A method and system for automatically optimizing the limiting angle of a saw chain blade

Abstract

The present invention discloses a method and system for automatically optimizing the limit angle of a saw chain blade, and relates to the technical field related to saw chain blade angle optimization. The method comprises: real-time data collection of the saw chain blade to obtain a blade state data set; constructing a fuzzy list of blade limit angles; performing deep association learning to obtain a state-angle association network; mapping the sawing task to the state-angle association network for matching; simulating the sawing task on the saw chain blade according to first blade limit angle information, performing cutting test analysis on the cutting simulation results, and generating cutting feedback information; performing dynamic optimization response to generate second blade limit angle information. The method solves the technical problem in the prior art that the saw chain blade angle adjustment lacks intelligence and cannot be optimized according to different cutting tasks and environmental changes, resulting in low cutting efficiency and cutting quality, and achieves the technical effect of improving the cutting efficiency, cutting quality and blade service life of actual cutting tasks.

Description

Technical Field

[0001] The present application relates to the technical field related to saw chain blade angle optimization, and in particular to a method and system for automatically optimizing the limiting angle of a saw chain blade. Background Art

[0002] Saw chain blades are widely used in cutting wood, metal, and other materials. To improve cutting efficiency and precision, the angle adjustment of the saw chain blade is crucial. Current saw chain blade angle adjustment methods often rely on manual experience or preset fixed angles, which can easily lead to incompatibility with different cutting tasks, thus affecting cutting results. For example, cutting tasks often rely on fixed angle settings, failing to make real-time adjustments based on different materials, cutting methods, or the actual state of the saw chain blade. This can result in low cutting efficiency and even increased blade wear, reducing service life. Furthermore, the limit angle adjustment lacks deep correlation and adaptive capabilities with multiple factors, such as blade status and cutting environment. This results in the blade's performance not being fully utilized in actual applications. Environmental changes, saw chain blade wear, and loss are not considered, impacting cutting quality and efficiency.

[0003] Therefore, in the current related technologies, there is a technical problem that the saw chain blade angle adjustment lacks intelligence and cannot be optimized according to different cutting tasks and environmental changes, resulting in low cutting efficiency and cutting quality. Therefore, this application proposes a method and system for automatically optimizing the limit angle of the saw chain blade. Summary of the Invention

[0004] This application provides a method and system for automatically optimizing the limiting angle of a saw chain blade, thereby solving the technical problems in the prior art of the saw chain blade angle adjustment lacking intelligence and being unable to be optimized according to different cutting tasks and environmental changes, resulting in low cutting efficiency and cutting quality. The application achieves the technical effect of improving the cutting efficiency, cutting quality and blade service life of actual cutting tasks.

[0005] The present application provides a method for automatically optimizing the limit angle of a saw chain blade, the method comprising: collecting data on the saw chain blade in real time according to a sawing task through a sensing device to obtain a blade state data set; performing fuzzy reasoning based on the blade state data set to construct a fuzzy list of blade limit angles; performing deep association learning on the blade state data set and the blade limit angle fuzzy list to obtain a state-angle association network; mapping the sawing task to the state-angle association network for matching, and extracting first blade limit angle information; simulating the sawing task on the saw chain blade according to the first blade limit angle information to obtain a cutting simulation result, performing a cutting test analysis on the cutting simulation result, and generating cutting feedback information; synchronizing the cutting feedback information to the sawing task to perform dynamic optimization response to the first blade limit angle information to generate second blade limit angle information.

[0006] In a possible implementation, the blade state data set and the blade limit angle fuzzy list are subjected to deep association learning to obtain a state-angle association network, and the following processing is also performed: the blade limit angle fuzzy list is traversed for defuzzification processing to generate multiple blade limit angle information; a deep neural network is used to synchronize the blade state data set and the multiple blade limit angle information to the deep neural network through the input layer for matching, and association capture is performed based on the matching results to determine multiple state-angle pairs; repair cross-validation is performed based on the multiple state-angle pairs, and cross-backtracking is performed based on the verification results to construct the state-angle association network.

[0007] In a possible implementation, the blade limit angle fuzzy list is traversed for defuzzification processing to generate multiple blade limit angle information, and the following processing is also performed: multiple sawing scene information is introduced, the blade limit angle fuzzy list is traversed and membership calculation is performed in combination with the multiple sawing scene information to generate a fuzzy membership degree, and the fuzzy membership degree includes membership area information and membership distribution information; based on the membership area information and the membership distribution information, the blade limit angle fuzzy list is weightedly calculated to generate multiple weight coefficients; according to the multiple weight coefficients, the centroid of the blade limit angle fuzzy list is calculated to determine multiple blade limit angle information, and the multiple blade limit angle information has a corresponding relationship with the multiple sawing scene information.

[0008] In a possible implementation, repair cross-validation is performed based on the multiple state-angle pairs, cross-backtracking is performed according to the validation results, and the state-angle association network is constructed, and the following processing is also performed: multiple angle constraints are set according to the multiple blade limit angle information in combination with the blade state data set; random extraction is performed based on the multiple state-angle pairs to obtain a first state-angle pair and a second state-angle pair; cross-validation is performed according to the first state-angle pair and the second state-angle pair to generate the validation result, and it is determined whether the validation result meets the multiple angle constraints; if the validation result does not meet any one of the multiple angle constraints, the first state-angle pair and the second state-angle pair are cross-backtracked according to the validation result to generate a backtracking result; the backtracking result is mapped to the multiple state-angles, and the first state-angle pair and the second state-angle pair are updated and iterated until the multiple angle constraints are met, thereby constructing the state-angle association network.

[0009] In a possible implementation, the sawing task is simulated on the saw chain blade according to the first blade limit angle information to obtain a cutting simulation result, and the following processing is also performed: the multiple sawing scene information is matched based on the sawing task to determine the simulated sawing environment information; according to the simulated sawing environment information, the sawing task is simulated in combination with the first blade limit angle information to perform multi-dimensional analysis to generate multi-dimensional cutting analysis data, wherein the multi-dimensional cutting analysis data includes cutting performance data, cutting wear data, and cutting stability data; based on the cutting performance data and in combination with the sawing task, performance calculation is performed to obtain cutting efficiency parameters; based on the cutting wear data and in combination with the sawing task, wear calculation is performed to obtain blade life parameters; based on the cutting stability data and in combination with the sawing task, stability calculation is performed to obtain blade amplitude parameters; the cutting efficiency parameters, the blade life parameters, and the blade amplitude parameters are added to the cutting simulation result.

