Supervision control system, method and equipment in industrial weaving process and storage medium

Abstract

The invention relates to the technical field of control systems, and provides a supervision control system, method and equipment in an industrial weaving process and a storage medium. The method comprises the following steps: acquiring engineering data and environment data in a weaving process through an intelligent sensor, and standardizing an original spatio-temporal data matrix formed by the engineering data and the environment data to generate an engineering matrix; attribute information including elasticity, fluid and heat conduction is generated based on the engineering matrix, and a space model representing the weaving process state is constructed based on the attribute information; generating a dynamic control parameter based on the deviation between the model parameter of the spatial model and the target trajectory; generating a tension signal for the dynamic control parameter and a preset target tension, and generating an execution instruction through the tension signal to control the weaving equipment to operate. A multi-physics field coupling space model is constructed, dynamic control parameters are generated according to the deviation between model parameters and a target track, and an accurate closed-loop supervision control process from data perception to equipment control is formed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of control systems, in particular to a supervisory control system, method, device and storage medium in an industrial weaving process. BACKGROUND

[0002] Industrial weaving is the core link of the textile industry, which converts fiber raw materials into various fabrics through weaving, knitting, non-woven and other processes, covering cotton spinning, chemical fiber, blended materials and other multi-materials, relying on automatic equipment such as air-jet looms and circular weaves to achieve efficient production, and the products are widely used in clothing, home textiles, automotive interiors and medical consumables fields. The global industrial weaving equipment market is in a rapid expansion phase. In 2021, the global automatic weaving equipment market size has reached a significant level, and it is expected to double by 2028. The growth rate in the Chinese market is particularly outstanding, with the market size accounting for about 30% of the global market in 2021, and it is expected to increase to more than 40% by 2028.

[0003] However, the limitations of traditional control methods are increasingly prominent. Classical control theory relies on accurate mathematical models, which is difficult to cope with time-varying, nonlinear and uncertain nature in industrial weaving. For example, in the complex process of yarn tension control and equipment coordination, traditional methods often result in insufficient control accuracy due to model simplification. SUMMARY

[0004] The present application provides a supervisory control system, method, device and storage medium in an industrial weaving process, which can at least partially solve the problem of insufficient supervisory control accuracy in the industrial weaving process.

[0005] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0006] According to one aspect of the present application, a supervisory control method in an industrial weaving process is provided, comprising: acquiring engineering data and environmental data in the weaving process through intelligent sensors; performing standardization processing on the original space-time data matrix composed of the engineering data and the environmental data to generate an engineering matrix; generating attribute information including elasticity, fluid and heat conduction based on the engineering matrix, and constructing a space model representing the state of the weaving process based on the attribute information; generating dynamic control parameters based on the deviation between the model parameters of the space model and the target trajectory; generating a tension signal from the dynamic control parameters and the preset target tension, and generating an execution instruction through the tension signal to control the operation of the weaving equipment.

[0007] In this application, based on the aforementioned scheme, the standardization process of the original spatiotemporal data matrix composed of the engineering data and the environmental data to generate the engineering matrix includes: performing wavelet transform on the original spatiotemporal data matrix composed of the engineering data and the environmental data to generate first data; calculating the mean and variance of the first data, and generating the engineering matrix based on the mean and variance.

[0008] In this application, based on the aforementioned scheme, the step of generating attribute information including elasticity, fluidity, and heat conduction based on the engineering matrix, and constructing a spatial model representing the state of the weaving process based on the attribute information, includes: determining the acceleration term, elastic restoring force, and nonlinear elastic force of the yarn displacement field based on the yarn displacement data in the engineering matrix; determining the hydrodynamic coupling term of the airflow field on the yarn based on the airflow velocity in the engineering matrix; and constructing a spatial model representing the state of the weaving process based on the acceleration term, elastic restoring force, nonlinear elastic force, and the hydrodynamic coupling term of the yarn displacement field.

[0009] In this application, based on the aforementioned scheme, determining the acceleration term, elastic restoring force, and nonlinear elastic force of the yarn displacement field based on the yarn displacement data in the engineering matrix includes: determining the acceleration term of the yarn displacement field as follows based on the yarn displacement data u and time t in the engineering matrix. The elastic restoring force is determined based on the yarn displacement data u and wave velocity c. Based on the yarn displacement data u and the preset nonlinear elastic coefficient a, the nonlinear elastic force is determined to be au. 3 .

[0010] In this application, based on the aforementioned scheme, determining the hydrodynamic coupling term of the airflow field on the yarn based on the airflow field velocity in the engineering matrix includes: determining the hydrodynamic coupling term of the airflow field on the yarn as β∫ based on the airflow field velocity in the engineering matrix. Ω v(τ,η)dηdτ; where β represents the hydrodynamic coupling coefficient, and v(τ,η) represents the velocity vector of the airflow field at time τ and spatial position η;

[0011] Specifically, based on the acceleration term, elastic restoring force, nonlinear elastic force, and hydrodynamic coupling term of the yarn displacement field, a spatial model representing the state of the weaving process is constructed, including: The spatial model representing the state of the weaving process is constructed based on the acceleration term, elastic restoring force, nonlinear elastic force, and hydrodynamic coupling term of the yarn displacement field as follows:

[0012]

[0013] Where F(t,ξ) represents the periodic force exerted by the loom at spatial coordinate ξ at time t.

