A glass steel production equipment whole life cycle management system

Abstract

The application discloses a kind of glass steel production equipment full life cycle management system, it is related to industrial equipment risk data processing technical field, comprising: acquisition module, for collecting the multi-modal operating signal of pultrusion mould, based on multi-modal operating signal, extract to obtain the multi-dimensional feature vector of explosive aggregation precursor;The application extracts positive feedback acceleration duration, energy jump amplitude and deviation cumulative area from the mould cavity wall surface temperature signal, dynamic pressure signal and ultrasonic flyover time signal of pultrusion mould in acquisition module, constitutes the multi-dimensional feature vector of explosive aggregation precursor, the identification timing of explosive aggregation risk is greatly advanced from the late stage of thermal runaway to the initial stage of positive feedback establishment, realizes the multi-physical field coupling comprehensive representation of the irreversible degree of thermal runaway process;The accuracy and forwardness of explosive aggregation risk determination are significantly improved by using numerical sequence increment analysis to identify accelerated evolution state for dynamic regulation.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment risk data processing technology, and in particular to a full life cycle management system for fiberglass production equipment. Background Technology

[0002] In the field of full lifecycle management of fiberglass pultrusion molding equipment, the current common approach for online monitoring and proactive protection against the extreme dynamic risk of sudden resin polymerization inside the mold during the curing process is a fixed-value alarm control technology based on conventional thermocouple feedback. The core principle of this technology is as follows: standard armored thermocouples are embedded in the mold wall of each heating zone of the pultrusion mold. A programmable logic controller continuously collects temperature values ​​at each measuring point with a second-level sampling period. The real-time temperature is compared with preset over-temperature alarm thresholds and upper safety thresholds. When the temperature at any measuring point exceeds the alarm threshold, an audible and visual warning signal is triggered. When the temperature exceeds the safety threshold, the heater power-off and traction shutdown interlock protection actions are executed, thereby achieving routine abnormal monitoring and emergency cut-off protection of the mold's thermal state. However, in actual pultrusion production scenarios, the internal flow channels of the mold may develop localized fouling and necking due to long-term resin residue deposition. Simultaneously, trace amounts of accelerator-like active impurities may accidentally enter the resin raw materials during storage and transportation. When resin clumps containing these impurities enter the fouling and necking region, the sudden drop in flow rate leads to a significant increase in local residence time. The exothermic reaction from the curing reaction rapidly accumulates in the necking region, where heat dissipation conditions deteriorate, creating a positive feedback thermal runaway cycle of temperature increase, reaction acceleration, intensified exothermic reaction, and further temperature increases. In this thermal runaway process, the temperature change in the necking region exhibits an accelerated upward trend, accompanied by dynamic pressure pulsations from bubble formation and collapse, and the resin sound velocity deviating from the normal curing trajectory. These three are not isolated phenomena, but rather correlated representations of the same physical process across different measurement dimensions. However, conventional thermocouples used in existing technologies can only provide the average temperature of the mold steel, failing to detect the acceleration of temperature changes or establish a causal relationship between temperature anomalies, pressure pulsations, and sound velocity deviations. This lack of understanding of the coupling relationships among multiple physical quantities means that existing technologies can only respond passively after explosive polymerization occurs, failing to proactively identify and prevent risks in the early stages of establishing positive feedback for thermal runaway. Summary of the Invention

[0003] The purpose of this invention is to solve the problem that existing technologies rely solely on the average temperature of conventional thermocouples for threshold alarms, which cannot detect changes in temperature acceleration and cannot establish a correlation between temperature anomalies and pressure pulsations and sound velocity deviations, resulting in a passive response only after explosive polymerization occurs. Therefore, this invention proposes a full life cycle management system for fiberglass production equipment.

[0004] To achieve the above objectives, the present invention employs the following technology: a full life-cycle management system for fiberglass production equipment, comprising: The acquisition module is used to acquire multimodal operating signals of the pultrusion die and extract multidimensional feature vectors of the precursor to explosive polymerization based on the multimodal operating signals. Among them, the multi-dimensional feature vector of the precursor to explosive polymerization includes the duration of positive feedback acceleration, the magnitude of energy jump, and the area of ​​deviation accumulation. The instruction module generates a raw explosive polymerization risk index reflecting the irreversibility of the thermal runaway process based on multi-dimensional feature vectors of explosive polymerization precursors; determines risk level markers based on the raw explosive polymerization risk index; and invokes control instruction combinations based on the risk level markers. The update module is used to obtain equipment status feedback data after the combined execution of control commands, construct an intervention effect evaluation sample by combining the burst risk index at the corresponding time, and obtain a hierarchical control strategy rule base based on the intervention effect evaluation sample.

[0005] Furthermore, methods for extracting multi-dimensional feature vectors of precursors to explosive fusion based on multi-modal operating signals include: Based on the mold cavity wall temperature signal of the multimodal operation signal, the mold cavity wall temperature signal is numerically differentiated to obtain the temperature change acceleration sequence; based on the temperature change acceleration sequence, the duration of continuous positive values ​​in the temperature change acceleration sequence is recorded as the positive feedback acceleration duration. Based on the dynamic pressure signal of multimodal operation signal, the dynamic pressure signal is decomposed in the frequency domain to extract the energy value of the bubble generation and collapse characteristic frequency band; based on the energy value, the amount by which the energy value at the current moment exceeds the average energy value during the steady-state operation period is recorded as the energy jump amplitude. Based on the ultrasonic fly-through time signal of the multimodal operation signal, the ultrasonic fly-through time signal is converted into sound velocity to obtain a real-time sound velocity sequence. The cumulative integral value of the deviation of the real-time sound velocity sequence from the expected solidified sound velocity trajectory within a preset observation window is recorded as the cumulative deviation area. Based on the duration of positive feedback acceleration, the magnitude of energy jump, and the area of ​​deviation accumulation, a multi-dimensional feature vector of the precursor to explosive fusion is obtained.

[0006] Furthermore, methods for generating a raw explosive polymerization risk index reflecting the irreversibility of the thermal runaway process based on multi-dimensional feature vectors of precursors to explosive polymerization include: Determine whether the duration of positive feedback acceleration is greater than the preset duration threshold, determine whether the energy jump is greater than the preset jump threshold, and determine whether the deviation cumulative area is greater than the preset cumulative area threshold. If the duration of positive feedback acceleration, the magnitude of energy jump, and the cumulative deviation area are all greater than their respective critical values, the thermal runaway process is determined to have entered the irreversible stage. Among them, the original explosive polymerization risk index is a preset extreme value that represents the irreversible state; If at least one of the positive feedback acceleration duration, energy jump magnitude, and deviation cumulative area does not exceed the corresponding critical value, the original explosive fusion risk index, which characterizes the progress of the irreversible process, is obtained by correlating and coupling the positive feedback acceleration duration, energy jump magnitude, and deviation cumulative area according to the degree of proximity of each characteristic quantity to the corresponding critical value.

[0007] Furthermore, methods for determining risk level labels based on the original explosive polymerization risk index include: Obtain the numerical sequence of the original explosive polymerization risk index at multiple consecutive sampling times, and analyze the incremental value of the numerical sequence per unit time based on the numerical sequence at multiple consecutive sampling times. If the increment value at the current moment is greater than the increment value at the previous moment, it is marked as an accelerated evolution state; if the increment value at the current moment is not greater than the increment value at the previous moment, it is marked as a non-accelerated evolution state. Obtain the value of the original explosive risk index at the current moment, and determine the basic risk level label based on the value. The basic risk level label includes the attention level label, the warning level label, and the emergency level label. If the original explosive risk index is in an accelerated evolution state, the basic risk level label will be upgraded by one level, and the upgraded risk level label will be the final risk level label; if the basic risk level label before the upgrade is already an emergency level label, the emergency level label will remain unchanged. If the original explosive polymerization risk index is in a non-accelerated evolution state, the basic risk level label will be used as the final determined risk level label.

[0008] Furthermore, the method of invoking control instruction combinations based on risk level markers includes: A hierarchical control strategy rule base is constructed, which stores the correspondence between attention level markers and first control instruction combinations, early warning level markers and second control instruction combinations, and emergency level markers and third control instruction combinations. When the final determined risk level is marked as "attention level", the first combination of control instructions is invoked; When the final determined risk level is marked as a warning level, the second combination of control instructions is invoked; When the final risk level is marked as emergency, the third set of control instructions is invoked.

[0009] Furthermore, the first control command combination, the second control command combination, and the third control command combination include: The first control command combination includes a traction speed reduction command; The second set of control commands includes a rapid decrease in traction speed and a forced cooling command for the target temperature zone. The third set of control commands includes a full-temperature zone heater cut-off command, an emergency shutdown command, and an active pressure relief device trigger command.

