Timing logic exception handling method and system for full automation at-211 separation

Abstract

The application discloses a timing logic abnormality processing method and system for full-process automatic At-211 separation, the method comprising: collecting real-time running data of multiple associated processes in the At-211 separation process, and calculating the pre-processed real-time running data to generate a multi-process synchronization timing matrix representing the running correlation degree of each process; through collecting the real-time running data of the multiple associated processes and generating the multi-process synchronization timing matrix, the running correlation degree between each process can be accurately reflected, the collaborative control degree between processes is evaluated through calculating the process control degree, and the overall running state is jointly evaluated in combination with the deviation control vector, so that the process combination with the risk of timing disorder can be identified in advance and an abnormal control identifier is generated, timing disorder caused by the destruction of the collaborative relationship of the associated processes such as negative pressure, conveying and airflow due to independent abnormal treatment is effectively avoided, and the collaboration and accuracy of the abnormal treatment are improved in the At-211 separation.

Description

Technical Field

[0001] This invention relates to the field of timing control technology, and more specifically to a timing logic exception handling method and system for fully automated At-211 separation. Background Technology

[0002] Currently, the fully automated At-211 separation system typically employs an open-loop control method based on a fixed time sequence when performing time-sequence logic control. The control program drives each actuator sequentially to complete the separation process according to preset time parameters. In anomaly handling, it often relies on data fed back from sensors, such as temperature exceeding limits or pressure abnormalities, to shut down the system.

[0003] However, the above-mentioned abnormal control methods still have the following defects: In collaborative operations involving multiple related processes, such as when liquids require negative pressure and airflow to be transported completely, if a pump malfunction causes insufficient negative pressure and then triggers a transport abnormality, these two related abnormalities will be identified and handled independently. This disrupts the original collaborative relationship, leading to disordered liquid transport sequence and affecting the effectiveness of abnormal handling. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for handling timing logic exceptions for fully automated At-211 separation, thus solving the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0006] A timing logic exception handling method for fully automated At-211 separation includes:

[0007] Step S1: Collect real-time running data of multiple related processes during the At-211 separation process, and calculate the pre-processed real-time running data to generate a multi-process synchronization time series matrix representing the degree of correlation between the running processes.

[0008] Step S2: Calculate the degree of correlation between each pair of processes in the multi-process synchronization timing matrix, and generate the process control degree representing the degree of collaborative control between the two processes;

[0009] Step S3: Based on the process control system, jointly evaluate the operating status of each process at the current moment to obtain the deviation control vector of the overall impact caused by the deviation of each process status.

[0010] Step S4: Based on the deviation control vector, identify the process combinations with time sequence disorder risk and generate an anomaly control identifier indicating that there is a time sequence disorder risk between the processes.

[0011] Furthermore, calculations are performed on the preprocessed real-time operational data to generate a multi-process synchronization time series matrix representing the degree of correlation between the operations of each process, including:

[0012] Collaborative analysis is performed on the preprocessed real-time operational data to generate dynamic feature vectors that reflect the instantaneous changing trends of each process.

[0013] Based on the dynamic feature vectors of each process, the consistency of control deviation direction and the difference in response time lag between the dynamic feature vectors of different processes are calculated, and a process coordination matrix representing the operational coordination between processes is generated.

[0014] Furthermore, calculations are performed on the preprocessed real-time operational data to generate a multi-process synchronization time-series matrix representing the degree of correlation between the operations of each process. This also includes:

[0015] A time-series recursive analysis is performed on the process coordination matrix to generate a dynamic coordination weight matrix representing the evolution trend;

[0016] Based on the real-time operation data and dynamic collaborative weight matrix of each process at the current moment, the correlation status of each process is analyzed, and a multi-process synchronization time sequence matrix representing the degree of correlation of each process is generated.

