An automated bayonet-type intelligent measuring system for wood

Abstract

The present invention relates to the field of timber gauge inspection and discloses an automated bayonet-type intelligent timber gauge inspection system. The system comprises a multi-source data acquisition module, which uses sensors, hardware interfaces, and a robotic arm to interactively acquire vehicle parking deviations, log end point clouds, and equipment operating status. A process coordination control module coordinates the movement of a truss-type robotic arm, inkjet printing, and data transfer according to a preset workflow, monitoring the interaction logic between PLC signals and axis control commands issued by a vision module in real time. A peripheral execution module includes a truss-type robotic arm for scanning and a license plate recognition camera. By utilizing the truss-type robotic arm, 3D vision sensors, and deep learning algorithms, the system automates the entire process, from vehicle entry verification and timber end scanning to inkjet printing and marking. This eliminates errors caused by manual intervention and improves gauge inspection efficiency and consistency.

Description

Technical Field

[0001] The invention relates to the technical field of wood measuring technology, in particular to an automated wood bayonet-type intelligent measuring system. Background Art

[0002] Timber operations at ports are a key link in the international trade supply chain, involving the loading and unloading, warehousing, measurement, and customs clearance of large quantities of logs. With the acceleration of smart port development, this sector is gradually integrating automated equipment with digital management systems. For example, laser scanning technology is used to achieve contactless three-dimensional timber modeling, combined with machine vision algorithms for intelligent determination of end diameter grades, and a coding and marking system is used to simultaneously record timber volume information. Measurement data is connected in real time to port operations systems and customs declaration platforms, forming a digital closed loop for the entire process, from cargo arrival, measurement and acceptance, to release and settlement. Some advanced ports have deployed intelligent robotic arms and unmanned inspection equipment to ensure operational safety while improving throughput efficiency, providing efficient and transparent data-based service support for the global timber trade.

[0003] Existing technologies rely on manual operations or semi-automated processes in vehicle entry verification, timber end face scanning, diameter grade calculation, and inkjet marking. These processes include manual confirmation of vehicle parking spaces, manual recording of measurement data, and manual verification of inkjet marking results. This results in low operational efficiency and is unable to meet the high efficiency requirements of large-scale industrial measurement. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an automated bayonet-type intelligent wood measuring system, which solves the problem that the existing technology relies on manual operation or semi-automatic processes in vehicle entry verification, wood end face scanning, diameter grade calculation and coding marking, manual confirmation of vehicle parking positions, manual recording of measuring data, manual verification of coding results, etc., resulting in low operating efficiency and inability to meet the high efficiency requirements of large-scale industrial measuring.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an automated bayonet-type intelligent measuring system for wood, comprising:

[0006] Multi-source data acquisition module, used to obtain vehicle parking deviation, log end point cloud and equipment operation status through sensors, hardware interfaces and robotic arms interaction;

[0007] The process collaborative control module is used to coordinate the movement of the truss robot arm, inkjet printing operation and data transfer according to the preset operation process, and monitor the interaction logic between the PLC signal and the axis control instructions issued by the vision module in real time;

[0008] Peripheral execution modules, including a truss-type robotic arm for scanning, a license plate recognition camera, a coding device, and a parking detection sensor;

[0009] Industrial communication interface module, used to interact with PLC through industrial protocols and synchronize data with vision modules and wood production system MES through communication protocols;

[0010] Dynamic visual monitoring module, used to display vehicle parking status, measurement progress, coding results and system alarm information in real time;

[0011] Dynamic environment adaptation module, used to dynamically adjust coding parameters and scanning strategies according to wood surface humidity, dust concentration and ambient temperature;

[0012] Edge computing nodes are deployed on the robotic arm control terminal for local real-time data processing and instruction optimization.

[0013] By adopting the above technical solutions, through truss-type robotic arms, 3D vision sensors and deep learning algorithms, the entire process from vehicle entry verification, wood end face scanning to inkjet marking is automated. The edge computing node completes point cloud noise reduction, three-dimensional reconstruction and diameter calculation locally, combined with principal component analysis and geometric feature processing to ensure diameter measurement accuracy. The process collaborative control module dynamically schedules robotic arm movement and equipment operation according to the preset operation process, monitors the instruction interaction logic in real time, avoids human intervention errors, greatly improves the efficiency and consistency of measurement, and significantly improves the efficiency of measurement.

[0014] Preferably, the vision module collects multi-view point cloud and image data of the end face of the log through a 3D vision sensor, determines the point cloud area of ​​a single log based on the YOLOX target detection algorithm and the DeeplabV3+ semantic segmentation algorithm, calculates the diameter grade through principal component analysis and generates a robot arm axis control instruction, which includes the target posture, movement speed and priority, and is sent to the process collaborative control module through the Socket protocol to coordinate the movement of the truss robot arm.

[0015] Preferably, the multi-source data acquisition module includes:

[0016] A vehicle parking position calibration unit, configured to detect a vehicle parking position deviation through a sensor and compare it with a preset threshold;

[0017] The end face 3D reconstruction unit is used to perform multi-view point cloud scanning of the log end face using a 3D camera mounted on a truss-type robotic arm, generating 3D data for diameter grade calculation;

[0018] The equipment status feedback unit is used to read the PLC-controlled robot arm axis position, gate opening and closing status, and sensor signals in real time.

[0019] The end face 3D reconstruction unit realizes full coverage acquisition of the end face point cloud through the coordinated movement of the truss-type robotic arm, and calculates the minimum diameter level based on geometric feature analysis.

[0020] Preferably, the process collaborative control module includes:

[0021] Segmented task scheduling unit, used to dynamically allocate control instructions according to the vehicle entry, scanning, coding, and release stages;

[0022] The instruction priority management unit is used to cache the robot arm motion instructions generated by the vision module and optimize the execution order according to the preset logic;

[0023] The cross-system status synchronization unit is used to forward the real-time axis position data of the PLC to the vision module and receive the job plan verification results from the MES.

[0024] Preferably, the industrial communication interface module includes:

[0025] PLC interaction unit, which uses industrial communication protocol to read and write PLC data blocks to control the opening and closing of gates and the movement parameters of the robotic arm;

[0026] The visual data channel unit uses a real-time transmission protocol to push PLC data to the visual module and obtains the axis control queue through the command receiving channel.

[0027] The data blocks of the industrial communication protocol are divided into:

[0028] Status reading area, used to obtain the coordinates of the robot arm axis, the vehicle parking status and the system operation mode flag;

[0029] The instruction writing area is used to issue axis motion target parameters, inkjet coding content and process start and stop control signals.

[0030] Preferably, the dynamic visual monitoring module distinguishes the operation stages by status coding:

[0031] Green indicates normal process execution, yellow indicates manual review or warning status, and red indicates system failure or emergency stop.