[0010] In a possible implementation, the cutting simulation results are subjected to a cutting test analysis to generate cutting feedback information, and the following processing is also performed: the sawing task is data segmented based on the cutting simulation results to determine multiple cutting stages; a cutting test is performed according to the multiple cutting stages in combination with the cutting efficiency parameters to obtain first blade cutting behavior test data; a cutting test is performed according to the multiple cutting stages in combination with the blade life parameters to obtain second blade cutting behavior test data; a cutting test is performed according to the multiple cutting stages in combination with the blade amplitude parameters to obtain third blade cutting behavior test data; the sawing behavior of the saw chain blade is analyzed according to the first blade cutting behavior test data, the second blade cutting behavior test data, and the third blade cutting behavior test data according to the first blade limit angle information to generate the cutting feedback information.

[0011] In a possible implementation, the cutting feedback information is synchronized to the sawing task to dynamically optimize the response to the first blade limit angle information and generate the second blade limit angle information, and the following processing is also performed: based on the first blade cutting behavior test data synchronized to the sawing task, the adaptability of the first blade limit angle information is evaluated to generate a first adaptability score; based on the second blade cutting behavior test data synchronized to the sawing task, the adaptability of the first blade limit angle information is evaluated to generate a second adaptability score; based on the third blade cutting behavior test data synchronized to the sawing task, the adaptability of the first blade limit angle information is evaluated to generate a third adaptability score; based on the first adaptability score, the second adaptability score, the third adaptability score combined with the cutting efficiency parameter, the blade life parameter, and the blade amplitude parameter, a sawing analysis is performed to obtain a first sawing effect, and there is a corresponding relationship between the first sawing effect and the first blade limit angle information; based on the multiple cutting stages combined with the first adaptability score, the second adaptability score, the third adaptability score are comprehensively analyzed to generate dynamic parameters to be optimized, and the first sawing effect is optimized in response to the dynamic parameters to be optimized to generate the second blade limit angle information.

[0012] The present application also provides a saw chain blade limit angle automatic optimization system, including: a blade state data set acquisition module, which is used to collect the saw chain blade in real time according to the sawing task through a sensing device to obtain a blade state data set; a fuzzy reasoning module, which is used to perform fuzzy reasoning based on the blade state data set to construct a blade limit angle fuzzy list; a deep association learning module, which is used to perform deep association learning on the blade state data set and the blade limit angle fuzzy list to obtain a state-angle association network; a first blade limit angle information extraction module, which is used to map the sawing task to the state-angle association network for matching and extract the first blade limit angle information; a cutting feedback information generation module, which is used to simulate the sawing task on the saw chain blade according to the first blade limit angle information to obtain a cutting simulation result, perform a cutting test analysis on the cutting simulation result, and generate cutting feedback information; a dynamic optimization response module, which is used to synchronize the cutting feedback information to the sawing task, perform dynamic optimization response to the first blade limit angle information, and generate second blade limit angle information.

[0013] This application proposes a method and system for automatically optimizing the limit angle of a saw chain blade. The method collects data from the saw chain blade in real time to obtain a blade state dataset. A fuzzy list of blade limit angles is constructed. Deep association learning is performed to obtain a state-angle association network. The sawing task is mapped to the state-angle association network for matching. The saw chain blade is simulated to perform a sawing task according to the first blade limit angle information, and the cutting simulation results are subjected to cutting test analysis to generate cutting feedback information. Dynamic optimization responses are performed to generate second blade limit angle information. This method solves the technical problem in the prior art of the saw chain blade's lack of intelligent angle adjustment, the inability to optimize according to different cutting tasks and environmental changes, and the resulting low cutting efficiency and quality. This method achieves the technical effect of improving the cutting efficiency, cutting quality, and blade life of actual cutting tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A schematic flow chart of a method for automatically optimizing the limiting angle of a saw chain blade provided in an embodiment of the present application.

[0016] Figure 2A schematic diagram of the structure of a saw chain blade limit angle automatic optimization system provided in an embodiment of the present application.

[0017] Explanation of the accompanying symbols: blade status data set acquisition module 10, fuzzy reasoning module 20, deep association learning module 30, first blade limit angle information extraction module 40, cutting feedback information generation module 50, dynamic optimization response module 60. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The embodiment of the present application provides a method for automatically optimizing the limiting angle of a saw chain blade, such as Figure 1 As shown, the method includes:

[0022] Step S100 : collecting data of the saw chain blade in real time according to the sawing task through a sensing device to obtain a blade status data set.

[0023] Preferably, a sensing device (a plurality of different sensors) is used to collect blade status data of the saw chain blade during the cutting task, including load data, temperature data, vibration data and wear data, etc., to form a blade status data set. Specifically, the load data refers to the magnitude of the force that the saw chain blade bears during the cutting process, including the cutting force, axial force, etc. during the cutting process. The load data can reflect the workload of the blade in actual use, thereby helping to judge whether the blade is in a normal working state, whether it is overloaded or abnormal; the temperature data refers to the temperature change generated by the saw chain blade during the cutting process. The friction during the cutting process will cause the surface temperature of the blade to rise. Excessive temperature may accelerate the wear of the blade and even cause deformation of the material or deterioration of the cutting quality. Temperature data helps monitor the operating temperature of the blade, detect overheating or abnormal temperature rise in time, and avoid blade damage; vibration data refers to the vibration generated by the saw chain blade during the cutting process. The vibration of the saw chain blade is closely related to its working state. Excessive vibration may indicate that the blade is unbalanced, damaged, or unstable during the cutting process. Vibration data helps to detect the stability of the blade and the smoothness of the cutting process; wear data refers to the degree of wear caused by the contact and friction between the blade and the material during long-term use. Wear data reflects the service life and wear of the blade. Excessively worn blades may cause reduced cutting effect or blade damage. Wear data can provide real-time feedback on the blade status, which helps to carry out timely maintenance or angle adjustment.

[0024] Step S200: performing fuzzy reasoning based on the blade status data set to construct a fuzzy list of blade limit angles.