[0017] Furthermore, the correlation degree between each pair of processes in the multi-process synchronization timing matrix is ​​calculated to generate a process control degree representing the degree of collaborative control between the two processes, including:

[0018] The synchronization time matrix of multiple processes is decomposed to generate the collaborative control participation degree of each process;

[0019] Based on the multi-process synchronization timing matrix, the phase deviation of the control loop of each process is calculated to obtain the phase control deviation factor representing the coordination of control actions between processes;

[0020] A control analysis is performed on the phase control deviation factor to generate control constraint weights that indicate the need for control intervention.

[0021] Furthermore, the correlation between each pair of processes in the multi-process synchronization timing matrix is ​​calculated to generate a process control degree representing the degree of collaborative control between the two processes, which also includes:

[0022] The collaborative control participation, phase control deviation factor and control constraint weight are fused, and a collaborative control value representing the degree of chaos in collaborative control between each pair of processes is calculated.

[0023] The collaborative control values ​​are analyzed to generate a process control degree that represents the degree of collaborative control between two processes.

[0024] Furthermore, based on the process control system, the operating status of each process at the current moment is jointly evaluated to obtain the deviation control vector of the overall impact caused by the deviation of each process status, including:

[0025] Based on the process control degree, calculate the influence weight and propagation path of each control loop on other loops, and generate control loop weights that represent the interdependence of inter-process control actions.

[0026] Analyze the real-time operating data of each process to generate a control deviation that represents the quality control of each process.

[0027] Furthermore, based on the process control system, the operating status of each process at the current moment is jointly evaluated to obtain the deviation control vector of the overall impact caused by the deviation of each process status, which also includes:

[0028] Based on the control loop weights and control deviation, the transmission response of the deviation between each loop is calculated to obtain the loop disturbance response degree, which reflects the impact of disturbance on each process under the current control action.

[0029] By analyzing the loop disturbance response, the deviation control vector of the overall impact caused by the state deviation of each process is obtained.

[0030] Furthermore, based on the deviation control vector, process combinations with temporal disorder risks are identified, and abnormal control identifiers indicating the existence of temporal disorder risks among processes are generated, including:

[0031] Based on the deviation control vector, the control deviation of adjacent processes is analyzed, and the process control deviation gradient, which reflects the trend of deviation change between adjacent control loops, is generated.

[0032] Furthermore, based on the deviation control vector, process combinations with timing disorder risks are identified, and abnormal control indicators representing the existence of timing disorder risks among processes are generated. This also includes:

[0033] Based on the process control deviation gradient, the cumulative effect and attenuation characteristics of deviation propagation along the process chain are analyzed, and a control propagation coefficient representing the deviation propagation trend is generated.

[0034] The control propagation coefficient is calculated to generate anomaly control indicators.

[0035] Furthermore, the timing logic exception handling system for fully automated At-211 separation, applied in the processing method described above, includes:

[0036] The correlation analysis unit is used to collect real-time operation data of multiple related processes during the At-211 separation process, and to calculate the pre-processed real-time operation data to generate a multi-process synchronization time series matrix representing the degree of correlation between the operation of each process.

[0037] The collaborative control unit is used to calculate the degree of correlation between each two processes in the multi-process synchronization timing matrix and generate a process control degree that represents the degree of collaborative control between the two processes.

[0038] The deviation control unit is used to jointly evaluate the operating status of each process at the current moment based on the process control degree, and obtain the deviation control vector of the overall impact caused by the deviation of each process status.

[0039] An anomaly control unit is used to identify process combinations with a risk of timing disorder based on the deviation control vector, and generate anomaly control flags indicating that there is a risk of timing disorder between the processes.