[0032] Preferably, the dynamic environment adaptation module includes:

[0033] Humidity compensation unit, which adjusts the inkjet printer's injection pressure and drying time by monitoring the surface humidity of the wood;

[0034] Dust suppression unit, dynamically removes dust interference on the end surface during scanning;

[0035] The temperature control logic unit automatically adjusts the camera exposure time and activates the temperature control device when the ambient temperature exceeds the preset range.

[0036] Preferably, the humidity compensation unit uses a humidity sensor with a measurement accuracy of ±1.5%RH and a collection frequency of 50ms. When the humidity of the wood surface is detected to be H>85%RH or H<30%RH, the inkjet printer parameters are adjusted by the following formula:

[0037]

[0038] in, injection pressure, Drying time, is the standard injection pressure, which is 0.3MPa. is the standard drying time, which is 200ms;

[0039] The dust suppression unit uses a dust concentration sensor with a detection range of 0.3μm-10μm. When the dust concentration C>500μg / m³ is detected three times in a row, the air pump is started to spray clean air at an air pressure of 0.2MPa≤Pa≤0.5MPa. The angle between the spray direction and the camera scanning direction is θ≤20°. The first spray time is 300ms. If C>400μg / m³ after cleaning, the air pressure is increased to 0.4MPa and extended to 500ms.

[0040] The temperature control logic unit uses a temperature sensor with an accuracy of ±0.5°C. When the ambient temperature T is less than 0°C or T is greater than 40°C, the 3D camera exposure time is adjusted using the following formula:

[0041]

[0042] in, is the camera exposure time at the current ambient temperature, This is the default exposure time at a standard ambient temperature of 20-30°C. It is the ambient temperature monitored in real time by the temperature sensor.

[0043] Preferably, the edge computing node includes:

[0044] The real-time data preprocessing unit uses statistical filtering algorithms to reduce noise on the point cloud data collected by the 3D camera. , calculate its Radius neighborhood point set The mean and , where the mean , standard deviation On point Euclidean distance from the mean When , remove the point ;

[0045] in, Indicates the first points, whose coordinates are , For point Center, radius The set of all neighboring points within the range, Neighborhood point set The number of point clouds in Neighborhood point set The Points, for point and the neighborhood mean The Euclidean distance of

[0046] The instruction delay optimization unit is used to build a priority queue to divide the control instructions into three levels of priority: safety instructions, measurement instructions, and coding instructions. Safety instructions include emergency stop instructions, which have a higher priority than measurement instructions, and measurement instructions have a higher priority than coding instructions. A preemptive scheduling algorithm is used to ensure that the response delay of safety instructions does not exceed 20ms, and timestamps are added to the robot arm movement instructions. , combined with the real-time feedback of the robot arm axis position by the programmable logic controller PLC , based on instruction plan execution time , current time and network transmission delay Dynamically adjust the instruction execution order to reduce the waiting time of the robot arm;

[0047] in, The timestamp of the robot arm motion instruction records the time when the instruction was generated. is the execution time of the instruction plan on the robot arm, is the current system time of the edge computing node, The network delay time for the control command to be transmitted from the edge computing node to the robotic arm controller;

[0048] The network interruption resuming unit uses a ring buffer with a storage capacity of not less than 1GB to temporarily store the gauge data, and records the timestamp of the last uploaded data when the network is interrupted and data number , after the network is restored, filter out the data timestamp later than And the data number Greater than The unsynchronized data is synchronized to the cloud server after verifying the data integrity through the CRC-32 check algorithm;

[0049] in, The timestamp of the data block to be transmitted. It is the unique number of the data block to be transferred, and increases in the order in which the data is generated.

[0050] The present invention also provides an automated bayonet-type intelligent measuring method for wood, comprising the following steps:

[0051] Step S1: Verify vehicle access rights through license plate recognition, control gate release, and guide vehicles to precise parking positions based on limit blocks and wireframes;

[0052] Step S2: Scan the vehicle outline and the layout of the wood stack to check whether the vehicle parking deviation meets the preset threshold and whether the distance between the wood stacks meets the preset range;

[0053] Step S3: driving the truss-type robotic arm carrying the 3D camera to scan the log end face along a preset path, adjusting the scanning parameters in conjunction with the dynamic environment adaptation module, reconstructing the 3D point cloud, calculating the diameter grade, and generating a coding instruction;

[0054] Step S4: Control the inkjet printer to perform end-face inkjet printing, and detect the inkjet marking status and marking result based on visual feedback;

[0055] Step S5: Process the measurement data locally through the edge computing node, bind it to the timber production system MES, generate a process log, and control the gate to release the vehicle;

[0056] Step S6: monitor the communication status of the device in real time, trigger an abnormal alarm and synchronize it to the visual interface.

[0057] The present invention provides an automated bayonet-type intelligent measuring system for wood, which has the following beneficial effects:

[0058] 1. This invention uses a truss-type robotic arm, 3D vision sensors, and deep learning algorithms to automate the entire process, from vehicle entry verification and timber end-face scanning to inkjet marking. The edge computing node locally performs point cloud noise reduction, 3D reconstruction, and diameter-grade calculation. This combines principal component analysis with geometric feature processing to ensure diameter-grade measurement accuracy. The process collaborative control module dynamically schedules robotic arm movement and equipment operation according to preset workflows, monitors command interaction logic in real time, avoids manual intervention errors, and significantly improves the efficiency and consistency of measurement.

[0059] 2. The present invention uses multiple sensors to monitor the surface humidity of the wood, the dust concentration at the end and the ambient temperature in real time, and dynamically adjusts the inkjet parameters and scanning strategies. The humidity compensation unit adjusts the injection pressure and drying time of the inkjet printer according to humidity changes to avoid ink diffusion caused by high humidity or nozzle clogging caused by low humidity. The dust suppression unit activates the airflow removal device when the dust concentration exceeds the standard to reduce the interference of dust on the point cloud data; the temperature control logic unit automatically adjusts the camera exposure time according to temperature fluctuations and links the heating or heat dissipation equipment to ensure stable imaging quality, effectively solving problems such as blurred inkjet printing and point cloud noise under complex climatic conditions, significantly improving the stability of the system in harsh environments, and breaking through the limitation of the existing technology that is highly dependent on environmental conditions.

[0060] 3. The present invention optimizes the transmission timing of control instructions through priority queues on edge computing nodes, ensuring rapid response to safety instructions, reducing the idling time of the robotic arm, and meeting the real-time control requirements of high-speed motion scenarios. The network interruption and transmission resumption unit caches the measurement data and device status when the network is interrupted, and accurately identifies the unsynchronized data and resumes the transmission completely after recovery, avoiding data loss and process interruption due to network fluctuations, and ensuring data integrity and operation continuity.