[0014] In the present application, based on the foregoing scheme, the dynamic control parameter is generated based on the deviation between the model parameters of the spatial model and the target trajectory, comprising: constructing a dynamic function based on the state tracking error between the model parameters of the spatial model and the target trajectory; solving the dynamic function by variational method to generate the dynamic control parameter.

[0015] In the present application, based on the foregoing scheme, the tension signal is generated based on the dynamic control parameter and the preset target tension, and the execution instruction is generated based on the tension signal to control the operation of the weaving equipment, comprising: determining the error signal based on the dynamic control parameter and the preset target tension; processing the error signal by a control algorithm to generate a tension signal; converting the tension signal into an execution instruction to instruct the weaving equipment to operate.

[0016] According to an aspect of the present application, there is provided a supervisory control system in an industrial weaving process, comprising:

[0017] An acquisition unit is configured to acquire engineering data and environmental data in a weaving process through an intelligent sensor;

[0018] A matrix unit is configured to perform standardization processing on an original space-time data matrix composed of the engineering data and the environmental data to generate an engineering matrix;

[0019] A model unit is configured to generate attribute information including elasticity, fluidity and heat conduction based on the engineering matrix, and construct a spatial model representing the state of the weaving process based on the attribute information;

[0020] A parameter unit is configured to generate a dynamic control parameter based on the deviation between the model parameters of the spatial model and the target trajectory;

[0021] A control unit is configured to generate a tension signal based on the dynamic control parameter and a preset target tension, and generate an execution instruction based on the tension signal to control the operation of the weaving equipment.

[0022] According to an aspect of the present application, there is provided a computer readable medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the supervisory control method in an industrial weaving process as described in the above embodiments.

[0023] According to an aspect of the present application, there is provided an electronic device, comprising: one or more processors; a storage device configured to store one or more programs, wherein the one or more programs are executed by the one or more processors to cause the one or more processors to implement the supervisory control method in an industrial weaving process as described in the above embodiments.

[0024] According to an aspect of the present application, a computer program product or computer program is provided, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the industrial weaving process supervision control method provided in the various optional implementation manners described above.

[0025] In the technical solution of the present application, engineering data and environmental data of the weaving process are collected in real time by intelligent sensors, and a unified dimension engineering matrix is generated after standardization processing. Based on the matrix, elasticity, fluid and heat conduction properties are extracted, and a multi-physical field coupled space model is constructed. Dynamic control parameters are generated according to the deviation of model parameters and target trajectory. Finally, the tension signal is generated by combining the dynamic parameters and the preset target tension, which is converted into an execution instruction to control the operation of the weaving equipment, forming a precise closed-loop supervision control process from data perception to equipment control.

[0026] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present application. BRIEF DESCRIPTION OF DRAWINGS

[0027] The accompanying drawings incorporated in and forming a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application. It is expressly understood that the drawings are only exemplary and are limiting of the present application, as, upon reading this description, those of ordinary skill in the art will appreciate various adaptations of the application that are within the scope of the claims. Therefore, and without departing from the spirit of the application, numerous other embodiments will be apparent to those skilled in the art.

[0028] Figure 1 A flow chart of the industrial weaving process supervision control method in an embodiment of the present application is schematically shown.

[0029] Figure 2 A flow chart of the space model of the weaving process state in an embodiment of the present application is schematically shown.

[0030] Figure 3 A schematic diagram of the industrial weaving process supervision control system in an embodiment of the present application is schematically shown.

[0031] Figure 4 A structural schematic diagram of a computer system of an electronic device suitable for implementing embodiments of the present application is shown. DETAILED DESCRIPTION

[0032] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art.

[0033] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the

[0034] The block diagrams in the drawings show only the functionality of the example implementations and do not imply any particular physical or architectural arrangement of the example implementations. No inference should be made regarding the architecture or configuration of a hardware implementation from the description of the example implementations provided herein. Also, the functionality provided by the example implementations can be split into additional components not explicitly described, for example, physical components. Additionally, the examples can be performed on electrical components, software routines, and / or any combination thereof. It is further noted that the example implementations described herein can be carried out by way of software or hardware components. The example implementations described herein can be implemented in one or more computer programs executing on one or more computer systems.

[0035] The flow diagrams depicted in the figures are merely exemplary and do not necessarily include all of the steps or operations set forth therein, nor do the flow diagrams necessarily include the order in which the steps or operations are presented. For example, some operations can be combined or partially combined, and some operations can be performed or omitted entirely. Additionally, the order of the steps or operations can be changed, depending on the implementation.

[0036] The implementation details of the technical solutions of the present application are described below:

[0037] Figure 1 A flow chart of an industrial weaving process monitoring control method according to an embodiment of the present application is shown. Referring to Figure 1 As shown, the industrial weaving process monitoring control method includes at least steps S110 to S150, which are described in detail as follows:

[0038] S110, obtaining engineering data and environmental data in the weaving process through an intelligent sensor.

[0039] In an embodiment of the present application, the intelligent sensors are selected according to the characteristics of the weaving process, for example, a high-precision tension sensor is installed at the warp beam of the loom to monitor the dynamic tension of the yarn, a laser displacement sensor is deployed at the weaving position to capture the fabric weft density in real time, the sampling frequency can be set to 1000 Hz, and humidity sensors and dust concentration sensors are arranged at key areas in the workshop to detect substances with a particle size greater than or equal to 0.3 microns. The sensor installation needs to follow the non-intrusive design principle, such as using a magnetic base to fix it to the loom frame, and using an industrial plug for the data interface to ensure anti-vibration performance.

