Intelligent program-controlled quantitative sealing system and method for water quality environment detection

Abstract

This invention relates to the field of water quality testing technology, and proposes an intelligent programmable quantitative sealing system and method for water quality environmental testing. The system includes a signal reconstruction module, a volume compensation module, a trigger monitoring module, a deviation decoupling module, and a closed-loop adjustment module. It monitors the voltage decay sequence and heat sealing pressure decay sequence during the sealing process, and performs exponential decay reconstruction on the voltage decay sequence to obtain a relaxation characteristic constant. The relaxation characteristic constant and the heat sealing pressure decay sequence are jointly inverted to correct the injection volume of subsequent injection cycles in real time. The sealing trigger time for the current sealing process is determined by a volume compensation factor. Thermodynamic deviation decoupling is performed on the real-time temperature and real-time pressure to obtain parameter correction values. The holding temperature and holding pressure of the current sealing process are dynamically adjusted. This invention can improve the efficiency of intelligent programmable quantitative sealing for water quality environmental testing.

Description

Technical Field

[0001] This invention relates to the field of water quality testing technology, and in particular to an intelligent programmable quantitative sealing system and method for water quality environmental testing. Background Technology

[0002] Existing quantitative sealing devices for water quality environmental testing generally use fixed timing and constant temperature and pressure parameters to complete the sealing operation. They lack the ability to monitor and analyze the dynamic sequence of voltage decay and heat sealing pressure decay during the sealing process in real time, cannot effectively extract the relaxation characteristics of the working conditions, and have difficulty identifying the dynamic fluctuations of quantitative disk volume deformation and heat sealing conditions.

[0003] Traditional sealing control methods lack adaptive volume compensation and decoupled control logic for thermal deviation. The injection volume uses a fixed set value and cannot be corrected in real time according to changes in operating conditions. The sealing trigger time cannot be adaptively adjusted based on operating condition deviations. Temperature and pressure parameters are coupled and interfere with each other, making independent and accurate correction difficult. This easily leads to problems such as large quantitative injection errors and poor sealing consistency, which restricts the operational accuracy and efficiency of quantitative sealing for water quality testing. Therefore, how to improve the efficiency of intelligent programmable quantitative sealing for water quality environmental testing has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides an intelligent programmable quantitative sealing system and method for water quality environmental monitoring, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an intelligent programmable quantitative sealing system for water quality environmental monitoring, characterized in that the system includes a signal reconstruction module, a volume compensation module, a trigger monitoring module, a deviation decoupling module, and a closed-loop adjustment module, wherein:

[0006] The signal reconstruction module is used to monitor the voltage decay sequence and heat sealing pressure decay sequence during the sealing process, and to perform exponential decay reconstruction on the voltage decay sequence to obtain the relaxation characteristic constant of the voltage decay sequence.

[0007] The volume compensation module is used to jointly invert the relaxation characteristic constant and the heat sealing pressure decay sequence to generate the volume compensation factor of the quantitative disk, and to correct the injection volume of subsequent injection cycles in real time according to the volume compensation factor.

[0008] The trigger monitoring module is used to determine the sealing trigger time of the current sealing process by the volume compensation factor, and to monitor the real-time temperature and real-time pressure of the current sealing process in real time.

[0009] The deviation decoupling module is used to perform thermodynamic deviation decoupling on the real-time temperature and the real-time pressure to obtain the parameter correction amount for this sealing process.

[0010] The closed-loop adjustment module is used to dynamically adjust the holding temperature and holding pressure of the current sealing process based on the parameter correction amount, until the temperature overshoot and pressure decay rate fall within the preset tolerance range.

[0011] In a preferred embodiment, when the signal reconstruction module executes the monitoring of the voltage attenuation sequence and heat sealing pressure attenuation sequence during the current sealing process, it is specifically used for:

[0012] During the monitoring of this sealing process, transient deviation tracking was performed on the voltage time-varying records of the dynamic response data to obtain the voltage decay sequence of the dynamic response data;

[0013] The pressure interface of the dynamic response data is subjected to creep response capture to obtain the pressure decay sequence of the dynamic response data.

[0014] In a preferred embodiment, when the signal reconstruction module performs exponential decay reconstruction on the voltage decay sequence to obtain the relaxation characteristic constant of the voltage decay sequence, it is specifically used for:

[0015] The voltage decay sequence is divided into equal-length window segments;

[0016] The window segment is subjected to monotonic regression fitting, and the slope of the fitted curve is used as the attenuation driving force coefficient.

[0017] The attenuation driving force coefficient in the middle of the sort is selected as the relaxation characteristic constant of the voltage attenuation sequence.

[0018] In a preferred embodiment, when the volume compensation module performs a joint inversion of the relaxation characteristic constant and the heat-sealing pressure decay sequence to generate the volume compensation factor for the quantitative disk, it is specifically used for:

[0019] Using the extreme value position as the registration basis, the heat sealing pressure decay sequence and the relaxation characteristic constant are dynamically normalized and aligned to obtain the synchronized time sequence of the relaxation characteristic constant;

[0020] Within each pressure fluctuation cycle of the synchronized time series, the median value of the heat sealing pressure decay sequence is extracted. The median value is used as a weighting coefficient to perform piecewise weighted integration on the synchronized time series to obtain the cumulative flow resistance of the relaxation characteristic constant.

[0021] The cumulative flow resistance of positive overshoot and negative loss is clamped to zero, and the cumulative flow resistance after bidirectional truncation is output as the volume compensation factor of the quantitative disk.

[0022] In a preferred embodiment, when the volume compensation module performs real-time correction of the injection volume for subsequent injection cycles based on the volume compensation factor, it is specifically used for:

[0023] Obtain the original volume setting for the current injection cycle, and embed the volume compensation factor as an additional bias term into the original volume setting to generate the pre-corrected volume of the original volume setting;

[0024] Based on the sliding window residual between the pre-corrected volume and the historical volume, the smoothed volume of the pre-corrected volume is iteratively updated;

[0025] The smoothing volume is superimposed and canceled out by the volume compensation factor to output the injection volume for subsequent injection cycles.

