Seal strip intelligent monitoring analysis and active prevention and control processing method and system under intelligent network connection environment

Abstract

This invention discloses a method and system for intelligent monitoring, analysis, and proactive prevention and control of sealing strips in an intelligent connected environment. It relates to the field of electronic digital data processing technology and includes a historical time-series acquisition and coding perturbation module, used to establish multiple monitoring zones along the length of the sealing strip, acquire historical compression information and real-time boundary response information of each monitoring zone, and apply a preset coding perturbation within the monitoring window to form an analyzable time-series sample; a rebound stage asymmetric identification and short-term sealing failure determination module, used to separate the compression stage and rebound stage of the time-series sample and extract asymmetric features reflecting the degree of abnormality in the rebound stage; and a pre-compensation control and closed-loop verification module, used to generate a pre-compensation control instruction set for the corresponding monitoring zone based on the short-term sealing failure risk and output it to the control output interface. This invention identifies and suppresses short-term sealing failures in the rebound stage before actual leakage occurs.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, specifically to a method and system for intelligent monitoring, analysis, and proactive prevention and control of sealing strips in an intelligent connected environment. Background Technology

[0002] Sealing strips are widely used to form continuous boundary isolation states. Their operational reliability depends not only on the initial compression state but also on the boundary response, recovery behavior, and local consistency along the length direction of the sealing strip during cyclic compression and rebound processes. The compression phase refers to the time period during which the sealing strip is compressed and kept in contact with the mating boundary. The rebound phase refers to the time period during which the sealing strip transitions from a compressed, contacted state to a naturally recovering state after the compression is reduced. The compression and rebound phases are divided by the trigger boundary of the coded perturbation sequence. With the development of intelligent monitoring technology, technical routes based on boundary signal acquisition, state modeling, and digital analysis have emerged around sealing state perception, life assessment, and predictive maintenance. At the same time, recent public research has gradually revealed that the state changes of the sealing interface have obvious time dependence, dynamic dependence, and local continuity dependence. Simply relying on static state quantities cannot fully reflect the true evolution of the sealing interface.

[0003] Existing technologies for monitoring the condition of sealing strips generally focus more on persistent anomalies, static compression states, existing leaks, and embedded sensor sampling results. However, they lack effective methods for addressing another, more subtle type of problem: after experiencing specific historical compression durations and rebound rhythms, sealing strips may only exhibit short-term micro-slits during the rebound phase. These micro-slits rapidly weaken before re-compression, making them difficult to detect with conventional static detection. Furthermore, whether a single local micro-slit truly evolves into a breach depends not only on the anomaly at that point itself but also on whether adjacent sections simultaneously exhibit a connectable state. Existing solutions typically cannot simultaneously answer the following three key questions: first, whether the current anomaly occurs during the compression or rebound phase; second, whether the current recovery is a true recovery or a false recovery with a significant lag; and third, whether multiple local anomalies along the length of the sealing strip have already created potential connectivity. Therefore, existing technologies struggle to provide sufficiently reliable early detection and targeted proactive prevention measures before leaks occur. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for intelligent monitoring, analysis and proactive prevention and control of sealing strips in an intelligent connected environment, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a smart monitoring, analysis and active prevention and control system for sealing strips in a smart connected environment, including a historical time series acquisition and coding disturbance module, used to establish multiple monitoring zones along the length of the sealing strip, acquire historical compression information and real-time boundary response information of each monitoring zone, and apply preset coding disturbance within the monitoring window to form an analyzable time series sample; The asymmetric identification and short-term sealing failure determination module for the springback stage is used to separate the compression stage and springback stage of the time series sample, extract asymmetric features that reflect the degree of abnormality in the springback stage, and determine the short-term sealing failure risk of each monitoring zone based on the continuous state between adjacent monitoring zones. The pre-compensation control and closed-loop verification module is used to generate a pre-compensation control instruction set for the corresponding monitoring zone based on the short-term sealing failure risk and output it to the control output interface. At the same time, it verifies and updates the risk status based on the feedback results after the pre-compensation control.

[0006] According to the above technical solution, the historical time series acquisition and coding perturbation module includes: a historical segment caching submodule, used to record the compaction duration, rebound duration, and previous risk status of each monitoring zone in multiple monitoring cycles; a coding perturbation generation submodule, used to generate a coding perturbation sequence composed of multiple micro-perturbation units without disrupting the continuity of the current sealing boundary, and apply the coding perturbation sequence to the monitoring window; and a response acquisition pre-compensation control submodule, used to acquire the displacement compensation response, edge pressure difference response, and edge impedance response corresponding to each monitoring zone, and organize them into a response sequence under a unified time axis. The rebound stage asymmetric identification and short-term sealing failure determination module includes: a phase slicing submodule, used to divide the response sequence into a compression stage segment and a rebound stage segment based on the trigger position and phase boundary of the coded perturbation sequence; an asymmetric feature extraction submodule, used to calculate the response intensity and recovery delay of the compression stage segment and the rebound stage segment respectively, and output the rebound stage asymmetric features at the monitoring zone level; and an adjacency continuity determination submodule, used to determine continuous abnormal segments by combining the rebound stage asymmetric features of each monitoring zone and their adjacency relationships, and output the short-term sealing failure risk status at the monitoring zone level. The pre-compensation control and closed-loop verification module includes: a strategy generation submodule, used to generate the pre-compensation clamping holding time, the partition rebound phase misalignment amount, and the number of pre-compensation control repetitions based on the short-term seal failure risk state; an instruction issuance submodule, used to send the pre-compensation control instruction set to the control output interface; and a closed-loop update submodule, used to verify the pre-compensation control effect based on the new round of response data after pre-compensation control, and update the risk state for the next monitoring cycle.