[0040] In an embodiment of the present application, a data acquisition system is constructed in cooperation with the edge computing cloud, the edge node uses an advanced reduced instruction set computer industrial control computer to carry a protocol stack, and the original sensor data is analyzed in real time, such as the analog signal of the tension sensor is converted through a 24-bit digital-to-analog converter, and data cleaning and feature extraction are completed synchronously, for example, the wavelet denoising algorithm is applied to filter out mechanical vibration interference, and key indicators such as tension fluctuation standard deviation and weft density deviation rate are calculated. The transmission layer uses wireless networking and wired Ethernet redundant backup to ensure that the transmission delay of key data is less than the set threshold, and the conventional monitoring data is uploaded to the industrial internet platform at a preset time period, and data interaction with the system is realized through the message queue telemetry transmission protocol.

[0041] A multi-modal data fusion engine is deployed in the cloud to perform spatio-temporal alignment of engineering parameters (tension, speed, density) and environmental parameters (temperature and humidity, dust), and to construct a digital twin model to simulate the weaving process. A quality prediction model is established using deep learning algorithms such as neural networks to identify defect types through historical data training, and a rule engine is developed to set threshold alarms. Finally, real-time working conditions are displayed through visual boards to support production scheduling and process optimization decisions.

[0042] The above process realizes the synchronous perception of engineering parameters (such as yarn tension, loom speed) and environmental parameters (such as workshop temperature and humidity, air flow rate) in the weaving process by deploying multiple types of intelligent sensors such as tension, displacement, and temperature and humidity. A multi-source heterogeneous data base is constructed to ensure that the control system has global situational awareness capability, providing real-time, high-dimensional original input for subsequent modeling and control, and solving the problem of local optimization caused by traditional single parameter control.

[0043] S120, standardizing the original spatio-temporal data matrix composed of the engineering data and the environmental data to generate an engineering matrix.

[0044] In an embodiment of the present application, when normalizing the original space-time data matrix, domain-specific specifications need to be implemented based on the characteristics of multi-source data. First, the data matrix structure is analyzed, and two types of heterogeneous data, engineering parameters (such as tension and speed) and environmental parameters (such as temperature and humidity and dust), are separated. For continuous engineering indicators, the Z-score method is used, and for count-type environmental data (such as yarn breakage frequency), the Min-Max scaling method is used to scale to the [0, 1] interval. A dynamic parameter updating mechanism is simultaneously established to real-time correct the parameters of the normalization process to adapt to the drift of process parameters, and a standardization parameter table is recorded to realize data traceability. The final generated engineering matrix retains the original space-time topology, converts heterogeneous data such as level tension and temperature into dimensionless relative values, meets the input scale uniformity requirement of the machine learning model, and supports data reverse reconstruction through the parameter table to ensure the explainability of process analysis.

[0045] In an embodiment of the present application, the original space-time data matrix composed of the engineering data and the environmental data is normalized to generate an engineering matrix, including:

[0046] The original space-time data matrix composed of the engineering data and the environmental data is subjected to wavelet transform to generate first data.

[0047] The mean and variance of the first data are calculated, and an engineering matrix is generated based on the mean and variance.

[0048] In an embodiment of the present application, the original space-time data matrix composed of the engineering data and the environmental data is normalized. In this process, the original space-time data matrix is subjected to wavelet transform of scale s to decompose the signal into a wavelet domain. Noise appears as high-frequency, low-amplitude wavelet coefficients in the wavelet domain. By threshold processing, noise coefficients are removed, and signal characteristics are retained. Sensor noise such as electromagnetic interference and mechanical vibration-induced spikes is effectively suppressed, and the signal-to-noise ratio is improved. The position-dependent mean and variance of the de-noised data are calculated to eliminate the influence of different dimensions, such as the order-of-magnitude difference between temperature and tension, so that the data conform to the standard normal distribution. The comparability of multi-source heterogeneous data is enhanced to provide a unified data processing scale for subsequent modeling, such as regression analysis and neural networks. The final generated normalized engineering matrix D'(t, ξ) is:

[0049]

[0050] where D(t, ξ) represents the original space-time data matrix, μ(ξ) represents the position mean, σ(ξ) represents the position variance, ξ represents the position, ψ represents the wavelet transform operator, and W s represents wavelet transform processing of scale s.

[0051] The wavelet transform in the technical scheme of the application has multi-resolution analysis capability, and the signal is analyzed in detail through scaling and translation operation. The data is mapped to a set range through linear transformation, thereby improving the signal-to-noise ratio in the data processing process. A high-precision and high-reliability data base is constructed to support upper-layer applications such as loom operation state monitoring and yarn quality prediction.

[0052] The above process effectively suppresses high-frequency noise such as mechanical vibration by performing multi-scale decomposition on the space-time data matrix through wavelet transform, and retains key features such as yarn elastic deformation; the scale range of different dimension parameters is unified through mean-variance standardization, and the interference of dimension difference on model training is eliminated. The technical effect of this step is to improve the data signal-to-noise ratio and model convergence efficiency, so that the subsequent space model can perform multi-physical field coupling analysis based on a unified benchmark.

