Bio-based nylon and nanoparticle composite spinning method and system

Abstract

The invention provides a bio-based nylon and nanoparticle composite spinning method and system, relates to the technical field of textile materials, and comprises the step of dynamically analyzing a heat transfer state by obtaining melt temperature distribution and a nano phase change material dispersion state. And according to the evaluation result and the flow field characteristics, establishing a temperature-viscosity coupling relationship, generating a viscosity regulation and control demand, and further calculating and generating a dynamic heat supply power distribution scheme through a phase change triggering time sequence model to adjust a heating energy input time sequence, so that the heat absorption and release process of the nano phase change material is matched with the melt flow. And dynamically correcting the phase change triggering model based on the spinning fiber quality feedback information. According to the invention, the temperature field in the composite spinning process is accurately and synchronously regulated and controlled, and the uniformity and stability of fiber quality are effectively improved.

Description

Technical Field

[0001] This invention relates to textile materials technology, and more particularly to a method and system for spinning bio-based nylon and nanoparticle composites. Background Technology

[0002] In the spinning process of bio-based nylon and nanoparticle composites, existing technologies typically employ static or segmented constant temperature control strategies. To improve melt flowability and fiber forming quality, the conventional approach is to set multiple independently temperature-controlled heating zones along the spinning channel and assign fixed heating power to each zone based on a pre-defined process curve. For functional composite melts containing nano-phase change materials, process design primarily relies on static thermophysical parameters provided by material suppliers, or on limited preliminary experiments to roughly determine the temperature setpoints for each zone. This control method assumes that the temperature distribution and nanoparticle dispersion are uniform and stable as the melt flows through each heating zone, thus attempting to maintain a globally or segmentally constant processing temperature environment.

[0003] However, the aforementioned conventional control methods have significant drawbacks. Because the dispersion state of nanophase change materials in a dynamic shear flow field changes in real time, there is a complex spatiotemporal coupling relationship between their phase change endothermic and exothermic behavior and the actual temperature field of the melt. A fixed heating power distribution scheme cannot respond to the dynamic fluctuations in the melt flow front position, local viscosity, and the real-time thermal state of the phase change material, leading to a mismatch between heat supply and the actual needs of the melt in both time and space. This easily causes unexpected local temperature surges or drops in the melt along the flow path, resulting in problems such as viscosity inhomogeneity, nanoparticle agglomeration, or disrupted phase change processes, ultimately introducing defects such as structural inhomogeneity, decreased mechanical properties, or unstable functional characteristics into the formed fibers. Existing methods lack the ability to precisely trace and close-loop correct temperature events during the melt flow process, resulting in lag in process adjustments and making it difficult to achieve stable preparation of high-quality fibers. Summary of the Invention

[0004] The present invention provides a method and system for composite spinning of bio-based nylon and nanoparticles, which can solve the problems in the prior art.

[0005] A first aspect of the present invention provides a method for spinning bio-based nylon and nanoparticle composites, comprising: The real-time temperature distribution data of the melt to be spun and the dispersion state characteristic parameters of the nano-phase change material in the melt are obtained. The real-time temperature distribution data is dynamically analyzed based on the enthalpy-temperature response relationship model of the phase change material to obtain the heat transfer state evaluation result. Based on the heat transfer status assessment results and the flow field distribution characteristics of the spinning channel, the temperature-viscosity coupling relationship at each point on the melt flow path is established, generating spatially distributed viscosity control requirements. According to the viscosity control requirements and the spinning speed constraints, a phase change triggering timing model of the phase change material in the flow shear field is established, and the spatiotemporal matching relationship between each heating section and the melt flow front is calculated, generating a dynamic heating power distribution scheme synchronized with the flow path. Based on the dynamic heating power distribution scheme, the energy input timing of each heating zone is adjusted so that the heat absorption and release process of the nano phase change material matches the time when the melt arrives at the corresponding heating zone. The quality characteristic data of the fiber after spinning is collected and used as feedback information. By tracing the temporal correlation between fiber defects and temperature fluctuation events in the melt flow process, the trigger advance in the phase change triggering timing model is dynamically corrected, forming a feedforward-feedback collaborative control of the spinning process.

[0006] The real-time temperature distribution data is dynamically analyzed based on the enthalpy-temperature response model of phase change materials to obtain the following heat transfer state assessment results: By spatially matching real-time temperature distribution data with the dispersion state characteristic parameters of nano-phase change materials in the melt, the degree of deviation between the local concentration distribution of phase change materials at each location point and the rate of temperature change at that location point is identified, and the uniformity evaluation result of phase change material distribution is obtained. Based on the latent heat of solid-liquid phase transition of phase change materials in the enthalpy-temperature response model, the theoretical heat absorption and release capacity of phase change materials at each location point at the current temperature is calculated. By establishing the transfer function of local concentration deviation on the thermal response delay of phase change materials, the theoretical heat absorption and release capacity is mapped to the effective heat absorption and release capacity of each location point under the actual dispersion state, and the actual heat absorption and release capacity distribution considering the influence of uneven dispersion is obtained. Based on the actual heat absorption and release capacity distribution and the temperature gradient change trend in the real-time temperature distribution data, the heat accumulation rate and heat dissipation rate at each location point inside the melt are calculated. Based on the difference between the heat accumulation rate and the heat dissipation rate, the heat transfer balance state at each location point is determined, and the heat transfer state evaluation result is obtained.

[0007] By establishing a transfer function for the thermal response delay of the phase change material due to local concentration deviation, the theoretical heat absorption and release capacity is mapped to the effective heat absorption and release capacity at each location point under the actual dispersion state, resulting in the actual heat absorption and release capacity distribution considering the influence of uneven dispersion, including: Based on the evaluation results of the uniformity of phase change material distribution, the deviation between the local concentration and the standard concentration at each location point is extracted. The deviation is used as the input variable of the transfer function to calculate the response delay time of the phase change material at each location point undergoing solid-liquid phase transition at the current temperature. Based on the response delay time and the rate of temperature change in the real-time temperature distribution data, the degree of lag in the actual participation of the phase change material in the heat absorption and release process at each location point is identified, and the degree of lag is quantified as the heat capacity contribution reduction ratio. The effective heat absorption and release capacity at each location point under the actual dispersion state is obtained by multiplying the theoretical heat absorption and release capacity with the heat capacity contribution reduction ratio. The effective heat absorption and release capacity is then spatially reconstructed along the melt flow path to obtain the actual heat absorption and release capacity distribution considering the effect of uneven dispersion.

[0008] Based on the heat transfer state assessment results and the flow field distribution characteristics of the spinning channel, a temperature-viscosity coupling relationship is established at each location along the melt flow path, generating spatially distributed viscosity control requirements, including: Extract the deviation of the heat transfer equilibrium state at each location point from the heat transfer state assessment results, and convert the deviation of the heat transfer equilibrium state into the temperature stability index of the corresponding location point. Based on the flow field distribution characteristics of the spinning channel, the flow velocity vector and local shear stress distribution of the melt at each location point are obtained. The temperature stability index and the local shear stress distribution are coupled and calculated to identify the sensitive range of viscosity response of the degree of restriction of melt molecular chain segment movement under temperature fluctuation at each location point. Based on the sensitive range, a dynamic response relationship between the temperature change and viscosity change at each location point is established to obtain the temperature-viscosity coupling relationship. The temperature-viscosity coupling relationship is then spatially expanded along the melt flow path to calibrate the temperature control amplitude required to maintain the target viscosity at each location point, thereby generating a spatially distributed viscosity control requirement.

