Method and system for optimizing joint cutting path of concrete pavement

Abstract

The invention discloses a concrete pavement joint cutting path optimization method and system. Original information of pavement kerfs is collected, and noise filtering and smoothing are carried out; identifying a direction deviation corresponding to the current path, performing path deviation judgment and determining a deviation correction demand; generating a speed correction instruction according to the speed deviation value and the cutting depth requirement; the angle fine adjustment increment is obtained by adjusting the advancing angle, extracting the depth change and evaluating the material hardness difference; according to correction of the angle and the speed, the machine is driven, and adjusted path data are collected again; a path deviation value is extracted through path comparison, the false seam straightness of the kerf is evaluated, an additional correction instruction of cutting parameters is generated according to a judgment result, and a target path and depth data are optimized; and width uniformity is determined, the cutting force change amplitude is fused to adjust the depth of the saw blade, and a final optimization control signal is obtained. The angle, the speed and the depth are rapidly and accurately adjusted during deviation, and the construction effect and quality are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of civil engineering, in particular to a concrete pavement joint cutting path optimization method and system. BACKGROUND

[0002] In the field of civil engineering, the construction quality of concrete pavement is directly related to the durability and safety of infrastructure, and is an important cornerstone to ensure traffic operation and urban development. Among them, the concrete pavement joint cutting construction is a key link to ensure the quality and service life of the pavement. Precise joint cutting of concrete pavement can control the expansion of pavement cracks and improve the durability and safety of the road.

[0003] Currently, joint cutting construction mainly relies on traditional machinery and manual adjustment. Existing equipment often cannot adapt to sudden changes in cutting force caused by uneven distribution of aggregates or hard inclusions in real time, which can easily cause joint cutting path deviation and affect the straightness and width uniformity of the false joint.

[0004] In addition, during the joint cutting process, the saw blade often experiences abnormal fluctuations in stress. The reasons are as follows: first, the mechanical actuator cannot obtain and analyze dynamic data such as cutting force, position coordinates and travel speed of the saw blade in real time, which makes it difficult to accurately determine the degree and direction of path deviation. For example, when the saw blade suddenly encounters hard inclusions, the sudden increase in cutting force can cause the machine to deviate laterally, but the existing system cannot quickly capture this change and make adjustments. Second, due to the lack of real-time data-driven path correction mechanism, the machine cannot restore the preset path by adjusting the travel angle and pushing speed. The lack of data acquisition and response mechanism makes it difficult to maintain a stable joint cutting path under complex working conditions, which affects the straightness and width uniformity of the false joint, i.e. the quality, safety and service life of the concrete pavement.

[0005] Therefore, how to obtain information such as joint cutting position, stress size, depth and travel speed in real time when the saw blade cutting force fluctuates abnormally, and achieve accurate path optimization by dynamically adjusting the travel angle and pushing speed of the machine, has become a key problem to improve the quality of concrete pavement joint cutting. SUMMARY

[0006] In order to solve the above technical problems, the present application provides a concrete pavement joint cutting path optimization method and corresponding concrete pavement joint cutting path optimization system, computing device and computer storage medium.

[0007] According to one aspect of the present application, a concrete pavement joint cutting path optimization method is provided, the method comprising: Collecting original information of the pavement joint cutting, and filtering noise from the original information to obtain smoothed original information; wherein the original information at least includes cutting force position data, travel speed data and cutting depth data; According to the smoothed cutting force position data, the direction deviation of the current path and the preset path is identified, if the direction deviation exceeds the preset deviation threshold, it is determined that the path deviates, and the deviation direction is determined according to the cutting force position change trend, and the deviation correction requirement is determined according to the deviation direction; The advancing speed of the saw blade is adjusted by the advancing speed, and the speed correction instruction is generated according to the speed deviation and the cutting depth requirement; Through the advancing speed and the deviation direction, the advancing angle is adjusted, the cutting force size change amplitude is identified, the depth change is extracted, the material hardness difference characteristics are evaluated, and the angle fine tuning increment is obtained; According to the angle fine tuning increment and the speed correction instruction, the mechanical actuator is driven to modify the advancing angle and the advancing speed in real time, the position coordinates and the advancing speed of the saw blade are reacquired, and the adjusted path data is obtained; The adjusted path data is compared with the preset path, the path deviation value is extracted, the false seam straightness of the cutting seam is evaluated, the false seam straightness is evaluated according to the path deviation value and the deviation correction requirement, if the false seam straightness is lower than the preset threshold, the additional correction instruction of the cutting parameter is generated, and the optimized target path and depth data are obtained; According to the optimized target path and depth data, the width change rule is identified to determine the width uniformity, the cutting force size change amplitude is fused, the saw blade depth is adjusted, the width control signal is determined, and the final optimized cutting seam path comprehensive control instruction is obtained.

[0008] In the above scheme, the original information of the pavement joint is collected, the original information is filtered to obtain smoothed original information, which further comprises: Based on the force sensor and the displacement encoder arranged on the saw blade spindle and the feeding mechanism, the cutting force position data, the advancing speed data and the cutting depth data are measured; wherein the force sensor is used to collect the three-axis torque data generated by the cutting vibration according to the preset sensing frequency, and the laser range finder is used to obtain the coordinate value of the saw blade relative to the pavement reference line to determine the cutting force position data; the pulse signal of the displacement encoder is used to calculate the speed change rate to determine the advancing speed data; the ultrasonic sensor is used to measure the depth data of the saw blade bottom to the concrete surface to determine the cutting depth data; The collected original information is preprocessed, the abnormal peak value exceeding the preset standard threshold in the original information amplitude value is identified, the median filter is used to remove the pulse interference, the time stamp alignment is adjusted according to the time delay characteristics within the sampling interval, the three-axis torque data is processed by low-pass filtering, the main frequency component of the cutting process is retained, and the filtered torque sequence, the aligned coordinate sequence and the speed sequence are outputted; Moving average processing is performed on the filtered torque sequence, coordinate sequence, and velocity sequence. The smoothing window size is determined based on the velocity change rate. If the velocity change rate exceeds a first threshold within a preset time, a small window sampling point is used; otherwise, a large window sampling point is used. Kalman filtering is performed on the coordinate sequence. The Kalman gain coefficient is adjusted according to the change trend of the depth data to obtain the smoothed coordinate sequence and generate the smoothed coordinate trajectory. The smoothed coordinate trajectory is time-synchronized using an interpolation algorithm. Based on the time delay characteristics, spline interpolation is used to fill in the missing sampling points. The resultant force application points at each moment are extracted from the torque sequence. The coordinate trajectory is fused to determine the cutting force application position. The smoothed cutting force position data, travel speed data, and cutting depth data are output.

[0009] In the above scheme, the step of identifying the directional deviation between the current path and the preset path based on the smoothed cutting force position data, determining that the path has deviated if the directional deviation exceeds the preset deviation threshold, determining the deviation direction based on the trend of cutting force position change, and determining the deviation correction requirement based on the deviation direction, further includes: Continuous position sequences are extracted from the smoothed cutting force position data. The direction vector of the current path is fitted by the least squares method. At the same time, the coordinate points of the preset path are read, the vertical distance from the position point to the preset path at each sampling time is calculated, and the deviation distance sequence is generated. The deviation change angle is calculated based on the ratio of the difference of the deviation distances of adjacent sampling points to the time interval. The deviation change rate is obtained by performing a difference operation on the deviation distance sequence. The change trend is judged by the consistency of the sign of the deviation change rate within the sliding window. If the deviation distance of multiple consecutive sampling points exceeds a preset distance threshold and the deviation change angle exceeds a preset angle threshold, it is determined that the path has deviated. The position of the starting point of the deviation and the deviation change rate are recorded. Based on the cutting force position sequence after the offset starting point, the radius of the arc formed by three adjacent points is calculated to obtain the path curvature. The ratio of the change in the lateral coordinate to the change in the longitudinal coordinate in the cutting force position sequence is extracted to determine the offset direction. The cumulative deviation value is obtained by accumulating the deviation distance sequence. The offset magnitude is determined by the product of the cumulative deviation value and the deviation change rate. The correction priority level is divided according to the magnitude of the offset. Based on the offset direction and correction priority level, an offset correction parameter set is generated, which includes the correction direction angle, correction displacement, and correction start position. If the cumulative deviation value exceeds the preset deviation threshold, the correction displacement is increased. The correction execution timing is adjusted according to the path curvature change, and the offset correction requirement including the correction parameter set and execution timing is output.

[0010] In the above scheme, the step of adjusting the saw blade's feed speed by adjusting the travel speed and generating a speed correction command based on the speed deviation and cutting depth requirements further includes: The difference between the current travel speed and the preset speed reference is obtained as the speed deviation. The ratio of the real-time cutting depth to the design depth threshold is read. The acceleration or deceleration requirement is determined according to the positive or negative sign of the speed deviation. The speed adjustment gradient is determined by the ratio of the load coefficient collected by the resistance sensor to the preset reference load. The target propulsion speed is calculated by multiplying the speed adjustment gradient by the current speed. If the load coefficient exceeds the preset upper limit threshold, the target propulsion speed is limited to a safe range. A preset mechanical response delay time constant is obtained. The difference between the target propulsion speed and the current speed is divided by the response delay time constant to obtain the speed change rate. The command transmission frequency is determined based on the ratio of the velocity change rate to the design depth threshold. Discrete velocity adjustment pulse sequences are generated according to the command transmission frequency. The velocity increment between adjacent pulses is limited according to the maximum allowable acceleration. The pulse sequences are arranged in chronological order to form a velocity correction command.

[0011] In the above scheme, the step of adjusting the travel angle by adjusting the propulsion speed and offset direction, identifying the magnitude of changes in cutting force, extracting depth changes, evaluating material hardness differences, and obtaining the angle fine-tuning increment further includes: A velocity vector is constructed based on the propulsion speed value and the offset direction. The deviation value between the current travel angle and the preset path angle is obtained. The cutting force variation amplitude between adjacent sampling points is calculated through the cutting force time sequence data collected by the force sensor. The depth change per unit time is extracted from the ultrasonic ranging data. The numerical sequence of the cutting force variation amplitude at different depth positions is recorded. The location and intensity of the cutting force mutation point are identified based on the numerical sequence. The material hardness change characteristics are judged by the correlation between the mutation point density and the deviation value. If the hardness change characteristics exceed the preset threshold, it is marked as a high hardness region. The resistance coefficient at each location is determined according to the distribution density of the high hardness region and the cutting force change amplitude. The lateral deflection torque is calculated by using the peak position of the resistance coefficient and the lateral and longitudinal components of the velocity vector. The theoretical deflection angle is obtained by the ratio of the deflection torque to the preset stiffness parameter of the saw blade bearing. The correction coefficient is determined according to the reverse compensation amount of the offset direction. The theoretical deflection angle is multiplied by the correction coefficient to obtain the preliminary adjustment angle value. The initial adjustment angle value is discretized, and the minimum adjustment step size is determined based on the stepping angle of the servo motor. The execution time interval of each step size is adjusted by the rate of change of the material hardness change characteristics. The initial adjustment angle value is decomposed into the sum of multiple minimum adjustment step sizes, and the angle fine-tuning increment sequence and the corresponding execution time table are output.

[0012] In the above scheme, the step of driving the mechanical actuator to modify the travel angle and advance speed in real time according to the angle fine-tuning increment and speed correction command, and re-acquiring the position coordinates and travel speed of the saw blade to obtain the adjusted path data further includes: The angle fine-tuning increment is converted into a pulse signal sequence for the servo motor. The frequency control parameters of the inverter are generated through the speed correction command. The pulse signal sequence is sent to the angle adjustment mechanism. At the same time, the frequency control parameters are input to the propulsion motor. The actual rotation angle fed back by the servo motor encoder and the actual operating frequency output by the inverter are obtained. The time difference between the control command issuance time and the mechanical response time is recorded. Based on the actual rotation angle and actual operating frequency, the steering mechanism and feed mechanism of the mechanical actuator are driven to perform adjustment actions. During the execution process, the coordinate value of the center point of the saw blade is obtained by the laser positioning instrument according to a fixed sampling rate. The instantaneous travel speed of the saw blade is measured by the incremental encoder. The sampling time is compensated and corrected according to the time difference to obtain the coordinate sequence and speed data at the corresponding time during the adjustment process. A time stamp is added to each coordinate point in the coordinate sequence, and coordinate points at the same time are paired and associated with velocity data. A continuous path trajectory segment is constructed through adjacent coordinate points. The velocity characteristic value of the segment is calculated based on the velocity value at the start and end points of each segment and the time interval, forming a coordinate point sequence containing timestamps and the velocity distribution of each trajectory segment. Using the starting and ending coordinates of the coordinate point sequence, the actual change in travel angle is calculated. The smoothness of speed adjustment is evaluated by the dispersion of speed characteristic values ​​of each segment in the speed distribution. If the deviation between the actual change and the command value exceeds a preset threshold, the path segment is marked. The adjusted path data is output, including the coordinate point sequence, speed distribution, and deviation marking information.

