A textile heald frame opening control system and method

Abstract

This invention belongs to the field of equipment control technology and discloses a textile heald frame opening control system and method. It employs a heterogeneous collaborative core control system using ARM Cortex-A9 and FPGA, coupled with a distributed multi-axis servo drive architecture. Each heald frame is equipped with an independent servo drive unit, enabling independent and precise adjustment of the opening height, opening time, and motion curve of a single heald frame. The system supports collaborative control of 4-16 heald frames, and incorporates a warp tension feedback electronic cam dynamic correction and phase compensation algorithm, providing various custom motion curves to quickly match different fabric characteristics. A multi-source sensing fusion system is constructed, using encoders, laser displacement, acceleration, tension, and other sensors to achieve real-time monitoring of heald frame position, equipment vibration, component temperature, warp tension, and lubrication status across all dimensions. An attention mechanism algorithm is used to filter key data and double-verify position information.

Description

Technical Field

[0001] This invention belongs to the field of equipment control technology, specifically a textile heald frame opening control system and method. Background Technology

[0002] The loom is the core equipment of the textile industry, and the shedding mechanism, as one of the five core mechanisms of the loom, directly determines weaving efficiency, fabric quality, and production flexibility. Existing textile heald frame shedding devices are mainly divided into two categories: mechanical transmission type and primary electronic control type. Both types present the following technical problems in actual production: Traditional mechanical opening devices such as linkage drive and eccentric wheel drive rely on complex mechanical transmission chains, resulting in rapid component wear, high failure rate, and high maintenance costs and labor requirements. Moreover, the opening parameters are entirely determined by the mechanical structure, and when changing the fabric type, the cam needs to be re-processed or the linkage length needs to be adjusted, with an adjustment cycle of up to several hours, which cannot meet the flexible production needs of small batches and multiple varieties.

[0003] Existing primary electronic control shedding devices mostly adopt a centralized drive mode, with all heald frames driven by a single motor. This makes it impossible to achieve independent motion adjustment of individual heald frames, making it difficult to weave fabrics with complex structures. They can only monitor basic parameters such as motor speed, lacking comprehensive perception of key operating indicators such as heald frame position accuracy, bearing wear, lubricating oil status, and warp tension. Fault diagnosis is mostly based on post-operation alarms, failing to provide early warning of gradual faults, resulting in long unplanned downtime.

[0004] Existing lubrication systems generally adopt passive splash lubrication, which cannot dynamically adjust the amount of lubrication according to the operating status of the equipment. This easily leads to problems such as insufficient lubrication at high speeds or excessive lubrication at low speeds, which not only accelerates component wear but also wastes lubricating oil and pollutes the production environment with oil. Summary of the Invention

[0005] The purpose of this invention is to provide a textile heald frame opening control system and method to solve one or more problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a textile heald frame opening control system, comprising the following modules: Furthermore, the core control module adopts a heterogeneous collaborative architecture of ARM Cortex-A9 main processor and Xilinx Artix-7 FPGA coprocessor, optimizing control logic and multi-module collaborative strategy. The ARM processor is used for upper-level logic scheduling, human-machine interaction response, data storage and industrial communication. It has a built-in 16GB eMMC storage unit for storing fabric process parameter templates and a process parameter self-optimization module, which can automatically correct process parameters based on real-time running data during the weaving process. The process parameter self-optimization aims to terminate the iteration with warp tension fluctuation ≤3%, heald frame vibration amplitude ≤0.3g, and stable weaving efficiency. When the running data meets the optimization target for 30 consecutive seconds, the algorithm automatically stops the iteration and locks the current optimal parameters. The optimized parameters take effect in the weaving control process in real time and are automatically saved to the process parameter template. If the parameters exceed the limit during the optimization process, the system immediately reverts to the previous version of stable parameters.

[0007] The FPGA coprocessor is used for real-time motion control, high-speed acquisition of encoder signals and multi-axis synchronous calculation. It is configured with a distributed control scheduling algorithm to realize parallel and collaborative control of multiple drive modules and multiple sensing modules. The core control module is equipped with multiple communication interfaces, including Ethernet, RS485, and CAN bus, which can interface with the loom main control system and the factory MES system to realize remote uploading of production data and remote monitoring of equipment. It is configured with a control command fault tolerance mechanism, which automatically switches to local backup control mode when communication is interrupted or commands are abnormal.

[0008] Furthermore, the power drive module adopts a distributed multi-axis servo drive architecture, with each helical frame corresponding to an independent servo drive unit, including a servo motor and a planetary reducer, incorporating a model predictive control algorithm. The servo motor has a built-in 17-bit absolute encoder for obtaining absolute position information, and the planetary reducer adopts a helical gear hard tooth surface design. The power drive module receives the position command from the core control module, predicts the deviation of the heald frame's motion trajectory in advance through the model predictive control algorithm, corrects the output speed and torque of the servo motor in real time, and drives the servo motor to convert the rotational motion into the up-and-down reciprocating linear motion of the heald frame through the ball screw pair, so as to realize the independent adjustment of the opening height, opening time and motion curve of a single heald frame. Configure a servo motor adaptive load adjustment strategy to dynamically adjust the motor drive torque based on changes in warp tension and heald frame movement resistance.

[0009] Model predictive control trajectory deviation correction formula: , express The servo motor control increment is used to directly correct the servo motor's speed and torque output; The model predictive control gain matrix is ​​a set of control weight parameters pre-calibrated by the system. express The reference position of the heald frame at any time is the point on the target trajectory of the heald frame movement set by the process. express The predicted position of the time frame is the value of the time frame position estimated in real time by the model prediction control algorithm; This represents the prediction time step, which is a time dimension parameter set by the system for predicting the motion trajectory of the frame.

[0010] Furthermore, the heald frame synchronization module is based on electronic cam technology and master-slave synchronous control architecture, supports the coordinated control of 4-16 heald frames, is configured with a warp tension feedback electronic cam dynamic correction algorithm, and an incremental encoder is installed on the loom spindle to collect the spindle rotation angle signal in real time and transmit it to the FPGA; the FPGA converts the spindle rotation angle into the target position command of each heald frame according to the preset electronic cam curve, and at the same time receives real-time data from the warp tension sensing unit; When abnormal fluctuations occur in warp tension, the phase and amplitude of the electronic cam curve are automatically corrected to compensate for the synchronization deviation caused by warp elastic deformation. The built-in phase dynamic compensation algorithm can adjust the motion phase of each heald frame in real time according to warp tension feedback, loom speed changes and mechanical transmission errors. It supports custom electronic cam curves and provides a variety of motion modes, including sine curves, modified trapezoidal curves and fifth-order polynomial curves. It can select the optimal motion curve according to the fabric characteristics. Configured with multi-hedron collaborative fault-tolerant control, when a minor fault occurs in the drive unit of one hedron frame, the motion parameters of other hedron frames are automatically adjusted.

