Cam shaft synchronous driving method for automatic control of textile equipment

Abstract

The invention provides a cam shaft synchronous driving method for automatic control of textile equipment, and relates to the technical field of automatic control, and the cam shaft synchronous driving method comprises the following steps: optimizing the rotating speed of a cam shaft based on the single-cycle target motion state of a driven part in combination with a first-stage prediction model of the motion state of the driven part to obtain a first target cam shaft rotating speed; when the first target cam shaft rotating speed does not belong to the cam shaft rotating speed rated interval, optimizing the cam shaft rotating speed and the cam pressure angle in combination with a follower motion state secondary prediction model to obtain a second target cam shaft rotating speed and a first target cam pressure angle; and when the second target cam shaft rotating speed belongs to the cam shaft rotating speed rated interval and the first target cam pressure angle belongs to the cam pressure angle rated interval, the second target cam shaft rotating speed and the first target cam pressure angle are sent to the user side. The technical problem that working parameters of an existing cam shaft cannot be dynamically adjusted according to process requirement changes is solved, and the operation stability and the spinning efficiency of spinning equipment are improved.

Description

Technical Field

[0001] The present application relates to the field of automation control technology, and in particular to a camshaft synchronous driving method for automation control of textile equipment. Background Art

[0002] In textile equipment, camshafts control processes such as fabric feeding, cutting and sewing by converting the rotational motion of the motor into linear motion. The traditional camshaft drive process usually relies on the motor to directly drive the camshaft through a reducer. Its design and operating parameters are fixed, which makes the equipment lack flexibility and adaptability during the processing and difficult to respond to changes in the movement state of the fabric in real time. At the same time, this design that relies on fixed parameters leads to a slow response speed of the equipment under different process requirements. When the load changes or the material properties are inconsistent, the speed and pressure angle cannot be adaptively adjusted, resulting in unstable movement of the follower, affecting the overall equipment performance, and causing problems such as unstable processing quality or low efficiency. Summary of the invention

[0003] The present application provides a camshaft synchronous drive method for automated control of textile equipment, which solves the technical problems in existing textile equipment such as fixed camshaft operating parameters, lack of dynamic adjustment capabilities, and difficulty in timely adjustment according to changes in process requirements. By adaptively adjusting the camshaft operating parameters to accurately match the motion requirements of the follower at different stages, the technical effect of improving the operating stability of the textile equipment and the textile efficiency is achieved.

[0004] In view of the above problems, the present application provides a camshaft synchronous drive method for automatic control of textile equipment, the method comprising: receiving a single-cycle target motion state of a follower, wherein the single-cycle target motion state of the follower includes process target speed timing information and return target speed timing information; activating a primary prediction model of the follower motion state and a secondary prediction model of the follower motion state according to the cam model; optimizing the camshaft speed based on the process target speed timing information and the return target speed timing information in combination with the primary prediction model of the follower motion state to obtain a first target camshaft speed; when the first target camshaft speed does not belong to the rated range of the camshaft speed, optimizing the camshaft speed and the cam pressure angle based on the process target speed timing information and the return target speed timing information in combination with the secondary prediction model of the follower motion state to obtain a second target camshaft speed and a first target cam pressure angle; when the second target camshaft speed belongs to the rated range of the camshaft speed, and the first target cam pressure angle belongs to the rated range of the camshaft pressure angle, sending the second target camshaft speed and the first target cam pressure angle to a user end.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages: Receive a single-cycle target motion state of a follower, wherein the single-cycle target motion state of the follower includes process target speed timing information and return target speed timing information, which provide accurate target parameters for subsequent optimization, making the optimization process more accurate. According to the cam model, enable the corresponding follower motion state primary prediction model and follower motion state secondary prediction model to evaluate the motion state of the follower. Based on the process target speed timing information and the return target speed timing information, the camshaft speed is optimized in combination with the follower motion state primary prediction model to obtain a first target camshaft speed; when the first target camshaft speed does not belong to the camshaft speed rated interval, the camshaft speed and cam pressure angle are optimized based on the process target speed timing information and the return target speed timing information, combined with the follower motion state secondary prediction model, to obtain a second target camshaft speed and a first target cam pressure angle; when the second target camshaft speed belongs to the camshaft speed rated interval, and the first target cam pressure angle belongs to the cam pressure angle rated interval, the second target camshaft speed and the first target cam pressure angle are sent to the user end. The camshaft speed and pressure angle are optimized through a multi-level prediction model, and combined with the rated range constraints, it is ensured that the most suitable operating parameters can be found under different loads and motion conditions, and problems such as equipment wear caused by excessive load are avoided.