[0007] The intelligent monitoring, analysis, and proactive prevention and control method for sealing strips in an intelligent connected environment operates according to the following steps: S1. Establish multiple monitoring zones along the length of the sealing strip, and collect historical compression information and real-time boundary response information for each monitoring zone; apply an coded perturbation sequence to the monitoring window within each monitoring cycle to form a monitoring sample with a clear temporal boundary; then perform unified time alignment and preprocessing on the monitoring sample to obtain the standardized response sequence required for subsequent analysis; S2. Within a monitoring cycle, the coded perturbation sequence first causes the target monitoring zone to enter the compaction stage, and then causes the target monitoring zone to enter the rebound stage. The compaction stage is the period during which the sealing strip is continuously in contact with the boundary, and the rebound stage is the period during which the sealing strip generates a recovery response after the compaction effect decreases. The standardized response sequence is divided into compaction stage segments and rebound stage segments according to the trigger boundary of the coded perturbation sequence. The response intensity of the compaction stage segment and the response intensity of the rebound stage segment are extracted respectively, and the recovery delay feature of the rebound stage is further extracted. The asymmetric result of the rebound stage of each monitoring zone is obtained based on the difference between the compaction stage and the rebound stage. S3. Perform adjacency correlation analysis on the asymmetric results of the rebound stage of each monitoring zone according to the length direction of the sealing strip; when multiple adjacent monitoring zones simultaneously meet the local abnormal conditions and continue to be maintained until the next monitoring cycle, they are determined to be continuous abnormal sections; output the short-term sealing failure risk status of each monitoring zone based on the continuous abnormal sections. S4. Generate a pre-compensation control instruction set based on the short-term sealing failure risk status of each monitoring zone. The pre-compensation control instruction set includes at least the pre-compensation clamping holding time, the zone rebound phase misalignment amount, and the number of pre-compensation control repetitions. Send the pre-compensation control instruction set through the control output interface to avoid multiple abnormal monitoring zones from opening simultaneously in the same rebound phase. Record the pre-compensation control parameters sent in this round and use them as prior information for the next monitoring cycle. S5. In the next monitoring cycle after executing the pre-compensation control instruction set, re-collect the response data of each monitoring zone; compare the asymmetric results of the rebound phase and the recovery delay characteristics before and after the pre-compensation control to determine whether the pre-compensation control strategy has achieved the expected suppression effect; if the expected suppression effect has not been achieved, increase the pre-compensation control intensity and update the risk status; if the expected suppression effect has been achieved, reduce the risk level of the corresponding monitoring zone and enter subsequent routine monitoring.

[0008] According to the above technical solution, step S1 includes the following sub-steps: S1-1, Divide the sealing strip along its length into... The monitoring zone, the first The monitoring zone in the first The historical pressure index within a monitoring period is defined as: ,in Indicates the first The monitoring zone in the first Historical pressure index within a monitoring period This indicates the cumulative compaction duration of the monitoring zone prior to the stated monitoring period. This indicates the cumulative rebound duration of the monitored zone prior to the stated monitoring period. This represents a tiny normal number to prevent the denominator from being zero; the historical compaction index is used to characterize whether the monitored area is in a compaction-dominated history or a rebound-dominated history. S1-2, Generate a monitoring window containing [data / information] within each monitoring cycle. The coded perturbation sequence of each perturbation unit has a preset duration and a preset trigger interval, and the trigger intervals of two adjacent perturbation units are different, so that different perturbation units can be distinguished on the time axis; the coded perturbation sequence is only used to activate the concealed state and is not used to destroy the current sealing boundary. S1-3, regarding the first Displacement compensation response data were collected from each monitoring zone. Edge pressure difference response and edge impedance response Since the displacement compensation response, edge pressure difference response, and edge impedance response have different dimensions and ranges, in order to ensure that the three types of responses can participate in subsequent stage difference analysis at the same scale, for any original response quantity... Standardize according to the following formula: ,in This represents the standardized response. This indicates the minimum value of the corresponding response quantity within the current monitoring window. This indicates the maximum value of the corresponding response quantity within the current monitoring window; S1-4. To uniformly represent the multi-source boundary responses of the same monitoring zone at the same time as a single analytical input, the first step is constructed based on the standardized processing results. The overall response value of each monitoring zone: ,in This represents the overall response value. Indicates displacement compensation response The standardization results Indicates edge pressure difference response The standardization results Indicates the edge impedance response The standardized results; the comprehensive response value serves as a unified input for subsequent separation analysis of the compaction and rebound stages.

[0009] According to the above technical solution, step S2 includes: S2-1. Based on the phase boundary of the coded perturbation sequence, the first... The monitoring zone in the first Comprehensive response value for each monitoring cycle Divided into compaction stage intervals and rebound phase range ; S2-2. To characterize the fluctuation of the overall response relative to its respective average level within the compression and rebound phases, and to provide a basis for subsequently constructing phase difference indicators, the average deviation intensity of the compression phase and the average deviation intensity of the rebound phase are calculated separately: , ,in, This indicates the average deviation from the compressing strength during the compaction stage. This indicates the average deviation from the strength during the rebound phase. Indicates the length of the compression stage interval. Indicates the length of the rebound phase interval. This represents the average comprehensive response within the compression phase interval. This represents the average comprehensive response within the rebound phase interval; the two average deviation intensities are used to characterize the response activity in different phases. S2-3. In order to compress the response difference between the compression stage and the springback stage into a single comparable index, the springback stage difference index is calculated based on the average deviation strength of the compression stage and the average deviation strength of the springback stage: ,in Indicates the difference in the rebound phase; when When it increases, it indicates that the first The abnormal response of each monitoring zone was significantly stronger during the rebound phase than during the compaction phase. S2-4. First, determine the recovery time from the start of the rebound phase until the stability condition is first met and maintained continuously for a preset duration. Then, normalize the recovery time relative to the length of the rebound phase interval and calculate the... Recovery delay metrics for each monitored zone during the rebound phase: , in Indicates the recovery delay index, This indicates the time from the start of the rebound phase until the condition is met. And maintain the duration continuously The first recovery time, This indicates the recovery threshold. The duration of stability is indicated; the recovery delay index is used to characterize the speed at which the monitored zone returns from an abnormal state to a stable state during the rebound phase.