[0013] In the above scheme, the step of driving the mechanical actuator to modify the travel angle and advance speed in real time according to the angle fine-tuning increment and speed correction command, and re-acquiring the position coordinates and travel speed of the saw blade to obtain the adjusted path data further includes: The steering mechanism of the mechanical actuator adopts a gear and rack transmission method. The servo motor drives the gear through a reducer. The gear meshes with the rack fixed on the frame to realize the lateral movement of the saw blade assembly. The feed mechanism adopts a ball screw transmission. The propulsion motor is connected to the ball screw through a coupling. The ball screw nut is fixed on the saw blade lifting frame. The rotation of the motor drives the saw blade forward. The laser positioning device is installed at the front end of the saw blade guard. The emitted laser beam is vertically irradiated onto the reflective target on the ground. The absolute coordinates of the center point of the saw blade are calculated by the principle of triangulation.

[0014] In the above scheme, the step of comparing the adjusted path data with the preset path, extracting the path deviation value, evaluating the dummy slit straightness, evaluating the dummy slit straightness based on the path deviation value and offset correction requirements, and generating additional correction instructions for cutting parameters if the dummy slit straightness is lower than a preset threshold to obtain optimized target path and depth data, further includes: The coordinate point sequence in the adjusted path data is compared point by point with the standard coordinate sequence of the preset path. The vertical distance from each coordinate point to the preset path is calculated as a local deviation value. The root mean square value of the path deviation is calculated based on the local deviation value. At the same time, the maximum value in the deviation value sequence is extracted as the peak deviation. The centerline of the actual cut is fitted using the least squares method based on the deviation value sequence. The angle difference and position offset between the fitted straight line and the ideal straight line are calculated. The straightness index of the dummy cut is obtained by the ratio of the root mean square value to the preset allowable deviation threshold. If the index is lower than the preset straightness threshold, it is determined that additional correction is required. Based on the dummy seam straightness index and peak deviation, obtain the depth data to be adjusted corresponding to the section where the deviation exceeds the threshold, adjust the cutting depth parameters according to the correspondence between the depth data to be adjusted and the deviation value, determine the lateral compensation amount through the angle difference, calculate the rate correction value according to the current feed rate and position offset, and form a correction instruction containing the depth adjustment value, rate correction value and lateral compensation amount. The control parameters are updated using the lateral compensation amount and depth adjustment value in the correction instruction. The original trajectory coordinates are corrected according to the lateral compensation amount to obtain the optimized target path coordinate sequence. The target cutting depth of each position point is obtained by superimposing the depth adjustment value with the original depth setting value. The optimized target path and depth data are then output.

[0015] In the above scheme, the step of identifying the width variation pattern and determining the width uniformity based on the optimized target path and depth data, integrating the magnitude of the cutting force variation, adjusting the saw blade depth, determining the width control signal, and obtaining the final optimized kerf path comprehensive control command further includes: Based on the coordinate point sequence and corresponding depth data on the target path, the kerf width at each position is measured by a laser scanner, the width difference between adjacent measurement points is extracted, the standard deviation of the width difference sequence is calculated as a uniformity index, and the cutting force time series data recorded by the force sensor is acquired to calculate the change amplitude of the cutting force per unit time. Based on the uniformity index, abnormal sections with width deviations exceeding the threshold are identified. The location coordinates of the abnormal sections are matched with the time series of the cutting force variation amplitude. The correlation strength is determined by calculating the Pearson correlation coefficient between the two. If the correlation strength exceeds the preset threshold, the saw blade feed depth parameter is adjusted according to the depth position corresponding to the cutting force peak. The cutting depth data of each path point is updated using the adjusted depth parameters. A control dataset is constructed by combining the target path coordinates and the updated depth data. Control priorities are assigned to different path segments according to the width uniformity index. The optimized cut path coordinate sequence is obtained through priority weighting. For the optimized path coordinate sequence and depth data, timing control points are generated according to a preset sampling period. Based on the width control requirements, the width deviation of each control point is converted into the lateral adjustment amount of the saw blade. The path coordinates, depth data and lateral adjustment amount are fused to form a comprehensive control command, and the final optimized kerf path and width control signal are output.

[0016] According to another aspect of the present invention, a concrete pavement joint cutting path optimization system is provided, comprising: The system includes a data acquisition module, a correction requirement determination module, a speed correction command generation module, an angle adjustment determination module, a path data adjustment module, an optimization module, and a comprehensive control signal generation module; among which, The data acquisition module is used to acquire the original information of the pavement cut, filter the noise of the original information, and obtain smoothed original information; wherein, the original information includes at least: cutting force position data, travel speed data, and cutting depth data; The correction requirement determination module is used to identify the directional deviation between the current path and the preset path based on the smoothed cutting force position data. If the directional deviation exceeds the preset deviation threshold, it is determined that the path has deviated, and the deviation direction is determined according to the trend of the cutting force position change. The deviation correction requirement is determined according to the deviation direction. The speed correction command generation module is used to adjust the saw blade's advance speed by adjusting the travel speed, and to generate speed correction commands based on the speed deviation and cutting depth requirements. The angle adjustment determination module is used to adjust the travel angle by means of the propulsion speed and offset direction, identify the magnitude of the cutting force change, extract the depth change, evaluate the material hardness difference characteristics, and obtain the angle fine-tuning increment. The path data adjustment module is used to drive the mechanical actuator to modify the travel angle and advance speed in real time according to the angle fine-tuning increment and speed correction command, and to re-collect the position coordinates and travel speed of the saw blade to obtain the adjusted path data. The optimization module is used to compare the adjusted path data with the preset path, extract the path deviation value, evaluate the dummy seam straightness of the cut, evaluate the dummy seam straightness according to the path deviation value and offset correction requirements, and if the dummy seam straightness is lower than the preset threshold, generate additional correction instructions for the cutting parameters to obtain the optimized target path and depth data. The integrated control signal generation module is used to identify the width variation pattern and determine the width uniformity based on the optimized target path and depth data, integrate the magnitude of the cutting force variation, adjust the saw blade depth, determine the width control signal, and obtain the final optimized kerf path integrated control command.

[0017] According to the technical solution provided by this invention, raw information including cutting force position data, travel speed data, and cutting depth data of pavement cutting is collected. Noise filtering is applied to the raw information to obtain smoothed raw information. Timestamp alignment ensures consistency of multi-source data on the time axis. Data filtering effectively suppresses pulse noise generated by mechanical impact, improving the accuracy and usability of data acquisition. Based on a preset deviation threshold, the current path is offset, and the offset magnitude is determined by the deviation change rate, thus determining the correction level. This correction mechanism allows for more reasonable correction arrangements and outputs offset correction requirements. Speed ​​deviation is calculated as the basis for precise control. Resistance information is collected to determine the load coefficient, accurately judging aggregate hardness. A gradient adjustment method is used to more reasonably determine the propulsion speed. By determining the command transmission frequency, the system rapidly approaches the target depth when far from it and smoothly transitions when approaching the target depth, improving the efficiency and stability of the adjustment process. By obtaining the deviation value between the current angle and the preset angle, the variation amplitude of the cutting force time series data is determined, and its value at different depths is obtained. This accurately identifies the location and intensity of abrupt change points, displays the material hardness variation characteristics, and determines the angle accordingly. The correction method rationally arranges the adjustment sequence and timing; by acquiring the actual rotation angle and operating frequency of the motor, it precisely drives the mechanical actuator to adjust the action, and associates the coordinate points and speed through time stamps to construct the adjusted trajectory segment, and acquires path data and related information to reflect the smoothness of the propulsion process and the path segments that need to be monitored by the terminal; by comparing the adjusted path with the preset path, it determines the straightness index and determines the corresponding correction amount and control parameters for different coordinate points, determines the initial correction command, and obtains the optimized target path and depth data, making the path adjustment method more in line with actual operation requirements; by identifying the width uniformity and combining the variation of cutting force, it rationally adjusts the saw blade depth, and further optimizes the kerf path and width control signal. Based on this, it finally completes the multi-directional, dynamic, and precise multi-category data collaborative adjustment for depth, speed, width, and angle, which effectively solves the business scenario problem of unstable cutting quality caused by material hardness differences, path deviations, and insufficient kerf straightness during saw blade cutting, significantly improves kerf straightness and width uniformity, effectively solves the quality control problem in cutting complex materials, and achieves high-precision and high-stability cutting effects.

[0018] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart illustrating a method for optimizing the cutting path of a concrete pavement according to an embodiment of the present invention is shown. Figure 2 A flowchart illustrating a method for pavement information acquisition and data preprocessing according to an embodiment of the present invention is shown. Figure 3 A flowchart illustrating a method for determining cut path offset and correction requirements according to an embodiment of the present invention is shown. Figure 4 A flowchart illustrating a method for generating correction instructions for saw blade feed speed correction according to an embodiment of the present invention is shown. Figure 5 A flowchart illustrating a method for fine-tuning the kerf angle based on material hardness assessment according to an embodiment of the present invention is shown. Figure 6 A flowchart illustrating a method for correcting path data using a road cutting mechanical actuator according to an embodiment of the present invention is shown. Figure 7 A flowchart illustrating a kerf path optimization method based on straightness according to an embodiment of the present invention is shown. Figure 8 A flowchart illustrating a method for generating a kerf path and its width control signal according to an embodiment of the present invention is shown. Figure 9 A structural block diagram of a concrete pavement joint cutting path optimization system according to an embodiment of the present invention is shown. Detailed Implementation

[0021] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0022] Figure 1A flowchart illustrating a method for optimizing the cutting path of concrete pavement joints according to an embodiment of the present invention is shown. The method includes the following steps: Step S101: Collect the original information of the pavement cut, filter the noise of the original information, and obtain the smoothed original information; wherein, the original information includes at least: cutting force position data, travel speed data, and cutting depth data.

[0023] Step S102: Based on the smoothed cutting force position data, identify the directional deviation between the current path and the preset path. If the directional deviation exceeds the preset deviation threshold, it is determined that the path has deviated. The deviation direction is determined according to the trend of the cutting force position change, and the deviation correction requirement is determined according to the deviation direction.

[0024] Step S103: Adjust the saw blade's advance speed by adjusting the travel speed, and generate a speed correction command based on the speed deviation and cutting depth requirements.

[0025] Step S104: Adjust the travel angle by adjusting the advance speed and offset direction, identify the magnitude of the cutting force change, extract the depth change, evaluate the material hardness difference characteristics, and obtain the angle fine-tuning increment.

[0026] Step S105: Based on the angle fine-tuning increment and speed correction instructions, drive the mechanical actuator to modify the travel angle and advance speed in real time, re-collect the position coordinates and travel speed of the saw blade, and obtain the adjusted path data.

[0027] Step S106: Compare the adjusted path data with the preset path, extract the path deviation value, evaluate the straightness of the dummy seam, evaluate the straightness of the dummy seam based on the path deviation value and offset correction requirements, and if the straightness of the dummy seam is lower than the preset threshold, generate additional correction instructions for the cutting parameters to obtain the optimized target path and depth data.

[0028] Step S107: Based on the optimized target path and depth data, identify the width variation pattern to determine the width uniformity, integrate the magnitude of the cutting force variation, adjust the saw blade depth, determine the width control signal, and obtain the final optimized kerf path integrated control command.

[0029] This embodiment provides a method for optimizing the cutting path of concrete pavement joints. Based on a comparison of the deviation with a preset path, the method dynamically determines the offset direction and generates correction requirements. Through coordinated adjustment of speed and angle, combined with the assessment of material hardness using cutting force changes and depth characteristics, the method drives a mechanical actuator in real time to optimize the travel angle and advance speed, ultimately forming an optimized cutting path and width control signal. This invention significantly improves the straightness and width uniformity of the cuts, effectively solves the quality control problem in cutting complex materials, and achieves high-precision and high-stability cutting results.

[0030] Figure 2 A flowchart illustrating a method for pavement information acquisition and data preprocessing according to an embodiment of the present invention is shown. like Figure 2 As shown, the method includes the following steps: Step S201: Based on the saw blade spindle and feed mechanism, force sensors and displacement encoders are installed to measure cutting force position data, travel speed data and cutting depth data.

[0031] Specifically, a force sensor is used to collect triaxial torque data generated by cutting vibration at a preset sensing frequency, and a laser rangefinder is used to obtain the coordinate values ​​of the saw blade relative to the road surface baseline to determine the cutting force position data. The velocity change rate is calculated using the pulse signal from the displacement encoder to determine the travel speed data; The depth of cut is determined by measuring the depth from the bottom of the saw blade to the concrete surface using an ultrasonic sensor.

[0032] Preferably, the rate of change of velocity is typically the instantaneous rate of change of velocity.

[0033] Specifically, during the construction of concrete pavement joint cutting, the arrangement of sensors directly affects the accuracy of data acquisition.