[0006] To summarize, the present application performs hierarchical control optimization according to the movement requirements of the follower at different stages, combined with the first-level prediction model of the follower's movement state and the second-level prediction model of the follower's movement state, to find the optimal camshaft speed and pressure angle, thereby achieving precise motion control of the follower and ensuring that the follower runs according to the predetermined motion trajectory and speed, thereby achieving the technical effect of improving the stability and reliability of the entire textile equipment, ensuring textile processing accuracy, and further improving textile processing efficiency and fabric quality.

[0007] 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic flow chart of a camshaft synchronous driving method for automated control of textile equipment provided in an embodiment of the present application.

[0009] Figure 2A schematic diagram of a flow chart for training a primary prediction model for a follower motion state and a secondary prediction model for a follower motion state in a camshaft synchronous drive method for automated control of textile equipment provided in an embodiment of the present application.

[0010] Figure 3 A schematic flow chart of obtaining a first target camshaft speed in a camshaft synchronous driving method for automated control of textile equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a camshaft synchronous drive method for automated control of textile equipment, thereby solving the technical problems in existing textile equipment such as fixed camshaft operating parameters, lack of dynamic adjustment capability, and difficulty in timely adjustment according to changes in process requirements. The camshaft operating parameters are adaptively adjusted to accurately match the motion requirements of the follower at different stages, thereby achieving the technical effect of improving the operating stability of the textile equipment and the textile efficiency.

[0012] like Figure 1 As shown, an embodiment of the present application provides a camshaft synchronous driving method for automatic control of textile equipment, the method comprising: Step S1: receiving a single-cycle target motion state of a driven member, wherein the single-cycle target motion state of the driven member includes a forward target speed timing information and a return target speed timing information.

[0013] Specifically, the follower refers to the component that is driven by the camshaft to move in the textile equipment, including the heald frame and the sley. The expected operating state of each follower in a complete working cycle, that is, the single-cycle target motion state of the follower, is obtained from the production process design document. The single-cycle target motion state of the follower includes the process target speed timing information and the return target speed timing information. Among them, the process target speed timing information refers to the sequence of speed changes of the follower during forward motion (i.e., the "weaving" stage in the weaving process) over time. The return target speed timing information refers to the sequence of speed changes of the follower during reverse motion (i.e., the "weaving back" stage in the weaving process) over time.

[0014] By receiving the single-cycle target motion state of the follower, the motion requirements of the follower can be clarified, thereby determining the specific goals of optimizing the camshaft working parameters and providing data reference for the subsequent optimization process.

[0015] Step S2: activating the primary prediction model of the follower motion state and the secondary prediction model of the follower motion state according to the cam model.

[0016] Specifically, since different types of camshafts have different physical and motion characteristics, it is necessary to select and activate the appropriate prediction model according to the specific model of the cam. The prediction model includes a primary prediction model for the motion state of the follower and a secondary prediction model for the motion state of the follower. These prediction models are pre-programmed or trained, and can predict the motion performance of the follower corresponding to the camshaft under different working conditions based on the working parameters of the cam. Among them, the primary prediction model for the motion state of the follower is used to predict the motion state of the follower at different camshaft speeds. The primary prediction model for the motion state of the follower is a more sophisticated prediction model used to predict the motion state of the follower at different camshaft speeds and different cam pressure angles.

[0017] Step S3: Based on the forward target speed timing information and the return target speed timing information, the camshaft speed is optimized in combination with the first-level prediction model of the follower motion state to obtain a first target camshaft speed.