[0026] In a preferred embodiment, when the volume compensation module iteratively updates the smoothed volume of the pre-corrected volume based on the sliding window residual between the pre-corrected volume and the historical volume, it is specifically used for:

[0027] The historical volumes are sorted by amplitude, and the largest and smallest amplitudes are removed. The historical volumes sorted in the middle are retained to form a reduced sequence.

[0028] The arithmetic mean of the reduced sequence is used as the reference volume, and the instantaneous deviation residual of the pre-corrected volume relative to the reference volume is extracted.

[0029] Based on the instantaneous deviation residual, the smoothed volume of the pre-corrected volume is calculated, wherein the formula for calculating the smoothed volume is:

[0030] ;

[0031] In the formula, For the smooth volume, The pre-corrected volume quantity, The preset convergence step size factor, For sign extraction operation, The instantaneous deviation residual, To perform the minimum value operation, For the absolute value operation, The preset scaling factor for the residual standard deviation. The total number of the historical volumes. This is the time-series index of the historical volume. This is the sequence number of the current iteration cycle. For the first The historical deviation residual of a historical volume This is the sequence number of the previous iteration cycle.

[0032] In a preferred embodiment, when the trigger monitoring module executes the sealing trigger time determined by the volume compensation factor for the current sealing process, it is specifically used for:

[0033] Read the sign and absolute value of the volume compensation factor, and scale the absolute value proportionally along the range of the relaxation characteristic constant to obtain the time offset step of the volume compensation factor;

[0034] The initial zero-crossing point of the heat-sealing pressure decay sequence is detected, and the first zero-crossing point detected is marked as the timing start point;

[0035] Based on the sign of the numerical value, the time offset step is superimposed and shifted with the starting point of the time sequence, and the shifted time is used as the sealing trigger time of the current sealing process.

[0036] In a preferred embodiment, when the deviation decoupling module performs thermodynamic deviation decoupling on the real-time temperature and the real-time pressure to obtain the parameter correction amount for the current sealing process, it is specifically used for:

[0037] By performing a sliding window difference on the real-time temperature, the steady-state temperature component in the real-time temperature is removed, and the temperature drift rate of the real-time temperature is obtained.

[0038] The real-time pressure is subjected to a first-order high-pass filter to eliminate the low-frequency creep component in the real-time pressure, thereby obtaining the pressure attenuation margin of the real-time pressure.

[0039] The temperature drift rate and the pressure attenuation margin are cross-multiplied and accumulated. The sign of the temperature drift rate determines the accumulation direction, and the absolute value of the pressure attenuation margin determines the accumulation amplitude. The parameter correction amount for this sealing process is then output.

[0040] In a preferred embodiment, the closed-loop adjustment module, when dynamically adjusting the holding temperature and holding pressure of the current sealing process based on the parameter correction amount, until the temperature overshoot and pressure decay rate fall within the preset tolerance range, is specifically used for:

[0041] The parameter correction amount is decomposed into a temperature correction component and a pressure correction component;

[0042] Apply the inverse superposition of the temperature correction component to the holding temperature of the current cycle, iteratively reduce the temperature overshoot, and obtain the temperature overshoot residual of the current cycle;

[0043] Apply a positive bias of the pressure correction component to the holding pressure of the current cycle, and successively compensate the pressure decay rate to obtain the pressure decay residual of the current cycle.

[0044] The adjustment is terminated immediately when neither the temperature overshoot residual nor the pressure attenuation residual exceeds the preset tolerance range; otherwise, the correction amount of the current cycle is added to the historical correction amount and fed into the next cycle, and the iteration continues until both residuals fall within the preset tolerance range.

[0045] To address the above problems, the present invention also provides an intelligent programmable quantitative sealing method for water quality environmental monitoring, the method comprising:

[0046] S01. Monitor the voltage decay sequence and heat sealing pressure decay sequence during this sealing process, and reconstruct the voltage decay sequence by exponential decay to obtain the relaxation characteristic constant of the voltage decay sequence.

[0047] S02. The relaxation characteristic constant and the heat sealing pressure decay sequence are jointly inverted to generate the volume compensation factor of the quantitative disk, and the injection volume of subsequent injection cycles is corrected in real time according to the volume compensation factor.

[0048] S03. The sealing trigger time of the current sealing process is determined by the volume compensation factor, and the real-time temperature and real-time pressure of the current sealing process are monitored in real time.

[0049] S04. Perform thermodynamic deviation decoupling on the real-time temperature and the real-time pressure to obtain the parameter correction amount for this sealing process;

[0050] S05. Based on the parameter correction amount, dynamically adjust the holding temperature and holding pressure of the current sealing process until the temperature overshoot and pressure decay rate fall within the preset tolerance range.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. This invention acquires the voltage decay sequence and heat sealing pressure decay sequence during the sealing process in real time, obtains the relaxation characteristic constant through exponential decay reconstruction, and generates a volume compensation factor by joint inversion. This can adaptively correct the injection volume during the injection cycle, accurately compensate for the measurement deviation caused by the volume deformation of the quantitative plate, and stably ensure the quantitative accuracy of water quality testing sampling.

[0053] 2. This invention relies on the volume compensation factor to autonomously adjust the sealing trigger time, while simultaneously decoupling the thermodynamic deviation between real-time temperature and real-time pressure. By using a closed-loop iterative mechanism to dynamically fine-tune and maintain the temperature and pressure, it quickly controls the temperature overshoot and pressure decay rate within the tolerance range, effectively improving the sealing quality and adaptability to working conditions, and significantly improving the operational efficiency and stability of quantitative sealing for water quality environmental testing. Attached Figure Description

[0054] Figure 1 This is a system architecture diagram of an intelligent programmable quantitative sealing system for water quality environmental monitoring provided in an embodiment of the present invention;

[0055] Figure 2 This is a schematic flowchart of an intelligent programmable quantitative sealing method for water quality environmental monitoring provided in an embodiment of the present invention.