[0010] According to the above technical solution, step S3 includes: S3-1, regarding the first Each monitoring zone constructs a local anomaly marker:

[0011] in, Indicates a local anomaly marker. This indicates the threshold for asymmetric judgment during the rebound phase. Indicates the threshold for determining recovery delay; when When, it indicates the first Each monitoring zone exhibits both significant rebound phase anomalies and significant recovery delays during the current monitoring period; S3-2, in the case of the first With one monitoring zone as the center and a radius of [missing information], Calculate the number of adjacent consecutive anomalies within the neighborhood: ,in Indicates the number of consecutive anomalies. The neighborhood radius is represented; the number of adjacent consecutive anomalies is used to characterize the relationship with the first... Are multiple adjacent monitoring zones simultaneously in a local abnormal state? S3-3. To avoid misjudging sporadic fluctuations in a single period as real risks, and to include both spatially continuous anomalies and temporally persistent anomalies in the assessment, a short-term sealing failure risk status is constructed based on the local anomaly markers of the current monitoring period, the local anomaly markers of the previous monitoring period, and the number of adjacent continuous anomalies in the current monitoring period:

[0012] in, This indicates a short-term seal failure risk status. Indicates the threshold for the number of adjacent consecutive anomalies; when When, it indicates the first The monitoring zone not only shows obvious anomalies at present, but the anomalies have continued into the next monitoring cycle, forming a continuous anomaly segment with the adjacent monitoring zone.

[0013] According to the above technical solution, step S4 includes: S4-1. To ensure that monitoring zones with high historical compaction levels and significant recovery delays have sufficient stabilization time before entering the rebound phase, the following measures are taken to meet the requirements: The monitoring zone generates a pre-compensation compaction holding time: ,in Indicates the first The monitoring zone in the first The pre-compensation compression holding time corresponding to each monitoring cycle This indicates the basic holding time; the pre-compensated compression holding time is used to increase the local stabilization time before entering the springback phase; S4-2. To prevent adjacent anomaly monitoring zones from entering the rebound state simultaneously, [the following conditions must be met]. The monitoring partition generates the partition rebound phase misalignment quantity: , in Indicates the first The monitoring zone in the first The rebound phase misalignment corresponding to each monitoring cycle The basic phase misalignment is represented; the rebound phase misalignment is used to prevent multiple adjacent anomaly monitoring zones from opening simultaneously in the same rebound phase. S4-3. To ensure that the number of repeated adjustments matches the degree of difference in the current rebound phase, the following conditions must be met: The monitoring zone generates a pre-compensation control repetition number: , ,in Indicates the first The monitoring zone in the first The number of pre-compensation control repetitions corresponding to each monitoring cycle. This represents the non-negative portion of the difference index during the rebound phase. This indicates rounding up; the number of pre-compensation control repetitions is used to control the execution intensity of the pre-compensation control action in the next monitoring cycle; S4-4, will , and Together they form the first The pre-compensation control instruction set for each monitoring zone is sent to the external adjustment execution unit through the control output interface.

[0014] According to the above technical solution, step S5 includes: S5-1, Recalculate the next monitoring cycle after executing the pre-compensation control instruction set. Rebound phase difference indicators for each monitoring zone and recovery delay indicators ; S5-2. To quantitatively compare the abnormal suppression effect before and after the implementation of pre-compensation control, the rebound stage suppression improvement rate and recovery delay improvement rate are calculated based on the changes in indicators before and after pre-compensation control: , ,in Indicates the rate of improvement in the rebound phase. The two improvement rates represent the recovery delay improvement rate; they are used to quantitatively determine the effectiveness of the current pre-compensation control instruction set. S5-3. In order to transform the improvement results into judgment conclusions that can be directly used in the next cycle, a judgment result on the effectiveness of the adjustment is constructed based on the rebound phase suppression improvement rate and the recovery delay improvement rate:

[0015] in, This indicates the result of the determination of the effectiveness of the adjustment. This indicates the threshold for the improvement rate during the rebound phase. This represents the threshold for the recovery delay improvement rate; S5-4. When the result of the adjustment effectiveness determination indicates that the current pre-compensation control has not achieved the expected effect, when When this happens, update the short-term seal failure risk status for the next monitoring cycle to a continuous risk status, and increase the pre-compensation control intensity as follows: , , ,when At that time, update the short-term seal failure risk status for the next monitoring cycle to a risk-free status: This completes the closed-loop verification and update of the risk status of each monitoring zone.

