A bamboo product flexible production line automatic scheduling method fusing digital twinning

Abstract

The application belongs to the technical field of digital twinning, and discloses a kind of bamboo product flexible production line automation scheduling method fusing digital twinning;Including: collecting data and carrying out data cleaning, obtaining accurate bamboo attribute data and correcting tool physical data;Tubular digital twinning and tool digital twinning are constructed;Based on bamboo digital twinning, the physical attribute change information of bamboo product is identified;Collect production line operation data and deduce processing path in combination with physical attribute change information;Based on tool digital twinning, tool processing data is obtained;Based on processing path and tool processing data, control resource allocation is carried out on production line equipment, and processing strategy is output;Real-time acquisition of environmental parameters, construction of strategy adjustment factor based on environmental parameters, parameter optimization of processing strategy using strategy adjustment factor, generation of optimized processing strategy, and sending to preset production line control terminal;Efficient automatic scheduling of bamboo product production line is realized.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and more specifically, to an automated scheduling method for a flexible bamboo product production line that integrates digital twins. Background Technology

[0002] With the continuous development of intelligent manufacturing technology, digital twins, as a key supporting technology that integrates physical and electronic information, have been widely used in various complex industrial manufacturing processes. Especially in the processing of natural materials such as bamboo, due to the high natural heterogeneity of its raw materials, the introduction of digital twins has become an important direction for the automated control and efficiency optimization of the processing process. However, existing technologies still pose certain challenges to achieving high-precision automated scheduling of production lines.

[0003] Bamboo's natural physical properties are complex. Each bamboo stalk exhibits variations in density, moisture content, texture, and the distribution of damaged areas. Existing technologies in bamboo production lines often overlook these differences, relying solely on images or rough statistical data collected during material handling. This leads to deviations in processing path planning and low production precision. For example, the lack of analysis of texture distribution trends in areas with dense knots or cracks results in burrs or fiber tearing during production, while also causing more severe wear on cutting tools. Furthermore, as the primary unit performing processing tasks on the production line, the wear status, usage load, and lifespan of cutting tools are difficult to monitor in real time. Existing technologies lack performance evaluation and lifespan prediction for cutting tools during use, leading to irrational tool allocation and increasing the risk of breakage or excessive wear on some tools, resulting in processing failures or excessive errors. In addition, existing technologies mostly execute processing tasks directly according to the system-set processing path, lacking dynamic awareness of external production environment parameters, resulting in poor production stability in complex workshop environments.

[0004] In view of this, the present invention proposes an automated scheduling method for a flexible bamboo product production line that integrates digital twins to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: an automated scheduling method for a flexible bamboo product production line integrating digital twins, comprising:

[0006] S1. Collect bamboo property data and production line tool physical data, and perform data cleaning to obtain accurate bamboo property data and corrected tool physical data;

[0007] S2. Construct a digital twin of bamboo based on accurate bamboo property data; construct a digital twin of the cutting tool based on corrected cutting tool physical data;

[0008] S3. Identify changes in the physical properties of bamboo products based on bamboo digital twins; collect production line operation data and deduce processing paths by combining physical property change information;

[0009] S4. Obtain tool processing data based on the tool digital twin; control and allocate resources for production line equipment based on the processing path and tool processing data, and output processing strategies;

[0010] S5. Acquire environmental parameters in real time, construct strategy adjustment factors based on environmental parameters, optimize the processing strategy using strategy adjustment factors, generate an optimized processing strategy, and send it to the preset production line control terminal.

[0011] Furthermore, the method for constructing a digital twin of bamboo based on accurate bamboo property data includes:

[0012] Precise bamboo attribute data includes bamboo images, texture parameters, contour parameters, and structural data; a bamboo processing coordinate system is constructed based on the structural data and spatially registered with a preset production line coordinate system; the bamboo texture angle distribution is calculated based on the texture parameters to obtain texture trend data; abnormal areas in the bamboo images are identified, and abnormal risk parameters are generated by combining the texture trend data; bamboo cross-sectional parameters are calculated based on the contour parameters; the texture trend data, abnormal risk parameters, bamboo cross-sectional parameters, and precise bamboo attribute data are encoded into structural feature vectors, and a digital twin of the bamboo is generated based on the bamboo processing coordinate system and the structural feature vectors.

[0013] Furthermore, the method of constructing a digital twin of a tool based on modified tool physical data includes:

[0014] Feature extraction is performed on the physical data of the corrected tool to output the tool state feature vector; the wear curve is fitted based on the tool state feature vector; the tool life change data is predicted based on the wear curve, and the tool feature set is obtained by combining the tool state feature vector; the machining scenario of the production line tool is modeled based on the corrected tool physical data, and the tool feature set is fused to obtain the tool digital twin.