[0010] Furthermore, methods for obtaining equipment status feedback data after the execution of control command combinations and constructing intervention effect evaluation samples by combining the burst risk index at the corresponding time include: After the combination of control commands is executed, the actual traction speed value, actual heating power value and actual cooling status value are continuously collected within the preset sampling time. The actual traction speed value, actual heating power value and actual cooling status value are used as equipment status feedback data. The equipment status feedback data is linked and bound with the explosive polymerization risk index calculated at the last time before the combined execution of control commands to form a sample for evaluating the effect of a single intervention. The single intervention effect evaluation samples are stored in the intervention effect evaluation sample library.

[0011] Furthermore, methods for obtaining a tiered management strategy rule base based on intervention effectiveness evaluation samples include: Based on the intervention effect evaluation sample, extract several single intervention effect evaluation samples corresponding to the intervention effect evaluation sample library; For each regulatory intervention, it is determined whether the original explosive risk index has fallen back to a safe range within a preset period after the intervention. If the original explosive risk index falls back to the safe range, the intervention will be marked as an effective intervention sample; otherwise, it will be marked as an ineffective intervention sample. Based on effective and ineffective intervention samples, the proportion of effective intervention samples corresponding to each combination of control instructions invoked under each risk level is statistically analyzed; based on the proportion of effective intervention samples, a hierarchical control strategy rule base is obtained.

[0012] Furthermore, based on the proportion of effective intervention samples, methods for obtaining a tiered control strategy rule base include: The comparison is based on the proportion of effective intervention samples, combined with a preset effectiveness threshold; When the proportion of effective intervention samples for a certain combination of control instructions under a certain risk level label is less than the preset effectiveness threshold, the control instruction combination corresponding to that risk level label is adjusted, and the adjusted combination is used to obtain a hierarchical control strategy rule base. When the proportion of effective intervention samples for a certain combination of control instructions under a certain risk level is not less than the preset effectiveness threshold, the combination of control instructions is kept unchanged, and a hierarchical control strategy rule base is obtained.

[0013] Furthermore, the method for adjusting the combination of control instructions corresponding to this risk level label includes: Based on the proportion of effective intervention samples, the combination of control instructions with the highest proportion of effective intervention samples under this risk level is determined as the candidate instruction combination; If there are multiple highest-value control instruction combinations with the same proportion of effective intervention samples under the risk level label, then the control instruction combination with the least impact on production continuity shall be selected as the candidate instruction combination. Based on the candidate instruction combination, the currently used control instruction combination under this risk level is replaced with the candidate instruction combination.

[0014] In summary, due to the adoption of the above-mentioned technology in the full life cycle management system for fiberglass production equipment, the beneficial effects of this invention are: This invention extracts the duration of positive feedback acceleration, the magnitude of energy jump, and the cumulative area of ​​deviation from the temperature signal of the mold cavity wall, dynamic pressure signal, and ultrasonic fly-through time signal of the pultrusion die through the acquisition module. This constitutes a multi-dimensional feature vector of the precursor to explosive polymerization, which significantly advances the identification of explosive polymerization risk from the middle and late stages of thermal runaway to the early stage of positive feedback establishment. It solves the technical problems of existing technologies that cannot sense changes in temperature acceleration and cannot establish the causal relationship between temperature anomalies, pressure pulsation, and sound velocity deviation by relying only on the average temperature of conventional thermocouples. It realizes a comprehensive characterization of the irreversibility of the thermal runaway process through multi-physics field coupling. This invention uses an instruction module to perform risk assessment and correlation coupling processing on the multi-dimensional feature vectors of explosive polymerization precursors, and generates an original explosive polymerization risk index that reflects the irreversibility of the thermal runaway process. It also uses incremental numerical sequence analysis to identify accelerated evolution states for dynamic adjustment, which solves the problems of risk underestimation and early warning lag caused by continuous numerical weighted fusion or fixed threshold interval mapping in the prior art. This invention significantly improves the accuracy and foresight of explosive polymerization risk assessment. This invention constructs a hierarchical control strategy rule base through an instruction module. Based on the attention level marker, the warning level marker, and the emergency level marker, it calls the traction speed reduction instruction, the traction speed reduction instruction and the target temperature zone forced cooling instruction, the full temperature zone heater cut-off instruction and the emergency shutdown instruction and the active pressure relief device trigger instruction, respectively. This solves the technical problem that the non-stop-as-you-go binary response mode in the prior art cannot adapt to the stage evolution characteristics of explosive polymerization risk, and achieves precise matching between the intervention intensity and the stage of risk evolution. This invention obtains the actual traction speed, actual heating power, and actual cooling status values ​​after the execution of the control command combination through an update module, and associates them with the bursting risk index at the corresponding time to form an intervention effect evaluation sample, which is then stored in the intervention effect evaluation sample library. Based on the statistical analysis of the proportion of effective intervention samples and the comparison of preset effectiveness thresholds, the hierarchical control strategy rule library is driven to adaptively correct itself. Furthermore, when selecting the best candidate command combination, a quantitative standard for the impact on production continuity is introduced. This solves the technical problem in the prior art that the control strategy is static and fixed and cannot learn and evolve from historical intervention experience, thus forming a closed loop of adaptive risk management throughout the entire life cycle that is continuously optimized as the equipment operates for a long time. Attached Figure Description

[0015] Figure 1 A system block diagram of the present invention is shown; Figure 2 A flowchart of the present invention is shown. Detailed Implementation