[0053] S130, based on the engineering matrix, generating attribute information containing elasticity, fluid and thermal conductivity, and constructing a space model representing the state of the weaving process based on the attribute information.

[0054] In an embodiment of the application, when generating attribute information and constructing a space model based on the engineering matrix, material constitutive relation and multi-physical field coupling analysis need to be integrated. Optionally, first, parameters such as tension, speed, temperature and humidity are extracted from the engineering matrix, the tension and strain data are converted into elastic modulus distribution through the constitutive equation of elastic mechanics, the fluid resistance field is calculated using the fluid dynamics model combined with the yarn inter-friction coefficient, and the thermal conductivity distribution is generated based on the Fourier heat conduction equation processing temperature gradient data; then the weaving area is discretized in space and time using a finite element analysis framework, each grid node integrates elasticity, fluid, and thermal conductivity, and the coupling of fluid and elastic deformation, such as yarn elongation caused by thermal stress, is performed, and finally a three-dimensional space model containing material deformation, fluid resistance and thermal conduction path is constructed. The model calibrates parameters such as elastic modulus correction coefficient through historical process data, and supports real-time data injection to realize dynamic updating, thereby providing multi-physical field collaborative digital mapping for quality prediction and process optimization.

[0055] As shown in Figure 3 In an embodiment of the application, based on the engineering matrix, attribute information containing elasticity, fluid and thermal conductivity is generated, and a space model representing the state of the weaving process is constructed based on the attribute information, including:

[0056] S310, based on the yarn displacement data in the engineering matrix, determining the acceleration term, elastic restoring force and nonlinear elastic force of the yarn displacement field;

[0057] S320, based on the air flow field velocity in the engineering matrix, determining the fluid dynamic coupling term of the air flow field to the yarn;

[0058] S330, constructing a space model representing the weaving process state based on the acceleration term of the yarn displacement field, the elastic restoring force, the nonlinear elastic force, and the fluid dynamic coupling term.

[0059] In an embodiment of the present application, the acceleration term of the yarn displacement field is determined based on the yarn displacement data u and the time t in the engineering matrix as The elastic restoring force is determined according to the yarn displacement data u and the wave speed c as The nonlinear elastic force is determined according to the yarn displacement data u and the preset nonlinear elastic coefficient a as au 3 .

[0060] In an embodiment of the present application, the fluid dynamic coupling term of the air flow field to the yarn is determined based on the air flow field velocity in the engineering matrix as β∫ Ω v(τ,η)dηdτ, wherein β represents the fluid dynamic coupling coefficient, v(τ,η) represents the velocity vector of the air flow field at time τ and spatial position η, reflecting the interaction strength of the fluid and the yarn.

[0061] In an embodiment of the present application, the space model representing the weaving process state is constructed based on the acceleration term of the yarn displacement field, the elastic restoring force, the nonlinear elastic force, and the fluid dynamic coupling term as:

[0062]

[0063] Wherein F(t,ξ) represents the periodic action force of the loom at space coordinate ξ at time t.

[0064] In the technical solution of the present application, partial differential equations are selected as the model framework based on the physical characteristics of the textile machinery system to describe the nonlinear coupling dynamics of the yarn and the air flow field.

[0065] The above process derives the acceleration term, the elastic restoring force, and the nonlinear elastic force based on the yarn displacement data, calculates the fluid dynamic coupling term in combination with the air flow field velocity, and constructs a three-dimensional space model covering the elasticity mechanics, fluid mechanics, and heat conduction. The technical effect of the model lies in realizing the collaborative representation of the multi-physical field of the weaving process, breaking through the description limitation of the traditional single-physical field model to the complex working conditions (such as the fluctuation of the yarn elastic modulus caused by the change of temperature and humidity), and providing more accurate state mapping for dynamic control.

[0066] S140, generating a dynamic control parameter based on the deviation between the model parameters of the space model and the target trajectory.

[0067] In an embodiment of the present application, when generating dynamic control parameters based on the deviation between the model parameters of the spatial model and the target trajectory, multiple control strategies and multiple physical field coupling mechanisms need to be integrated. First, real-time acquisition of parameters such as elastic deformation, fluid resistance, and heat conduction of the weaving equipment is performed through a sensor network, and the preset values of the target trajectory are compared to calculate the deviation in each dimension, such as the tension deviation rate and the temperature gradient difference. Subsequently, a hierarchical control architecture is adopted, the bottom layer uses control algorithms to quickly respond to significant deviations, and the tension servo motor or the workshop temperature and humidity control system is adjusted to achieve coarse adjustment; the middle layer introduces fuzzy control to handle nonlinear coupling effects, such as when the yarn friction coefficient changes due to temperature and humidity, the parameters are dynamically corrected to maintain stable tension; the top layer deploys model predictive control based on the spatial model of multiple physical field coupling relationships, such as elastic deformation caused by thermal stress, to roll-optimize the control input sequence in the future time domain. By solving the quadratic programming problem containing elastic, fluid, and heat conduction constraints, the optimal control parameters that take into account process requirements and equipment capabilities are generated, and finally the adjusted tension set value, fluid valve opening, and heat source power parameters are sent to the actuator to form a closed-loop control.

