Method and system for real-time monitoring of nitrogen oxide gas concentration in bismuth target dissolution process

Abstract

The application provides a method and system for real-time monitoring of nitrogen oxide gas concentration in a bismuth target dissolving process, and relates to the technical field of data processing. The method comprises the following steps: selecting three concentration values in time sequence to form a time correlation group, superimposing the change values of each time position in time sequence, calculating the concentration change disturbance degree in unit time, obtaining the gas disturbance index, marking the time periods with different concentration change degrees, combining the concentration values belonging to the same time period as stage concentration data, recursively superimposing the concentration changes between adjacent time periods, calculating the stability degree of the concentration change trend, obtaining the stage response coefficient, dynamically adjusting the change amplitude of the stage concentration data, identifying the nitrogen oxide gas concentration change state in the bismuth target dissolving process, and obtaining the real-time concentration sequence. The application can capture the dynamic change of the nitrogen oxide gas concentration in the bismuth target dissolving process.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for real-time monitoring of nitrogen oxide gas concentration during the dissolution of a bismuth target. Background Technology

[0002] Currently, monitoring of nitrogen oxide gas concentration during bismuth target dissolution primarily relies on traditional online gas analysis techniques, such as optical absorption spectrometry, non-dispersive infrared spectroscopy, and electrochemical sensors. These techniques use sensors installed in the dissolution reaction zone to monitor the gas in real time, converting the detected gas concentration into electronic signals and transmitting them to a data processing system. This system can display the nitrogen oxide gas concentration in real time, facilitating the control and optimization of gas release during the dissolution process.

[0003] However, it may have shortcomings when dealing with environments with drastic changes in gas concentration or complex conditions. For example, when existing gas sensors are used for real-time concentration monitoring, the concentration of nitrogen oxide gas may change rapidly with different stages of the dissolution process during the chemical reaction of bismuth target dissolution. The sensor may not be able to accurately reflect the dynamic changes in concentration in a short period of time, and the gas sensor may not be able to adapt to the rapid fluctuations in nitrogen oxide concentration during the dissolution process in real time, resulting in lag in monitoring data. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for real-time monitoring of nitrogen oxide gas concentration during the dissolution of a bismuth target, in order to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A first aspect is a method for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution, the method comprising:

[0007] Acquire the concentration-time data of nitrogen oxide gas in the bismuth target dissolution reaction region;

[0008] Based on the concentration-time data, three consecutive concentration values ​​are selected in chronological order to form a time-related group, and labeled as the preceding concentration value, the middle concentration value, and the following concentration value, respectively, to obtain the time-related set;

[0009] Based on the time association set, the change values ​​between the preceding concentration value and the intermediate concentration value and between the intermediate concentration value and the subsequent concentration value are extracted within each time association group to obtain the change value sequence;

[0010] Based on the sequence of changes, the changes at each time point are superimposed in chronological order to calculate the degree of concentration disturbance per unit time, thus obtaining the gas disturbance index.

[0011] Based on the temporal position of the gas disturbance index, the time periods with different degrees of concentration change are marked in the concentration time data according to the order of change of the gas disturbance index, thus obtaining the change marking sequence;

[0012] Based on the time range marked by the change marker sequence, extract the corresponding concentration values ​​from the concentration time data, and combine the concentration values ​​belonging to the same time period into stage concentration data to obtain the stage concentration dataset;

[0013] Based on the phase concentration dataset, the concentration changes between adjacent time periods are recursively superimposed to calculate the stability of the concentration change trend and obtain the phase response coefficient.

[0014] Based on the stage response coefficient, the change range of stage concentration data is dynamically adjusted to identify the change state of nitrogen oxide gas concentration during the dissolution of bismuth target and obtain the real-time concentration sequence.

[0015] Furthermore, based on the sequence of changes, the changes at each time point are sequentially superimposed to calculate the degree of concentration disturbance per unit time, yielding the gas disturbance index, including:

[0016] By calculating the magnitude of the change in intermediate concentration value relative to preceding concentration value and subsequent concentration value relative to intermediate concentration value, the basic intensity of concentration change per unit time is determined, and the local change magnitude term is obtained.

[0017] By calculating the degree of coupling between two consecutive changes, the degree of coordinated change in concentration at adjacent time points is identified, and the change coupling term is obtained.

[0018] The local variation amplitude term and the variation coupling term are fused together to calculate the instantaneous concentration disturbance degree at a single time position, thus obtaining the single-point disturbance term;

[0019] The adjustment ratio of the influence of the current time position on the disturbance of the previous time position is calculated based on the single-point disturbance term, the degree of inheritance of the current time position on the historical disturbance state is determined, and the recursive adjustment term is obtained.

[0020] The gas disturbance index at the current time position is calculated by fusing the single-point disturbance term and the recursive adjustment term.

[0021] Furthermore, based on the temporal position of the gas disturbance index, time periods with different degrees of concentration change are marked in the concentration time data according to the order of change of the gas disturbance index, resulting in a change marker sequence, including:

[0022] Based on the gas disturbance index, calculate the change range of the gas disturbance index between adjacent time positions, identify the changing trend of the disturbance degree per unit time, and obtain disturbance change data;

[0023] Based on the disturbance change data, identify the time locations where the disturbance change transitions from an enhanced state to a weakened state and from a weakened state to an enhanced state, and obtain the change boundary data;

[0024] Based on the temporal position of the change boundary data in the concentration-time data, the concentration-time data between adjacent change boundaries are divided into multiple time periods to obtain a time period set;

[0025] Based on the time period set, the concentration time data within the same time period are assigned a unified segment identifier, and the segment identifiers are written into the corresponding time positions in chronological order to obtain the change marker sequence.

[0026] Furthermore, based on the gas disturbance index, the variation range of the gas disturbance index between adjacent time positions is calculated to identify the changing trend of the disturbance degree per unit time, thus obtaining disturbance change data, including:

[0027] Arbitrarily select two adjacent time positions for gas disturbance indices and calculate the difference between them at the corresponding time positions to obtain disturbance difference data;

[0028] Based on the disturbance difference data, the disturbance change amplitude of the gas disturbance index between adjacent time positions is determined, and a correspondence is established between the disturbance change amplitude and the corresponding time position to obtain the disturbance amplitude data;

[0029] Based on the disturbance difference data, the disturbance change direction of the gas disturbance index between adjacent time positions is determined, and the disturbance change direction is combined with the corresponding disturbance amplitude data to obtain the disturbance direction data;

[0030] Based on the disturbance direction data, the concentration time data at each time position is continuously recorded in chronological order to determine the trend of disturbance degree change per unit time and obtain disturbance change data.

[0031] Furthermore, based on the time period range marked by the change marker sequence, the corresponding concentration values ​​in the concentration time data are extracted, and the concentration values ​​belonging to the same time period are combined into stage concentration data to obtain the stage concentration dataset, including:

[0032] By identifying the range of continuous time positions in the change marker sequence that maintains consistent segment identifiers, the start and end time positions of each time period are determined, thus obtaining the time period boundary data;

[0033] Based on the time period boundary data, the time position range of each time period is located in the concentration time data, and the concentration value within each time period range is extracted to obtain the concentration data within the segment.

[0034] Based on the concentration data within a segment, the concentration values ​​belonging to the same time period are combined in chronological order to form a continuous arrangement structure, thus obtaining the stage concentration data;

[0035] The concentration data for each time period are collected in chronological order, and a corresponding time period identifier is written for each concentration data period to obtain the concentration dataset.

[0036] Furthermore, based on the phase concentration dataset, the concentration changes between adjacent time periods are recursively superimposed to calculate the stability of the concentration change trend, thus obtaining the phase response coefficient, including:

[0037] Based on the concentration values ​​of each time period in the phase concentration dataset, calculate the average value of all concentration values ​​within that time period, determine the unified reference benchmark for the concentration data of that time period, and obtain the intra-segment scale term;

[0038] Based on the concentration values ​​within each time period, calculate the change and transformation relationship between three adjacent concentration values, identify the degree of consistency of the morphology of the concentration change trajectory within that time period, and obtain the morphological item within the segment;

[0039] Based on the difference between the initial concentration value of the next time period and the final concentration value of the previous time period, the degree of continuity of concentration changes between adjacent time periods is calculated to obtain the inter-segment connection term.