[0025] Preferably, the working status data of the blade is analyzed using a fuzzy logic method to infer multiple possible suitable blade limit angle ranges and construct a fuzzy list of blade limit angles, wherein fuzzy reasoning is a method for dealing with uncertainty and ambiguity, and reasoning is usually performed by converting precise values ​​into fuzzy sets. Fuzzy reasoning is mainly based on multiple data in the blade status data set (such as load, temperature, vibration, wear, etc.) to determine the working angle range of the blade. Specifically, the blade status data (such as load data, temperature data, vibration data, wear data, etc.) collected from the sensing device is fuzzified, that is, converted into fuzzy values ​​according to predetermined fuzzy rules. For example, load data can be fuzzified into categories such as low load, medium load, and high load; temperature data can be fuzzified into categories such as low temperature, normal temperature, and high temperature; vibration data can be fuzzified into categories such as light vibration, medium vibration, and strong vibration; wear data can be fuzzified into categories such as slight wear, medium wear, and severe wear. The system defines a rule base to map the relationship between the input fuzzy state data and the blade angle. For example, if the blade is heavily loaded, hot, and severely worn, the corresponding inference rule will derive a smaller limit angle to avoid excessive wear or overheating. Finally, by reasoning about different state combinations, multiple possible angle ranges are derived and a fuzzy list of blade limit angles is constructed, representing the angle range that the blade should adopt under specific operating conditions. Through this fuzzification and reasoning approach, the system can dynamically adjust the blade angle according to different operating conditions, thereby improving cutting results, extending blade life, and achieving a more efficient cutting process.

[0026] Step S300 : performing deep association learning on the blade state data set and the blade limit angle fuzzy list to obtain a state-angle association network.

[0027] Preferably, deep learning (such as deep neural network and other algorithms) is used to deeply analyze and learn the blade state data collected from the sensor and the fuzzy list of blade limit angles obtained according to fuzzy reasoning, and an accurate association model between the blade state and the limit angle is established, namely, a state-angle association network, which can help the system automatically predict and adjust the most appropriate blade limit angle according to different blade working states. Specifically, the complex relationship between the blade state data set and the fuzzy angle list is trained and learned through deep learning algorithms (such as deep neural networks), including taking the blade state data (load, temperature, vibration, wear, etc.) as input data, and the angle value in the fuzzy angle list as the target output data, and inputting these data as training samples into the deep neural network. A deep learning model (such as a convolutional neural network (CNN), a recurrent neural network (RNN), or a multi-layer perceptron (MLP)) is used to train the relationship between blade status data and angle fuzzy values. Parameters are optimized through multiple iterations to learn the mapping relationship between blade status and limit angles. Finally, the optimal mapping relationship between blade status data and the fuzzy angle list is found through a deep learning network, and a state-angle association network is constructed. This network can automatically predict the most appropriate blade limit angle or angle range based on the blade status data collected in real time. It can also recommend the most appropriate blade angle for different working conditions (such as different loads, different temperatures, different wear conditions, etc.), so that the cutting task can always maintain the best cutting effect under different conditions, reduce blade loss, and improve cutting accuracy and efficiency.

[0028] Furthermore, step S300 also includes step S310, traversing the blade limit angle fuzzy list for defuzzification processing to generate multiple blade limit angle information; step S320, using a deep neural network, synchronizing the blade state data set and the multiple blade limit angle information through the input layer to the deep neural network for matching, performing association capture based on the matching results, and determining multiple state-angle pairs; step S330, performing repair cross-validation based on the multiple state-angle pairs, performing cross-backtracking based on the verification results, and constructing the state-angle association network.

[0029] Preferably, traversing the fuzzy list of blade limit angles for defuzzification processing refers to converting the fuzzy angle values ​​obtained in the fuzzy reasoning process into specific and definite numerical values, thereby determining multiple blade limit angle information, wherein defuzzification refers to defuzzifying the fuzzy angle values ​​through a certain algorithm (such as the maximum membership method, the weighted average method, etc.), converting the fuzzy angle interval into a single angle or a specific angle range, and the multiple blade limit angle information determined represent the optimal angle value or angle range that should be adopted under different blade working conditions; a deep neural network (DNN) is used to match and associate the blade state data with the blade limit angle information, specifically, the previously collected blade The state data set (for example, load, temperature, vibration, wear, etc.) is input into the input layer of the deep neural network together with multiple defuzzified blade limit angle information and converted into the network's input feature vector. By training the deep neural network, the network will learn and capture the matching pattern between the blade state and angle based on the relationship between the input blade state data (such as load, temperature, etc.) and the limit angle information. The network automatically identifies and associates the most suitable angle information with the current state of the blade, and then associates and captures it based on the matching results to determine multiple state-angle pairs. Each state-angle pair includes a specific blade working state (such as load, temperature, vibration, etc.) and the corresponding optimal blade limit angle.

[0030] Preferably, repair cross-validation is performed based on multiple state-angle pairs, that is, the generated state-angle pairs are optimized and verified to ensure the accuracy and reliability of the state-angle association network. Specifically, through a cross-validation method (such as K-fold cross-validation), the performance of the neural network is trained and tested on different data subsets. If the matching results of some state-angle pairs are found to be inaccurate during the cross-validation process, repair is performed (including adjusting network parameters, retraining the network or adjusting input data features, etc.), that is, adjusting and optimizing these state-angle pairs until better matching and optimization results are obtained; then cross-backtracking is performed based on the verification results, that is, backtracking check The state-angle pairs generated by the model are verified to be consistent with the actual situation, and further optimization is performed based on the verification results. For example, through backtracking analysis, inaccurately matched or poorly performing state-angle pairs are found, analyzed and adjusted. Ultimately, through backtracking and repair, the deep neural network will continuously optimize itself to generate a more accurate state-angle association network, which represents the precise relationship between different blade working states (such as load, temperature, vibration, etc.) and the corresponding blade limit angles. It can automatically predict and select the most suitable limit angle based on real-time blade working state data; and ensure the optimal selection of blade angles in each cutting task, improving cutting efficiency and quality.

[0031] Furthermore, step S310 also includes step S311, introducing multiple sawing scene information, traversing the blade limit angle fuzzy list and performing membership calculation in combination with the multiple sawing scene information to generate a fuzzy membership degree, wherein the fuzzy membership degree includes membership area information and membership distribution information; step S312, performing weighted calculation on the blade limit angle fuzzy list based on the membership area information and the membership distribution information to generate multiple weight coefficients; step S313, performing centroid calculation on the blade limit angle fuzzy list according to the multiple weight coefficients to determine multiple blade limit angle information, and the multiple blade limit angle information has a corresponding relationship with the multiple sawing scene information.