[0061] 4. The dynamic visualization monitoring module of the present invention uses status coding to display the operation stage and abnormality level in real time, and combines the graded early warning mechanism with sound and light alarm to achieve rapid fault location and manual intervention guidance. When the axis position of the robot arm exceeds the limit, the environmental parameters are abnormal, or the safety grating is triggered, the system immediately locks the interface and shuts down urgently. At the same time, it records the fault information to prompt the direction of investigation, constructing a multi-dimensional safety monitoring system, significantly improving the equipment operation reliability and operator safety, and making up for the defects of the traditional system's early warning lag and low fault handling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a partial architecture diagram of an automated bayonet-type intelligent measuring system for wood according to the present invention;

[0063] Figure 2 This is a partial architecture diagram of an automated bayonet-type intelligent measuring system for wood according to the present invention;

[0064] Figure 3 This is a schematic diagram of the operation process of an automated bayonet-type intelligent measuring system for wood according to the present invention;

[0065] Figure 4 This is a schematic diagram of laser radar scanning of the present invention;

[0066] Figure 5 It is a schematic side view of the structure of the truss type scanning device of the present invention;

[0067] Figure 6A schematic diagram of the scanning path of the truss-type robotic arm of the present invention;

[0068] Figure 7 Schematic diagram of the truss coordinate relationship of the present invention;

[0069] Figure 8 This is a flow chart of an automated bayonet-type intelligent measuring method for wood according to the present invention;

[0070] Figure 9 Schematic diagram of the relationship between the modules of the present invention. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0072] Please see the attached Figure 1 - Attachment Figure 9 The embodiment of the present invention provides an automated bayonet-type intelligent measuring system for wood, comprising:

[0073] Multi-source data acquisition module, used to obtain vehicle parking deviation, log end point cloud and equipment operation status through sensors, hardware interfaces and robotic arms interaction;

[0074] The process collaborative control module is used to coordinate the movement of the truss robot arm, inkjet printing operation and data transfer according to the preset operation process, and monitor the interaction logic between the PLC signal and the axis control instructions issued by the vision module in real time;

[0075] Peripheral execution modules, including a truss-type robotic arm for scanning, a license plate recognition camera, a coding device, and a parking detection sensor;

[0076] Industrial communication interface module, used to interact with PLC through industrial protocols and synchronize data with vision modules and wood production system MES through communication protocols;

[0077] Dynamic visual monitoring module, used to display vehicle parking status, measurement progress, coding results and system alarm information in real time;

[0078] Dynamic environment adaptation module, used to dynamically adjust coding parameters and scanning strategies according to wood surface humidity, dust concentration and ambient temperature;

[0079] Edge computing nodes are deployed on the robotic arm control terminal for local real-time data processing and instruction optimization.

[0080] The vision module collects multi-view point cloud and image data of the log end face through 3D vision sensors, determines the point cloud area of ​​a single log based on the YOLOX target detection algorithm and DeeplabV3+ semantic segmentation algorithm, calculates the diameter grade through principal component analysis and generates the robot arm axis control instructions. The instructions include the target position, movement speed and priority, and are sent to the process collaborative control module through the Socket protocol to coordinate the movement of the truss robot arm.

[0081] Specifically, the visual module collects multi-view point clouds and 3864×2192 resolution color images of the log end face through the LME-M laser 3D camera mounted on the truss-type robotic arm, and establishes the conversion relationship between the camera, truss and global coordinate system through Zhang's calibration method; the collected data is first subjected to voxel downsampling and statistical filtering for noise reduction, and then the end face ROI area is detected by the YOLOX algorithm, and the single log point cloud is segmented by the DeeplabV3+ algorithm. After the multi-view point cloud is aligned with the ICP algorithm, the center of mass and the main direction are calculated using PCA, and the edge distance is measured at 15° intervals along the circumference as the diameter grade; based on the detection results Generates robot arm control instructions including target posture (±1mm accuracy in the global coordinate system), motion speed (50mm / s for scanning, 150mm / s for air cutting) and priority (safety > detection > inkjet printing), and sends them to the process collaborative control module in JSON format via the Socket protocol. At the same time, it receives the axis position signal (100Hz frequency) feedback from the PLC in real time and dynamically adjusts the instructions to achieve full process automation from point cloud acquisition, end face detection, diameter calculation to motion control. The diameter measurement error is less than 0.1mm, and the single end face processing time is ≤200ms, supporting high-precision ruler inspection operations in complex environments.

[0082] The multi-source data acquisition module includes:

[0083] A vehicle parking position calibration unit, configured to detect a vehicle parking position deviation through a sensor and compare it with a preset threshold;

[0084] The end face 3D reconstruction unit is used to perform multi-view point cloud scanning of the log end face using a 3D camera mounted on a truss-type robotic arm, generating 3D data for diameter grade calculation;

[0085] The equipment status feedback unit is used to read the PLC-controlled robot arm axis position, gate opening and closing status, and sensor signals in real time.

[0086] The end face 3D reconstruction unit achieves full coverage acquisition of the end face point cloud through the coordinated movement of the truss-type robotic arm, and calculates the minimum diameter based on geometric feature analysis.

[0087] Specifically, the vehicle parking calibration unit is implemented by installing a laser radar on the inner side of the ground rail of the measuring workstation. The radar has a 360° scanning angle and a measurement accuracy of 1mm, and outputs the wheel point cloud data in real time through the network port at a sampling frequency of 20,000 times / second, including distance and angle information. The system first pre-processes the collected point cloud data, uses a pass-through filter to remove ground noise, and then uses a statistical filter to identify and eliminate outliers to ensure the reliability of valid point cloud data. The wheel contour is fitted based on the pre-processed point cloud, and the distance between the front wheel and the limit block is calculated: the point cloud of wheel A is identified, the nearest edge point to the limit block is extracted, and the vertical distance between this point and the reference plane of the limit block is calculated. is the Y coordinate of the edge point of wheel A, is the Y coordinate of the limit block; detect the deviation between the vehicle and the ground rail: based on the point cloud position of wheels A, B, C, and D, fit the direction vector of the vehicle's longitudinal axis , calculate the direction vector between it and the center line of ground rail A / B Angle .like And the average lateral offset of each wheel from the center line of the ground rail is , then the deviation is determined to be qualified. Compare with the stop threshold ±50mm and verify at the same time and , generating a "parking pass / fail" signal which is transmitted to the process collaborative control module, providing a basis for vehicle parking judgment.