[0015] Furthermore, the method for identifying changes in the physical properties of bamboo products based on a bamboo digital twin includes:

[0016] Set the processing path direction and detect the parameter changes of adjacent spatial sub-regions of the bamboo digital twin along the processing path direction; set the partition change threshold and divide the bamboo digital twin into spatial sub-regions, and mark the disturbance type of each type of spatial sub-region to obtain a set of physical disturbance regions; perform semantic encoding on the set of physical disturbance regions and output physical attribute change information.

[0017] Furthermore, the method for deriving the processing path includes:

[0018] The processing path is set by combining information on changes in physical properties and the direction of the processing path; the processing path is divided into segments based on the type of disturbance, including segments with sudden density changes, moisture fluctuations, texture changes, and bamboo damage; and the path parameters of each disturbance path segment are adjusted based on the production line operation data.

[0019] A rate correction field is configured for density abrupt change segments, and the path running speed is adjusted based on the rate correction field; a time buffer field is set to adjust the running rhythm of moisture fluctuation segments, and the path running rhythm is output; a cutting angle adjustment value is set based on the texture change distribution of texture change segments, and the physical direction of the path is corrected using the cutting angle adjustment value to obtain the corrected physical direction of the path; a path offset is constructed to avoid malignant damage areas in bamboo damage segments, and the path obstacle avoidance information is output; the adjusted path running speed, path running rhythm, corrected physical direction of the path, and path obstacle avoidance information are connected to the path to be processed to obtain the processed path.

[0020] Furthermore, the method for acquiring tool machining data based on the tool digital twin includes:

[0021] The machining scene model of the digital twin of the tool is used as an index to search for historical tool machining information corresponding to the scene; the tool feature set is extracted and the state change data is fitted; the tool machining data is obtained by combining the historical tool machining information and state change data corresponding to the scene.

[0022] Furthermore, the method for controlling resource allocation for production line equipment includes:

[0023] Based on the path segment type of the machining path, select the corresponding functional tool to obtain candidate tool combinations; extract the corresponding data from the tool machining data for each group of candidate tools and perform performance evaluation to obtain machining evaluation indicators; obtain the machining execution requirements and equipment load capacity values ​​of the production line equipment, and simulate the machining environment for each group of candidate tools based on the machining path and the machining execution requirements and equipment load capacity values, outputting candidate tool simulation data; calculate the path segment-tool coupling matching value of the corresponding candidate tool based on the candidate tool simulation data, select candidate tools whose path segment-tool coupling matching value and machining evaluation indicators are both greater than the preset target threshold as optional tools, and assign the optional tools to the corresponding path segments; encode the control resource allocation process into a path structure, which is the machining strategy.

[0024] Furthermore, the method for constructing the strategy adjustment factor based on environmental parameters includes:

[0025] Obtain equipment status change parameters, and construct an environment-equipment status quantification matrix based on environmental parameters and equipment status change parameters; combine the environment-equipment status quantification matrix with the path parameters of each path segment in the processing path for fitting analysis to identify the environment-sensitive path parameters of the corresponding path segments; design corresponding action instructions based on the environment-sensitive path parameters to adjust the equipment, and output the strategy adjustment factor.

[0026] Furthermore, the method of optimizing the processing strategy parameters using the strategy adjustment factor includes:

[0027] The corresponding execution parameters in the processing strategy are weighted using a strategy adjustment factor to obtain a weighted processing strategy. The weighted processing strategy is then optimized and its effectiveness is evaluated. If the expected effect is achieved, the optimized processing strategy is output; otherwise, the iteration is repeated until the expected effect is achieved.

[0028] An automated scheduling system for a flexible bamboo product production line integrating digital twins, used to implement an automated scheduling method for a flexible bamboo product production line integrating digital twins, characterized by comprising:

[0029] The data acquisition module is used to collect bamboo property data and production line tool physical data, and to perform data cleaning to obtain accurate bamboo property data and corrected tool physical data.

[0030] The twin construction module is used to construct digital twins of bamboo based on accurate bamboo property data; and to construct digital twins of cutting tools based on modified tool physical data.

[0031] The path generation module is used to identify changes in the physical properties of bamboo products based on the digital twin of bamboo; it collects production line operation data and combines it with the information on changes in physical properties to deduce the processing path;

[0032] The resource allocation module is used to acquire tool processing data based on the tool digital twin; control resource allocation for production line equipment based on the processing path and tool processing data; and output processing strategies.

[0033] The strategy optimization module is used to acquire environmental parameters in real time, construct strategy adjustment factors based on environmental parameters, optimize the processing strategy using strategy adjustment factors, generate an optimized processing strategy, and send it to the preset production line control terminal; the modules are connected to each other via wired and / or wireless means.