[0032] Preferably, by performing fuzzy calculation, weighting and optimization on the relationship between different sawing scenarios and blade angle fuzzy values, the blade angle most suitable for different sawing scenarios can be further accurately determined. Specifically, multiple sawing scene information is introduced, that is, multiple working conditions and environmental factors in the cutting task are introduced, such as different material types such as wood, metal, plastic, different cutting methods such as straight cutting, curve cutting, deep cutting, different cutting speeds, and different ambient temperatures and humidity. These sawing scene information are closely related to the blade status (load, temperature, vibration, wear, etc.).

[0033] Preferably, the fuzzy list of blade limit angles is traversed and membership calculation is performed in combination with multiple sawing scene information, that is, the fuzzy membership is calculated in combination with the blade limit angle and the sawing scene information to measure the degree of adaptation between the blade angle and the sawing scene information, that is, the degree of membership of the angle in a specific sawing scene. The value of the membership is usually between 0 and 1, 0 means completely unapplicable, and 1 means completely applicable. The fuzzy membership includes membership area information and membership distribution information. The membership area indicates the size of the adaptation area of ​​the angle value in the sawing scene. A larger membership area indicates that the angle has a wider range of applicability in the scene; the membership distribution indicates the distribution of the membership within the fuzzy angle value range. The membership distribution describes how the membership of each angle value is distributed in the entire angle range, reflecting the relative importance of different angles in different scenes. For example, if the membership of an angle value in a specific sawing scene is 0.8 and in another scene it is 0.5, then the angle value is more adaptable in the first scene and the membership area will be larger.

[0034] Preferably, weights are assigned according to the information of the belonging area and the belonging distribution, and the fuzzy list of blade limit angles is weightedly calculated to generate multiple weight coefficients. Angle values ​​with higher weight coefficients are more applicable in specific scenarios, while angles with lower weight coefficients are less suitable for use in the scenario. Then, the most suitable blade limit angle for each sawing scenario is determined by the centroid method, wherein the centroid method is a commonly used fuzzy defuzzification method for determining the center of gravity or center point of a set. Specifically, according to the weighted blade limit angle list, the center of gravity for each scenario is calculated, for example, an optimal angle value is determined based on the weighted average of the product of the membership degree of each angle and the weight coefficient, which represents the optimal angle after considering multiple factors (such as belonging area, distribution, and weight coefficient). Then, multiple blade limit angle information is determined, and the multiple blade limit angle information corresponds to the multiple sawing scene information, that is, each sawing scene has an optimal blade limit angle or angle range, so that the system can automatically adjust the blade angle according to different cutting tasks and environmental conditions to achieve an efficient and accurate cutting process.

[0035] Furthermore, step S330 also includes step S331, setting multiple angle constraints based on the multiple blade limit angle information in combination with the blade state data set; step S332, performing random extraction based on the multiple state-angle pairs to obtain a first state-angle pair and a second state-angle pair; step S333, cross-validating according to the first state-angle pair and the second state-angle pair to generate the verification result, and judging whether the verification result meets the multiple angle constraints; step S334, if the verification result does not meet any one of the multiple angle constraints, cross-backtracking the first state-angle pair and the second state-angle pair according to the verification result to generate a backtracking result; step S335, mapping the backtracking result to the multiple state-angles, updating and iterating the first state-angle pair and the second state-angle pair until the multiple angle constraints are met, and constructing the state-angle association network.

[0036] Preferably, multiple angle constraints are set based on multiple blade limit angle information combined with a blade state data set to define a reasonable range or limit of the blade angle under a specific working state. For example, the blade angle cannot exceed a certain maximum or minimum value to avoid excessive cutting or excessive wear; the blade angle should adapt to specific workloads, such as a smaller cutting angle should be used under high loads, and a larger angle can be used under low loads; the blade angle is related to the ambient temperature, and the blade angle should be reduced when the temperature is too high to avoid overheating of the blade; the change in blade angle should follow certain rate and amplitude limits to avoid the impact of sudden changes on cutting performance; and then two are randomly extracted from multiple state-angle pairs as the first state-angle pair and the second state-angle pair, respectively. The first state-angle pair and the second state-angle pair can be adjacent or non-adjacent.

[0037] Preferably, the first state-angle pair and the second state-angle pair are verified to check whether they meet all angle constraints. If one of the state-angle pairs does not meet the angle constraints, it means that the pair is not applicable in the current situation and needs to be optimized or adjusted. For example, if the angle value is not within the allowable range, or the angle does not match the working conditions such as load and temperature, cross-validation will mark this non-compliance and determine whether the verification result meets multiple angle constraints. If the verification result finds that a state-angle pair does not meet the constraints, it needs to be corrected through cross-backtracking to obtain a backtracking result, that is, the non-compliant state-angle pairs are adjusted according to the verification results to ensure that they meet the predetermined angle constraints. For example, if the angle of a state-angle pair exceeds the maximum value, the angle can be modified through backtracking to ensure that it meets the predetermined range.

[0038] Preferably, the backtracking results are mapped to multiple state-angles, and the first state-angle pair and the second state-angle pair are updated and iterated until multiple angle constraints are met, that is, the state-angle pair is updated according to the corrected backtracking results and remapped to multiple state-angle pairs. Specifically, the backtracking results are checked to see where the problem occurred, and the problematic ones are eliminated and replaced. Random extraction is performed again based on multiple state-angle pairs to continue cross-validation until all state-angle pairs meet the angle constraints, and finally a complete state-angle association network is generated, which represents the relationship between different blade states (such as load, temperature, vibration, etc.) and the optimal limit angle. The state-angle association network can accurately describe the relationship between the blade state and the limit angle, ensuring that the optimal blade angle selection can be achieved in various sawing scenarios, thereby improving cutting efficiency and accuracy.

[0039] Step S400: Map the sawing task to the state-angle association network for matching, and extract the first blade limit angle information.

[0040] Preferably, mapping the sawing task to the state-angle association network for matching means that, according to the current sawing task, the state-angle association network is used to select and extract the most suitable blade limit angle, so as to achieve the optimal cutting effect. Specifically, the current sawing task parameters (such as load, temperature, cutting method, etc.) are converted into blade state, and then the blade angle that best matches the state is found in the state-angle association network. For example, if the current sawing task is performed under a "high load" and "high temperature" environment, this task is mapped to the two states of "high load" and "high temperature", and the corresponding optimal angle in the state-angle association network is found, that is, the first blade limit angle information is extracted to ensure that the optimal blade limit angle can be automatically selected and adjusted in each sawing task, thereby optimizing the cutting effect, improving cutting efficiency, reducing blade wear, and extending the service life of the blade.