[0040] Based on the concentration data of two adjacent time periods, the direction of change between the initial and final concentration values ​​of each time period is calculated to obtain the inter-segment direction term.

[0041] By fusing the intra-segment scale term, intra-segment morphology term, inter-segment connection term, and inter-segment direction term, and recursively updating the change relationship between adjacent time periods, the stage response coefficient is obtained.

[0042] Furthermore, based on the stage response coefficient, the variation range of the stage concentration data is dynamically adjusted to identify the change state of nitrogen oxide gas concentration during the bismuth target dissolution process, thereby obtaining a real-time concentration sequence, including:

[0043] Based on the phase concentration dataset, the phase response coefficients for each time period are written into the time position of the concentration value within that time period to obtain the corresponding response data.

[0044] Based on the corresponding response data, calculate the difference between each concentration value in each time period and the initial concentration value in that time period, determine the magnitude of change of each concentration value relative to the starting position of the time period, and obtain the concentration change data;

[0045] The magnitude of the change in each concentration value is adjusted proportionally based on the concentration change data, and the adjustment magnitude is superimposed with the initial concentration value for that time period to obtain the adjusted concentration data.

[0046] By arranging the regulated concentration data in chronological order and then sequentially splicing them at the connection points of adjacent time periods according to the temporal continuity, a real-time concentration sequence is obtained.

[0047] Secondly, a system for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution, the system comprising:

[0048] The data module is used to acquire the concentration and time data of nitrogen oxide gas in the bismuth target dissolution reaction region;

[0049] The association module is used to select three consecutive concentration values ​​in chronological order based on concentration-time data to form a time association group, and label them as the preceding concentration value, the middle concentration value, and the following concentration value, respectively, to obtain the time association set;

[0050] The change module is used to extract the change values ​​between the preceding concentration value and the intermediate concentration value and between the intermediate concentration value and the subsequent concentration value within each time association set, based on the time association set, to obtain the change value sequence;

[0051] The perturbation module is used to calculate the degree of concentration change per unit time by sequentially superimposing the change values ​​at each time position according to the time sequence, and obtaining the gas perturbation index.

[0052] The segmentation module is used to mark time periods with different degrees of concentration change in the concentration time data according to the time position of the gas disturbance index and the order of change of the gas disturbance index, so as to obtain the change mark sequence.

[0053] The phase module is used to extract the corresponding concentration values ​​from the concentration time data according to the time range marked by the change marker sequence, and combine the concentration values ​​belonging to the same time period into phase concentration data to obtain the phase concentration dataset.

[0054] The response module is used to recursively superimpose the concentration changes between adjacent time periods based on the phase concentration dataset, calculate the stability of the concentration change trend, and obtain the phase response coefficient.

[0055] The monitoring module is used to dynamically adjust the change range of the stage concentration data according to the stage response coefficient, identify the change state of nitrogen oxide gas concentration during the dissolution of bismuth target, and obtain the real-time concentration sequence.

[0056] The above-described solution of the present invention has at least the following beneficial effects:

[0057] This invention calculates the degree of concentration change disturbance per unit time to form a gas disturbance index, integrating instantaneous changes into a comprehensive disturbance index. This allows concentration change information to reflect the cumulative dynamics in a time series in numerical form. The disturbance index can characterize the amplitude and continuity of concentration changes at different time locations, providing a quantitative basis for subsequent time period division. It can distinguish between continuous fluctuations and sudden fluctuations, clarify the strength of the change amplitude and trend, and achieve a unified quantitative expression of concentration fluctuations. This provides basic data for gas analysis to continuously calculate and judge dynamic changes.

[0058] This invention analyzes the time series of gas disturbance indexes, marks time periods with different concentration changes according to the order of change, and divides continuous time series data into several structured segments. The concentration change state within each segment remains relatively consistent, transforming the original continuous data into a segmented data structure. This provides a clear time range for the extraction of stage concentration data, facilitating subsequent calculation of stage response coefficients, dynamic adjustment of concentration change amplitude, and formation of real-time concentration sequences. It transforms a single continuous sequence into a structured sequence that can be analyzed in segments, allowing the change patterns within each stage to be analyzed independently and ensuring that the data from different stages have clear boundaries during processing.

[0059] This invention extracts corresponding concentration values ​​based on the time range marked by the change marker sequence and combines them into stage concentration data. The original continuous sequence is abstracted into a hierarchical dataset. The concentration values ​​within each stage are arranged in chronological order, maintaining the continuity and temporal order of changes within each stage. This allows the concentration time series to be represented at different scales, facilitating the analysis of changes within stages and the relationships between stages in subsequent calculations. It provides a basis for hierarchical analysis, enabling continuous concentration data to not only reflect short-term changes but also to perform overall trend calculations at the stage level, thus satisfying the hierarchical modeling of concentration changes.

[0060] This invention obtains the stage response coefficient by calculating the stability of the concentration change trend, and mathematically describes the continuity, trend and change pattern of changes between adjacent time periods, realizing the systematic and quantitative processing of stage concentration changes. The stage response coefficient can clearly define the degree of influence of each stage on the concentration changes of adjacent stages, providing a quantitative basis for subsequent dynamic adjustment and real-time concentration sequence generation, forming a complete data processing path from changes within a stage to the analysis of continuity between stages, and realizing the quantitative description of nitrogen oxide concentration changes in both time and space during the dissolution process.

[0061] This invention dynamically adjusts the amplitude of changes in stage concentration data and generates a real-time concentration sequence based on the stage response coefficient. It integrates the quantitative changes within a stage and the continuity between stages into a continuous, time-sequential real-time concentration data sequence, which can simultaneously reflect instantaneous fluctuations and stage change patterns. This transforms the original discrete measurement data into a continuous and traceable dynamic concentration sequence during the calculation process, providing the detection system with directly applicable dynamic data. It realizes a complete processing chain from the original time series to the quantitative stage data, and then to the continuous real-time concentration sequence. This allows the data processing results to fully reflect the temporal and stage characteristics of gas concentration changes during the dissolution process, meeting the requirements of dynamic concentration monitoring. Attached Figure Description

[0062] Figure 1 This is a flowchart of a method for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution provided by an embodiment of the present invention. Detailed Implementation

[0063] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0064] like Figure 1 As shown, embodiments of the present invention propose a method for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution, the method comprising:

[0065] Acquire the concentration-time data of nitrogen oxide gas in the bismuth target dissolution reaction region;

[0066] Based on the concentration-time data, three consecutive concentration values ​​are selected in chronological order to form a time-related group, and labeled as the preceding concentration value, the middle concentration value, and the following concentration value, respectively, to obtain the time-related set;

[0067] Based on the time association set, the change values ​​between the preceding concentration value and the intermediate concentration value and between the intermediate concentration value and the subsequent concentration value are extracted within each time association group to obtain the change value sequence;

[0068] Based on the sequence of changes, the changes at each time point are superimposed in chronological order to calculate the degree of concentration disturbance per unit time, thus obtaining the gas disturbance index.

[0069] Based on the temporal position of the gas disturbance index, the time periods with different degrees of concentration change are marked in the concentration time data according to the order of change of the gas disturbance index, thus obtaining the change marking sequence;

[0070] Based on the time range marked by the change marker sequence, extract the corresponding concentration values ​​from the concentration time data, and combine the concentration values ​​belonging to the same time period into stage concentration data to obtain the stage concentration dataset;

[0071] Based on the phase concentration dataset, the concentration changes between adjacent time periods are recursively superimposed to calculate the stability of the concentration change trend and obtain the phase response coefficient.