[0011] Warp tension feedback electronic cam target position correction formula: , This indicates the final target position of the heald frame. It is the precise positioning value of the heald frame output by the system and is used to determine the actual target position of the opening of the heald frame. This indicates the rotation angle of the loom spindle, which is acquired in real time by an incremental encoder installed on the loom spindle. This indicates the preset original position of the electronic cam, including the position values ​​corresponding to three basic curves: sine curve, modified trapezoidal curve, and fifth-order polynomial curve. This represents the warp tension correction coefficient, used to calibrate the weight of the influence of warp tension changes on the heald frame position; This represents the warp tension deviation value, which is the difference between the real-time collected warp tension and the tension set in the process. This represents the mechanical transmission error compensation coefficient, used to calibrate the weight of the influence of mechanical transmission clearance on the position of the heald frame. It represents the mechanical transmission error, which is the positional deviation caused by the gaps and wear of transmission components such as guide rails and reducers.

[0012] Formula for dynamic phase compensation of frame motion: , This indicates the phase of the heald frame motion after compensation, used to ensure that there is no phase deviation in the coordinated motion of multiple heald frames; This indicates the initial phase setting, which is the pre-set basic phase value for the heald frame movement in the process parameters; This represents the loom speed compensation coefficient, used to calibrate the weight of the influence of changes in the loom spindle speed on the phase of the heald frame; This indicates the spindle speed deviation of the loom, which is the difference between the real-time collected spindle speed and the process-set speed. This represents the warp tension phase correction coefficient, used to calibrate the weight of the influence of warp tension changes on the heald frame phase. This represents the warp tension deviation value, which is the difference between the real-time collected warp tension and the tension set in the process. This represents the phase compensation coefficient for transmission error, used to calibrate the weight of the influence of mechanical transmission error on the phase of the heald frame. It represents the mechanical transmission error, which is the positional deviation caused by the gaps and wear of transmission components such as guide rails and reducers.

[0013] Furthermore, the state perception module includes a position perception unit, a motion state perception unit, a lubrication state perception unit, a temperature perception unit, and a warp tension perception unit, and is configured with an attention mechanism algorithm for multi-source perception data fusion. The position sensing unit consists of a servo motor with a built-in encoder and a laser displacement sensor at the end of the heald frame, providing dual verification of the actual position of the heald frame. The motion state sensing unit is a triaxial accelerometer mounted on the heald frame beam and the reducer housing, monitoring the vibration amplitude and frequency of the heald frame and identifying early fault characteristics. The lubrication state sensing unit includes a radar level sensor, an oil quality sensor, and a flow sensor, which monitor the lubricating oil level, oil contamination level, and oil supply flow at each lubrication point, respectively. The temperature sensing unit consists of PT100 platinum resistance sensors distributed at the servo motor, reducer, and guide rail slider, monitoring the operating temperature of key components. The warp tension sensing unit is a tension sensor installed between the warp beam and the heald frame, collecting warp tension change data in real time. All sensing data is transmitted to the core control module in real time via the CAN bus. Key feature data is filtered and redundant information is removed through the attention mechanism algorithm. The system is configured with a self-check and compensation function for sensing data anomalies. When a sensor shows abnormal data, it automatically switches to a backup sensor or uses historical data interpolation for compensation.

[0014] Furthermore, the fault early warning module has a built-in fault knowledge base and a multi-parameter fusion anomaly detection algorithm. It adopts a combination of LSTM long short-term memory neural network and attention mechanism. The fault knowledge base stores the feature parameters, causes and handling methods of common faults, including three major categories: mechanical faults, electrical faults and lubrication faults. It is equipped with a fault feature self-learning module, which can automatically update the fault knowledge base according to the fault data in actual operation. A two-level early warning mechanism is adopted. For threshold-type faults, an audible and visual warning is issued when the detected parameters exceed the preset threshold. For gradual faults, an Attention-LSTM neural network is used to analyze the trend changes of vibration, temperature and position data, extract early fault features and issue early warnings. When a fault occurs, the system automatically locates the fault position, generates a fault code and standardized handling suggestions, and pushes them to operators and equipment managers through the human-machine interface and remote communication interface. For minor and repairable faults, the system automatically activates the fault self-healing control strategy, adjusts relevant control parameters, and supports fault history data query and statistical analysis.

[0015] Furthermore, the lubrication control module includes a variable frequency lubricating oil pump, a multi-channel solenoid valve group, a lubricating oil preheating unit, and a three-stage filtration unit, incorporating a lubrication demand prediction model and a closed-loop feedback control strategy. The variable frequency lubricating oil pump can dynamically adjust the oil supply pressure based on the loom speed, running time, key component temperature, and vibration data, using the lubrication demand prediction model. The multi-channel solenoid valve group corresponds to each key lubrication point, and the core control module independently controls the oil supply time and quantity for each lubrication point. The lubricating oil preheating unit automatically starts when the ambient temperature is too low. The three-stage filtration unit is used to remove metal debris and impurities from the lubricating oil. The core control module constructs a lubrication regulation closed loop based on the data from the lubrication status sensing unit, collects the oil status, component temperature and vibration data of each lubrication point in real time, and feeds them back to the lubrication demand prediction model to dynamically optimize the lubrication strategy. When the loom is running at high speed, the oil supply and frequency are automatically increased; when the temperature of a certain lubrication point rises abnormally, the oil supply to the corresponding point is temporarily increased; when the oil contamination level exceeds the standard, an early warning for replacing the lubricating oil is issued, and the oil supply pressure and frequency are automatically adjusted. The lubricating oil regeneration status monitoring function is configured, and the lubricating oil regeneration capacity is monitored in real time through an oil quality sensor.

[0016] Furthermore, the human-machine interaction module adopts a capacitive touch screen, supports dual operation of multi-touch and physical buttons, and is configured with automatic recommendation and manual fine-tuning linkage functions for control parameters. The main interface displays operating parameters in real time, including loom speed, position of each heald frame, warp tension, lubricating oil level, and temperature of key components, and shows the parameter change trend in the form of curves. It also has a function to display the operating status of the control strategy. The parameter configuration interface supports setting the number of heald frames, shedding height, shedding time, electronic cam curve, lubrication parameters, and fault warning thresholds. All parameter modifications take effect in real time. It has a built-in process parameter management function, which supports the creation, editing, deletion, and one-click recall of process parameters. It can import and export process parameters via USB interface or SD card. It can also configure a process parameter comparison and analysis function to compare the weaving effect and system operating status under different process parameters. It features a fault alarm pop-up window that displays the fault code, fault location, and handling steps. It supports alarm information confirmation and historical alarm query, and can be set to display the fault self-repair progress.