[0034] In one embodiment, the force sensor is a triaxial piezoelectric sensor, mounted on the bearing housing of the saw blade spindle and connected to the spindle via a flexible coupling, enabling real-time monitoring of the axial, radial, and tangential forces generated during the cutting process. The displacement encoder is an incremental photoelectric encoder, installed at the end of the lead screw of the feed mechanism, generating 2000 pulse signals per revolution. The instantaneous feed speed of the saw blade is calculated by pulse counting and time interval measurement. The laser rangefinder is a two-dimensional scanning laser sensor, mounted on a fixed bracket of the kerfing machine frame. Using a pre-marked baseline on the cutter surface as a reference, it measures the vertical distance from the center point of the saw blade to the baseline in real time, with a measurement accuracy down to the millimeter level.

[0035] For example, when collecting cutting vibration data, a triaxial force sensor converts mechanical vibration into an electrical signal, which is then amplified and filtered by a signal conditioning circuit. When the saw blade cuts quartz aggregate in concrete, the cutting force changes abruptly, causing a spike in the voltage signal output by the sensor. An ultrasonic sensor is vertically mounted on the saw blade guard. By emitting 40kHz ultrasonic pulses and receiving reflected signals, the distance from the bottom of the saw blade to the concrete surface is calculated based on the time difference, thereby obtaining the real-time cutting depth. The sensing frequency is set to a constant value to ensure the sampling synchronization of each sensor and avoid data timing errors caused by inconsistent sampling rates.

[0036] Step S202: Preprocess the acquired raw information, identify abnormal peak values ​​in the raw information amplitude that exceed the preset standard threshold, remove pulse interference by median filtering, adjust the timestamp alignment according to the time delay characteristics within the sampling interval, process the triaxial torque data by low-pass filtering, retain the main frequency components of the cutting process, and output the filtered torque sequence, aligned coordinate sequence and velocity sequence.

[0037] In one possible embodiment, the preprocessing of the raw signal employs a hierarchical processing strategy. First, statistical analysis is performed on the signal amplitude, calculating the signal mean and standard deviation under normal cutting conditions. When the amplitude of a sampling point exceeds the mean plus a preset multiple of the standard deviation, it is determined to be an abnormal peak. A median filter uses a five-point sliding window, sorting the data within the window and taking the median value as the output, effectively suppressing impulse noise generated by electromagnetic interference or mechanical shock. Timestamp alignment is achieved by comparing the trigger times of each sensor, using the earliest trigger signal as a reference to adjust the timestamps of other sensor data, ensuring consistency of multi-source data on the time axis.

[0038] Step S203: Perform moving average processing on the filtered torque sequence, coordinate sequence, and velocity sequence. Determine the smoothing window size based on the velocity change rate. If the velocity change rate exceeds a first threshold within a preset time, use a small window sampling point number; otherwise, use a large window sampling point number. Perform Kalman filtering processing on the coordinate sequence. Adjust the Kalman gain coefficient according to the change trend of the depth data to obtain the smoothed coordinate sequence and generate the smoothed coordinate trajectory.

[0039] It should be noted that the dynamic window size adjustment mechanism in moving average processing is the key to optimizing the smoothing effect.

[0040] Preferably, the rate of change of speed is obtained by dividing the speed difference between two adjacent sampling periods by the time interval, reflecting the stability of the cutting process. When the saw blade encounters densely distributed hard aggregate, the rate of change of speed increases sharply. In this case, using a small window can retain more detailed information and avoid the loss of useful signals caused by over-smoothing. Conversely, when cutting homogeneous concrete areas, the speed change is gradual, and using a large window can better eliminate random noise. The state equation of the Kalman filter describes the dynamic relationship between the saw blade position and speed, and the observation equation correlates the measurement value of the laser rangefinder with the state variable. The Kalman gain coefficient is dynamically adjusted according to the changing trend of the depth data. When the depth changes drastically, the measurement noise covariance is increased, reducing the confidence in the new measurement value; when the depth change is stable, the process noise covariance is reduced, improving the tracking performance of the filter. This adaptive adjustment mechanism enables the filter to maintain good estimation accuracy under different cutting conditions.

[0041] Step S204: The smoothed coordinate trajectory is processed for time synchronization using an interpolation algorithm. The missing sampling points are filled in using spline interpolation based on the time delay characteristics. The resultant force application points at each moment are extracted from the torque sequence. The coordinate trajectory is fused to determine the cutting force application position. The smoothed cutting force position data, travel speed data, and cutting depth data are output.

[0042] In one embodiment, spline interpolation employs a cubic spline function to construct a piecewise polynomial between adjacent sampling points, ensuring the continuity of the second derivative of the interpolation curve. When a sensor misses a sampling point due to communication delay or data loss, the coefficients of the interpolation polynomial are calculated based on the position and slope information of the preceding and following valid data points to generate an estimate of the missing time.

[0043] Preferably, the extraction of the resultant force application point in the torque sequence is based on the principle of force vector composition. The axial, radial, and tangential forces measured by the triaxial force sensor are vector-superimposed in a Cartesian coordinate system to obtain the magnitude and direction of the resultant force. Based on the torque balance principle, and combined with the saw blade's geometry and installation position, the offset of the resultant force application point relative to the saw blade center is calculated.

[0044] Specifically, the fusion of coordinate trajectories and the point of application of the resultant force employs a weighted average method. Different weighting coefficients are assigned based on the positional accuracy of the laser rangefinder and the measurement reliability of the force sensor. When the cutting force is stable, the weight of position measurement is increased; when encountering abnormal cutting conditions, the weight of the point of application of the force is increased. This dynamic weighting strategy improves the accuracy of determining the cutting force position.

[0045] For example, when the kerf depth reaches two-thirds of the design requirement, the steel mesh inside the concrete may exert additional resistance on the saw blade. At this point, the force sensor detects a sharp increase in tangential force. The aforementioned fusion algorithm can accurately identify the actual stress location on the saw blade, providing reliable data support for subsequent path correction. The smoothed output data includes the cutting force location coordinates, travel speed value, and cutting depth value at each sampling time. This data constitutes the basic information for kerf path optimization control.

[0046] Based on the above method, the original information can be obtained, noise can be filtered, and smoothed original information can be obtained. The consistency of multi-source data on the time axis can be ensured by timestamp alignment. Data filtering can effectively suppress impulse noise generated by mechanical shock, thereby improving the accuracy and usability of data acquisition.

[0047] Figure 3 A flowchart illustrating a method for determining cut path offset and correction requirements according to an embodiment of the present invention is shown. like Figure 3As shown, the method includes the following steps: Step S301: Extract a continuous position sequence from the smoothed cutting force position data, fit the direction vector of the current path using the least squares method, read the preset path coordinate points, calculate the vertical distance from the position point to the preset path at each sampling time, generate a deviation distance sequence, and calculate the deviation change angle based on the ratio of the difference in deviation distances between adjacent sampling points to the time interval.

[0048] Specifically, in the process of identifying the deviation of the cutting path in concrete pavement, the least squares method fitting is based on the temporal characteristics of the cutting force position data.

[0049] Specifically, a position sequence within a fixed time window is extracted from the smoothed data stream. Each position point contains horizontal and vertical coordinates, and the straight line parameters are solved by constructing an overdetermined system of equations. The direction vector is determined by fitting the slope and intercept of the straight line. The slope reflects the angle of the saw blade's travel direction, and the intercept reflects the initial position of the path in the coordinate system. The preset path coordinate points are obtained by digitizing the construction drawings and stored in the controller's memory at the same sampling frequency. Each coordinate point corresponds to a specific mileage position. The vertical distance is calculated using the point-to-line distance formula. When the actual position of the saw blade is a certain sampling point, the signed value of the vertical distance is obtained through the algebraic relationship between the coordinates of that point and the preset path straight line equation. A positive value indicates that the saw blade is biased to the right of the road surface, and a negative value indicates that it is biased to the left. The deviation change angle is converted into angular dimensions by the ratio of the difference in deviation distance between two adjacent sampling periods to the sampling time interval, combined with the saw blade's travel speed. When the saw blade travels at a constant speed, the deviation change angle directly reflects the curvature of the path.

[0050] Step S302: Perform differential operation on the deviation distance sequence to obtain the deviation change rate. Determine the change trend by the consistency of the sign of the deviation change rate within the sliding window. If the deviation distance of multiple consecutive sampling points exceeds a preset distance threshold and the deviation change angle exceeds a preset angle threshold, it is determined that the path has deviated. Record the position of the offset starting point and the deviation change rate.

[0051] Specifically, if the deviation distance of multiple consecutive sampling points does not exceed a preset distance threshold, or the deviation change angle does not exceed a preset angle threshold, then it is determined that the path has not deviated.

[0052] In one possible embodiment, the difference operation performs a first-order difference on the deviation distance sequence, and the resulting deviation change rate sequence reflects the speed of path deviation. The sliding window is set to contain 5 to 10 consecutive sampling points, and all deviation change rates within the window having the same sign indicate a stable offset trend.

[0053] Step S303: Based on the cutting force position sequence after the offset starting point, calculate the radius of the arc formed by three adjacent points to obtain the path curvature, extract the ratio of the change in the lateral coordinate to the change in the longitudinal coordinate in the cutting force position sequence to determine the offset direction, obtain the cumulative deviation value by accumulating the deviation distance sequence, determine the offset magnitude based on the product of the cumulative deviation value and the deviation change rate, and classify the correction priority level according to the magnitude of the offset.

[0054] Preferably, the preset distance threshold is set based on the pavement width and construction accuracy requirements, typically one-tenth of the dummy joint design width. The preset angle threshold considers the saw blade's mechanical response capability; excessive angle changes may cause the saw blade to jam or break. The path curvature is calculated using the three-point arc method, selecting three consecutive points after the offset starting point. A unique arc is determined through these three points, and the reciprocal of the arc radius is the curvature value at that position. The magnitude of the curvature value directly affects the formulation of the correction strategy; a larger curvature indicates a more severe path curvature, requiring a stronger correction force. The offset direction is determined by calculating the ratio of the change in lateral coordinates to the change in longitudinal coordinates in the position sequence; the arctangent of this ratio gives the offset direction angle. The cumulative deviation value is obtained by accumulating the deviation distance sequence, reflecting the overall severity of the offset. The offset magnitude is determined by multiplying the cumulative deviation value by the deviation change rate, which comprehensively considers both the magnitude and speed of the offset. The correction priority is divided into three levels: Level 1 corresponds to minor deviations, which are corrected gradually; Level 2 corresponds to moderate deviations, which require immediate adjustment; and Level 3 corresponds to severe deviations, which triggers the emergency correction mechanism.

[0055] Step S304: Based on the offset direction and correction priority level, generate an offset correction parameter set including the correction direction angle, correction displacement, and correction start position. If the cumulative deviation value exceeds a preset deviation threshold, increase the correction displacement. Adjust the correction execution timing according to the path curvature change, and output the offset correction requirement including the correction parameter set and execution timing.

[0056] Preferably, the generation of the correction parameter set follows a graded response principle. The correction direction angle is determined based on the opposite direction of the offset direction, and the inertia of the saw blade is considered for compensation. The initial value of the correction displacement is set according to the proportional relationship of the cumulative deviation value. When the cumulative deviation value exceeds the preset deviation threshold, the correction displacement increases according to the preset amplification factor to ensure rapid return to the preset path.

[0057] In one embodiment, the impact of path curvature variation on the timing of correction execution is reflected in the decomposition of correction actions. When the path curvature variation is gradual, correction actions can be executed continuously; when the curvature suddenly increases, the correction actions are decomposed into multiple small-amplitude adjustments, and the path state is reassessed after each adjustment to avoid oscillations caused by over-correction.

[0058] Specifically, the output format for offset correction requirements includes two parts: a correction parameter set and an execution sequence. The correction parameter set is stored in structured data format, including the numerical value of the correction direction angle, the magnitude of the correction displacement, and the mileage marker of the correction start position. The execution sequence defines the execution order and time interval of each correction action, achieving smooth path adjustment through timing control.

[0059] For example, when a saw blade encounters a band of obliquely distributed hard aggregate during cutting, the asymmetrical distribution of cutting resistance causes the saw blade to deflect towards the side with less resistance. Through the aforementioned deviation identification and correction requirement determination mechanism, the controller can detect the anomaly at the initial stage of the deflection.

[0060] Based on the above method, the curvature of the path can be clearly reflected, thereby accurately determining whether a deviation has occurred, scientifically determining the priority of correction, triggering an emergency correction mechanism, and accurately determining the path correction sequence, correction parameters, and correction requirements.

[0061] Figure 4 A flowchart illustrating a method for generating correction instructions for saw blade feed speed correction according to an embodiment of the present invention is shown. like Figure 4 As shown, the method includes the following steps: Step S401: Obtain the difference between the current travel speed and the preset speed reference as the speed deviation, read the ratio of the real-time cutting depth to the design depth threshold, determine the acceleration or deceleration requirement according to the positive or negative sign of the speed deviation, and determine the speed adjustment gradient by the ratio of the load coefficient collected by the resistance sensor to the preset reference load.