[0088] The end face 3D reconstruction unit uses an LME-M laser 3D camera with 1440 contour points and an inspection speed of 360,000 points / second. It is mounted on the crossbeam of a truss-type robotic arm. The camera transmits a point cloud, including depth maps and color image data, via a GigE interface with a resolution of 3864×2192. The truss-type robotic arm is divided into three groups: truss_1, truss_2, and truss_3, covering the front, middle, and rear areas, respectively. Each truss group can move horizontally along the X-axis of the ground rail coordinate system, and the crossbeam can move vertically along the Z-axis, with a default speed of 50 mm / s. During scanning, the trusses are initially positioned at the front of the vehicle (base_p1), the middle of the vehicle (base_p2), and the rear of the vehicle (base_p3). Upon receiving the scan command, the span beam moves downward at a constant speed from the top, while the camera simultaneously collects point clouds. External calibration is used to obtain the camera-truss pose relationship (truss(j)_T_camera(i)) and the truss pose (base_T_truss(j)) in the base coordinate system. The point cloud (cam_p) in the camera coordinate system is then converted to the point cloud (base_p) in the global coordinate system. The YOLOX deep learning network is used to detect the end face of the wood, and the DeeplabV3+ semantic segmentation algorithm is used to extract the point cloud of a single log. After eliminating background interference, the point cloud of the single log is averaged to calculate the center of mass coordinates. The PCA algorithm is used to analyze the main direction of the point cloud. Rays are generated every 15° from the center of mass to measure the edge intersection distance. The average value is used as the diameter grade, with an accuracy of <0.1mm.

[0089] The equipment status feedback unit establishes communication with the PLC through the ModbusTCP protocol. The process collaborative control module reads the DB6 data block of the PLC with a period of 100ms to obtain the robot arm axis position data and equipment status signal; the robot arm axis position data is stored in DINT type, where the X1 axis lateral position corresponds to the truss_1 truss lateral movement coordinate, the X2 axis lateral position corresponds to the truss_2 truss lateral movement coordinate, the X3 axis lateral position corresponds to the truss_3 truss lateral movement coordinate, the unit is mm, the Y1 axis vertical position corresponds to the truss_1 span beam lifting coordinate, the Y2 axis vertical position corresponds to the truss_2 span beam lifting coordinate, the Y3 axis vertical position corresponds to the truss_3 span beam lifting coordinate, the unit is mm, the Z1 axis height position corresponds to the truss The vertical fine-tuning displacement of the 3D camera of s_1, the height position of the Z2 axis corresponds to the vertical fine-tuning displacement of the 3D camera of truss_2, and the height position of the Z3 axis corresponds to the vertical fine-tuning displacement of the 3D camera of truss_3. The units are all mm. The rotation angle of the R1 axis corresponds to the pitch angle of the truss_1 camera pan / tilt, the rotation angle of the R2 axis corresponds to the pitch angle of the truss_2 camera pan / tilt, and the rotation angle of the R3 axis corresponds to the pitch angle of the truss_3 camera pan / tilt. The units are all 0.1°. The device status signal is stored in Bool type. The first bit of the 0th byte of the DB6 data block stores the true or false value of the vehicle parking completion status. The second bit of the 0th byte stores the system operation mode value. 0 represents manual mode, 1 represents automatic mode, and 2 represents maintenance mode. The third bit of the 0th byte stores the true or false value of the safety grating trigger status. The process collaborative control module pushes the read axis position data to the vision module in real time via the UDP protocol as a basis for judging the movement status of the robot arm. For example, if "X1 axis movement is completed", the DB6.106 point is True, triggering the camera to take a photo. The gate opening and closing status and sensor signals, such as limit switch triggering, are collected through the PLC digital input module, converted into Bool signals, and stored in the corresponding points of DB6. For example, "Vehicles allowed to enter" corresponds to the DB5.0 point. To ensure data consistency among multiple modules, the system adopts a double buffering mechanism. After the central control module reads the PLC data, it is first stored in the local cache and then pulled by the vision module in real time. If the reading fails for three consecutive times, the network disconnection alarm is triggered. At the same time, the last valid data is cached for emergency use by the system.

[0090] The process collaborative control module includes:

[0091] Segmented task scheduling unit, used to dynamically allocate control instructions according to the vehicle entry, scanning, coding, and release stages;

[0092] The instruction priority management unit is used to cache the robot arm motion instructions generated by the vision module and optimize the execution order according to the preset logic;

[0093] The cross-system status synchronization unit is used to forward the real-time axis position data of the PLC to the vision module and receive the job plan verification results from the MES.

[0094] Specifically, the segmented task scheduling unit dynamically allocates instructions for each stage based on the preset work process. When the vehicle enters the site, the license plate recognition information is used to verify the authority with the MES system. After confirming the legality, the PLC is controlled to open the gate and guide the vehicle to the designated position. The parking detection signal is combined to determine whether the scanning conditions are met. During the scanning stage, the truss-type robotic arm is coordinated to move according to the planned path, and the 3D camera is driven to complete the multi-view point cloud acquisition of the log end face. During the process, the robotic arm position and scanning progress are matched in real time, and the dynamic environment adaptation module is linked to adjust the parameters to adapt to environmental changes. During the coding stage, the coding instructions are generated according to the diameter calculation results, and the coding device is controlled to complete the information marking on the wood end face, and the coding quality is verified through visual feedback. During the release stage, the measurement data is bound to the license plate information and uploaded to the MES system, a process log is generated, and the gate is controlled to release the vehicle. At the same time, the equipment reset instruction is triggered to prepare for the next process.

[0095] The instruction priority management unit receives and caches the robot arm motion instructions generated by the vision module, and prioritizes the instructions according to the preset logic: safety-related instructions, such as emergency stop and over-limit alarm, are given the highest execution priority to ensure a quick response in the event of a system anomaly. Instructions related to detection accuracy, such as scan path planning and axis motion control, are given the second highest priority to ensure the accuracy of the measurement data. Maintenance instructions, such as equipment status query and parameter configuration, have the lowest priority to avoid interfering with core business processes. The management unit monitors the PLC device status and instruction queue progress in real time, dynamically adjusts the execution order, and reduces robot arm idling and waiting time through queue optimization algorithms to ensure efficient collaboration in multi-tasking scenarios.

[0096] The cross-system status synchronization unit achieves data exchange through an industrial communication interface, acquiring real-time operational data such as the robot arm's axis position and gate status from the PLC. This data is converted into a unified format and then pushed to the vision module, providing a real-time position reference for scanning path planning and camera posture adjustment. The unit also communicates bidirectionally with the MES system, submitting a license plate information verification plan during the entry phase, receiving vehicle permissions and task parameters returned by the MES, and uploading data such as diameter grade, length, and inkjet coding content after the inspection is completed, thus achieving closed-loop management of production plans and inspection results. A caching mechanism is used to handle network delays or disconnections, ensuring complete synchronization of historical data after an anomaly recovery, and maintaining data consistency between the system and external platforms.