[0056] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. 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.

[0058] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0059] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0060] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0061] In practice, the server-side equipment deployed in the intelligent programmable quantitative sealing system for water quality environmental monitoring may consist of one or more devices. The aforementioned intelligent programmable quantitative sealing system for water quality environmental monitoring can be implemented as: a business instance, a virtual machine, or hardware devices. For example, this intelligent programmable quantitative sealing system for water quality environmental monitoring can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this intelligent programmable quantitative sealing system for water quality environmental monitoring can be understood as software deployed on a cloud node, used to provide intelligent programmable quantitative sealing for water quality environmental monitoring to various user terminals. Alternatively, this intelligent programmable quantitative sealing system for water quality environmental monitoring can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, this intelligent programmable quantitative sealing system for water quality environmental monitoring can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide intelligent programmable quantitative sealing for water quality environmental monitoring to various user terminals.

[0062] In terms of implementation, the intelligent programmable quantitative sealing system for water quality environmental monitoring and the user terminal are mutually compatible. That is, if the intelligent programmable quantitative sealing system for water quality environmental monitoring is implemented as an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the intelligent programmable quantitative sealing system for water quality environmental monitoring is implemented as a website, then the user terminal is implemented as a webpage; or if the intelligent programmable quantitative sealing system for water quality environmental monitoring is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0063] like Figure 1 The figure shown is a system architecture diagram of an intelligent programmable quantitative sealing system for water quality environmental monitoring provided in an embodiment of the present invention.

[0064] The intelligent programmable quantitative sealing system 10 for water quality environmental monitoring described in this invention can be installed on a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed on the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the intelligent programmable quantitative sealing system 10 for water quality environmental monitoring may include a signal reconstruction module 11, a volume compensation module 12, a trigger monitoring module 13, a deviation decoupling module 14, and a closed-loop adjustment module 15. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0065] In this embodiment of the invention, in the intelligent programmable quantitative sealing system for water quality environmental monitoring, each of the above-mentioned modules can be implemented independently and can be called upon with other modules. This "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the intelligent programmable quantitative sealing system for water quality environmental monitoring provided by this embodiment of the invention, the applicable scope of the system architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the system. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0066] The following describes, with reference to specific embodiments, each component and its specific workflow of the intelligent programmable quantitative sealing system for water quality environmental monitoring:

[0067] The signal reconstruction module 11 is used to monitor the voltage decay sequence and heat sealing pressure decay sequence of the current sealing process, and to perform exponential decay reconstruction on the voltage decay sequence to obtain the relaxation characteristic constant of the voltage decay sequence.

[0068] In this embodiment of the invention, when the signal reconstruction module executes the voltage attenuation sequence and heat sealing pressure attenuation sequence for monitoring the current sealing process, it is specifically used for:

[0069] During the monitoring of this sealing process, transient deviation tracking was performed on the voltage time-varying records of the dynamic response data to obtain the voltage decay sequence of the dynamic response data;

[0070] The pressure interface of the dynamic response data is subjected to creep response capture to obtain the pressure decay sequence of the dynamic response data.

[0071] When the signal reconstruction module performs exponential decay reconstruction on the voltage decay sequence to obtain the relaxation characteristic constant of the voltage decay sequence, it is specifically used for:

[0072] The voltage decay sequence is divided into equal-length window segments;

[0073] The window segment is subjected to monotonic regression fitting, and the slope of the fitted curve is used as the attenuation driving force coefficient.

[0074] The attenuation driving force coefficient in the middle of the sort is selected as the relaxation characteristic constant of the voltage attenuation sequence.

[0075] During the sealing process, voltage values ​​in the dynamic response data are continuously collected, and all collected voltage values ​​are arranged in chronological order to form a continuous time-varying voltage record.

[0076] The voltage value at the current time is extracted from the voltage time-varying record and the voltage value at the previous time. The difference between the two values ​​is obtained. The differences obtained at all times are arranged in chronological order to obtain the voltage decay sequence.

[0077] During the sealing process, the pressure values ​​at the pressure interface in the dynamic response data are continuously collected, and all the collected pressure values ​​are arranged in chronological order to form a continuous time-varying pressure record.

[0078] The pressure values ​​are extracted slowly over time from the time-varying pressure record. All the extracted slowly changing values ​​are arranged in chronological order to obtain the pressure decay sequence.

[0079] The obtained voltage decay sequence is divided into segments of fixed time length, resulting in multiple window segments of identical length. Each window segment contains all voltage decay values ​​within the corresponding time period.

[0080] For each window segment obtained by division, the voltage decay values ​​in the segments are connected sequentially according to the time sequence to form a continuous curve.

[0081] Adjust the overall trend of the continuous curve to make it exhibit a single trend, while minimizing the total deviation between each point on the curve and the original voltage attenuation value at the corresponding position. Use the slope of the adjusted curve as the attenuation driving force coefficient corresponding to the window segment.

[0082] Arrange the attenuation driving force coefficients corresponding to all window segments in ascending order of their numerical values, and select the attenuation driving force coefficient in the middle position after sorting, which is taken as the relaxation characteristic constant of the voltage attenuation sequence.

[0083] The beneficial effects include the ability to acquire complete and continuous dynamic data on voltage and pressure during the sealing process, ensuring the temporal correspondence and integrity of the data. By extracting the voltage value difference at each time step to generate a voltage decay sequence, the instantaneous change characteristics of voltage are accurately captured. Similarly, by extracting the slow change in pressure values ​​to generate a pressure decay sequence, the creep characteristics of the pressure interface are accurately reflected. Dividing the voltage decay sequence into equal-length segments and fitting a single trend curve effectively extracts the decay driving characteristics of each time period. Selecting the coefficient at the middle position as the relaxation characteristic constant avoids the influence of extreme fluctuations, obtaining stable and representative core characteristic parameters, and providing accurate and reliable characteristic basis for subsequent volume compensation and sealing control.