[0018] Specifically, the process target speed timing information and return target speed timing information received in step S1 are used as optimization targets, and the camshaft speed is adjusted through an optimization algorithm. After each adjustment, the follower motion characteristics (process speed timing information and return speed timing information) at the current speed are predicted using the first-level prediction model of the follower motion state, and compared with the optimization target (process target speed timing information and return target speed timing information). The camshaft speed is continued to be adjusted and optimized based on the comparison result, and through continuous adjustment and optimization, the ideal camshaft speed that matches the process target speed timing information and the return target speed timing information is found, that is, the first target camshaft speed.

[0019] Since the cam pressure angle and profile are design parameters and usually do not change, when optimizing, the camshaft speed is first optimized using the first-level prediction model of the follower motion state to find the camshaft speed that meets the follower motion requirements to achieve the minimum cost optimization.

[0020] Step S4: When the first target camshaft speed does not belong to the rated camshaft speed range, the camshaft speed and the cam pressure angle are optimized based on the process target speed timing information and the return target speed timing information and combined with the secondary prediction model of the follower motion state to obtain the second target camshaft speed and the first target cam pressure angle.

[0021] Specifically, the camshaft speed rating range refers to the acceptable speed range of the camshaft determined by the design limitations or process requirements of the equipment. If the speed exceeds this range, the equipment may not be able to operate stably. For the first target camshaft speed obtained in step S3, check whether it is within the camshaft speed rating range. If the first target camshaft speed exceeds the camshaft speed rating range, it means that the preliminary optimization result does not meet the mechanical structure safety requirements of the equipment and needs further optimization.

[0022] At this time, the same method as the optimization process in step S3 is used, with the process target speed timing information and the return target speed timing information as the optimization target, and the camshaft speed and cam pressure angle are adjusted simultaneously through the algorithm, and the follower motion state secondary prediction model is used to predict the follower motion state after each adjustment. After the joint optimization of the secondary prediction model, the second target camshaft speed and the first target cam pressure angle that match the process target speed timing information and the return target speed timing information are obtained.

[0023] On the premise of ensuring the safe operation of textile equipment, when optimizing the camshaft speed alone cannot meet the movement requirements of the follower, the camshaft speed and pressure angle are jointly optimized by combining a more complex secondary prediction model to further improve the movement accuracy and efficiency of the follower, making the movement of the follower smoother.

[0024] Step S5: When the second target camshaft speed belongs to the camshaft speed rated interval, and the first target cam pressure angle belongs to the cam pressure angle rated interval, the second target camshaft speed and the first target cam pressure angle are sent to the user end.

[0025] Specifically, the cam pressure angle rating range is the pressure angle range allowed for safe operation of the equipment. If the pressure angle is too large or too small, it will lead to reduced system efficiency or increased mechanical wear, affecting equipment life and product quality.

[0026] For the second target camshaft speed and the first target cam pressure angle obtained by optimization in step S4, check whether they are within their respective safe and effective ranges. If the second target camshaft speed belongs to the rated range of the camshaft speed, and the first target cam pressure angle belongs to the rated range of the cam pressure angle, this indicates that the joint optimization result can meet the movement requirements of the follower and will not exceed the bearing limit of the mechanical structure of the textile equipment. At this time, the joint optimization result, i.e., the second target camshaft speed and the first target cam pressure angle, is sent to the user's operating terminal or control system, so that the automatic control system adjusts the camshaft speed according to the second target camshaft speed, and manually adjusts the cam pressure angle to the first target cam pressure angle to meet the movement requirements of the follower, thereby ensuring the stable operation of the textile equipment and improving the fabric quality.

[0027] Furthermore, the method described in the embodiment of the present application also includes: When the first target camshaft speed belongs to the rated camshaft speed range, the first target camshaft speed is sent to the user end.

[0028] Specifically, in step S3, if the first target camshaft speed is already in the rated range of the camshaft speed, it means that the first target camshaft speed at this time meets the safety requirements of the mechanical structure of the equipment, and there is no need to perform subsequent optimization steps. The first target camshaft speed can be directly sent to the user end for adjustment, so that the follower can move accurately according to the predetermined speed curve, thereby improving production efficiency and fabric quality.