[0068] Optionally, the process updates the model parameters once every preset time interval, ensuring that when the yarn variety is switched or the environment is suddenly changed, the dynamic control parameters can still maintain the weaving precision within the preset range.

[0069] In an embodiment of the present application, based on the deviation between the model parameters of the spatial model and the target trajectory, dynamic control parameters are generated, including:

[0070] Based on the state tracking error between the model parameters of the spatial model and the target trajectory, a dynamic function is constructed;

[0071] The dynamic function is solved by the variational method to generate dynamic control parameters.

[0072] In an embodiment of the present application, the trained nonlinear dynamic model is applied to a real-time control scenario. First, the model parameters of the spatial model, such as the fluid dynamic coupling coefficient β and the wave speed c, are loaded, and real-time sensor data is accessed. Based on the state tracking error between the model parameters of the spatial model and the target trajectory, a dynamic function J is constructed as:

[0073]

[0074] Where λ, γ, k, and δ represent the control energy weight, the chattering suppression coefficient, the terminal constraint factor, and the control dimension, respectively, x and x * represent the actual displacement and the target displacement of the yarn, v represents the dynamic control parameter, i and k represent the identification and the total number of the dynamic control parameter, respectively; v(T) and v f represent the end of the sequence of the dynamic control parameter and the target value, respectively, and T represents the duration of the dynamic control.

[0075] In an embodiment of the present application, the dynamic function is solved by variational method or by Lagrange equation, and the dynamic control parameter is derived to achieve the balance between state tracking, energy consumption and system stability.

[0076] The control performance is significantly improved by multi-objective optimization. First, the term λ||v 2 The control accuracy and energy consumption are effectively balanced, and the waste of energy consumption caused by excessive adjustment is avoided. Second, the term γ is designed to suppress high-frequency chattering, reduce the wear of mechanical parts (such as bearings), and prolong the service life of the equipment. Finally, the terminal constraint δ||v(T)-v f || 2 The end state of the control sequence is strictly ensured to meet the process requirements, such as quickly adjusting to a safe state when the yarn breaks, so that the breakage rate is reduced. In addition, the real-time performance of the variational method meets the rapid response requirements of industrial scenarios.

[0077] The above process provides the basis for solving the dynamic control parameter through the model parameters of the spatial model, and the real-time sensor data is used as state feedback. The output is the control parameter, which is used to adjust the system state through the actuator (such as a servo motor), forming a closed loop of model prediction, control decision and state update.

[0078] For example, when the yarn displacement deviates from the target value, the model prediction error ||x-x * || 2 The control law triggers the motor voltage adjustment, and finally returns the system to a stable state through closed-loop feedback. This process ensures seamless integration of theoretical models and actual engineering, and realizes complete application from nonlinear dynamics analysis to industrial control.

[0079] The above process solves the deviation function of the model parameters and the target trajectory by variational method, and dynamically adjusts the control input sequence. The control system has adaptive ability, which can rollingly optimize the control strategy according to the real-time working condition (such as the change of elastic coefficient caused by yarn variety switching), and solve the oscillation and overshoot problem of fixed parameter control in nonlinear and time-varying system.

[0080] S150, generating a tension signal from the dynamic control parameter and the preset target tension, and generating an execution instruction from the tension signal to control the operation of the weaving equipment.

[0081] In an embodiment of the present application, the process of generating a tension signal from the dynamic control parameter and the preset target tension and converting it into an execution instruction needs to be combined with real-time control algorithm and industrial communication protocol to achieve accurate driving: first, the dynamic control parameter and the target tension are weighted and fused to generate a corrected tension set value through a feedforward compensation mechanism to correct environmental disturbances such as fluctuations in yarn elastic modulus caused by temperature and humidity changes.

[0082] Subsequently, a digital signal processor is used to execute an anti-integral saturation control algorithm, a deviation calculation is performed on the set value and the real-time tension sensor feedback value, and a pulse modulation control signal or a current signal is output; an execution instruction is transmitted to a servo driver through an industrial bus, and a yarn tension servo motor or a hydraulic actuator is driven to adjust the tension. Finally, through closed loop verification, the actual tension data after execution is returned to the control model, the control parameters are dynamically corrected, the millisecond level closed loop control from signal generation to device response is formed, the tension fluctuation control in the weaving process is ensured within the preset range, and the process requirements of high-end fabrics are met.

[0083] In an embodiment of the present application, a tension signal is generated based on the dynamic control parameters and the preset target tension, and an execution instruction is generated based on the tension signal to control the operation of the weaving equipment, comprising:

[0084] Based on the dynamic control parameters and the preset target tension, an error signal is determined;

[0085] The error signal is processed by a control algorithm to generate a tension signal;

[0086] The tension signal is converted into an execution instruction to instruct the operation of the weaving equipment.

[0087] The dynamic control parameters and the real-time sensor feedback yarn tension data are received. Subsequently, the ideal tension value is set according to the process requirements, and the deviation between the current tension and the ideal value, i.e. the error signal, is calculated, which provides a target reference and an initial adjustment direction for subsequent control.

[0088] A high-order control algorithm is used to process the error signal, and a high-order derivative term is extended to capture the dynamic change characteristics of the tension, and the tension signal F(t) is generated as:

[0089]

[0090] Wherein, e(t) represents the error signal, K x , K y , K z represent the current error parameter, the static error parameter and the error change parameter, t and τ represent time and time integral factor respectively, a n represents an online identification coefficient, and n represents the identification of the online identification coefficient.