[0009] Based on the viscosity control requirements and spinning speed constraints, a phase change triggering timing model of the phase change material in the flow shear field is established. The spatiotemporal matching relationship between each heating section and the melt flow front is calculated, and a dynamic heating power distribution scheme synchronized with the flow path is generated, including: Extract the target temperature control range required for each position point from the viscosity control requirements, perform correlation calculations between the target temperature control range and the spinning speed constraint, and calculate the flow time required for the melt to flow from each heating section to the corresponding position point. Based on the shear stress accumulated by the phase change material in the flow shear field during the flow time, the dynamic offset effect of shear stress on the solid-liquid phase transition temperature threshold of the phase change material is identified, and the influence law of shear stress on the triggering time of solid-liquid phase transition of the phase change material is established. By coupling the influence law with the flow time, the actual moment when the phase change material in each heating section triggers phase change during melt flow is determined, and a phase change triggering timing model is obtained. Based on the time point at which the phase change material triggers phase change in each heating section as determined in the phase change triggering timing model, the starting time of the phase change material at the time point is predicted to release or absorb latent heat. The starting time is compared with the time when the melt flow front reaches the corresponding position point, and the time difference between the two is calculated. The time difference is converted into the heating advance or delay of each heating section. The heating start time of each heating section is adjusted forward or backward according to the heating advance or delay. The duration of each heating section is determined according to the target temperature control range, and a dynamic heating power distribution scheme synchronized with the flow path is generated.

[0010] Using the quality characteristic data as feedback information, and by tracing the temporal correlation between fiber defects and temperature fluctuation events in the melt flow process, the trigger advance in the phase change triggering timing model is dynamically corrected, forming a feedforward-feedback collaborative control of the spinning process, including: The spatial distribution and defect type of fiber defects are extracted from the quality characteristic data. Combined with the spinning speed constraint, the position of the heating section and its temperature state of the melt in the flow process corresponding to the moment of defect formation are calculated by spatiotemporal backward calculation. Based on the location of the heating section and the temperature state, the temperature change trajectory before and after the defect formation time is extracted from the real-time temperature distribution data. Temperature fluctuation events that deviate from the steady-state temperature path in the temperature change trajectory are identified by trajectory deviation determination. The temperature fluctuation events are then causally correlated with the defect feature type. Based on the results of the causal relationship mapping, the degree of delayed impact of the temperature fluctuation event on the triggering time of the solid-liquid phase transition of the phase change material is analyzed and quantified by the delayed propagation chain, and the degree of delayed impact is converted into the correction increment of the trigger advance of the corresponding heating segment in the phase change triggering timing model. The correction increment is superimposed on the current trigger advance of the phase change trigger timing model, and the heating start time of each heating section is updated by redefining the timing. The updated heating start time is then synchronously applied to the real-time generation of the dynamic heating power allocation scheme, forming a feedforward-feedback collaborative control of the spinning process.

[0011] Identifying temperature fluctuation events that deviate from the steady-state temperature path in the temperature change trajectory by determining trajectory deviation, and mapping the temperature fluctuation events to the defect feature types through causal correlation, includes: Based on the temperature state, a temperature evolution baseline for the heating section under steady-state conditions is constructed. The deviation between the temperature value at each time point on the temperature change trajectory and the temperature value at the corresponding time point on the temperature evolution baseline is calculated to obtain the trajectory deviation distribution. A time-series scan of the trajectory deviation distribution is performed to identify deviation segments whose deviation magnitude exceeds the steady-state allowable range and whose duration spans the critical duration of the phase change material's thermal response. The temperature change process corresponding to the deviation segment is marked as a temperature fluctuation event. Extract the peak intensity and duration of the temperature fluctuation event from the temperature fluctuation event, and combine the peak intensity and duration of the fluctuation event into a feature vector of the temperature fluctuation event. Based on the defect size distribution corresponding to the defect feature type, a size correlation weight is established between the defect size distribution and the peak intensity of the fluctuation. Based on the defect density distribution corresponding to the defect feature type, a density correlation weight is established between the defect density distribution and the duration of the fluctuation. The feature vector is mapped to the defect feature type through the size correlation weight and the density correlation weight to obtain the causal correlation mapping result.

[0012] A second aspect of the present invention provides a bio-based nylon and nanoparticle composite spinning system, comprising: The temperature analysis unit is used to acquire real-time temperature distribution data of the melt to be spun and dispersion state characteristic parameters of the nano-phase change material in the melt. Based on the enthalpy-temperature response relationship model of the phase change material, the real-time temperature distribution data is dynamically analyzed to obtain the heat transfer state evaluation result. The viscosity control unit is used to establish the temperature-viscosity coupling relationship at each point on the melt flow path based on the heat transfer state assessment results and the flow field distribution characteristics of the spinning channel, generate spatially distributed viscosity control requirements, and, based on the viscosity control requirements and spinning speed constraints, calculate the spatiotemporal matching relationship between each heating section and the melt flow front by establishing a phase change triggering timing model of the phase change material in the flow shear field, and generate a dynamic heating power distribution scheme synchronized with the flow path. The heating matching unit is used to adjust the energy input timing of each heating section based on the dynamic heating power distribution scheme, so that the heat absorption and release process of the nano phase change material matches the time when the melt arrives at the corresponding heating section. The collaborative control unit is used to collect quality characteristic data of the fiber after spinning and forming. The quality characteristic data is used as feedback information. By tracing the temporal correlation between fiber defects and temperature fluctuation events in the melt flow process, the trigger advance in the phase change triggering timing model is dynamically corrected to form a feedforward-feedback collaborative control of the spinning process.

[0013] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0015] The beneficial effects of this application are as follows: This method enables precise dynamic analysis and control of the temperature distribution in spinning melts. Based on a model of the enthalpy-temperature response relationship of phase change materials, real-time temperature data can be analyzed to directly assess the heat transfer state within the melt, providing a reliable basis for subsequent precise temperature control. This method avoids the lag and limitations of traditional temperature monitoring, allowing for a holistic understanding of the melt's thermal state.

[0016] By establishing a temperature-viscosity coupling relationship based on heat transfer status and flow field characteristics, viscosity control requirements corresponding to spatial location can be generated. By establishing a phase change triggering time sequence model for phase change materials in a shear field, the spatiotemporal matching relationship between the heating section and the melt flow front can be accurately calculated. The resulting dynamic heating power allocation scheme ensures the synchronization of energy input and melt flow process in time and space.

[0017] By adjusting the energy input timing of each heating zone using a dynamic heating scheme, the heat absorption or release process of the nano-phase change material can be precisely matched to the moment when the melt flows through a specific location. This matching effectively offsets the natural temperature drop or shear heat generation of the melt during its flow, achieving point-to-point and time-based compensation of the temperature at each point along the melt's path, thereby maintaining the stability of the melt viscosity. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the process for the composite spinning method of bio-based nylon and nanoparticles according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the causal relationship mapping between temperature fluctuation events and defect feature types in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0020] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0021] Figure 1 This is a schematic diagram of the process for the composite spinning method of bio-based nylon and nanoparticles according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes: The real-time temperature distribution data of the melt to be spun and the dispersion state characteristic parameters of the nano-phase change material in the melt are obtained. The real-time temperature distribution data is dynamically analyzed based on the enthalpy-temperature response relationship model of the phase change material to obtain the heat transfer state evaluation result. Based on the heat transfer status assessment results and the flow field distribution characteristics of the spinning channel, the temperature-viscosity coupling relationship at each point on the melt flow path is established, generating spatially distributed viscosity control requirements. According to the viscosity control requirements and the spinning speed constraints, a phase change triggering timing model of the phase change material in the flow shear field is established, and the spatiotemporal matching relationship between each heating section and the melt flow front is calculated, generating a dynamic heating power distribution scheme synchronized with the flow path. Based on the dynamic heating power distribution scheme, the energy input timing of each heating zone is adjusted so that the heat absorption and release process of the nano phase change material matches the time when the melt arrives at the corresponding heating zone. The quality characteristic data of the fiber after spinning is collected and used as feedback information. By tracing the temporal correlation between fiber defects and temperature fluctuation events in the melt flow process, the trigger advance in the phase change triggering timing model is dynamically corrected, forming a feedforward-feedback collaborative control of the spinning process.