[0016] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention utilizes coded perturbation to excite hidden states, then uses phase-separation analysis to clearly distinguish between abnormalities in the rebound stage and normalities in the compression stage, further utilizes adjacent continuity judgment to identify whether multiple local anomalies have met the conditions for forming a continuous breach zone, and finally outputs pre-compensation control commands to actively correct the boundary state of the next cycle. Compared with solutions that only consider static compression, single-point anomalies, or final leakage results, this solution can detect problems earlier, more accurately distinguish between true and false anomalies, and advance the results from monitoring to a closed-loop process of monitoring + analysis + active prevention and control, thus being more conducive to maintaining the consistency and continuity of the sealing boundary. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the overall modular structure of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 The present invention provides a technical solution: a smart monitoring, analysis and active prevention and control system for sealing strips in a smart connected environment, including a historical time series acquisition and coding disturbance module, which is used to establish multiple monitoring zones along the length of the sealing strip, acquire historical compression information and real-time boundary response information of each monitoring zone, and apply preset coding disturbance within the monitoring window to form an analyzable time series sample; The asymmetric identification and short-term sealing failure determination module for the springback stage is used to separate the compression stage and springback stage of the time series sample, extract asymmetric features that reflect the degree of abnormality in the springback stage, and determine the short-term sealing failure risk of each monitoring zone based on the continuous state between adjacent monitoring zones. The pre-compensation control and closed-loop verification module is used to generate a pre-compensation control instruction set for the corresponding monitoring zone based on the risk of short-term sealing failure and output it to the control output interface. At the same time, it verifies and updates the risk status based on the feedback results after pre-compensation control. The historical time series acquisition and coding perturbation module includes: a historical segment caching submodule, used to record the compaction duration, rebound duration, and previous risk status of each monitoring zone in multiple monitoring cycles; a coding perturbation generation submodule, used to generate a coding perturbation sequence composed of multiple micro-perturbation units without disrupting the continuity of the current sealing boundary, and apply the coding perturbation sequence to the monitoring window; and a response acquisition pre-compensation control submodule, used to acquire the displacement compensation response, edge pressure difference response, and edge impedance response corresponding to each monitoring zone, and organize them into a response sequence under a unified time axis. The asymmetric identification and short-term sealing failure determination module for the springback stage includes: a phase slicing submodule, used to divide the response sequence into compression stage segments and springback stage segments based on the trigger position and phase boundary of the coded disturbance sequence; an asymmetric feature extraction submodule, used to calculate the response intensity and recovery delay of the compression stage segments and the springback stage segments respectively, and output the springback stage asymmetric features at the monitoring zone level; and an adjacency and continuity determination submodule, used to determine continuous abnormal segments by combining the springback stage asymmetric features of each monitoring zone and their adjacency relationships, and output the short-term sealing failure risk status at the monitoring zone level. The pre-compensation control and closed-loop verification module includes: a strategy generation submodule, used to generate the pre-compensation clamping holding time, the zone rebound phase misalignment amount, and the number of pre-compensation control repetitions based on the short-term seal failure risk status; an instruction issuance submodule, used to send the pre-compensation control instruction set to the control output interface; and a closed-loop update submodule, used to verify the pre-compensation control effect based on the new round of response data after pre-compensation control, and update the risk status for the next monitoring cycle. The intelligent monitoring, analysis, and proactive prevention and control method for sealing strips in an intelligent connected environment operates according to the following steps: S1. Establish multiple monitoring zones along the length of the sealing strip, and collect historical compression information and real-time boundary response information for each monitoring zone; apply a coded perturbation sequence to the monitoring window within each monitoring cycle to form monitoring samples with clear temporal boundaries; then perform unified time alignment and preprocessing on the monitoring samples to obtain the standardized response sequence required for subsequent analysis; S2. Within a monitoring cycle, the coded perturbation sequence first causes the target monitoring zone to enter the compaction stage, and then causes the target monitoring zone to enter the rebound stage. The compaction stage is the period during which the sealing strip is continuously in contact with the boundary, and the rebound stage is the period during which the sealing strip produces a recovery response after the compaction effect decreases. Based on the trigger boundary of the coded perturbation sequence, the standardized response sequence is divided into compaction stage segments and rebound stage segments. The response intensity of the compaction stage segment and the response intensity of the rebound stage segment are extracted respectively, and the recovery delay characteristics of the rebound stage are further extracted. Based on the difference between the compaction stage and the rebound stage, the asymmetric results of the rebound stage of each monitoring zone are obtained. S3. Perform adjacency correlation analysis on the asymmetric results of the rebound stage of each monitoring zone according to the length direction of the sealing strip; when multiple adjacent monitoring zones simultaneously meet the local abnormal conditions and continue to be maintained until the next monitoring cycle, they are judged as continuous abnormal sections; output the short-term sealing failure risk status of each monitoring zone based on the continuous abnormal sections. S4. Generate a pre-compensation control instruction set based on the short-term sealing failure risk status of each monitoring zone. The pre-compensation control instruction set shall include at least the pre-compensation clamping holding time, the zone rebound phase misalignment amount, and the number of pre-compensation control repetitions. Send the pre-compensation control instruction set through the control output interface to avoid multiple abnormal monitoring zones from opening simultaneously in the same rebound phase. Record the pre-compensation control parameters sent in this round and use them as prior information for the next monitoring cycle. S5. In the next monitoring cycle after executing the pre-compensation control instruction set, re-collect the response data of each monitoring zone; compare the asymmetric results of the rebound phase and the recovery delay characteristics before and after the pre-compensation control to determine whether the pre-compensation control strategy has achieved the expected suppression effect; if the expected suppression effect has not been achieved, increase the pre-compensation control intensity and update the risk status; if the expected suppression effect has been achieved, reduce the risk level of the corresponding monitoring zone and enter subsequent routine monitoring. This implementation method does not confirm the result only after the sealing strip has shown persistent leakage. Instead, it applies a slight and discernible disturbance without damaging the current sealing boundary, thus exposing boundary response differences that are not easily apparent in a static state. The purpose of this is not simply to increase the number of detection actions, but to transform short-term anomalies, which are the most difficult to capture in conventional detection, into repeatable and comparable time-series responses. Subsequent stage separation, continuous judgment, and pre-compensation control then form a complete closed loop from identifying the potential problem to suppressing it. Compared to methods that rely solely on static compression states, single-point anomaly amplitudes, or the final leakage result for judgment, this solution focuses more on the dynamic evolution process before the anomaly forms, thus making it easier to identify and address the problem before the short-term sealing failure spreads.

[0020] S1 includes the following sub-steps: S1-1, Divide the sealing strip along its length into... The monitoring zone, the first The monitoring zone in the first The historical pressure index within a monitoring period is defined as: ,in Indicates the first The monitoring zone in the first Historical pressure index within a monitoring period This indicates the cumulative duration of compaction in the monitoring zone prior to the monitoring period. This indicates the cumulative duration of rebound in the monitored area prior to the monitoring period. This indicates a small normal number that prevents the denominator from being zero; the historical compaction index is used to characterize whether the monitored area is in a history dominated by compaction or rebound. S1-2, Generate a monitoring window containing [data / information] within each monitoring cycle. The coded perturbation sequence of each perturbation unit has a preset duration and a preset trigger interval, and the trigger intervals of two adjacent perturbation units are different, so that different perturbation units can be distinguished on the time axis; the coded perturbation sequence is only used to excite the concealed state and is not used to destroy the current sealing boundary. The coded perturbation used here is not intended to artificially create faults, but rather to subject the sealing strip to a set of distinguishable boundary changes within a very small range, thereby revealing subtle recovery differences that were originally hidden in the static fit state. The conventional approach is to directly collect signals such as pressure, displacement, or impedance while the sealing strip remains under its original pressure state. While this method can reflect the current state, it is difficult to determine whether the state is only temporarily stable, and even more difficult to determine whether local areas will temporarily open once the compression is reduced. This approach does not wait for anomalies to surface on their own, but first uses slight, controlled, and time-distinguishable perturbations to probe the true response behavior of the sealing strip. This yields not a single static measurement, but a response trajectory with causal relationships, allowing subsequent analysis to truly identify which areas are only superficially stable and which areas actually harbor hidden recovery anomalies.