[0017] The present invention also provides a method for controlling the opening of textile heald frames, based on the above-described system, comprising the following specific steps: System initialization: Configure the loom process parameters through the core control module, select the electronic cam curve that is suitable for the fabric characteristics, set the opening height, opening time and synchronization accuracy parameters of each heald frame, and store the parameters in the built-in storage unit of the core control module to complete the initial deployment of the control strategy; Sensing data acquisition and transmission: After the control process is started, each sensing unit of the state sensing module works synchronously. Through encoders, laser displacement sensors, acceleration sensors, PT100 platinum resistance sensors and tension sensors, the system collects data on heald frame position, motion vibration, key component temperature, warp tension and lubrication status in real time. After being filtered and processed by the attention mechanism algorithm of multi-source sensing data fusion, the data is transmitted to the core control module through the CAN bus. Core control and power drive: Based on the received sensing data, the core control module performs collaborative calculations with the main processor and FPGA coprocessor, uses model predictive control algorithms to predict the deviation of the heald frame motion trajectory, generates position control commands for each heald frame, and transmits them to the power drive module; after receiving the commands, the power drive module uses a servo motor and planetary reducer to convert the rotational motion into linear motion of the heald frame, and dynamically adjusts the driving torque according to the changes in warp tension; Heald frame synchronization and fault warning: The heald frame synchronization module is based on electronic cam technology and master-slave synchronous control architecture. It converts the target position command according to the spindle rotation angle signal, and corrects the phase and amplitude of the cam curve by combining the warp tension data. It adjusts the motion phase of each heald frame through a phase dynamic compensation algorithm to achieve coordinated motion of multiple heald frames. At the same time, the fault warning module analyzes the sensing data in real time, identifies fault characteristics through the Attention-LSTM algorithm, issues warnings and locates faults, and automatically starts a self-repair strategy for minor faults. Lubrication control and human-machine interaction: The lubrication control module dynamically adjusts the oil supply pressure, oil supply quantity and oil supply frequency based on lubrication status data and through a prediction model, and starts the preheating and filtration functions; the human-machine interaction module displays the operating parameters in real time and supports manual fine-tuning of parameters and calling process templates.

[0018] The beneficial effects of this invention are as follows: 1. This invention adopts a heterogeneous collaborative core control of ARM Cortex-A9 and FPGA, coupled with a distributed multi-axis servo drive architecture. Each heald frame is equipped with an independent servo drive unit, which can realize independent and precise adjustment of the opening height, opening time and motion curve of a single heald frame. The system supports collaborative control of 4-16 heald frames, and is equipped with a warp tension feedback electronic cam dynamic correction and phase compensation algorithm, providing a variety of custom motion curves that can quickly match different fabric characteristics. Process parameters support one-click recall and self-optimization. Changing fabric types does not require modification of mechanical parts, and the adjustment time is reduced from several hours to minutes. It is suitable for small-batch, multi-variety flexible production, improving the weaving capacity and production efficiency of complex fabrics.

[0019] 2. This invention constructs a multi-source sensing fusion system, using various sensors such as encoders, laser displacement, acceleration, and tension to achieve real-time monitoring of heald frame position, equipment vibration, component temperature, warp tension, and lubrication status across all dimensions. It also incorporates an attention mechanism algorithm to filter key data and double-verify position information. The fault early warning module employs an Attention-LSTM neural network and a two-level early warning mechanism to identify gradual faults in advance, quickly respond to threshold-type faults, automatically locate the fault position and push processing solutions, and achieve self-repair for minor faults. This allows for early fault prediction, reduces sudden equipment downtime, lowers maintenance costs and production losses, and effectively extends equipment lifespan.

[0020] 3. This invention employs an active centralized lubrication system, incorporating a lubrication demand prediction model and a closed-loop feedback control strategy. Through a variable frequency oil pump and a multi-channel solenoid valve assembly, it achieves independent dynamic adjustment of the oil supply pressure, time, and quantity at each lubrication point. The system features lubricant preheating, three-stage filtration, and regeneration status monitoring functions. It can precisely supply oil as needed based on loom speed, component temperature, and vibration data, avoiding lubricant waste and oil pollution on the production site. Precise lubrication effectively reduces wear on mechanical components, lowers equipment failure rates and maintenance frequency, and extends the service life of the lubricant. Attached Figure Description

[0021] Figure 1 A flowchart of the workflow for this invention system is provided; Figure 2 This is a flowchart of the frame opening control sub-flowchart of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figures 1 to 2 As shown, this embodiment of the invention provides a textile heald frame opening control system, comprising the following modules: In this embodiment of the invention, the core control module adopts a heterogeneous collaborative architecture of an ARM Cortex-A9 main processor and a Xilinx Artix-7 FPGA coprocessor, optimizing the control logic and multi-module collaborative strategy. The ARM processor is used for upper-level logic scheduling, human-machine interaction response, data storage and industrial communication. It has a built-in 16GB eMMC storage unit for storing fabric process parameter templates and is configured with a process parameter self-optimization module, which can automatically correct process parameters based on real-time operating data such as warp tension, heald frame vibration and fabric density during the weaving process. The process parameter self-optimization module adopts a hybrid optimization algorithm combining fuzzy PID and particle swarm optimization, consisting of a parameter acquisition layer, a fuzzy inference layer, an optimization solution layer, and a parameter correction layer. The algorithm input consists of four types of core textile process data: warp tension fluctuation coefficient, heald frame vibration acceleration, weaving speed, and fabric density. The optimization objectives are to reduce tension fluctuation, reduce mechanical vibration, and improve weaving efficiency. The algorithm output consists of four types of control parameters: shedding height correction, shedding time offset, and electronic cam curve amplitude and phase correction. The optimization rules are set in combination with the characteristics of textile fabrics: when the warp tension fluctuation is >3%, the sheath height is automatically reduced; when the vibration amplitude is >0.5g, the slope of the motion curve is optimized; and the phase parameters are compensated synchronously when the loom speed changes. The algorithm runs for 50ms and iterates and optimizes in real time according to the weaving status.

[0024] Process parameter templates are categorized and stored according to fabric type, yarn material, and weaving speed. It supports storing no less than 1,000 sets of customized templates. Each template includes a complete set of parameters such as the number of heald frames, shedding parameters, cam curve, lubrication strategy, and warning threshold. When a template is called, the system automatically verifies parameter compatibility, loads all parameters with one click, and completes hardware initialization. It supports template copying, editing, and encryption protection, and can be imported and exported in batches via external storage devices.

[0025] The system first obtains the real-time warp tension value through the tension sensing unit, compares the tension fluctuation amplitude with the preset standard range, and then activates the electronic cam dynamic correction algorithm to adjust the curve amplitude and basic phase. Then, combined with the spindle speed and mechanical transmission error data, a second precise fine adjustment is made through the phase dynamic compensation algorithm. The two algorithms are executed in a coordinated manner in the order of amplitude correction first and phase compensation later, and the calculation and parameter update are completed every 5ms, keeping the warp tension stable and the multi-heald frame movement synchronized throughout the process.