[0040] In summary, the present invention has the following main beneficial effects:

[0041] By collecting real-time operational data from multiple related processes during the At-211 separation process, collaborative analysis and time-series recursive analysis are performed on the preprocessed data to generate a multi-process synchronous time-series matrix. This matrix accurately reflects the degree of operational correlation and dynamic collaborative relationship between each process. By decomposing and performing phase analysis on the multi-process synchronous time-series matrix, the process control degree is calculated, which reflects the degree of collaborative control between each pair of processes. This enables a holistic evaluation of related processes rather than isolated judgments. Based on this, a joint evaluation is performed using control loop weights and control deviations to obtain a deviation control vector. This vector accurately reflects the impact of each process's state deviation on the overall process and the disturbance propagation path. Furthermore, through analysis of process control deviation gradients and control propagation coefficients, process combinations with potential time-series disorder risks are identified, and abnormal control indicators are generated. This prevents time-series disorder caused by the disruption of the original collaborative relationship due to independent abnormal handling of strongly related processes such as negative pressure, conveying, and airflow. This solution can identify time-series logic anomalies during the fully automated operation of the At-211 separation process, improve the response speed of anomaly handling, and ensure the continuous and stable operation of the separation process. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the steps of the timing logic exception handling method for fully automated At-211 separation according to the present invention.

[0043] Figure 2 This is a schematic diagram of the timing logic exception handling method for fully automated At-211 separation according to the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] refer to Figure 1 and Figure 2 A timing logic exception handling method for fully automated At-211 separation, including:

[0046] Step S1: Collect real-time running data of multiple related processes during the At-211 separation process, and calculate the pre-processed real-time running data to generate a multi-process synchronization time series matrix representing the degree of correlation between the running processes.

[0047] Real-time operational data includes medium temperature, cavity pressure, fluid flow rate, and tank level for each process.

[0048] Step S2: Calculate the degree of correlation between each pair of processes in the multi-process synchronization timing matrix, and generate the process control degree representing the degree of collaborative control between the two processes;

[0049] Step S3: Based on the process control system, jointly evaluate the operating status of each process at the current moment to obtain the deviation control vector of the overall impact caused by the deviation of each process status.

[0050] Step S4: Based on the deviation control vector, identify the process combinations with time sequence disorder risk and generate an anomaly control identifier indicating that there is a time sequence disorder risk between the processes.

[0051] In one embodiment, the preprocessed real-time running data is calculated to generate a multi-process synchronization time series matrix representing the degree of correlation between the operations of each process, including:

[0052] Collaborative analysis is performed on the preprocessed real-time operating data to generate dynamic feature vectors reflecting the instantaneous change trends of each process. Specifically, for the real-time operating data of a single process, the four parameters of medium temperature, cavity pressure, fluid flow rate, and tank level at the current moment and the two adjacent sampling moments are calculated for their respective rates of change. After normalizing the rates of change to the 0-1 interval, they are summed to obtain the single-point trend value of the process at the current moment. The single-point trend values ​​of all processes are arranged in order of process number to form a dynamic feature vector reflecting the instantaneous change trends of each process.

[0053] Based on the dynamic feature vectors of each process, the consistency of control deviation direction and the difference in response time lag between the dynamic feature vectors of different processes are calculated to generate a process coordination matrix representing the operational coordination between processes. Specifically, this includes: selecting any two processes, namely process A and process B; for the five consecutive dynamic feature vector sequences of the two processes before the current time, comparing the direction of increase or decrease of the values ​​of the dynamic feature vectors of the two processes at each sampling point; if the values ​​of the two processes both increase or decrease, the sampling point is determined to be in the same direction and is recorded as a positive match; otherwise, it is recorded as a direction divergence; counting the number of positive matches in these five sampling points, dividing the number by 5, and normalizing the calculation result to the 0-1 interval, the direction consistency coefficient of the two processes in this time period can be obtained.

[0054] In the dynamic feature vector sequence of five consecutive sampling points, the time points when the dynamic feature vector values ​​of the two processes reach their maximum values ​​are marked respectively. The maximum value is the peak value. The absolute value of the interval between the peak values ​​of the two processes is calculated. At the same time, the difference in the number of sampling points between the starting point of the numerical change of process A and the starting point of the numerical change of process B is identified. The absolute value is added to the difference in the number of sampling points to obtain the response time lag difference between the two processes. For example, if the single-point trend value of process A changes from 0.5 to 0.6, the sampling point at 0.5 is the starting point of the change.