[0016] The following will describe, with reference to the accompanying drawings of the embodiments of the present invention, a full life-cycle management system for fiberglass production equipment according to the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0017] To more clearly and intuitively demonstrate the practical application effects and advantages of the fiberglass production equipment lifecycle management system method of the present invention, and to verify its feasibility and effectiveness, the present invention will be further described below with reference to embodiments. Through specific scenario simulations and data calculations, the method is explained in detail how it plays a role in actual energy storage battery fault prediction, helping readers to better understand the technical details and practical value of the invention. The present invention will be further described below with reference to embodiments; Example 1: See Figure 1 - Figure 2 A full lifecycle management system for fiberglass production equipment, comprising: The acquisition module acquires multimodal operating signals of the pultrusion die, including die cavity wall temperature signal, dynamic pressure signal and ultrasonic fly-through time signal. Based on the multimodal operating signals, a multidimensional feature vector of the precursor to explosive polymerization is extracted. Among them, the multi-dimensional feature vector of the precursor to explosive polymerization includes the duration of positive feedback acceleration, the magnitude of energy jump, and the area of ​​deviation accumulation. It should be noted that the methods for extracting multi-dimensional feature vectors of precursors to explosive fusion based on multimodal operating signals include: Based on the temperature signal of the mold cavity wall using multimodal operating signals, numerical differentiation is performed on the temperature signal to obtain a temperature change acceleration sequence. The duration of continuous positive values ​​in this sequence is recorded as the positive feedback acceleration duration. This solves the technical problem in existing technologies where conventional thermocouples can only provide the average temperature of the mold steel and cannot detect temperature acceleration changes. During the establishment of positive feedback in thermal runaway, the temperature in the necking region does not rise uniformly but rather accelerates. Existing technologies compare absolute temperature values ​​using a fixed threshold, triggering an alarm only when the temperature has reached a high level, at which point the thermal runaway process is often already in its mid-to-late stages. This solution, by extracting the duration of continuous positive values ​​in the temperature change acceleration sequence, can identify the established cumulative effect of self-driven thermal runaway even when the absolute temperature is still within the normal range. This significantly advances the identification of explosive polymerization risk from the mid-to-late stages of thermal runaway to the initial stage of positive feedback establishment, providing a crucial response time window for subsequent graded intervention. The length of the numerical differential processing window in the temperature change acceleration sequence calculation is determined based on the sampling frequency of the mold cavity wall temperature signal and the response time requirements for identifying pre-burst precipitation. Specifically, the differential calculation window length is the reciprocal of the sampling frequency multiplied by the number of sampling points within the window, which is set to five sampling points to achieve a balance between response speed and noise suppression. If the temperature signal sampling frequency is ten times per second, the differential calculation window length is 0.5 seconds. This setting ensures that the temperature change acceleration calculation can respond promptly to the temperature change trend while effectively suppressing the interference of high-frequency noise on the differential results. The condition for continuously maintaining a positive value is that the value of the temperature change acceleration sequence is greater than zero. Based on the dynamic pressure signal from multimodal operating signals, this method performs frequency domain decomposition on the dynamic pressure signal to extract the energy value of the characteristic frequency band of bubble formation and collapse. Based on the energy value, the amount by which the current energy value exceeds the average energy value during steady-state operation is recorded as the energy jump amplitude. This solves the technical problem that existing technologies cannot establish a causal relationship between temperature anomalies and pressure pulsations. During the thermal runaway process, the rapid temperature rise is accompanied by the violent formation and collapse of bubbles inside the resin. This process is manifested in the dynamic pressure signal as a significant energy jump in a specific frequency band. Existing pressure monitoring technologies only focus on exceeding the absolute pressure value limit, failing to identify the characteristic frequency band of bubble formation and collapse, nor using the energy jump amplitude of this frequency band as an independent characterization dimension for explosive polymerization precursors. This method, by extracting the energy value of the characteristic frequency band of bubble formation and collapse and calculating its jump amplitude relative to the steady-state baseline, supplements the characterization information of the thermal runaway process from the mechanical effect dimension. This allows the identification of explosive polymerization risk to no longer rely on a single temperature dimension, but achieves joint perception of thermal and mechanical effects, significantly reducing the risk of missed detection due to single sensor failure or signal interference. The characteristic frequency band for bubble formation and collapse is determined based on the typical frequency range of bubble formation and collapse before resin burst polymerization in the pultrusion die. Specifically, burst polymerization simulation experiments are conducted on this type of resin in a necking die model, dynamic pressure signals are simultaneously acquired and time-frequency analysis is performed, and the frequency range in which the dynamic pressure pulsation energy is most concentrated during bubble formation and collapse is extracted as this characteristic frequency band; if no experimental data is available, a default frequency range is used, i.e., the lower limit frequency is set to 10 Hz and the upper limit frequency is set to 200 Hz. This frequency range covers the main energy concentration area of ​​dynamic pressure pulsation generated during the formation, expansion and collapse of resin bubbles under pultrusion process conditions. Based on the ultrasonic fly-through time signal of multimodal operation signal, the sound velocity of the ultrasonic fly-through time signal is converted to obtain a real-time sound velocity sequence. The cumulative integral value of the deviation of the real-time sound velocity sequence from the expected curing sound velocity trajectory within a preset observation window is recorded as the cumulative deviation area. This solves the technical problem that existing technologies cannot establish a causal relationship between temperature anomalies and sound velocity deviation. During the thermal runaway process, the resin phase changes from liquid to gel to solid, and the propagation speed of ultrasound during this process also undergoes characteristic changes. When local thermal runaway is caused by fouling and impurities in the necking region, the actual curing trajectory of the resin will deviate from the expected curing sound velocity trajectory under normal curing process. Existing ultrasonic monitoring only focuses on the instantaneous value of sound velocity or the determination of a single curing endpoint, and does not use the cumulative effect of sound velocity deviation as a quantitative indicator of explosive polymerization risk. This scheme, by calculating the cumulative integral value of the deviation of the real-time sound velocity sequence from the expected curing sound velocity trajectory within a preset observation window, characterizes the cumulative degree of abnormal resin phase evolution from two dimensions: spatial scale and duration. The larger the deviation from the cumulative area, the longer the phase anomaly has persisted and the wider its impact, indicating that the thermal runaway process is closer to the irreversible stage. The introduction of this characteristic quantity elevates the assessment of explosive polymerization risk from judging the instantaneous state to quantifying the cumulative effect of the process. The desired curing sound velocity trajectory is established in advance based on the standard curve of the sound velocity of this type of resin changing with curing time under standard curing process conditions. This standard curve is obtained by a combination of differential scanning calorimetry and ultrasonic monitoring experiments. The duration of the preset observation window is determined based on the typical time span from the appearance of explosive polymerization precursors to the entry into the irreversible stage. Based on the duration of positive feedback acceleration, the magnitude of energy jump, and the area of ​​deviation from cumulative accumulation, a multi-dimensional feature vector of pre-burst polymerization is obtained, solving the fundamental problem of the lack of understanding of the coupling relationship of multiple physical quantities in existing technologies. During the thermal runaway process, the accelerated temperature rise in the necking region, the dynamic pressure pulsations caused by bubble formation and collapse, and the deviation of resin sound velocity from the normal curing trajectory are not isolated phenomena, but rather a correlated representation of the same physical process across three measurement dimensions: thermal, mechanical, and acoustic effects. Conventional thermocouples used in existing technologies can only provide a single-dimensional average temperature value, failing to detect changes in temperature acceleration and unable to establish a causal relationship between temperature anomalies and pressure pulsations and sound velocity deviations. This scheme, for the first time, integrates the duration of positive feedback acceleration, the magnitude of energy jump, and the area of ​​deviation accumulation into a unified multi-dimensional feature vector for explosive polymerization precursors. It comprehensively characterizes the irreversibility of the thermal runaway process from three dimensions: time accumulation, intensity jump, and spatial scale. This allows the system's perception of explosive polymerization risk to leap from the numerical exceedance of a single physical quantity to a comprehensive understanding of the multi-physics coupled evolution process. The above approach advances the timing of explosive polymerization risk identification from after temperature exceedance to the early stage of positive feedback establishment. Cross-validation through three independent physical dimensions reduces false alarms and provides a quantifiable cumulative characterization of the irreversible critical state. Among the multi-dimensional feature vectors of pre-burst polymerization, the duration of positive feedback acceleration characterizes the thermal runaway process from a time accumulation dimension, i.e., the length of time the self-driven thermal runaway state has lasted; the energy jump magnitude characterizes the thermal runaway process from an intensity jump dimension, i.e., the jump magnitude of the intensity of bubble collapse activity relative to the steady state; and the deviation accumulation area characterizes the thermal runaway process from a spatial scale dimension, i.e., the cumulative scale of the resin phase abnormally deviating from the normal curing trajectory. These three dimensions are independent yet causally related, together constituting a multi-dimensional process characterization of the irreversibility of the thermal runaway process.

[0018] The instruction module generates a raw explosive polymerization risk index reflecting the irreversibility of the thermal runaway process based on multi-dimensional feature vectors of explosive polymerization precursors; determines risk level markers based on the raw explosive polymerization risk index; and invokes control instruction combinations based on the risk level markers. It should be noted that the methods for generating the original explosive polymerization risk index, reflecting the irreversibility of the thermal runaway process, based on the multi-dimensional feature vector of the precursor to explosive polymerization include: Determine whether the duration of positive feedback acceleration is greater than the preset duration threshold, determine whether the energy jump is greater than the preset jump threshold, and determine whether the deviation cumulative area is greater than the preset cumulative area threshold. If the duration of positive feedback acceleration, the magnitude of energy jump, and the area of ​​deviation all exceed their respective critical values, the thermal runaway process is determined to have entered an irreversible stage. The original explosive polymerization risk index is taken as a preset extreme value representing the irreversible state. This solves the technical problem that existing risk assessment methods based on continuous numerical weighted fusion cannot reflect the physical critical phenomena of thermal runaway. Existing multi-indicator fusion generally uses linear weighted summation, resulting in a continuously changing value, implicitly assuming a linear additive relationship between risk and each indicator. However, the thermal runaway process exhibits a clear physical critical phenomenon. When the positive feedback cumulative effect represented by the duration of positive feedback acceleration, the bubble collapse intensity represented by the magnitude of energy jump, and the phase deviation scale represented by the area of ​​deviation simultaneously reach their respective critical points, the system will undergo an irreversible phase transition. At this point, the risk state undergoes a qualitative change rather than a quantitative one. This scheme introduces this physical critical phenomenon into the generation logic of the explosive polymerization risk index through a determination mechanism that directly takes the extreme value when all three characteristics simultaneously exceed criticality. When all three dimensions of the characteristic quantity exceed the critical value, regardless of the specific value of each characteristic quantity, the system directly determines that the thermal runaway process has entered the irreversible stage, and the original explosive polymerization risk index is directly taken as the preset extreme value representing the irreversible state. This determination method based on physical critical phenomena enables the risk index to truly reflect the qualitative change characteristics of the thermal runaway process, avoiding the risk underestimation problem that may occur near the critical state in continuous numerical fusion methods; the above method eliminates the risk underestimation near the critical state, avoids the dilution of key early warning information when multiple indicators are fused, and ensures that the irreversible determination is based on the strict physical condition that all three characteristics are simultaneously supercritical; If at least one of the positive feedback acceleration duration, energy jump magnitude, and deviation cumulative area does not exceed the corresponding critical value, the original explosive fusion risk index, which characterizes the progress of the irreversible process, is obtained by correlating and coupling the positive feedback acceleration duration, energy jump magnitude, and deviation cumulative area according to the degree of proximity of each characteristic quantity to the corresponding critical value. The specific method for handling the correlation and coupling is as follows: First, the first approximation ratio of the positive feedback acceleration duration relative to a preset duration threshold; second, the second approximation ratio of the energy jump magnitude relative to a preset jump magnitude threshold; and third, the third approximation ratio of the deviation accumulation area relative to a preset accumulation area threshold. These three approximation ratios are then combined to obtain the original explosive polymerization risk index. The combination method involves taking the maximum value among the first, second, and third approximation ratios to reflect the physical nature of thermal runaway, where the proximity of any dimension's characteristic quantity to the threshold has a dominant influence on the overall risk. The aforementioned correlation and coupling processing method solves the technical problem of diluted early warning signals in key dimensions caused by averaging in existing technologies when fusing multiple indicators. During thermal runaway, if any of the three dimensions—the duration of positive feedback acceleration, the magnitude of energy jump, and the area of ​​deviation from the cumulative area—approaches its critical value first, it means that the thermal runaway process is approaching an irreversible state in that dimension, which should raise a high level of alert in the system. The weighted averaging or summing methods used in existing technologies may result in a fusion result that remains at a moderate level even when one dimension is close to the critical value, while other dimensions are low, thus masking the true urgency of the risk. This scheme adopts a correlation and coupling method that takes the maximum value among the three closest proportions, ensuring that the original explosive burst risk index is dominated by the feature dimension closest to the critical value. This design reflects the "barrel effect" in the physics of thermal runaway. The "barrel effect" here refers to the fact that the safety boundary of the system is determined by the weakest dimension; that is, any feature quantity in any dimension approaching the critical value has a dominant influence on the overall risk. The system should quantify risk based on the most urgent dimension, rather than seeking averages or compromises among dimensions. In a specific calculation embodiment, if the duration of positive feedback acceleration has reached most of its critical value, the energy jump is only a small part of its critical value, and the deviation from the cumulative area is at a moderate level relative to its critical value, then the proportion of positive feedback acceleration duration is the largest among the three proximity proportions, and the original explosive polymerization risk index takes this maximum value; the system identifies the most pressing risk dimension as the positive feedback acceleration dimension and outputs the corresponding risk index.