[0022] In one optional implementation, the real-time temperature distribution data is dynamically analyzed based on the enthalpy-temperature response model of the phase change material to obtain the heat transfer state assessment results, including: By spatially matching real-time temperature distribution data with the dispersion state characteristic parameters of nano-phase change materials in the melt, the degree of deviation between the local concentration distribution of phase change materials at each location point and the rate of temperature change at that location point is identified, and the uniformity evaluation result of phase change material distribution is obtained. Based on the latent heat of solid-liquid phase transition of phase change materials in the enthalpy-temperature response model, the theoretical heat absorption and release capacity of phase change materials at each location point at the current temperature is calculated. By establishing the transfer function of local concentration deviation on the thermal response delay of phase change materials, the theoretical heat absorption and release capacity is mapped to the effective heat absorption and release capacity of each location point under the actual dispersion state, and the actual heat absorption and release capacity distribution considering the influence of uneven dispersion is obtained. Based on the actual heat absorption and release capacity distribution and the temperature gradient change trend in the real-time temperature distribution data, the heat accumulation rate and heat dissipation rate at each location point inside the melt are calculated. Based on the difference between the heat accumulation rate and the heat dissipation rate, the heat transfer balance state at each location point is determined, and the heat transfer state evaluation result is obtained.

[0023] During the heating process of the spinning melt, real-time temperature distribution data is collected by temperature sensors deployed at various locations within the heating zone. The sensor sampling frequency is set to 10 to 50 times per second. The collected data is then processed by low-pass filtering to remove high-frequency noise before entering the data processing module. The dispersion characteristics of the nano-phase change material in the melt are acquired in real time by an online optical detection device. This device measures the concentration distribution of phase change material particles in the melt using laser scattering, achieving a detection resolution at the micrometer level and scanning the entire length of the heating zone. The coordinates of each temperature measurement point in the real-time temperature distribution data are spatially matched with the concentration distribution data acquired by the optical detection device. The matching process uses the nearest neighbor interpolation method to ensure that the local concentration value of the phase change material corresponding to each temperature measurement point is obtained through interpolation calculation, with the interpolation error controlled within 5%.

[0024] To identify the deviation between the local concentration distribution of phase change material (PCM) at each location point and the rate of temperature change at that location point, the rate of temperature change at each location point is first calculated within a continuous time window. The time window length is set to 0.5 to 2 seconds. The rate of temperature change is obtained by subtracting the temperature value of the previous moment from the current temperature value and then dividing by the time interval. Simultaneously, the local concentration value of PCM at that location point is extracted and compared with the average concentration value of PCM in the heating zone. The concentration deviation rate is calculated as the local concentration value minus the average concentration value, divided by the average concentration value, and then multiplied by 100%. When the absolute value of the concentration deviation rate exceeds 15%, it is determined that there is uneven dispersion at that location point. Further analysis of the correlation between the rate of temperature change and the local concentration deviation rate reveals that when the rate of temperature change increases or decreases in the same direction as the concentration deviation rate, the dispersion state of PCM at that location point has a significant impact on the temperature response. This yields an assessment result of the PCM distribution uniformity, which is stored as a data record consisting of location point number, concentration deviation rate, temperature change rate, and uniformity level.

[0025] Based on the latent heat of solid-liquid phase transition of phase change materials in the enthalpy-temperature response model, the theoretical heat absorption and release capacity of the phase change material at each location point at the current temperature is calculated. The enthalpy-temperature response model is pre-determined using differential scanning calorimetry to determine the phase transition temperature range and latent heat value of the nano-phase change material. The phase transition temperature range is typically between 150°C and 180°C, and the latent heat value ranges from 100 joules to 200 joules per gram. The theoretical heat absorption / release capacity is calculated using the temperature value at the current location as input. It is determined whether the temperature value falls within the phase change temperature range. If the temperature value is lower than the lower limit of the phase change temperature, the phase change material at that location is in a solid phase state, and the theoretical heat absorption / release capacity is 0. If the temperature value is higher than the upper limit of the phase change temperature, the phase change material is in a liquid phase state, and the theoretical heat absorption / release capacity is also 0. If the temperature value is within the phase change temperature range, the phase change material is in a solid-liquid coexistence state. The theoretical heat absorption / release capacity is calculated by linear interpolation based on the relative position of the temperature value within the phase change temperature range. The interpolation result is multiplied by the mass of the phase change material at that location to obtain the theoretical heat absorption / release capacity value.

[0026] By establishing a transfer function for the thermal response delay of phase change materials (PCMs) due to local concentration deviations, the theoretical heat absorption / release capacity is mapped to the effective heat absorption / release capacity at each location under actual dispersion conditions. The transfer function is constructed based on experimental calibration data. By measuring the actual heat absorption / release response time and intensity of the PCM under different concentration deviation conditions, a curve relating the concentration deviation rate to the thermal response delay coefficient is fitted. The thermal response delay coefficient is defined as the ratio of the actual heat absorption / release response time to the response time under ideal uniform dispersion conditions. When the concentration deviation rate is 0, the delay coefficient is 1; as the absolute value of the concentration deviation rate increases, the delay coefficient increases accordingly, indicating an intensified thermal response delay. The effective heat absorption / release capacity is equal to the theoretical heat absorption / release capacity divided by the thermal response delay coefficient, yielding the actual heat absorption / release capacity distribution considering the effects of uneven dispersion. This distribution is stored as a mapping between location point numbers and corresponding effective heat absorption / release capacity values, with an update frequency consistent with the temperature data acquisition frequency.

[0027] Based on the actual heat absorption / release capacity distribution and the temperature gradient trend in real-time temperature distribution data, the heat accumulation rate and heat dissipation rate at each location point inside the melt are calculated. The temperature gradient trend is obtained by dividing the temperature difference between adjacent locations by the distance between those locations, and then comparing the changes in the spatial temperature gradient over consecutive time intervals to determine whether the temperature gradient is increasing or decreasing. The heat accumulation rate is calculated by considering the sum of the heat input power received from the heating system at that location and the heat absorption rate of the phase change material. The heat input power is determined by the heating section control system using a proportional-integral-derivative (PID) control algorithm based on the deviation between the set temperature and the actual temperature. The heat absorption rate of the phase change material is equal to the effective heat absorption / release capacity multiplied by the temperature change rate.

[0028] The heat dissipation rate is calculated by considering the sum of the heat conduction rate from the current location to adjacent locations and the heat radiation rate to the environment. The heat conduction rate is calculated using Fourier's law from the spatial temperature gradient and the thermal conductivity of the melt, with the melt thermal conductivity ranging from 0.2 W to 0.5 W per meter per Kelvin. The heat radiation rate is calculated using the Stefan-Boltzmann law by multiplying the fourth power of the temperature difference between the current location and the ambient temperature by the emissivity, with the emissivity ranging from 0.3 to 0.7. The ambient temperature is obtained in real-time through an ambient temperature sensor deployed outside the heating section. The sum of the calculated heat conduction and heat radiation rates yields the total heat dissipation rate at that location, reflecting the melt's heat dissipation capacity at that point.

[0029] Based on the difference between the rate of heat accumulation and the rate of heat dissipation, the heat transfer balance at each location point is determined. When the absolute value of the difference is less than a preset threshold, the location point is considered to be in a state of heat transfer balance. The threshold is set between 50 watts and 200 watts per cubic meter per second, with the specific value dynamically adjusted according to the heat capacity and heat exchange conditions of the heating section. When the difference is positive and exceeds the threshold, the location point is considered to be in a state of heat accumulation, with the temperature showing an upward trend. Upon receiving this status information, the control system reduces the heating power input at that location point or enhances heat dissipation measures. When the difference is negative and the absolute value exceeds the threshold, the location point is considered to be in a state of heat dissipation, with the temperature showing a downward trend. The control system correspondingly increases the heating power input at that location point or reduces the heat dissipation intensity.

