Automatic control system and method for log processing

Abstract

The invention discloses an automatic control system and method for log processing, and the system comprises a visual scanning mechanism, a feeding mechanism, a cutting mechanism and a control mechanism, and the visual scanning mechanism collects surface images of logs in a multi-view manner; the feeding mechanism drives logs to move towards the cutting mechanism, and the logs are cut by the cutting mechanism; the control mechanism is used for controlling the whole log cutting production process and comprises a man-machine interaction module, a data transmission module and a data processing module, and the data transmission module is used for receiving instructions of the visual scanning mechanism and issuing the instructions of the data processing module. The data processing module receives image data of the visual scanning mechanism according to the data transmission module and conducts three-dimensional modeling and cutting path calculation. The man-machine interaction module is used for achieving dynamic adjustment of machining parameters and whole-process data monitoring. According to the automatic cutting method, automation, intelligence and high efficiency of log cutting are achieved.

Description

Technical Field

[0001] The present application belongs to the technical field of log cutting, and in particular relates to an automated control system and method for log processing. Background Art

[0002] During the wood processing process, the size of the finished square timber / rectangular wood strips / wooden boards is determined according to demand, and then workers perform rough cutting and fine cutting according to standard procedures. However, due to the low precision in the manual cutting process, the utilization rate of the logs (i.e., the yield rate) is usually not high or unstable, resulting in a waste of resources. In addition, this processing method has the problem of low efficiency, long manual operation time and difficulty in batch processing. Manual operation has a large safety risk and is prone to accidents. Therefore, an automated control system and method for log processing are urgently needed to solve the above problems. Summary of the Invention

[0003] The object of the present invention is to provide an automated control system for log processing to overcome the deficiencies in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] The present application discloses an automated control system for log processing, comprising a visual scanning mechanism, a feeding mechanism, a cutting mechanism and a control mechanism, wherein:

[0006] The visual scanning mechanism collects surface images of logs from multiple perspectives;

[0007] The feeding mechanism drives the logs to move toward the cutting mechanism, and the cutting mechanism completes the cutting;

[0008] The control mechanism is used to control the entire log cutting production process, including a human-computer interaction module, a data transmission module, and a data processing module. The data transmission module is used to receive instructions from the visual scanning mechanism and issue instructions to the data processing module. The data processing module receives image data from the visual scanning mechanism according to the data transmission module and performs three-dimensional modeling and calculates the cutting path. The human-computer interaction module is used to realize dynamic adjustment of processing parameters and full-process data monitoring.

[0009] Furthermore, in the above-mentioned automated control system for log processing, the visual scanning mechanism includes several cameras, and acquires images of both end faces of the log and surface images of the log at four different angles.

[0010] Furthermore, in the above-mentioned automated control system for log processing, the steps of calculating the cutting path by the data processing module are as follows:

[0011] A1: Determine the cutting surface with the greatest curvature: Based on the 3D model of the log, obtain the cross section with the greatest curvature;

[0012] A2: Obtain the largest inscribed prism: Slice the log parallel to the cutting plane with the greatest curvature, forming several slices of equal thickness. Calculate the maximum inscribed rectangle on each slice plane. The desired maximum prism is obtained by multiplying the maximum inscribed rectangles within all slices.

[0013] A3: Implementing a decision tree algorithm using a doubly linked list: Use a doubly linked list data structure to simulate the stacking of wood during cutting within the largest column.

[0014] A4: Select the optimal cutting path: Sum the number of placed nodes in each chain and select the chain with the largest number of strips / planks as the optimal cutting path.

[0015] Furthermore, in the above-mentioned automated control system for log processing, the human-computer interaction module includes a host computer, a slave computer and a production management platform. The host computer and the slave computer communicate in two directions for obtaining the real-time status of the cutting mechanism and issuing control instructions. The host computer and the data processing module communicate in two directions for feeding back the real-time status of the system to the production management platform and receiving the cutting path calculated by the data processing module.

[0016] Furthermore, in the above-mentioned automated control system for log processing, the host computer is a computer and / or a touch screen and / or an industrial computer, and the slave computer includes a sensor and a PLC and / or a single-chip microcomputer.