[0091] The proportional term quickly responds to the current error, the integral term eliminates long-term static error, the differential term suppresses the error change trend, and the high-order derivative term adapts to the nonlinear fluctuation through online learning, such as the tension change caused by environmental disturbance. Through the synergistic effect of the four terms, the system generates an accurate control signal.

[0092] The technical advantages of this step are reflected in three aspects. First, the high-order control algorithm significantly reduces tension overshoot, reduces start-up stage error, and ensures that the steady-state error is less than 2%. Second, the online identification of high-order coefficient improves the robustness of the system to environmental disturbances such as temperature changes. Third, the actuator modulation precision is high, such as motor speed control within ±0.1 r / min, meeting the high-precision weaving requirements. In addition, the real-time feedback mechanism ensures that the control signal always adapts to the actual working condition changes.

[0093] Based on the control parameters and real-time sensor data, the output is an actuator adjustment signal, such as a motor pulse width modulation signal. These signals are converted into actual tension adjustment actions through physical systems such as servo motors, and the adjusted tension data is fed back to the data acquisition link to form a closed loop of decision, execution, and feedback. For example, when the system detects that the tension is out of limits, the motor speed is quickly adjusted to make the tension return to the safe range, and the new data is used for subsequent model optimization. This process ensures the precise synchronization of control instructions and actual execution.

[0094] The above process fuses dynamic control parameters with target tension to generate error signals, which are processed by an anti-integral saturation control algorithm to form execution instructions, forming a closed-loop control loop that precisely drives servo motors through industrial buses to adjust tension, ensuring that the deviation between actual process parameters and target trajectories continuously converges, solving the quality fluctuation problem caused by environmental disturbances in traditional open-loop control.

[0095] In the technical solution of the present application, engineering data and environmental data during the weaving process are obtained through intelligent sensors; the original space-time data matrix composed of the engineering data and the environmental data is standardized to generate an engineering matrix; based on the engineering matrix, attribute information including elasticity, fluidity, and thermal conductivity is generated, and a space model representing the state of the weaving process is constructed based on the attribute information; based on the deviation between the model parameters of the space model and the target trajectory, dynamic control parameters are generated; the dynamic control parameters and the preset target tension generate a tension signal, and the execution instruction is generated through the tension signal to control the operation of the weaving equipment. Through intelligent sensors, engineering data and environmental data of the weaving process are collected in real time, and a unified dimension engineering matrix is generated after standardization processing; based on the matrix, the elasticity, fluidity, and thermal conductivity attributes are extracted to construct a multi-physical field coupled space model; dynamic control parameters are generated according to the deviation between the model parameters and the target trajectory; finally, the tension signal is generated by combining the dynamic parameters and the preset target tension, and the execution instruction is converted to control the operation of the weaving equipment, forming a precise closed-loop supervisory control process from data perception to equipment control.

[0096] The following introduces an embodiment of an industrial weaving process supervision control system of the present application, which can be used to execute the industrial weaving process supervision control method in the above embodiments of the present application. It can be understood that the industrial weaving process supervision control system can be a computer program (including program code) running in a computer device, for example, the industrial weaving process supervision control system is an application software; the industrial weaving process supervision control system can be used to execute the corresponding steps in the method provided by the embodiments of the present application. For details of the embodiments of the industrial weaving process supervision control system of the present application that are not disclosed, please refer to the above embodiments of the industrial weaving process supervision control method of the present application.

[0097] Figure 3 A block diagram of an industrial weaving process supervision control system according to an embodiment of the present application is shown.

[0098] Referring to Figure 3 According to an embodiment of the present application, the industrial weaving process supervision control system comprises:

[0099] The acquisition unit 310 is configured to acquire engineering data and environmental data in the weaving process through an intelligent sensor;

[0100] The matrix unit 320 is configured to perform standardization processing on the original space-time data matrix composed of the engineering data and the environmental data to generate an engineering matrix;

[0101] The model unit 330 is configured to generate attribute information including elasticity, fluidity and heat conduction based on the engineering matrix, and construct a spatial model representing the state of the weaving process based on the attribute information;

[0102] The parameter unit 340 is configured to generate dynamic control parameters based on the deviation between the model parameters of the spatial model and the target trajectory;

[0103] The control unit 350 is configured to generate a tension signal based on the dynamic control parameters and a preset target tension, and generate an execution instruction through the tension signal to control the operation of the weaving equipment.

[0104] In the present application, based on the foregoing scheme, the standardization processing on the original space-time data matrix composed of the engineering data and the environmental data to generate an engineering matrix comprises: performing wavelet transform on the original space-time data matrix composed of the engineering data and the environmental data to generate first data; calculating the mean and variance of the first data, and generating an engineering matrix based on the mean and variance.

[0105] In the present application, based on the foregoing scheme, the generation of attribute information including elasticity, fluidity, and thermal conduction based on the engineering matrix, the construction of a spatial model representing the weaving process state based on the attribute information, comprises: determining the acceleration term, the elastic restoring force, and the nonlinear elastic force of the yarn displacement field based on the yarn displacement data in the engineering matrix; determining the fluid dynamic coupling term of the air flow field to the yarn based on the air flow field velocity in the engineering matrix; constructing a spatial model representing the weaving process state based on the acceleration term, the elastic restoring force, and the nonlinear elastic force of the yarn displacement field and the fluid dynamic coupling term.