[0041] Step S500 , simulating the sawing task on the saw chain blade according to the first blade limit angle information to obtain a cutting simulation result, performing a cutting test analysis on the cutting simulation result, and generating cutting feedback information.

[0042] Preferably, the selected blade limit angle information is applied to the simulation of the sawing task, the cutting effect is predicted by simulating the cutting process, and then the simulation result is verified by actual cutting test, and feedback information is generated to further optimize the blade angle. Specifically, the first blade limit angle information is used to simulate how the blade performs in the actual cutting process through computer simulation or simulation technology to evaluate the cutting effect of the blade in the sawing task at a specific limit angle. For example, the simulation may include calculating the contact force between the blade and the material, the movement trajectory of the blade, the cutting speed, the cutting depth and other factors, and then obtaining the cutting simulation result, which includes the detailed performance of the blade in performing the cutting task at the selected angle, such as whether the simulated cutting can achieve the expected accuracy requirements, and whether the blade performs well at a specific limit angle. The speed of performing the cutting task at the desired angle, vibration, temperature changes, etc. during the cutting process, as well as the friction between the blade and the material, load changes during cutting, etc.; the simulated cutting simulation results are then subjected to cutting test analysis, that is, the simulated cutting results are compared with the actual physical cutting process, or the simulation results are verified through actual cutting experiments, and then cutting feedback information is generated, that is, the feedback data obtained from the cutting test analysis is used to evaluate the effect of the cutting task execution, which may include whether the cutting accuracy meets the expectations, whether the cutting speed is appropriate, whether the blade is excessively worn, whether overheating occurs, whether it operates stably, etc., whether there are abnormalities such as excessive vibration, noise, and increased cutting resistance, so as to help optimize the blade angle or cutting conditions and further improve cutting performance and efficiency.

[0043] Furthermore, step S500 also includes step S510, matching the multiple sawing scene information based on the sawing task to determine the simulated sawing environment information; step S520, simulating the execution of the sawing task according to the simulated sawing environment information and combining the first blade limit angle information to perform multi-dimensional analysis and generate multi-dimensional cutting analysis data, wherein the multi-dimensional cutting analysis data includes cutting performance data, cutting wear data, and cutting stability data; step S530, performing performance calculation based on the cutting performance data combined with the sawing task to obtain cutting efficiency parameters; step S540, performing wear calculation based on the cutting wear data combined with the sawing task to obtain blade life parameters; step S550, performing stability calculation based on the cutting stability data combined with the sawing task to obtain blade amplitude parameters; step S560, adding the cutting efficiency parameters, the blade life parameters, and the blade amplitude parameters to the cutting simulation results.

[0044] Preferably, a comprehensive evaluation of the sawing task and the cutting performance of the blade under the simulated environment is performed through multi-dimensional data analysis, and the cutting task is deeply analyzed and optimized by combining parameters such as cutting efficiency, wear, and stability. Specifically, matching multiple sawing scene information based on the sawing task means selecting and matching sawing scene information related to the task according to the requirements of the sawing task (for example, the task of cutting wood, metal, or plastic). For example, when cutting metal, a higher load and a smaller angle are required, while when cutting wood, a larger angle and a lower load may be required, thereby determining the simulated sawing environment information, including factors such as temperature, load, friction, and cutting speed during the cutting process; Combined with the simulated sawing environment information and the first blade limit angle information, the sawing task is simulated and a multi-dimensional analysis is performed. That is, when performing the sawing task in a simulated environment, not only the cutting effect of the blade is analyzed, but also the cutting process is analyzed from multiple angles to generate multi-dimensional cutting analysis data, including cutting performance data (cutting efficiency, cutting speed, cutting accuracy, etc.), cutting wear data (blade wear during the cutting process, including blade wear speed, wear position, wear degree, etc.), and cutting stability data (whether the blade can maintain a stable working state during the cutting process, including vibration, temperature changes, and whether the blade is unstable or out of control during the cutting process).

[0045] Preferably, performance calculation is performed based on the cutting performance data in combination with the sawing task, that is, the cutting efficiency parameters are calculated based on the cutting speed, cutting depth, cutting accuracy, etc. The cutting efficiency usually refers to the amount of cutting tasks completed per unit time; wear calculation is performed based on the cutting wear data in combination with the sawing task, and the service life of the blade (blade life parameter) is predicted, which reflects the durability and replacement cycle of the blade under specific sawing tasks and environmental conditions. For example, the wear rate of the blade cutting under high load will be faster than that under low load, so its life parameter will be relatively short; stability calculation is performed based on the cutting stability data in combination with the sawing task, and the blade amplitude parameter is calculated. The blade amplitude parameter reflects the intensity and amplitude of the blade vibration during the cutting process. Excessive vibration will affect the cutting accuracy and accelerate blade wear; finally, the cutting efficiency parameters, blade life parameters and blade amplitude parameters are integrated into the cutting simulation results, which provide data support for subsequent optimization decisions and blade performance adjustments, ensuring the efficient execution of each cutting task and the best use of the blade.

[0046] Furthermore, step S500 also includes step S570, performing data segmentation on the sawing task based on the cutting simulation result to determine multiple cutting stages; step S580, performing a cutting test according to the multiple cutting stages in combination with the cutting efficiency parameters to obtain first blade cutting behavior test data; step S590, performing a cutting test according to the multiple cutting stages in combination with the blade life parameters to obtain second blade cutting behavior test data; step S5100, performing a cutting test according to the multiple cutting stages in combination with the blade amplitude parameters to obtain third blade cutting behavior test data; step S5200, performing a sawing behavior analysis on the saw chain blade according to the first blade cutting behavior test data, the second blade cutting behavior test data, and the third blade cutting behavior test data according to the first blade limit angle information to generate the cutting feedback information.