[0021] In this embodiment, the coded perturbation sequence corresponds to the minute compression adjustment action applied by the external adjustment execution unit to the corresponding boundary adjustment part of the target monitoring partition. Specifically, within the monitoring window, the external adjustment execution unit applies short-term, amplitude-limited compression changes to the corresponding position of the target monitoring partition according to the trigger time and duration corresponding to each micro-perturbation unit in the coded perturbation sequence. This causes the target monitoring partition to first enter the compression stage, and then enter the rebound stage after the compression effect is reduced. The perturbation is limited to the minute adjustment range required for detection to ensure that the sealing strip maintains the continuity of the normal sealing boundary, while obtaining continuous response data that can be used for stage difference analysis within the same monitoring cycle.

[0022] S1-3, regarding the first Displacement compensation response data were collected from each monitoring zone. Edge pressure difference response and edge impedance response Since the displacement compensation response, edge pressure difference response, and edge impedance response have different dimensions and ranges, in order to ensure that the three types of responses can participate in subsequent stage difference analysis at the same scale, for any original response quantity... Standardize according to the following formula: ,in This represents the standardized response. This indicates the minimum value of the corresponding response quantity within the current monitoring window. This indicates the maximum value of the corresponding response quantity within the current monitoring window; S1-4. To uniformly represent the multi-source boundary responses of the same monitoring zone at the same time as a single analytical input, the first step is constructed based on the standardization processing results. The overall response value of each monitoring zone: ,in This represents the overall response value. Indicates displacement compensation response The standardization results Indicates edge pressure difference response The standardization results Indicates the edge impedance response The standardized results; the comprehensive response value serves as the unified input for subsequent separation analysis of the compaction and rebound stages; Step S2 includes: S2-1. Based on the phase boundary of the coded perturbation sequence, the first... The monitoring zone in the first Comprehensive response value for each monitoring cycle Divided into compaction stage intervals and rebound phase range ; S2-2. To characterize the fluctuation of the overall response relative to its respective average level within the compression and rebound phases, and to provide a basis for subsequently constructing phase difference indicators, the average deviation intensity of the compression phase and the average deviation intensity of the rebound phase are calculated separately: , ,in, This indicates the average deviation from the compressing strength during the compaction stage. This indicates the average deviation from the strength during the rebound phase. Indicates the length of the compression stage interval. Indicates the length of the rebound phase interval. This represents the average comprehensive response within the compression phase interval. This represents the average overall response within the rebound phase interval; two average deviation intensities are used to characterize the response activity in different phases. S2-3. To compress the response difference between the compression stage and the springback stage into a single comparable index, the springback stage difference index is calculated based on the average deviation strength of the compression stage and the average deviation strength of the springback stage: ,in Indicates the difference in the rebound phase; when When it increases, it indicates that the first The abnormal response of each monitoring zone was significantly stronger during the rebound phase than during the compaction phase. S2-4. First, determine the recovery time from the start of the rebound phase until the stability condition is first met and maintained continuously for a preset duration. Then, normalize the recovery time relative to the length of the rebound phase interval and calculate the... Recovery delay metrics for each monitored zone during the rebound phase: , in Indicates the recovery delay index, This indicates the time from the start of the rebound phase until the condition is met. And maintain the duration continuously The first recovery time, This indicates the recovery threshold. The duration of stability is indicated; the recovery delay index is used to characterize the speed at which the monitored area returns from an abnormal state to a stable state during the rebound phase. This implementation method does not treat all responses within a monitoring cycle together, but instead specifically distinguishes between the compaction stage and the rebound stage. This is because the sealing strip faces completely different boundary conditions in these two stages. During the compaction stage, boundary adhesion is often maintained by external compression; even if potential problems exist in localized weak areas, they may be temporarily suppressed and not become apparent. However, upon entering the rebound stage, the external compression decreases, and the sealing strip's own recovery ability begins to dominate the boundary state. At this point, it becomes clearer which areas can quickly recover stability and which areas will experience short-term opening. Conventional methods, if only considering the average state over the entire cycle, can easily misinterpret the surface stability of the compaction stage as overall stability, thus missing truly dangerous short-term anomalies. The ingenuity of this solution lies in isolating the small segment of the process most likely to expose hidden dangers, shifting the focus of identification from whether there are obvious anomalies to whether anomalies will be exposed after the compaction is released. This change in perspective directly improves the ability to identify precursors to short-term seal failure.

[0023] Step S3 includes: S3-1, regarding the first Each monitoring zone constructs a local anomaly marker:

[0024] in, Indicates a local anomaly marker. This indicates the threshold for asymmetric judgment during the rebound phase. Indicates the threshold for determining recovery delay; when When, it indicates the first Each monitoring zone exhibits both significant rebound phase anomalies and significant recovery delays during the current monitoring period; S3-2, in the case of the first With one monitoring zone as the center and a radius of [missing information], Calculate the number of adjacent consecutive anomalies within the neighborhood: ,in Indicates the number of consecutive anomalies. Represents the neighborhood radius; the number of adjacent consecutive anomalies is used to characterize the relationship with the first... Are multiple adjacent monitoring zones simultaneously in a local abnormal state? S3-3. To avoid misjudging sporadic fluctuations in a single period as real risks, and to include both spatially continuous anomalies and temporally persistent anomalies in the assessment, a short-term sealing failure risk status is constructed based on the local anomaly markers of the current monitoring period, the local anomaly markers of the previous monitoring period, and the number of adjacent continuous anomalies in the current monitoring period:

[0025] in, This indicates a short-term seal failure risk status. Indicates the threshold for the number of adjacent consecutive anomalies; when When, it indicates the first The monitoring zone not only shows obvious anomalies at present, but the anomalies have continued into the next monitoring cycle, forming a continuous anomaly segment with the adjacent monitoring zone; In actual boundary conditions, an anomaly in a single monitoring zone does not necessarily mean the sealing strip is in a dangerous state, as the anomaly could be merely a local disturbance, a transient fluctuation, or acquisition noise. What truly leads to short-term sealing failure is not isolated anomalies, but rather the simultaneous manifestation of anomalies in several adjacent areas within a short period, and these anomalies do not disappear naturally in the next monitoring cycle. Conventional methods often involve judging each zone separately, assuming a problem at whichever point exceeds the limit. While simple, this approach is prone to two biases: first, overemphasizing occasional anomalies; and second, viewing multiple weak anomalies in isolation, ignoring the fact that they may be spatially contiguous into an anomaly band. The role of this solution is to reorganize the previously scattered local information to determine whether these anomalies have a tendency to continuously expand. The originality of this approach lies in shifting risk assessment from a single-point level to a grasp of the overall boundary continuity, thus more closely approximating the actual sealing failure process.