[0072] Based on the stage response coefficient, the change range of stage concentration data is dynamically adjusted to identify the change state of nitrogen oxide gas concentration during the dissolution of bismuth target and obtain the real-time concentration sequence.

[0073] In this embodiment of the invention, the concentration-time data of nitrogen oxide gas in the bismuth target dissolution reaction region is acquired to achieve a digital description of the dynamic changes of the gas during the dissolution process, providing a basic data structure for subsequent data analysis. Based on the concentration-time data, three consecutive concentration values ​​are selected sequentially to form a time-related group, and labeled as the preceding concentration value, the intermediate concentration value, and the subsequent concentration value, respectively, to obtain a time-related set. This achieves a preliminary structured characterization of the concentration change trend, providing a clear time dependency for subsequent value extraction and perturbation calculation. Based on the time-related set, the preceding concentration value and its time dependence are extracted within each time-related group. The changes between intermediate concentration values ​​and between intermediate and subsequent concentration values ​​are used to obtain a sequence of changes, enabling the system to identify the magnitude and direction of concentration changes and providing input data for subsequent calculations. This also clearly distinguishes between instantaneous fluctuations and phased trends. Based on the sequence of changes, the changes at each time point are superimposed in chronological order to calculate the degree of concentration change disturbance per unit time, resulting in a gas disturbance index. This achieves dynamic quantification of concentration changes and integrates instantaneous changes into a comprehensive index, clearly reflecting the temporal distribution and magnitude of concentration fluctuations, providing a quantitative basis for subsequent time period division and phased concentration data extraction.

[0074] Based on the temporal location of the gas disturbance index, time periods with different degrees of concentration change are marked in the concentration time data according to the order of change of the gas disturbance index, resulting in a change marker sequence. This enables segmented and structured processing of continuous concentration time series, providing clear segment-level logical relationships for data processing and ensuring the continuity and accuracy of analysis between stages. Based on the time period range marked by the change marker sequence, the corresponding concentration values ​​in the concentration time data are extracted, and concentration values ​​belonging to the same time period are combined into stage concentration data, resulting in a stage concentration dataset. This achieves hierarchical representation of the data, enabling simultaneous quantitative analysis of concentration changes within and between stages, providing a data foundation for subsequent calculations. Based on the phased concentration dataset, the concentration changes between adjacent time periods are recursively superimposed to calculate the stability of the concentration change trend and obtain the phased response coefficient. This enables a quantitative description of the concentration change trend between phases, links the changes in different phases, provides a basis for dynamically adjusting the phased concentration data, and ensures that the real-time concentration sequence has complete historical and current status information. Based on the phased response coefficient, the change amplitude of the phased concentration data is dynamically adjusted to identify the nitrogen oxide gas concentration change state during the bismuth target dissolution process, obtain the real-time concentration sequence, realize the complete data processing chain for the dissolution process, ensure that both instantaneous and phased changes are reflected, and meet the requirements for continuous gas concentration monitoring.

[0075] Specifically, acquiring the concentration and time data of nitrogen oxide gas in the bismuth target dissolution reaction region includes:

[0076] A set of nitrogen oxide gas sensors is arranged in the bismuth target dissolution reaction apparatus. These sensors can be optical absorption sensors, non-dispersive infrared sensors, or electrochemical sensors. The sensors are installed at different heights and directions covering the dissolution reaction area to ensure comprehensive acquisition of the nitrogen oxide gas concentration distribution within the reaction area. When detecting gas concentration, the sensors convert the actual nitrogen oxide concentration signal into an electrical or digital signal, which is then transmitted in real time to the central data processing system via a data acquisition module. The data processing system records the concentration value once per second or millisecond according to a preset sampling period. Simultaneously, it performs noise processing on the raw acquired signal, including smoothing short-term fluctuations using a moving average method, removing outliers using median filtering, or eliminating high-frequency noise using an exponential weighted smoothing method. The system records the corresponding timestamp information for each sampling point and stores it as a continuous concentration time series data table. This table may include fields such as time, sensor number, original concentration value, and smoothed value.

[0077] Specifically, based on concentration-time data, three consecutive concentration values ​​are selected sequentially in chronological order to form a time-related group, which is then labeled as the preceding concentration value, the intermediate concentration value, and the following concentration value, resulting in a time-related set, which includes:

[0078] The system iterates through the concentration time series using a sliding window algorithm, with a fixed window length of three sampling points. Each time the window slides forward one sampling point, a new time-related set is generated. In each time-related set, the first sampling point is marked as the preceding concentration value, the second as the intermediate concentration value, and the third as the subsequent concentration value. The sampling time of each value is recorded to ensure consistency of time intervals. The system stores all generated time-related sets in a time-related set data structure, which can be stored in array, list, or table format, recording the preceding, intermediate, and subsequent concentration values ​​and their corresponding time information for each set. During operation, the system automatically checks for missing sampling points or outliers. For missing or outlier data, interpolation methods such as linear interpolation are used for correction to ensure the continuity and integrity of the time-related set. The system can also sort and index the time-related set as needed to quickly access concentration data for any time period, providing complete, continuous, and traceable data input for subsequent calculations of change values.

[0079] Specifically, based on the time correlation set, the changes between preceding and intermediate concentration values, and between intermediate and subsequent concentration values, are extracted within each time correlation group to obtain a change value sequence, which includes:

[0080] For each time-related group, the system first calculates the difference between the intermediate concentration value and the preceding concentration value to obtain the change between the preceding and intermediate values, and records the intermediate time point corresponding to this change. Then, it calculates the difference between the subsequent concentration value and the intermediate concentration value to obtain the change between the intermediate and subsequent values, and records the corresponding subsequent time point. The change values ​​can be obtained using a direct difference method or normalized according to the time interval, for example, by dividing the concentration difference by the time interval to obtain the concentration change rate per unit time. The system stores the two change values ​​of each time-related group sequentially into the change value sequence, while recording the corresponding time-related group number, so that the change values ​​can be associated with the original time series and the time-related set in the calculation of the disturbance index. To further ensure data continuity and stability, the system can smooth the change value sequence, for example, by using a moving average or weighted average method to eliminate local abnormal fluctuations, and calculate the cumulative change trend for each change value for subsequent analysis. This step transforms the original concentration measurements into continuous, quantitative change indicators that reflect the instantaneous magnitude and direction of concentration changes within each time period. This provides a sufficient data foundation for disturbance analysis and phase division, while ensuring that change information at all time points is preserved and traceable.

[0081] In a preferred embodiment of the present invention, based on the sequence of change values, the change values ​​at each time position are sequentially superimposed according to time order to calculate the degree of concentration change disturbance per unit time, thereby obtaining a gas disturbance index, including:

[0082] By calculating the magnitude of the change in intermediate concentration value relative to preceding concentration value and subsequent concentration value relative to intermediate concentration value, the basic intensity of concentration change per unit time is determined, and the local change magnitude term is obtained.

[0083] By calculating the degree of coupling between two consecutive changes, the degree of coordinated change in concentration at adjacent time points is identified, and the change coupling term is obtained.

[0084] The local variation amplitude term and the variation coupling term are fused together to calculate the instantaneous concentration disturbance degree at a single time position, thus obtaining the single-point disturbance term;

[0085] The adjustment ratio of the influence of the current time position on the disturbance of the previous time position is calculated based on the single-point disturbance term, the degree of inheritance of the current time position on the historical disturbance state is determined, and the recursive adjustment term is obtained.

[0086] The gas disturbance index at the current time position is calculated by fusing the single-point disturbance term and the recursive adjustment term.

