting coefficients representing deviations in operating parameters; Weighting coefficients representing position and attitude deviations; The weighting coefficient representing the deviation of the work surface inspection; ,and , , .

4. The online evaluation and closed-loop feedback control method for construction robot operation quality according to claim 1, characterized in that, In step S2, extracting the connection deviation between the current sampling point and adjacent work areas includes the following steps: S215. Determine the boundary of the work area corresponding to the current sampling point and the boundary of the work area adjacent to the work area corresponding to the current sampling point. Extract the work surface detection data at the corresponding points of the boundary along the work area boundary corresponding to the current sampling point and the adjacent work area boundary according to the same boundary sampling order. Record them as the work surface detection data on the boundary of the work area corresponding to the current sampling point. Inspection data of the working surface at the boundary of the adjacent working area ; S216. Based on the detection data of the work surface on the boundary of the work area corresponding to the current sampling point. Inspection data of the working surface at the boundary of the adjacent working area Calculate the connection deviation components at corresponding boundary points And based on the connection deviation components of each boundary point Determine the boundary difference value between the current sampling point and the adjacent work area. ; S217. For sampling points within a local continuous work unit where adjacent work areas exist, determine the boundary difference reference value. ; S218. Based on the boundary difference value between the current sampling point and adjacent work areas. and boundary difference reference value Extract the connection deviation between the current sampling point and the adjacent work area. : ； in: This is a function for calculating the connection deviation between the boundary difference value and the boundary difference reference value.

5. The online evaluation and closed-loop feedback control method for construction robot operation quality according to claim 1, characterized in that, In step S2, the joint judgment and generation of real-time operation quality assessment results and deviation data of the construction robot include the following steps: S219. Obtain the local quality deviation of the current sampling point. and the connection deviation between the current sampling point and the adjacent work area. ; S220, Local quality deviation based on the current sampling point and the connection deviation between the current sampling point and the adjacent work area. Determine the deviation combination state of the current sampling point. Wherein, the deviation combination state of the current sampling point This refers to the local quality deviation and connection deviation Whether the deviation type combination exceeds the preset threshold; S221, Deviation combination state based on the current sampling point Determine the combination state of the deviation from the current sampling point. Corresponding joint judgment weight coefficient And the local quality deviation of the current sampling point. and the connection deviation between the current sampling point and the adjacent work area. Perform a joint determination to obtain a joint determination value. : ； in ; S222, Based on joint decision value Generate real-time operation quality assessment results and deviation data for the construction robot, whereby the deviation data includes local quality deviations at the current sampling point. Connection deviation between the current sampling point and adjacent work areas The current sampling point's deviation combination state With joint judgment value .

6. The online evaluation and closed-loop feedback control method for the operation quality of construction robots according to claim 5, characterized in that, In S220, based on the local quality deviation of the current sampling point and the connection deviation between the current sampling point and the adjacent work area. Determine the deviation combination state of the current sampling point. Includes the following steps: S220.1, Based on a preset local quality deviation threshold Local quality deviation at the current sampling point The system identifies whether the local quality deviation exceeds a preset threshold, thus obtaining the local quality deviation identification result for the current sampling point. ; S220.2, Based on a preset connection deviation threshold The connection deviation between the current sampling point and the adjacent work area The system identifies whether the connection deviation exceeds a preset threshold, thus obtaining the connection deviation identification result between the current sampling point and the adjacent work area. ; S220.3 Identify the local quality deviation results of the current sampling point. Identification results of the connection deviation between the current sampling point and the adjacent work area By performing corresponding combinations, the deviation combination state of the current sampling point is obtained. .

7. The online evaluation and closed-loop feedback control method for construction robot operation quality according to claim 1, characterized in that, In step S3, the feedback control command generation includes the following steps: S31. Obtain deviation data and extract the local quality deviation of the current sampling point, the connection deviation between the current sampling point and the adjacent work area, the deviation combination status of the current sampling point and the joint judgment value from the deviation data; S32. Obtain the construction design benchmark data corresponding to the current sampling point, and determine the target operation parameters corresponding to the current sampling point based on the construction design benchmark data; S33. Based on the local quality deviation of the current sampling point, the connection deviation between the current sampling point and the adjacent work area, the deviation combination state of the current sampling point, the joint judgment value, and the target work parameters, determine the work parameter correction amount corresponding to the current sampling point. S34. Based on the operation parameter correction amount and preset control rules, generate operation parameter correction control instructions for the construction robot.

8. The online evaluation and closed-loop feedback control method for construction robot operation quality according to claim 7, characterized in that, In step S33, determining the correction amount of the operation parameters corresponding to the current sampling point includes the following steps: S33.

1. Based on the deviation combination state of the current sampling point, determine the operation parameters to be corrected corresponding to the current sampling point; S33.

2. Based on the local quality deviation of the current sampling point, the connection deviation between the current sampling point and the adjacent work area, and the joint judgment value, determine the deviation correction direction corresponding to the work parameter to be corrected; S33.

3. Based on the target operation parameters and the deviation correction direction, determine the parameter offset corresponding to the operation parameters to be corrected; S33.

4. Based on the parameter offset, determine the correction amount of the operation parameters corresponding to the current sampling point.

9. The online evaluation and closed-loop feedback control method for the operation quality of construction robots according to claim 1, characterized in that, In step S4, the closed-loop feedback dynamic execution includes the following steps: S41. Obtain the operation parameter correction control command for the construction robot, and parse the operation parameters to be adjusted and the parameter adjustment amount corresponding to the operation parameter correction control command for the construction robot. S42. Based on the control command for correcting the operation parameters of the construction robot, the operation parameters of the construction robot to be adjusted are dynamically adjusted to obtain the adjusted operation parameters of the construction robot. S43. Control the construction robot to continue performing operations based on the adjusted operation parameters of the construction robot; S44. After completing the dynamic adjustment, continue to execute the operation data collection, operation quality assessment and feedback control instruction generation to continuously update the adjusted construction robot operation parameters. S45. Based on the continuously updated control instructions for correcting the operation parameters of the construction robot, the system cyclically executes the dynamic adjustment of the operation parameters of the construction robot, the acquisition of operation data, the evaluation of operation quality and the generation of feedback control instructions, until a closed-loop feedback control of the operation quality of the construction robot is achieved.

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