[0084] The volume compensation module 12 is used to perform joint inversion of the relaxation characteristic constant and the heat sealing pressure decay sequence to generate the volume compensation factor of the quantitative disk, and to correct the injection volume of subsequent injection cycles in real time according to the volume compensation factor.

[0085] In this embodiment of the invention, when the volume compensation module performs a joint inversion of the relaxation characteristic constant and the heat-sealing pressure decay sequence to generate the volume compensation factor of the quantitative disk, it is specifically used for:

[0086] Using the extreme value position as the registration basis, the heat sealing pressure decay sequence and the relaxation characteristic constant are dynamically normalized and aligned to obtain the synchronized time sequence of the relaxation characteristic constant;

[0087] Within each pressure fluctuation cycle of the synchronized time series, the median value of the heat sealing pressure decay sequence is extracted. The median value is used as a weighting coefficient to perform piecewise weighted integration on the synchronized time series to obtain the cumulative flow resistance of the relaxation characteristic constant.

[0088] The cumulative flow resistance of positive overshoot and negative loss is clamped to zero, and the cumulative flow resistance after bidirectional truncation is output as the volume compensation factor of the quantitative disk.

[0089] When the volume compensation module performs real-time correction of the injection volume for subsequent injection cycles based on the volume compensation factor, it is specifically used for:

[0090] Obtain the original volume setting for the current injection cycle, and embed the volume compensation factor as an additional bias term into the original volume setting to generate the pre-corrected volume of the original volume setting;

[0091] Based on the sliding window residual between the pre-corrected volume and the historical volume, the smoothed volume of the pre-corrected volume is iteratively updated;

[0092] The smoothing volume is superimposed and canceled out by the volume compensation factor to output the injection volume for subsequent injection cycles.

[0093] When the volume compensation module iteratively updates the smoothed volume of the pre-corrected volume based on the sliding window residual between the pre-corrected volume and the historical volume, it is specifically used for:

[0094] The historical volumes are sorted by amplitude, and the largest and smallest amplitudes are removed. The historical volumes sorted in the middle are retained to form a reduced sequence.

[0095] The arithmetic mean of the reduced sequence is used as the reference volume, and the instantaneous deviation residual of the pre-corrected volume relative to the reference volume is extracted.

[0096] Based on the instantaneous deviation residual, the smoothed volume of the pre-corrected volume is calculated, wherein the formula for calculating the smoothed volume is:

[0097] ;

[0098] In the formula, For the smooth volume, The pre-corrected volume quantity, The preset convergence step size factor, For sign extraction operation, The instantaneous deviation residual, To perform the minimum value operation, For the absolute value operation, The preset scaling factor for the residual standard deviation. The total number of the historical volumes. This is the time-series index of the historical volume. This is the sequence number of the current iteration cycle. For the first The historical deviation residual of a historical volume This is the sequence number of the previous iteration cycle.

[0099] The timing points where the values ​​in the heat sealing pressure decay sequence reach their limits are locked. These points are used as a unified reference benchmark to adjust the timing arrangement rhythm of the relaxation characteristic constants so that the timing change rhythms of the two are matched and corresponded. After the regularization and alignment process is completed, the synchronized timing sequence of the relaxation characteristic constants is obtained.

[0100] The synchronous time series is divided into pressure fluctuation intervals according to the natural fluctuation of pressure. All values ​​of the heat sealing pressure decay sequence contained in each interval are sorted out one by one, and the value in the middle position of the numerical arrangement is selected as the median value of the corresponding interval.

[0101] The median value extracted from each pressure fluctuation interval is set as the weight reference standard. The synchronized time series is segmented according to the divided intervals. Each segment is then accumulated and integrated layer by layer in combination with the set weight reference standard. After integration, the flow resistance accumulation of the relaxation characteristic constant is obtained.

[0102] Distinguish between the portion of the accumulated flow resistance that deviates upward from the standard range and the portion that falls short of the standard range downward, and unify the values ​​of both types of deviations to a baseline zero value.

[0103] The cumulative flow resistance after bidirectional numerical limiting is directly used as the volume compensation factor of the quantitative disk for output.

[0104] The original volume setting of the current injection cycle is retrieved, and the volume compensation factor is incorporated into the original volume setting as a fixed offset reference. After the numerical fusion arrangement is completed, the pre-corrected volume of the original volume setting is generated.

[0105] Historical volume data from previous injection cycles are collected and compared with the temporal arrangement of the pre-corrected volume data. The differences in arrangement between the two in continuous time intervals are analyzed to form a sliding window residual. Based on the temporal arrangement changes of the residual, the arrangement of the pre-corrected volume data is continuously optimized and updated, and a smooth volume of the pre-corrected volume data is gradually formed through iteration.

[0106] All historical volumes are arranged in order of their numerical value. The values ​​at the beginning and end of the arrangement are removed, and the remaining historical volumes in the middle positions are retained to form a reduced sequence.

[0107] Find the average value of all values ​​within the reduced sequence and set this average value as the baseline volume.

[0108] By comparing the arrangement difference between the pre-corrected volume and the baseline volume, the instantaneous deviation residual formed between the two is accurately extracted.

[0109] Based on the arrangement and drop state of the instantaneous deviation residual, the numerical arrangement of the pre-corrected volume is regularized and harmonized, and finally the smoothed volume of the pre-corrected volume is obtained after regularization and harmonization.

[0110] The generated smooth volume and the volume compensation factor are mutually offset and adapted. After the adaptation is completed, the injection volume used in subsequent injection cycles is determined and output.

[0111] The pre-corrected volume is generated by embedding the volume compensation factor as an additional bias term into the original volume setting in the current injection cycle. The convergence step size factor and the residual standard deviation scaling factor are fixed values ​​set in advance. The instantaneous deviation residual is obtained by the difference between the pre-corrected volume and the reference volume. The total number of historical volumes is the number of historical volumes retained in the sliding window. The historical deviation residual is the difference between each historical volume and the corresponding reference volume.