[0034] The technical effects and advantages of the automated scheduling method for a flexible bamboo product production line integrating digital twins, as described in this invention:

[0035] By constructing dual digital twins of bamboo and cutting tools, dynamic control and efficient optimization of the bamboo processing process are achieved, significantly improving processing accuracy and stability. Based on the local disturbance characteristics of the bamboo's physical properties perceived by the bamboo digital twin, and combined with the cutting tool state evolution trend information, control resource allocation and path parameter adjustment are performed, reducing the risk of errors and damage caused by material defects or tool wear. The processing path can adjust its running speed, cycle time, and angle in real time according to factors such as density, moisture, texture, and damage, ensuring cutting stability and finished product quality. At the same time, the generated strategy has environmental parameter adaptive capabilities, automatically optimizing key process parameters under external changes such as temperature and humidity fluctuations, enhancing the robustness and adaptability of the manufacturing process. Through full-process closed-loop control and feedback optimization, continuous iteration and performance improvement of the processing strategy are achieved, reducing manual intervention while improving the automation scheduling level and resource utilization efficiency of the production line. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of an automated scheduling method for a flexible bamboo product production line integrating digital twins, according to the present invention.

[0037] Figure 2 This is a schematic diagram of an automated scheduling system for a flexible bamboo product production line that integrates digital twins, according to the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1

[0039] Please see Figure 1As shown in this embodiment, an automated scheduling method for a flexible bamboo product production line integrating digital twins includes:

[0040] S1. Collect bamboo property data and production line tool physical data, and perform data cleaning to obtain accurate bamboo property data and corrected tool physical data;

[0041] S2. Construct a digital twin of bamboo based on accurate bamboo property data; construct a digital twin of the cutting tool based on corrected cutting tool physical data;

[0042] S3. Identify changes in the physical properties of bamboo products based on bamboo digital twins; collect production line operation data and deduce processing paths by combining physical property change information;

[0043] S4. Obtain tool processing data based on the tool digital twin; control and allocate resources for production line equipment based on the processing path and tool processing data, and output processing strategies;

[0044] S5. Acquire environmental parameters in real time, construct strategy adjustment factors based on environmental parameters, optimize the processing strategy using strategy adjustment factors, generate an optimized processing strategy, and send it to the preset production line control terminal.

[0045] High-precision cameras are deployed in the production line to acquire bamboo image type data. At the same time, bamboo-related attribute data, such as density distribution information, moisture distribution information, and abnormal area distribution information of knots or cracks, are obtained by querying existing data and material picking records. The physical data of the production line tools includes attribute data related to the tools used to cut bamboo in the production line, such as tool number, model, usage time, size, and sharpness. The data cleaning process includes standardization, missing value filling, and data denoising to obtain more accurate bamboo attribute data and correct tool physical data with higher data quality.

[0046] Methods for constructing digital twins of bamboo based on accurate bamboo property data include:

[0047] Precise bamboo attribute data includes bamboo images, texture parameters, contour parameters, and structural data. All of the precise bamboo attribute data is cleaned data. Bamboo images refer to images of the bamboo surface. Texture parameters are used to reflect information such as the texture direction and texture gradient of the processed bamboo surface. Contour parameters include bamboo edge line data and other related data obtained based on edge detection. Structural data refers to the distribution of density and moisture in areas such as bamboo, as well as the overall size and structure of the bamboo.

[0048] A bamboo processing coordinate system is constructed based on structural data and spatially registered with a preset production line coordinate system. The bamboo processing coordinate system uses the largest bamboo stalk as the reference to construct the coordinate axes, ensuring that any bamboo stalk can be included. At the same time, the spatial positions of various equipment in the production line are used as reference points. A three-point attitude solution algorithm is used to spatially register the bamboo processing coordinate system with the production line coordinate system, achieving spatial consistency registration between the bamboo and the production line. Based on texture parameters, the bamboo texture angle distribution is calculated to obtain texture trend data. The calculation of bamboo texture angle distribution refers to constructing a continuous sliding window to traverse the bamboo surface to obtain the texture direction angle of local areas. The texture direction angles of all areas are statistically fitted to construct a texture direction map, which is used to describe the main direction shape of the bamboo surface texture, i.e., texture trend data.