[0029] Furthermore, the method described in the embodiment of the present application also includes: When the second target camshaft speed does not belong to the rated camshaft speed interval, or / and the first target cam pressure angle does not belong to the rated cam pressure angle interval, the camshaft speed and the cam pressure angle are updated based on the process target speed timing information and the return target speed timing information in combination with the secondary prediction model of the follower motion state to obtain a third target camshaft speed and a second target cam pressure angle; when the third target camshaft speed belongs to the rated camshaft speed interval, and the second target cam pressure angle belongs to the rated cam pressure angle interval, the third target camshaft speed and the second target cam pressure angle are sent to the user end; otherwise, the third target camshaft speed and the second target cam pressure angle are continued to be updated; when a convergence solution is still not generated after a preset number of directed optimization attempts, a camshaft adjustment instruction is generated and sent to the user end.

[0030] Specifically, when any one of the second target camshaft speed and the first target cam pressure angle obtained in step S4 does not satisfy the corresponding rated interval or both do not satisfy the corresponding rated interval, it is necessary to update the camshaft speed and the cam pressure angle based on the progress target speed timing information and the return target speed timing information of the follower, combined with the secondary prediction model of the motion state of the follower, and further adjust the camshaft speed and the cam pressure angle according to the aforementioned similar optimization process to generate a new optimization result, i.e., the third target camshaft speed and the second target cam pressure angle.

[0031] The third target camshaft speed and the second target cam pressure angle are judged in the rated interval. When the third target camshaft speed belongs to the rated interval of the camshaft speed and the second target cam pressure angle belongs to the rated interval of the cam pressure angle, the third target camshaft speed and the second target cam pressure angle are sent to the user end. When any one of the third target camshaft speed and the second target cam pressure angle does not meet the corresponding rated interval or both do not meet the corresponding rated interval, the above-mentioned updating optimization and judgment process is repeated to find the optimization result that the camshaft speed belongs to the rated interval of the camshaft speed and the cam pressure angle belongs to the rated interval of the cam pressure angle, and the optimization result is sent to the user end.

[0032] If the update is continued until the number of optimization attempts reaches the preset number of directional optimization attempts and the camshaft speed and cam pressure angle that meet the conditions are still not found, a camshaft adjustment instruction is generated to instruct the staff to manually intervene and make adjustments to break through the current optimization bottleneck.

[0033] Further, such as Figure 2 As shown, according to the cam model, the primary prediction model of the motion state of the follower and the secondary prediction model of the motion state of the follower are activated, including: According to the cam model and the initial pressure angle, a dataset for constructing a primary prediction model of the motion state of the follower is collected, wherein the dataset for constructing the primary prediction model of the motion state of the follower includes a first camshaft speed timing record dataset, a first process identification speed timing dataset, and a first return identification speed timing dataset; the first camshaft speed timing record dataset is used as input, and the first process identification speed timing dataset and the first return identification speed timing dataset are used as supervision to train the primary prediction model of the motion state of the follower; according to the cam model, a dataset for constructing a secondary prediction model of the motion state of the follower is collected, wherein the dataset for constructing the secondary prediction model of the motion state of the follower includes a second camshaft speed timing record dataset, a pressure angle record dataset, a second process identification speed timing dataset, and a second return identification speed timing dataset; the second camshaft speed timing record dataset and the pressure angle record dataset are used as input, and the second process identification speed timing dataset and the second return identification speed timing dataset are used as supervision to train the secondary prediction model of the motion state of the follower.

[0034] Specifically, according to the aforementioned cam model and initial pressure angle, the operating data of the cam synchronous drive structure of the textile equipment under the same model and cam pressure angle are collected, including the rotational speed of the camshaft at different time points, and the speed change data of the follower in the process and the speed change data in the return. These data are preprocessed and sorted and summarized in chronological order to generate a first camshaft speed timing record data set, a first process identification speed timing data set, and a first return identification speed timing data set. These data sets together constitute a data set for constructing a first-level prediction model of the follower motion state, which is used to train the first-level prediction model of the follower motion state.