[0019] It should be noted that the method for extracting the duration of positive feedback acceleration is derived from the physical mechanism of positive feedback in thermal runaway. During thermal runaway, the rate of heat release from the curing reaction is exponentially dependent on temperature. When the local heat generation rate exceeds the heat dissipation rate, the temperature change acceleration changes from zero or negative to positive and remains so, indicating that the positive feedback loop of thermal runaway has been established and entered a self-driven state. That is, the thermal runaway process no longer relies on external heating but is maintained and accelerated by its own heat release. This scheme records the duration of continuous positive values ​​in the temperature change acceleration sequence as the duration of positive feedback acceleration. The derivation logic is as follows: First, the temperature change acceleration sequence is obtained by numerically differentiating the temperature signal of the mold cavity wall. The calculation window for numerical differentiation is determined based on the signal sampling frequency and the response time requirements for identifying pre-burst polymerization. Then, it is determined whether the value of each sampling point in the sequence is greater than zero. The number of consecutively greater than zero sampling points is multiplied by the sampling interval to obtain the duration. The physical dimension of this characteristic quantity is time, consistent with the dimension of the preset duration threshold. In a specific calculation embodiment, if the temperature change acceleration sequence is greater than zero at five consecutive sampling points and the sampling interval is 0.2 seconds, the duration of positive feedback acceleration is one second. As the thermal runaway process intensifies, this duration accumulates continuously. When it exceeds the preset duration threshold, it indicates that the self-driven thermal runaway has accumulated to a point where it cannot decay naturally. The method for extracting the energy jump amplitude originates from bubble dynamics and fluid acoustics mechanisms. During resin curing, localized temperature increases cause dissolved gases to precipitate and form bubbles. The generation, expansion, and collapse of these bubbles in the viscoelastic resin generate dynamic pressure pulsations with characteristic frequencies. The characteristic frequency band for bubble generation and collapse is determined based on the typical frequency range of bubble generation and collapse before resin bursting in the pultrusion die. This frequency band corresponds to the main energy concentration area of ​​the dynamic pressure pulsations generated during the bubble generation and collapse process. This scheme extracts the energy value of this characteristic frequency band by performing frequency domain decomposition on the dynamic pressure signal, and records the amount by which the current energy value exceeds the average energy value during steady-state operation as the energy jump amplitude. Frequency domain decomposition can be achieved using bandpass filtering, with the filter passband range set according to the bubble collapse characteristic frequency. The energy value is characterized by the root mean square value of the filtered signal within a sliding window, and the average energy value during steady-state operation is taken as the moving average of this energy value during stable equipment operation. This characteristic quantity is in the form of a dimensionless ratio or a logarithmic ratio, consistent with the dimensions of the preset jump amplitude threshold. In a specific calculation embodiment, when the average energy during steady-state operation is a certain benchmark value, and the energy value at the current moment reaches several times the benchmark value, the energy jump is the corresponding multiple or decibel value; when the intensity of bubble collapse activity intensifies, this amplitude value increases accordingly. The steady-state operating energy average refers to the moving average of the energy values ​​in the characteristic frequency band of bubble generation and collapse during the period when the equipment is running stably and no signs of impending explosive aggregation appear. This moving average is updated using an exponentially weighted moving average method, with a smoothing coefficient set to 0.2. Whenever the system determines that it is not in any risk level state at the current moment and the duration of positive feedback acceleration is zero, the energy value of this characteristic frequency band calculated at the current moment is included in the moving average update. If the system runs continuously for more than a preset stable period without any risk event, the energy statistical characteristics within this period are automatically used as the steady-state baseline. This update method allows the steady-state baseline to adapt to the slow changes in the energy baseline caused by changes in mold condition or environmental drift during long-term equipment operation. The method for extracting the deviation cumulative area is based on the theory of ultrasonic wave propagation in viscoelastic media. During the curing process, the resin undergoes a phase transition from liquid to gel to solid, with its bulk modulus and shear modulus continuously changing, causing the ultrasonic wave propagation velocity to evolve along a characteristic trajectory. The desired curing acoustic velocity trajectory is pre-established based on a standard curve of the acoustic velocity versus curing time under standard curing conditions for this type of resin. This standard curve is obtained through a combination of differential scanning calorimetry and ultrasonic monitoring experiments. When local thermal runaway occurs in the necking region due to fouling and impurities, the actual curing process deviates from the normal trajectory, and the real-time acoustic velocity sequence deviates from the desired curing acoustic velocity trajectory. This method accumulates and integrates this deviation within a preset observation window, recording it as the deviation cumulative area. The real-time acoustic velocity is obtained by the ratio of the ultrasonic flight time to the known propagation distance, and the deviation is the absolute value of the difference between the real-time acoustic velocity and the desired acoustic velocity at the corresponding moment. The physical dimension of the deviation cumulative area is the product of the acoustic velocity unit and the time unit, consistent with the dimension of the preset cumulative area critical value. In a specific calculation embodiment, if the preset observation window duration is a fixed value, the sound velocity deviation accumulates continuously over time, and the integral result is the area of ​​the accumulated deviation. The longer the phase anomaly lasts or the greater the deviation, the larger the area value, indicating that the thermal runaway process is closer to the irreversible stage.

[0020] Specifically, the preset observation window duration in the deviation from the cumulative area calculation is determined based on the typical time span from the appearance of pre-explosive polymerization precursors to the entry into the irreversible stage. Specifically, explosive polymerization simulation experiments are conducted on this type of resin in a necking mold model, recording the time span from the moment the duration of positive feedback acceleration first exceeds the preset duration threshold to the moment explosive polymerization occurs. The average of multiple experiments is taken as the preset observation window duration. If no experimental data is available, the default value is used, i.e., the preset observation window duration is set to ten seconds. This setting ensures that the calculation window for the deviation from the cumulative area covers the critical period of the thermal runaway process evolving from the reversible to the irreversible stage, ensuring that the cumulative integration result can effectively reflect the cumulative scale of the abnormal phase evolution.