[0062] Specifically, in the process of controlling the cutting speed of concrete pavement joints, real-time calculation of speed deviation is the foundation for achieving precise control.

[0063] Specifically, the preset speed benchmark is pre-set based on the concrete strength grade and aggregate density. It is typically set at a lower value at the initial stage of cutting, and gradually increased after the saw blade is fully engaged in cutting. The current travel speed is collected in real time by an encoder, with an update frequency no less than the preset sampling rate per second. A positive speed deviation indicates that the actual speed exceeds the benchmark value and needs to be reduced; a negative value indicates that the speed is insufficient and needs to be increased.

[0064] For example, the load factor is obtained by comparing the output current of the cutting resistance sensor with the rated current. When the saw blade cuts homogeneous concrete, the load factor remains relatively stable; when encountering reinforcing steel or hard aggregate, the load factor increases sharply. The speed adjustment gradient is determined based on the ratio of the load factor to a preset reference load; the larger the ratio, the steeper the adjustment gradient and the faster the speed changes.

[0065] Step S402: Calculate the target propulsion speed by multiplying the speed adjustment gradient by the current speed. If the load coefficient exceeds the preset upper limit threshold, limit the target propulsion speed within a safe range. Obtain the preset mechanical response delay time constant. Divide the difference between the target propulsion speed and the current speed by the response delay time constant to obtain the speed change rate.

[0066] In one possible embodiment, the target propulsion speed is calculated using a proportional adjustment principle. The speed adjustment gradient is used as a proportional coefficient, multiplied by the current speed to obtain a preliminary target value. The mechanical response delay time constant reflects the time interval from issuing a control command to the actual mechanical response. This constant is obtained through prior system identification experiments, and its typical value is between tens and hundreds of milliseconds.

[0067] Step S403: Determine the command transmission frequency based on the ratio of the velocity change rate to the design depth threshold, generate a discrete velocity adjustment pulse sequence according to the command transmission frequency, limit the velocity increment between adjacent pulses according to the maximum allowable acceleration, and arrange the pulse sequence in time order to form a velocity correction command.

[0068] The calculation of the velocity change rate takes into account the influence of mechanical inertia. The difference between the target's advancing velocity and the current velocity represents the magnitude of the velocity adjustment required. Dividing this by the response delay time constant yields the velocity change per unit time. This rate of change determines the density of the subsequent pulse sequence.

[0069] In one possible embodiment, the command transmission frequency is dynamically adjusted based on the ratio of the speed change rate to the design depth threshold. When a rapid speed change is required, the command transmission frequency is increased; when approaching the target depth, the frequency is decreased to achieve a smooth transition. Discrete speed adjustment pulse sequences are generated at a defined frequency, with each pulse representing a speed increment command. The speed increment between adjacent pulses is limited by the maximum acceleration to prevent excessively rapid speed changes from impacting the saw blade.

[0070] For example, in the final stage when the kerf depth is close to the design value, the command sending frequency is automatically reduced, the speed increment is reduced, and a smooth speed transition is achieved to avoid overcutting due to inertia.

[0071] Based on the above method, the feed speed of the saw blade can be reasonably adjusted according to the obtained load conditions. The required adjustment value can be quickly determined with a high sampling frequency, ensuring the timeliness and efficiency of speed adjustment. At the same time, by limiting the speed increment, it is prevented that excessively rapid speed changes will cause impact on the saw blade, thus ensuring the smoothness and safety of the speed correction process.

[0072] Figure 5A flowchart illustrating a method for fine-tuning the kerf angle based on material hardness assessment according to an embodiment of the present invention is shown. like Figure 5 As shown, the method includes the following steps: Step S501: Construct a velocity vector based on the propulsion speed value and offset direction, obtain the deviation value between the current travel angle and the preset path angle, calculate the cutting force change amplitude between adjacent sampling points through the cutting force time sequence data collected by the force sensor, extract the depth change per unit time from the ultrasonic ranging data, and record the numerical sequence of the cutting force change amplitude at different depth positions.

[0073] Specifically, in the angle adjustment control of concrete pavement joints, the construction of the velocity vector is a key step in achieving precise path control. The propulsion velocity value contains two components: magnitude and direction. The velocity vector is formed by combining the instantaneous velocity obtained by the encoder with the direction angle measured by the gyroscope. The offset direction is determined by the geometric relationship between the current position point and the preset path, and the left or right direction of the offset is determined by vector cross product operation. The current travel angle is measured in real time by a magnetometer or photoelectric encoder, and the difference between the current travel angle and the preset path angle reflects the degree of deviation from the path.

[0074] For example, the calculation of the cutting force variation amplitude is based on continuously sampled force sensor data. Within each sampling period, the output value of the triaxial force sensor is recorded, the difference in axial force between two adjacent sampling points is calculated, and the vector sum of the three axial differences is taken as the total cutting force variation amplitude. When the saw blade cuts from a soft cement paste area into an area containing granite aggregate, the cutting force will rise sharply in a short period of time, with the variation amplitude reaching several times the normal value. The extraction of depth variation considers not only the vertical feed of the saw blade but also the depth fluctuations caused by uneven road surfaces. An ultrasonic ranging sensor emits pulses at a fixed frequency, and the distance from the bottom of the saw blade to the concrete surface is calculated by measuring the echo time. The depth variation per unit time is obtained by performing a first-order difference on the depth data; this variation directly affects the magnitude of the cutting load. The numerical sequence stores the cutting force variation amplitude corresponding to each depth position in chronological order, forming a depth-force amplitude mapping relationship.

[0075] Step S502: Based on the numerical sequence, identify the location and intensity of the cutting force mutation point, determine the material hardness change characteristics by the correlation between the mutation point density and the deviation value, and mark the hardness change characteristics as a high hardness region if the hardness change characteristics exceed a preset threshold. Determine the resistance coefficient at each location based on the distribution density of the high hardness region and the cutting force change amplitude.

[0076] In one possible embodiment, statistical methods are used to determine the characteristics of material hardness changes. A sliding window is used to analyze the numerical sequence; when multiple consecutive abrupt changes exceeding a certain multiple of the average value appear within the window, it is identified as a hardness abrupt change region. The density of abrupt changes is characterized by the number of abrupt changes per unit length; a higher density indicates stronger material inhomogeneity. The correlation between the deviation value and the density of abrupt changes is calculated using the Pearson correlation coefficient; when the correlation coefficient exceeds a preset threshold, it indicates a causal relationship between path deviation and material hardness changes. The drag coefficient is determined based on the ratio of the cutting force variation amplitude to the standard cutting force. In high-hardness regions, the drag coefficient increases significantly, forming a spatial distribution map of the drag coefficient.

[0077] Step S503: Using the peak position of the resistance coefficient, the lateral deflection torque is calculated by combining the lateral and longitudinal components of the velocity vector. The theoretical deflection angle is obtained by the ratio of the deflection torque to the preset stiffness parameter of the saw blade bearing. The correction coefficient is determined according to the reverse compensation amount of the offset direction. The theoretical deflection angle is multiplied by the correction coefficient to obtain the preliminary adjustment angle value.

[0078] Preferably, the calculation of the lateral deflection torque takes into account the geometric characteristics of the saw blade and the position of the cutting point. The velocity vector is decomposed into a longitudinal component parallel to the preset path and a lateral component perpendicular to the trajectory. The product of the lateral component and the resistance coefficient generates a lateral force. This lateral force acts on the cutting point of the saw blade, forming a lever arm with the center of rotation of the saw blade, generating a torque that causes the saw blade to deflect. The preset stiffness parameters of the saw blade bearing are pre-determined through material mechanics experiments, reflecting the bearing system's ability to resist deformation. The theoretical deflection angle is equal to the deflection torque divided by the stiffness parameter; this angle represents the natural deflection tendency of the saw blade under the current cutting conditions. The correction coefficient is determined according to the offset direction. When the offset direction is the same as the deflection tendency, the correction coefficient is greater than 1, requiring a larger compensation amount; when the direction is opposite, the correction coefficient is less than 1 to avoid over-correction. The initial adjustment angle value is obtained by multiplying the theoretical deflection angle by the correction coefficient; this value is the basis for subsequent discretization processing. Angle discretization processing converts continuous angle adjustments into discrete instructions executable by the stepper motor. The step angle of a servo motor is determined by the motor's physical structure, with typical values ​​of 1.8 degrees or 0.9 degrees, which is the smallest unit of angle adjustment. The initial adjustment angle value is divided by the step angle to obtain the required number of steps, and rounding is used to ensure the number of steps is an integer.

[0079] Step S504: Discretize the initial adjustment angle value, determine the minimum adjustment step size based on the stepping angle of the servo motor, adjust the execution time interval of each step size by the rate of change of the material hardness change characteristics, decompose the initial adjustment angle value into the sum of multiple minimum adjustment step sizes, and output the angle fine-tuning increment sequence and the corresponding execution time table.

[0080] In one possible embodiment, the dynamic adjustment of the execution time interval is based on real-time monitoring of material hardness variation characteristics. When the rate of hardness change is large, it indicates that the saw blade is traversing a soft-hard interface region; in this case, the execution time interval is shortened, allowing for more frequent and precise angle adjustments. Conversely, in homogeneous material regions, the time interval is lengthened to reduce unnecessary adjustments.

[0081] For example, when the saw blade moves from a plain concrete area into a heavily reinforced area, the material hardness increases dramatically, and the system detects that the cutting force at multiple consecutive sampling points exceeds the threshold. Based on torque calculations, the theoretical deflection angle is 3.6 degrees, with a correction factor of 1.2, resulting in an initial adjustment angle of 4.32 degrees. If the step angle is 0.9 degrees, five step pulses are required. Considering the drastic change in hardness, the execution time interval of the five pulses is set to a shorter value to quickly complete the angle adjustment. The output format of the angle fine-tuning increment sequence includes the amplitude and trigger time of each step pulse. The execution timetable is arranged chronologically to ensure that pulse transmission does not conflict. This precise angle control mechanism can compensate for path deviations caused by material inhomogeneity in real time, maintaining the straightness accuracy of the cut.

[0082] Based on the above method, the hardness change characteristics at different locations can be accurately identified by the correspondence between depth position and cutting force change amplitude, the distribution of hardness regions can be scientifically determined, the initial adjustment angle value can be determined by calculating the lateral deflection torque, and then the execution step size and time interval can be adjusted according to the actual situation of the motor, which greatly improves the applicability of angle adjustment, making it adaptable to the motor parameters in the specific implementation process and ensuring the smooth progress of the adjustment process.

[0083] Figure 6 A flowchart illustrating a method for correcting path data using a road cutting mechanical actuator according to an embodiment of the present invention is shown. like Figure 6 As shown, the method includes the following steps: Step S601: Convert the angle fine-tuning increment into a pulse signal sequence for the servo motor, generate frequency control parameters for the inverter through a speed correction command, send the pulse signal sequence to the angle adjustment mechanism, and simultaneously input the frequency control parameters to the propulsion motor. Obtain the actual rotation angle fed back by the servo motor encoder and the actual operating frequency output by the inverter, and record the time difference between the control command issuance time and the mechanical response time.

[0084] Specifically, in the mechanical control of cutting joints in concrete pavement, the conversion of angle fine-tuning increments into pulse signal sequences is the foundation for achieving precise control.

[0085] In one possible embodiment, the angle fine-tuning increment is input to the controller in the form of an angle value. The pulse generation module inside the controller converts the angle value into a corresponding number of pulses based on the microstepping settings of the servo motor. Each pulse represents the smallest angle unit of motor rotation, and the frequency of the pulse sequence determines the speed of angle adjustment. The speed correction command includes a target speed value and acceleration time parameters. The inverter calculates a frequency change curve based on these parameters to achieve a smooth speed transition. When the angle fine-tuning increment is 2.7 degrees, the servo motor's step angle is 0.9 degrees, and the microstepping is 4, the actual minimum resolvable angle is 0.225 degrees, requiring 12 pulses to complete the adjustment. The pulse signal sequence is transmitted to the servo driver via a differential signal line, and the driver converts the pulses into corresponding current waveforms to drive the motor rotation. Simultaneously, the target frequency value in the speed correction command is sent to the inverter via an analog interface or communication protocol, and the inverter adjusts the output frequency according to the preset acceleration curve.

[0086] Furthermore, the time difference between the issuance of control commands and the mechanical response mainly consists of three parts: signal transmission delay, driver processing delay, and mechanical inertia delay. Signal transmission delay depends on the communication method and distance, typically in the microsecond range; driver processing delay includes signal decoding and power amplification time, approximately in the millisecond range; mechanical inertia delay is the most significant source of delay, related to load inertia and driving torque, and can reach tens of milliseconds. Accurate measurement of this time difference is crucial for subsequent timing compensation.