[0035] A supervised learning algorithm is used to construct an initial model, such as a long short-term memory neural network. The first camshaft speed time series record data set is used as a training data set, and the first process identification speed time series data set and the first return identification speed time series data set are used as supervision data sets. The initial model is supervised and trained. During the training process, the model learns how to predict the corresponding follower process speed and return speed based on the camshaft speed. By comparing the prediction results with the supervision data set, the model continuously adjusts the internal parameters until convergence, so that the prediction results on the training data set are close to the supervision data set, and a trained follower motion state first-level prediction model is obtained. The model can predict the follower process speed and return speed under a specific cam model and cam pressure angle based on the input camshaft speed.

[0036] According to the camshaft model, the operation data of the cam synchronous drive structure of the textile equipment under this model is collected, including the rotation speed of the camshaft at different time points, different cam pressure angle data, and the speed change data of the follower in the process and the speed change data in the return, and the second camshaft rotation speed timing record data set, the pressure angle record data set, the second process identification speed timing data set and the second return identification speed timing data set are sorted and generated. A similar model training step is adopted, with the second camshaft rotation speed timing record data set and the pressure angle record data set as the input data set, and the second process identification speed timing data set and the second return identification speed timing data set as the supervision data set, to train the secondary prediction model of the motion state of the follower. The secondary prediction model of the motion state of the follower can predict the process speed and return speed of the follower under the corresponding cam model according to the input camshaft rotation speed data and cam pressure angle data.

[0037] Through the above steps, a customized prediction model can be trained for each cam model to improve the accuracy and efficiency of the prediction of the motion state of the follower, providing an accurate and reliable judgment tool for the subsequent optimization of working parameters.

[0038] Further, taking the first camshaft speed time series record data set as input, taking the first process mark speed time series data set and the first return mark speed time series data set as supervision, training the first level prediction model of the follower motion state also includes: A first loss function for motion state prediction is constructed, wherein the first loss function for motion state prediction is used to count the proportion of first speed deviation time series data of the first process prediction speed time series data and the first process identification speed time series data that are greater than or equal to the speed deviation threshold value in any training; a second loss function for motion state prediction is constructed, wherein the second loss function for motion state prediction is used to count the proportion of second speed deviation time series data of the first return prediction speed time series data and the first return identification speed time series data that are greater than or equal to the speed deviation threshold value in any training; the first camshaft speed time series record data set is used as input, and the first process identification speed time series data set and the first return identification speed time series data set are used as supervision to train the long short-term memory neural network, when in N consecutive trainings, at least more than f (0.9*N) times: the first loss function for motion state prediction and the second loss function for motion state prediction are both less than or equal to the first loss threshold value, a primary prediction model for the motion state of the follower is generated, wherein f (0.9*N) represents rounding up 0.9*N.

[0039] Specifically, in the process of training the first-level prediction model of the motion state of the follower, the prediction results of the model are evaluated by constructing a loss function to further optimize the performance of the first-level prediction model of the motion state of the follower.

[0040] First, a motion state prediction first loss function is constructed to measure the accuracy of the first process speed predicted by the model after each training. The function first calculates the difference between all the first process prediction speed time series data output by the model after each training and the corresponding actual first process identification speed time series data to obtain M first speed deviation time series data, and then compares the M first speed deviation time series data with the preset speed deviation threshold, and counts the proportion of data greater than or equal to the speed deviation threshold in the M first speed deviation time series data, and uses the calculation result as the first loss value. Among them, the speed deviation threshold is the upper limit of the speed difference determined according to the actual model prediction accuracy requirements. When the first speed deviation time series data is greater than or equal to the speed deviation threshold, it is considered that the deviation is too large and the prediction is inaccurate.

[0041] A similar method is used to construct the second loss function for motion state prediction. The second loss function measures the accuracy of the first return speed predicted by the model after each training by counting the proportion of the second speed deviation time series data between the first return prediction speed time series data and the first return identification speed time series data that is greater than or equal to the speed deviation threshold.