[0087] In this embodiment of the invention, by calculating the change amplitude of the intermediate concentration value relative to the preceding concentration value and the subsequent concentration value relative to the intermediate concentration value, the basic intensity of the concentration change per unit time is determined, resulting in a local change amplitude term. This quantifies the instantaneous concentration change at each time point relative to adjacent time points, providing a basic quantitative indicator for the calculation of the disturbance index. By calculating the coupling degree between two consecutive change amplitudes, the degree of coordinated change in concentration changes between adjacent time positions is identified, resulting in a change coupling term. This provides a quantification of the synergy of changes at adjacent time positions, capturing continuous patterns and trend reversals in concentration changes, and providing time-dependent information for the dynamic calculation of the disturbance index. The local change amplitude term and the change coupling term are fused together to calculate the instantaneous concentration disturbance degree at a single time position, resulting in a single-point disturbance term. This term then combines the local change amplitude with the phase change amplitude. The neighbor-to-neighbor relationship is comprehensively reflected as a single quantitative index, realizing a comprehensive characterization of instantaneous concentration disturbances and providing a direct calculation basis for subsequent recursive adjustment and disturbance index generation. Based on the single-point disturbance term, the adjustment ratio of the current time position to the disturbance at the previous time position is calculated, the degree of inheritance of the current time position to the historical disturbance state is determined, and the recursive adjustment term is obtained, realizing the fusion of historical disturbance information and current disturbance quantity. This enables data processing to reflect the continuity and cumulative effect of disturbances in the time series, providing a quantitative basis for generating a continuous and dynamically responsive gas disturbance index. The single-point disturbance term and the recursive adjustment term are fused to calculate the gas disturbance index at the current time position. By comprehensively considering instantaneous disturbances and historical disturbance states, a dynamic quantitative characterization of nitrogen oxide concentration changes during dissolution is achieved, providing a sufficient and complete quantitative data basis for subsequent operations.

[0088] Specifically, the following steps are taken: First, by calculating the magnitude of the change in concentration relative to the preceding and subsequent concentration values ​​relative to the intermediate concentration value, the basic intensity of concentration change per unit time is determined, yielding a local magnitude change term. Second, by calculating the coupling degree between two consecutive magnitude changes, the degree of coordinated change in concentration between adjacent time positions is identified, yielding a change coupling term. Third, the local magnitude change term and the change coupling term are fused to calculate the instantaneous concentration disturbance at a single time position, yielding a single-point disturbance term. Fourth, based on the single-point disturbance term, the adjustment ratio of the current time position to the disturbance at the previous time position is calculated to determine the degree of inheritance of the historical disturbance state at the current time position, yielding a recursive adjustment term. Fifth, the single-point disturbance term and the recursive adjustment term are fused to calculate the gas disturbance index at the current time position, specifically including:

[0089] First, the data processing system iterates through each time-related group in the time-related set. For each time-related group, it extracts the preceding concentration value, the intermediate concentration value, and the subsequent concentration value. The system calculates the change between the preceding and intermediate concentration values ​​by subtracting the intermediate concentration value from the intermediate concentration value, and divides it by the corresponding sampling time interval to obtain the rate of change per unit time. Then, it calculates the change between the intermediate and subsequent concentration values ​​by subtracting the intermediate concentration value from the subsequent concentration value, and similarly divides it by the time interval to obtain the rate of change per unit time. In order to form a single local change amplitude term, the system can perform a weighted average or sum of squares operation on the changes between the preceding and intermediate concentrations and the changes between the intermediate and subsequent concentrations to obtain a value representing the basic intensity of the concentration change at the current time position. At the same time, to ensure data continuity, outliers or abrupt changes can be smoothed using median filtering or moving average, so that the local change amplitude term can accurately reflect the intensity of the concentration change at that time point relative to the neighboring time points.

[0090] The system performs correlation analysis or product operations on two adjacent change amplitudes to calculate a coupling coefficient, which characterizes the consistency of the direction and amplitude of concentration changes at continuous time points. If the change directions are the same, the coupling coefficient is positive, indicating a continuous trend in concentration change; if the directions are opposite, the coupling coefficient is negative, indicating fluctuations or reversals in concentration. The system associates and saves each coupling coefficient with its corresponding time point so that local amplitude terms and coupling information can be integrated in subsequent calculations. The system uses weighted or nonlinear functions to integrate the local change amplitude term and the change coupling term at the current time point into a single value to quantify the instantaneous disturbance intensity. For example, the local change amplitude term can be multiplied by a coefficient and summed with the coupling term multiplied by another coefficient to form a single-point disturbance term. The system records each single-point disturbance term with a corresponding timestamp to maintain the continuity of the time series. During data processing, the calculation results can be smoothed or normalized so that the single-point disturbance terms form a continuously comparable data sequence over time.

[0091] The system performs a weighted summation calculation of the single-point disturbance term at the current time point and the single-point disturbance term at the previous time point, sets a historical inheritance coefficient, and recursively generates a recursive adjustment term according to a formula: the current recursive adjustment term is the historical inheritance coefficient multiplied by the previous recursive adjustment term, plus 1, minus the historical inheritance coefficient, and then multiplied by the current single-point disturbance term. The system dynamically adjusts the value of the historical inheritance coefficient as needed to balance the impact of historical disturbances on the current concentration change. The recursive adjustment term is recorded in relation to the corresponding time point, providing historical dependency information for subsequent gas disturbance index calculations. The system performs weighted or function calculations on the single-point disturbance term and the recursive adjustment term at the current time point, such as using linear combination or square root operation, to obtain the final gas disturbance index value, and stores it along with the corresponding timestamp as a disturbance index time series. The system can perform smoothing processing or outlier detection on the disturbance index series to ensure numerical continuity and reliability. This gas disturbance index series can be used to mark boundary points of concentration changes, generate change marker sequences, and serve as input for constructing concentration datasets in subsequent stages.

[0092] In a preferred embodiment of the present invention, based on the time position of the gas disturbance index, time periods with different degrees of concentration change are marked in the concentration time data according to the order of change of the gas disturbance index, resulting in a change marking sequence, including:

[0093] Based on the gas disturbance index, calculate the change range of the gas disturbance index between adjacent time positions, identify the changing trend of the disturbance degree per unit time, and obtain disturbance change data;

[0094] Based on the disturbance change data, identify the time locations where the disturbance change transitions from an enhanced state to a weakened state and from a weakened state to an enhanced state, and obtain the change boundary data;

[0095] Based on the temporal position of the change boundary data in the concentration-time data, the concentration-time data between adjacent change boundaries are divided into multiple time periods to obtain a time period set;

[0096] Based on the time period set, the concentration time data within the same time period are assigned a unified segment identifier, and the segment identifiers are written into the corresponding time positions in chronological order to obtain the change marker sequence.

[0097] In this embodiment of the invention, based on the gas disturbance index, the variation amplitude of the gas disturbance index between adjacent time positions is calculated, the changing trend of the disturbance degree per unit time is identified, and disturbance change data is obtained. This provides direct input for subsequent identification of concentration change boundaries, enabling data processing to clearly reflect the characteristics of disturbance state changes at each time point during the dissolution process. Based on the disturbance change data, the time positions where the disturbance change transitions from an enhancing state to a weakening state and from a weakening state to an enhancing state are identified, obtaining change boundary data and clearly marking the reversal point of the disturbance trend. This enables data processing to distinguish different concentration change stages in the time series, providing a reliable boundary basis for subsequent generation of time period divisions. Based on the time position of the change boundary data in the concentration time data, adjacent... Concentration time data between change boundaries are divided into multiple time periods, resulting in a time period set. This achieves segmented structuring of continuous concentration data over time, enabling modular description of concentration changes over time. This provides a data structure foundation for the analysis and dynamic monitoring of different change stages during the dissolution process. Based on the time period set, concentration time data within the same time segment are assigned a unified segment identifier. These segment identifiers are then written into their corresponding time positions in chronological order, resulting in a change marker sequence. This achieves structured marking of the concentration time series, ensuring that each time point clearly corresponds to its corresponding concentration change stage. This guarantees that subsequent concentration data extraction and stage response analysis can be accurately processed according to time periods, providing a clear temporal structure and logical order for the data processing chain.