[0112] This calculation process controls the magnitude of a single correction by introducing a convergence step size factor, determines the direction of correction by combining the sign of the instantaneous deviation residual, and limits the maximum magnitude of a single correction by referring to the dispersion of historical deviation residuals, thereby achieving smooth processing of the pre-corrected volume and eliminating the impact of single data fluctuations on the injection volume.

[0113] As the iteration cycle progresses, the smoothed volume will gradually approach the stable baseline volume, and the magnitude of a single correction will be automatically adjusted according to the changes in the dispersion of historical data, ultimately keeping the injection volume in a stable output state.

[0114] The beneficial effects include achieving precise temporal matching between the relaxation characteristic constant and the heat-sealing pressure decay sequence, ensuring the correspondence of the change rhythms of the two types of data, and providing a unified temporal basis for subsequent feature fusion processing. By extracting the median value within each pressure fluctuation cycle as a weight for accumulation and integration, a stable cumulative flow resistance can be obtained. Bidirectional numerical constraint processing can filter out abnormal deviations and generate an accurate and reliable quantitative disk volume compensation factor. Based on the volume compensation factor, the initial correction of the injection volume is completed. Then, combined with the screening of historical volumes and benchmark references, a smooth volume is obtained through residual normalization. Finally, the superimposed and offset output injection volume can accurately adapt to the actual state of the quantitative disk, stably ensuring the accuracy and consistency of injection quantification.

[0115] The trigger monitoring module 13 is used to determine the sealing trigger time of the current sealing process based on the volume compensation factor;

[0116] In this embodiment of the invention, when the trigger monitoring module executes the sealing trigger time determined by the volume compensation factor for the current sealing process, it is specifically used for:

[0117] Read the sign and absolute value of the volume compensation factor, and scale the absolute value proportionally along the range of the relaxation characteristic constant to obtain the time offset step of the volume compensation factor;

[0118] The initial zero-crossing point of the heat-sealing pressure decay sequence is detected, and the first zero-crossing point detected is marked as the timing start point;

[0119] Based on the sign of the numerical value, the time offset step is superimposed and shifted with the starting point of the time sequence, and the shifted time is used as the sealing trigger time of the current sealing process.

[0120] The system fully reads the positive and negative attribute identifiers and corresponding numerical volume information carried by the volume compensation factor, separating the unique numerical symbol part and the independent absolute value content from the overall information of the volume compensation factor. It comprehensively analyzes the entire value range covered by the relaxation characteristic constant, clearly defining the start and end boundaries of this value interval and clarifying the complete span range within the interval. The absolute value content of the separated volume compensation factor is incorporated into the overall span of the relaxation characteristic constant's value interval. According to the overall length of the value interval, continuous and equal scale segments are evenly divided, and the absolute value of the volume compensation factor is placed at the corresponding position on the interval scale according to a unified scale division standard. The absolute value and the value range are adapted and normalized according to the arrangement rule of equal scale intervals. Based on the normalized correspondence, the volume of a time period with a fixed duration is calculated. The volume of this time period is defined as the time offset step of the volume compensation factor. The entire adaptation and scaling process follows a unified normalization standard. The matching is performed according to the inherent span of the relaxation characteristic constant value range throughout the process, without changing the interval boundary and scale division rules, and stably generating a time offset step with fixed attributes.

[0121] Following the original time sequence, the numerical content of all time nodes within the heat sealing pressure decay sequence is systematically analyzed. Starting from the initial time node at the very beginning of the sequence, the crossing status verification of the reference zero point is carried out node by node. Strictly adhering to the time sequence without skipping any intermediate nodes, the numerical change trend of each time node is compared with its adjacent preceding and following nodes, continuously investigating the switching node positions where the value transitions from one side of the reference zero point to the other. During the node-by-node investigation, the time node that first completes the reference zero point crossing switch in the heat sealing pressure decay sequence structure is accurately identified and marked. This marked node is established as the time starting point of the heat sealing pressure decay sequence. After marking, the node position remains constant. Zero-crossing point screening is carried out throughout the process based on the original time sequence arrangement logic, ensuring that the selected node is the earliest in the time sequence among all zero-crossing points, thus accurately locating and marking the time starting point.

[0122] Identify the positive or negative category of the numerical symbols derived from the volume compensation factor, and determine the fixed offset direction of the time series position based on the category attribute. Using the time series starting point with fixed markings as the reference position, and following the offset direction determined by matching the numerical symbols, arrange the complete duration span corresponding to the previously generated time offset step in a coherent and sequential manner, fitting the overall time series arrangement. Along the predetermined offset direction, superimpose and merge the complete duration of the time offset step onto the fixed time position of the time series starting point, completing the unidirectional translation adjustment of the overall time series position. The translation process maintains the continuity and integrity of the time series arrangement, without interrupting the time series context or changing the predetermined offset direction. After completing the time overlay and time sequence position translation, the time position is fixed at the final adjusted time position. This fixed time position is directly set as the exclusive sealing trigger time for this sealing process. This time position serves as the standard moment for starting the sealing process. The offset direction is strictly defined by numerical symbols throughout the process, and the time sequence position is shifted in full using the predetermined time offset step size. This ensures that the finally established sealing trigger time can accurately match the attribute characteristics of the volume compensation factor and the time sequence benchmark of the heat sealing pressure decay sequence.

[0123] The beneficial effects include the ability to accurately separate the attribute and numerical information of the volume compensation factor, and to complete a proportional adaptation transformation based on the inherent value range of the relaxation characteristic constant, generating a time offset step that highly matches the operating conditions. By verifying the benchmark zero-point crossing state of the heat sealing pressure decay sequence point by point, the earliest zero-crossing point in the time series is locked as a unified time series benchmark, ensuring the uniqueness and accuracy of the time series starting point. By combining the numerical sign to determine the offset direction and completing the precise shift of the time series position, the final generated sealing trigger time can adaptively change the volume of the quantitative disk, accurately match the dynamic operating conditions of the sealing process, provide accurate timing basis for subsequent sealing operations, and improve the accuracy and consistency of the sealing trigger timing.