[0087] Step S602: Based on the actual rotation angle and actual operating frequency, drive the steering mechanism and feed mechanism of the mechanical actuator to perform adjustment actions. During the execution process, obtain the coordinate value of the center point of the saw blade by the laser positioning instrument according to a fixed sampling rate, use the incremental encoder to measure the instantaneous travel speed of the saw blade, and compensate and correct the sampling time according to the time difference to obtain the coordinate sequence and speed data at the corresponding time during the adjustment process.

[0088] In one possible embodiment, the steering mechanism of the kerfing machine employs a rack and pinion transmission. A servo motor drives the gear through a reducer, and the gear meshes with a rack fixed to the frame, enabling lateral movement of the saw blade assembly. The feed mechanism uses a ball screw drive, with a propulsion motor connected to the screw via a coupling. The screw nut is fixed to the saw blade lifting frame, and the motor's rotation propels the saw blade forward. A laser positioning device is installed at the front end of the saw blade guard, emitting a laser beam that vertically illuminates a reflective target on the ground. The absolute coordinates of the saw blade's center point are calculated using triangulation principles. An incremental encoder is coaxially mounted with the propulsion motor, outputting a pulse signal proportional to the rotational speed. The pulse frequency divided by the encoder's line count yields the rotational speed, which is then multiplied by the transmission ratio and wheel diameter to obtain the linear velocity.

[0089] Preferably, the time difference compensation adopts a predictive compensation method. A model of the relationship between delay time and control quantity is established based on historical data, and the delay effect is considered in advance when sending control commands. Specifically, the compensation method is to subtract the time difference from the sampling time to obtain the compensated time stamp, thus aligning the sensor data and control commands on the time axis. The compensated coordinate sequence can accurately reflect the motion trajectory of the saw blade under specific control actions.

[0090] Step S603: Add a time stamp to each coordinate point in the coordinate sequence, pair and associate coordinate points at the same time with velocity data, construct continuous path trajectory segments through adjacent coordinate points, calculate the velocity characteristic value of each segment based on the velocity value of the start and end points and the time interval, and form a coordinate point sequence containing timestamps and the velocity distribution of each trajectory segment.

[0091] Specifically, the pairing and association of coordinate points and velocity data is achieved through timestamp matching. The control system maintains a unified clock, and all sensor data and control commands are accompanied by precise timestamps. During data processing, the time window is set to half of the sampling period; coordinate and velocity data falling within the same window are considered measurements taken at the same moment. Adjacent coordinate points are connected by straight line segments to form an initial trajectory, which is then smoothed using spline interpolation to obtain a continuous path curve. The calculation of velocity characteristic values ​​includes not only the average velocity but also the rate of change and fluctuation range of velocity; these characteristic values ​​collectively describe the motion characteristics of each trajectory segment.

[0092] Step S604: Using the starting and ending coordinates of the coordinate point sequence, calculate the actual change in the travel angle. Evaluate the smoothness of the speed adjustment by the dispersion of the speed characteristic values ​​of each segment in the speed distribution. If the deviation between the actual change and the command value exceeds a preset threshold, mark the path segment and output the adjusted path data, which includes the coordinate point sequence, speed distribution, and deviation marking information.

[0093] In one embodiment, the actual change in travel angle is calculated using a vector analysis method. The start and end points of the coordinate point sequence are taken to construct a displacement vector; the angle between this vector and the preset path direction represents the actual angle change. The dispersion of the velocity distribution is evaluated by calculating the standard deviation of the velocity characteristic values ​​for each segment; a smaller standard deviation indicates smoother velocity control. When the deviation between the actual change and the command value exceeds a preset threshold, the system determines that there is a control deviation in that path segment, requiring compensation and correction in the next control cycle.

[0094] For example, in a cutting operation, the angle fine-tuning command requires a 3-degree leftward adjustment and an increase in speed from 1.2 m / min to 1.5 m / min. Data collected after execution shows the starting point coordinates as the origin, the ending point coordinates as an actual deflection of 2.8 degrees, and the speed fluctuating between 1.48 and 1.52 m / min. The system calculates the angle deviation to be 0.2 degrees, and the speed dispersion is within the allowable range. This segment is marked as a slight deviation, and the corresponding deviation information is added to the output path data.

[0095] The adjusted path data serves as feedback information for closed-loop control, used to evaluate control effectiveness and optimize control parameters. The coordinate point sequence provides the actual motion trajectory of the saw blade, the speed distribution reflects the smoothness of the propulsion process, and deviation markers indicate path segments requiring special attention.

[0096] Based on the above method, by obtaining the correction data of the angle and speed of the mechanical actuator, the adjusted path data is obtained. Based on this, the stability of the propulsion process and the key path segments to focus on are further reflected through the coordinate point sequence, speed distribution and deviation marking information in the path data, so that users can intuitively understand the operation of the current adjustment process.

[0097] Figure 7 A flowchart illustrating a kerf path optimization method based on straightness according to an embodiment of the present invention is shown. like Figure 7 As shown, the method includes the following steps: Step S701: Compare the coordinate point sequence in the adjusted path data with the standard coordinate sequence of the preset path point by point, calculate the vertical distance from each coordinate point to the preset path as a local deviation value, calculate the root mean square value of the path deviation based on the local deviation value, and extract the maximum value in the deviation value sequence as the peak deviation.

[0098] Specifically, the root mean square value of the path deviation is obtained by summing the squares of the local deviation values ​​and then taking the square root.

[0099] Specifically, in the quality assessment of concrete pavement cuts, accurate calculation of path deviation is the basis for judging the straightness of the cuts.

[0100] The adjusted path data includes a sequence of coordinate points collected during the actual cutting process, with each coordinate point having a corresponding timestamp and location information. The standard coordinate sequence of the preset path is extracted from the construction design drawings and interpolated at the same sampling interval to ensure a one-to-one correspondence with the actual coordinate points. The vertical distance is calculated using the point-to-line distance formula; for each straight line segment on the preset path, the vertical distance from the actual coordinate point to that line is calculated.

[0101] For example, in a point-by-point comparison process, each local deviation value represents the lateral offset at that location. When the saw blade is affected by uneven resistance and shifts, the deviation value will show a continuous changing trend. The root mean square value is calculated by summing the squares of all local deviation values, dividing by the number of points, and then taking the square root. This value reflects the average level of the overall deviation. The peak deviation is extracted by traversing the sequence of deviation values ​​to find the deviation with the largest absolute value, which represents the maximum degree of deviation during the cutting process.

[0102] Step S702: Fit the centerline of the actual cut using the least squares method based on the deviation value sequence, calculate the angle difference and position offset between the fitted straight line and the ideal straight line, and obtain the dummy cut straightness index by the ratio of the root mean square value to the preset allowable deviation threshold. If the index is lower than the preset straightness threshold, it is determined that additional correction is required.

[0103] It should be noted that the process of fitting the actual cut centerline using the least squares method involves constructing an overdetermined system of equations. Using the actual coordinates of the points as input, the parameters of the straight line equation are solved by minimizing the sum of squared residuals. The fitted straight line represents the overall orientation of the actual cut, and comparing it with the ideal straight line reveals systematic deviations. The angle difference is obtained by calculating the difference between the arctangent values ​​of the slopes of the two straight lines, while the positional offset is the perpendicular distance between the two lines at a specific location. The dummy cut straightness index is calculated by the ratio of the root mean square value to a preset allowable deviation threshold; a smaller ratio indicates better straightness. The preset straightness threshold is determined based on the pavement grade and usage requirements; airport runways typically have stricter requirements than ordinary roads.

[0104] Step S703: Based on the dummy seam straightness index and peak deviation, obtain the depth data to be adjusted corresponding to the section where the deviation exceeds the threshold, adjust the cutting depth parameter according to the correspondence between the depth data to be adjusted and the deviation value, determine the lateral compensation amount through the angle difference, calculate the rate correction value according to the current feed rate and position offset, and form a correction instruction containing the depth adjustment value, rate correction value and lateral compensation amount.

[0105] In one possible implementation, a sliding window detection method is used to identify segments with deviations exceeding a threshold. A window of fixed length moves along the path, and when the average deviation within the window exceeds a set value, the segment is marked as an abnormal segment. The cutting depth data corresponding to these segments is extracted from sensor records. By analyzing the correlation between depth changes and deviation values, it can be determined whether the deviation is caused by an inappropriate cutting depth. When it is found that excessive depth leads to increased resistance and thus deviation, the subsequent cutting depth needs to be reduced; when the depth is too small, resulting in an incomplete cut, the depth value needs to be increased.

[0106] Preferably, the lateral compensation amount is determined based on the angle difference and the distance from the current position to the target endpoint. The compensation amount calculation takes into account the remaining path length, employing gradual compensation on longer remaining paths to avoid path unevenness caused by abrupt adjustments. The rate correction value is determined based on the magnitude and direction of the position offset. When the offset is large, the advance speed is appropriately reduced to allow more time for lateral adjustment; when the path is close to the ideal trajectory, the speed can be appropriately increased to improve efficiency. The various parameters in the correction command are interrelated. The depth adjustment value affects the cutting resistance, which in turn affects the required lateral compensation amount; the rate correction value determines the execution time of the compensation action.

[0107] Step S704: Update the control parameters using the lateral compensation amount and depth adjustment value in the correction instruction; correct the original trajectory coordinates according to the lateral compensation amount to obtain the optimized target path coordinate sequence; obtain the target cutting depth of each position point by superimposing the depth adjustment value with the original depth setting value; and output the optimized target path and depth data.

[0108] Specifically, the optimized target path coordinate sequence is obtained by superimposing lateral compensation on the original trajectory. The correction amount for each coordinate point is calculated individually based on its position and local deviation. The correction amount is smaller in the initial stage, gradually transitioning to the required compensation value to ensure the continuity of the path. The determination of the target cutting depth not only considers the original design depth but also makes dynamic adjustments based on material properties and cutting conditions.

[0109] In one possible implementation, when multiple consecutive coordinate points are detected to be biased towards the same side, and the deviation values ​​are gradually increasing, a systematic offset trend is determined. In this case, the correction instruction includes not only compensation for the current position but also preventative adjustment parameters. This early intervention prevents the deviation from further increasing.

[0110] For example, in a certain kerf cutting operation, comparison revealed that the actual path deviated to the right, with a root mean square deviation of 8 mm and a peak deviation of 12 mm. Least squares fitting showed an angular deviation of 1.5 degrees between the overall path and the preset path. System analysis found that the deviation was concentrated in the section where the cutting depth reached 80 mm, indicating that the excessive depth caused lateral instability. The correction command adjusted the subsequent cutting depth to 75 mm, set the lateral compensation to 10 mm to the left, and reduced the feed rate to 0.9 times the original.

[0111] The optimized target path and depth data provide precise control targets for the next stage of the kerfing operation.

[0112] Based on the above method, the straightness of the current adjusted path is accurately judged by comparing the straightness. Based on the lateral compensation amount and the depth adjustment value, the problems in the current cutting process are determined by comprehensive consideration. The advancing speed is intelligently adjusted according to the offset. The speed is reduced when the adjustment demand is large and increased when the adjustment demand is small. While preserving the adjustment space and time and ensuring the quality and integrity of the adjustment, the execution efficiency is also taken into account.

[0113] Figure 8 A flowchart illustrating a method for generating a kerf path and its width control signal according to an embodiment of the present invention is shown. like Figure 8 As shown, the method includes the following steps: Step S801: Based on the coordinate point sequence and corresponding depth data on the target path, the kerf width at each position is measured by a laser scanner, the width difference between adjacent measurement points is extracted, the standard deviation of the width difference sequence is calculated as a uniformity index, and the cutting force time series data recorded by the force sensor is acquired to calculate the variation amplitude of the cutting force per unit time.

[0114] Specifically, in the final optimization stage of concrete pavement joint width control, the measurement accuracy of the laser scanner directly affects the evaluation results of width uniformity. The laser scanner uses a line laser projection method, vertically illuminating the kerf surface at a fixed distance behind the saw blade. An image sensor captures the deformation of the laser line. The kerf width is calculated by the distance between two edge points on the laser line. The measurement frequency is synchronized with the saw blade's forward speed, ensuring a width value is obtained at fixed intervals. The width difference between adjacent measurement points reflects local variations in the kerf width. The standard deviation, obtained by summing the squares of all differences, dividing by the number of measurement points, is used as a uniformity indicator.

[0115] For example, a cutting force sensor is installed on the support structure of the saw blade spindle to monitor the reaction force during the cutting process in real time. Time-series force data is recorded at a constant sampling rate, with each data point containing triaxial force components and a timestamp. The magnitude of change per unit time is obtained by calculating the difference in force values ​​between adjacent sampling points; this magnitude reflects the stability of the cutting process. When the saw blade encounters reinforcing steel or large aggregate particles, the cutting force undergoes a sudden change, with a significantly increased magnitude of variation.