[0021] It should be noted that the physical source for directly determining that the thermal runaway process has entered the irreversible stage is the critical phase transition phenomenon in the thermal runaway process. During the positive feedback evolution of thermal runaway, when the cumulative effect of positive feedback acceleration (represented by the duration of positive feedback acceleration), the intensity of bubble collapse (represented by the energy jump amplitude), and the scale of phase deviation (represented by the deviation from the cumulative area) simultaneously reach their respective critical points, the system will undergo an irreversible phase transition, resulting in a qualitative rather than quantitative change in the risk state. Based on this, this scheme sets the following judgment rule: when the duration of positive feedback acceleration exceeds a preset duration critical value, the energy jump amplitude exceeds a preset jump amplitude critical value, and the deviation from the cumulative area exceeds a preset cumulative area critical value, the thermal runaway process is determined to have entered the irreversible stage, and the original explosive polymerization risk index is taken as a preset extreme value representing the irreversible state. The derivation of this judgment rule is based on the multidimensional concurrent characteristics of the thermal runaway physical critical phenomenon; only when all three dimensions of the characteristic quantities exceed the critical scale can the irreversible condition be truly satisfied. The initial settings for the three critical values ​​are as follows: the preset duration critical value is derived from the thermal runaway induction period obtained from the adiabatic thermal experiment of this type of resin; the preset jump amplitude critical value is derived from the upper limit of the statistical distribution of energy values ​​during the stable operation of the equipment; and the preset cumulative area critical value is derived from the minimum value of the deviation from the cumulative area reached before the occurrence of explosive polymerization in historical explosive polymerization events. The dimensions of the three critical values ​​are consistent with the corresponding characteristic quantities. In a specific calculation embodiment, when the positive feedback acceleration duration exceeds the critical duration, the energy jump amplitude exceeds the critical multiple, and the deviation from the cumulative area exceeds the critical area value, the original explosive polymerization risk index jumps directly to the extreme value, and the system determines that thermal runaway is irreversible.

[0022] Specifically, the criteria for setting the preset duration threshold, preset jump threshold, and preset cumulative area threshold include: The preset duration threshold is determined based on the shortest duration required for the establishment of positive feedback for thermal runaway under adiabatic conditions for this type of resin. Specifically, the duration of the thermal runaway induction period is obtained by conducting adiabatic calorimetry experiments on this type of resin, and one-third of this induction period duration is taken as the preset duration threshold. The adiabatic calorimetry experiment is conducted in an adiabatic accelerating calorimeter. The resin sample is placed in an adiabatic environment and heated to the curing reaction initiation temperature at a constant heating rate. Then, the adiabatic tracking mode is switched, and the temperature change curve of the sample over time is recorded. The time interval from the moment when the temperature begins to rise continuously to the moment when the temperature rise rate reaches the preset steep rise threshold is determined as the duration of the thermal runaway induction period. This setting method allows the preset duration threshold to reflect the self-accelerating characteristics of the resin system under adiabatic conditions, providing a physically based critical benchmark for the early identification of pre-explosive polymerization precursors. The preset jump amplitude threshold is determined based on the normal fluctuation range of the energy value of the characteristic frequency band of bubble formation and collapse during the steady-state curing process of this type of resin. Specifically, the statistical distribution characteristics of the energy value of this characteristic frequency band during the stable operation period of the equipment are collected, and five times the upper limit of the statistical distribution is taken as the preset jump amplitude threshold. The stable operation period is selected from the production period when the equipment is running continuously and normally without any signs of impending explosive polymerization, and the statistical sample size is not less than the preset minimum sample size. The upper limit of the statistical distribution is determined using the interquartile range method, that is, the upper quartile plus one interquartile range is taken as the upper limit of normal fluctuation. This upper limit is multiplied by a safety amplification factor to obtain the preset jump amplitude threshold, to ensure that normal process fluctuations will not trigger false alarms, while ensuring that abnormal jumps can be sensitively detected. The preset cumulative area threshold is determined based on the maximum allowable cumulative deviation of the acoustic velocity during the curing process of this type of resin. Specifically, by analyzing the evolution data of the cumulative area deviation in historical burst events of this type of resin on the pultrusion equipment, the cumulative area deviation value corresponding to a preset observation window duration before the burst event is selected as a reference benchmark. This reference benchmark is multiplied by a safety factor of 0.8 to obtain the preset cumulative area threshold. If there are no historical burst event records for this equipment, historical burst event data of the same type of resin on the same specification equipment is used as a substitute benchmark; if there is still no usable data, a reference benchmark is obtained through laboratory burst simulation experiments. In the laboratory burst simulation experiment, resin clusters containing accelerator impurities are artificially injected into the necking mold model, and ultrasonic fly-through time signals are simultaneously collected and the cumulative area deviation is calculated. The maximum value of the cumulative area deviation before the burst event is recorded as the reference benchmark. The above three critical values ​​are initially set using the experimental calibration values ​​of this type of resin when the system is first deployed. During the operation of the system, they can be dynamically corrected based on the cumulative data of the intervention effect evaluation samples.

[0023] It should be noted that the methods for determining the risk level label based on the original explosive polymerization risk index include: Obtain the numerical sequence of the original explosive polymerization risk index at multiple consecutive sampling times, and analyze the incremental value of the numerical sequence per unit time based on the numerical sequence at multiple consecutive sampling times. If the increment value at the current moment is greater than the increment value at the previous moment, it is marked as an accelerated evolution state; if the increment value at the current moment is not greater than the increment value at the previous moment, it is marked as a non-accelerated evolution state. Obtain the value of the original explosive risk index at the current moment, and determine the basic risk level label based on the value. The basic risk level label includes the attention level label, the warning level label, and the emergency level label. The standard for dividing the numerical range of the basic risk level marker is as follows: When the original explosive polymerization risk index is less than the first risk threshold, it is designated as a basic concern level; when the original explosive polymerization risk index is between the first and second risk thresholds, it is designated as a basic early warning level; when the original explosive polymerization risk index is greater than the second risk threshold, it is designated as a basic emergency level. The first and second risk thresholds are determined based on the evolution curve of the original explosive polymerization risk index before an explosive polymerization event in historical production data for this type of resin. Specifically, the lowest effective value for early warning level intervention in historical explosive polymerization events is taken as the first risk threshold, and the lowest effective value for emergency level intervention in historical explosive polymerization events is taken as the second risk threshold. The above-mentioned method of setting each risk threshold solves the technical problem in the prior art that fixed thresholds cannot adapt to individual differences in different equipment and changes in the characteristics of different resin systems. The alarm thresholds in the prior art are usually based on empirical values ​​or manufacturer-recommended values, which remain unchanged for a long time once set. They do not take into account the different risk evolution patterns caused by differences in the service life, wear degree, and mold structure of different pultrusion equipment, nor do they take into account the differences in curing characteristics caused by activity fluctuations of different batches of resin. The first and second risk thresholds in this scheme are not fixed empirical values, but are determined based on the evolution curve of the original polymerization risk index before a polymerization event occurred, using historical production data of this type of resin on this specific equipment. Specifically, the lowest effective threshold for early warning-level intervention in historical polymerization events is taken as the first risk threshold, and the lowest effective threshold for emergency-level intervention in historical polymerization events is taken as the second risk threshold. This method of determining thresholds based on the equipment's own historical data allows the risk level range to be divided in accordance with the actual risk evolution characteristics of this specific equipment. As equipment operating data accumulates, the threshold settings will become more accurate, thereby effectively balancing the false alarm rate and the false negative rate. The initial settings for the first and second risk thresholds are based on historical polymerization event data of this type of resin on similar pultrusion equipment. If there are no historical polymerization event records for this specific equipment, historical data from similar equipment is used as the initial setting reference. Specifically, the evolution curve of the original polymerization risk index before a polymerization event occurs in similar equipment is collected. The minimum value of the original polymerization risk index at the intervention time in events where early warning-level intervention successfully curbs polymerization is extracted as the initial value of the first risk threshold, and the minimum value of the original polymerization risk index at the intervention time in events where emergency-level intervention successfully curbs polymerization is extracted as the initial value of the second risk threshold. If there is no available historical successful intervention data, expert experience values ​​are used as the initial settings, where the initial value of the first risk threshold is set to 30% of the theoretical maximum value of the original polymerization risk index, and the initial value of the second risk threshold is set to 60% of the theoretical maximum value of the original polymerization risk index. As the operating data of this specific equipment accumulates, the system will adjust the two thresholds based on its own historical data. If the original explosive risk index is in an accelerated evolution state, the basic risk level label will be upgraded by one level, and the upgraded risk level label will be the final risk level label; if the basic risk level label before the upgrade is already an emergency level label, the emergency level label will remain unchanged. If the original explosive growth risk index is in a non-accelerated evolution state, the basic risk level marker is used as the final determined risk level marker; this solves the technical problem in existing technologies where risk level determination relies solely on the current value while ignoring the risk evolution trend. Existing risk grading technologies generally use a fixed threshold interval mapping method, comparing the current value of the monitored indicator with a preset value interval, and outputting the corresponding risk level based on which interval it falls into. This method has a fundamental flaw: the same risk index value, if in an accelerating upward trend, indicates a much greater urgency of explosive growth than the same value in a steady or decelerating growth trend. For example, if the original explosive growth risk index is a moderate value but the increment value continues to expand, it indicates that the risk is rapidly deteriorating; while the same moderate value, if the increment value continues to shrink, indicates that the risk is easing. This solution analyzes the change in the increment value of the numerical sequence within a unit of time to identify whether the original explosive growth risk index is in an accelerating or non-accelerated evolution state, and adjusts the basic risk level upward for those in an accelerating evolution state. This allows the system to raise the risk level in advance when the risk index is still at a moderate level but has already shown signs of accelerating deterioration, thereby triggering higher-level intervention measures and proactively blocking the thermal runaway process before the risk worsens further.