[0098] Specifically, based on the disturbance change data, the time points at which the disturbance changes transition from an enhancing state to a weakening state and from a weakening state to an enhancing state are identified, and the change boundary data is obtained, including:

[0099] The system first traverses the disturbance change data sequence chronologically, determining the direction of change in disturbance amplitude at each time point. Continuously increasing disturbances are marked as enhancing states, and continuously decreasing disturbances are marked as weakening states. When the system detects a disturbance change that transitions from a continuously enhancing state to a continuously weakening state, or vice versa, it records this time point as the change boundary time position, while simultaneously saving the corresponding disturbance amplitude and original concentration value information. To reduce false inflection points caused by instantaneous noise or sensor malfunctions, the system sets amplitude thresholds and duration conditions for the identification of change boundaries. Only when the disturbance change amplitude exceeds the preset threshold and the state persists for a certain period is it confirmed as a valid change boundary. The system stores the time positions of all valid change boundaries, along with their corresponding disturbance amplitudes and state information, as change boundary data, used to mark inflection points in concentration change trends.

[0100] Specifically, based on the temporal position of the change boundary data within the concentration-time data, the concentration-time data between adjacent change boundaries is divided into multiple time periods, resulting in a time period set, which includes:

[0101] The system reads the time positions of each pair of consecutive change boundaries from the change boundary data, extracts the original concentration time data within that time range, and combines them into a time period. Each time period contains all concentration sampling points located between the two change boundaries, and generates a unique identifier for each time period to distinguish different stages. The system sequentially arranges the concentration data within each time period, retaining its corresponding timestamp and sampling order information to ensure that subsequent stage analysis and recursive calculations can accurately reference the data within the time period. During the segmentation process, the system can record the length of the time period and the concentration fluctuation amplitude for subsequent feature extraction and trend analysis of the stage concentration data. It can also merge or smooth small time periods with insignificant fluctuations over short periods to maintain the stability and logical continuity of the time period segmentation. Finally, all time periods are arranged in chronological order to form a complete time period set, with each time period containing a corresponding concentration value sequence and time information, providing structured data input for subsequent processing.

[0102] In a preferred embodiment of the present invention, based on the gas disturbance index, the change amplitude of the gas disturbance index between adjacent time positions is calculated, the changing trend of the disturbance degree per unit time is identified, and disturbance change data is obtained, including:

[0103] Arbitrarily select two adjacent time positions for gas disturbance indices and calculate the difference between them at the corresponding time positions to obtain disturbance difference data;

[0104] Based on the disturbance difference data, the disturbance change amplitude of the gas disturbance index between adjacent time positions is determined, and a correspondence is established between the disturbance change amplitude and the corresponding time position to obtain the disturbance amplitude data;

[0105] Based on the disturbance difference data, the disturbance change direction of the gas disturbance index between adjacent time positions is determined, and the disturbance change direction is combined with the corresponding disturbance amplitude data to obtain the disturbance direction data;

[0106] Based on the disturbance direction data, the concentration time data at each time position is continuously recorded in chronological order to determine the trend of disturbance degree change per unit time and obtain disturbance change data.

[0107] In this embodiment of the invention, two adjacent time positions of gas disturbance index are arbitrarily selected, and the difference between them at the corresponding time positions is calculated to obtain disturbance difference data. This clearly identifies the amplitude of disturbance at each time point, providing accurate input for subsequent calculation of disturbance change amplitude and direction. Based on the disturbance difference data, the disturbance change amplitude of the gas disturbance index between adjacent time positions is determined, and a correspondence is established between the disturbance change amplitude and the corresponding time position to obtain disturbance amplitude data. This provides quantified change intensity information for each time point, distinguishing significant changes from minor fluctuations, and providing a reliable quantitative basis for subsequent calculations. Based on the disturbance difference data, the disturbance change direction of the gas disturbance index between adjacent time positions is determined, and the disturbance change direction is combined with the corresponding disturbance amplitude data to obtain disturbance direction data. This clearly reflects the trend of disturbance change at each time point, providing a logical basis for identifying change boundaries and dividing time periods. Based on the disturbance direction data, the concentration time data at each time position is continuously recorded in chronological order to determine the trend of disturbance degree change per unit time, obtaining disturbance change data. This ensures a complete, continuous, and traceable data foundation when dividing change boundaries and generating phase concentration data.

[0108] Specifically, based on the disturbance difference data, the disturbance change amplitude of the gas disturbance index between adjacent time positions is determined, and a correspondence is established between the disturbance change amplitude and the corresponding time position to obtain the disturbance amplitude data, which specifically includes:

[0109] The system first extracts the perturbation index difference between every two consecutive time points from the perturbation difference data sequence, and takes the absolute value of each difference to obtain the perturbation change amplitude per unit time. For each calculated amplitude value, the system associates it with the corresponding current time position and records it to form a structured data entry, while retaining the original perturbation index value and timestamp information corresponding to that amplitude. During data processing, the system can smooth the amplitude data, for example, by using moving average or local weighting methods to eliminate local extrema caused by sensor noise or sudden anomalies, so that the perturbation amplitude data can truly reflect the intensity and variation characteristics of gas perturbation over a continuous time period.

[0110] Specifically, based on the disturbance difference data, the direction of change of the gas disturbance index between adjacent time positions is determined, and the direction of change of disturbance is combined with the corresponding disturbance amplitude data to obtain the disturbance direction data, which specifically includes:

[0111] The system determines the direction of change for each disturbance difference. If the disturbance index at the current time point is higher than that at the previous time point, it is marked as an increasing state; if it is lower than that at the previous time point, it is marked as a decreasing state. Simultaneously, each direction marker is associated with the disturbance amplitude data generated in the previous step, forming a complete combination of direction and amplitude data entry. The system can correct for direction reversals within extremely short timeframes through continuity checks to avoid misjudgments caused by noise.

[0112] Specifically, based on the disturbance direction data, the concentration time data at each time position is continuously recorded in chronological order to determine the trend of disturbance degree change per unit time, thus obtaining disturbance change data, including:

[0113] The system correlates perturbation direction data with raw concentration time data in chronological order. At each time point, it records the corresponding concentration value, perturbation amplitude, and perturbation direction, arranging them in continuous chronological order to form a continuous recording sequence. The system can perform statistical analysis on the continuous recording sequence, such as calculating the duration of continuous enhancement or weakening states and the cumulative change amplitude, and append these statistical characteristics to the data entries at each time point to identify change boundaries, divide time periods, and generate staged concentration datasets in subsequent steps.

[0114] In a preferred embodiment of the present invention, based on the time period range marked by the change marker sequence, the corresponding concentration values ​​in the concentration time data are extracted, and the concentration values ​​belonging to the same time period are combined into stage concentration data to obtain a stage concentration dataset, including:

[0115] By identifying the range of continuous time positions in the change marker sequence that maintains consistent segment identifiers, the start and end time positions of each time period are determined, thus obtaining the time period boundary data;

[0116] Based on the time period boundary data, the time position range of each time period is located in the concentration time data, and the concentration value within each time period range is extracted to obtain the concentration data within the segment.

[0117] Based on the concentration data within a segment, the concentration values ​​belonging to the same time period are combined in chronological order to form a continuous arrangement structure, thus obtaining the stage concentration data;

[0118] The concentration data for each time period are collected in chronological order, and a corresponding time period identifier is written for each concentration data period to obtain the concentration dataset.

[0119] In this embodiment of the invention, by identifying the continuous time range where the segment identifiers in the change marker sequence remain consistent, the start and end time positions of each time period are determined, obtaining time period boundary data. Continuous concentration time data is divided into meaningful stages according to perturbation characteristics, providing a clear time range for subsequent stage concentration data extraction and recursive analysis. Based on the time period boundary data, the time position range of each time period is located in the concentration time data, and the concentration values ​​within each time period range are extracted to obtain intra-segment concentration data. This achieves structured storage of concentration information at continuous time points, allowing data within each time period to be analyzed and processed independently, providing reliable input for subsequent steps. Based on the intra-segment concentration data, concentration values ​​belonging to the same time period range are combined into a continuous arrangement structure according to time order to obtain stage concentration data, achieving a hierarchical description of different concentration change stages during the dissolution process, providing a structured data foundation for subsequent steps. The stage concentration data of each time period are collected in chronological order, and corresponding time period identifiers are written for each stage concentration data to obtain a stage concentration dataset. This achieves structured storage of the entire concentration time series in the stage dimension, providing unified, continuous, and traceable input data for subsequent operations, and establishing a complete data framework for dynamic concentration analysis of the dissolution process.