[0116] Step S802: Based on the uniformity index, identify abnormal sections where the width deviation exceeds the threshold, match the position coordinates of the abnormal sections with the time series of the cutting force variation amplitude, and determine the correlation strength by calculating the Pearson correlation coefficient between the two. If the correlation strength exceeds the preset threshold, adjust the saw blade feed depth parameter according to the depth position corresponding to the cutting force peak.

[0117] Preferably, the identification of abnormal segments is based on statistical principles. A baseline value for the uniformity index is set, and when the width deviation of a local segment exceeds a preset multiple of the baseline value, the segment is marked as abnormal. Matching of position coordinates and time series is achieved through timestamp alignment. Considering the physical distance of the saw blade from the cutting point to the measurement point, the time series needs to be time-shifted accordingly. The Pearson correlation coefficient is calculated using a standard formula, treating the width deviation sequence and the cutting force change sequence as two variables, and calculating their linear correlation coefficient. The closer the absolute value of the correlation coefficient is to 1, the stronger the correlation between the two. When the correlation strength exceeds the threshold of 0.7, the system determines that the width anomaly is mainly caused by cutting force fluctuations. The adjustment of the depth parameter is based on empirical rules; the depth corresponding to the peak cutting force usually needs to be reduced to decrease cutting resistance and improve width control.

[0118] Step S803: Update the cutting depth data of each path point using the adjusted depth parameters, construct a control dataset by combining the target path coordinates and the updated depth data, assign control priorities to different path segments according to the width uniformity index, and obtain the optimized cut path coordinate sequence through priority weighting.

[0119] Specifically, the control dataset integrates multi-dimensional information. Each path point includes three-dimensional coordinates, cutting depth, feed rate, and width control parameters. The updated depth value is obtained by adding an adjustment amount to the original depth, with the adjustment amount proportional to the peak cutting force. Control priority allocation considers the severity and location importance of width deviations; path segments with larger deviations and more critical locations receive higher priority. Priority weighting assigns a weight coefficient to each path point, with the weight coefficient proportional to the priority, ensuring the control accuracy of high-weight points is prioritized during path optimization.

[0120] Step S804: For the optimized path coordinate sequence and depth data, generate timing control points according to a preset sampling period, convert the width deviation of each control point into the saw blade lateral adjustment amount according to the width control requirements, integrate the path coordinates, depth data and lateral adjustment amount to form a comprehensive control command, and output the final optimized kerf path and width control signal.

[0121] Preferably, the generation of timing control points follows a fixed sampling period, typically 100 milliseconds. Each control point corresponds to a specific time and position, containing all control parameters that need to be executed at that time. The conversion from width deviation to lateral adjustment is based on the proportional relationship of control theory; the larger the deviation, the larger the required lateral adjustment, but an upper limit is set to prevent over-adjustment. Lateral adjustment is achieved by controlling the lateral movement mechanism of the saw blade assembly, and the adjustment amount is converted into the number of pulses of the stepper motor. The upper limit can be determined by the user according to actual needs and is not limited here.

[0122] Specifically, the formation of integrated control commands involves the coordination of multiple parameters. Path coordinates determine the spatial position of the saw blade, depth values ​​control the vertical feed of the saw blade, and lateral adjustment corrects the lateral offset of the path. These parameters are organized into a sequence of control commands according to their timing relationship. Each command includes the execution time, target position, depth setting, and lateral correction value. The control system executes these commands sequentially according to the timing sequence to achieve precise control of the kerf path and width.

[0123] In one possible implementation, when a periodic fluctuation in the kerf width is detected within a certain segment, the system analyzes the relationship between the fluctuation frequency and the saw blade rotation speed. If the frequency is a multiple of the rotation speed, it is determined that the fluctuation may be caused by saw blade eccentricity or vibration. In this case, in addition to adjusting the depth and lateral position, it is also necessary to adjust the saw blade rotation speed to avoid the resonant frequency.

[0124] For example, when processing a section of concrete pavement containing a dense steel mesh, the system detected a width uniformity index of 5 mm, far exceeding the 2 mm threshold. Correlation analysis showed a strong correlation between the cutting force variation and the width deviation, with a correlation coefficient of 0.85. The system adjusted the cutting depth of this section from 100 mm to 90 mm, set the lateral compensation to 8 mm to the left, and reduced the feed speed by 20%. After executing the optimized control commands, the width uniformity index decreased to 1.8 mm, meeting the quality requirements.

[0125] Understandably, the final output of the cut path and width control signals is the integrated result of the entire optimization process, which includes a complete control chain from deviation detection and cause analysis to parameter adjustment, thus realizing closed-loop control of the cut quality of concrete pavement.

[0126] Based on the above method, by evaluating the uniformity of the width and determining its correlation coefficient, the cause of the width anomaly can be determined. This allows for a more reasonable and targeted selection of ways to improve width control. Furthermore, by coordinating multiple parameters and scientifically determining the comprehensive control command based on the temporal relationship, various important parameters in the kerf cutting process are taken into account. This scientifically completes the dynamic adjustment during the kerf cutting process, effectively solving the problem of unstable cutting quality caused by material hardness differences, path deviations, and insufficient kerf straightness in the saw blade cutting process. It significantly improves the kerf straightness and width uniformity, effectively solves the quality control problem in cutting complex materials, and achieves high-precision and high-stability cutting results.

[0127] Figure 9 A structural block diagram of a concrete pavement joint cutting path optimization system according to an embodiment of the present invention is shown. like Figure 9As shown, the system includes: a data acquisition module 901, a correction requirement determination module 902, a speed correction command generation module 903, an angle adjustment determination module 904, a path data adjustment module 905, an optimization module 906, and a comprehensive control signal generation module 907; among which, The data acquisition module 901 is used to acquire the original information of the pavement cut, filter the noise of the original information, and obtain smoothed original information; wherein, the original information includes at least: cutting force position data, travel speed data, and cutting depth data.

[0128] Specifically, the data acquisition module 901 is further used for, Force sensors and displacement encoders are installed on the saw blade spindle and feed mechanism to measure cutting force position data, travel speed data, and cutting depth data. Specifically, the force sensor collects triaxial torque data generated by cutting vibration at a preset sensing frequency, while a laser rangefinder obtains the coordinate values ​​of the saw blade relative to the road surface baseline to determine the cutting force position data. The velocity change rate is calculated using the pulse signal from the displacement encoder to determine the travel speed data. An ultrasonic sensor measures the depth from the bottom of the saw blade to the concrete surface to determine the cutting depth data. The collected raw information is preprocessed to identify abnormal peak values ​​in the raw information amplitude that exceed the preset standard threshold. Median filtering is used to remove pulse interference. The timestamp alignment is adjusted according to the time delay characteristics within the sampling interval. The triaxial torque data is processed by low-pass filtering to retain the main frequency components of the cutting process. The filtered torque sequence, aligned coordinate sequence, and velocity sequence are output. Moving average processing is performed on the filtered torque sequence, coordinate sequence, and velocity sequence. The smoothing window size is determined based on the velocity change rate. If the velocity change rate exceeds a first threshold within a preset time, a small window sampling point is used; otherwise, a large window sampling point is used. Kalman filtering is performed on the coordinate sequence. The Kalman gain coefficient is adjusted according to the change trend of the depth data to obtain the smoothed coordinate sequence and generate the smoothed coordinate trajectory. The smoothed coordinate trajectory is time-synchronized using an interpolation algorithm. Based on the time delay characteristics, spline interpolation is used to fill in the missing sampling points. The resultant force application points at each moment are extracted from the torque sequence. The coordinate trajectory is fused to determine the cutting force application position. The smoothed cutting force position data, travel speed data, and cutting depth data are output.

[0129] The correction requirement determination module 902 is used to identify the directional deviation between the current path and the preset path based on the smoothed cutting force position data. If the directional deviation exceeds the preset deviation threshold, it is determined that the path has deviated, and the deviation direction is determined according to the trend of the cutting force position change. The deviation correction requirement is determined according to the deviation direction.

[0130] Specifically, the correction requirement determination module 902 is further used for, Continuous position sequences are extracted from the smoothed cutting force position data. The direction vector of the current path is fitted by the least squares method. At the same time, the coordinate points of the preset path are read, the vertical distance from the position point to the preset path at each sampling time is calculated, and the deviation distance sequence is generated. The deviation change angle is calculated based on the ratio of the difference of the deviation distances of adjacent sampling points to the time interval. The deviation change rate is obtained by performing a difference operation on the deviation distance sequence. The change trend is judged by the consistency of the sign of the deviation change rate within the sliding window. If the deviation distance of multiple consecutive sampling points exceeds a preset distance threshold and the deviation change angle exceeds a preset angle threshold, it is determined that the path has deviated. The position of the starting point of the deviation and the deviation change rate are recorded. Based on the cutting force position sequence after the offset starting point, the radius of the arc formed by three adjacent points is calculated to obtain the path curvature. The ratio of the change in the lateral coordinate to the change in the longitudinal coordinate in the cutting force position sequence is extracted to determine the offset direction. The cumulative deviation value is obtained by accumulating the deviation distance sequence. The offset magnitude is determined by the product of the cumulative deviation value and the deviation change rate. The correction priority level is divided according to the magnitude of the offset. Based on the offset direction and correction priority level, an offset correction parameter set is generated, which includes the correction direction angle, correction displacement, and correction start position. If the cumulative deviation value exceeds the preset deviation threshold, the correction displacement is increased. The correction execution timing is adjusted according to the path curvature change, and the offset correction requirement including the correction parameter set and execution timing is output.

[0131] The speed correction command generation module 903 is used to adjust the saw blade's advance speed by adjusting the travel speed, and to generate speed correction commands based on the speed deviation and cutting depth requirements.

[0132] Specifically, the speed correction instruction generation module 903 is further used to: The difference between the current travel speed and the preset speed reference is obtained as the speed deviation. The ratio of the real-time cutting depth to the design depth threshold is read. The acceleration or deceleration requirement is determined according to the positive or negative sign of the speed deviation. The speed adjustment gradient is determined by the ratio of the load coefficient collected by the resistance sensor to the preset reference load. The target propulsion speed is calculated by multiplying the speed adjustment gradient by the current speed. If the load coefficient exceeds the preset upper limit threshold, the target propulsion speed is limited to a safe range. A preset mechanical response delay time constant is obtained. The difference between the target propulsion speed and the current speed is divided by the response delay time constant to obtain the speed change rate. The command transmission frequency is determined based on the ratio of the velocity change rate to the design depth threshold. Discrete velocity adjustment pulse sequences are generated according to the command transmission frequency. The velocity increment between adjacent pulses is limited according to the maximum allowable acceleration. The pulse sequences are arranged in chronological order to form a velocity correction command.

[0133] The angle adjustment determination module 904 is used to adjust the travel angle by adjusting the propulsion speed and offset direction, identify the magnitude of the cutting force change, extract the depth change, evaluate the material hardness difference characteristics, and obtain the angle fine-tuning increment.

[0134] Specifically, the angle adjustment determination module 904 is further used for, A velocity vector is constructed based on the propulsion speed value and the offset direction. The deviation value between the current travel angle and the preset path angle is obtained. The cutting force variation amplitude between adjacent sampling points is calculated through the cutting force time sequence data collected by the force sensor. The depth change per unit time is extracted from the ultrasonic ranging data. The numerical sequence of the cutting force variation amplitude at different depth positions is recorded. The location and intensity of the cutting force mutation point are identified based on the numerical sequence. The material hardness change characteristics are judged by the correlation between the mutation point density and the deviation value. If the hardness change characteristics exceed the preset threshold, it is marked as a high hardness region. The resistance coefficient at each location is determined according to the distribution density of the high hardness region and the cutting force change amplitude. The lateral deflection torque is calculated by using the peak position of the resistance coefficient and the lateral and longitudinal components of the velocity vector. The theoretical deflection angle is obtained by the ratio of the deflection torque to the preset stiffness parameter of the saw blade bearing. The correction coefficient is determined according to the reverse compensation amount of the offset direction. The theoretical deflection angle is multiplied by the correction coefficient to obtain the preliminary adjustment angle value. The initial adjustment angle value is discretized, and the minimum adjustment step size is determined based on the stepping angle of the servo motor. The execution time interval of each step size is adjusted by the rate of change of the material hardness change characteristics. The initial adjustment angle value is decomposed into the sum of multiple minimum adjustment step sizes, and the angle fine-tuning increment sequence and the corresponding execution time table are output.

[0135] The path data adjustment module 905 is used to drive the mechanical actuator to modify the travel angle and advance speed in real time according to the angle fine-tuning increment and speed correction command, and to re-collect the position coordinates and travel speed of the saw blade to obtain the adjusted path data.