[0017] The present application also discloses a method for log processing, which utilizes the above-mentioned automated control system and includes the following steps:

[0018] B1. Obtain the data of both end faces and surface of the log through multi-angle visual scanning by the visual scanning mechanism;

[0019] B2. Upload to the cloud server for 3D modeling and cutting path optimization calculation;

[0020] B3. Send the optimized processing instructions to the upper computer, and control the cutting mechanism to execute the cutting instructions through the lower computer;

[0021] B4. Monitor the processing process and collect production data through the production management platform.

[0022] Furthermore, in the above-mentioned method for log processing, the host computer uses a Modbus RTU-to-JSON decoder and a ring buffer to process the data of the slave computer sensors and feeds it back to the production management platform.

[0023] Furthermore, in the above-mentioned method for log processing, step B4 includes: establishing a processing quality traceability file and automatically generating a material utilization analysis report.

[0024] Furthermore, in the above-mentioned method for log processing, the production management platform includes a production scheduling algorithm: weights are calculated based on priority, urgency and production time, and a genetic algorithm is used to dynamically respond to equipment failures, emergency orders and raw material deviation events.

[0025] Furthermore, in the above-mentioned method for log processing, the production management platform is based on the multi-objective optimization algorithm of NSGA-II, which simultaneously optimizes cutting efficiency, saw blade wear and energy consumption indicators; dynamic parameter adjustment strategy: the knot area automatically reduces the speed by 20%, the high-density area increases the speed by 15% and reduces the cutting amount by 5%

[0026] Compared with the prior art, the advantages of the present invention are:

[0027] 1) Improved processing yield and log utilization: Visual scanning technology accurately captures log morphological data and uses cloud-based algorithms to calculate the optimal cutting path, effectively improving log utilization. Compared with traditional processing methods that rely on manual judgment, this technology can accurately plan cutting paths, reduce log waste, and maximize yield.

[0028] 2) Improved processing accuracy and quality: The cloud server's cutting path planning algorithm, based on a three-dimensional log model, can precisely control the angle and position of each cut to ensure processing accuracy. Automated equipment performs cutting operations through remote control from the host computer module, reducing errors caused by manual operation and ensuring stable and consistent processing quality.

[0029] 3) Improved production efficiency: The intelligent scheduling module uses intelligent algorithms to automatically schedule and optimize production tasks, reducing manual intervention, achieving efficient collaboration and seamless connection of equipment, avoiding equipment idleness and task conflicts, and significantly improving production efficiency. In particular, the ability to dynamically adjust production plans can quickly respond to changes in the production site.

[0030] 4) Reduced labor costs: Through automated equipment control and remote monitoring, reliance on manual labor is greatly reduced, especially in the cutting, processing, and finished product handling processes, which are almost all automated. This reduces the error rate of manual operations, lowers staffing and labor costs;

[0031] 5) Operational convenience: Through the cloud-based production management platform, managers can remotely monitor the entire production process, check equipment status, adjust processing parameters, and modify production plans at any time. The host computer module eliminates the need for managers to directly contact the equipment, greatly improving the convenience and flexibility of operation.

[0032] 6) Optimization of equipment energy consumption: The intelligent scheduling module can reasonably allocate the working time of equipment, avoid frequent equipment start-up and shutdown and excessive use, and reduce energy consumption. At the same time, by monitoring the operating status of the equipment, the system can detect faults in advance, avoid long downtime caused by equipment failure, and further improve energy utilization efficiency.

[0033] 7) Process savings and process standardization: Through information exchange and process control technology, each production link realizes data sharing and automated operation, avoiding information transmission delays and human errors; at the same time, the process is standardized and normalized, reducing unnecessary processes and improving the standardization of overall production;

[0034] 8) Improved production safety: Remote control and real-time monitoring of equipment status reduce the chances of direct contact between humans and equipment, thus reducing potential safety hazards. At the same time, fault alarms and equipment status feedback functions can promptly identify potential problems, ensuring the safety and continuity of the production process.