[0030] The heat transfer status assessment results are output as data records consisting of location point numbers, heat accumulation rates, heat dissipation rates, and equilibrium status indicators. These data records are transmitted to the central control unit via a communication interface for subsequent control strategy adjustments. The transmission protocol adopts the industrial Ethernet standard to ensure real-time performance and reliability. The assessment results are stored using a circular buffer mechanism. The buffer capacity is set to store historical data from the most recent 1 to 4 hours. Older data exceeding the capacity is overwritten chronologically, ensuring efficient use of data storage space. The heat transfer status assessment results also include a thermal equilibrium deviation index for each location point. This index is calculated by dividing the absolute value of the difference between the heat accumulation rate and the heat dissipation rate by a threshold. A higher value indicates a more severe deviation from the thermal equilibrium state, providing a quantitative basis for control system adjustments.

[0031] In one optional implementation, by establishing a transfer function for the thermal response delay of the phase change material due to local concentration deviation, the theoretical heat absorption / release capacity is mapped to the effective heat absorption / release capacity at each location point under the actual dispersion state, resulting in an actual heat absorption / release capacity distribution considering the influence of uneven dispersion, including: Based on the evaluation results of the uniformity of phase change material distribution, the deviation between the local concentration and the standard concentration at each location point is extracted. The deviation is used as the input variable of the transfer function to calculate the response delay time of the phase change material at each location point undergoing solid-liquid phase transition at the current temperature. Based on the response delay time and the rate of temperature change in the real-time temperature distribution data, the degree of lag in the actual participation of the phase change material in the heat absorption and release process at each location point is identified, and the degree of lag is quantified as the heat capacity contribution reduction ratio. The effective heat absorption and release capacity at each location point under the actual dispersion state is obtained by multiplying the theoretical heat absorption and release capacity with the heat capacity contribution reduction ratio. The effective heat absorption and release capacity is then spatially reconstructed along the melt flow path to obtain the actual heat absorption and release capacity distribution considering the effect of uneven dispersion.

[0032] In the composite spinning process of bio-based nylon and nanoparticles, the uniformity of phase change material dispersion in the melt directly affects its thermal response characteristics. A high-precision optical sensor was used to monitor the nanoparticle distribution density at different spatial locations within the melt flow channel in real time. A monitoring section was set every 5 mm to collect local concentration data at each section. A preset standard concentration value of 2.5 wt% was set, and the deviation between the measured concentration and the standard concentration at each monitoring point was calculated. When the measured concentration at a certain location was 2.1 wt%, the deviation was -0.4 wt%, indicating that the phase change material was sparsely distributed at that location.

[0033] Establish transfer function The effect of concentration deviation on the phase change response is described, where the time constant τ is positively correlated with the deviation. For the aforementioned deviation of -0.4 wt%, τ = 0.18 seconds was obtained through experimental calibration. When the melt temperature rises at a rate of 3.2 °C per second through the melting point of the phase change material (218 °C), ideally, the phase change should be triggered immediately. However, due to insufficient local concentration, there is a delay in actual triggering. Substituting the temperature change rate and the time constant into the transfer function, the response delay time at this location is calculated to be 0.23 seconds. This delay causes the phase change material to miss the optimal endothermic opportunity.

[0034] By comparing the theoretical phase change process with the actual response curve, the degree of hysteresis was identified. Within a 0.5-second time window as the melt flows through this location, the phase change material theoretically absorbs 42 J / g of heat with an enthalpy value. However, due to a 0.23-second delay, the actual time for heat absorption is only 0.27 seconds, reducing the absorbed heat to 23 J / g. The ratio of the actual absorbed heat to the theoretical value is defined as the heat capacity contribution reduction ratio, which is 0.55 at this location. For regions with positive concentration deviations, such as a measured concentration of 2.9 wt%, excessive aggregation of the phase change material leads to localized heat transfer obstruction. Although the delay time is shortened to 0.08 seconds, the effective contact area of ​​the aggregated state decreases, and the reduction ratio still reaches 0.72.

[0035] The theoretical heat absorption / release capacity at each location point is multiplied point-by-point with the corresponding reduction ratio. The theoretical heat absorption / release capacity, calculated based on the phase change material ratio, is 168 J / kg·℃. Under the aforementioned reduction ratio of 0.55, the effective heat absorption / release capacity at this point is corrected to 92.4 J / kg·℃. A spatial grid containing 237 discrete nodes is established along the entire flow path of the melt from the screw outlet to the spinneret, with each node assigned the corrected effective heat absorption / release capacity value. A three-dimensional interpolation algorithm is used to smooth the values ​​between nodes, generating a continuous actual heat absorption / release capacity distribution field. This distribution field exhibits obvious spatial non-uniformity: at the end of the screw metering section, due to strong shearing, the dispersion is good, and the effective heat absorption / release capacity is maintained at 78% of the theoretical value; while in the melt convergence region, flow stagnation causes nanoparticle sedimentation, and the effective value drops to 43% of the theoretical value, forming a low heat capacity channel. This actual distribution data is transferred to the temperature-viscosity coupled calculation module to provide an accurate physical property basis for subsequent heating power allocation.

[0036] In one optional implementation, based on the heat transfer state assessment results and the flow field distribution characteristics of the spinning channel, a temperature-viscosity coupling relationship is established at each point along the melt flow path, generating spatially distributed viscosity control requirements, including: Extract the deviation of the heat transfer equilibrium state at each location point from the heat transfer state assessment results, and convert the deviation of the heat transfer equilibrium state into the temperature stability index of the corresponding location point. Based on the flow field distribution characteristics of the spinning channel, the flow velocity vector and local shear stress distribution of the melt at each location point are obtained. The temperature stability index and the local shear stress distribution are coupled and calculated to identify the sensitive range of viscosity response of the degree of restriction of melt molecular chain segment movement under temperature fluctuation at each location point. Based on the sensitive range, a dynamic response relationship between the temperature change and viscosity change at each location point is established to obtain the temperature-viscosity coupling relationship. The temperature-viscosity coupling relationship is then spatially expanded along the melt flow path to calibrate the temperature control amplitude required to maintain the target viscosity at each location point, thereby generating a spatially distributed viscosity control requirement.

[0037] In the process of spinning bio-based nylon and nanoparticle composites, the deviation of the heat transfer equilibrium state at each location point is extracted from the heat transfer state assessment results. Specifically, for each monitoring point along the melt flow path, the absolute value of the temperature difference between the actual temperature value at that point and the ideal temperature value predicted based on the enthalpy-temperature response relationship model of the phase change material is calculated as the deviation of the heat transfer equilibrium state. A larger deviation value indicates that the heat transfer at that point is in a non-equilibrium state, with local overheating or insufficient cooling. The extracted heat transfer equilibrium state deviation is then normalized and converted into a temperature stability index, with a value ranging from 0 to 1; a value closer to 1 indicates better temperature stability.

[0038] Based on the flow field distribution characteristics of the spinning channel, the flow velocity vector of the melt at each location point is obtained using an online rheometer or pressure sensor array. The flow velocity vector includes axial and radial velocity components. The axial velocity reflects the rate at which the melt propagates along the spinning channel, while the radial velocity reflects the uniformity of the melt distribution across the channel cross-section. Simultaneously, the local shear stress distribution at each location point is calculated based on the channel geometry and the flow velocity gradient. The calculation of shear stress considers the velocity boundary layer effect of the melt near the wall, with particular attention paid to the high-shear region within the spinneret inlet convergence section and the spinneret orifice.

[0039] When coupling the temperature stability index with the local shear stress distribution in the calculation, a weighted function is used to establish the correlation between the two. Locations with lower temperature stability indices are assigned higher shear sensitivity coefficients, indicating that the melt at these locations is more prone to abrupt viscosity changes under temperature fluctuations. By setting a viscosity response threshold, the sensitivity range of each location to the degree of restriction on the movement of melt molecular chains under temperature fluctuations is identified. The criterion for determining the sensitivity range is: when the viscosity change rate caused by temperature change exceeds the set threshold, the location is marked as a viscosity-sensitive region. Locations with higher nanoparticle aggregation or larger shear stress gradients often correspond to sensitive regions.