[0097] Industrial communication interface modules include:

[0098] PLC interaction unit, which uses industrial communication protocol to read and write PLC data blocks to control the opening and closing of gates and the movement parameters of the robotic arm;

[0099] The visual data channel unit uses a real-time transmission protocol to push PLC data to the visual module and obtains the axis control queue through the command receiving channel.

[0100] Specifically, the PLC interaction unit uses the Modbus TCP industrial communication protocol to establish a data exchange channel with the PLC, enabling read and write operations on the PLC data block. The data block is divided into a status read area and a command write area. The status read area acquires real-time information such as the coordinates of each robot axis, X / Y / Z axis position and rotation angle, vehicle parking status, pass / fail signals, system operation mode flags, and automatic / manual / maintenance modes, providing the process collaborative control module with information on the equipment's operating status. The command write area receives control commands from the process collaborative control module, including target parameters for robot axis motion, such as positioning coordinates, movement speed, inkjet content, diameter grade, batch information, and process start and stop control signals, such as gate opening and closing and scanning start / stop. These commands are then driven by the PLC output module to execute actions on peripheral devices. A heartbeat check mechanism is implemented during communication, sending a connection check frame every 50ms to ensure stable communication with the PLC. Three consecutive check failures trigger a network disconnection alarm, while the latest valid commands are cached until communication is restored.

[0101] The visual data channel unit uses the UDP real-time transport protocol to establish a high-speed data channel between the PLC and the vision module. First, data such as the robot arm axis position and gate opening and closing status from the PLC status reading area is packaged in a fixed format, such as JSON, containing timestamps, device IDs, data types, and parameter values. This data is then pushed to the vision module at a 100Hz frequency, providing a real-time position reference for the 3D camera's scan path planning and point cloud coordinate conversion. Second, the axis control queue generated by the vision module is obtained through the command receiving channel. This contains parameters such as target coordinates, motion priority, and speed curves. After protocol conversion, it is written to the PLC command writing area, enabling direct control of the robot arm's motion based on visual inspection results. To avoid data conflicts, a dual buffer mechanism is implemented: the sending buffer updates the PLC status data in real time, while the receiving buffer temporarily stores vision module commands. The process collaborative control module schedules execution based on priority, ensuring command transmission latency is less than 20ms, meeting real-time control requirements.

[0102] The data blocks of the industrial communication protocol are divided into:

[0103] Status reading area, used to obtain the coordinates of the robot arm axis, the vehicle parking status and the system operation mode flag;

[0104] The instruction writing area is used to issue axis motion target parameters, inkjet coding content and process start and stop control signals.

[0105] Specifically, the status read area establishes a data exchange channel through industrial communication protocols, such as ModbusTCP, to obtain real-time system operating status information. This area stores the coordinate data of each axis of the robot arm, including the position coordinates and rotation angles of the X / Y / Z axes, stored in DINT type with an accuracy of 1mm / 0.1°, the vehicle parking status signal, a Bool type (0 indicates deviation exceeds the limit, 1 indicates parking is qualified), and the system operation mode flag, 0 for manual mode, 1 for automatic mode, and 2 for maintenance mode. The central control module polls and reads the status read area data in a 100ms cycle, ensuring data integrity through data verification algorithms such as CRC16, providing a real-time device status reference for the process collaborative control module. For example, when the robot arm's X-axis position is detected to be outside the preset travel range, the safety shutdown logic is triggered. If the vehicle parking status signal is 0, the scanning process is prohibited from starting, ensuring that the actions of each module are synchronized with the actual status of the device.

[0106] The instruction write area receives and executes control commands issued by the system via an industrial communication protocol. These commands include axis motion target parameters, including X / Y / Z axis target coordinates and speed, stored in array format (e.g., [X:1000, Y:500, Z:800, Speed:200mm / s]), inkjet coding content (string type, such as "D20-20230801-001" for a 20cm diameter), production date and batch number, and process start / stop control signals (Bool type, 1 for start and 0 for stop), supporting single-step process control or full process start / stop. The PLC controller monitors the instruction write area in real time. When a new command is detected, it uses ladder logic to analyze the command type. If it is an axis motion command, it triggers the servo driver to drive the robot arm according to the preset speed curve. If it is a coding command, it controls the printer to read the content and perform end-face marking. If it is a process start / stop signal, it activates peripheral devices such as gates and sensors to switch their operating states. After the instruction is executed, the PLC automatically clears the processed instruction and writes the execution status feedback, success / failure, to ensure that the instruction is not executed repeatedly and is traceable.

[0107] The dynamic visual monitoring module distinguishes the operation stages through status codes:

[0108] Green indicates normal process execution, yellow indicates manual review or warning status, and red indicates system failure or emergency stop.

[0109] Specifically, the green status is normal execution: when the vehicle parking deviation is within the preset threshold, the robotic arm moves according to the planned path, the inkjet printer operates normally and the communication status of each device is stable, the icons of the corresponding stages in the interface, such as "Scanning" and "Coding Completed", are displayed in green. At the same time, the process progress bar is filled with green to indicate that the current step is completed and there are no abnormalities. The real-time data area synchronously displays parameters such as the robotic arm axis coordinates and the number of scanned point clouds, indicating that the system is in an efficient and stable operation state.

[0110] Yellow Status Manual Review / Warning: The system triggers a yellow warning when the vehicle's parking deviation approaches a threshold, such as ±45mm, the wood surface moisture exceeds the coding parameter adaptation range, or the PLC reads data with a delay exceeding 50ms for three consecutive times. The corresponding stage icon flashes yellow, and a warning pop-up window appears in the upper right corner of the interface, such as "Critical parking deviation, manual confirmation recommended." The warning time and reason are recorded in the log area, allowing operators to intervene to review or manually adjust parameters to ensure that the process continues under monitoring.

[0111] Red Fault / Emergency Stop: If the robotic arm axis position exceeds the hardware limit, the 3D camera fails to capture point clouds for five consecutive times, or the safety light barrier detects a person intruding, the system immediately triggers a red fault state. The entire interface background flashes red, all motion devices come to an emergency stop, and the module icon corresponding to the fault point is highlighted red with a specific fault code, such as "E001: Robotic Arm Overtravel." An audible and visual alarm sounds a buzzer, and the operation interface is locked until the fault is manually corrected and confirmed to be corrected, ensuring system safety.

[0112] The dynamic environment adaptation module includes:

[0113] Humidity compensation unit, which adjusts the inkjet printer's injection pressure and drying time by monitoring the surface humidity of the wood;

[0114] Dust suppression unit, dynamically removes dust interference on the end surface during scanning;

[0115] The temperature control logic unit automatically adjusts the camera exposure time and activates the temperature control device when the ambient temperature exceeds the preset range.