[0035] 9) Environmental benefits: By improving log utilization and equipment energy efficiency, it reduces raw material waste and energy consumption; it has certain environmental benefits and contributes to energy conservation, emission reduction and sustainable use of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 Shown is a schematic flow chart of an automated control system for log processing in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following is a detailed description of the technical solutions in the embodiments of the present invention, with reference to the accompanying drawings. Obviously, the described embodiments are only a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0039] Combine Figure 1 As shown, an automated control system for log processing includes a visual scanning mechanism, a feeding mechanism, a cutting mechanism and a control mechanism, wherein the hardware equipment such as the feeding mechanism and the cutting mechanism can utilize the existing structure and be configured with a lower computer composed of a PLC and sensors, etc. The lower computer communicates with the upper computer in two directions through the Modbus TCP communication protocol. The upper computer uses an existing industrial computer and is connected to the cloud server through Ethernet. It is responsible for controlling the cutting mechanism, receiving tasks and parameters sent from the server cloud, and feedback on the equipment operation status. The visual scanning mechanism uses an industrial-grade 3D TOF camera (model: TM461-E2 PM806-E1) to scan four different angles and cross-sections of the log and generate a high-resolution three-dimensional point cloud map. The resolution of each image is 2048x2048 pixels. The visual scanning mechanism transmits the image to the cloud server through the Gigabit Ethernet M12 X-Code aviation interface to complete the image data acquisition. The cloud server uses the ECSg7 Alibaba Cloud server, equipped with a 16-core CPU, 64GB memory, and 1TB SSD hard drive, and connected to the existing MES system or sharing the same server with the MES system, as a data processing module, supports processing 100 log image data per second, and performs 3D modeling and calculates cutting paths, and can store log images and processing data, etc. The production management platform is a Web application platform based on Swoole+Go+Vue and runs on a cloud server. It is used for management scheduling, equipment status monitoring, equipment parameter adjustment, production data summary analysis, and displays log processing tasks, equipment status, equipment parameters, real-time processing data, task progress and other information through a visual interface.

[0040] Log processing includes the following steps:

[0041] B1. Multi-angle visual scanning of the log's two end faces and surface data is acquired through a visual scanning mechanism: After the log is forked into the existing production line's loading platform, it is moved to the cutting position / scanning position by a feeding mechanism and secured with conventional track pins. The visual scanning mechanism's camera acquires images of the log's two end faces and the log's surface at four different angles (either by rotating the log or the camera, or by using multiple cameras at different angles). The camera transmits the images to the cloud server via a Gigabit Ethernet M12 X-Code aviation interface to complete image data acquisition. At least four high-definition images are generated for each log, with a total data volume of approximately 160MB.

[0042] B2. Upload to the cloud server for 3D modeling and cutting path optimization calculation: After receiving the image data, the cloud server uses image processing algorithms to model the log and generate an accurate 3D model. The algorithm determines the most curved cutting surface, knots, and shape of the log, and calculates the optimal cutting path to maximize material utilization. The specific steps include:

[0043] A1: Determine the cutting surface with the greatest curvature: Based on the 3D model of the log, obtain the cross section with the greatest curvature. This involves the following steps:

[0044] A11: Read the generated 3D log model;

[0045] A12: Take five first-section circles along the length of the log, including both ends. If there are depressions or knots on the log surface, add second-section circles at the depressions and knots. The more cross-section circles you select, the more accurate the cutting surface with the greatest curvature will be, but this will also increase the computational effort.

[0046] A13: Calculate the center of the cross-sectional circle: Take any point A on the arc of the cross-sectional circle and connect it to another point B on the arc. Move point B so that the areas of the cross-sectional circles on both sides of line AB are equal. Similarly, find another line CD. The intersection of AB and CD is the center of the circle. Points A and C do not coincide.

[0047] A14: Connect the centers of the two end faces of the log to form a baseline. Select the center of the cross-section circle with the largest distance from the baseline and form a judgment triangle with the baseline. The cross-section where the judgment triangle is located is the most curved cross-section, that is, the cutting surface with the greatest curvature.