[0026] The FPGA coprocessor is used for real-time motion control, high-speed acquisition of encoder signals and multi-axis synchronous operation, with a response delay of no more than 1 microsecond. It is configured with a distributed control scheduling algorithm to realize parallel and collaborative control of multiple drive modules and multiple sensing modules, so as to avoid the paralysis of the entire system due to the failure of a single module. The ARM main processor and the FPGA coprocessor use the AXI high-speed parallel bus for bidirectional real-time data interaction. The bus clock frequency is stable at 100MHz and the data transmission delay is less than 0.5 microseconds. The ARM main processor mainly completes upper-level tasks such as process parameter parsing, human-machine interaction instruction processing, fault data storage, and remote communication data packaging. It then transmits the processed standard control instructions to the FPGA in a fixed data frame format. The FPGA coprocessor is responsible for receiving high-speed real-time data such as spindle encoder signals, servo position signals, and multi-sensor acquisition signals, and completing low-level real-time control tasks such as multi-axis synchronous calculation, position closed-loop calculation, and electronic cam curve generation. It also transmits data such as the real-time position of the helical frame, synchronization status, and module operation flag bit back to the ARM in real time. The distributed control scheduling algorithm adopts a hybrid scheduling mode of hardware interrupt priority and time-slice polling. It allocates the highest priority interrupt resources to servo drive control and position perception feedback, and allocates polling time slices to auxiliary modules such as lubrication regulation and temperature monitoring.

[0027] The distributed multi-axis servo drive architecture adopts a unified hardware clock reference, with a high-precision clock signal provided by the FPGA coprocessor. All servo drive units share the same clock source, and the clock synchronization error is less than 1 microsecond. The system periodically calibrates the operating phase of each servo unit through synchronization messages to avoid time differences and position deviations in multi-axis motion, ensuring that the 4-16-chip heald frame always maintains coordinated synchronization in high-speed reciprocating motion.

[0028] The core control module is equipped with multiple communication interfaces such as Ethernet, RS485, and CAN bus, which can interface with the loom main control system and the factory MES system to realize remote uploading of production data and remote monitoring of equipment. It is configured with a control command fault tolerance mechanism, which automatically switches to local backup control mode when communication is interrupted or commands are abnormal.

[0029] The control program for the local backup control mode is pre-stored in the built-in storage unit of the core control module. It contains basic weaving control parameters and standard motion curves adapted to conventional fabrics and can independently complete basic shedding control tasks. The system continuously monitors the communication status and command validity of Ethernet, RS485, and CAN bus in each control cycle. When external communication fails to receive valid data for three consecutive control cycles, the command checksum error rate exceeds 10%, or the command data format is abnormal, the fault-tolerant switching mechanism is immediately triggered. During the switching process, the system maintains the current heald frame position, opening height, motion curve and other key operating parameters unchanged, without interrupting the heald frame opening motion and weaving process, automatically disconnects the abnormal external communication link, and activates the local backup control program to maintain normal equipment operation; after the external communication is restored to normal and the command data is verified to be correct, the system automatically synchronizes the main control command and the latest process parameters, and smoothly switches back to the original control mode.

[0030] The Ethernet interface uses the TCP / IP protocol to transmit production data and fault information, with a data upload cycle of 1 second, and is used to connect to the factory MES system to achieve remote monitoring; the CAN bus interface is used for real-time communication between the core module and the servo and sensing units, with an interaction cycle of 1 millisecond; the RS485 interface is used for data transmission of auxiliary modules, with a cycle of 10 milliseconds; each interface uses a standard data frame format for transmission, including device address, command type, data content, and check bit.

[0031] In this embodiment of the invention, the power drive module adopts a distributed multi-axis servo drive architecture, with each helical frame corresponding to an independent servo drive unit, including a servo motor and a planetary reducer, and incorporates a model predictive control algorithm. The servo motor has a built-in 17-bit absolute encoder for obtaining absolute position information, and the planetary reducer adopts a helical gear hard tooth surface design to reduce transmission error and operating noise. The model predictive control algorithm is a linear time-varying model predictive controller for the reciprocating motion of textile heald frames. It consists of a trajectory prediction unit, a deviation calculation unit, an optimal solution unit, and a command output unit. The algorithm's prediction time domain is set to 25, the control time domain is set to 6, and the control period is 1ms. The algorithm's input data includes five types of parameters: target position of the heald frame, real-time position, motion resistance, warp tension feedback value, and loom spindle speed. With the goal of minimizing the position tracking error of the heald frame, constraints on motor speed and torque output are set to predict the motion trajectory deviation within the next 25 control cycles in real time. The optimal control quantity is solved by quadratic programming, and the real-time speed and torque correction commands of the servo motor are output to convert the rotational motion into linear motion of the heald frame, thereby realizing independent control of the opening height and opening time of a single heald frame.

[0032] The power drive module receives the position command from the core control module, predicts the deviation of the heald frame's motion trajectory in advance through the model predictive control algorithm, corrects the output speed and torque of the servo motor in real time, and drives the servo motor to convert the rotational motion into the up-and-down reciprocating linear motion of the heald frame through the ball screw pair, so as to realize the independent adjustment of the opening height, opening time and motion curve of a single heald frame. The servo motor output shaft is rigidly and flexibly connected to the input end of the ball screw pair through a high-elasticity plum blossom-shaped coupling, which can effectively compensate for radial and axial deviations during transmission and reduce operating vibration and transmission noise. The ball screw pair is made of high-strength alloy material and precision machined. It has a silent and dustproof integrated structure design, a lead of 5mm, a transmission efficiency of no less than 95%, and a repeatability of 0.005mm. Each servo drive unit independently corresponds to a set of ball screw pairs and a single heald frame. There is no mechanical linkage or transmission interference between the various transmission mechanisms. The opening height, movement speed, and curve shape adjustment of a single heald frame will not interfere with the operating status of other heald frames.

[0033] The distributed multi-axis servo drive architecture supports seamless switching between independent control and collaborative control modes. When weaving conventional fabrics, the collaborative control mode is enabled, and the heald frame synchronization module uniformly schedules the multi-axis motion. When weaving complex jacquard fabrics, it can be switched to independent control mode with one click. A single axis can be decoupled from the overall synchronization constraint and adjust the shedding parameters and motion curves independently. The switching process is uniformly coordinated by the FPGA coprocessor, which automatically completes phase synchronization and position calibration, without motion impact or position deviation.

[0034] Configure a servo motor adaptive load adjustment strategy to dynamically adjust the motor drive torque based on changes in warp tension and heald frame movement resistance, thereby avoiding motor overload or insufficient power.

[0035] In this embodiment of the invention, the heald frame synchronization module is based on electronic cam technology and master-slave synchronous control architecture, supports the coordinated control of 4-16 heald frames, is configured with a warp tension feedback electronic cam dynamic correction algorithm, and an incremental encoder is installed on the loom spindle to collect the spindle rotation angle signal in real time and transmit it to the FPGA; the FPGA converts the spindle rotation angle into the target position command of each heald frame according to the preset electronic cam curve, and at the same time receives the real-time data from the warp tension sensing unit; When abnormal fluctuations occur in warp tension, the phase and amplitude of the electronic cam curve are automatically corrected to compensate for the synchronization deviation caused by the elastic deformation of the warp. The built-in phase dynamic compensation algorithm can adjust the motion phase of each heald frame in real time according to the warp tension feedback, loom speed changes and mechanical transmission errors. It supports custom electronic cam curves and provides multiple motion modes such as sine curve, modified trapezoidal curve, and fifth-order polynomial curve. It can select the optimal motion curve according to the fabric characteristics to reduce the impact of heald frame movement and mechanical vibration. The system has a built-in electronic cam curve library and a custom editing tool. For regular fabrics, a modified trapezoidal curve is selected by default to reduce motion impact. For high-density fabrics, a fifth-order polynomial curve is selected to ensure smooth operation. For high-speed woven fabrics, a sine curve is selected to reduce warp tension fluctuations. Custom curves can be generated by manually inputting displacement, velocity, and acceleration parameters through the human-machine interface. The curve parameters are written to the FPGA storage unit in real time. When switching curves, the system automatically completes phase synchronization calibration.