[0049] Abnormal areas in bamboo images are identified, and abnormal risk parameters are generated by combining texture trend data. In this embodiment, abnormal areas refer to areas such as knots, wormholes, cracks, and decay on the surface of bamboo. Since the texture near abnormal areas is chaotic, the abnormal areas are projected onto the chaotic texture area of ​​the texture direction map, and the abnormal level of the corresponding abnormal area is identified by querying historical processing cases, which is the abnormal risk parameter. Bamboo cross-sectional parameters are calculated based on contour parameters, including cross-sectional area, thickness, perimeter, and curvature. The bamboo cross-sectional structure diagram is drawn based on the contour parameters, and the above-mentioned bamboo cross-sectional parameters are extracted from the diagram.

[0050] Texture trend data, abnormal risk parameters, bamboo cross-section parameters, and precise bamboo attribute data are encoded into structural feature vectors. A digital twin of bamboo is generated based on the bamboo processing coordinate system and the structural feature vectors. In this embodiment, the precise bamboo attribute data is used as the modeling basis. A virtual three-dimensional geometric model of bamboo is constructed in combination with the bamboo processing coordinate system. Other data in the structural feature vectors, except for the precise bamboo attribute data, are mapped to the three-dimensional geometric model to obtain a digital twin of bamboo that can change in conjunction with the subsequent processing path.

[0051] Methods for constructing a digital twin of a tool based on modified tool physical data include:

[0052] Feature extraction is performed on the physical data of the modified tool to output a tool state feature vector. The tool state feature vector includes data that reflects the overall usage status of the tool, such as tool number, total machining time, and load level. A wear curve is fitted based on the tool state feature vector. In this embodiment, the tool state feature vector is used as input data, and an exponential decay function is used to fit the changes in the tool state to construct a wear curve. The wear curve is used to reflect the performance degradation of the tool.

[0053] Tool life variation data is predicted based on wear curves, and tool feature sets are obtained by combining tool state feature vectors. The current tool life stage is determined by trend analysis based on the wear curve values. In this embodiment, the life stage is divided into three types: initial stable stage, linear degradation stage, and nonlinear abrupt change stage, and thresholds are set for each type. A performance variation model for the corresponding tool model is constructed by querying the data of the tool model, and the performance variation model is used to predict tool life variation data. Tool life variation data and tool state feature vectors are concatenated to obtain tool features, and the tool features of all tools are integrated to obtain a tool feature set.

[0054] Based on the corrected tool physical data, a machining scenario model is created for the production line tools. The tool feature set is then fused to obtain a tool digital twin. Machining scenario modeling refers to constructing a structured representation model to depict the process of performing machining tasks using production line tools. This model includes labels such as machining target parameters and material properties, and establishes a mapping relationship between the labels and the tool machining effects. The tool machining effects include parameters such as error, wear rate, and surface fineness. The tool feature set is then mapped onto the constructed machining scenario model and labeled to obtain the tool digital twin.

[0055] Methods for identifying changes in the physical properties of bamboo products based on bamboo digital twins include:

[0056] The processing path direction is set, and the parameter change values ​​of adjacent spatial sub-regions of the bamboo digital twin are detected along the processing path direction. The processing path direction refers to the forward direction of the path used to execute each process of the production line in sequence. For example, if a grooving process is required for a certain surface area of ​​a bamboo, the process traverses each bamboo section from the left end to the right until the target area is reached. Then, the cutting tool is used to push the cutting tool along the traversal direction. The processing path direction is the direction from the left end to the target area. Each bamboo section is treated as a spatial sub-region, and the parameter change values ​​between different bamboo sections are obtained. The parameter change values ​​include data such as density gradient difference, moisture content change, damage structure difference, and texture offset.

[0057] A partition change threshold is set, and the bamboo digital twin is divided into spatial sub-regions based on the parameter change value and the partition change threshold. The perturbation type is marked for each type of spatial sub-region to obtain a set of physical perturbation regions. A partition change threshold including information such as density, moisture content, damage area and texture angle is set. The parameter change value is compared with the partition change threshold, and the perturbation type label of the corresponding spatial sub-region is set, including density change, moisture content fluctuation, texture change and damage perturbation.

[0058] Semantic encoding is performed on the set of physical disturbance regions to output physical property change information. In this embodiment, the set of physical disturbance regions is generated into structured data items, and the data items include fields related to parameter changes such as disturbance type and disturbance location, which are physical property change information, used to reflect the changes in bamboo-related physical parameters and the disturbance type corresponding to the cause of the changes.

[0059] Methods for deriving processing paths include:

[0060] The processing path is set by combining the physical property change information and the processing path direction. In this embodiment, the path trajectory is constructed with the processing path direction as the main axis, and the area traversed by the path trajectory is a safe area or a physical disturbance area with small parameter change value, so as to obtain the processing path; each bamboo material corresponds to a processing path, and the processing path derived later also corresponds one-to-one with the bamboo material.