[0116] The humidity compensation unit uses a humidity sensor with a measurement accuracy of ±1.5%RH and a sampling frequency of 50ms. When the humidity on the wood surface is detected to be H>85%RH or H<30%RH, the printer parameters are adjusted using the following formula:

[0117] ;

[0118] ;

[0119] in, injection pressure, Drying time, is the standard injection pressure, which is 0.3MPa. is the standard drying time, which is 200ms;

[0120] The dust suppression unit uses a dust concentration sensor with a detection range of 0.3μm-10μm. When the dust concentration C>500μg / m³ is detected for three consecutive times, the air pump is started to spray clean air at an air pressure of 0.2MPa≤Pa≤0.5MPa. The angle between the spray direction and the camera scanning direction is θ≤20°. The first spray time is 300ms. If C>400μg / m³ after cleaning, the air pressure is increased to 0.4MPa and extended to 500ms.

[0121] The temperature control logic unit uses a temperature sensor with an accuracy of ±0.5°C. When the ambient temperature T is less than 0°C or T is greater than 40°C, the 3D camera exposure time is adjusted using the following formula:

[0122] ;

[0123] in, is the camera exposure time at the current ambient temperature, This is the default exposure time at a standard ambient temperature of 20-30°C. It is the ambient temperature monitored in real time by the temperature sensor.

[0124] Specifically, the dynamic environment adaptation module uses multi-sensor real-time monitoring and linkage with the equipment to achieve adaptive adjustment of the gauge parameters in complex environments: the humidity compensation unit uses the SHT30 humidity sensor (accuracy ±1.5%RH, acquisition frequency 50ms). When the humidity of the wood surface is higher than 85%RH, the inkjet printer's injection pressure is increased by 20% from the standard 0.3MPa and the drying time is extended to 300ms. When the humidity is lower than 30%RH, the pressure is reduced by 10% and the drying time is shortened to 160ms to avoid ink diffusion or clogging. The dust suppression unit uses the DSM501A dust sensor (detection range 0.3μm -10μm), when the concentration exceeds 500μg / m³ for three consecutive tests, the air pump will spray clean air at 0.2MPa pressure and 20° angle for 300ms. If the standard is not met, the pressure will be increased to 0.4MPa for 500ms to remove dust interference from the end surface; the temperature control logic unit uses a PT100 temperature sensor (accuracy ±0.5℃). When the low temperature is <0℃, the camera exposure time is increased by 10% and a 50W heating film is started. When the high temperature is >40℃, the exposure time is reduced by 15% and a 1500RPM fan and air conditioner are linked to control the temperature. Scanning is paused when the temperature fluctuates by more than 2℃ / min to ensure stable point cloud acquisition and coding quality.

[0125] Edge computing nodes include:

[0126] The real-time data preprocessing unit uses statistical filtering algorithms to reduce noise on the point cloud data collected by the 3D camera. , calculate its Radius neighborhood point set The mean and , where the mean , standard deviation On point Euclidean distance from the mean When the elimination point ;

[0127] in, Indicates the first points, whose coordinates are , For point Center, radius The set of all neighboring points within the range, Neighborhood point set The number of point clouds in Neighborhood point set The Points, for point and the neighborhood mean The Euclidean distance of

[0128] The instruction delay optimization unit is used to build a priority queue to divide the control instructions into three levels of priority: safety instructions, measurement instructions, and coding instructions. Safety instructions include emergency stop instructions, which have a higher priority than measurement instructions, and measurement instructions have a higher priority than coding instructions. A preemptive scheduling algorithm is used to ensure that the response delay of safety instructions does not exceed 20ms, and timestamps are added to the robot arm movement instructions. , combined with the real-time feedback of the robot arm axis position by the programmable logic controller PLC , based on instruction plan execution time , current time and network transmission delay Dynamically adjust the instruction execution order to reduce the waiting time of the robot arm;

[0129] in, The timestamp of the robot arm motion instruction records the time when the instruction was generated. is the execution time of the instruction plan on the robot arm, is the current system time of the edge computing node, The network delay time for the control command to be transmitted from the edge computing node to the robotic arm controller;

[0130] The network interruption resuming unit uses a ring buffer with a storage capacity of not less than 1GB to temporarily store the gauge data, and records the timestamp of the last uploaded data when the network is interrupted and data number , after the network is restored, filter out the data timestamp later than And the data number Greater than The unsynchronized data is synchronized to the cloud server after verifying the data integrity through the CRC-32 check algorithm;

[0131] in, The timestamp of the data block to be transmitted. It is the unique number of the data block to be transferred, and increases in the order in which the data is generated.

[0132] Specifically, the edge computing node is deployed at the robot control terminal to locally process data and optimize control instructions: the real-time data preprocessing unit eliminates point cloud outliers through statistical filtering, uses the iterative nearest point algorithm to splice multi-view point clouds, calculates the log diameter based on principal component analysis, measures the average edge distance at 15° intervals along the circumference, and the single-end processing delay is ≤150ms, with a diameter accuracy of <0.1mm; the instruction delay optimization unit constructs a three-level priority queue (safety > measurement > inkjet printing), and safety instructions are responded to within 20ms through hardware interrupts. The order of robot arm motion instructions is dynamically adjusted in combination with PLC axis position feedback to reduce idling time. The dynamic adjustment of motion instructions is achieved through a time collaboration algorithm: time parameter synchronization includes instruction generation timestamps Recorded by the edge node system clock, network transmission delay The average round-trip delay between the PLC and the edge node is measured at a frequency of 10Hz using the Ping protocol, and the planned execution time is Based on exercise distance With preset speed curve Points calculation ; Dynamic reordering logic includes calculating the time point when the instruction is fully executed ,

[0133] Real-time assessment of remaining waiting time ,when And there is a command queue When the instruction is given, the execution order of the two instructions is exchanged, and the idling suppression mechanism includes the axis position based on PLC feedback Determine the state of the robot arm. If the idle time is 10ms, activate the pre-load of the instructions to be executed. Indicates the timestamp of instruction generation. Indicates network transmission delay, Indicates the planned execution time. Indicates the movement distance, Indicates the preset speed curve. Indicates the time point when the instruction is fully executed. Indicates the remaining waiting time. Indicates the current time, and Represent the predicted execution time points of the new instruction and the current instruction respectively, Indicates the axis position;

[0134] The network-disconnected data resumption unit uses a 1GB circular buffer to temporarily store data, records the timestamp and data number when interrupted, and filters out unsynchronized data after recovery based on timestamps later than the interruption time and numbers greater than the interruption number. After CRC-32 verification, multi-threaded data resumption is carried out, supporting 72 hours of offline operation to ensure data integrity and no loss.