[0040] Based on the identified sensitive regions, a dynamic response relationship between temperature and viscosity changes at each location point is established. This relationship is characterized using piecewise linear or polynomial fitting, with fitting parameters obtained by experimentally measuring the viscosity data of bio-based nylon melt at different temperatures. For composite melts containing nano-phase change materials, the fitting model needs to incorporate an additional nanoparticle concentration correction factor, which is dynamically adjusted based on the dispersion state characteristic parameters. The established temperature-viscosity coupling relationship is spatially expanded along the melt flow path according to the flow time series, forming a one-dimensional or two-dimensional temperature-viscosity field distribution map.

[0041] In the unfolded temperature-viscosity field, the temperature control range required to maintain the target viscosity is calibrated at each location point. The target viscosity is preset according to the spinning process requirements; typically, the melt viscosity at the front end of the spinneret needs to be controlled within a specific range to ensure fiber forming quality. The temperature control range is calculated by substituting the deviation between the actual viscosity and the target viscosity at the current location point into the inverse function of the temperature-viscosity coupling relationship to obtain the required temperature compensation. The generated spatially distributed viscosity control requirements are output in the form of data tables or control commands, providing a precise control basis for the power distribution of subsequent heating sections, achieving uniform and controllable melt viscosity throughout the entire flow path.

[0042] In one optional implementation, based on the viscosity control requirements and spinning speed constraints, a phase change triggering timing model of the phase change material in the flow shear field is established. The spatiotemporal matching relationship between each heating zone and the melt flow front is calculated, and a dynamic heating power allocation scheme synchronized with the flow path is generated, including: Extract the target temperature control range required for each position point from the viscosity control requirements, perform correlation calculations between the target temperature control range and the spinning speed constraint, and calculate the flow time required for the melt to flow from each heating section to the corresponding position point. Based on the shear stress accumulated by the phase change material in the flow shear field during the flow time, the dynamic offset effect of shear stress on the solid-liquid phase transition temperature threshold of the phase change material is identified, and the influence law of shear stress on the triggering time of solid-liquid phase transition of the phase change material is established. By coupling the influence law with the flow time, the actual moment when the phase change material in each heating section triggers phase change during melt flow is determined, and a phase change triggering timing model is obtained. Based on the time point at which the phase change material triggers phase change in each heating section as determined in the phase change triggering timing model, the starting time of the phase change material at the time point is predicted to release or absorb latent heat. The starting time is compared with the time when the melt flow front reaches the corresponding position point, and the time difference between the two is calculated. The time difference is converted into the heating advance or delay of each heating section. The heating start time of each heating section is adjusted forward or backward according to the heating advance or delay. The duration of each heating section is determined according to the target temperature control range, and a dynamic heating power distribution scheme synchronized with the flow path is generated.

[0043] In the specific implementation process, the target temperature control amplitude values ​​at different axial positions within the spinning channel are read from the viscosity control requirement mapping table. This value represents the amount of temperature increase or decrease required to achieve the optimal viscosity of the melt at that position, expressed in degrees Celsius. The extracted target temperature control amplitude is divided by the currently set spinning speed to obtain the required rate of temperature change per unit length of melt flow. Combining this with the geometric parameters of the spinning channel, the path length traversed by the melt from the inlet section of each heating zone to the target position is calculated through integration, and then divided by the spinning speed to obtain the corresponding flow time.

[0044] To investigate the shear stress process within the flow time window, velocity gradient distribution data of the melt in the channel were collected, and the instantaneous shear stress of each flow layer was calculated using the constitutive equation of the bio-based nylon melt. The shear stress was then cumulatively integrated over time to obtain the cumulative shear stress load borne by the phase change material particles during flow. Using a pre-established shear-phase change threshold relationship database, the phase change temperature threshold offset corresponding to the cumulative shear stress was retrieved. This offset typically indicates a decrease in the solid-liquid transition temperature due to increased shear stress, and the offset magnitude is closely related to the surface energy and dispersion state of the nanoparticles. The standard phase change temperature was algebraically summed with the offset to obtain the dynamic phase change trigger temperature under actual operating conditions.

[0045] The intersection of the dynamic phase change trigger temperature and the temperature evolution curve of the melt along the flow path is used to determine the moment when the melt temperature first reaches the trigger threshold. This moment is the actual time point at which the phase change material begins to undergo phase change during flow in a specific heating section, constituting the core parameter of the phase change triggering timing model. For endothermic phase change processes, the trigger moment corresponds to the starting point where latent heat begins to be absorbed; for exothermic processes, it corresponds to the starting point where latent heat begins to be released.

[0046] The estimated arrival time of the melt flow front at each target location is calculated using flow field simulation. This time is then compared with the phase change trigger time. A positive time difference indicates that heating needs to be started earlier to compensate for the phase change lag when the phase change trigger time is later than the arrival time of the flow front; conversely, a negative time difference indicates that the heating start time needs to be delayed. The spatial distance offset is obtained by multiplying the time difference by the spinning speed, and the starting position of the power output in each heating section is adjusted accordingly. For sections requiring earlier heating, the original heating start time is moved forward by the corresponding time difference; for sections requiring delayed heating, the heating start time is postponed accordingly.

[0047] Based on the target temperature control range and the latent heat value of the phase change material, the total heat input required to achieve the set temperature change is calculated. Dividing the total heat input by the heating power yields the continuous heating duration for each heating section. By combining the start-up time adjustment and duration parameters of each heating section, a dynamic heating power allocation scheme is generated, including time axis coordinates, spatial location identifiers, power amplitude, and operating time period. This scheme is stored in a data table and sent to the controllers of each heating section in real time, achieving precise synchronization between the heating sequence and the melt flow state, ensuring that the nano-phase change material completes the predetermined heat absorption and release control effect precisely when the melt reaches a specific location.

[0048] In one optional implementation, the quality characteristic data is used as feedback information. By tracing the temporal correlation between fiber defects and temperature fluctuation events in the melt flow process, the trigger advance in the phase change triggering timing model is dynamically corrected, forming a feedforward-feedback collaborative control of the spinning process, including: The spatial distribution and defect type of fiber defects are extracted from the quality characteristic data. Combined with the spinning speed constraint, the position of the heating section and its temperature state of the melt in the flow process corresponding to the moment of defect formation are calculated by spatiotemporal backward calculation. Based on the location of the heating section and the temperature state, the temperature change trajectory before and after the defect formation time is extracted from the real-time temperature distribution data. Temperature fluctuation events that deviate from the steady-state temperature path in the temperature change trajectory are identified by trajectory deviation determination. The temperature fluctuation events are then causally correlated with the defect feature type. Based on the results of the causal relationship mapping, the degree of delayed impact of the temperature fluctuation event on the triggering time of the solid-liquid phase transition of the phase change material is analyzed and quantified by the delayed propagation chain, and the degree of delayed impact is converted into the correction increment of the trigger advance of the corresponding heating segment in the phase change triggering timing model. The correction increment is superimposed on the current trigger advance of the phase change trigger timing model, and the heating start time of each heating section is updated by redefining the timing. The updated heating start time is then synchronously applied to the real-time generation of the dynamic heating power allocation scheme, forming a feedforward-feedback collaborative control of the spinning process.

[0049] When extracting the spatial distribution and defect type of fiber defects from quality characteristic data, an online optical inspection system scans the surface of the formed fiber point by point to collect the spatial coordinates of defects such as fiber diameter variation, abrupt surface roughness changes, and cross-sectional inhomogeneity. Specifically, the fiber is divided into several equidistant unit segments along its length, with each unit segment ranging from 0.5 mm to 2 mm in length. The defect type identifier for each unit segment is recorded; for example, diameter abrupt defects are labeled as type A, and surface melting inhomogeneity defects are labeled as type B. Combining the spinning speed constraint, the actual flow position of the melt in the spinneret to cooling zone corresponding to the formation of the defect unit segment is calculated based on the product of the fiber's acquisition time at the detection point and the spinning linear velocity. Through spatiotemporal backward calculation, the spatial coordinates of a defect point on the fiber are mapped back to the heating zone number on the melt flow path, such as the outlet position of the third heating zone. Simultaneously, the real-time temperature data at that position at the time of defect formation is extracted to confirm whether the melt temperature at that time is within ±3 degrees Celsius of the target temperature range.