[0106] In the present application, based on the foregoing scheme, the determination of the acceleration term, the elastic restoring force, and the nonlinear elastic force of the yarn displacement field based on the yarn displacement data in the engineering matrix comprises: determining the acceleration term of the yarn displacement field based on the yarn displacement data u and time t in the engineering matrix as determining the elastic restoring force based on the yarn displacement data u and the wave speed c determining the nonlinear elastic force as au based on the yarn displacement data u and the preset nonlinear elastic coefficient a 3 .

[0107] In the present application, based on the foregoing scheme, the determination of the fluid dynamic coupling term of the air flow field to the yarn based on the air flow field velocity in the engineering matrix comprises: determining the fluid dynamic coupling term of the air flow field to the yarn based on the air flow field velocity in the engineering matrix as Ω v(τ,η)dηdτ; wherein β represents the fluid dynamic coupling coefficient, v(τ,η) represents the velocity vector of the air flow field at time τ and spatial position η;

[0108] wherein the construction of a spatial model representing the weaving process state based on the acceleration term, the elastic restoring force, and the nonlinear elastic force of the yarn displacement field and the fluid dynamic coupling term comprises: constructing a spatial model representing the weaving process state based on the acceleration term, the elastic restoring force, and the nonlinear elastic force of the yarn displacement field and the fluid dynamic coupling term as

[0109]

[0110] In the present application, based on the foregoing scheme, the generation of dynamic control parameters based on the deviation between the model parameters of the spatial model and the target trajectory comprises: constructing a dynamic function based on the state tracking error between the model parameters of the spatial model and the target trajectory; generating dynamic control parameters by solving the dynamic function through variational method.

[0111] In this application, based on the aforementioned scheme, the step of generating a tension signal from the dynamic control parameters and the preset target tension, and generating an execution command through the tension signal to control the operation of the weaving equipment, includes: determining an error signal based on the dynamic control parameters and the preset target tension; processing the error signal through a control algorithm to generate a tension signal; and converting the tension signal into an execution command to instruct the weaving equipment to operate.

[0112] In this application's technical solution, engineering and environmental data during the weaving process are acquired through intelligent sensors. The original spatiotemporal data matrix composed of the engineering and environmental data is standardized to generate an engineering matrix. Based on the engineering matrix, attribute information including elasticity, fluidity, and heat conduction is generated. A spatial model representing the weaving process state is constructed based on this attribute information. Dynamic control parameters are generated based on the deviation between the model parameters of the spatial model and the target trajectory. A tension signal is generated based on the dynamic control parameters and a preset target tension, and an execution command is generated through the tension signal to control the operation of the weaving equipment. The process involves real-time acquisition of engineering and environmental data during the weaving process by intelligent sensors, followed by standardization to generate a uniform-dimensional engineering matrix. Elasticity, fluidity, and heat conduction attributes are extracted from this matrix to construct a multi-physics coupled spatial model. Dynamic control parameters are generated based on the deviation between the model parameters and the target trajectory. Finally, the dynamic parameters are combined with the preset target tension to generate a tension signal, which is then converted into an execution command to control the operation of the weaving equipment, forming a precise closed-loop supervisory control process from data perception to equipment control.

[0113] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0114] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not impose any limitations on the function and scope of use of the embodiments of this application.

[0115] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes based on a program stored in a read-only memory 402 or a program loaded from a storage section 408 into a random access memory 403, such as executing the monitoring and control method for the industrial weaving process described in the above embodiment. The random access memory 403 also stores various programs and data required for system operation. The central processing unit 401, the read-only memory 402, and the random access memory 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0116] The following components are connected to the input / output interface 405: an input portion 406 including input devices such as a keyboard and a mouse; an output portion 407 including output devices such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage portion 408 including a hard disk; and a communication portion 409 including a network interface card such as a LAN (Local Area Network) card, a modem, and the like. The communication portion 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as necessary. A removable media 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is attached to the drive 410 as necessary, so that a computer program read therefrom is installed in the storage portion 408 as necessary.

[0117] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing computer programs for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication portion 409, and / or installed from the removable media 411. When the computer program is executed by the central processing unit 401, various functions defined in the system of the present application are executed.

[0118] It should be noted that the computer-readable medium in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present application, the computer-readable signal medium can include a data signal carrying computer-readable computer programs in a baseband or as a part of a carrier wave. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate or transmit programs for use by or in conjunction with an instruction execution system, device or apparatus. The computer programs contained in the computer-readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, or the like, or any suitable combination thereof.

[0119] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In the flowcharts or block diagrams, each block can represent a module, a program segment or a part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than that shown in the drawings. For example, two blocks represented in succession can actually be executed substantially in parallel, and sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams or flowcharts, and the combination of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0120] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described can also be located in a processor. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0121] According to an aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method provided in the various optional implementation manners described above.

[0122] As another aspect, the present application also provides a computer readable medium, which can be included in the electronic device described in the above embodiments, or can exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the industrial weaving process supervision control method described in the above embodiments.

[0123] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, the division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into several modules or units.