[0047] Preferably, by analyzing the cutting task in stages, combining multiple cutting performance parameters (such as cutting efficiency, blade life, blade amplitude, etc.), and conducting tests, the behavior data of the blade at different stages are generated, and finally cutting feedback information is generated based on these data for comprehensive analysis, thereby realizing the performance optimization and adjustment of the saw chain blade. Specifically, based on the cutting simulation results, the entire sawing process is divided into multiple cutting stages, each stage represents a different stage in the cutting process (for example, the starting stage, the acceleration stage, the stable stage, the ending stage, etc.), and then a cutting test is conducted in combination with the cutting efficiency parameters, that is, an actual cutting test is conducted based on the cutting efficiency parameters, the saw chain blade is simulated or actually operated to cut, and the first blade cutting behavior test data is obtained, such as cutting speed, efficiency loss, etc.; combined with the blade life parameters, actual cutting tests are conducted at different cutting stages. Test, evaluate the wear of the blade at each stage, and obtain the second blade cutting behavior test data, including the degree of blade wear and life change at each stage; according to the blade amplitude parameter, test at each cutting stage, observe the vibration of the blade at different stages, and obtain the third blade cutting behavior test data, including the vibration of the blade at different cutting stages, and the relationship between amplitude change and cutting quality; combine the first blade cutting behavior test data, the second blade cutting behavior test data, and the third blade cutting behavior test data to perform sawing behavior analysis, including comparing the data at each stage to find out the advantages and disadvantages of the blade at different stages, thereby generating cutting feedback information, usually including cutting efficiency feedback, blade wear feedback, and cutting stability feedback, to ensure the efficient completion of the sawing task and improve the service life and stability of the blade.

[0048] Step S600: Synchronize the cutting feedback information to the sawing task to dynamically optimize the first blade limit angle information to generate second blade limit angle information.

[0049] Preferably, the cutting feedback information obtained from the cutting test is fed back into the sawing task so that the blade limit angle is dynamically optimized and adjusted according to the actual performance, thereby generating new, more optimized second blade limit angle information to adapt to different cutting conditions and improve the cutting effect. Specifically, the cutting feedback information is combined with the sawing task and a response is made according to the feedback result, that is, the blade limit angle is adjusted in real time according to the feedback information. By taking the cutting feedback information (such as efficiency, wear, stability, etc.) as input, the first blade limit angle is dynamically adjusted, and a new, more suitable angle is selected to perform the next cutting task, thereby generating the second blade limit angle information. That is, the new blade angle generated after the dynamic optimization response can better adapt to the current sawing task requirements and environmental conditions. For example, if the first blade limit angle performs poorly in a high-load, high-temperature environment, the limit angle is adjusted according to the cutting feedback information so that the blade can provide higher efficiency and lower wear in the next cutting task. The second blade limit angle information will be used in subsequent cutting tasks to ensure that the blade is always in the best cutting state under different working conditions, improve the cutting effect, extend the blade life, and ensure the stability and efficiency of the cutting process.

[0050] Furthermore, step S600 also includes step S610, evaluating the adaptability of the first blade limit angle information based on the first blade cutting behavior test data synchronized to the sawing task to generate a first adaptability score; step S620, evaluating the adaptability of the first blade limit angle information based on the second blade cutting behavior test data synchronized to the sawing task to generate a second adaptability score; step S630, evaluating the adaptability of the first blade limit angle information based on the third blade cutting behavior test data synchronized to the sawing task to generate a third adaptability score; step S640, performing sawing analysis based on the first adaptability score, the second adaptability score, and the third adaptability score in combination with the cutting efficiency parameter, the blade life parameter, and the blade amplitude parameter to obtain a first sawing effect, and the first sawing effect has a corresponding relationship with the first blade limit angle information; step S650, performing a comprehensive analysis based on the multiple cutting stages in combination with the first adaptability score, the second adaptability score, and the third adaptability score to generate dynamic parameters to be optimized, optimizing the first sawing effect according to the dynamic parameters to be optimized, and generating the second blade limit angle information.

[0051] Preferably, by performing fitness evaluation and multi-dimensional sawing analysis on the first blade limit angle information, integrating various cutting behavior data (such as cutting efficiency, blade life, blade amplitude, etc.), and then dynamically optimizing the blade limit angle according to the results of the comprehensive analysis, a new, optimized second blade limit angle information is generated to improve the cutting effect, reduce blade wear, and improve stability and efficiency. Specifically, the first blade cutting behavior test data, the second blade cutting behavior test data, and the third blade cutting behavior test data are synchronized to the sawing task, and the fitness evaluation of the first blade limit angle information is performed to generate a first fitness score (indicating the first The first blade limit angle information is used to determine the cutting efficiency adaptability under the first blade limit angle), the second adaptability score (indicating the cutting wear adaptability under the first blade limit angle) and the third adaptability score (indicating the cutting vibration adaptability under the first blade limit angle), and then the first, second and third adaptability scores are combined with the cutting efficiency parameters, blade life parameters and blade amplitude parameters to perform sawing analysis, evaluate the comprehensive performance of the first blade limit angle information under different cutting tasks, and obtain the first sawing effect, which describes the comprehensive performance of the blade when performing a specific task, such as cutting efficiency, blade life, cutting stability, etc., wherein the first sawing effect corresponds to the first blade limit angle information.

[0052] Preferably, a comprehensive analysis is performed based on multiple cutting stages in combination with the first adaptation score, the second adaptation score and the third adaptation score, that is, in the cutting task, the adaptation score and parameters (such as cutting efficiency, blade life, amplitude, etc.) of each stage will be used to generate dynamic parameters to be optimized. For example, if the first adaptation score indicates that the efficiency of a certain cutting stage is low, the stage is identified as the part to be optimized, and the parameters (such as blade angle, cutting speed, etc.) are adjusted to improve the efficiency. Similarly, based on the blade life and amplitude parameters, the blade angle is optimized to reduce vibration and extend the blade service life. Finally, based on the generated dynamic parameters to be optimized, the first blade limit angle information is optimized and responded to, and a new, more suitable blade angle, namely the second blade limit angle information, is generated, thereby ensuring that the blade is always in the best working state in different sawing tasks and stages, improving cutting efficiency, extending blade life, and optimizing the stability of the cutting process.

[0053] In the above, refer to Figure 1 A method for automatically optimizing the limiting angle of a saw chain blade according to an embodiment of the present invention is described in detail. Figure 2 A saw chain blade limit angle automatic optimization system according to an embodiment of the present invention is described.

[0054] According to an embodiment of the present invention, a saw chain blade limit angle automatic optimization system is designed to address the technical issues in the prior art, such as the lack of intelligent saw chain blade angle adjustment, the inability to optimize according to different cutting tasks and environmental changes, and the resulting low cutting efficiency and quality. This system achieves the technical effect of improving the cutting efficiency, cutting quality, and blade life of actual cutting tasks. The saw chain blade limit angle automatic optimization system includes: a blade state data set acquisition module 10, a fuzzy reasoning module 20, a deep association learning module 30, a first blade limit angle information extraction module 40, a cutting feedback information generation module 50, and a dynamic optimization response module 60.