[0050] Based on the location of the heating zone and the temperature state, the temperature change trajectory within a time window of 30 to 60 seconds before and after the defect formation time is extracted from the stored real-time temperature distribution data. A reference curve for the steady-state temperature path is established, which reflects the temperature decay law of the melt along the flow path under normal spinning conditions, typically showing a linear decreasing trend from the spinneret to the cooling zone. The point-by-point deviation between the actual temperature change trajectory and the reference curve is calculated. When the deviation value of a certain segment exceeds 5% of the reference temperature, it is identified as a temperature fluctuation event. The fluctuation amplitude, duration, and frequency of the temperature fluctuation event are further analyzed. For example, a fluctuation event with an amplitude of 8 degrees Celsius and a duration of 15 seconds is causally mapped to fiber defect characteristic types. Specifically, diameter abrupt defect A usually corresponds to a temperature drop event, and surface melting inhomogeneity defect B usually corresponds to a temperature oscillation event. By establishing a mapping database between defect types and fluctuation characteristics, automated correlation matching is achieved.

[0051] Based on the causal relationship mapping results, the degree of delay in the triggering time of the solid-liquid phase transition of the phase change material is quantified through delayed transmission chain analysis. A heat transfer delay model is established, which considers the thermal response time constant of the nano-phase change material, typically 3 to 10 seconds, and the thermal diffusion rate within the melt. When a temperature fluctuation event causes the actual temperature of a certain heating section to be lower than the critical temperature for the solid-liquid phase transition of the phase change material, the delay time for phase transition triggering is calculated, for example, a 5-second delay. This delay time is a quantitative indicator of the degree of delay. The delay time is converted into a correction increment for the advance of the heating section triggering. The conversion relationship is determined by a compensation coefficient, which is calculated based on the melt flow rate and the heat capacity of the heating section. For example, a 5-second delay corresponds to a correction increment that advances the heating start time by 7 seconds.

[0052] The correction increment is superimposed on the current trigger advance of the phase change triggering timing model. For example, if the original trigger advance is 20 seconds, after adding the correction increment of 7 seconds, it is updated to 27 seconds. The heating start-up time of each heating section is updated by redefining the timing. Specifically, the heating power input is started 27 seconds in advance based on the expected arrival time of the melt flow front at the heating section. The updated heating start-up time is synchronously applied to the real-time generation of the dynamic heating power allocation scheme. By adjusting the power output curve of each heating section in real time, the nano-phase change material can trigger an endothermic or exothermic response in a timely manner when flowing through the section, thereby suppressing the downstream transmission of temperature fluctuations and forming a feedforward-feedback collaborative control of the spinning process to achieve continuous optimization of fiber quality.

[0053] In one optional implementation, identifying temperature fluctuation events that deviate from the steady-state temperature path in the temperature change trajectory by determining the trajectory deviation, and causally mapping the temperature fluctuation events to the defect feature types includes: Based on the temperature state, a temperature evolution baseline for the heating section under steady-state conditions is constructed. The deviation between the temperature value at each time point on the temperature change trajectory and the temperature value at the corresponding time point on the temperature evolution baseline is calculated to obtain the trajectory deviation distribution. A time-series scan of the trajectory deviation distribution is performed to identify deviation segments whose deviation magnitude exceeds the steady-state allowable range and whose duration spans the critical duration of the phase change material's thermal response. The temperature change process corresponding to the deviation segment is marked as a temperature fluctuation event. Extract the peak intensity and duration of the temperature fluctuation event from the temperature fluctuation event, and combine the peak intensity and duration of the fluctuation event into a feature vector of the temperature fluctuation event. Based on the defect size distribution corresponding to the defect feature type, a size correlation weight is established between the defect size distribution and the peak intensity of the fluctuation. Based on the defect density distribution corresponding to the defect feature type, a density correlation weight is established between the defect density distribution and the duration of the fluctuation. The feature vector is mapped to the defect feature type through the size correlation weight and the density correlation weight to obtain the causal correlation mapping result.

[0054] like Figure 2 As shown, the method includes: A temperature evolution baseline for the heating section under steady-state conditions is constructed based on the temperature state, which is derived from the temperature state of the melt at the moment of defect formation, calculated by spatiotemporal backwards. The construction of the temperature evolution baseline relies on a steady-state thermal equilibrium model for the heating section. This model is obtained through long-term data collection and statistical analysis of the temperature distribution of the heating section under steady-state operating conditions, with a collection time of no less than 24 hours and a sampling interval of 0.1 to 1 second. The steady-state thermal equilibrium model records the temperature evolution patterns of each heating section under different combinations of spinning speed and melt flow rate, establishing a correspondence between temperature values ​​and time to form a temperature evolution baseline database. For a specific heating section location and a specific combination of operating parameters, a matching temperature evolution baseline is retrieved from the database. If no perfectly matching record exists, linear interpolation is performed using temperature evolution baselines with adjacent operating parameters, with the interpolation error controlled within 2 degrees Celsius.

[0055] The deviation between the temperature values ​​at each time point on the temperature change trajectory and the corresponding temperature values ​​at the temperature evolution baseline is calculated to obtain the trajectory deviation distribution. The temperature change trajectory is derived from the temperature change trajectory before and after the defect formation time extracted from real-time temperature distribution data. This trajectory includes temperature measurements at each time point in a continuous time series. The deviation amplitude is calculated by subtracting the baseline temperature value at the corresponding time point in the temperature evolution baseline from the measured temperature value in the temperature change trajectory at each time point to obtain the temperature deviation at each time point. A positive temperature deviation indicates that the measured temperature is higher than the steady-state baseline temperature, and a negative value indicates that the measured temperature is lower than the steady-state baseline temperature. The temperature deviations at all time points are arranged in chronological order to form a trajectory deviation distribution data sequence. The time resolution of this sequence is consistent with the temperature data acquisition frequency, typically 10 to 100 times per second. The trajectory deviation distribution is stored in the form of timestamp-deviation amplitude pairs, and the data structure adopts a time series database format, supporting efficient time series query and range retrieval operations.

[0056] A time-series scan of the trajectory deviation distribution is performed to identify deviation segments whose deviation magnitude exceeds the steady-state tolerance range and whose duration spans the critical duration of the phase change material's thermal response. The time-series scan employs a sliding window mechanism, with the window length set to 1 to 3 times the critical duration of the phase change material's thermal response. The critical duration of the phase change material's thermal response is calculated based on the thermal diffusivity and particle size of the nano-phase change material, typically ranging from 0.5 to 5 seconds. The steady-state tolerance range is determined based on the statistical characteristics of temperature fluctuations in the heating section under steady-state conditions. By calculating the standard deviation of temperature deviation under steady-state conditions, the steady-state tolerance range is set to 2 to 3 times the standard deviation to ensure that normal steady-state fluctuations are not misjudged as temperature fluctuation events. The sliding window gradually moves from the start time to the end time on the trajectory deviation distribution data sequence, with each move being 10% to 50% of the window length. Within each window position, it checks for time periods where the absolute value of the deviation magnitude continuously exceeds the steady-state tolerance range. If the duration of continuous deviations from the steady-state allowable range within a window reaches the critical duration of the phase change material's thermal response, this time period is marked as a deviation segment. The start time of a deviation segment is defined as the moment when the absolute value of the deviation first exceeds the steady-state allowable range, and the end time is defined as the moment when the absolute value of the deviation last exceeds the steady-state allowable range. The temperature change process corresponding to the deviation segment is marked as a temperature fluctuation event. The temperature fluctuation event record includes the start time, end time, corresponding heating section location, and complete temperature change trajectory data within the deviation segment.