[0026] Step S4 includes: S4-1. To ensure that monitoring zones with high historical compaction levels and significant recovery delays have sufficient stabilization time before entering the rebound phase, the following measures are taken to meet the requirements: The monitoring zone generates a pre-compensation compaction holding time: ,in Indicates the first The monitoring zone in the first The pre-compensation compression holding time corresponding to each monitoring cycle Indicates the base holding time; the pre-compensated compaction holding time is used to increase the local stabilization time before entering the springback phase; S4-2. To prevent adjacent anomaly monitoring zones from entering the rebound state simultaneously, [the following conditions must be met]. The monitoring partition generates the partition rebound phase misalignment quantity: , in Indicates the first The monitoring zone in the first The rebound phase misalignment corresponding to each monitoring cycle The basic phase misalignment is indicated; the rebound phase misalignment is used to prevent multiple adjacent anomaly monitoring zones from opening simultaneously during the same rebound phase. S4-3. To ensure that the number of repeated adjustments matches the degree of difference in the current rebound phase, the following conditions must be met: The monitoring zone generates a pre-compensation control repetition number: , ,in Indicates the first The monitoring zone in the first The number of pre-compensation control repetitions corresponding to each monitoring cycle. This represents the non-negative portion of the difference index during the rebound phase. This indicates rounding up; the number of repetitions of the pre-compensation control is used to control the intensity of the pre-compensation control action in the next monitoring cycle; S4-4, will , and Together they form the first The pre-compensation control instruction set for each monitoring zone is sent to the external adjustment and execution unit through the control output interface; This implementation method, after identifying continuous abnormal sections, does not stop at the level of alarms or waiting for maintenance, but directly corrects the boundary adjustment method for the next monitoring cycle in advance. The reason for this is that short-term sealing failures are often not caused by the absolute failure of a single area, but rather by multiple relatively weak boundary areas simultaneously entering an unstable state, eventually forming a continuous opening. In conventional techniques, even if some weak areas are found, it usually only prompts subsequent inspections; the actual boundary action rhythm does not change, so the risk may still be concentrated in the next cycle. The originality of this solution lies in directly transforming the risks identified in the previous cycle into the control basis for the next cycle, prioritizing the disruption of the temporal synchronicity of these abnormal areas, and allowing more sufficient stabilization time for slower-recovering areas. In other words, this solution does not wait for the risk to develop into a result before processing it, but proactively changes its formation conditions before the risk is most likely to form, reducing the probability of continuous abnormal sections expanding into actual failures from the source.

[0027] Step S5 includes: S5-1, Recalculate the next monitoring cycle after executing the pre-compensation control instruction set. Rebound phase difference indicators for each monitoring zone and recovery delay indicators ; S5-2. To quantitatively compare the abnormal suppression effect before and after the implementation of pre-compensation control, the rebound stage suppression improvement rate and recovery delay improvement rate are calculated based on the changes in indicators before and after pre-compensation control: , ,in Indicates the rate of improvement in the rebound phase. This indicates the recovery delay improvement rate; the two improvement rates are used to quantitatively determine the effectiveness of the current pre-compensation control instruction set. S5-3. In order to transform the improvement results into judgment conclusions that can be directly used in the next cycle, the adjustment effectiveness judgment results are constructed based on the rebound phase suppression improvement rate and recovery delay improvement rate:

[0028] in, This indicates the result of the determination of the effectiveness of the adjustment. This indicates the threshold for the improvement rate during the rebound phase. This represents the threshold for the recovery delay improvement rate; S5-4. When the result of the adjustment effectiveness determination indicates that the current pre-compensation control has not achieved the expected effect, when When this happens, update the short-term seal failure risk status for the next monitoring cycle to a continuous risk status, and increase the pre-compensation control intensity as follows: , , ,when At that time, update the short-term seal failure risk status for the next monitoring cycle to a risk-free status: This completes the closed-loop verification and update of the risk status of each monitoring zone.

[0029] In this implementation, closed-loop updates are not merely formal feedback, but rather a means to continuously correct the system's understanding of the true boundary state of the sealing strip. Since the sealing strip's resilience, local fit, and abnormal expansion trends can all change over time, relying solely on the judgment results and control intensity from the previous cycle can easily lead to two problems: first, excessively strong control may continue even when the original risk has diminished; second, the original risk may not have been eliminated but may be mistakenly considered to have returned to normal. Conventional solutions at this level often simply compare the changes before and after processing to determine the effectiveness of control. This solution goes further, directly using the comparison results for risk status updates and control intensity adjustments in the next cycle. In this way, the system does not rigidly execute a set of preset strategies, but rather corrects its judgments after each monitoring session to better reflect the true boundary state. Therefore, the overall prevention and control process is more stable and better adaptable to differences between different regions and cycles.

[0030] Unlike conventional sealing strip detection, which relies heavily on static state sampling, single-point anomaly detection, or post-event leakage confirmation, this implementation method proactively exposes hidden recovery anomalies through slight disturbances before persistent failure occurs at the sealing boundary. It then identifies truly risky abnormal responses by utilizing the difference between the compaction and rebound phases. Combined with continuous judgments of adjacent areas and cycles, scattered anomalies are elevated to continuous anomaly segments that can be used for prevention and control decisions. Furthermore, this implementation method does not stop at the monitoring level but directly transforms the identification results into the basis for pre-compensation control in the next cycle. This allows the system to change the boundary adjustment rhythm before risks materialize, thereby reducing the possibility of simultaneous instability in multiple weak areas. Because this implementation method connects early exposure, accurate identification, proactive suppression, and closed-loop correction into a complete chain, it not only improves the ability to identify short-term sealing failure precursors but also enhances the matching degree between subsequent control actions and the actual boundary state.