[0120] Specifically, by identifying a continuous time range where the segment identifiers in the change marker sequence remain consistent, the start and end time positions of each time period are determined, thus obtaining the time period boundary data, which includes:

[0121] The system first reads the segment identifier for each time point from the change marker sequence and traverses it chronologically, identifying consecutive time points with the same segment identifier as the same continuous time range. During the traversal, the system records the start time position of each continuous range (the timestamp of the first time point of the continuous segment) and the end time position (the timestamp of the last time point of the continuous segment). When the system detects a change in the segment identifier, it records the current continuous range as a complete time period boundary and begins identifying the next continuous segment. To ensure the accuracy of the division, the system can merge or adjust excessively short continuous time ranges and remove minor false segments caused by noise or abnormal fluctuations, thus forming effective and stable time period boundary data. Each time period boundary data entry includes the corresponding segment identifier, start time, end time, and index information of the concentration value within the segment.

[0122] Specifically, based on the concentration data within a segment, concentration values ​​belonging to the same time period are grouped into a continuous arrangement according to time sequence to obtain the stage concentration data, which includes:

[0123] Based on the time-boundary data generated in the previous step, the system extracts all concentration values ​​within each time period range from the original concentration time series and arranges them in chronological order to form a continuous sequence, ensuring that the concentration values ​​at each time point maintain the same order as the sampling time within the sequence. The system adds a time period identifier, start and end time information to each stage of concentration data unit and performs an integrity check on the concentration values ​​within the segment. For missing or abnormal data, the system corrects them through interpolation or smoothing, making the data within the segment continuous and analyzable. Simultaneously, the system can record statistical information about the concentration within the segment, such as the maximum, minimum, average, and fluctuation range, providing a reference for subsequent stage response coefficient calculations and dynamic adjustments.

[0124] In a preferred embodiment of the present invention, based on a phased concentration dataset, the concentration changes between adjacent time periods are recursively superimposed to calculate the stability of the concentration change trend and obtain a phased response coefficient, including:

[0125] Based on the concentration values ​​of each time period in the phase concentration dataset, calculate the average value of all concentration values ​​within that time period, determine the unified reference benchmark for the concentration data of that time period, and obtain the intra-segment scale term;

[0126] Based on the concentration values ​​within each time period, calculate the change and transformation relationship between three adjacent concentration values, identify the degree of consistency of the morphology of the concentration change trajectory within that time period, and obtain the morphological item within the segment;

[0127] Based on the difference between the initial concentration value of the next time period and the final concentration value of the previous time period, the degree of continuity of concentration changes between adjacent time periods is calculated to obtain the inter-segment connection term.

[0128] Based on the concentration data of two adjacent time periods, the direction of change between the initial and final concentration values ​​of each time period is calculated to obtain the inter-segment direction term.

[0129] By fusing the intra-segment scale term, intra-segment morphology term, inter-segment connection term, and inter-segment direction term, and recursively updating the change relationship between adjacent time periods, the stage response coefficient is obtained.

[0130] In this embodiment of the invention, based on the concentration values ​​of each time period in the stage concentration dataset, the average value of all concentration values ​​within that time period is calculated to determine a unified reference benchmark for the concentration data of that time period, thus obtaining an intra-segment scale term. This provides a unified concentration benchmark for each stage, ensuring that concentration changes within and between stages are clearly reflected in the quantitative calculation. Based on the concentration values ​​within each time period, the transformation relationship between three adjacent concentration values ​​is calculated to identify the consistency of the concentration change trajectory within that time period, thus obtaining an intra-segment morphology term. This achieves a quantitative description of the intra-segment concentration change trajectory, clearly identifying the trend pattern of intra-segment concentration, and providing a reference for the continuity and consistency of intra-segment changes for subsequent stage response coefficient calculations. Based on the difference between the starting concentration value of the next time period and the ending concentration value of the previous time period, the adjacent time... The continuity of concentration changes between segments is analyzed to obtain inter-segment connection terms, which quantifies the continuity and connection characteristics of concentration between stages and clarifies the concentration connection relationship between each stage. Based on the concentration data of two adjacent time periods, the direction of change between the initial and final concentration values ​​of each time period is calculated to obtain inter-segment direction terms, which clearly quantifies the change trend between each stage and provides directional information for subsequent calculations, enabling the system to continuously analyze and quantify the concentration trend between stages. By fusing intra-segment scale terms, intra-segment morphology terms, inter-segment connection terms, and inter-segment direction terms, the change relationship between adjacent time periods is recursively updated to obtain stage response coefficients, which achieve a comprehensive quantitative description of the concentration change trend of each stage and provide a quantitative basis for subsequent dynamic adjustment of stage concentration amplitude and generation of real-time concentration sequences.

[0131] Specifically, based on the concentration values ​​of each time period in the stage concentration dataset, the average value of all concentration values ​​within that time period is calculated to determine a unified reference benchmark for the concentration data of that time period, thus obtaining the intra-segment scale term; based on the concentration values ​​within each time period, the transformation relationship between three adjacent concentration values ​​is calculated to identify the consistency of the morphology of the concentration change trajectory within that time period, thus obtaining the intra-segment morphology term; based on the difference between the starting concentration value of the next time period and the ending concentration value of the previous time period, the continuity of concentration changes between adjacent time periods is calculated, thus obtaining the inter-segment connection term; based on the stage concentration data of two adjacent time periods, the direction of change between the starting and ending concentration values ​​of each time period is calculated, thus obtaining the inter-segment direction term; the intra-segment scale term, intra-segment morphology term, inter-segment connection term, and inter-segment direction term are fused to recursively update the change relationship between adjacent time periods, thus obtaining the stage response coefficient, specifically including:

[0132] The system first extracts all continuous concentration values ​​within each time period from the phased concentration dataset and arranges these values ​​in chronological order. Then, the system averages the concentration values ​​for that time period to obtain a numerical value representing a unified reference benchmark for the overall concentration level of that period. The system can simultaneously record the maximum concentration, minimum concentration, and concentration fluctuation range within that time period for subsequent analysis of intra-segment variation amplitudes. The intra-segment scaling term provides a consistent concentration reference for each time period, ensuring that concentration data from each phase can be quantified under the same standard when comparing different time periods or performing recursive analysis. Within each time period, the system analyzes three consecutive concentration values ​​in chronological order to determine the relationship between the directions of concentration change, such as continuous increase, continuous decrease, or reversal. The system counts the frequency and duration of continuous change patterns within that time period and saves the statistical results as an intra-segment morphological term. The system can smooth or correct extreme or abnormal reversals to ensure that the intra-segment morphological term accurately reflects the trend and pattern of concentration changes.

[0133] The system analyzes each pair of consecutive time periods, extracting the initial concentration value of the subsequent time period and the final concentration value of the previous time period, calculating the difference between them, and recording the result as an inter-segment transition term. Based on the characteristics of the actual dissolution process, the system can apply a threshold to determine the magnitude of the difference, identifying whether the continuous change is smooth or has significant abrupt changes, and storing the difference information in association with the corresponding time period identifier. The inter-segment transition term quantifies the continuity of concentration changes between stages, ensuring logical connections between data processing segments. The system compares the initial and final concentration values ​​within each time period to determine whether the concentration shows an upward trend, a downward trend, or a flat state within that time period, recording the result as an inter-segment direction term. Simultaneously, the inter-segment direction information is associated with and saved with the time period identifier and start and end times, providing directional reference for subsequent recursive analysis. The inter-segment direction term reflects the overall concentration change trend within each time period, providing directional information for generating stage response coefficients.