[0061] The processing path is divided into segments based on the disturbance type, including density change segments, moisture fluctuation segments, texture change segments, and bamboo damage segments. The path segments are mapped to the disturbance types to divide the path segments, generating a set of path segments containing path segment numbers and disturbance type labels. The path parameters are adjusted for each disturbed path segment based on the production line operation data. Corresponding strategies are adopted to adjust the path parameters for different disturbed path segments in each processing path.

[0062] A rate correction field is configured for density abrupt change segments, and the path running speed is adjusted based on the rate correction field. The rate correction field is obtained by substituting the density gradient change value of the density abrupt change segment into an exponential decay function. The original path running speed is weighted using the rate correction field to obtain the adjusted running speed, ensuring that the production line equipment will not experience cracking of the blades or other abnormal wear due to large density differences and high speed when processing density abrupt change areas because of excessive speed.

[0063] A time buffer field is set to adjust the operating cycle time of the moisture fluctuation segment. The output path operating cycle time is obtained by subtracting the maximum and minimum moisture content in the moisture fluctuation segment. An environmental response compensation coefficient is obtained by querying existing data. The product of the moisture fluctuation value and the environmental response compensation coefficient is used as the cycle time buffer time. This value is used as the time buffer field to control the buffer time before the equipment starts to process the moisture fluctuation segment, so as to realize the operating cycle time delay and reduce the impact of moisture fluctuation on situations such as changes in cutting resistance and instability of thermal diffusion effect.

[0064] The cutting angle adjustment value is set based on the texture change distribution of the texture change segment. The physical direction of the path is corrected by the cutting angle adjustment value to obtain the corrected physical direction of the path. The texture direction vector sequence of the corresponding area of ​​the texture change segment is extracted by a preset fixed step size. The original cutting angle is compared with the texture direction to obtain the angle deviation sequence, which is the physical direction of the path. A dynamically adjustable cutting angle adjustment value is set. Based on the specific working conditions, this value is used to correct the angle deviation of the physical direction of the path, so that the cutting direction is consistent with the texture direction, reducing the risk of fiber tearing caused by reverse cutting.

[0065] The method constructs a path offset to avoid severely damaged areas in bamboo sections and outputs path obstacle avoidance information. In this embodiment, the specific coordinates of, for example, severe knots or cracks are determined to identify severely damaged areas. The Euclidean distance between the original path to be processed and the severely damaged area is calculated as the path offset. The path offset is used to adjust the trajectory of the path to be processed while marking the obstacle avoidance areas. This yields path obstacle avoidance information, including the path segment number corresponding to the obstacle avoidance area and displacement deviation information. The adjusted path running speed, path running rhythm, corrected path physical direction, and path obstacle avoidance information are then connected to the path to be processed to obtain the processing path. In this embodiment, the processing path for each bamboo section is obtained by mapping four types of adjustment fields to the path to be processed and adjusting the corresponding parameters.

[0066] Methods for obtaining tool machining data based on tool digital twins include:

[0067] The machining scenario model of the tool digital twin is used as an index to search for historical tool machining information corresponding to the scenario. The machining task corresponding to the current tool digital twin is used as the scenario index to search for tool information entries of similar machining tasks in the historical records, including tool machining information such as wear rate, machining error and thermal expansion coefficient.

[0068] Extract tool feature set fitting state change data, where state change data refers to the process of tool usage state change constructed based on tool life change data and related data reflecting the comprehensive usage state of the tool in the tool feature set, and also includes predictions of future usage state trends; combine historical corresponding scenario tool processing information and state change data to obtain tool processing data, which includes information such as the type of task that can be processed for the corresponding tool model, the range of suitable processing rates, the maximum processing path length, and recommended processing tasks.

[0069] Methods for controlling and allocating resources for production line equipment include:

[0070] Based on the path segment type of the processing path, select the corresponding functional tool to obtain the candidate tool combination. By using density change segment, moisture fluctuation segment, texture change segment and bamboo damage segment as screening conditions, search the preset tool database for tool entries with corresponding task execution functions, and match the tools with the path segments.

[0071] For each group of candidate cutting tools, corresponding data from the tool processing data are extracted and performance is evaluated to obtain processing evaluation indicators. In this embodiment, the corresponding data of the candidate cutting tools in the tool processing data are extracted, and corresponding evaluation dimensions are designed according to the requirements of the current processing task, such as mean error, remaining life, processing stability, and historical processing effect. A performance scoring function is constructed using a weighted scoring method, and the corresponding evaluation dimension data measured in the experiment are quantified and weighted based on real working conditions, and the output is the processing evaluation indicator.