[0024] It should be noted that the methods for invoking control command combinations based on risk level markers include: A hierarchical control strategy rule base is constructed, which stores the correspondence between attention level markers and first control instruction combinations, early warning level markers and second control instruction combinations, and emergency level markers and third control instruction combinations. Among them, the first control command combination includes a traction speed slow-down command; the second control command combination includes a traction speed rapid-down command and a target temperature zone forced cooling command; the third control command combination includes a full-temperature zone heater cut-off command, an emergency stop command, and an active pressure relief device trigger command. When the final determined risk level is marked as "attention level", the first combination of control instructions is invoked; When the final determined risk level is marked as a warning level, the second combination of control instructions is invoked; When the final risk level is marked as emergency, the third set of control instructions is invoked. The above approach addresses the technical problem that the existing binary response mode, which operates on an "once a stop, go" basis, cannot adapt to the phased evolution of explosive polymerization risks. Existing technologies typically employ a single-threshold binary action mechanism for abnormal responses: when the monitored value exceeds the safety threshold, heater power-off and traction shutdown interlock protection actions are executed; when the monitored value does not exceed the threshold, normal operation is maintained. This binary mode ignores the phased nature of the evolution of explosive polymerization risks from their inception to irreversibility. In the inception stage, the positive feedback loop of thermal runaway has just been established, and only a moderate reduction in traction speed is needed to effectively curb further risk development. In the stage where the risk significantly increases, a single speed adjustment is insufficient to control the risk, requiring simultaneous forced cooling measures. When the risk has entered the irreversible stage, any delaying measures are ineffective, and immediate implementation of system-wide shutdown and active pressure relief protection is necessary to minimize accident losses. This solution constructs a three-level control command system including attention level, early warning level, and emergency level, ensuring that the intervention intensity is precisely matched to the risk evolution stage. This avoids production interruptions caused by over-response and prevents accidents caused by under-response.

[0025] It should be noted that the active pressure relief device is set at the preset pressure relief position of the pultrusion die. The preset pressure relief position is located on the side wall or end cap of the die cavity. This position is determined by finite element simulation analysis to be a stress concentration area of ​​the die structure or a weak area on the pressure wave propagation path. The active pressure relief device includes a pressure relief plug, a shearing pin, and a trigger actuator. The pressure relief plug is embedded in the pressure relief hole of the mold wall, with one end of the pressure relief plug flush with the inner wall of the mold cavity. The shearing pin passes through the pressure relief plug and the mold wall, fixing the pressure relief plug in the pressure relief hole. The shearing strength of the shearing pin is determined according to the design pressure limit of the mold cavity. When the instantaneous pressure in the mold cavity exceeds the design pressure limit, the shearing pin is sheared, and the pressure relief plug is pushed out under the pressure impact, forming a pressure relief channel. The trigger actuator is electrically connected to the instruction module and is used to receive the active pressure relief device trigger instruction from the third control instruction combination. The trigger actuator includes an electromagnetic drive push rod. When the active pressure relief device trigger instruction is received, the electromagnetic drive push rod impacts the pressure relief plug, actively shears off the shear pin, opens the pressure relief channel in advance, and guides the pressure shock wave to be released in a preset safe direction. The external outlet of the pressure relief hole faces the safe area of ​​the workshop or is connected to a pressure relief collection container to collect the high-temperature resin that may be sprayed out with the release of pressure during pressure relief, so as to avoid injury to operators and surrounding equipment. The aforementioned active pressure relief device solves the technical problem in existing technologies where the direction of pressure shock wave release and the consequences of accidents cannot be effectively controlled when explosive polymerization has already occurred. Existing emergency shut-off protection can only shut down the heater and traction machine after the temperature exceeds the limit, but it lacks any means of guiding or controlling the pressure shock wave that has already formed during explosive polymerization. The pressure shock wave will propagate along the mold cavity and impact weak points in the mold structure, causing the mold to crack and become unusable instantly. Simultaneously, high-temperature resin will spray in unpredictable directions, seriously endangering the personal safety of on-site operators. This solution, by setting up an active pressure relief device including a pressure relief plug, shear pin, and trigger actuator, actively triggers the pressure relief device to open the pressure relief channel in advance when it is determined that the thermal runaway process has entered an irreversible stage, guiding the pressure shock wave to a preset safe direction for release. This design achieves active control of the consequences of explosive polymerization accidents, transforming uncontrollable mold bursting into controllable directional pressure relief. It protects the integrity of the main mold structure and avoids random spraying of high-temperature resin into the operating area, fundamentally reducing the risk of equipment loss and personnel injury in explosive polymerization accidents.

[0026] The update module obtains equipment status feedback data after the execution of the control command combination, constructs an intervention effect evaluation sample by combining the burst risk index at the corresponding time, and obtains a hierarchical control strategy rule base based on the intervention effect evaluation sample.

[0027] It should be noted that the methods for obtaining equipment status feedback data after the execution of control command combinations and constructing an intervention effect evaluation sample by combining it with the burst risk index at the corresponding time include: After the combination of control commands is executed, the actual traction speed value, actual heating power value and actual cooling status value are continuously collected within the preset sampling time. The actual traction speed value, actual heating power value and actual cooling status value are used as equipment status feedback data. The equipment status feedback data is linked with the explosive polymerization risk index calculated last time before the combination of control commands is executed to form a single intervention effect evaluation sample. The single intervention effect evaluation sample also includes the risk level label of this control intervention, the type of control command combination invoked, the timestamp of the intervention execution, and the recovery time required for the explosive polymerization risk index to fall back to the safe range after the intervention. The criteria for determining whether the original explosive polymerization risk index has fallen back to the safe range in the intervention effect assessment are as follows: the original explosive polymerization risk index is less than 50% of the first risk threshold, the duration of positive feedback acceleration is zero, and the energy jump is less than 30% of the preset jump threshold. When all three conditions are met, the original explosive polymerization risk index is determined to have fallen back to the safe range. This definition method ensures that the risk status has significantly fallen from below the concern level, the self-driving mechanism of thermal runaway has completely subsided, and the bubble collapse activity has returned to normal levels, thereby confirming that the intervention has completely eliminated the explosive polymerization risk. The single intervention effect evaluation samples are stored in the intervention effect evaluation sample library. The intervention effect evaluation sample library adopts a circular queue storage structure and sets a maximum number of samples to be stored. When the number of samples stored in the intervention effect evaluation sample library reaches the maximum number of samples to be stored, the newly stored samples automatically overwrite the oldest stored samples to ensure that the sample library always retains the intervention effect data of the most recent period, so that strategy optimization can adapt to changes in the recent status of the equipment. The maximum number of stored samples is determined based on the production cycle and strategy update frequency requirements to ensure that the samples accumulated in the sample library can cover multiple intervention records of the equipment under different operating conditions and risk levels, providing a sufficient statistical basis for subsequent statistics on the proportion of effective intervention samples. The above approach addresses the technical problems of existing technologies where regulatory intervention and effect evaluation are disconnected, and historical intervention experience cannot be effectively accumulated. Existing technologies employ an open-loop model for abnormal responses, where the system returns to normal monitoring after the action is executed. This model neither records the specific effects of the intervention nor establishes a causal relationship between the pre-intervention risk state and the post-intervention equipment state. This open-loop model prevents the system from answering key management questions such as whether the intervention was effective, whether the intervention intensity was appropriate, and which intervention strategy is optimal for the same risk level. Each intervention is treated as an isolated event, failing to provide experience for subsequent optimization. This solution links and binds the equipment state feedback data after the combined execution of regulatory commands with the pre-intervention burst risk index, forming a structured single-intervention effect evaluation sample and storing it in a sample library. This transforms each regulatory intervention from an isolated action into a traceable, evaluable, and comparable unit of experience. The establishment of the intervention effect evaluation sample library provides a data foundation for the statistical evaluation and optimal adjustment of subsequent control strategies, enabling the system to continuously learn and evolve from historical intervention experience.