[0048] A2: Obtain the largest inscribed cylinder, including the following steps:

[0049] A21: Slice the log in a direction parallel to the cutting surface with the greatest curvature to form n slices of the same thickness, which are recorded as slices Q1, Q2, ..., Q n ;

[0050] A22: Select slice Q1 and evenly find i points on the upper arc of the cross section of slice Q1, denoted as points A1, A2, ..., A5. The straight lines passing through two adjacent points (A1, A2), (A2, A3), ..., (A4, A5) are denoted as lines L1, L2, ..., L4, for a total of four straight lines.

[0051] A23: Find i points evenly spaced on the lower arc of the slice Q1 section, and record them as points B1, B2, ..., B i ;

[0052] A24: Calculate the distances from points B1, B2, ..., B5 to lines L1, L2, ..., L4, respectively. The distances from points B1, B2, ..., B5 to line L1 are denoted by D. 11 、D 12 、D 13 、D 14 and D 15 , take D 11 、D 12 、D 13 、D 14 、D 15 The minimum value in 1min , ..., the distances from points B1, B2...B5 to line L4 are denoted as D 41 、D 42 、D 43 、D 44 and D 45 , take D 41 、D 42 、D 43 、D 44 、D 45 The minimum value in 4min , in D 1min 、D 2min ...D 4min Calculate the maximum value D max , i is an integer greater than or equal to 3, and the larger the value of i, the more accurate the maximum inscribed rectangle in the slice will be, but it will also increase the amount of calculation;

[0053] A25:D max The corresponding line segment is the width of the largest inscribed rectangle of the slice, D max The line segment formed by the corresponding straight line (L1) and the two end faces is the length of the maximum inscribed rectangle of the slice. The maximum inscribed rectangle R1 in the slice Q1 is determined by the length and width;

[0054] A26: Loop through steps S22 to S25 to complete slicing Q2...Q n The calculation of the maximum inscribed rectangle is recorded as R2, R3...R n ;

[0055] A27: Rectangle R1, R2, R3...R n The maximum prism to be obtained is formed by calculus. The thinner the slice, the more corresponding slices there are. That is, the larger n is, the more accurate the inscribed maximum prism is, but it also increases the amount of calculation.

[0056] A3: Implementing the decision tree algorithm with a doubly linked list includes the following steps:

[0057] A31: Place the required timber or strips by length or width at the bottom of the largest column cross-section circle as the first layer of node chain, and record the number of timber or strips included;

[0058] A32: The next layer is divided into two node chains according to width or length, and each node chain records the number of square timber or wood strips contained;

[0059] A33: Repeat step S32 until the total width or height of the stacked timber or wood strips exceeds the cross-sectional circle of the largest column;

[0060] A4: Select the optimal cutting path: Sum the number of node chains placed within each chain and select the chain containing the largest number of strips / planks as the optimal cutting path;

[0061] B3. Send the optimal cutting path to the host computer, and use the slave computer to control the cutting mechanism to execute the cutting instructions: After the host computer receives the cutting path sent from the cloud, it controls the saw machine (cutting mechanism) through the PLC (slave computer) to execute the cutting task. The cutting mechanism accurately moves the double-headed saw according to the set path and begins processing the logs. Throughout the cutting process, regular sensors are used to provide real-time feedback on the equipment status, such as cutting progress and equipment operating temperature. After each work order log specification is cut and processed, the system automatically starts the next task according to the schedule (automatically or manually by the operator). The host computer uses the Modbus RTU-to-JSON decoder and ring buffer to process the data from the slave computer sensors and feed it back to the production management platform. The production management platform uses the TCP communication protocol to monitor the designated port in real time. The PLC pushes the cutting mechanism's operating status, energy consumption, and other sensor data to the production management platform in real time through the designated communication protocol. The production management platform processes and presents the data to the user end. Sensor data stream processing includes:

[0062] class DataPipeline:

[0063] def__init__(self):

[0064] self.buffer = CircularBuffer(1024) # Circular buffer

[0065] self.decoder = ModbusRTU_to_JSON() #Protocol conversion

[0066] def on_data_received(self,raw_data):

[0067] parsed=self.decoder.transform(raw_data)

[0068] if validate_checksum(parsed):

[0069] self.buffer.push(parsed)

[0070] emit_signal('data_updated');