[0072] The processing execution requirements and equipment load capacity values ​​of the production line equipment are obtained. Based on the processing path, the processing environment of each group of candidate tools is simulated according to the processing execution requirements and equipment load capacity values. The simulation data of candidate tools is output. The processing execution requirements refer to the limitations on the processing task, such as the task cycle time, the allowable vibration amplitude range, and the production line operating speed range. The equipment load capacity values ​​refer to the maximum theoretical values ​​of the production line equipment load, such as the maximum force on the tool, the upper limit of the tool operating speed, the upper limit of the equipment response delay, and the upper limit of the equipment temperature. In this embodiment, the simulation of each group of candidate tools is combined with the simulation environment of the entire production line for a certain processing task scenario using a tool digital twin, and the processing process of the selected candidate tools is deduced. The output candidate tool simulation data includes information reflecting the simulation effect, such as the simulated value of processing error, the simulated value of remaining life, and the completion degree of processing task.

[0073] Based on the candidate tool simulation data, the path segment-tool coupling matching value of the corresponding candidate tool is calculated. Candidate tools whose path segment-tool coupling matching value and machining evaluation index are both greater than the preset target threshold are selected as optional tools, and the optional tools are assigned to the corresponding path segments. The path segment-tool coupling matching value is obtained by quantifying and weighting the data of each dimension in the candidate tool simulation data, where the weights can be dynamically adjusted based on the specific working conditions. The tool allocation is achieved by screening optional tools and matching them with the corresponding appropriate path segments.

[0074] The control resource allocation process is encoded as a path structure, which is the machining strategy. In the process of allocating control resources in the production line, the cutting tool is the main entity that performs machining tasks. The process of obtaining suitable selectable cutting tools through performance evaluation and environmental simulation and allocating them to the corresponding path segments is the control resource allocation process of the cutting tool. Other equipment in the production line, such as fixtures and machine tools, are allocated resources by comparing their operating parameters with historical high-efficiency operation records to obtain the optimal operating parameters of the corresponding equipment. The control resource allocation process of the complete production line is encoded and encapsulated as a structure, which the system can directly call to read the machining strategy.

[0075] Methods for constructing strategy adjustment factors based on environmental parameters include:

[0076] The equipment status change parameters are obtained, and an environment-equipment status quantification matrix is ​​constructed based on the environmental parameters and the equipment status change parameters. The environmental parameters include data such as temperature, humidity and air pressure of the space where the production line is located; the equipment status parameters include data such as the temperature change rate of the main shaft of the complete production line, vibration amplitude and cooling time, which reflect the overall status change of the production line; the environment-equipment status quantification matrix is ​​obtained by normalizing all parameters and arranging them according to the corresponding dimensions of the parameters.

[0077] By combining the environmental-equipment state quantification matrix and the path parameters of each path segment in the processing path, a fitting analysis is performed to identify the environmentally sensitive path parameters of the corresponding path segments. The path parameters refer to the path running speed and path running cycle adjusted when deriving the processing path. In this embodiment, a nonlinear regression fitting method is used to perform fitting analysis on the path parameters and the environmental-equipment state quantification matrix. When a certain path parameter is highly correlated with a certain parameter or a certain type of parameter in the environmental-equipment state quantification matrix, it is determined to be an environmentally sensitive path parameter.

[0078] Based on environmentally sensitive path parameters, corresponding action commands are designed to adjust the equipment and output strategy adjustment factors. Among them, corresponding control actions are formulated for the identified environmentally sensitive path parameters, such as reducing the spindle speed, extending the buffer time, or adjusting the cutting direction angle deviation value. The production line adjusts the equipment by recognizing the action commands, adjusts by constructing a simulated environment, and quantifies the adjustment process. For example, if the speed is reduced to a certain value, the change in speed is used as the strategy adjustment factor for the spindle speed.

[0079] Methods for optimizing processing strategies using strategy adjustment factors include:

[0080] A weighted processing strategy is obtained by weighting the corresponding execution parameters in the processing strategy using strategy adjustment factors. New control fields are generated for each dimension by multiplying and weighting the corresponding parameters of each strategy adjustment factor with the parameters in the processing strategy. The original processing strategy is then updated based on these new control fields. The weighted processing strategy is then optimized using an algorithm and its effectiveness evaluated. In this embodiment, the processing effect is evaluated by simulating a processing scenario and applying the weighted processing strategy. Evaluation criteria include indicators such as processing time, path offset, and control resource utilization efficiency. If the expected effect is achieved, the optimized processing strategy is output; otherwise, the iteration continues until the expected effect is achieved. It should be noted that if the expected effect is not achieved, the execution parameters in the processing strategy are optimized using an optimization algorithm. The optimal solution is searched through repeated iterations, and the processing strategy that achieves the expected effect is ultimately selected as the optimized processing strategy.