[0055] A blade state data set acquisition module 10 is used to collect the saw chain blade in real time according to the sawing task through a sensing device to obtain a blade state data set; a fuzzy reasoning module 20 is used to perform fuzzy reasoning based on the blade state data set to construct a blade limit angle fuzzy list; a deep association learning module 30 is used to perform deep association learning on the blade state data set and the blade limit angle fuzzy list to obtain a state-angle association network; a first blade limit angle information extraction module 40 is used to map the sawing task to the state-angle association network for matching and extract the first blade limit angle information; a cutting feedback information generation module 50 is used to simulate the sawing task on the saw chain blade according to the first blade limit angle information to obtain a cutting simulation result, perform a cutting test analysis on the cutting simulation result, and generate cutting feedback information; a dynamic optimization response module 60 is used to synchronize the cutting feedback information to the sawing task to perform dynamic optimization response to the first blade limit angle information to generate second blade limit angle information.

[0056] The specific configuration of the deep association learning module 30 will be described in detail below. The deep association learning module 30 further includes: traversing the blade limit angle fuzzy list to perform defuzzification processing to generate multiple blade limit angle information; using a deep neural network, synchronizing the blade state dataset and the multiple blade limit angle information through the input layer to the deep neural network for matching, performing association capture based on the matching results, and determining multiple state-angle pairs; performing repair cross-validation based on the multiple state-angle pairs, and performing cross-backtracking based on the verification results to construct the state-angle association network.

[0057] The specific configuration of the deep association learning module 30 will be described in detail below. The deep association learning module 30 further includes: introducing multiple sawing scene information, traversing the blade limit angle fuzzy list and performing membership calculation in combination with the multiple sawing scene information to generate a fuzzy membership degree, wherein the fuzzy membership degree includes membership area information and membership distribution information; performing weighted calculation on the blade limit angle fuzzy list based on the membership area information and the membership distribution information to generate multiple weight coefficients; performing centroid calculation on the blade limit angle fuzzy list according to the multiple weight coefficients to determine multiple blade limit angle information, wherein the multiple blade limit angle information corresponds to the multiple sawing scene information.

[0058] The specific configuration of the deep association learning module 30 will be described in detail below. The deep association learning module 30 further includes: setting multiple angle constraints based on the multiple blade limit angle information combined with the blade state data set; performing random extraction based on the multiple state-angle pairs to obtain a first state-angle pair and a second state-angle pair; performing cross-validation according to the first state-angle pair and the second state-angle pair to generate the verification result, and judging whether the verification result meets the multiple angle constraints; if the verification result does not meet any of the multiple angle constraints, then cross-backtracking the first state-angle pair and the second state-angle pair according to the verification result to generate a backtracking result; mapping the backtracking result to the multiple state-angles, updating and iterating the first state-angle pair and the second state-angle pair until the multiple angle constraints are met, and constructing the state-angle association network.

[0059] The specific configuration of the cutting feedback information generation module 50 will be described in detail below. The cutting feedback information generation module 50 further includes: matching the multiple sawing scene information based on the sawing task to determine the simulated sawing environment information; according to the simulated sawing environment information, combined with the first blade limit angle information, simulating the execution of the sawing task to perform multi-dimensional analysis to generate multi-dimensional cutting analysis data, the multi-dimensional cutting analysis data including cutting performance data, cutting wear data, and cutting stability data; performing performance calculation based on the cutting performance data combined with the sawing task to obtain cutting efficiency parameters; performing wear calculation based on the cutting wear data combined with the sawing task to obtain blade life parameters; performing stability calculation based on the cutting stability data combined with the sawing task to obtain blade amplitude parameters; adding the cutting efficiency parameters, the blade life parameters, and the blade amplitude parameters to the cutting simulation results.

[0060] The specific configuration of the cutting feedback information generation module 50 will be described in detail below. The cutting feedback information generation module 50 further includes: segmenting the sawing task data based on the cutting simulation results to determine multiple cutting stages; performing a cutting test according to the multiple cutting stages in combination with the cutting efficiency parameter to obtain first blade cutting behavior test data; performing a cutting test according to the multiple cutting stages in combination with the blade life parameter to obtain second blade cutting behavior test data; performing a cutting test according to the multiple cutting stages in combination with the blade amplitude parameter to obtain third blade cutting behavior test data; and analyzing the sawing behavior of the saw chain blade using the first blade cutting behavior test data, the second blade cutting behavior test data, and the third blade cutting behavior test data according to the first blade limit angle information to generate the cutting feedback information.

[0061] The specific configuration of the dynamic optimization response module 60 will be described in detail below. The dynamic optimization response module 60 further includes: evaluating the adaptability of the first blade limit angle information based on the first blade cutting behavior test data synchronized to the sawing task to generate a first adaptability score; evaluating the adaptability of the first blade limit angle information based on the second blade cutting behavior test data synchronized to the sawing task to generate a second adaptability score; evaluating the adaptability of the first blade limit angle information based on the third blade cutting behavior test data synchronized to the sawing task to generate a third adaptability score; performing sawing analysis based on the first adaptability score, the second adaptability score, and the third adaptability score in combination with the cutting efficiency parameter, the blade life parameter, and the blade amplitude parameter to obtain a first sawing effect, wherein the first sawing effect has a corresponding relationship with the first blade limit angle information; performing a comprehensive analysis based on the multiple cutting stages in combination with the first adaptability score, the second adaptability score, and the third adaptability score to generate dynamic parameters to be optimized, performing an optimization response to the first sawing effect according to the dynamic parameters to be optimized, and generating the second blade limit angle information.

[0062] A saw chain blade limit angle automatic optimization system provided by an embodiment of the present invention can execute a saw chain blade limit angle automatic optimization method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0063] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0064] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for automatically optimizing the limiting angle of a saw chain blade, characterized in that: The method comprises: The sensor equipment collects the saw chain blade in real time according to the sawing task to obtain the blade status data set; Performing fuzzy reasoning based on the blade state data set to construct a fuzzy list of blade limit angles; Performing deep correlation learning on the blade state dataset and the blade limit angle fuzzy list to obtain a state-angle correlation network; Mapping the sawing task to the state-angle association network for matching, and extracting the first blade limit angle information; Simulating the sawing task on the saw chain blade according to the first blade limit angle information to obtain a cutting simulation result, performing a cutting test analysis on the cutting simulation result, and generating cutting feedback information; Synchronizing the cutting feedback information to the sawing task to dynamically optimize the first blade limit angle information to generate second blade limit angle information; Performing deep correlation learning on the blade state dataset and the blade limit angle fuzzy list to obtain a state-angle correlation network, the method comprising: Traversing the blade limit angle fuzzy list to perform defuzzification processing to generate multiple blade limit angle information; Using a deep neural network, the blade state data set and the plurality of blade limit angle information are synchronized to the deep neural network through an input layer for matching, and association capture is performed based on the matching results to determine a plurality of state-angle pairs; Repair cross-validation is performed based on the multiple state-angle pairs, cross-backtracking is performed according to the validation results, and the state-angle association network is constructed.