[0124] The deviation decoupling module 14 is used to perform thermodynamic deviation decoupling on the real-time temperature and the real-time pressure to obtain the parameter correction amount for the current sealing process.

[0125] In this embodiment of the invention, when the deviation decoupling module performs thermodynamic deviation decoupling on the real-time temperature and the real-time pressure to obtain the parameter correction amount for the current sealing process, it is specifically used for:

[0126] By performing a sliding window difference on the real-time temperature, the steady-state temperature component in the real-time temperature is removed, and the temperature drift rate of the real-time temperature is obtained.

[0127] The real-time pressure is subjected to a first-order high-pass filter to eliminate the low-frequency creep component in the real-time pressure, thereby obtaining the pressure attenuation margin of the real-time pressure.

[0128] The temperature drift rate and the pressure attenuation margin are cross-multiplied and accumulated. The sign of the temperature drift rate determines the accumulation direction, and the absolute value of the pressure attenuation margin determines the accumulation amplitude. The parameter correction amount for this sealing process is then output.

[0129] Real-time temperature values ​​within a continuous time series are extracted to form a coherent time series interval. This interval is then shifted position by position along the direction of time, sequentially covering real-time temperature records across all time series dimensions. Real-time temperature values ​​at consecutive time points within each sliding time series interval are compared, and the differences in variation between adjacent values ​​are analyzed to fully capture the dynamic fluctuations in temperature over time. From the complete real-time temperature records, components that remain constant over long periods are selected, completely separated from and removed from the original temperature records, completing the stripping of steady-state temperature components. Only temperature content carrying dynamic time series fluctuation characteristics is retained. This retained content is then regularized and integrated according to the original time series arrangement, resulting in the temperature drift rate of the real-time temperature.

[0130] Following a chronological arrangement, the system comprehensively collects real-time pressure data generated throughout the sealing process. It then identifies components within these real-time pressure values ​​that evolve slowly at a gradual pace, precisely categorizing those exhibiting long-term, slow-changing characteristics. This identified, gradually evolving content is then completely eliminated, removing low-frequency creep components from the real-time pressure data and retaining only pressure values ​​reflecting short-term dynamic fluctuations. The remaining real-time pressure dynamic values, after removing useless components, are then rearranged chronologically, integrating all effective dynamic pressure data and uniformly standardizing it to ultimately determine the real-time pressure attenuation margin.

[0131] Following a one-to-one correspondence between time-series nodes, the content of each time-series point of the temperature drift rate is paired with the corresponding time-series point of the pressure attenuation margin, ensuring precise alignment of the two types of features in the time dimension. For each pair of aligned time-series points, mutual correlation and overlay processing is performed, with overlay operations completed sequentially for each time-series point, continuously accumulating content layer by layer. The overall accumulation process is guided by the inherent positive and negative attributes of the temperature drift rate, maintaining a consistent logical flow throughout. Referring to the inherent numerical standard of the pressure attenuation margin, the content filling scale for each round of accumulation is determined, using the same scale to match the amplitude of all time-series nodes. After traversing all time-series nodes to complete full cross-correlation and layer-by-layer accumulation, the integrated overall feature content is directly output and fixed as the parameter correction amount for this sealing process.

[0132] The beneficial effects include the ability to accurately isolate the steady-state components of real-time temperature by comparing it with adjacent values ​​through positional shifting of the sliding time interval, fully capturing the dynamic fluctuation characteristics of temperature, and generating a temperature drift rate that accurately reflects the temperature change trend. By identifying and eliminating the slowly evolving components in real-time pressure, the short-term dynamic fluctuation information of pressure is effectively separated, yielding a pure pressure attenuation margin. Based on precise time-series alignment, the cross-correlation and layer-by-layer accumulation of the two types of features are completed. The correction direction is determined by combining the attributes of the temperature features, and the correction amplitude is determined by referring to the volume of the pressure features. The final generated parameter correction can accurately decouple the coupling interference of temperature and pressure, providing a reliable basis for the dynamic adjustment of subsequent sealing parameters and improving the accuracy and consistency of temperature and pressure control during the sealing process.

[0133] The closed-loop adjustment module 15 is used to dynamically adjust the holding temperature and holding pressure of the current sealing process based on the parameter correction amount, until the temperature overshoot and pressure decay rate fall within the preset tolerance range.

[0134] In this embodiment of the invention, the closed-loop adjustment module, based on the parameter correction amount, dynamically adjusts the holding temperature and holding pressure of the current sealing process until the temperature overshoot and pressure decay rate fall within the preset tolerance range. Specifically, it is used for:

[0135] The parameter correction amount is decomposed into a temperature correction component and a pressure correction component;

[0136] Apply the inverse superposition of the temperature correction component to the holding temperature of the current cycle, iteratively reduce the temperature overshoot, and obtain the temperature overshoot residual of the current cycle;

[0137] Apply a positive bias of the pressure correction component to the holding pressure of the current cycle, and successively compensate the pressure decay rate to obtain the pressure decay residual of the current cycle.

[0138] The adjustment is terminated immediately when neither the temperature overshoot residual nor the pressure attenuation residual exceeds the preset tolerance range; otherwise, the correction amount of the current cycle is added to the historical correction amount and fed into the next cycle, and the iteration continues until both residuals fall within the preset tolerance range.

[0139] The parameter correction quantities generated during this sealing process are categorized according to their control and adaptation attributes. They are then independently split into temperature control and pressure control attributes. After splitting, a temperature correction component is formed specifically for temperature control, and a pressure correction component is formed specifically for pressure control. The splitting process fully preserves the inherent control attributes of the parameter correction quantities and does not change the internal control and adaptation logic, so that the two types of components can be matched with the subsequent adjustment and adaptation requirements of temperature and pressure.

[0140] Retrieve the holding temperature that is currently being maintained during the sealing operation cycle, and superimpose the temperature correction component in a way that is opposite to the original temperature control. Continue to reduce the part of the holding temperature that deviates from the standard state in accordance with the time sequence, and continuously converge the deviation of temperature overshoot. After the superposition and convergence operation is completed, freeze the remaining deviation of the holding temperature, and establish the frozen deviation as the temperature overshoot residual of the current cycle.