[0031] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0032] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A system for intelligent monitoring, analysis, and active prevention and control processing of a sealing strip in an intelligent networking environment, characterized in that: It includes a historical time series acquisition and coding perturbation module, which is used to establish multiple monitoring zones along the length of the sealing strip, collect historical compaction information and real-time boundary response information of each monitoring zone, and apply preset coding perturbation within the monitoring window to form analyzable time series samples; The asymmetric identification and short-term sealing failure determination module for the springback stage is used to separate the compression stage and springback stage of the time series sample, extract asymmetric features that reflect the degree of abnormality in the springback stage, and determine the short-term sealing failure risk of each monitoring zone based on the continuous state between adjacent monitoring zones. The pre-compensation control and closed-loop verification module is used to generate a pre-compensation control instruction set for the corresponding monitoring zone based on the short-term sealing failure risk and output it to the control output interface. At the same time, it verifies and updates the risk status based on the feedback results after the pre-compensation control. The historical time series acquisition and coding perturbation module includes: a historical segment caching submodule, used to record the compaction duration, rebound duration, and previous risk status of each monitoring zone in multiple monitoring cycles; a coding perturbation generation submodule, used to generate a coding perturbation sequence composed of multiple micro-perturbation units without disrupting the continuity of the current sealing boundary, and apply the coding perturbation sequence to the monitoring window; and a response acquisition pre-compensation control submodule, used to acquire the displacement compensation response, edge pressure difference response, and edge impedance response corresponding to each monitoring zone, and organize them into a response sequence under a unified time axis. The rebound stage asymmetric identification and short-term sealing failure determination module includes: a phase slicing submodule, used to divide the response sequence into a compression stage segment and a rebound stage segment based on the trigger position and phase boundary of the coded perturbation sequence; an asymmetric feature extraction submodule, used to calculate the response intensity and recovery delay of the compression stage segment and the rebound stage segment respectively, and output the rebound stage asymmetric features at the monitoring zone level; and an adjacency continuity determination submodule, used to determine continuous abnormal segments by combining the rebound stage asymmetric features of each monitoring zone and their adjacency relationships, and output the short-term sealing failure risk status at the monitoring zone level. The pre-compensation control and closed-loop verification module includes: a strategy generation submodule, used to generate the pre-compensation clamping holding time, the partition rebound phase misalignment amount, and the number of pre-compensation control repetitions based on the short-term seal failure risk state; an instruction issuance submodule, used to send the pre-compensation control instruction set to the control output interface; and a closed-loop update submodule, used to verify the pre-compensation control effect based on the new round of response data after pre-compensation control, and update the risk state for the next monitoring cycle.

2. The method for intelligent monitoring and analysis of sealing strips and active prevention and control processing in an intelligent networking environment, characterized in that: The method is applied to the intelligent monitoring, analysis, and proactive prevention and control system for sealing strips in an intelligent connected environment as described in claim 1, and includes the following steps: S1. Establish multiple monitoring zones along the length of the sealing strip, and collect historical compression information and real-time boundary response information for each monitoring zone; apply an coded perturbation sequence to the monitoring window within each monitoring cycle to form a monitoring sample with a clear temporal boundary; then perform unified time alignment and preprocessing on the monitoring sample to obtain the standardized response sequence required for subsequent analysis; S2. Within a monitoring cycle, the coded perturbation sequence first causes the target monitoring zone to enter the compaction stage, and then causes the target monitoring zone to enter the rebound stage. The compaction stage is the period during which the sealing strip is continuously in contact with the boundary, and the rebound stage is the period during which the sealing strip generates a recovery response after the compaction effect decreases. The standardized response sequence is divided into compaction stage segments and rebound stage segments according to the trigger boundary of the coded perturbation sequence. The response intensity of the compaction stage segment and the response intensity of the rebound stage segment are extracted respectively, and the recovery delay feature of the rebound stage is further extracted. The asymmetric result of the rebound stage of each monitoring zone is obtained based on the difference between the compaction stage and the rebound stage. S3. Perform adjacency correlation analysis on the asymmetric results of the rebound stage of each monitoring zone according to the length direction of the sealing strip; when multiple adjacent monitoring zones simultaneously meet the local abnormal conditions and continue to be maintained until the next monitoring cycle, they are determined to be continuous abnormal sections; output the short-term sealing failure risk status of each monitoring zone based on the continuous abnormal sections. S4. Generate a pre-compensation control instruction set based on the short-term sealing failure risk status of each monitoring zone. The pre-compensation control instruction set includes at least the pre-compensation clamping holding time, the zone rebound phase misalignment amount, and the number of pre-compensation control repetitions. Send the pre-compensation control instruction set through the control output interface to avoid multiple abnormal monitoring zones from opening simultaneously in the same rebound phase. Record the pre-compensation control parameters sent in this round and use them as prior information for the next monitoring cycle. S5. In the next monitoring cycle after executing the pre-compensation control instruction set, re-collect the response data of each monitoring zone; compare the asymmetric results of the rebound phase and the recovery delay characteristics before and after the pre-compensation control to determine whether the pre-compensation control strategy has achieved the expected suppression effect; if the expected suppression effect has not been achieved, increase the pre-compensation control intensity and update the risk status; if the expected suppression effect has been achieved, reduce the risk level of the corresponding monitoring zone and enter subsequent routine monitoring. 3.The method of claim 2, wherein the method further comprises: S1 includes the following sub-steps: S1-1, Divide the sealing strip along its length into... The monitoring zone, the first The monitoring zone in the first The historical pressure index within a monitoring period is defined as: ,in Indicates the first The monitoring zone in the first Historical pressure index within a monitoring period This indicates the cumulative compaction duration of the monitoring zone prior to the stated monitoring period. This indicates the cumulative rebound duration of the monitored zone prior to the stated monitoring period. This represents a small positive number that prevents the denominator from being zero. S1-2, Generate a monitoring window containing [data / information] within each monitoring cycle. The coded perturbation sequence of each perturbation unit has a preset duration and a preset trigger interval, and the trigger intervals of two adjacent perturbation units are different, so that different perturbation units can be distinguished on the time axis; the coded perturbation sequence is only used to activate the concealed state and is not used to destroy the current sealing boundary. S1-3, regarding the first Displacement compensation response data were collected from each monitoring zone. Edge pressure difference response and edge impedance response Since the displacement compensation response, edge pressure difference response, and edge impedance response have different dimensions and ranges, in order to ensure that the three types of responses can participate in subsequent stage difference analysis at the same scale, for any original response quantity... Standardize according to the following formula: ,in This represents the standardized response. This indicates the minimum value of the corresponding response quantity within the current monitoring window. This indicates the maximum value of the corresponding response quantity within the current monitoring window; S1-4. To uniformly represent the multi-source boundary responses of the same monitoring zone at the same time as a single analytical input, the first step is constructed based on the standardization processing results. The overall response value of each monitoring zone: ,in This represents the overall response value. Indicates displacement compensation response The standardization results Indicates edge pressure difference response The standardization results Indicates the edge impedance response The standardization results.