[0055] Divide the directional consistency coefficient by the sum of the response time lag difference and 1 to obtain the operational coordination coefficient between the two processes. Calculate the operational coordination coefficients of all processes in pairs in turn. Fill all the operational coordination coefficients into the matrix in the order of process number, with rows representing the baseline process, columns representing the comparison process, and diagonal elements preset to 1, to form a process coordination matrix that represents the operational coordination between each process.

[0056] In one embodiment, the calculation of the preprocessed real-time running data to generate a multi-process synchronization time series matrix representing the degree of correlation between the operations of each process further includes:

[0057] A time-series recursive analysis is performed on the process coordination matrix to generate a dynamic coordination weight matrix representing the evolution trend. Specifically, this includes: for the process coordination matrix of the current time and the previous six consecutive sampling times, these seven process coordination matrices are used to form an analysis sequence; for the running coordination coefficients at the same position in the process coordination matrix, the running coordination coefficients at the seven sampling times are extracted to form the evolution sequence at that position.

[0058] For each evolution sequence, calculate its standard deviation, and simultaneously calculate the difference sequence between adjacent time points of each evolution sequence. Use 1.5 times the standard deviation as the amplitude threshold; take half the length of the evolution sequence and round down to obtain the length threshold.

[0059] The maximum length of consecutive identical signs in the difference sequence is calculated, where consecutive identical signs are either positive or negative. For the consecutive identical sign segment corresponding to this maximum length, the absolute value of its cumulative change is calculated, which is the absolute value of the algebraic sum of all differences within the segment. If the maximum length of consecutive identical signs is greater than or equal to a length threshold, and the corresponding absolute value of cumulative change is greater than or equal to an amplitude threshold, then the trend direction is determined based on the sign of the differences in that segment: if all differences in the consecutive identical sign segment are positive, it is a unidirectional strengthening trend; if all differences in the consecutive identical sign segment are negative, it is a unidirectional weakening trend; if the number of alternating positive and negative values ​​in the difference sequence exceeds two-thirds of the difference sequence length, it is determined to be a high-frequency oscillation trend; if the difference between the maximum and minimum values ​​of the evolution sequence is less than 2 times the standard deviation, and the standard deviation of the evolution sequence is less than 0.1, it is determined to be a steady-state trend.

[0060] Among them, the unidirectional strengthening trend is assigned a base weight of 0.9, the unidirectional weakening trend is assigned a base weight of 0.3, the high-frequency oscillation trend is assigned a base weight of 0.1, and the steady-state trend is assigned a base weight of 0.6. The unidirectional strengthening trend indicates that the inter-process synergy is continuously improving and contributes significantly to the overall process stability. It should be assigned a high weight of 0.9 to strengthen its impact. The unidirectional weakening trend mainly reflects the continuous deterioration of the inter-process synergy. It should be assigned a lower weight of 0.3 to warn of risks. The high-frequency oscillation trend indicates that the synergy relationship fluctuates violently and is difficult to predict. Therefore, it is assigned the lowest weight of 0.1 to reduce its interference. The steady-state trend indicates that the synergy relationship is stable. It is assigned a medium weight of 0.6 to maintain normal attention.

[0061] Multiply the average of the three most recent values ​​in the evolution sequence by the base weight to obtain the evolution weight at that position. Fill the evolution weights of all positions into the corresponding positions of the original matrix, and fix the diagonal elements to 1 to generate a dynamic collaborative weight matrix that represents the evolution trend.