[0124] From the above description of the embodiments, those skilled in the art will readily appreciate that the example embodiments described herein can be implemented by software and / or by hardware coupled with software. Accordingly, the technical solutions of the embodiments of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, U disk, mobile hard disk, etc.) or network, and includes several instructions to make a computing device (which can be a personal computer, server, touch terminal, or network device, etc.) execute the methods according to the embodiments of the present application.

[0125] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice in the art to which the application pertains.

[0126] It is to be understood that the application is not limited to the precise construction already described above and shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application should only be limited by the claims appended hereto.

Claims

1. A method of supervisory control in an industrial weaving process, characterized by, The method comprises the following steps: obtaining engineering data and environmental data in the weaving process through intelligent sensors; standardizing the original space-time data matrix composed of the engineering data and the environmental data to generate an engineering matrix; based on the engineering matrix, generating attribute information including elasticity, fluid and heat conduction, and constructing a space model representing the state of the weaving process based on the attribute information; generating dynamic control parameters based on the deviation between the model parameters of the space model and the target trajectory; generating a tension signal based on the dynamic control parameters and the preset target tension, and generating an execution instruction through the tension signal to control the operation of the weaving equipment.

2. The method for supervisory control of an industrial weaving process according to claim 1, characterized in that, The standardization processing of the original space-time data matrix composed of the engineering data and the environmental data to generate an engineering matrix comprises: wavelet transform is performed on the original space-time data matrix composed of the engineering data and the environmental data to generate first data; the mean and variance of the first data are calculated, and the engineering matrix is generated based on the mean and variance.

3. The method of supervisory control of an industrial weaving process according to claim 1, characterized in that, Based on the engineering matrix, attribute information including elasticity, fluid and heat conduction is generated, and a space model representing the state of the weaving process is constructed based on the attribute information, which comprises: based on the yarn displacement data in the engineering matrix, determining the acceleration term, elastic restoring force and nonlinear elastic force of the yarn displacement field; based on the air flow field velocity in the engineering matrix, determining the fluid dynamic coupling term of the air flow field to the yarn; based on the acceleration term, elastic restoring force, nonlinear elastic force of the yarn displacement field and the fluid dynamic coupling term, a space model representing the state of the weaving process is constructed.

4. The method for supervisory control of an industrial weaving process according to claim 3, characterized in that, Based on the yarn displacement data in the engineering matrix, the acceleration term, elastic restoring force and nonlinear elastic force of the yarn displacement field are determined, which comprises: Based on the yarn displacement data u and time t in the engineering matrix, the acceleration term of the yarn displacement field is determined as The elastic restoring force is determined according to the yarn displacement data u and the wave speed c The nonlinear elastic force is determined according to the yarn displacement data u and the preset nonlinear elastic coefficient a au 3 .

5. The method for supervisory control of an industrial weaving process according to claim 4, characterized in that, based on the air flow field velocity in the engineering matrix, the fluid dynamic coupling term of the air flow field to the yarn is determined, which comprises: determining a fluid dynamic coupling term of the air flow field to the yarn as β∫ Ω v(τ,η)dηdτ; where β represents a fluid dynamic coupling coefficient, v(τ,η) represents a velocity vector of the air flow field at time τ and spatial location η; wherein, based on the acceleration term, elastic restoring force, nonlinear elastic force of the yarn displacement field and the fluid dynamic coupling term, a space model representing the state of the weaving process is constructed, which comprises: based on the acceleration term, elastic restoring force, nonlinear elastic force of the yarn displacement field and the fluid dynamic coupling term, a space model representing the state of the weaving process is constructed as follows: wherein, F(t,ξ) represents the periodic force of the loom at space coordinate ξ at time t.

6. The method of in-process supervisory control of a weaving process according to claim 1, characterized in that, Based on the deviation between the model parameters of the space model and the target trajectory, dynamic control parameters are generated, which comprises: constructing a dynamic function based on the state tracking error between the model parameters of the space model and the target trajectory; solving the dynamic function through variational method to generate dynamic control parameters.

7. The method of in-process supervisory control of a weaving process according to claim 1, characterized in that, Based on the dynamic control parameters and the preset target tension, an error signal is determined; processing the error signal through a control algorithm to generate a tension signal; the tension signal is converted into an execution instruction to instruct the operation of the weaving equipment. The method comprises the following steps:

8. A supervisory control system in an industrial weaving process, characterized by an acquisition unit is configured to obtain engineering data and environmental data in the weaving process through intelligent sensors; ​ a matrix unit configured to normalize a raw spatiotemporal data matrix composed of the engineering data and the environmental data to generate an engineering matrix; a model unit configured to generate attribute information including elasticity, fluidity, and thermal conduction based on the engineering matrix, and construct a spatial model representing a state of the weaving process based on the attribute information; a parameter unit configured to generate dynamic control parameters based on a deviation between model parameters of the spatial model and a target trajectory; a control unit configured to generate a tension signal based on the dynamic control parameters and a preset target tension, and generate execution instructions to control the weaving equipment to operate based on the tension signal.

9. A computer readable medium having stored thereon a computer program, characterized in that The computer program, when executed by a processor, implements the supervisory control method in an industrial weaving process according to any one of claims 1 to 7.

10. An electronic device, comprising: comprise: one or more processors; a memory device configured to store one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the supervisory control method in an industrial weaving process according to any one of claims 1 to 7.

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