4. The method for intelligent monitoring, analysis, and proactive prevention and control of sealing strips in an intelligent connected environment according to claim 3, characterized in that: Step S2 includes: S2-1. Based on the phase boundary of the coded perturbation sequence, the first... The monitoring zone in the first Comprehensive response value for each monitoring cycle Divided into compaction stage intervals and rebound phase range ; S2-2. To characterize the fluctuation of the overall response relative to its respective average level within the compression and rebound phases, and to provide a basis for subsequently constructing phase difference indicators, the average deviation intensity of the compression phase and the average deviation intensity of the rebound phase are calculated separately: , ,in, This indicates the average deviation from the compressing strength during the compaction stage. This indicates the average deviation from the strength during the rebound phase. Indicates the length of the compression stage interval. Indicates the length of the rebound phase interval. This represents the average comprehensive response within the compression phase interval. This represents the average overall response within the rebound phase interval; S2-3. In order to compress the response difference between the compression stage and the springback stage into a single comparable index, the springback stage difference index is calculated based on the average deviation strength of the compression stage and the average deviation strength of the springback stage: ,in Indicates the difference in the rebound phase; when When it increases, it indicates that the first The abnormal response of each monitoring zone was significantly stronger during the rebound phase than during the compaction phase. S2-4. First, determine the recovery time from the start of the rebound phase until the stability condition is first met and maintained continuously for a preset duration. Then, normalize the recovery time relative to the length of the rebound phase interval and calculate the... Recovery delay metrics for each monitored zone during the rebound phase: , in Indicates the recovery delay index, This indicates the time from the start of the rebound phase until the condition is met. And maintain the duration continuously The first recovery time, This indicates the recovery threshold. Indicates the duration of stability.

5. The method for intelligent monitoring, analysis, and proactive prevention and control of sealing strips in an intelligent connected environment according to claim 4, characterized in that: Step S3 includes: S3-1, regarding the first Each monitoring zone constructs a local anomaly marker: in, Indicates a local anomaly marker. This indicates the threshold for asymmetric judgment during the rebound phase. Indicates the threshold for determining recovery delay; when When, it indicates the first Each monitoring zone exhibits both significant rebound phase anomalies and significant recovery delays during the current monitoring period; S3-2, in the case of the first With one monitoring zone as the center and a radius of [missing information], Calculate the number of adjacent consecutive anomalies within the neighborhood: ,in Indicates the number of consecutive anomalies. Indicates the neighborhood radius; S3-3. To avoid misjudging sporadic fluctuations in a single period as real risks, and to include both spatially continuous anomalies and temporally persistent anomalies in the assessment, a short-term sealing failure risk status is constructed based on the local anomaly markers of the current monitoring period, the local anomaly markers of the previous monitoring period, and the number of adjacent continuous anomalies in the current monitoring period: in, This indicates a short-term seal failure risk status. Indicates the threshold for the number of adjacent consecutive anomalies; when When, it indicates the first The monitoring zone not only shows obvious anomalies at present, but the anomalies have continued into the next monitoring cycle, forming a continuous anomaly segment with the adjacent monitoring zone.

6. The method for intelligent monitoring, analysis, and proactive prevention and control of sealing strips in an intelligent connected environment according to claim 5, characterized in that: Step S4 includes: S4-1. To ensure that monitoring zones with high historical compaction levels and significant recovery delays have sufficient stabilization time before entering the rebound phase, the following measures are taken to meet the requirements: The monitoring zone generates a pre-compensation compaction holding time: ,in Indicates the first The monitoring zone in the first The pre-compensation compression holding time corresponding to each monitoring cycle Indicates the duration of basic retention; S4-2. To prevent adjacent anomaly monitoring zones from entering the rebound state simultaneously, [the following conditions must be met]. The monitoring partition generates the partition rebound phase misalignment quantity: , in Indicates the first The monitoring zone in the first The rebound phase misalignment corresponding to each monitoring cycle Represents the fundamental phase misalignment; S4-3. To ensure that the number of repeated adjustments matches the degree of difference in the current rebound phase, the following conditions must be met: The monitoring zone generates a pre-compensation control repetition number: , ,in Indicates the first The monitoring zone in the first The number of pre-compensation control repetitions corresponding to each monitoring cycle. This represents the non-negative portion of the difference index during the rebound phase. Indicates rounding up; S4-4, will , and Together they form the first The pre-compensation control instruction set for each monitoring zone is sent to the external adjustment execution unit through the control output interface.

7. The method for intelligent monitoring, analysis, and proactive prevention and control of sealing strips in an intelligent connected environment according to claim 6, characterized in that: Step S5 includes: S5-1, Recalculate the next monitoring cycle after executing the pre-compensation control instruction set. Rebound phase difference indicators for each monitoring zone and recovery delay indicators ; S5-2. To quantitatively compare the abnormal suppression effect before and after the implementation of pre-compensation control, the rebound stage suppression improvement rate and recovery delay improvement rate are calculated based on the changes in indicators before and after pre-compensation control: , ,in Indicates the rate of improvement in the rebound phase. Indicates the recovery delay improvement rate; S5-3. In order to transform the improvement results into judgment conclusions that can be directly used in the next cycle, a judgment result on the effectiveness of the adjustment is constructed based on the rebound phase suppression improvement rate and the recovery delay improvement rate: in, This indicates the result of the determination of the effectiveness of the adjustment. This indicates the threshold for the improvement rate during the rebound phase. This represents the threshold for the recovery delay improvement rate; S5-4. When the result of the adjustment effectiveness determination indicates that the current pre-compensation control has not achieved the expected effect, when When this happens, update the short-term seal failure risk status for the next monitoring cycle to a continuous risk status, and increase the pre-compensation control intensity as follows: , , ,when At that time, update the short-term seal failure risk status for the next monitoring cycle to a risk-free status: This completes the closed-loop verification and update of the risk status of each monitoring zone.

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