[0081] This embodiment achieves dynamic control and efficient optimization of the bamboo processing process by constructing dual digital twins of bamboo and cutting tools, significantly improving processing accuracy and stability. Based on the local disturbance characteristics of the bamboo's physical properties perceived by the bamboo digital twin, and combined with the cutting tool state evolution trend information, it controls resource allocation and path parameter adjustments, reducing the risk of errors and damage caused by material defects or tool wear. The processing path can adjust its running speed, cycle time, and angle in real time according to factors such as density, moisture, texture, and damage, ensuring cutting stability and finished product quality. At the same time, the generated strategy has environmental parameter adaptive capabilities, automatically optimizing key process parameters under external changes such as temperature and humidity fluctuations, enhancing the robustness and adaptability of the manufacturing process. Through full-process closed-loop control and feedback optimization, continuous iteration and performance improvement of the processing strategy are achieved, reducing manual intervention while improving the automation scheduling level and resource utilization efficiency of the production line. Example 2

[0082] Please see Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. An automated scheduling system for a flexible bamboo product production line integrating digital twins is provided, including:

[0083] The data acquisition module is used to collect bamboo property data and production line tool physical data, and to perform data cleaning to obtain accurate bamboo property data and corrected tool physical data.

[0084] The twin construction module is used to construct digital twins of bamboo based on accurate bamboo property data; and to construct digital twins of cutting tools based on modified tool physical data.

[0085] The path generation module is used to identify changes in the physical properties of bamboo products based on the digital twin of bamboo; it collects production line operation data and combines it with the information on changes in physical properties to deduce the processing path;

[0086] The resource allocation module is used to acquire tool processing data based on the tool digital twin; control resource allocation for production line equipment based on the processing path and tool processing data; and output processing strategies.

[0087] The strategy optimization module is used to acquire environmental parameters in real time, construct strategy adjustment factors based on environmental parameters, optimize the processing strategy using strategy adjustment factors, generate an optimized processing strategy, and send it to the preset production line control terminal; the modules are connected to each other via wired and / or wireless means.

[0088] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0089] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0090] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0091] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0092] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0093] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0094] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0095] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. An automated scheduling method for a flexible bamboo product production line integrating digital twins, characterized in that, include: S1. Collect bamboo property data and production line tool physical data, and perform data cleaning to obtain accurate bamboo property data and corrected tool physical data; S2. Construct a digital twin of bamboo based on accurate bamboo property data; construct a digital twin of the cutting tool based on corrected cutting tool physical data; S3. Identify changes in the physical properties of bamboo products based on bamboo digital twins; Collect production line operation data and combine it with information on changes in physical properties to deduce processing paths, including: The processing path is set by combining information on changes in physical properties and the direction of the processing path; the processing path is divided into segments based on the type of disturbance, including segments with sudden density changes, moisture fluctuations, texture changes, and bamboo damage; and the path parameters of each disturbance path segment are adjusted based on the production line operation data. A rate correction field is configured for density abrupt change segments, and the path running speed is adjusted based on the rate correction field; a time buffer field is set to adjust the running rhythm for moisture fluctuation segments, and the path running rhythm is output; a cutting angle adjustment value is set based on the texture change distribution of texture change segments, and the physical direction of the path is corrected using the cutting angle adjustment value to obtain the corrected physical direction of the path; a path offset is constructed to avoid malignant damage areas in bamboo damage segments, and the path obstacle avoidance information is output; the adjusted path running speed, path running rhythm, corrected physical direction of the path, and path obstacle avoidance information are connected to the path to be processed to obtain the processed path; S4. Obtain tool processing data based on the tool digital twin; control and allocate resources for production line equipment based on the processing path and tool processing data, and output processing strategies; S5. Acquire environmental parameters in real time, construct strategy adjustment factors based on environmental parameters, optimize the processing strategy using strategy adjustment factors, generate an optimized processing strategy, and send it to the preset production line control terminal.

2. The automated scheduling method for a flexible bamboo product production line integrating digital twins according to claim 1, characterized in that, The method for constructing a digital twin of bamboo based on accurate bamboo property data includes: Precise bamboo attribute data includes bamboo images, texture parameters, contour parameters, and structural data; a bamboo processing coordinate system is constructed based on the structural data and spatially registered with a preset production line coordinate system; the bamboo texture angle distribution is calculated based on the texture parameters to obtain texture trend data; abnormal areas in the bamboo images are identified, and abnormal risk parameters are generated by combining the texture trend data; bamboo cross-sectional parameters are calculated based on the contour parameters; the texture trend data, abnormal risk parameters, bamboo cross-sectional parameters, and precise bamboo attribute data are encoded into structural feature vectors, and a digital twin of the bamboo is generated based on the bamboo processing coordinate system and the structural feature vectors.