[0036] The warp tension feedback closed-loop adjustment is based on real-time tension data. When the tension is higher than the standard value, the system slightly reduces the movement speed of the heald frame and reduces the opening height to release the tension. When the tension is lower than the standard value, the positioning accuracy of the heald frame is appropriately increased and the cam phase is compensated to tighten the tension. The adjustment process is gradually corrected with a period of 5ms to avoid sudden tension changes that could lead to yarn breakage or loosening.

[0037] Configured with multi-hedron collaborative fault-tolerant control, when a minor fault occurs in the drive unit of one hedron frame, the motion parameters of other hedron frames are automatically adjusted.

[0038] The multi-frame collaborative fault-tolerant control has a multi-dimensional fault judgment mechanism. The judgment criteria for minor faults are that the servo motor output torque deviation does not exceed 10%, the deviation between the actual position of the frame and the target position does not exceed 0.1mm, the unit communication data packet loss rate does not exceed 5%, and there are no serious abnormalities such as hardware overload or short circuit. When the system detects that a single heald frame drive unit meets the conditions for a minor fault, it immediately executes a fault-tolerant control strategy. First, it locks the current stable position of the faulty heald frame and stops its dynamic movement to prevent the fault from escalating. Simultaneously, it reduces the movement speed of adjacent heald frames by 15%, slightly corrects the phase and amplitude parameters of the electronic cam curve, and compensates for changes in warp tension in real time. In the fault-tolerant state, the system continuously monitors the status of the faulty unit. If the fault is automatically eliminated, it quickly restores normal collaborative control. If the fault persists, it retains the running status and pushes a manual intervention prompt to avoid unplanned downtime of the entire machine.

[0039] Once the faulty heddle frame unit returns to normal and all operating indicators return to the allowable range, the system automatically initiates the fault-tolerant exit process. First, it gradually restores the movement speed of adjacent heddle frames to the original set value, then synchronously corrects the electronic cam curve to the initial state, and finally releases the position lock of the faulty heddle frame, restoring normal collaborative control of the entire heddle frame. The recovery process uses a smooth and gradual adjustment of parameters, without impact or sudden tension changes.

[0040] In this embodiment of the invention, the state perception module includes a position perception unit, a motion state perception unit, a lubrication state perception unit, a temperature perception unit, and a warp tension perception unit, and is configured with an attention mechanism algorithm for multi-source perception data fusion; The attention mechanism is a multi-sensor feature weighted attention module for textile weaving scenarios, consisting of a four-level structure: feature normalization layer, feature mapping layer, weight allocation layer, and feature fusion layer. First, the five types of sensing data—position, vibration, temperature, tension, and lubrication—are normalized and preprocessed to map the data uniformly to the 0-1 interval. The input data dimension is an N×5 time-series matrix within the sampling period, where N is the number of sampling points per period. The weighting layer calculates the contribution weight of each sensor data through two fully connected networks and a sigmoid activation function. It assigns high weights of 0.85-1.0 to the two core data types, heald frame position and warp tension, and dynamic weights of 0.2-0.8 to auxiliary data such as temperature and lubrication, eliminating invalid and redundant data. Finally, it outputs a fused one-dimensional key feature vector, which is directly transmitted to the core control module for motion control and fault diagnosis.

[0041] The optimization rules are set in combination with the characteristics of textile fabrics: when the warp tension fluctuation is >3%, the sheath height is automatically reduced; when the vibration amplitude is >0.5g, the slope of the motion curve is optimized; and the phase parameters are compensated synchronously when the loom speed changes. The algorithm runs for 50ms and iterates and optimizes in real time according to the weaving status.

[0042] Process parameter templates are categorized and stored according to fabric type, yarn material, and weaving speed. It supports storing no less than 1,000 sets of customized templates. Each template includes a complete set of parameters such as the number of heald frames, shedding parameters, cam curve, lubrication strategy, and warning threshold. When a template is called, the system automatically verifies parameter compatibility, loads all parameters with one click, and completes hardware initialization. It supports template copying, editing, and encryption protection, and can be imported and exported in batches via external storage devices.

[0043] The system first obtains the real-time warp tension value through the tension sensing unit, compares the tension fluctuation amplitude with the preset standard range, and then activates the electronic cam dynamic correction algorithm to adjust the curve amplitude and basic phase. Then, combined with the spindle speed and mechanical transmission error data, a second precise fine adjustment is made through the phase dynamic compensation algorithm. The two algorithms are executed in a coordinated manner in the order of amplitude correction first and phase compensation later, and the calculation and parameter update are completed every 5ms, keeping the warp tension stable and the multi-heald frame movement synchronized throughout the process.

[0044] The FPGA coprocessor is used for real-time motion control, high-speed acquisition of encoder signals and multi-axis synchronous operation, with a response delay of no more than 1 microsecond. It is configured with a distributed control scheduling algorithm to realize parallel and collaborative control of multiple drive modules and multiple sensing modules, so as to avoid the paralysis of the entire system due to the failure of a single module. The ARM main processor and the FPGA coprocessor use the AXI high-speed parallel bus for bidirectional real-time data interaction. The bus clock frequency is stable at 100MHz and the data transmission delay is less than 0.5 microseconds. The ARM main processor mainly completes upper-level tasks such as process parameter parsing, human-machine interaction instruction processing, fault data storage, and remote communication data packaging. It then transmits the processed standard control instructions to the FPGA in a fixed data frame format. The FPGA coprocessor is responsible for receiving high-speed real-time data such as spindle encoder signals, servo position signals, and multi-sensor acquisition signals, and completing low-level real-time control tasks such as multi-axis synchronous calculation, position closed-loop calculation, and electronic cam curve generation. It also transmits data such as the real-time position of the helical frame, synchronization status, and module operation flag bit back to the ARM in real time. The distributed control scheduling algorithm adopts a hybrid scheduling mode of hardware interrupt priority and time-slice polling. It allocates the highest priority interrupt resources to servo drive control and position perception feedback, and allocates polling time slices to auxiliary modules such as lubrication regulation and temperature monitoring.

[0045] The distributed multi-axis servo drive architecture adopts a unified hardware clock reference, with a high-precision clock signal provided by the FPGA coprocessor. All servo drive units share the same clock source, and the clock synchronization error is less than 1 microsecond. The system periodically calibrates the operating phase of each servo unit through synchronization messages to avoid time differences and position deviations in multi-axis motion, ensuring that the 4-16-chip heald frame always maintains coordinated synchronization in high-speed reciprocating motion.