[0062] Based on the real-time operating data and dynamic collaborative weight matrix of each process at the current moment, the correlation status of each process is analyzed to generate a multi-process synchronous time-series matrix representing the degree of correlation between the operations of each process. Specifically, this includes: normalizing the medium temperature, cavity pressure, fluid flow rate, and tank level of each process at the current moment to the 0-1 range. Among them, the medium temperature directly affects the distillation efficiency and nuclide migration rate, which is the core factor determining the separation purity, so it is assigned a weight of 0.3. The fluid flow rate controls the material transmission speed and reaction contact time, which is directly related to the timeliness of process connection, so it is assigned a weight of 0.3. The cavity pressure, as an auxiliary adjustment parameter, mainly affects the gas-liquid phase balance and has relatively low sensitivity to time-series coordination, so it is assigned a weight of 0.2. The tank level reflects the capacity status and is a lagging monitoring indicator, which has the least impact on the instantaneous trend, so it is assigned a weight of 0.2. The four normalized parameters are multiplied by their corresponding weights and then summed to obtain the instantaneous state factor of the process.

[0063] Calculate the absolute value of the difference between the instantaneous state factors of processes A and B, and subtract this difference from 1 to obtain the instantaneous matching degree. The instantaneous matching degree reflects the degree of consistency between the operating states of the two processes at the current moment.

[0064] For the dynamic feature vectors of the two processes at five consecutive sampling points before the current time, calculate the square of the difference between the two dynamic feature vectors at each sampling point. The square root of the arithmetic mean of these five squares is used as the trend deviation factor. The trend deviation factor reflects the degree of deviation between the two processes on the recent running trajectory. The smaller the value, the closer the trend is.

[0065] Then, the instant matching degree is multiplied by each element in the dynamic collaborative weight matrix, and then divided by the trend deviation factor plus 1 to obtain the correlation degree between the two processes. The correlation degrees of all processes are filled into the matrix in the order of process number, with the row number representing the baseline process, the column number representing the comparison process, and the diagonal element fixed at 1, thus generating a multi-process synchronization time sequence matrix that represents the degree of correlation of each process.

[0066] By performing collaborative analysis on preprocessed real-time operating data to generate dynamic feature vectors, calculating the consistency of control deviation direction and response time lag differences between processes to construct a process collaboration matrix, and then forming a dynamic collaboration weight matrix through time-series recursive analysis, and combining process instantaneous state factors, immediate matching degree and trend deviation factors to generate a multi-process synchronous time-series matrix, it can accurately reflect the degree of operational correlation and collaborative evolution trend between related processes, identify changes in collaboration and potential anomalies between related processes, avoid anomaly handling from disrupting the collaborative relationship between processes, effectively ensure the time-series stability and smooth connection of the At-211 separation process, and reduce the adverse effects of time-series disorder on separation efficiency and purity.

[0067] In one embodiment, the correlation degree between every two processes in the multi-process synchronization timing matrix is ​​calculated to generate a process control degree representing the degree of collaborative control between the two processes, including:

[0068] The multi-process synchronization time sequence matrix is ​​decomposed to generate the collaborative control participation degree of each process. Specifically, in the multi-process synchronization time sequence matrix, for each process, all correlation degrees in the row except itself are sorted, and the arithmetic mean of the three correlation degrees with the largest values ​​is calculated as the strong correlation mean of the process. At the same time, the arithmetic mean of all correlation degrees in the row is calculated as the overall correlation mean of the process. The difference between the strong correlation mean and the overall correlation mean is used as the correlation concentration degree of the process.

[0069] Collect the instantaneous state factors of the process at ten consecutive sampling points before the current moment to form a state evolution sequence. Calculate the standard deviation of the sequence and then calculate the sum of the absolute values ​​of the rate of change of the four operating parameters of the process at the current moment. Multiply the standard deviation by the sum and take the square root to obtain the state fluctuation contribution of the process. The state fluctuation contribution reflects the activity level of the process's own operating state and its potential impact on the overall coordinated control.