[0134] The system combines the aforementioned four types of indicators according to preset rules or weights to generate a stage response coefficient for each time period, and records the coefficient along with the corresponding time period identifier and start and end times. During processing, the system can normalize each indicator to ensure the uniformity and comparability of the fused calculation results. The stage response coefficient can comprehensively reflect the concentration level within a stage, the pattern of change within a stage, the continuity between stages, and the direction of change. It provides a complete quantitative basis for subsequent dynamic adjustment of stage concentration and generation of real-time concentration sequences, realizing a complete data processing closed loop from stage concentration data to stage response coefficients. This ensures that the concentration change trend of each time period is fully characterized in quantitative analysis, providing reliable input for subsequent monitoring.

[0135] In a preferred embodiment of the present invention, the variation range of the stage concentration data is dynamically adjusted according to the stage response coefficient to identify the change state of nitrogen oxide gas concentration during the dissolution of the bismuth target, thereby obtaining a real-time concentration sequence, including:

[0136] Based on the phase concentration dataset, the phase response coefficients for each time period are written into the time position of the concentration value within that time period to obtain the corresponding response data.

[0137] Based on the corresponding response data, calculate the difference between each concentration value in each time period and the initial concentration value in that time period, determine the magnitude of change of each concentration value relative to the starting position of the time period, and obtain the concentration change data;

[0138] The magnitude of the change in each concentration value is adjusted proportionally based on the concentration change data, and the adjustment magnitude is superimposed with the initial concentration value for that time period to obtain the adjusted concentration data.

[0139] By arranging the regulated concentration data in chronological order and then sequentially splicing them at the connection points of adjacent time periods according to the temporal continuity, a real-time concentration sequence is obtained.

[0140] In this embodiment of the invention, based on the phased concentration dataset, the phased response coefficients for each time period are written into the time position of the concentration value within that time period to obtain response-corresponding data. A mapping relationship between the concentration value and its dynamic response characteristics is established, providing complete input for subsequent dynamic adjustment of the concentration amplitude and generation of real-time concentration sequences. Based on the response-corresponding data, the difference between each concentration value within each time period and the initial concentration value of that time period is calculated to determine the change amplitude of each concentration value relative to the starting position of the time period, obtaining concentration change data. This ensures that the concentration change at each time point reflects both the actual measurement trend and the dynamic response between phases, providing operable quantitative data for generating continuous real-time concentration sequences. The adjustment of the concentration values ​​based on the concentration change data is proportional, and the adjustment is superimposed on the initial concentration value of the time period to obtain the regulated concentration data. This enables dynamic adjustment of the concentration value within the segment, providing basic data for generating continuous, time-series real-time concentrations, allowing the data to simultaneously reflect instantaneous changes and stage response characteristics. By arranging the regulated concentration data in chronological order and sequentially splicing adjacent time periods according to their temporal continuity, a real-time concentration sequence is obtained. This sequence reflects the dynamic changes in nitrogen oxide gas concentration during the bismuth target dissolution process, enabling continuous quantitative description of concentration changes and providing a reliable data foundation for real-time monitoring of the dissolution process.

[0141] Specifically, based on the response data, the difference between each concentration value within each time period and the initial concentration value of that time period is calculated to determine the magnitude of change of each concentration value relative to the starting position of the time period, thus obtaining concentration change data, including:

[0142] The system first extracts the stage response coefficients and concentration value sequences within each time period from the response data generated in the previous step. Then, for each time point within each time period, it calculates the difference between the concentration value at that time point and the initial concentration value of that time period, recording the difference as the concentration change amplitude at that time point. During processing, the system can interpolate or smooth abnormal or missing data to ensure that the concentration change amplitude at each time point within a segment is continuous and analyzable, while maintaining the time sequence and corresponding stage identifiers. The concentration change data provides a quantitative description of the concentration at each time point relative to the starting position of the stage, making the trend of data change within the segment clear and traceable, providing basic quantitative information for subsequent dynamic adjustment.

[0143] Specifically, the adjustment process involves proportionally adjusting the magnitude of changes in each concentration value based on the concentration change data, and then overlaying the adjusted magnitude with the initial concentration value for that time period to obtain the adjusted concentration data. This includes:

[0144] The system iterates through the concentration change data within each time period, multiplying the concentration change amplitude at each time point by the corresponding stage response coefficient or other adjustment factor for that time period to reflect the dynamic response characteristics of concentration changes within the period. Then, the adjusted amplitude is superimposed with the initial concentration value for that time period to generate the adjusted concentration value for each time point. The system can smooth or normalize the adjustment results within a period to ensure the continuity and consistency of the data over time, while retaining the timestamp and stage identifier information for each time point. The adjusted concentration data combines intra-period concentration changes with stage response characteristics, ensuring that concentration changes not only reflect the original measured values ​​but also demonstrate the dynamic response effects between stages.

[0145] Specifically, by arranging the adjusted concentration data in chronological order and sequentially splicing them at the connection points of adjacent time periods according to temporal continuity, a real-time concentration sequence is obtained, including:

[0146] The system sequentially splices the regulated concentration data within each time period. At the junctions of adjacent time periods, the system checks temporal continuity to ensure that the end time of one time period corresponds to the start time of the next. Necessary smoothing is performed at the transitions between segments to eliminate potential abrupt changes or discontinuities. After splicing, the system generates a complete real-time concentration sequence. Each time point includes the regulated concentration value, timestamp, and corresponding stage information. The system also performs consistency and integrity checks on the entire sequence to ensure no missing or duplicate time points. The real-time concentration sequence fully reflects the dynamic changes in nitrogen oxide gas concentration during the bismuth target dissolution process, providing a reliable basis for dissolution process control and analysis.

[0147] Embodiments of the present invention also provide a system for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution, the system comprising:

[0148] The data module is used to acquire the concentration and time data of nitrogen oxide gas in the bismuth target dissolution reaction region;

[0149] The association module is used to select three consecutive concentration values ​​in chronological order based on concentration-time data to form a time association group, and label them as the preceding concentration value, the middle concentration value, and the following concentration value, respectively, to obtain the time association set;

[0150] The change module is used to extract the change values ​​between the preceding concentration value and the intermediate concentration value and between the intermediate concentration value and the subsequent concentration value within each time association set, based on the time association set, to obtain the change value sequence;

[0151] The perturbation module is used to calculate the degree of concentration change per unit time by sequentially superimposing the change values ​​at each time position according to the time sequence, and obtaining the gas perturbation index.

[0152] The segmentation module is used to mark time periods with different degrees of concentration change in the concentration time data according to the time position of the gas disturbance index and the order of change of the gas disturbance index, so as to obtain the change mark sequence.

[0153] The phase module is used to extract the corresponding concentration values ​​from the concentration time data according to the time range marked by the change marker sequence, and combine the concentration values ​​belonging to the same time period into phase concentration data to obtain the phase concentration dataset.

[0154] The response module is used to recursively superimpose the concentration changes between adjacent time periods based on the phase concentration dataset, calculate the stability of the concentration change trend, and obtain the phase response coefficient.

[0155] The monitoring module is used to dynamically adjust the change range of the stage concentration data according to the stage response coefficient, identify the change state of nitrogen oxide gas concentration during the dissolution of bismuth target, and obtain the real-time concentration sequence.