[0046] The core control module is equipped with multiple communication interfaces such as Ethernet, RS485, and CAN bus, which can interface with the loom main control system and the factory MES system to realize remote uploading of production data and remote monitoring of equipment. It is configured with a control command fault tolerance mechanism, which automatically switches to local backup control mode when communication is interrupted or commands are abnormal.

[0047] The control program for the local backup control mode is pre-stored in the built-in storage unit of the core control module. It contains basic weaving control parameters and standard motion curves adapted to conventional fabrics and can independently complete basic shedding control tasks. The system continuously monitors the communication status and command validity of Ethernet, RS485, and CAN bus in each control cycle. When external communication fails to receive valid data for three consecutive control cycles, the command checksum error rate exceeds 10%, or the command data format is abnormal, the fault-tolerant switching mechanism is immediately triggered. During the switching process, the system maintains the current heald frame position, opening height, motion curve and other key operating parameters unchanged, without interrupting the heald frame opening motion and weaving process, automatically disconnects the abnormal external communication link, and activates the local backup control program to maintain normal equipment operation; after the external communication is restored to normal and the command data is verified to be correct, the system automatically synchronizes the main control command and the latest process parameters, and smoothly switches back to the original control mode.

[0048] The Ethernet interface uses the TCP / IP protocol to transmit production data and fault information, with a data upload cycle of 1 second, and is used to connect to the factory MES system to achieve remote monitoring; the CAN bus interface is used for real-time communication between the core module and the servo and sensing units, with an interaction cycle of 1 millisecond; the RS485 interface is used for data transmission of auxiliary modules, with a cycle of 10 milliseconds; each interface uses a standard data frame format for transmission, including device address, command type, data content, and check bit.

[0049] In this embodiment of the invention, the power drive module adopts a distributed multi-axis servo drive architecture, with each helical frame corresponding to an independent servo drive unit, including a servo motor and a planetary reducer, and incorporates a model predictive control algorithm. The servo motor has a built-in 17-bit absolute encoder for obtaining absolute position information, and the planetary reducer adopts a helical gear hard tooth surface design to reduce transmission error and operating noise. The model predictive control algorithm is a linear time-varying model predictive controller for the reciprocating motion of textile heald frames. It consists of a trajectory prediction unit, a deviation calculation unit, an optimal solution unit, and a command output unit. The algorithm's prediction time domain is set to 25, the control time domain is set to 6, and the control period is 1ms. The algorithm's input data includes five types of parameters: target position of the heald frame, real-time position, motion resistance, warp tension feedback value, and loom spindle speed. With the goal of minimizing the position tracking error of the heald frame, constraints on motor speed and torque output are set to predict the motion trajectory deviation within the next 25 control cycles in real time. The optimal control quantity is solved by quadratic programming, and the real-time speed and torque correction commands of the servo motor are output to convert the rotational motion into linear motion of the heald frame, thereby realizing independent control of the opening height and opening time of a single heald frame.

[0050] The power drive module receives the position command from the core control module, predicts the deviation of the heald frame's motion trajectory in advance through the model predictive control algorithm, corrects the output speed and torque of the servo motor in real time, and drives the servo motor to convert the rotational motion into the up-and-down reciprocating linear motion of the heald frame through the ball screw pair, so as to realize the independent adjustment of the opening height, opening time and motion curve of a single heald frame. The servo motor output shaft is rigidly and flexibly connected to the input end of the ball screw pair through a high-elasticity plum blossom-shaped coupling, which can effectively compensate for radial and axial deviations during transmission and reduce operating vibration and transmission noise. The ball screw pair is made of high-strength alloy material and precision machined. It has a silent and dustproof integrated structure design, a lead of 5mm, a transmission efficiency of no less than 95%, and a repeatability of 0.005mm. Each servo drive unit independently corresponds to a set of ball screw pairs and a single heald frame. There is no mechanical linkage or transmission interference between the various transmission mechanisms. The opening height, movement speed, and curve shape adjustment of a single heald frame will not interfere with the operating status of other heald frames.

[0051] The distributed multi-axis servo drive architecture supports seamless switching between independent control and collaborative control modes. When weaving conventional fabrics, the collaborative control mode is enabled, and the heald frame synchronization module uniformly schedules the multi-axis motion. When weaving complex jacquard fabrics, it can be switched to independent control mode with one click. A single axis can be decoupled from the overall synchronization constraint and adjust the shedding parameters and motion curves independently. The switching process is uniformly coordinated by the FPGA coprocessor, which automatically completes phase synchronization and position calibration, without motion impact or position deviation.

[0052] Configure a servo motor adaptive load adjustment strategy to dynamically adjust the motor drive torque based on changes in warp tension and heald frame movement resistance, thereby avoiding motor overload or insufficient power.

[0053] In this embodiment of the invention, the heald frame synchronization module is based on electronic cam technology and master-slave synchronous control architecture, supports the coordinated control of 4-16 heald frames, is configured with a warp tension feedback electronic cam dynamic correction algorithm, and an incremental encoder is installed on the loom spindle to collect the spindle rotation angle signal in real time and transmit it to the FPGA; the FPGA converts the spindle rotation angle into the target position command of each heald frame according to the preset electronic cam curve, and at the same time receives the real-time data from the warp tension sensing unit; When abnormal fluctuations occur in warp tension, the phase and amplitude of the electronic cam curve are automatically corrected to compensate for the synchronization deviation caused by the elastic deformation of the warp. The built-in phase dynamic compensation algorithm can adjust the motion phase of each heald frame in real time according to the warp tension feedback, loom speed changes and mechanical transmission errors. It supports custom electronic cam curves and provides multiple motion modes such as sine curve, modified trapezoidal curve, and fifth-order polynomial curve. It can select the optimal motion curve according to the fabric characteristics to reduce the impact of heald frame movement and mechanical vibration. The system has a built-in electronic cam curve library and a custom editing tool. For regular fabrics, a modified trapezoidal curve is selected by default to reduce motion impact. For high-density fabrics, a fifth-order polynomial curve is selected to ensure smooth operation. For high-speed woven fabrics, a sine curve is selected to reduce warp tension fluctuations. Custom curves can be generated by manually inputting displacement, velocity, and acceleration parameters through the human-machine interface. The curve parameters are written to the FPGA storage unit in real time. When switching curves, the system automatically completes phase synchronization calibration.

[0054] The warp tension feedback closed-loop adjustment is based on real-time tension data. When the tension is higher than the standard value, the system slightly reduces the movement speed of the heald frame and reduces the opening height to release the tension. When the tension is lower than the standard value, the positioning accuracy of the heald frame is appropriately increased and the cam phase is compensated to tighten the tension. The adjustment process is gradually corrected with a period of 5ms to avoid sudden tension changes that could lead to yarn breakage or loosening.

[0055] Configured with multi-hedron collaborative fault-tolerant control, when a minor fault occurs in the drive unit of one hedron frame, the motion parameters of other hedron frames are automatically adjusted.