[0135] This embodiment also provides an automated bayonet-type intelligent measuring method for wood, comprising the following steps:

[0136] Step S1: Verify vehicle access rights through license plate recognition, control gate release, and guide vehicles to precise parking positions based on limit blocks and wireframes;

[0137] Step S2: Scan the vehicle outline and the layout of the wood stack to check whether the vehicle parking deviation meets the preset threshold and whether the distance between the wood stacks meets the preset range;

[0138] Step S3: driving the truss-type robotic arm carrying the 3D camera to scan the log end face along a preset path, adjusting the scanning parameters in conjunction with the dynamic environment adaptation module, reconstructing the 3D point cloud, calculating the diameter grade, and generating a coding instruction;

[0139] Step S4: Control the inkjet printer to perform end-face inkjet printing, and detect the inkjet marking status and marking result based on visual feedback;

[0140] Step S5: Process the measurement data locally through the edge computing node, bind it to the timber production system MES, generate a process log, and control the gate to release the vehicle;

[0141] Step S6: monitor the communication status of the device in real time, trigger an abnormal alarm and synchronize it to the visual interface.

[0142] Specifically, in step S1, license plate recognition is used to verify vehicle access, control gate release, and guide vehicles to precise parking positions based on stop blocks and wireframes. When a vehicle enters, the license plate recognition camera captures the license plate information and transmits it to the process collaborative control module. This information is then verified with the MES system, and a legitimate vehicle triggers the PLC to open the gate. A LiDAR on the inner side of the track collects wheel point cloud data at a rate of 20,000 times per second. After a direct filter removes ground clutter and a statistical filter removes outliers, the wheel profile is fitted and the actual distance from the stop block and the left-right deviation are calculated. This is then compared to a preset threshold of ±50mm to generate a "parking pass / fail" signal. If a vehicle passes, it proceeds to the next step; if it fails, the gate closes and a warning is displayed on the monitoring interface.

[0143] In step S2, the vehicle outline and the layout of the wood stack are scanned to verify whether the vehicle parking deviation meets the preset threshold and whether the wood stack spacing meets the preset range. The truss-type robotic arm equipped with a 3D camera scans the vehicle outline and the end face of the wood stack, and extracts the single log point cloud through the DeeplabV3+ semantic segmentation algorithm. Combined with the robotic arm axis position data fed back in real time by the PLC, the longitudinal parking distance of the vehicle, the lateral deviation and the spacing between adjacent end faces of the wood stack are calculated. If the parking deviation is within ±50mm and the spacing is not less than 20cm, it is judged to be qualified. Otherwise, the process is suspended and a yellow warning is triggered to prompt manual intervention.

[0144] In step S3, three groups of truss-type robotic arms scan the wood end faces at the front, middle, and rear of the vehicle, respectively. The 3D camera moves at a constant speed along the span beam to collect multi-view point clouds, and converts the point clouds into global coordinate system data through external parameter calibration. The dynamic environment adaptation module monitors humidity, dust concentration, and ambient temperature in real time, automatically adjusts the inkjet printer's injection pressure and drying time, starts the air pump to remove dust, adjusts the camera's exposure time, and links the temperature control equipment to ensure a stable scanning environment. The edge computing node uses the ICP algorithm to stitch the point cloud. After PCA analyzes the main direction of the point cloud, it generates rays at 15° intervals along the circumference to measure the edge intersection distance. The average value is taken as the diameter grade (accuracy <0.1mm), and a coding instruction containing information such as diameter grade and batch is generated.

[0145] In step S4, the process collaborative control module writes the coding instructions into the PLC's instruction writing area, driving the printer to print information such as diameter grade and batch size on the end face of the wood, with the printing position error controlled within ±2mm. After coding is completed, the 3D camera scans the end face again, using an image recognition algorithm to detect the clarity and integrity of the coding, as well as the position of the nail. If the coding is blurred, missing, or offset, the system marks the wood as abnormal and triggers re-coding or manual review. The dynamic visual monitoring module records warning information.

[0146] In step S5, the edge computing node preprocesses the point cloud data collected by the 3D camera, performing noise reduction, stitching, and diameter-level calculations to ensure data accuracy. The cross-system status synchronization unit binds the inspection data with license plate information, operation time, and other information, and uploads it to the MES system via the ModbusTCP protocol, generating a complete process log containing vehicle information, inspection results, and process duration. Once data synchronization is complete, the process collaborative control module sends a command to the PLC to open the exit gate, allowing the vehicle to exit the inspection area and triggering a device reset command to prepare for the next process.

[0147] In step S6, the device status feedback unit polls the PLC data block with a period of 100ms to obtain the robot arm axis position, gate status, sensor signals, etc., and pushes them to the visual module and dynamic visualization monitoring module through the UDP protocol. The monitoring interface distinguishes the operation stages by status coding: green indicates normal execution of the process, yellow indicates manual review or warning status, and red indicates system failure or emergency stop. When the network is interrupted, the network resuming unit caches unuploaded data and resumes the transmission through the MQTT protocol after recovery; when an emergency failure occurs, the system immediately triggers the red status, all motion equipment shuts down urgently, the interface flashes red, and an audible and visual alarm sounds until the fault is manually checked and confirmed to be resolved.

[0148] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An automated bayonet-type intelligent measuring system for wood, characterized in that: include: Multi-source data acquisition module, used to obtain vehicle parking deviation, log end point cloud and equipment operation status through sensors, hardware interfaces and robotic arms interaction; The process collaborative control module is used to coordinate the movement of the truss robot arm, inkjet printing operation and data transfer according to the preset operation process, and monitor the interaction logic between the PLC signal and the axis control instructions issued by the vision module in real time; Peripheral execution modules, including a truss-type robotic arm for scanning, a license plate recognition camera, a coding device, and a parking detection sensor; Industrial communication interface module, used to interact with PLC through industrial protocols and synchronize data with vision modules and wood production system MES through communication protocols; Dynamic visual monitoring module, used to display vehicle parking status, measurement progress, coding results and system alarm information in real time; Dynamic environment adaptation module, used to dynamically adjust coding parameters and scanning strategies according to wood surface humidity, dust concentration and ambient temperature; Edge computing nodes are deployed on the robotic arm control terminal for local real-time data processing and instruction optimization; The dynamic environment adaptation module includes: Humidity compensation unit, which adjusts the inkjet printer's injection pressure and drying time by monitoring the surface humidity of the wood; Dust suppression unit, dynamically removes dust interference on the end surface during scanning; Temperature control logic unit, which automatically adjusts the camera exposure time and activates the temperature control device when the ambient temperature exceeds the preset range; The humidity compensation unit uses a humidity sensor with a measurement accuracy of ±1.5% RH and a sampling frequency of 50ms. When the humidity on the wood surface is detected to be H>85% RH or H<30% RH, the printer parameters are adjusted using the following formula: Where, P injection pressure, t d Drying time, P0 is the standard injection pressure, the value is 0.3MPa, t d0 is the standard drying time, which is 200ms; The dust suppression unit uses a dust concentration sensor with a detection range of 0.3μm-10μm. When the dust concentration C is > 500μg / m 3 When the air pump is started, the clean air flow is sprayed at an air pressure of 0.2MPa≤Pa≤0.5MPa. The angle between the spray direction and the camera scanning direction is θ≤20°. The first spray time is 300ms. If C>400μg / m 3 Then the air pressure is increased to 0.4MPa and extended to 500ms; The temperature control logic unit uses a temperature sensor with an accuracy of ±0.5°C. When the ambient temperature T is less than 0°C or T is greater than 40°C, the 3D camera exposure time is adjusted using the following formula: Among them, T exp is the camera exposure time at the current ambient temperature, T exp0 is the default exposure time under a standard ambient temperature of 20-30°C, and T is the ambient temperature monitored in real time by the temperature sensor.