[0141] Retrieve the pressure holding pressure that is currently maintaining operation during the sealing cycle, and adjust the pressure correction component in the same direction as the original pressure control. Fill the gap caused by the attenuation of the pressure holding pressure as the time changes in each cycle, and make up for the state loss caused by the pressure attenuation in turn. After the compensation operation is completed, fix the remaining attenuation of the pressure holding pressure, and establish the fixed attenuation content as the pressure attenuation residual of the current cycle.

[0142] Referring to the pre-set tolerance range boundary standards, the interval positions of temperature overshoot residual and pressure decay residual are checked simultaneously. If both types of residuals are within the tolerance range boundary, all temperature and pressure adjustment behaviors are directly terminated. If either type of residual is outside the tolerance range boundary, all correction content generated in the current cycle is integrated and collected with the historical correction content accumulated in the previous period. The collected overall content is sent to the next sealing operation cycle to continue to participate in the control. The process of component superposition residual verification and correction content collection and transmission is repeatedly executed until the temperature overshoot residual and pressure decay residual fall within the preset tolerance range at the same time, and the iterative adjustment process is completely ended.

[0143] The beneficial effects include the ability to precisely decompose parameter correction amounts into independent components adapted to temperature and pressure control, fully preserving the original control logic and achieving independent and precise control of temperature and pressure parameters, avoiding mutual interference between the two types of parameters. By inversely superimposing the temperature correction component, the temperature overshoot amplitude can be effectively converged; by positively placing the pressure correction component, the pressure attenuation gap can be successively filled, generating accurate temperature overshoot residuals and pressure attenuation residuals respectively. Relying on the dual residual synchronous verification mechanism, adjustments under compliant conditions can be terminated in a timely manner; when compliant conditions are not met, continuous iterative optimization is achieved by integrating historical correction content, ensuring that both temperature overshoot and pressure attenuation rate fall within the preset range simultaneously. This effectively improves the accuracy and stability of temperature and pressure control during the sealing process, ensuring consistent sealing quality.

[0144] Reference Figure 2 The diagram shown is a flowchart illustrating an intelligent programmable quantitative sealing method for water quality environmental monitoring according to an embodiment of the present invention. In this embodiment, the intelligent programmable quantitative sealing method for water quality environmental monitoring includes:

[0145] S01. Monitor the voltage decay sequence and heat sealing pressure decay sequence during this sealing process, and reconstruct the voltage decay sequence by exponential decay to obtain the relaxation characteristic constant of the voltage decay sequence.

[0146] S02. The relaxation characteristic constant and the heat sealing pressure decay sequence are jointly inverted to generate the volume compensation factor of the quantitative disk, and the injection volume of subsequent injection cycles is corrected in real time according to the volume compensation factor.

[0147] S03. The sealing trigger time of the current sealing process is determined by the volume compensation factor, and the real-time temperature and real-time pressure of the current sealing process are monitored in real time.

[0148] S04. Perform thermodynamic deviation decoupling on the real-time temperature and the real-time pressure to obtain the parameter correction amount for this sealing process;

[0149] S05. Based on the parameter correction amount, dynamically adjust the holding temperature and holding pressure of the current sealing process until the temperature overshoot and pressure decay rate fall within the preset tolerance range.

[0150] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0151] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent programmable quantitative sealing system for water quality environmental monitoring, characterized in that, The system includes a signal reconstruction module, a volume compensation module, a trigger monitoring module, a deviation decoupling module, and a closed-loop adjustment module, wherein: The signal reconstruction module is used to monitor the voltage decay sequence and heat sealing pressure decay sequence during the sealing process, and to perform exponential decay reconstruction on the voltage decay sequence to obtain the relaxation characteristic constant of the voltage decay sequence. The volume compensation module is used to jointly invert the relaxation characteristic constant and the heat sealing pressure decay sequence to generate the volume compensation factor of the quantitative disk, and to correct the injection volume of subsequent injection cycles in real time according to the volume compensation factor. The trigger monitoring module is used to determine the sealing trigger time of the current sealing process by the volume compensation factor, and to monitor the real-time temperature and real-time pressure of the current sealing process in real time. The deviation decoupling module is used to perform thermodynamic deviation decoupling on the real-time temperature and the real-time pressure to obtain the parameter correction amount for this sealing process. The closed-loop adjustment module is used to dynamically adjust the holding temperature and holding pressure of the current sealing process based on the parameter correction amount, until the temperature overshoot and pressure decay rate fall within the preset tolerance range.

2. The intelligent programmable quantitative sealing system for water quality environmental monitoring as described in claim 1, characterized in that, When the signal reconstruction module executes the monitoring of the voltage attenuation sequence and heat sealing pressure attenuation sequence during this sealing process, it is specifically used for: During the monitoring of this sealing process, transient deviation tracking was performed on the voltage time-varying records of the dynamic response data to obtain the voltage decay sequence of the dynamic response data; The pressure interface of the dynamic response data is subjected to creep response capture to obtain the pressure decay sequence of the dynamic response data.

3. The intelligent programmable quantitative sealing system for water quality environmental monitoring as described in claim 1, characterized in that, When the signal reconstruction module performs exponential decay reconstruction on the voltage decay sequence to obtain the relaxation characteristic constant of the voltage decay sequence, it is specifically used for: The voltage decay sequence is divided into equal-length window segments; The window segment is subjected to monotonic regression fitting, and the slope of the fitted curve is used as the attenuation driving force coefficient. The attenuation driving force coefficient in the middle of the sort is selected as the relaxation characteristic constant of the voltage attenuation sequence.