[0056] The multi-frame collaborative fault-tolerant control has a multi-dimensional fault judgment mechanism. The judgment criteria for minor faults are that the servo motor output torque deviation does not exceed 10%, the deviation between the actual position of the frame and the target position does not exceed 0.1mm, the unit communication data packet loss rate does not exceed 5%, and there are no serious abnormalities such as hardware overload or short circuit. When the system detects that a single heald frame drive unit meets the conditions for a minor fault, it immediately executes a fault-tolerant control strategy. First, it locks the current stable position of the faulty heald frame and stops its dynamic movement to prevent the fault from escalating. Simultaneously, it reduces the movement speed of adjacent heald frames by 15%, slightly corrects the phase and amplitude parameters of the electronic cam curve, and compensates for changes in warp tension in real time. In the fault-tolerant state, the system continuously monitors the status of the faulty unit. If the fault is automatically eliminated, it quickly restores normal collaborative control. If the fault persists, it retains the running status and pushes a manual intervention prompt to avoid unplanned downtime of the entire machine.

[0057] Once the faulty heddle frame unit returns to normal and all operating indicators return to the allowable range, the system automatically initiates the fault-tolerant exit process. First, it gradually restores the movement speed of adjacent heddle frames to the original set value, then synchronously corrects the electronic cam curve to the initial state, and finally releases the position lock of the faulty heddle frame, restoring normal collaborative control of the entire heddle frame. The recovery process uses a smooth and gradual adjustment of parameters, without impact or sudden tension changes.

[0058] In this embodiment of the invention, the state perception module includes a position perception unit, a motion state perception unit, a lubrication state perception unit, a temperature perception unit, and a warp tension perception unit, and is configured with an attention mechanism algorithm for multi-source perception data fusion; The attention mechanism is a multi-sensor feature weighted attention module for textile weaving scenarios, consisting of a four-level structure: feature normalization layer, feature mapping layer, weight allocation layer, and feature fusion layer. First, the five types of sensing data—position, vibration, temperature, tension, and lubrication—are normalized and preprocessed to map the data uniformly to the 0-1 interval. The input data dimension is an N×5 time-series matrix within the sampling period, where N is the number of sampling points per period. The weighting layer calculates the contribution weight of each sensor data through two fully connected networks and a sigmoid activation function. It assigns high weights of 0.85-1.0 to the two core data types, heald frame position and warp tension, and dynamic weights of 0.2-0.8 to auxiliary data such as temperature and lubrication, eliminating invalid and redundant data. Finally, it outputs a fused one-dimensional key feature vector, which is directly transmitted to the core control module for motion control and fault diagnosis.

Claims

1. A textile heald frame opening control system, characterized in that, It includes a core control module, a power drive module, a system synchronization module, a status perception module, a fault early warning module, a lubrication control module, and a human-machine interaction module; The core control module adopts a heterogeneous collaborative architecture, with the main processor and coprocessor working together. The main processor is used for upper-level logic scheduling, data storage, industrial communication and process parameter self-optimization, while the coprocessor is used for real-time motion control and multi-axis synchronous calculation. The power drive module adopts a distributed multi-axis servo drive architecture, which configures an independent servo drive unit for each heel frame, receives control commands and corrects the output in real time, and dynamically adjusts the drive torque according to the running resistance. The heald frame synchronization module is based on an electronic cam and master-slave synchronization control architecture, supports multi-heal frame collaborative control, converts the spindle rotation angle signal into heald frame target position command, adjusts motion parameters in combination with warp tension and transmission error, and is configured with multi-heal frame collaborative fault-tolerant control function. The status perception module includes multiple sensing units, which monitor the position of the heald frame, the movement status of the equipment, the lubrication status, the temperature of the components and the warp tension, respectively. The collected data is transmitted to the core control module, and the system is configured with a self-check and compensation function for abnormal sensing data. The fault early warning module has a built-in fault knowledge base and anomaly detection algorithm, automatically updates fault knowledge, adopts a two-level early warning mechanism, locates the fault location and pushes processing suggestions, and supports automatic repair of minor faults and fault data query and statistics. The lubrication control module adopts an active centralized lubrication system, which integrates multiple lubrication-related units, dynamically adjusts lubrication parameters according to the equipment operating status, and is equipped with a lubricating oil status monitoring function. The human-machine interaction module adopts a capacitive touch screen, supports dual operation of multi-touch and physical buttons, displays equipment operating parameters and control status in real time, and supports process parameter setting, calling and fault alarm information push.

2. The textile heald frame opening control system according to claim 1, characterized in that, The core control module adopts a heterogeneous collaborative architecture of ARM Cortex-A9 main processor and Xilinx Artix-7 FPGA coprocessor; the ARM processor is used for upper-level logic scheduling, human-machine interaction response, data storage and industrial communication, and has a built-in 16GB eMMC storage unit for storing fabric process parameter templates and configuring a process parameter self-optimization module, which can automatically correct process parameters according to real-time running data during the weaving process. The FPGA coprocessor is used for real-time motion control, high-speed acquisition of encoder signals and multi-axis synchronous calculation. It is configured with a distributed control scheduling algorithm to realize parallel and collaborative control of multiple drive modules and multiple sensing modules. The core control module is equipped with multiple communication interfaces such as Ethernet, RS485, and CAN bus, which can interface with the loom main control system and the factory MES system to realize remote uploading of production data and remote monitoring of equipment. The core control module is configured with a control command fault tolerance mechanism, which automatically switches to the local backup control mode when communication is interrupted or commands are abnormal.

3. The textile heald frame opening control system according to claim 2, characterized in that, Each frame of the power drive module corresponds to an independent servo drive unit, including a servo motor and a planetary reducer. The servo drive unit incorporates a model predictive control algorithm. The servo motor has a built-in 17-bit absolute encoder for obtaining absolute position information, and the planetary reducer adopts a helical gear hardened tooth surface design. The power drive module receives the position command from the core control module, predicts the deviation of the heald frame's motion trajectory in advance through the model predictive control algorithm, and corrects the output speed and torque of the servo motor in real time. The servo motor converts the rotational motion into the up-and-down reciprocating linear motion of the heald frame through the ball screw pair, realizing the independent adjustment of the opening height, opening time and motion curve of a single heald frame. Configure a servo motor adaptive load adjustment strategy to dynamically adjust the motor drive torque based on changes in warp tension and heald frame motion resistance.

4. A textile heald frame opening control system according to claim 3, characterized in that, The heald frame synchronization module supports the coordinated control of 4-16 heald frames and is configured with a warp tension feedback electronic cam dynamic correction and phase compensation algorithm. An incremental encoder is installed on the loom spindle to collect the spindle rotation angle signal in real time and transmit it to the FPGA. The FPGA converts the spindle rotation angle into the target position command of each heald frame according to the preset electronic cam curve, and at the same time receives real-time data from the warp tension sensing unit. When abnormal fluctuations occur in warp tension, the phase and amplitude of the electronic cam curve are automatically corrected to compensate for the synchronization deviation caused by the elastic deformation of the warp. The built-in phase dynamic compensation algorithm can adjust the motion phase of each heald frame in real time according to the warp tension feedback, loom speed changes and mechanical transmission errors. It supports custom electronic cam curves and provides a variety of motion modes, including sine curves, modified trapezoidal curves, and fifth-order polynomial curves, and can select the optimal motion curve according to the fabric characteristics. The multi-hedron collaborative fault-tolerant control function is as follows: when a minor fault occurs in the drive unit of a certain hedron frame, the hedron frame synchronization module automatically adjusts the motion parameters of other hedron frames.