[0028] Specifically, the method for determining the maximum number of samples to be stored in the intervention effect evaluation sample bank is shown in the following example: The maximum number of samples to be stored in the intervention effect evaluation sample library is determined based on the average daily intervention frequency and strategy update cycle requirements of the pultrusion production line. The estimated total number of interventions at each risk level expected to occur within a calendar month under normal production conditions is taken as the maximum number of samples to be stored. The average daily intervention frequency is estimated based on the historical operating data of the equipment or statistical data of similar equipment. If no historical data is available, the default value is used, i.e., the maximum number of samples to be stored is set to one hundred. This setting method ensures that the intervention effect data of the most recent period is always stored in the sample library, which not only ensures that the statistics of the proportion of effective intervention samples have a sufficient statistical basis, but also avoids the excessive influence of outdated data on strategy updates due to an excessively large sample library.

[0029] It should be noted that the methods for obtaining the hierarchical management strategy rule base based on the intervention effect evaluation sample include: Based on the intervention effect evaluation sample, extract several single intervention effect evaluation samples corresponding to the intervention effect evaluation sample library; For each regulatory intervention, it is determined whether the original explosive risk index has fallen back to a safe range within a preset period after the intervention. If the original explosive risk index falls back to the safe range, the intervention will be marked as an effective intervention sample; otherwise, it will be marked as an ineffective intervention sample. Based on effective and ineffective intervention samples, the proportion of effective intervention samples corresponding to each combination of control commands invoked under each risk level is statistically analyzed. This proportion is then compared with a preset effectiveness threshold. This statistical evaluation mechanism based on the proportion of effective intervention samples solves the technical problem of lacking quantitative evaluation methods for the effectiveness of control strategies in existing technologies. Existing control strategies, once deployed, remain fixed for a long period, lacking both a mechanism to evaluate the effectiveness of the current strategy and a mechanism to identify whether a better alternative strategy exists. This solution uses whether the original explosive risk index falls back to a safe range within a preset period after intervention as an objective criterion for intervention effectiveness. Each control intervention is marked as an effective or ineffective intervention sample, and the proportion of effective intervention samples corresponding to each combination of control commands under each risk level is statistically analyzed. The proportion of effective intervention samples directly reflects the success rate of intervention for that command combination at that risk level, providing objective data support for strategy adjustment decisions. When the proportion of effective intervention samples for a certain command combination remains consistently low, the system can automatically identify that the strategy is no longer suitable for the current equipment state, thereby triggering a strategy adjustment process. The preset validity threshold is set based on the following criteria: The minimum acceptable intervention success rate under this risk level; the preset effectiveness thresholds corresponding to different risk levels can be set separately, wherein the preset effectiveness threshold corresponding to the emergency level is higher than the preset effectiveness threshold corresponding to the early warning level, and the preset effectiveness threshold corresponding to the early warning level is higher than the preset effectiveness threshold corresponding to the attention level, so as to reflect the safety management principle that the higher the risk level, the stricter the requirements for intervention effectiveness. The preset validity threshold uses the initial default value when the system is first deployed. During system operation, it can be adjusted according to the configuration instructions of the equipment management personnel to adapt to the different trade-offs between safety margin and production efficiency of different production lines. When the proportion of effective intervention samples for a certain combination of control instructions under a certain risk level label is less than the preset effectiveness threshold, the control instruction combination corresponding to that risk level label is adjusted, and the adjusted combination is used to obtain a hierarchical control strategy rule base. When the proportion of effective intervention samples for a certain combination of control instructions under a certain risk level is not less than the preset effectiveness threshold, the combination of control instructions is kept unchanged, and a hierarchical control strategy rule base is obtained. The hierarchical control strategy rule base will be applied to the matching of control strategies in subsequent production cycles; The above approach addresses the technical problems of static, fixed control strategies and their inability to dynamically evolve with equipment status changes in existing technologies. During long-term service, pultrusion equipment experiences gradual deterioration due to factors such as increased fouling on the die inner wall, aging heating elements leading to changes in temperature distribution, and wear on the traction mechanism causing decreased speed control accuracy. These status changes all contribute to a gradual decrease in the effectiveness of existing control strategies. Static strategies in existing technologies cannot detect this degradation and may continue to execute ineffective interventions even when the strategy has failed, failing to effectively control risks and causing unnecessary production disruptions. This solution continuously compares the percentage of effective intervention samples for each instruction combination with a preset effectiveness threshold. When the percentage falls below the threshold, strategy adjustments are automatically triggered, allowing the hierarchical control strategy rule base to dynamically evolve with equipment status changes. The preset effectiveness thresholds are set hierarchically, with the threshold corresponding to the emergency level being higher than that of the early warning level, and the threshold corresponding to the early warning level being higher than that of the concern level. This reflects the safety management principle that higher risk levels require more stringent intervention effectiveness.

[0030] Specifically, an example of a preset validity threshold is as follows: The preset effectiveness thresholds are set according to the risk level. The preset effectiveness threshold for the "Attention" level is set to 60%, for the "Warning" level to 75%, and for the "Emergency" level to 90%. This setting reflects the safety management principle that the higher the risk level, the stricter the requirements for intervention effectiveness. The preset effectiveness thresholds use the above default values ​​during the initial system deployment. During system operation, equipment managers can adjust the preset effectiveness thresholds for each risk level by using configuration commands through the human-machine interface, based on the production line's trade-off between safety margin and production efficiency. Increasing the threshold raises the trigger threshold for strategy adjustments, reducing the frequency of strategy changes but potentially tolerating lower effectiveness; decreasing the threshold lowers the trigger threshold for strategy adjustments, more actively eliminating inefficient strategies but potentially increasing the frequency of strategy changes.

[0031] Specifically, the methods for adjusting the combination of control instructions corresponding to this risk level label include: Based on the proportion of effective intervention samples, the combination of control instructions with the highest proportion of effective intervention samples under this risk level is determined as the candidate instruction combination; If multiple combinations of control commands with the same percentage of effective intervention samples exist under a given risk level, the combination with the least impact on production continuity is selected as the candidate command combination. This approach addresses the technical problem in existing technologies where strategy adjustments only consider effectiveness while neglecting production economics. When multiple candidate strategies have comparable effectiveness, the degree of impact on production continuity can vary significantly. For example, a slow traction speed reduction command only slightly reduces the production cycle time, allowing production to continue; while a rapid traction speed reduction command causes a significant drop in the production cycle time, and a forced cooling command for the target temperature zone requires a long recovery time; an emergency shutdown command directly leads to production interruption. This solution further selects the command combination with the least impact on production continuity from among the multiple candidate command combinations with the highest percentage of effective intervention samples, achieving a two-tiered strategy selection that prioritizes effectiveness and secondarily prioritizes economics. This design ensures that the evolution of the hierarchical control strategy rule base moves not only towards more precise risk management but also towards lower-cost intervention methods, maximizing overall production efficiency while ensuring a safety baseline. The quantitative standard for the impact on production continuity is as follows: the impact level is divided according to the magnitude of the impact of different control command combinations on the traction speed of the pultrusion production line and whether a shutdown is triggered. Specifically, the control command combination containing only the traction speed reduction command has the least impact on production continuity; the control command combination containing the traction speed reduction command has a moderate impact on production continuity; and the control command combination containing the emergency shutdown command has the greatest impact on production continuity. When it is necessary to select among multiple command combinations with the same degree of impact on production continuity, the inclusion of a forced cooling command for the target temperature zone in the command combination is further compared. Command combinations containing forced cooling commands have a greater impact on production continuity than command combinations that do not contain forced cooling commands because the subsequent heating time required to resume production is longer. Based on the candidate instruction combination, the currently used control instruction combination under this risk level is replaced with the candidate instruction combination.

[0032] In summary, this solution achieves adaptive closed-loop management of explosive polymerization risk throughout the entire lifecycle of FRP pultrusion production equipment through a three-layer closed-loop architecture comprising a data acquisition module, an instruction module, and an update module. The data acquisition module extracts three dimensions of precursory features of explosive polymerization: duration of positive feedback acceleration, magnitude of energy jump, and area of ​​deviation from cumulative total area. This addresses the technical problem of existing technologies that can only perceive the instantaneous value of a single physical quantity and cannot characterize the irreversibility of the thermal runaway process. The instruction module, through risk assessment, dynamic adjustment, and matching of graded control strategies, solves the technical problems of delayed judgment and rigid response modes in existing technologies. The update module, through the construction of intervention effect evaluation samples, the statistics of the proportion of effective intervention samples, and the operation of a two-layer strategy optimization, solves the technical problems of static and rigid control strategies in existing technologies that cannot learn and evolve from historical experience. The three modules work together to continuously optimize the system's risk perception capabilities and control decision-making level as the equipment operates over time, truly achieving adaptive risk management throughout the entire equipment lifecycle.