4. The intelligent programmable quantitative sealing system for water quality environmental monitoring as described in claim 1, characterized in that, When the volume compensation module performs a joint inversion of the relaxation characteristic constant and the heat-sealing pressure decay sequence to generate the volume compensation factor for the quantitative disk, it is specifically used for: Using the extreme value position as the registration basis, the heat sealing pressure decay sequence and the relaxation characteristic constant are dynamically normalized and aligned to obtain the synchronized time sequence of the relaxation characteristic constant; Within each pressure fluctuation cycle of the synchronized time series, the median value of the heat sealing pressure decay sequence is extracted. The median value is used as a weighting coefficient to perform piecewise weighted integration on the synchronized time series to obtain the cumulative flow resistance of the relaxation characteristic constant. The cumulative flow resistance of positive overshoot and negative loss is clamped to zero, and the cumulative flow resistance after bidirectional truncation is output as the volume compensation factor of the quantitative disk.

5. The intelligent programmable quantitative sealing system for water quality environmental monitoring as described in claim 1, characterized in that, When the volume compensation module performs real-time correction of the injection volume for subsequent injection cycles based on the volume compensation factor, it is specifically used for: Obtain the original volume setting for the current injection cycle, and embed the volume compensation factor as an additional bias term into the original volume setting to generate the pre-corrected volume of the original volume setting; Based on the sliding window residual between the pre-corrected volume and the historical volume, the smoothed volume of the pre-corrected volume is iteratively updated; The smoothing volume is superimposed and canceled out by the volume compensation factor to output the injection volume for subsequent injection cycles.

6. The intelligent programmable quantitative sealing system for water quality environmental monitoring as described in claim 5, characterized in that, When the volume compensation module executes the sliding window residual based on the pre-corrected volume and the historical volume to iteratively update the smoothed volume of the pre-corrected volume, it is specifically used for: The historical volumes are sorted by amplitude, and the largest and smallest amplitudes are removed. The historical volumes sorted in the middle are retained to form a reduced sequence. The arithmetic mean of the reduced sequence is used as the reference volume, and the instantaneous deviation residual of the pre-corrected volume relative to the reference volume is extracted. Based on the instantaneous deviation residual, the smoothed volume of the pre-corrected volume is calculated, wherein the formula for calculating the smoothed volume is: ； In the formula, For the smooth volume, The pre-corrected volume quantity, The preset convergence step size factor, For sign extraction operation, The instantaneous deviation residual, To perform the minimum value operation, For the absolute value operation, The preset scaling factor for the residual standard deviation. The total number of the historical volumes. This is the time-series index of the historical volume. This is the sequence number of the current iteration cycle. For the first The historical deviation residual of a historical volume This is the sequence number of the previous iteration cycle.

7. The intelligent programmable quantitative sealing system for water quality environmental monitoring as described in claim 1, characterized in that, When the trigger monitoring module executes the sealing trigger time determined by the volume compensation factor for the current sealing process, it is specifically used for: Read the sign and absolute value of the volume compensation factor, and scale the absolute value proportionally along the range of the relaxation characteristic constant to obtain the time offset step of the volume compensation factor; The initial zero-crossing point of the heat-sealing pressure decay sequence is detected, and the first zero-crossing point detected is marked as the timing start point; Based on the sign of the numerical value, the time offset step is superimposed and shifted with the starting point of the time sequence, and the shifted time is used as the sealing trigger time of the current sealing process.

8. The intelligent programmable quantitative sealing system for water quality environmental monitoring as described in claim 1, characterized in that, When the deviation decoupling module performs thermodynamic deviation decoupling on the real-time temperature and the real-time pressure to obtain the parameter correction amount for this sealing process, it is specifically used for: By performing a sliding window difference on the real-time temperature, the steady-state temperature component in the real-time temperature is removed, and the temperature drift rate of the real-time temperature is obtained. The real-time pressure is subjected to a first-order high-pass filter to eliminate the low-frequency creep component in the real-time pressure, thereby obtaining the pressure attenuation margin of the real-time pressure. The temperature drift rate and the pressure attenuation margin are cross-multiplied and accumulated. The sign of the temperature drift rate determines the accumulation direction, and the absolute value of the pressure attenuation margin determines the accumulation amplitude. The parameter correction amount for this sealing process is then output.

9. The intelligent programmable quantitative sealing system for water quality environmental monitoring as described in claim 1, characterized in that, The closed-loop adjustment module, based on the parameter correction, dynamically adjusts the holding temperature and pressure during the sealing process until the temperature overshoot and pressure decay rate fall within the preset tolerance range. Specifically, it is used for: The parameter correction amount is decomposed into a temperature correction component and a pressure correction component; Apply the inverse superposition of the temperature correction component to the holding temperature of the current cycle, iteratively reduce the temperature overshoot, and obtain the temperature overshoot residual of the current cycle; Apply a positive bias of the pressure correction component to the holding pressure of the current cycle, and successively compensate the pressure decay rate to obtain the pressure decay residual of the current cycle. The adjustment is terminated immediately when neither the temperature overshoot residual nor the pressure attenuation residual exceeds the preset tolerance range; otherwise, the correction amount of the current cycle is added to the historical correction amount and fed into the next cycle, and the iteration continues until both residuals fall within the preset tolerance range.

10. A smart, programmable, quantitative sealing method for water quality environmental monitoring, characterized in that, The method is used in the intelligent programmable quantitative sealing system for water quality environmental monitoring as described in claim 1. S01. Monitor the voltage decay sequence and heat sealing pressure decay sequence during this sealing process, and reconstruct the voltage decay sequence by exponential decay to obtain the relaxation characteristic constant of the voltage decay sequence. S02. The relaxation characteristic constant and the heat sealing pressure decay sequence are jointly inverted to generate the volume compensation factor of the quantitative disk, and the injection volume of subsequent injection cycles is corrected in real time according to the volume compensation factor. S03. The sealing trigger time of the current sealing process is determined by the volume compensation factor, and the real-time temperature and real-time pressure of the current sealing process are monitored in real time. S04. Perform thermodynamic deviation decoupling on the real-time temperature and the real-time pressure to obtain the parameter correction amount for this sealing process; S05. Based on the parameter correction amount, dynamically adjust the holding temperature and holding pressure of the current sealing process until the temperature overshoot and pressure decay rate fall within the preset tolerance range.

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