5. A textile heald frame opening control system according to claim 4, characterized in that, The state perception module includes a position perception unit, a motion state perception unit, a lubrication state perception unit, a temperature perception unit, and a warp tension perception unit, and is configured with an attention mechanism algorithm for multi-source perception data fusion. The position sensing unit consists of a servo motor with a built-in encoder and a laser displacement sensor at the end of the heald frame, providing dual verification of the actual position of the heald frame. The motion state sensing unit is a triaxial accelerometer mounted on the heald frame beam and the reducer housing, monitoring the vibration amplitude and frequency of the heald frame and identifying early fault characteristics. The lubrication state sensing unit includes a radar level sensor, an oil quality sensor, and a flow sensor, which monitor the lubricating oil level, oil contamination level, and oil supply flow at each lubrication point, respectively. The temperature sensing unit consists of PT100 platinum resistance sensors distributed at the servo motor, reducer, and guide rail slider, monitoring the operating temperature of key components. The warp tension sensing unit is a tension sensor installed between the warp beam and the heald frame, collecting warp tension change data in real time. All sensing data is transmitted to the core control module in real time via the CAN bus. Key feature data is filtered and redundant information is removed through the attention mechanism algorithm. The sensing data anomaly self-check and compensation function is as follows: when a sensor shows data anomaly, it automatically switches to the backup sensor or uses historical data interpolation compensation.

6. A textile heald frame opening control system according to claim 5, characterized in that, The fault early warning module has a built-in fault knowledge base and a multi-parameter fusion anomaly detection algorithm, which adopts a combination of LSTM long short-term memory neural network and attention mechanism. The fault knowledge base stores the feature parameters, causes and handling methods of common faults, including three categories: mechanical faults, electrical faults and lubrication faults. It is equipped with a fault feature self-learning module, which can automatically update the fault knowledge base according to the fault data in actual operation. The two-level early warning mechanism is as follows: for threshold-type faults, an audible and visual early warning is issued when the detection parameter exceeds a preset threshold. For gradual faults, Attention-LSTM neural network is used to analyze the trend changes of vibration, temperature and location data, extract early fault features and issue early warnings. When a fault occurs, the system automatically locates the fault location, generates a fault code and standardized handling suggestions, and pushes them to relevant personnel through the human-machine interface and remote communication interface; For minor, repairable faults, the system automatically initiates a fault self-healing control strategy and adjusts relevant control parameters. Supports querying and statistical analysis of historical fault data.

7. A textile heald frame opening control system according to claim 6, characterized in that, The lubrication control module includes a variable frequency lubricating oil pump, a multi-channel solenoid valve group, a lubricating oil preheating unit, and a three-stage filtration unit, incorporating a lubrication demand prediction model and a closed-loop feedback control strategy. The variable frequency lubricating oil pump can dynamically adjust the oil supply pressure based on the loom speed, running time, key component temperature, and vibration data, using the lubrication demand prediction model. The multi-channel solenoid valve group corresponds to each key lubrication point, and the core control module independently controls the oil supply time and quantity for each lubrication point. The lubricating oil preheating unit automatically starts when the ambient temperature is too low, and the three-stage filtration unit is used to remove impurities from the lubricating oil. The core control module constructs a lubrication regulation closed loop based on the data from the lubrication status sensing unit, collects the oil status, component temperature and vibration data of each lubrication point in real time, and feeds them back to the lubrication demand prediction model to dynamically optimize the lubrication strategy. When the loom is running at high speed, the oil supply and frequency are automatically increased; when the temperature of a certain lubrication point rises abnormally, the oil supply to the corresponding point is temporarily increased; when the oil contamination level exceeds the standard, an early warning for replacing the lubricating oil is issued, and the oil supply pressure and frequency are automatically adjusted; the lubricating oil status monitoring function includes lubricating oil regeneration status monitoring, which monitors the lubricating oil regeneration capacity in real time through an oil quality sensor.

8. A textile heald frame opening control system according to claim 7, characterized in that, The human-machine interaction module is configured with a control parameter self-recommendation and manual fine-tuning linkage function. The main interface displays the loom speed, the position of each heald frame, the warp tension, the lubricating oil level, and the operating parameters of key components in real time, and shows the parameter change trend in the form of curves. It also has a control strategy operation status display function. The parameter configuration interface supports setting the number of heald frames, opening height, opening time, electronic cam curve, lubrication parameters, and fault warning thresholds. All parameter modifications take effect in real time. Built-in process parameter management function, supporting the creation, editing, deletion and one-click recall of process parameters, can import and export process parameters via USB interface or SD card, and configure process parameter comparison and analysis function; It features a fault alarm pop-up window that displays the fault code, fault location, and handling steps. It supports alarm information confirmation and historical alarm query, and can be set to display the fault self-repair progress.

9. A method for controlling the opening of a textile heald frame, based on the system described in any one of claims 1-8, characterized in that, Includes the following steps: System initialization: Configure the loom process parameters through the core control module, select the electronic cam curve that is suitable for the fabric characteristics, set the opening height, opening time and synchronization accuracy parameters of each heald frame, and store the parameters in the built-in storage unit of the core control module to complete the initial deployment of the control strategy; Sensing data acquisition and transmission: After the control process is started, each sensing unit of the state sensing module works synchronously to collect data on heald frame position, motion vibration, key component temperature, warp tension and lubrication status in real time. After being filtered and processed by the attention mechanism algorithm of multi-source sensing data fusion, the data is transmitted to the core control module. Core control and power drive: Based on the received sensing data, the core control module, through collaborative calculations between the main processor and the coprocessor, predicts the deviation of the heald frame's motion trajectory, generates position control commands for each heald frame, and transmits them to the power drive module; after receiving the commands, the power drive module drives the heald frames to perform reciprocating linear motion and dynamically adjusts the driving torque according to changes in warp tension. Heald frame synchronization and fault warning: The heald frame synchronization module converts the spindle rotation angle signal into a target position command for the heald frame, and corrects the motion parameters in combination with the warp tension data to achieve coordinated movement of multiple heald frames; at the same time, the fault warning module analyzes the sensing data in real time, identifies fault characteristics and issues warnings, locates the fault location and pushes processing suggestions, and automatically starts a self-repair strategy for minor faults. Lubrication control and human-machine interaction: The lubrication control module dynamically adjusts lubrication parameters according to the equipment operating status and activates the corresponding lubricating oil treatment function; the human-machine interaction module displays the equipment operating parameters in real time and supports manual fine-tuning of parameters and calling process templates.

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