[0156] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0157] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0158] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0159] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution, characterized in that, The method includes: Acquire the concentration-time data of nitrogen oxide gas in the bismuth target dissolution reaction region; Based on the concentration-time data, three consecutive concentration values ​​are selected in chronological order to form a time-related group, and labeled as the preceding concentration value, the middle concentration value, and the following concentration value, respectively, to obtain the time-related set; Based on the time association set, the change values ​​between the preceding concentration value and the intermediate concentration value and between the intermediate concentration value and the subsequent concentration value are extracted within each time association group to obtain the change value sequence; Based on the sequence of changes, the changes at each time point are sequentially superimposed to calculate the degree of concentration disturbance per unit time, resulting in a gas disturbance index, including: By calculating the magnitude of the change in intermediate concentration value relative to preceding concentration value and subsequent concentration value relative to intermediate concentration value, the basic intensity of concentration change per unit time is determined, and the local change magnitude term is obtained. By calculating the degree of coupling between two consecutive changes, the degree of coordinated change in concentration at adjacent time points is identified, and the change coupling term is obtained. The local variation amplitude term and the variation coupling term are fused together to calculate the instantaneous concentration disturbance degree at a single time position, thus obtaining the single-point disturbance term; The adjustment ratio of the influence of the current time position on the disturbance of the previous time position is calculated based on the single-point disturbance term, the degree of inheritance of the current time position on the historical disturbance state is determined, and the recursive adjustment term is obtained. The single-point disturbance term and the recursive adjustment term are fused to calculate the gas disturbance index at the current time position. Based on the time position of the gas disturbance index, the time periods with different concentration changes are marked in the concentration time data according to the order of change of the gas disturbance index, thus obtaining the change marking sequence. Based on the time range marked by the change marker sequence, extract the corresponding concentration values ​​from the concentration time data, and combine the concentration values ​​belonging to the same time period into stage concentration data to obtain the stage concentration dataset; Based on the phase concentration dataset, the concentration changes between adjacent time periods are recursively superimposed to calculate the stability of the concentration change trend and obtain the phase response coefficient. Based on the stage response coefficient, the change range of stage concentration data is dynamically adjusted to identify the change state of nitrogen oxide gas concentration during the dissolution of bismuth target and obtain the real-time concentration sequence.

2. The method for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution according to claim 1, characterized in that, Based on the temporal location of the gas disturbance index, time periods with different degrees of concentration change are marked in the concentration time data according to the order of change of the gas disturbance index, resulting in a change marker sequence, including: Based on the gas disturbance index, calculate the change range of the gas disturbance index between adjacent time positions, identify the changing trend of the disturbance degree per unit time, and obtain disturbance change data; Based on the disturbance change data, identify the time locations where the disturbance change transitions from an enhanced state to a weakened state and from a weakened state to an enhanced state, and obtain the change boundary data; Based on the temporal position of the change boundary data in the concentration-time data, the concentration-time data between adjacent change boundaries are divided into multiple time periods to obtain a time period set; Based on the time period set, the concentration time data within the same time period are assigned a unified segment identifier, and the segment identifiers are written into the corresponding time positions in chronological order to obtain the change marker sequence.

3. The method for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution according to claim 2, characterized in that, Based on the gas disturbance index, the variation range of the gas disturbance index between adjacent time positions is calculated to identify the changing trend of the disturbance degree per unit time, and the disturbance change data is obtained, including: Arbitrarily select two adjacent time positions for gas disturbance indices and calculate the difference between them at the corresponding time positions to obtain disturbance difference data; Based on the disturbance difference data, the disturbance change amplitude of the gas disturbance index between adjacent time positions is determined, and a correspondence is established between the disturbance change amplitude and the corresponding time position to obtain the disturbance amplitude data; Based on the disturbance difference data, the disturbance change direction of the gas disturbance index between adjacent time positions is determined, and the disturbance change direction is combined with the corresponding disturbance amplitude data to obtain the disturbance direction data; Based on the disturbance direction data, the concentration time data at each time position is continuously recorded in chronological order to determine the trend of disturbance degree change per unit time and obtain disturbance change data.

4. The method for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution according to claim 3, characterized in that, Based on the time range marked by the change marker sequence, the corresponding concentration values ​​in the concentration time data are extracted, and the concentration values ​​belonging to the same time period are combined into stage concentration data to obtain the stage concentration dataset, including: By identifying the range of continuous time positions in the change marker sequence that maintains consistent segment identifiers, the start and end time positions of each time period are determined, thus obtaining the time period boundary data; Based on the time period boundary data, the time position range of each time period is located in the concentration time data, and the concentration value within each time period range is extracted to obtain the concentration data within the segment. Based on the concentration data within a segment, the concentration values ​​belonging to the same time period are combined in chronological order to form a continuous arrangement structure, thus obtaining the stage concentration data; The concentration data for each time period are collected in chronological order, and a corresponding time period identifier is written for each concentration data period to obtain the concentration dataset.

5. The method for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution according to claim 4, characterized in that, Based on the phased concentration dataset, the concentration changes between adjacent time periods are recursively superimposed to calculate the stability of the concentration change trend, thus obtaining the phased response coefficient, including: Based on the concentration values ​​of each time period in the phase concentration dataset, calculate the average value of all concentration values ​​within that time period, determine the unified reference benchmark for the concentration data of that time period, and obtain the intra-segment scale term; Based on the concentration values ​​within each time period, calculate the change and transformation relationship between three adjacent concentration values, identify the degree of consistency of the morphology of the concentration change trajectory within that time period, and obtain the morphological item within the segment; Based on the difference between the initial concentration value of the next time period and the final concentration value of the previous time period, the degree of continuity of concentration changes between adjacent time periods is calculated to obtain the inter-segment connection term. Based on the concentration data of two adjacent time periods, the direction of change between the initial and final concentration values ​​of each time period is calculated to obtain the inter-segment direction term. By fusing the intra-segment scale term, intra-segment morphology term, inter-segment connection term, and inter-segment direction term, and recursively updating the change relationship between adjacent time periods, the stage response coefficient is obtained.

6. The method for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution according to claim 5, characterized in that, Based on the stage response coefficient, the amplitude of changes in stage concentration data is dynamically adjusted to identify the state of nitrogen oxide gas concentration changes during bismuth target dissolution, resulting in a real-time concentration sequence, including: Based on the phase concentration dataset, the phase response coefficients for each time period are written into the time position of the concentration value within that time period to obtain the corresponding response data. Based on the corresponding response data, calculate the difference between each concentration value in each time period and the initial concentration value in that time period, determine the magnitude of change of each concentration value relative to the starting position of the time period, and obtain the concentration change data; The magnitude of the change in each concentration value is adjusted proportionally based on the concentration change data, and the adjustment magnitude is superimposed with the initial concentration value for that time period to obtain the adjusted concentration data. By arranging the regulated concentration data in chronological order and then sequentially splicing them at the connection points of adjacent time periods according to the temporal continuity, a real-time concentration sequence is obtained.

7. A system for real-time monitoring of nitrogen oxide gas concentration during bismuth target dissolution, characterized in that, The system is used to perform the method as described in any one of claims 1 to 6, the system comprising: The data module is used to acquire the concentration and time data of nitrogen oxide gas in the bismuth target dissolution reaction region; The association module is used to select three consecutive concentration values ​​in chronological order based on concentration-time data to form a time association group, and label them as the preceding concentration value, the middle concentration value, and the following concentration value, respectively, to obtain the time association set; The change module is used to extract the change values ​​between the preceding concentration value and the intermediate concentration value and between the intermediate concentration value and the subsequent concentration value within each time association set, based on the time association set, to obtain the change value sequence; The perturbation module is used to calculate the degree of concentration change per unit time by sequentially superimposing the change values ​​at each time position according to the time sequence, and obtaining the gas perturbation index. The segmentation module is used to mark time periods with different degrees of concentration change in the concentration time data according to the time position of the gas disturbance index and the order of change of the gas disturbance index, so as to obtain the change mark sequence. The phase module is used to extract the corresponding concentration values ​​from the concentration time data according to the time range marked by the change marker sequence, and combine the concentration values ​​belonging to the same time period into phase concentration data to obtain the phase concentration dataset. The response module is used to recursively superimpose the concentration changes between adjacent time periods based on the phase concentration dataset, calculate the stability of the concentration change trend, and obtain the phase response coefficient. The monitoring module is used to dynamically adjust the change range of the stage concentration data according to the stage response coefficient, identify the change state of nitrogen oxide gas concentration during the dissolution of bismuth target, and obtain the real-time concentration sequence.

8. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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