[0136] Specifically, the path data adjustment module 905 is further used for: The angle fine-tuning increment is converted into a pulse signal sequence for the servo motor. The frequency control parameters of the inverter are generated through the speed correction command. The pulse signal sequence is sent to the angle adjustment mechanism. At the same time, the frequency control parameters are input to the propulsion motor. The actual rotation angle fed back by the servo motor encoder and the actual operating frequency output by the inverter are obtained. The time difference between the control command issuance time and the mechanical response time is recorded. Based on the actual rotation angle and actual operating frequency, the steering mechanism and feed mechanism of the mechanical actuator are driven to perform adjustment actions. During the execution process, the coordinate value of the center point of the saw blade is obtained by the laser positioning instrument according to a fixed sampling rate. The instantaneous travel speed of the saw blade is measured by the incremental encoder. The sampling time is compensated and corrected according to the time difference to obtain the coordinate sequence and speed data at the corresponding time during the adjustment process. A time stamp is added to each coordinate point in the coordinate sequence, and coordinate points at the same time are paired and associated with velocity data. A continuous path trajectory segment is constructed through adjacent coordinate points. The velocity characteristic value of the segment is calculated based on the velocity value at the start and end points of each segment and the time interval, forming a coordinate point sequence containing timestamps and the velocity distribution of each trajectory segment. Using the starting and ending coordinates of the coordinate point sequence, the actual change in travel angle is calculated. The smoothness of speed adjustment is evaluated by the dispersion of speed characteristic values ​​of each segment in the speed distribution. If the deviation between the actual change and the command value exceeds a preset threshold, the path segment is marked. The adjusted path data is output, including the coordinate point sequence, speed distribution, and deviation marking information.

[0137] Preferably, the steering mechanism of the mechanical actuator adopts a gear and rack transmission method. The servo motor drives the gear through a reducer. The gear meshes with the rack fixed on the frame to realize the lateral movement of the saw blade assembly. The feed mechanism adopts a ball screw transmission. The propulsion motor is connected to the ball screw through a coupling. The ball screw nut is fixed on the saw blade lifting frame. The rotation of the motor drives the saw blade forward. The laser positioning instrument is installed at the front end of the saw blade guard. The emitted laser beam is vertically irradiated onto the reflective target on the ground. The absolute coordinates of the center point of the saw blade are calculated by the principle of triangulation.

[0138] The optimization module 906 is used to compare the adjusted path data with the preset path, extract the path deviation value, evaluate the dummy seam straightness of the cut, evaluate the dummy seam straightness according to the path deviation value and offset correction requirements, and if the dummy seam straightness is lower than the preset threshold, generate additional correction instructions for the cutting parameters to obtain the optimized target path and depth data.

[0139] Specifically, the optimization module 906 is further used to: The coordinate point sequence in the adjusted path data is compared point by point with the standard coordinate sequence of the preset path. The vertical distance from each coordinate point to the preset path is calculated as a local deviation value. The root mean square value of the path deviation is calculated based on the local deviation value. At the same time, the maximum value in the deviation value sequence is extracted as the peak deviation. The centerline of the actual cut is fitted using the least squares method based on the deviation value sequence. The angle difference and position offset between the fitted straight line and the ideal straight line are calculated. The straightness index of the dummy cut is obtained by the ratio of the root mean square value to the preset allowable deviation threshold. If the index is lower than the preset straightness threshold, it is determined that additional correction is required. Based on the dummy seam straightness index and peak deviation, obtain the depth data to be adjusted corresponding to the section where the deviation exceeds the threshold, adjust the cutting depth parameters according to the correspondence between the depth data to be adjusted and the deviation value, determine the lateral compensation amount through the angle difference, calculate the rate correction value according to the current feed rate and position offset, and form a correction instruction containing the depth adjustment value, rate correction value and lateral compensation amount. The control parameters are updated using the lateral compensation amount and depth adjustment value in the correction instruction. The original trajectory coordinates are corrected according to the lateral compensation amount to obtain the optimized target path coordinate sequence. The target cutting depth of each position point is obtained by superimposing the depth adjustment value with the original depth setting value. The optimized target path and depth data are then output.

[0140] The integrated control signal generation module 907 is used to identify the width variation pattern and determine the width uniformity based on the optimized target path and depth data, integrate the magnitude of the cutting force variation, adjust the saw blade depth, determine the width control signal, and obtain the final optimized kerf path integrated control command.

[0141] Specifically, the integrated control signal generation module 907 is further used for: Based on the coordinate point sequence and corresponding depth data on the target path, the kerf width at each position is measured by a laser scanner, the width difference between adjacent measurement points is extracted, the standard deviation of the width difference sequence is calculated as a uniformity index, and the cutting force time series data recorded by the force sensor is acquired to calculate the change amplitude of the cutting force per unit time. Based on the uniformity index, abnormal sections with width deviations exceeding the threshold are identified. The location coordinates of the abnormal sections are matched with the time series of the cutting force variation amplitude. The correlation strength is determined by calculating the Pearson correlation coefficient between the two. If the correlation strength exceeds the preset threshold, the saw blade feed depth parameter is adjusted according to the depth position corresponding to the cutting force peak. The cutting depth data of each path point is updated using the adjusted depth parameters. A control dataset is constructed by combining the target path coordinates and the updated depth data. Control priorities are assigned to different path segments according to the width uniformity index. The optimized cut path coordinate sequence is obtained through priority weighting. For the optimized path coordinate sequence and depth data, timing control points are generated according to a preset sampling period. Based on the width control requirements, the width deviation of each control point is converted into the lateral adjustment amount of the saw blade. The path coordinates, depth data and lateral adjustment amount are fused to form a comprehensive control command, and the final optimized kerf path and width control signal are output.

[0142] The concrete pavement cutting path optimization system provided in this embodiment includes: a data acquisition module, a correction requirement determination module, a speed correction command generation module, an angle adjustment determination module, a path data adjustment module, an optimization module, and a comprehensive control signal generation module. The data acquisition module is used to acquire the original information of the pavement cutting, filter noise from the original information, and obtain smoothed original information. The original information includes at least: cutting force position data, travel speed data, and cutting depth data. The correction requirement determination module is used to identify the directional deviation between the current path and a preset path based on the smoothed cutting force position data. If the directional deviation exceeds a preset deviation threshold, it determines that the path has deviated, determines the deviation direction based on the trend of cutting force position changes, and determines the deviation correction requirement based on the deviation direction. The speed correction command generation module is used to adjust the saw blade's advance speed by adjusting the travel speed and generate a speed correction command based on the speed deviation and cutting depth requirements. The angle adjustment determination module is used to... The system adjusts the travel angle by controlling the advance speed and offset direction, identifies the magnitude of changes in cutting force, extracts depth changes, and assesses material hardness differences to obtain the angle fine-tuning increment. The path data adjustment module drives the mechanical actuator to modify the travel angle and advance speed in real time based on the angle fine-tuning increment and speed correction command, and re-acquires the position coordinates and travel speed of the saw blade to obtain the adjusted path data. The optimization module compares the adjusted path data with the preset path, extracts the path deviation value, evaluates the dummy kerf straightness, and evaluates the dummy kerf straightness based on the path deviation value and offset correction requirements. If the dummy kerf straightness is lower than the preset threshold, additional correction commands for cutting parameters are generated to obtain the optimized target path and depth data. The integrated control signal generation module identifies the width variation pattern to determine the width uniformity based on the optimized target path and depth data, integrates the magnitude of changes in cutting force, adjusts the saw blade depth, determines the width control signal, and obtains the final optimized kerf path integrated control command.The concrete pavement cutting path optimization system provided in this embodiment collects raw information of pavement cutting, including cutting force location data, travel speed data, and cutting depth data. Noise filtering is applied to the raw information to obtain smoothed raw information. Timestamp alignment ensures consistency of multi-source data along the time axis. Data filtering effectively suppresses impulse noise generated by mechanical impact, improving the accuracy and usability of data acquisition. Based on a preset deviation threshold, the system determines the deviation magnitude and correction level by using the deviation change rate. This correction mechanism allows for more reasonable correction arrangements and outputs deviation correction requirements. Calculating speed deviation serves as the basis for precise control. Resistance information is collected to determine the load coefficient, accurately judging aggregate hardness. A gradient adjustment method is used to more reasonably determine the propulsion speed. By determining the command sending frequency, the system rapidly approaches the target depth when far from it and smoothly transitions when approaching the target depth, improving the efficiency and stability of the adjustment process. By obtaining the deviation value between the current angle and the preset angle, the system determines the variation amplitude of the cutting force time-series data, derives its value at different depths, and accurately identifies the location and intensity of abrupt change points, displaying the material hardness variation characteristics, and based on this... The system determines the angle correction method and rationally arranges the adjustment sequence and timing. By acquiring the actual rotation angle and operating frequency of the motor, it precisely drives the mechanical actuator to adjust its actions. Time stamps are used to associate coordinate points and speeds, constructing the adjusted trajectory segment and acquiring path data and related information to reflect the smoothness of the propulsion process and the path segments requiring terminal attention. By comparing the adjusted path with the preset path, the system determines the straightness index and assigns corresponding correction amounts and control parameters for different coordinate points, establishing the initial correction command and deriving optimized target path and depth data, making the path adjustment method more aligned with actual operational needs. By identifying width uniformity and combining it with the variation in cutting force, the saw blade depth is rationally adjusted, further optimizing the kerf path and width control signals. Based on this, multi-directional, dynamic, and precise multi-category data collaborative adjustment of depth, speed, width, and angle is ultimately achieved. This effectively solves the problem of unstable cutting quality caused by material hardness differences, path deviations, and insufficient kerf straightness during saw blade cutting, significantly improving kerf straightness and width uniformity, effectively solving the quality control challenges in cutting complex materials, and achieving high-precision and high-stability cutting results.

[0143] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0144] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0145] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0146] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0147] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0148] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0149] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimizing the cutting path of concrete pavement joints, characterized in that, include: The raw information of the pavement cut is collected, and noise is filtered to obtain smoothed raw information; the raw information includes at least: cutting force location data, travel speed data, and cutting depth data; Based on the smoothed cutting force position data, the directional deviation between the current path and the preset path is identified. If the directional deviation exceeds the preset deviation threshold, it is determined that the path has deviated. The direction of deviation is determined according to the trend of cutting force position change, and the deviation correction requirement is determined according to the direction of deviation. The saw blade's feed speed is adjusted by the travel speed, and a speed correction command is generated based on the speed deviation and cutting depth requirements. By adjusting the travel angle based on the propulsion speed and offset direction, identifying the magnitude of changes in cutting force, extracting depth changes, and assessing material hardness differences, the angle fine-tuning increment is obtained. Based on the angle fine-tuning increment and speed correction commands, the mechanical actuator is driven to modify the travel angle and advance speed in real time, and the position coordinates and travel speed of the saw blade are re-acquired to obtain the adjusted path data. The adjusted path data is compared with the preset path, the path deviation value is extracted, the straightness of the dummy cut is evaluated, and the straightness of the dummy cut is evaluated based on the path deviation value and the offset correction requirements. If the straightness of the dummy cut is lower than the preset threshold, additional correction instructions for the cutting parameters are generated to obtain the optimized target path and depth data. Based on the optimized target path and depth data, the width variation pattern is identified to determine the width uniformity. The magnitude of the cutting force variation is integrated, the saw blade depth is adjusted, the width control signal is determined, and the final optimized kerf path integrated control command is obtained.

2. The method for optimizing the cutting path of concrete pavement joints according to claim 1, characterized in that, The process of collecting the original information of pavement cuts, filtering noise from the original information to obtain smoothed original information, further includes: Force sensors and displacement encoders are installed on the saw blade spindle and feed mechanism to measure cutting force position data, travel speed data, and cutting depth data. Specifically, the force sensor collects triaxial torque data generated by cutting vibration at a preset sensing frequency, while a laser rangefinder obtains the coordinate values ​​of the saw blade relative to the road surface baseline to determine the cutting force position data. The velocity change rate is calculated using the pulse signal from the displacement encoder to determine the travel speed data. An ultrasonic sensor measures the depth from the bottom of the saw blade to the concrete surface to determine the cutting depth data. The collected raw information is preprocessed to identify abnormal peak values ​​in the raw information amplitude that exceed the preset standard threshold. Median filtering is used to remove pulse interference. The timestamp alignment is adjusted according to the time delay characteristics within the sampling interval. The triaxial torque data is processed by low-pass filtering to retain the main frequency components of the cutting process. The filtered torque sequence, aligned coordinate sequence, and velocity sequence are output. Moving average processing is performed on the filtered torque sequence, coordinate sequence, and velocity sequence. The smoothing window size is determined based on the velocity change rate. If the velocity change rate exceeds a first threshold within a preset time, a small window sampling point is used; otherwise, a large window sampling point is used. Kalman filtering is performed on the coordinate sequence. The Kalman gain coefficient is adjusted according to the change trend of the depth data to obtain the smoothed coordinate sequence and generate the smoothed coordinate trajectory. The smoothed coordinate trajectory is time-synchronized using an interpolation algorithm. Based on the time delay characteristics, spline interpolation is used to fill in the missing sampling points. The resultant force application points at each moment are extracted from the torque sequence. The coordinate trajectory is fused to determine the cutting force application position. The smoothed cutting force position data, travel speed data, and cutting depth data are output.