3. The automated scheduling method for a flexible bamboo product production line integrating digital twins according to claim 2, characterized in that, The method of constructing a digital twin of a tool based on modified tool physical data includes: Feature extraction is performed on the physical data of the corrected tool to output the tool state feature vector; the wear curve is fitted based on the tool state feature vector; the tool life change data is predicted based on the wear curve, and the tool feature set is obtained by combining the tool state feature vector; the machining scenario of the production line tool is modeled based on the corrected tool physical data, and the tool feature set is fused to obtain the tool digital twin.

4. The automated scheduling method for a flexible bamboo product production line integrating digital twins according to claim 3, characterized in that, The method for identifying changes in the physical properties of bamboo products based on bamboo digital twins includes: Set the processing path direction and detect the parameter changes of adjacent spatial sub-regions of the bamboo digital twin along the processing path direction; set the partition change threshold and divide the bamboo digital twin into spatial sub-regions, and mark the disturbance type of each type of spatial sub-region to obtain a set of physical disturbance regions; perform semantic encoding on the set of physical disturbance regions and output physical attribute change information.

5. The automated scheduling method for a flexible bamboo product production line integrating digital twins according to claim 4, characterized in that, The methods for acquiring tool machining data based on a tool digital twin include: The machining scene model of the digital twin of the tool is used as an index to search for historical tool machining information corresponding to the scene; the tool feature set is extracted and the state change data is fitted; the tool machining data is obtained by combining the historical tool machining information and state change data corresponding to the scene.

6. The automated scheduling method for a flexible bamboo product production line integrating digital twins according to claim 5, characterized in that, The methods for controlling and allocating resources for production line equipment include: Based on the path segment type of the machining path, select the corresponding functional tool to obtain candidate tool combinations; extract the corresponding data from the tool machining data for each group of candidate tools and perform performance evaluation to obtain machining evaluation indicators; obtain the machining execution requirements and equipment load capacity values ​​of the production line equipment, and simulate the machining environment for each group of candidate tools based on the machining path and the machining execution requirements and equipment load capacity values, outputting candidate tool simulation data; calculate the path segment-tool coupling matching value of the corresponding candidate tool based on the candidate tool simulation data, select candidate tools whose path segment-tool coupling matching value and machining evaluation indicators are both greater than the preset target threshold as optional tools, and assign the optional tools to the corresponding path segments; encode the control resource allocation process into a path structure, which is the machining strategy.

7. The automated scheduling method for a flexible bamboo product production line integrating digital twins according to claim 6, characterized in that, The method of constructing strategy adjustment factors based on environmental parameters includes: Obtain equipment status change parameters, and construct an environment-equipment status quantification matrix based on environmental parameters and equipment status change parameters; combine the environment-equipment status quantification matrix with the path parameters of each path segment in the processing path for fitting analysis to identify the environment-sensitive path parameters of the corresponding path segments; design corresponding action instructions based on the environment-sensitive path parameters to adjust the equipment, and output the strategy adjustment factor.

8. The automated scheduling method for a flexible bamboo product production line integrating digital twins according to claim 7, characterized in that, The method of optimizing the processing strategy parameters using a strategy adjustment factor includes: The corresponding execution parameters in the processing strategy are weighted using a strategy adjustment factor to obtain a weighted processing strategy. The weighted processing strategy is then optimized and its effectiveness is evaluated. If the expected effect is achieved, the optimized processing strategy is output; otherwise, the iteration is repeated until the expected effect is achieved.

9. An automated scheduling system for a flexible bamboo product production line integrating digital twins, used to implement the automated scheduling method for a flexible bamboo product production line integrating digital twins as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to collect bamboo property data and production line tool physical data, and to perform data cleaning to obtain accurate bamboo property data and corrected tool physical data. The twin construction module is used to construct digital twins of bamboo based on accurate bamboo property data; and to construct digital twins of cutting tools based on modified tool physical data. The path generation module is used to identify changes in the physical properties of bamboo products based on the digital twin of bamboo; it collects production line operation data and combines it with the information on changes in physical properties to deduce the processing path; The resource allocation module is used to acquire tool processing data based on the tool digital twin; control resource allocation for production line equipment based on the processing path and tool processing data; and output processing strategies. The strategy optimization module is used to acquire environmental parameters in real time, construct strategy adjustment factors based on environmental parameters, optimize the processing strategy using strategy adjustment factors, generate an optimized processing strategy, and send it to the preset production line control terminal; the modules are connected to each other via wired and / or wireless means.

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