[0042] In the training process of the first-level prediction model of the motion state of the follower, the long short-term memory neural network (LSTM) is selected as the model architecture, and the first camshaft speed time series record data set is used as input, and the first process identification speed time series data set and the first return identification speed time series data set are used as supervision to train the long short-term memory neural network. After each training, the accuracy of the model prediction results is evaluated using the first and second loss functions defined above. When the first and second loss functions are less than or equal to the first loss threshold in more than f (0.9*N) trainings in N consecutive trainings, the model is considered to have converged, and the final first-level prediction model of the motion state of the follower is generated. Among them, f (0.9*N) means rounding up 0.9*N, and the first loss threshold is the upper limit of the loss function value pre-set according to the actual model prediction accuracy requirements.

[0043] Through the customized loss function, the model can not only learn the law of follower speed change, but also ensure the stability and accuracy of the prediction. In the training process of the secondary prediction model of the follower motion state, a similar function construction logic is used to construct the corresponding loss function to evaluate the accuracy of the model training results.

[0044] Further, such as Figure 3 As shown, step S3 of the embodiment of the present application also includes: Obtain a random assigned value of the camshaft speed, process the random assigned value of the camshaft speed through the first-level prediction model of the follower motion state, and generate first process speed timing information and first return speed timing information; based on the motion state prediction of a first loss function, calculate the first loss value of the first process speed timing information and the process target speed timing information; based on the motion state prediction of a second loss function, calculate the second loss value of the first return speed timing information and the return target speed timing information; when the first loss value and the second loss value are both less than or equal to a second loss threshold, add the random assigned value of the camshaft speed to the first target camshaft speed.

[0045] Specifically, based on the forward target speed timing information and the return target speed timing information, the camshaft speed is optimized in combination with the first-level prediction model of the driven member motion state, and the specific process of obtaining the first target camshaft speed includes: First, a series of camshaft speed values ​​are randomly generated, and these random camshaft speed values ​​are sequentially input into the above-trained primary prediction model of the follower motion state to predict the corresponding first process speed timing information and first return speed timing information of the follower.

[0046] The first loss function of motion state prediction is used to calculate the deviation ratio between the predicted first process speed timing information and the process target speed timing information, which is recorded as the first loss value. The second loss function of motion state prediction is used to calculate the deviation ratio between the predicted first return speed timing information and the return target speed timing information, which is recorded as the second loss value.

[0047] Compare the calculated first loss value and the second loss value with the set second loss threshold. When the first loss value and the second loss value are both less than or equal to the second loss threshold, it indicates that the motion state under the current speed assignment is close to or meets the target speed requirement. Among them, the second loss threshold is the upper limit of the difference between the estimated follower operating state corresponding to each camshaft speed random assignment and the follower target motion state. Add the camshaft speed random assignment that meets the conditions to the first target camshaft speed set to provide parameter selection for subsequent optimization or practical application.

[0048] The above steps combine simulation optimization and prediction models. Through repeated iterations and evaluations, the optimal or satisfactory camshaft speed is gradually approached, thereby improving the operating efficiency and stability of the camshaft synchronous drive structure.

[0049] Further, when the first loss value and the second loss value are both less than or equal to a second loss threshold, the camshaft speed is randomly assigned and added to the first target camshaft speed, including: When the number of the first target camshaft speeds meets a preset number, the first target camshaft speed is output.

[0050] Specifically, the process of random assignment of camshaft speed, prediction, calculation of loss value, judgment and collection is continuously performed, and the random assignment of camshaft speed whose first loss value and second loss value are both less than or equal to the second loss threshold is added to the first target camshaft speed, until the number in the first target camshaft speed meets the preset number, and a complete set of optimized speeds, i.e., the first target camshaft speed, is output. The preset number is set according to actual application requirements and optimization goals.

[0051] Further, when the directional optimization is performed for a preset number of times and still no convergence solution is generated, a camshaft adjustment instruction is generated and sent to the user end, and then the following is further included: Receive user feedback data of the camshaft adjustment instruction from the user end, and determine whether the user feedback data has a target motion state updated in a single cycle by the follower; if so, perform a camshaft drive control process according to the target motion state updated in a single cycle by the follower.