2. The method for automatically optimizing the limiting angle of a saw chain blade according to claim 1, characterized in that: Traversing the blade limit angle fuzzy list to perform defuzzification processing to generate multiple blade limit angle information, the method includes: Introducing multiple sawing scene information, traversing the blade limit angle fuzzy list and combining the multiple sawing scene information to perform membership calculation to generate a fuzzy membership degree, wherein the fuzzy membership degree includes membership area information and membership distribution information; Performing weighted calculation on the blade limit angle fuzzy list based on the membership area information and the membership distribution information to generate a plurality of weight coefficients; The centroid of the blade limit angle fuzzy list is calculated according to the multiple weight coefficients to determine multiple blade limit angle information, and the multiple blade limit angle information has a corresponding relationship with the multiple sawing scene information.

3. The method for automatically optimizing the limiting angle of a saw chain blade according to claim 1, wherein: Performing repair cross-validation based on the multiple state-angle pairs, performing cross-backtracking according to the validation results, and constructing the state-angle association network, the method includes: Setting a plurality of angle constraint conditions according to the plurality of blade limit angle information in combination with the blade status data set; Perform random extraction based on the multiple state-angle pairs to obtain a first state-angle pair and a second state-angle pair; Perform cross-validation according to the first state-angle pair and the second state-angle pair to generate the validation result, and determine whether the validation result satisfies multiple angle constraints; If the verification result does not meet any of the multiple angle constraints, cross-backtrack the first state-angle pair and the second state-angle pair according to the verification result to generate a backtracking result; The backtracking results are mapped to the multiple state-angles, and the first state-angle pair and the second state-angle pair are updated and iterated until the multiple angle constraints are satisfied, thereby constructing the state-angle association network.

4. The method for automatically optimizing the limiting angle of a saw chain blade according to claim 2, wherein: The method includes: simulating the sawing task on the saw chain blade according to the first blade limit angle information to obtain a cutting simulation result. Matching the plurality of sawing scene information based on the sawing task to determine simulated sawing environment information; According to the simulated sawing environment information, combined with the first blade limit angle information, the sawing task is simulated and multi-dimensional analysis is performed to generate multi-dimensional cutting analysis data, wherein the multi-dimensional cutting analysis data includes cutting performance data, cutting wear data, and cutting stability data; Performing performance calculation based on the cutting performance data in combination with the sawing task to obtain a cutting efficiency parameter; Perform wear calculation based on the cutting wear data in combination with the sawing task to obtain blade life parameters; Performing stability calculation based on the cutting stability data in combination with the sawing task to obtain blade amplitude parameters; The cutting efficiency parameter, the blade life parameter, and the blade amplitude parameter are added to the cutting simulation result.

5. The method for automatically optimizing the limiting angle of a saw chain blade according to claim 4, characterized in that: The cutting simulation results are subjected to cutting test analysis to generate cutting feedback information, the method comprising: Segmenting the sawing task based on the cutting simulation result to determine a plurality of cutting stages; Performing a cutting test according to the plurality of cutting stages in combination with the cutting efficiency parameter to obtain first blade cutting behavior test data; Performing a cutting test according to the plurality of cutting stages in combination with the blade life parameter to obtain second blade cutting behavior test data; Performing a cutting test according to the plurality of cutting stages in combination with the blade amplitude parameter to obtain third blade cutting behavior test data; The first blade cutting behavior test data, the second blade cutting behavior test data, and the third blade cutting behavior test data are used to perform sawing behavior analysis on the saw chain blade according to the first blade limit angle information to generate the cutting feedback information.

6. The method for automatically optimizing the limiting angle of a saw chain blade according to claim 5, characterized in that: Synchronizing the cutting feedback information to the sawing task to dynamically optimize the first blade limit angle information to generate second blade limit angle information, the method comprising: Generating a first adaptation score by evaluating the adaptability of the first blade limit angle information based on the first blade cutting behavior test data synchronized to the sawing task; Generating a second adaptation score based on evaluating the adaptability of the first blade limit angle information synchronized to the sawing task based on the second blade cutting behavior test data; Generating a third adaptation score based on evaluating the adaptability of the first blade limit angle information synchronized to the sawing task based on the third blade cutting behavior test data; performing sawing analysis based on the first adaptability score, the second adaptability score, the third adaptability score in combination with the cutting efficiency parameter, the blade life parameter, and the blade amplitude parameter to obtain a first sawing effect, wherein the first sawing effect corresponds to the first blade limit angle information; A comprehensive analysis is performed based on the multiple cutting stages in combination with the first adaptation score, the second adaptation score, and the third adaptation score to generate dynamic parameters to be optimized, and the first sawing effect is optimized according to the dynamic parameters to be optimized to generate the second blade limit angle information.

7. A saw chain blade limit angle automatic optimization system, characterized in that: The system is used to implement the method for automatically optimizing the limiting angle of a saw chain blade according to any one of claims 1 to 6, and the system comprises: The blade status data set acquisition module is used to collect the saw chain blade in real time according to the sawing task through the sensing device to obtain the blade status data set; A fuzzy reasoning module, configured to perform fuzzy reasoning based on the blade state data set to construct a fuzzy list of blade limit angles; A deep association learning module is used to perform deep association learning on the blade state data set and the blade limit angle fuzzy list to obtain a state-angle association network; A first blade limit angle information extraction module is used to map the sawing task to the state-angle association network for matching, and extract the first blade limit angle information; a cutting feedback information generating module, configured to simulate the sawing task performed on the saw chain blade according to the first blade limit angle information, obtain a cutting simulation result, perform a cutting test analysis on the cutting simulation result, and generate cutting feedback information; A dynamic optimization response module is used to synchronize the cutting feedback information with the sawing task to dynamically optimize the first blade limit angle information and generate second blade limit angle information.

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