3. The method for optimizing the cutting path of concrete pavement joints according to claim 1, characterized in that, The step of identifying the directional deviation between the current path and a preset path based on the smoothed cutting force position data, determining that the path has deviated if the directional deviation exceeds a preset deviation threshold, and determining the deviation direction based on the trend of cutting force position change, and determining the deviation correction requirement based on the deviation direction, further includes: Continuous position sequences are extracted from the smoothed cutting force position data. The direction vector of the current path is fitted by the least squares method. At the same time, the coordinate points of the preset path are read, the vertical distance from the position point to the preset path at each sampling time is calculated, and the deviation distance sequence is generated. The deviation change angle is calculated based on the ratio of the difference of the deviation distances of adjacent sampling points to the time interval. The deviation change rate is obtained by performing a difference operation on the deviation distance sequence. The change trend is judged by the consistency of the sign of the deviation change rate within the sliding window. If the deviation distance of multiple consecutive sampling points exceeds a preset distance threshold and the deviation change angle exceeds a preset angle threshold, it is determined that the path has deviated. The position of the starting point of the deviation and the deviation change rate are recorded. Based on the cutting force position sequence after the offset starting point, the radius of the arc formed by three adjacent points is calculated to obtain the path curvature. The ratio of the change in the lateral coordinate to the change in the longitudinal coordinate in the cutting force position sequence is extracted to determine the offset direction. The cumulative deviation value is obtained by accumulating the deviation distance sequence. The offset magnitude is determined by the product of the cumulative deviation value and the deviation change rate. The correction priority level is divided according to the magnitude of the offset. Based on the offset direction and correction priority level, an offset correction parameter set is generated, which includes the correction direction angle, correction displacement, and correction start position. If the cumulative deviation value exceeds the preset deviation threshold, the correction displacement is increased. The correction execution timing is adjusted according to the path curvature change, and the offset correction requirement including the correction parameter set and execution timing is output.

4. The method for optimizing the cutting path of concrete pavement joints according to claim 1, characterized in that, The step of adjusting the saw blade's feed speed by adjusting the travel speed and generating a speed correction command based on the speed deviation and cutting depth requirements further includes: The difference between the current travel speed and the preset speed reference is obtained as the speed deviation. The ratio of the real-time cutting depth to the design depth threshold is read. The acceleration or deceleration requirement is determined according to the positive or negative sign of the speed deviation. The speed adjustment gradient is determined by the ratio of the load coefficient collected by the resistance sensor to the preset reference load. The target propulsion speed is calculated by multiplying the speed adjustment gradient by the current speed. If the load coefficient exceeds the preset upper limit threshold, the target propulsion speed is limited to a safe range. A preset mechanical response delay time constant is obtained. The difference between the target propulsion speed and the current speed is divided by the response delay time constant to obtain the speed change rate. The command transmission frequency is determined based on the ratio of the velocity change rate to the design depth threshold. Discrete velocity adjustment pulse sequences are generated according to the command transmission frequency. The velocity increment between adjacent pulses is limited according to the maximum allowable acceleration. The pulse sequences are arranged in chronological order to form a velocity correction command.

5. The method for optimizing the cutting path of concrete pavement joints according to claim 1, characterized in that, The process of adjusting the travel angle by controlling the propulsion speed and offset direction, identifying the magnitude of changes in cutting force, extracting depth changes, evaluating material hardness differences, and obtaining the angle fine-tuning increment further includes: A velocity vector is constructed based on the propulsion speed value and the offset direction. The deviation value between the current travel angle and the preset path angle is obtained. The cutting force variation amplitude between adjacent sampling points is calculated through the cutting force time sequence data collected by the force sensor. The depth change per unit time is extracted from the ultrasonic ranging data. The numerical sequence of the cutting force variation amplitude at different depth positions is recorded. The location and intensity of the cutting force mutation point are identified based on the numerical sequence. The material hardness change characteristics are judged by the correlation between the mutation point density and the deviation value. If the hardness change characteristics exceed the preset threshold, it is marked as a high hardness region. The resistance coefficient at each location is determined according to the distribution density of the high hardness region and the cutting force change amplitude. The lateral deflection torque is calculated by using the peak position of the resistance coefficient and the lateral and longitudinal components of the velocity vector. The theoretical deflection angle is obtained by the ratio of the deflection torque to the preset stiffness parameter of the saw blade bearing. The correction coefficient is determined according to the reverse compensation amount of the offset direction. The theoretical deflection angle is multiplied by the correction coefficient to obtain the preliminary adjustment angle value. The initial adjustment angle value is discretized, and the minimum adjustment step size is determined based on the stepping angle of the servo motor. The execution time interval of each step size is adjusted by the rate of change of the material hardness change characteristics. The initial adjustment angle value is decomposed into the sum of multiple minimum adjustment step sizes, and the angle fine-tuning increment sequence and the corresponding execution time table are output.

6. The method for optimizing the cutting path of concrete pavement joints according to claim 1, characterized in that, The step of driving the mechanical actuator to modify the travel angle and advance speed in real time according to the angle fine-tuning increment and speed correction command, re-collecting the position coordinates and travel speed of the saw blade, and obtaining the adjusted path data further includes: The angle fine-tuning increment is converted into a pulse signal sequence for the servo motor. The frequency control parameters of the inverter are generated through the speed correction command. The pulse signal sequence is sent to the angle adjustment mechanism. At the same time, the frequency control parameters are input to the propulsion motor. The actual rotation angle fed back by the servo motor encoder and the actual operating frequency output by the inverter are obtained. The time difference between the control command issuance time and the mechanical response time is recorded. Based on the actual rotation angle and actual operating frequency, the steering mechanism and feed mechanism of the mechanical actuator are driven to perform adjustment actions. During the execution process, the coordinate value of the center point of the saw blade is obtained by the laser positioning instrument according to a fixed sampling rate. The instantaneous travel speed of the saw blade is measured by the incremental encoder. The sampling time is compensated and corrected according to the time difference to obtain the coordinate sequence and speed data at the corresponding time during the adjustment process. A time stamp is added to each coordinate point in the coordinate sequence, and coordinate points at the same time are paired and associated with velocity data. A continuous path trajectory segment is constructed through adjacent coordinate points. The velocity characteristic value of the segment is calculated based on the velocity value at the start and end points of each segment and the time interval, forming a coordinate point sequence containing timestamps and the velocity distribution of each trajectory segment. Using the starting and ending coordinates of the coordinate point sequence, the actual change in travel angle is calculated. The smoothness of speed adjustment is evaluated by the dispersion of speed characteristic values ​​of each segment in the speed distribution. If the deviation between the actual change and the command value exceeds a preset threshold, the path segment is marked. The adjusted path data is output, including the coordinate point sequence, speed distribution, and deviation marking information.

7. The method for optimizing the cutting path of concrete pavement joints according to claim 6, characterized in that, The step of driving the mechanical actuator to modify the travel angle and advance speed in real time according to the angle fine-tuning increment and speed correction command, re-collecting the position coordinates and travel speed of the saw blade, and obtaining the adjusted path data further includes: The steering mechanism of the mechanical actuator adopts a gear and rack transmission method. The servo motor drives the gear through a reducer. The gear meshes with the rack fixed on the frame to realize the lateral movement of the saw blade assembly. The feed mechanism adopts a ball screw transmission. The propulsion motor is connected to the ball screw through a coupling. The ball screw nut is fixed on the saw blade lifting frame. The rotation of the motor drives the saw blade forward. The laser positioning device is installed at the front end of the saw blade guard. The emitted laser beam is vertically irradiated onto the reflective target on the ground. The absolute coordinates of the center point of the saw blade are calculated by the principle of triangulation.

8. The method for optimizing the cutting path of concrete pavement joints according to claim 1, characterized in that, The process of comparing the adjusted path data with the preset path, extracting the path deviation value, evaluating the dummy slit straightness, evaluating the dummy slit straightness based on the path deviation value and offset correction requirements, and generating additional correction instructions for cutting parameters if the dummy slit straightness is below a preset threshold, thereby obtaining optimized target path and depth data, further includes: The coordinate point sequence in the adjusted path data is compared point by point with the standard coordinate sequence of the preset path. The vertical distance from each coordinate point to the preset path is calculated as a local deviation value. The root mean square value of the path deviation is calculated based on the local deviation value. At the same time, the maximum value in the deviation value sequence is extracted as the peak deviation. The centerline of the actual cut is fitted using the least squares method based on the deviation value sequence. The angle difference and position offset between the fitted straight line and the ideal straight line are calculated. The straightness index of the dummy cut is obtained by the ratio of the root mean square value to the preset allowable deviation threshold. If the index is lower than the preset straightness threshold, it is determined that additional correction is required. Based on the dummy seam straightness index and peak deviation, obtain the depth data to be adjusted corresponding to the section where the deviation exceeds the threshold, adjust the cutting depth parameters according to the correspondence between the depth data to be adjusted and the deviation value, determine the lateral compensation amount through the angle difference, calculate the rate correction value according to the current feed rate and position offset, and form a correction instruction containing the depth adjustment value, rate correction value and lateral compensation amount. The control parameters are updated using the lateral compensation amount and depth adjustment value in the correction instruction. The original trajectory coordinates are corrected according to the lateral compensation amount to obtain the optimized target path coordinate sequence. The target cutting depth of each position point is obtained by superimposing the depth adjustment value with the original depth setting value. The optimized target path and depth data are then output.

9. The method for optimizing the cutting path of concrete pavement joints according to claim 1, characterized in that, The process of identifying width variation patterns and determining width uniformity based on optimized target path and depth data, integrating the magnitude of cutting force variation, adjusting saw blade depth, determining width control signals, and obtaining the final optimized kerf path integrated control command further includes: Based on the coordinate point sequence and corresponding depth data on the target path, the kerf width at each position is measured by a laser scanner, the width difference between adjacent measurement points is extracted, the standard deviation of the width difference sequence is calculated as a uniformity index, and the cutting force time series data recorded by the force sensor is acquired to calculate the change amplitude of the cutting force per unit time. Based on the uniformity index, abnormal sections with width deviations exceeding the threshold are identified. The location coordinates of the abnormal sections are matched with the time series of the cutting force variation amplitude. The correlation strength is determined by calculating the Pearson correlation coefficient between the two. If the correlation strength exceeds the preset threshold, the saw blade feed depth parameter is adjusted according to the depth position corresponding to the cutting force peak. The cutting depth data of each path point is updated using the adjusted depth parameters. A control dataset is constructed by combining the target path coordinates and the updated depth data. Control priorities are assigned to different path segments according to the width uniformity index. The optimized cut path coordinate sequence is obtained through priority weighting. For the optimized path coordinate sequence and depth data, timing control points are generated according to a preset sampling period. Based on the width control requirements, the width deviation of each control point is converted into the lateral adjustment amount of the saw blade. The path coordinates, depth data and lateral adjustment amount are fused to form a comprehensive control command, and the final optimized kerf path and width control signal are output.

10. A concrete pavement joint cutting path optimization system, characterized in that, include: The system includes a data acquisition module, a correction requirement determination module, a speed correction command generation module, an angle adjustment determination module, a path data adjustment module, an optimization module, and a comprehensive control signal generation module; among which, The data acquisition module is used to acquire the original information of the pavement cut, filter the noise of the original information, and obtain smoothed original information; wherein, the original information includes at least: cutting force position data, travel speed data, and cutting depth data; The correction requirement determination module is used to identify the directional deviation between the current path and the preset path based on the smoothed cutting force position data. If the directional deviation exceeds the preset deviation threshold, it is determined that the path has deviated, and the deviation direction is determined according to the trend of the cutting force position change. The deviation correction requirement is determined according to the deviation direction. The speed correction command generation module is used to adjust the saw blade's advance speed by adjusting the travel speed, and to generate speed correction commands based on the speed deviation and cutting depth requirements. The angle adjustment determination module is used to adjust the travel angle by means of the propulsion speed and offset direction, identify the magnitude of the cutting force change, extract the depth change, evaluate the material hardness difference characteristics, and obtain the angle fine-tuning increment. The path data adjustment module is used to drive the mechanical actuator to modify the travel angle and advance speed in real time according to the angle fine-tuning increment and speed correction command, and to re-collect the position coordinates and travel speed of the saw blade to obtain the adjusted path data. The optimization module is used to compare the adjusted path data with the preset path, extract the path deviation value, evaluate the dummy seam straightness of the cut, evaluate the dummy seam straightness according to the path deviation value and offset correction requirements, and if the dummy seam straightness is lower than the preset threshold, generate additional correction instructions for the cutting parameters to obtain the optimized target path and depth data. The integrated control signal generation module is used to identify the width variation pattern and determine the width uniformity based on the optimized target path and depth data, integrate the magnitude of the cutting force variation, adjust the saw blade depth, determine the width control signal, and obtain the final optimized kerf path integrated control command.

Images