[0057] The peak intensity and duration of temperature fluctuations are extracted from the event. The peak intensity is defined as the maximum absolute value of the deviation within a given segment. This is determined by iterating through the deviation data at all time points within the segment and finding the deviation with the largest absolute value. The duration is defined as the time difference between the end and start times of the segment, expressed in seconds. The peak intensity and duration are combined to form a feature vector for the temperature fluctuation event. This feature vector is represented by a two-dimensional numerical array, where the first element is the peak intensity and the second element is the duration. The feature vector is precisiond to 0.1 degrees Celsius for peak intensity and 0.01 seconds for duration to ensure the accuracy of subsequent mapping calculations.

[0058] Based on the defect size distribution corresponding to the defect feature type, a size correlation weight is established between the defect size distribution and the peak intensity of the fluctuation. The defect size distribution is derived from the statistical results of fiber defect sizes extracted from the quality feature data, and is represented as a probability density distribution of defect length or diameter. The size correlation weight is established through training on historical data, collecting a large number of fiber samples with labeled defect types and size distributions, and simultaneously acquiring the peak intensity data of corresponding temperature fluctuation events. Statistical analysis is used to determine the defect size distribution characteristics corresponding to different peak intensity intervals. The peak intensity of the fluctuation is divided into several intensity intervals, with interval widths set from 2 degrees Celsius to 10 degrees Celsius. For each intensity interval, the defect size distribution caused by temperature fluctuation events within that interval is statistically analyzed, and the similarity between this size distribution and the size distribution of the target defect feature type is calculated. The similarity calculation uses the cross-entropy or cosine similarity measurement method between distributions. The size correlation weight is defined as a similarity value, ranging from 0 to 1. A higher similarity indicates a stronger correlation between the peak intensity of the fluctuation and the target defect feature type.

[0059] Based on the defect density distribution corresponding to the defect feature type, a density correlation weight is established between the defect density distribution and the duration of fluctuation. The defect density distribution is derived from the spatial distribution density of fiber defects extracted from the quality feature data, and is represented as the probability density distribution of the number of defects per unit length of fiber. The establishment of the density correlation weight is also achieved through training on historical data, collecting fiber samples with labeled defect density distributions and the duration data of corresponding temperature fluctuation events. The duration of fluctuation is divided into several duration intervals, with interval widths set from 0.5 seconds to 2 seconds. For each duration interval, the defect density distribution caused by the temperature fluctuation event is statistically analyzed, and the similarity between this density distribution and the density distribution of the target defect feature type is calculated. The density correlation weight is defined as a similarity value, ranging from 0 to 1. A higher similarity indicates a stronger correlation between the duration of fluctuation and the target defect feature type.

[0060] The feature vector is mapped to the defect feature type using size-related weights and density-related weights, resulting in a causal correlation mapping. The mapping process involves searching for the corresponding size-related weight value in the size-related weight table based on the peak intensity value of the fluctuation in the feature vector, and simultaneously searching for the corresponding density-related weight value in the density-related weight table based on the fluctuation duration value. If the peak intensity or duration of the fluctuation does not fall exactly on the predefined interval boundary, a linear interpolation method is used to calculate the corresponding correlation weight value. The size-related weight and density-related weight are multiplied to obtain a comprehensive correlation value, which reflects the comprehensive correlation strength between the temperature fluctuation event and the specific defect feature type. The comprehensive correlation degree is calculated for all candidate defect feature types, and the defect feature type with the highest comprehensive correlation degree is selected as the mapping target for the temperature fluctuation event, establishing a causal relationship between the temperature fluctuation event and the defect feature type. The causal correlation mapping result is stored in the form of a correlation record consisting of a temperature fluctuation event identifier, a defect feature type identifier, and a comprehensive correlation degree value. The record also includes detailed information such as the peak intensity of the fluctuation, the duration of the fluctuation, the size-related weight, and the density-related weight, for subsequent causal analysis and control strategy optimization. The overall correlation threshold is set to 0.3 to 0.6. When the overall correlation is lower than the threshold, it is determined that the temperature fluctuation event has no significant causal relationship with the defect feature type, and no mapping relationship is established.

[0061] A second aspect of the present invention provides a bio-based nylon and nanoparticle composite spinning system, comprising: The temperature analysis unit is used to acquire real-time temperature distribution data of the melt to be spun and dispersion state characteristic parameters of the nano-phase change material in the melt. Based on the enthalpy-temperature response relationship model of the phase change material, the real-time temperature distribution data is dynamically analyzed to obtain the heat transfer state evaluation result. The viscosity control unit is used to establish the temperature-viscosity coupling relationship at each point on the melt flow path based on the heat transfer state assessment results and the flow field distribution characteristics of the spinning channel, generate spatially distributed viscosity control requirements, and, based on the viscosity control requirements and spinning speed constraints, calculate the spatiotemporal matching relationship between each heating section and the melt flow front by establishing a phase change triggering timing model of the phase change material in the flow shear field, and generate a dynamic heating power distribution scheme synchronized with the flow path. The heating matching unit is used to adjust the energy input timing of each heating section based on the dynamic heating power distribution scheme, so that the heat absorption and release process of the nano phase change material matches the time when the melt arrives at the corresponding heating section. The collaborative control unit is used to collect quality characteristic data of the fiber after spinning and forming. The quality characteristic data is used as feedback information. By tracing the temporal correlation between fiber defects and temperature fluctuation events in the melt flow process, the trigger advance in the phase change triggering timing model is dynamically corrected to form a feedforward-feedback collaborative control of the spinning process.

[0062] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0063] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0064] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for spinning bio-based nylon and nanoparticle composites, characterized in that, include: The real-time temperature distribution data of the melt to be spun and the dispersion state characteristic parameters of the nano-phase change material in the melt are obtained. The real-time temperature distribution data is dynamically analyzed based on the enthalpy-temperature response relationship model of the phase change material to obtain the heat transfer state evaluation result. Based on the heat transfer status assessment results and the flow field distribution characteristics of the spinning channel, the temperature-viscosity coupling relationship at each point on the melt flow path is established, generating spatially distributed viscosity control requirements. According to the viscosity control requirements and the spinning speed constraints, a phase change triggering timing model of the phase change material in the flow shear field is established, and the spatiotemporal matching relationship between each heating section and the melt flow front is calculated, generating a dynamic heating power distribution scheme synchronized with the flow path. Based on the dynamic heating power distribution scheme, the energy input timing of each heating zone is adjusted so that the heat absorption and release process of the nano phase change material matches the time when the melt arrives at the corresponding heating zone. The quality characteristic data of the fiber after spinning is collected and used as feedback information. By tracing the temporal correlation between fiber defects and temperature fluctuation events in the melt flow process, the trigger advance in the phase change triggering timing model is dynamically corrected, forming a feedforward-feedback collaborative control of the spinning process.

2. The method according to claim 1, characterized in that, The real-time temperature distribution data is dynamically analyzed based on the enthalpy-temperature response model of phase change materials to obtain the following heat transfer state assessment results: By spatially matching real-time temperature distribution data with the dispersion state characteristic parameters of nano-phase change materials in the melt, the degree of deviation between the local concentration distribution of phase change materials at each location point and the rate of temperature change at that location point is identified, and the uniformity evaluation result of phase change material distribution is obtained. Based on the latent heat of solid-liquid phase transition of phase change materials in the enthalpy-temperature response model, the theoretical heat absorption and release capacity of phase change materials at each location point at the current temperature is calculated. By establishing the transfer function of local concentration deviation on the thermal response delay of phase change materials, the theoretical heat absorption and release capacity is mapped to the effective heat absorption and release capacity of each location point under the actual dispersion state, and the actual heat absorption and release capacity distribution considering the influence of uneven dispersion is obtained. Based on the actual heat absorption and release capacity distribution and the temperature gradient change trend in the real-time temperature distribution data, the heat accumulation rate and heat dissipation rate at each location point inside the melt are calculated. Based on the difference between the heat accumulation rate and the heat dissipation rate, the heat transfer balance state at each location point is determined, and the heat transfer state evaluation result is obtained.