[0071] B4. Monitoring the processing process and collecting production data through the production management platform: The production management platform generates and adjusts production scheduling tasks through the intelligent scheduling module to ensure the optimal operation of all equipment and avoid task conflicts. Managers can monitor the production process in real time through the production management platform, viewing the operating status of equipment, the progress of current tasks, and production efficiency. The system also provides a fault alarm function. When equipment fails, the production management platform automatically notifies maintenance personnel via text message and allows managers to remotely inspect and restore equipment. Managers can remotely start, stop, or adjust cutting mechanism parameters through the production management platform to optimize production processes and ensure equipment operates in optimal conditions. The production management platform enables standardized management of the production process, including unified control and scheduling of multiple links such as log loading, cutting, and finished product processing. Intelligent algorithms are used to automatically schedule and optimize production tasks. This module generates optimal production plans based on factors such as real-time equipment status, log inventory, and production task priority. The cutting mechanism is remotely controlled through the server cloud platform, achieving a seamless connection from the cloud to production equipment. This module will improve the system's automation level and make the entire log processing process more flexible and efficient.

[0072] The production management platform provides a six-state parameter input panel: idle run, feed, processing, output, end, and reset input points. Users input the corresponding processing speed parameters based on the processing technical indicators of the log species. Through the TCP communication protocol, the speed parameters are sent to the PLC of the lower computer to realize hardware synchronous execution of instructions and support refined control of the motion state of the cutting mechanism.

[0073] Production scheduling is based on priority, urgency and production time calculation weights: priority is divided into three levels: high priority, medium priority and low priority, with corresponding weight coefficients of 3, 2 and 1 respectively; work order deadline is divided into three levels: high time consumption, medium time consumption and low time consumption, with corresponding weight coefficients of 3, 2 and 1 respectively; and urgency is divided into three levels: high urgency, medium urgency and low urgency, with corresponding weight coefficients of 3, 2 and 1 respectively. The specific level can be selected when entering the work order; weight calculation method: various factors are combined to calculate a total weight, and then sorted according to the total weight; for example: priority The priority weight accounts for 50%. The smaller the weight, the less important the priority is in determining the final sorting of work orders. The urgency weight accounts for 20%. The smaller the weight, the weaker the influence of urgency in determining the sorting of work orders. The production time weight accounts for 10%. The smaller the weight, the weaker the influence of production time in determining the sorting of work orders. The corresponding total weight = 50% (priority weight coefficient) + 20% (urgency weight coefficient) + 10% (production time coefficient). The production schedule also has a real-time rescheduling trigger mechanism: when the following events occur, the genetic algorithm is triggered to recalculate: equipment failure (through OPC UA event subscription), emergency insertion order (weight coefficient > 2.5), raw material characteristic deviation > 15% (through visual quality inspection feedback).

[0074] During the cutting process, the production management platform uses the NSGA-II multi-objective optimization algorithm to simultaneously optimize cutting efficiency, saw blade wear, and energy consumption. A dynamic parameter adjustment strategy automatically reduces the saw speed by 20% in knotty areas and increases the saw speed by 15% and reduces the saw feed by 5% in high-density areas. The NSGA-II multi-objective optimization algorithm is as follows:

[0075] def objective_function(params):

[0076] speed,blade_pos=params

[0077] #Goal 1: Cutting efficiency (maximization)

[0078] efficiency=calculate_cutting_efficiency(speed)

[0079] #Goal 2: Saw blade wear (minimize)

[0080] wear=predict_blade_wear(speed,wood_hardness)

[0081] #Goal 3: Energy consumption (minimization)

[0082] energy=estimate_energy_consumption(speed)

[0083] return[efficiency,-wear,-energy]#Multi-target output.

[0084] The production management platform can establish processing quality traceability files and automatically generate material utilization analysis reports: data including the cutting path, processing accuracy, yield rate, etc. of each log processing will be automatically stored in the cloud to facilitate later production optimization and analysis; the system can generate statistical reports based on historical data, and managers can analyze equipment utilization, production efficiency and log utilization to further optimize the processing process.