[0052] Specifically, since the automatic optimization cannot find a satisfactory solution, a camshaft adjustment instruction is generated and sent to the user end. After receiving the camshaft adjustment instruction, the user end will make manual adjustments according to the actual situation and send the adjusted results back as user feedback data. The received user feedback data is analyzed to determine whether it contains the single-cycle update target motion state of the follower, that is, the user updates the expected motion state of the follower according to the actual situation. If it is included, the optimization target is updated according to the single-cycle update target motion state of the follower contained in the user feedback, and the aforementioned steps S1 to S5 are repeated to perform camshaft drive control.

[0053] By introducing a user feedback loop, it is ensured that even if no automatic optimization solution is found, accurate control of the follower can still be achieved through user participation. It can effectively integrate automatic optimization and manual adjustment to improve the adaptability and solution capabilities for complex problems.

[0054] In summary, the camshaft synchronous driving method for automatic control of textile equipment provided in the embodiments of the present application has the following technical effects: Receive a single-cycle target motion state of a follower, wherein the single-cycle target motion state of the follower includes process target speed timing information and return target speed timing information, which provide accurate target parameters for subsequent optimization, making the optimization process more accurate. According to the cam model, enable the corresponding follower motion state primary prediction model and follower motion state secondary prediction model to evaluate the motion state of the follower. Based on the process target speed timing information and the return target speed timing information, the camshaft speed is optimized in combination with the follower motion state primary prediction model to obtain a first target camshaft speed; when the first target camshaft speed does not belong to the camshaft speed rated interval, the camshaft speed and cam pressure angle are optimized based on the process target speed timing information and the return target speed timing information, combined with the follower motion state secondary prediction model, to obtain a second target camshaft speed and a first target cam pressure angle; when the second target camshaft speed belongs to the camshaft speed rated interval, and the first target cam pressure angle belongs to the cam pressure angle rated interval, the second target camshaft speed and the first target cam pressure angle are sent to the user end. The camshaft speed and pressure angle are optimized through a multi-level prediction model, and combined with the rated range constraints, it is ensured that the most suitable operating parameters can be found under different loads and motion conditions, and problems such as equipment wear caused by excessive load are avoided.

[0055] In general, the embodiment of the present application performs hierarchical control optimization according to the motion requirements of the follower at different stages, combined with the primary prediction model of the motion state of the follower and the secondary prediction model of the motion state of the follower, finds the optimal camshaft speed and pressure angle, realizes precise motion control of the follower, ensures that the follower runs according to the predetermined motion trajectory and speed, thereby achieving the technical effect of improving the stability and reliability of the entire textile equipment, ensuring the textile processing accuracy, and then improving the textile processing efficiency and fabric quality. The above description of the disclosed embodiments enables professionals and technicians in this field to implement or use the present application. Various modifications to these embodiments will be obvious to professionals and technicians in this field, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features disclosed herein.

Claims

1. A camshaft synchronous driving method for automatic control of textile equipment, characterized in that: include: Receiving a single-cycle target motion state of a driven member, wherein the single-cycle target motion state of the driven member includes a forward target speed timing information and a return target speed timing information; According to the cam model, the primary prediction model of the motion state of the follower and the secondary prediction model of the motion state of the follower are activated; Based on the forward target speed timing information and the return target speed timing information, the camshaft speed is optimized in combination with the first-level prediction model of the follower motion state to obtain a first target camshaft speed; When the first target camshaft speed does not belong to the rated camshaft speed range, optimizing the camshaft speed and the cam pressure angle based on the forward target speed timing information and the return target speed timing information and in combination with the secondary prediction model of the follower motion state, to obtain a second target camshaft speed and a first target cam pressure angle; When the second target camshaft speed belongs to the camshaft speed rated interval, and the first target cam pressure angle belongs to the cam pressure angle rated interval, the second target camshaft speed and the first target cam pressure angle are sent to the user end.

2. The method according to claim 1, characterized in that Also includes: When the first target camshaft speed belongs to the rated camshaft speed range, the first target camshaft speed is sent to the user end.