3. The method according to claim 2, characterized in that, By establishing a transfer function for the thermal response delay of the phase change material due to local concentration deviation, the theoretical heat absorption and release capacity is mapped to the effective heat absorption and release capacity at each location point under the actual dispersion state, resulting in the actual heat absorption and release capacity distribution considering the influence of uneven dispersion, including: Based on the evaluation results of the uniformity of phase change material distribution, the deviation between the local concentration and the standard concentration at each location point is extracted. The deviation is used as the input variable of the transfer function to calculate the response delay time of the phase change material at each location point undergoing solid-liquid phase transition at the current temperature. Based on the response delay time and the rate of temperature change in the real-time temperature distribution data, the degree of lag in the actual participation of the phase change material in the heat absorption and release process at each location point is identified, and the degree of lag is quantified as the heat capacity contribution reduction ratio. The effective heat absorption and release capacity at each location point under the actual dispersion state is obtained by multiplying the theoretical heat absorption and release capacity with the heat capacity contribution reduction ratio. The effective heat absorption and release capacity is then spatially reconstructed along the melt flow path to obtain the actual heat absorption and release capacity distribution considering the effect of uneven dispersion.

4. The method according to claim 1, characterized in that, Based on the heat transfer state assessment results and the flow field distribution characteristics of the spinning channel, a temperature-viscosity coupling relationship is established at each location along the melt flow path, generating spatially distributed viscosity control requirements, including: Extract the deviation of the heat transfer equilibrium state at each location point from the heat transfer state assessment results, and convert the deviation of the heat transfer equilibrium state into the temperature stability index of the corresponding location point. Based on the flow field distribution characteristics of the spinning channel, the flow velocity vector and local shear stress distribution of the melt at each location point are obtained. The temperature stability index and the local shear stress distribution are coupled and calculated to identify the sensitive range of viscosity response of the degree of restriction of melt molecular chain segment movement under temperature fluctuation at each location point. Based on the sensitive range, a dynamic response relationship between the temperature change and viscosity change at each location point is established to obtain the temperature-viscosity coupling relationship. The temperature-viscosity coupling relationship is then spatially expanded along the melt flow path to calibrate the temperature control amplitude required to maintain the target viscosity at each location point, thereby generating a spatially distributed viscosity control requirement.

5. The method according to claim 1, characterized in that, Based on the viscosity control requirements and spinning speed constraints, a phase change triggering timing model of the phase change material in the flow shear field is established. The spatiotemporal matching relationship between each heating section and the melt flow front is calculated, and a dynamic heating power distribution scheme synchronized with the flow path is generated, including: Extract the target temperature control range required for each position point from the viscosity control requirements, perform correlation calculations between the target temperature control range and the spinning speed constraint, and calculate the flow time required for the melt to flow from each heating section to the corresponding position point. Based on the shear stress accumulated by the phase change material in the flow shear field during the flow time, the dynamic offset effect of shear stress on the solid-liquid phase transition temperature threshold of the phase change material is identified, and the influence law of shear stress on the triggering time of solid-liquid phase transition of the phase change material is established. By coupling the influence law with the flow time, the actual moment when the phase change material in each heating section triggers phase change during melt flow is determined, and a phase change triggering timing model is obtained. Based on the time point at which the phase change material triggers phase change in each heating section as determined in the phase change triggering timing model, the starting time of the phase change material at the time point is predicted to release or absorb latent heat. The starting time is compared with the time when the melt flow front reaches the corresponding position point, and the time difference between the two is calculated. The time difference is converted into the heating advance or delay of each heating section. The heating start time of each heating section is adjusted forward or backward according to the heating advance or delay. The duration of each heating section is determined according to the target temperature control range, and a dynamic heating power distribution scheme synchronized with the flow path is generated.

6. The method according to claim 1, characterized in that, Using the quality characteristic data as feedback information, and by tracing the temporal correlation between fiber defects and temperature fluctuation events in the melt flow process, the trigger advance in the phase change triggering timing model is dynamically corrected, forming a feedforward-feedback collaborative control of the spinning process, including: The spatial distribution and defect type of fiber defects are extracted from the quality characteristic data. Combined with the spinning speed constraint, the position of the heating section and its temperature state of the melt in the flow process corresponding to the moment of defect formation are calculated by spatiotemporal backward calculation. Based on the location of the heating section and the temperature state, the temperature change trajectory before and after the defect formation time is extracted from the real-time temperature distribution data. Temperature fluctuation events that deviate from the steady-state temperature path in the temperature change trajectory are identified by trajectory deviation determination. The temperature fluctuation events are then causally correlated with the defect feature type. Based on the results of the causal relationship mapping, the degree of delayed impact of the temperature fluctuation event on the triggering time of the solid-liquid phase transition of the phase change material is analyzed and quantified by the delayed propagation chain, and the degree of delayed impact is converted into the correction increment of the trigger advance of the corresponding heating segment in the phase change triggering timing model. The correction increment is superimposed on the current trigger advance of the phase change trigger timing model, and the heating start time of each heating section is updated by redefining the timing. The updated heating start time is then synchronously applied to the real-time generation of the dynamic heating power allocation scheme, forming a feedforward-feedback collaborative control of the spinning process.

7. The method according to claim 6, characterized in that, Identifying temperature fluctuation events that deviate from the steady-state temperature path in the temperature change trajectory by determining trajectory deviation, and mapping the temperature fluctuation events to the defect feature types through causal correlation, includes: Based on the temperature state, a temperature evolution baseline for the heating section under steady-state conditions is constructed. The deviation between the temperature value at each time point on the temperature change trajectory and the temperature value at the corresponding time point on the temperature evolution baseline is calculated to obtain the trajectory deviation distribution. A time-series scan of the trajectory deviation distribution is performed to identify deviation segments whose deviation magnitude exceeds the steady-state allowable range and whose duration spans the critical duration of the phase change material's thermal response. The temperature change process corresponding to the deviation segment is marked as a temperature fluctuation event. Extract the peak intensity and duration of the temperature fluctuation event from the temperature fluctuation event, and combine the peak intensity and duration of the fluctuation event into a feature vector of the temperature fluctuation event. Based on the defect size distribution corresponding to the defect feature type, a size correlation weight is established between the defect size distribution and the peak intensity of the fluctuation. Based on the defect density distribution corresponding to the defect feature type, a density correlation weight is established between the defect density distribution and the duration of the fluctuation. The feature vector is mapped to the defect feature type through the size correlation weight and the density correlation weight to obtain the causal correlation mapping result.

8. A bio-based nylon and nanoparticle composite spinning system for implementing the method of any one of claims 1-7, characterized in that, include: The temperature analysis unit is used to acquire real-time temperature distribution data of the melt to be spun and dispersion state characteristic parameters of the nano-phase change material in the melt. Based on the enthalpy-temperature response relationship model of the phase change material, the real-time temperature distribution data is dynamically analyzed to obtain the heat transfer state evaluation result. The viscosity control unit is used to establish the temperature-viscosity coupling relationship at each point on the melt flow path based on the heat transfer state assessment results and the flow field distribution characteristics of the spinning channel, generate spatially distributed viscosity control requirements, and, based on the viscosity control requirements and spinning speed constraints, calculate the spatiotemporal matching relationship between each heating section and the melt flow front by establishing a phase change triggering timing model of the phase change material in the flow shear field, and generate a dynamic heating power distribution scheme synchronized with the flow path. The heating matching unit is used to adjust the energy input timing of each heating section based on the dynamic heating power distribution scheme, so that the heat absorption and release process of the nano phase change material matches the time when the melt arrives at the corresponding heating section. The collaborative control unit is used to collect quality characteristic data of the fiber after spinning and forming. The quality characteristic data is used as feedback information. By tracing the temporal correlation between fiber defects and temperature fluctuation events in the melt flow process, the trigger advance in the phase change triggering timing model is dynamically corrected to form a feedforward-feedback collaborative control of the spinning process.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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