[0033] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the present invention's technology and inventive concept of a full life cycle management system for fiberglass production equipment, should be covered within the scope of protection of the present invention.

Claims

1. A full life-cycle management system for fiberglass production equipment, characterized in that, include: The acquisition module is used to acquire multimodal operating signals of the pultrusion die and extract multidimensional feature vectors of the precursor to explosive polymerization based on the multimodal operating signals. Among them, the multi-dimensional feature vector of the precursor to explosive polymerization includes the duration of positive feedback acceleration, the magnitude of energy jump, and the area of ​​deviation accumulation. The instruction module generates a raw explosive polymerization risk index reflecting the irreversibility of the thermal runaway process based on multi-dimensional feature vectors of explosive polymerization precursors; determines risk level markers based on the raw explosive polymerization risk index; and invokes control instruction combinations based on the risk level markers. The update module is used to obtain equipment status feedback data after the combined execution of control commands, construct an intervention effect evaluation sample by combining the burst risk index at the corresponding time, and obtain a hierarchical control strategy rule base based on the intervention effect evaluation sample.

2. The fiberglass production equipment lifecycle management system according to claim 1, characterized in that, Methods for extracting multi-dimensional feature vectors of precursors to explosive fusion based on multimodal operating signals include: Based on the mold cavity wall temperature signal of the multimodal operation signal, the mold cavity wall temperature signal is numerically differentiated to obtain the temperature change acceleration sequence; based on the temperature change acceleration sequence, the duration of continuous positive values ​​in the temperature change acceleration sequence is recorded as the positive feedback acceleration duration. Based on the dynamic pressure signal of multimodal operation signal, the dynamic pressure signal is decomposed in the frequency domain to extract the energy value of the bubble generation and collapse characteristic frequency band; based on the energy value, the amount by which the energy value at the current moment exceeds the average energy value during the steady-state operation period is recorded as the energy jump amplitude. Based on the ultrasonic fly-through time signal of the multimodal operation signal, the ultrasonic fly-through time signal is converted into sound velocity to obtain a real-time sound velocity sequence. The cumulative integral value of the deviation of the real-time sound velocity sequence from the expected solidified sound velocity trajectory within a preset observation window is recorded as the cumulative deviation area. Based on the duration of positive feedback acceleration, the magnitude of energy jump, and the area of ​​deviation accumulation, a multi-dimensional feature vector of the precursor to explosive fusion is obtained.

3. The fiberglass production equipment lifecycle management system according to claim 1, characterized in that, Methods for generating a raw explosive polymerization risk index reflecting the irreversibility of the thermal runaway process based on multi-dimensional feature vectors of precursors to explosive polymerization include: Determine whether the duration of positive feedback acceleration is greater than the preset duration threshold, determine whether the energy jump is greater than the preset jump threshold, and determine whether the deviation cumulative area is greater than the preset cumulative area threshold. If the duration of positive feedback acceleration, the magnitude of energy jump, and the cumulative deviation area are all greater than their respective critical values, the thermal runaway process is determined to have entered the irreversible stage. Among them, the original explosive polymerization risk index is a preset extreme value that represents the irreversible state; If at least one of the positive feedback acceleration duration, energy jump magnitude, and deviation cumulative area does not exceed the corresponding critical value, the original explosive fusion risk index, which characterizes the progress of the irreversible process, is obtained by correlating and coupling the positive feedback acceleration duration, energy jump magnitude, and deviation cumulative area according to the degree of proximity of each characteristic quantity to the corresponding critical value.

4. The fiberglass production equipment full life cycle management system according to claim 3, characterized in that, Methods for determining risk level labels based on the original explosive polymerization risk index include: Obtain the numerical sequence of the original explosive polymerization risk index at multiple consecutive sampling times, and analyze the incremental value of the numerical sequence per unit time based on the numerical sequence at multiple consecutive sampling times. If the increment value at the current moment is greater than the increment value at the previous moment, it is marked as an accelerated evolution state; if the increment value at the current moment is not greater than the increment value at the previous moment, it is marked as a non-accelerated evolution state. Obtain the value of the original explosive risk index at the current moment, and determine the basic risk level label based on the value. The basic risk level label includes the attention level label, the warning level label, and the emergency level label. If the original explosive risk index is in an accelerated evolution state, the basic risk level label will be upgraded by one level, and the upgraded risk level label will be the final risk level label; if the basic risk level label before the upgrade is already an emergency level label, the emergency level label will remain unchanged. If the original explosive polymerization risk index is in a non-accelerated evolution state, the basic risk level label will be used as the final determined risk level label.

5. The fiberglass production equipment lifecycle management system according to claim 4, characterized in that, Methods for invoking control command combinations based on risk level markers include: A hierarchical control strategy rule base is constructed, which stores the correspondence between attention level markers and first control instruction combinations, early warning level markers and second control instruction combinations, and emergency level markers and third control instruction combinations. When the final determined risk level is marked as "attention level", the first combination of control instructions is invoked; When the final determined risk level is marked as a warning level, the second combination of control instructions is invoked; When the final risk level is marked as emergency, the third set of control instructions is invoked.

6. The fiberglass production equipment full life cycle management system according to claim 5, characterized in that, The first control command combination, the second control command combination, and the third control command combination include: The first control command combination includes a traction speed reduction command; The second set of control commands includes a rapid decrease in traction speed and a forced cooling command for the target temperature zone. The third set of control commands includes a full-temperature zone heater cut-off command, an emergency shutdown command, and an active pressure relief device trigger command.

7. The fiberglass production equipment lifecycle management system according to claim 1, characterized in that, Methods for obtaining equipment status feedback data after the execution of a combination of control commands, and constructing an intervention effect evaluation sample by combining it with the burst risk index at the corresponding time point, include: After the combination of control commands is executed, the actual traction speed value, actual heating power value and actual cooling status value are continuously collected within the preset sampling time. The actual traction speed value, actual heating power value and actual cooling status value are used as equipment status feedback data. The equipment status feedback data is linked and bound with the explosive polymerization risk index calculated at the last time before the combined execution of control commands to form a sample for evaluating the effect of a single intervention. The single intervention effect evaluation samples are stored in the intervention effect evaluation sample library.

8. The fiberglass production equipment lifecycle management system according to claim 1, characterized in that, Methods for obtaining a tiered management strategy rule base based on intervention effect evaluation samples include: Based on the intervention effect evaluation sample, extract several single intervention effect evaluation samples corresponding to the intervention effect evaluation sample library; For each regulatory intervention, it is determined whether the original explosive risk index has fallen back to a safe range within a preset period after the intervention. If the original explosive risk index falls back to the safe range, the intervention will be marked as an effective intervention sample; otherwise, it will be marked as an ineffective intervention sample. Based on effective and ineffective intervention samples, the proportion of effective intervention samples corresponding to each combination of control instructions invoked under each risk level is statistically analyzed; based on the proportion of effective intervention samples, a hierarchical control strategy rule base is obtained.

9. The fiberglass production equipment lifecycle management system according to claim 8, characterized in that, Methods for obtaining a hierarchical control strategy rule base based on the proportion of effective intervention samples include: The comparison is based on the proportion of effective intervention samples, combined with a preset effectiveness threshold; When the proportion of effective intervention samples for a certain combination of control instructions under a certain risk level label is less than the preset effectiveness threshold, the control instruction combination corresponding to that risk level label is adjusted, and the adjusted combination is used to obtain a hierarchical control strategy rule base. When the proportion of effective intervention samples for a certain combination of control instructions under a certain risk level is not less than the preset effectiveness threshold, the combination of control instructions is kept unchanged, and a hierarchical control strategy rule base is obtained.

10. The fiberglass production equipment lifecycle management system according to claim 9, characterized in that, The methods for adjusting the combination of control instructions corresponding to this risk level marker include: Based on the proportion of effective intervention samples, the combination of control instructions with the highest proportion of effective intervention samples under this risk level is determined as the candidate instruction combination; If there are multiple highest-value control instruction combinations with the same proportion of effective intervention samples under the risk level label, then the control instruction combination with the least impact on production continuity shall be selected as the candidate instruction combination. Based on the candidate instruction combination, the currently used control instruction combination under this risk level is replaced with the candidate instruction combination.

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