3. The method according to claim 1, characterized in that Also includes: When the second target camshaft speed does not belong to the rated camshaft speed interval, or / and the first target cam pressure angle does not belong to the rated cam pressure angle interval, based on the forward target speed timing information and the return target speed timing information, the camshaft speed and the cam pressure angle are updated in combination with the secondary prediction model of the follower motion state to obtain a third target camshaft speed and a second target cam pressure angle; When the third target camshaft speed belongs to the camshaft speed rated interval, and the second target cam pressure angle belongs to the cam pressure angle rated interval, sending the third target camshaft speed and the second target cam pressure angle to the user end; otherwise, continue to update the third target camshaft speed and the second target cam pressure angle; When a converged solution is not generated after the directional optimization is performed for a preset number of times, a camshaft adjustment instruction is generated and sent to the user end.

4. The method according to claim 1, characterized in that According to the cam model, the primary prediction model of the follower motion state and the secondary prediction model of the follower motion state are activated, including: According to the cam model and the initial pressure angle, a data set for constructing a primary prediction model of the motion state of the follower is collected, wherein the data set for constructing the primary prediction model of the motion state of the follower includes a first camshaft speed time series record data set, a first process mark speed time series data set, and a first return mark speed time series data set; Taking the first camshaft speed time series record data set as input, and taking the first process mark speed time series data set and the first return mark speed time series data set as supervision, training the first level prediction model of the follower motion state; According to the cam model, a data set for constructing a secondary prediction model of the motion state of the follower is collected, wherein the data set for constructing the secondary prediction model of the motion state of the follower includes a second camshaft speed time series record data set, a pressure angle record data set, a second process mark speed time series data set, and a second return mark speed time series data set; The second camshaft speed timing record data set and the pressure angle record data set are used as input, and the second process identification speed timing data set and the second return identification speed timing data set are used as supervision to train the secondary prediction model of the follower motion state.

5. The method according to claim 4, characterized in that Taking the first camshaft speed time series record data set as input, taking the first process mark speed time series data set and the first return mark speed time series data set as supervision, training the first level prediction model of the follower motion state includes: Constructing a first loss function for motion state prediction, wherein the first loss function for motion state prediction is used to count the proportion of first speed deviation time series data of first process prediction speed time series data and first process identification speed time series data that is greater than or equal to a speed deviation threshold value in any training; Constructing a second loss function for motion state prediction, wherein the second loss function for motion state prediction is used to count the proportion of the second speed deviation time series data of the first return prediction speed time series data and the first return identification speed time series data that is greater than or equal to the speed deviation threshold value in any training; The first camshaft speed timing record data set is used as input, and the first process identification speed timing data set and the first return identification speed timing data set are used as supervision to train the long short-term memory neural network. When, in N consecutive trainings, at least more than f (0.9*N) times: the first loss function for motion state prediction and the second loss function for motion state prediction are both less than or equal to the first loss threshold, the first-level prediction model of the motion state of the follower is generated, wherein f (0.9*N) represents rounding up to 0.9*N.

6. The method according to claim 5, characterized in that Based on the forward target speed timing information and the return target speed timing information, the camshaft speed is optimized in combination with the primary prediction model of the follower motion state to obtain a first target camshaft speed, including: Obtaining a random value of a camshaft speed, processing the random value of the camshaft speed through the primary prediction model of the follower motion state, and generating first process speed timing information and first return speed timing information; Calculate a first loss value of the first process speed timing information and the process target speed timing information based on the motion state prediction first loss function; Predicting a second loss function based on the motion state, calculating a second loss value of the first return speed timing information and the return target speed timing information; When the first loss value and the second loss value are both less than or equal to a second loss threshold, the camshaft speed is randomly assigned and added to the first target camshaft speed.

7. The method according to claim 6, characterized in that When the first loss value and the second loss value are both less than or equal to a second loss threshold, randomly assigning a value to the camshaft speed to the first target camshaft speed includes: When the number of the first target camshaft speeds meets a preset number, the first target camshaft speed is output.

8. The method according to claim 3, characterized in that When the directional optimization fails to generate a converged solution after a preset number of times, a camshaft adjustment instruction is generated and sent to the user end, and then the following steps are further included: Receive user feedback data of the camshaft adjustment instruction from the user end, and determine whether the user feedback data has a target motion state updated in a single cycle by the follower; if so, perform a camshaft drive control process according to the target motion state updated in a single cycle by the follower.

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