[0085] In summary, the present invention uses visual technology and cloud-based path planning to control cutting accuracy within 0.2mm, greatly improving processing quality; compared with traditional methods, the system's log utilization rate is increased by about 15%-20%, reducing material waste; the system uses intelligent scheduling to reduce equipment idle time, and the overall production efficiency is improved by about 25%; through the scheduling module's reasonable scheduling of equipment working hours, equipment energy consumption is reduced by about 10%; throughout the entire production process, manual participation is limited to loading and troubleshooting, reducing labor costs by more than 30%.

[0086] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0087] The above is only a specific implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An automated control system for log processing, characterized in that: It includes visual scanning mechanism, feeding mechanism, cutting mechanism and control mechanism, among which, The visual scanning mechanism collects surface images of logs from multiple perspectives; The feeding mechanism drives the logs to move toward the cutting mechanism, and the cutting mechanism completes the cutting; The control mechanism is used to control the entire log cutting production process, including a human-computer interaction module, a data transmission module, and a data processing module. The data transmission module is used to receive instructions from the visual scanning mechanism and issue instructions to the data processing module. The data processing module receives image data from the visual scanning mechanism according to the data transmission module and performs three-dimensional modeling and calculates the cutting path. The human-computer interaction module is used to realize dynamic adjustment of processing parameters and full-process data monitoring.

2. The automated control system for log processing according to claim 1, characterized in that: The visual scanning mechanism includes several cameras and acquires images of both end faces of the log and surface images of the log at four different angles.

3. The automated control system for log processing according to claim 1, characterized in that: The steps of calculating the cutting path by the data processing module are as follows: A1: Determine the cutting surface with the greatest curvature: Based on the 3D model of the log, obtain the cross section with the greatest curvature; A2: Obtain the largest inscribed prism: Slice the log parallel to the cutting plane with the greatest curvature, forming several slices of equal thickness. Calculate the maximum inscribed rectangle on each slice plane. The desired maximum prism is obtained by multiplying the maximum inscribed rectangles within all slices. A3: Implementing a decision tree algorithm using a doubly linked list: Use a doubly linked list data structure to simulate the stacking of wood during cutting within the largest column. A4: Select the optimal cutting path: Sum the number of placed nodes in each chain and select the chain with the largest number of strips / planks as the optimal cutting path.

4. The automated control system for log processing according to claim 1, characterized in that: The human-computer interaction module includes a host computer, a slave computer and a production management platform. The host computer and the slave computer communicate with each other in two directions to obtain the real-time status of the cutting mechanism and issue control instructions. The host computer and the data processing module communicate with each other in two directions to feed back the real-time status of the system to the production management platform and receive the cutting path calculated by the data processing module.

5. The automated control system for log processing according to claim 4, characterized in that: The host computer is a computer and / or a touch screen and / or an industrial computer, and the slave computer includes a sensor and a PLC and / or a single chip microcomputer.

6. A method for log processing, characterized in that: The automated control system according to any one of claims 1 to 5 comprises the following steps: B1. Obtain the data of both end faces and surface of the log through multi-angle visual scanning by the visual scanning mechanism; B2. Upload to the cloud server for 3D modeling and cutting path optimization calculation; B3. Send the optimized processing instructions to the upper computer, and control the cutting mechanism to execute the cutting instructions through the lower computer; B4. Monitor the processing process and collect production data through the production management platform.

7. A method for log processing according to claim 6, characterized in that: The host computer uses a Modbus RTU-to-JSON decoder and a ring buffer to process the data from the lower computer sensors and feed it back to the production management platform.

8. The method for log processing according to claim 6, characterized in that: The step B4 includes: establishing a processing quality traceability file and automatically generating a material utilization analysis report.

9. The method for log processing according to claim 6, characterized in that: The production management platform includes a production scheduling algorithm that calculates weights based on priority, urgency, and production time, and dynamically responds to equipment failures, emergency orders, and raw material deviation events based on a genetic algorithm.

10. The method for log processing according to claim 6, characterized in that: The production management platform is based on the multi-objective optimization algorithm of NSGA-II, which simultaneously optimizes cutting efficiency, saw blade wear and energy consumption indicators; the dynamic parameter adjustment strategy: the knotty area automatically reduces the speed by 20%, and the high-density area increases the speed by 15% and reduces the feed amount by 5%.

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