2. The automated bayonet-type intelligent measuring system for wood according to claim 1, characterized in that: The vision module collects multi-view point cloud and image data of the log end face through a 3D vision sensor, determines the point cloud area of ​​a single log based on the YOLOX target detection algorithm and the DeeplabV3+ semantic segmentation algorithm, calculates the diameter grade through principal component analysis and generates a robot arm axis control instruction. The instruction includes the target posture, movement speed and priority, and is sent to the process collaborative control module through the Socket protocol to coordinate the movement of the truss robot arm.

3. The automated bayonet-type intelligent measuring system for wood according to claim 1, characterized in that: The multi-source data acquisition module includes: A vehicle parking position calibration unit, configured to detect a vehicle parking position deviation through a sensor and compare it with a preset threshold; The end face 3D reconstruction unit is used to perform multi-view point cloud scanning of the log end face using a 3D camera mounted on a truss-type robotic arm, generating 3D data for diameter grade calculation; Equipment status feedback unit, used to read the PLC-controlled mechanical arm axis position, gate opening and closing status and sensor signals in real time; The end face 3D reconstruction unit realizes full coverage acquisition of the end face point cloud through the coordinated movement of the truss-type robotic arm, and calculates the minimum diameter level based on geometric feature analysis.

4. The automated bayonet-type intelligent measuring system for wood according to claim 1, characterized in that: The process collaborative control module includes: Segmented task scheduling unit, used to dynamically allocate control instructions according to the vehicle entry, scanning, coding, and release stages; The instruction priority management unit is used to cache the robot arm motion instructions generated by the vision module and optimize the execution order according to the preset logic; The cross-system status synchronization unit is used to forward the real-time axis position data of the PLC to the vision module and receive the job plan verification results from the MES.

5. The automated bayonet-type intelligent measuring system for wood according to claim 1, characterized in that: The industrial communication interface module includes: PLC interaction unit, which uses industrial communication protocol to read and write PLC data blocks to control the opening and closing of gates and the movement parameters of the robotic arm; The visual data channel unit uses a real-time transmission protocol to push PLC data to the visual module and obtain the axis control queue through the command receiving channel; The data blocks of the industrial communication protocol are divided into: Status reading area, used to obtain the coordinates of the robot arm axis, the vehicle parking status and the system operation mode flag; The instruction writing area is used to issue axis motion target parameters, inkjet coding content and process start and stop control signals.

6. The automated bayonet-type intelligent measuring system for wood according to claim 1, characterized in that: The dynamic visual monitoring module distinguishes the operation stages through status codes: Green indicates normal process execution, yellow indicates manual review or warning status, and red indicates system failure or emergency stop.

7. The automated bayonet-type intelligent measuring system for wood according to claim 1, characterized in that: The edge computing node includes: The real-time data preprocessing unit uses statistical filtering algorithm to reduce the noise of the point cloud data collected by the 3D camera. i (x i ,y i ,z i ), calculate its r radius neighborhood point set N i The mean and σ, where the mean Standard deviation When point P i Euclidean distance from the mean When it is greater than 2σ, the point P is eliminated. i ; Among them, P i (x i ,y i ,z i ) represents the i-th point in the point cloud data, and its coordinates are (x i ,y i ,z i ), N i Point P i is the set of all neighborhood points within the center and radius r, |N i | is the neighborhood point set N i The number of point clouds in P j is the neighborhood point set N i The jth point in Point P i and the neighborhood mean The Euclidean distance of The instruction delay optimization unit is used to build a priority queue to divide the control instructions into three levels of priority: safety instructions, measurement instructions, and coding instructions. Safety instructions include emergency stop instructions, which have a higher priority than measurement instructions, and measurement instructions have a higher priority than coding instructions. A preemptive scheduling algorithm is used to ensure that the response delay of safety instructions does not exceed 20ms. A timestamp t is added to the robot arm movement instructions. cmd , combined with the real-time feedback of the robot arm axis position P of the programmable logic controller PLC axis (t), based on the instruction plan execution time t plan , current time t now and network transmission delay t trans Dynamically adjust the instruction execution order to reduce the waiting time of the robot arm; Among them, t cmd The timestamp of the robot arm motion instruction, recording the time when the instruction was generated, t plan is the execution time of the instruction plan on the robot arm, t now is the current system time of the edge computing node, t trans The network delay time for the control command to be transmitted from the edge computing node to the robotic arm controller; The network interruption resuming unit uses a ring buffer with a storage capacity of not less than 1GB to temporarily store the gauge data, and records the timestamp of the last uploaded data when the network is interrupted. last and data number ID last , after the network is restored, filter out the data timestamp t i later than t last And data number ID i Greater than ID last The unsynchronized data is synchronized to the cloud server after verifying the data integrity through the CRC-32 check algorithm; Among them, t i The generation timestamp of the data block to be continued, ID i It is the unique number of the data block to be transferred, and increases in the order in which the data is generated.

8. An automated bayonet-type intelligent measuring method for wood, used in the automated bayonet-type intelligent measuring system for wood according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step S1: Verify vehicle access rights through license plate recognition, control gate release, and guide vehicles to precise parking positions based on limit blocks and wireframes; Step S2: Scan the vehicle outline and the layout of the wood stack to check whether the vehicle parking deviation meets the preset threshold and whether the distance between the wood stacks meets the preset range; Step S3: driving the truss-type robotic arm carrying the 3D camera to scan the log end face along a preset path, adjusting the scanning parameters in conjunction with the dynamic environment adaptation module, reconstructing the 3D point cloud, calculating the diameter grade, and generating the inkjet coding instructions; Step S4: Control the inkjet printer to perform end face inkjet printing, and detect the inkjet marking status and marking result based on visual feedback; Step S5: Process the measurement data locally through the edge computing node, bind it to the timber production system MES, generate a process log, and control the gate to release the vehicle; Step S6: monitor the communication status of the device in real time, trigger an abnormal alarm and synchronize it to the visual interface.

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