[0070] Multiply the correlation concentration by the state fluctuation contribution, then multiply by the instantaneous state factor of the process itself at the current moment, and normalize the calculation result to the 0-1 range to generate the collaborative control participation degree of each process. The closer the collaborative control participation degree is to 1, the more core the process occupies in the overall collaborative control, and the greater the impact of its operating state changes on the global time sequence logic. The closer the collaborative control participation degree is to zero, the more peripheral the process is in the control position.

[0071] Based on the multi-process synchronization timing matrix, the phase deviation of each process control loop is calculated to obtain the phase control deviation factor representing the coordination of control actions between processes. Specifically, this includes: arranging the instantaneous state factors of each process at fifteen consecutive sampling points before the current time to form a state change sequence of each process control loop; for each process state change sequence, marking the inflection point where the instantaneous state factor changes from a continuous decrease to a continuous increase (this point is the local minimum point), and the inflection point where the instantaneous state factor changes from a continuous increase to a continuous decrease (this point is the local maximum point); and using the time sequence corresponding to all inflection points as the response phase marker point of the process control loop; if there is no inflection point within fifteen consecutive sampling points, the median point of the sequence is taken as the response phase marker point.

[0072] For the control loops of two processes A and B, the time sequence of their respective response phase markers is extracted. On the time axis of fifteen sampling points, the minimum absolute value of the interval between each response phase marker of process A and each response phase marker of process B is found. The arithmetic mean of all minimum absolute values ​​is calculated and the arithmetic mean is normalized to the 0-1 interval to obtain the control loop phase deviation between the two processes.

[0073] Find the correlation between processes A and B from the multi-process synchronization timing matrix. Multiply the control loop phase deviation by 1 and subtract the difference in correlation to obtain the weighted phase deviation. Sum the weighted phase deviations of all process pairs and divide by the total number of process pairs. Normalize the calculation result to the 0-1 interval to obtain the overall average weighted phase deviation. The average weighted phase deviation is the phase control deviation factor that represents the coordination of control actions between processes. The lower the value of the phase control deviation factor, the better the phase synchronization of the control actions of each process.

[0074] The phase control deviation factor is analyzed to generate control constraint weights that indicate the need for control intervention. Specifically, this includes: collecting the phase control deviation factor of twelve consecutive sampling points before the current time, constructing a phase drift sequence, calculating the cumulative offset of the phase drift sequence, extracting the difference between adjacent times in the sequence and accumulating it to obtain the cumulative phase drift. The cumulative phase drift reflects the degree of continuous deviation of the phase deviation of the control action between processes.

[0075] The eigenvalue decomposition of the multi-process synchronization time matrix is ​​performed, the absolute value of each eigenvalue is calculated, and the maximum value among these absolute values ​​is found, which is the spectral radius at the current time. The rate of change of this spectral radius compared with the spectral radius at the previous time is calculated, that is, the spectral radius at the current time is subtracted from the spectral radius at the previous time and then divided by the spectral radius at the previous time to obtain the rate of change. This rate of change characterizes the dynamic tightness change of the process association structure.

[0076] If the cumulative phase drift is greater than 0.3 and the rate of change of the spectral radius is positive and >0.1, it indicates that the phase deviation continues to expand and the process-related structure tends to be tense. In this case, the baseline constraint weight is set to 0.85. If the cumulative phase drift is less than 0.1 and the rate of change of the spectral radius is negative and the absolute value is >0.15, it indicates that the phase deviation converges and the related structure tends to be loose. In this case, the baseline constraint weight is set to 0.25. In other cases, the baseline constraint weight is set to 0.55. The phase control deviation factor at the current moment is added to the baseline constraint weight, and the calculation result is normalized to the 0-1 interval to obtain the control constraint weight that indicates the need for control intervention.

[0077] In one embodiment, calculating the correlation between every two processes in the multi-process synchronization timing matrix and generating a process control degree representing the degree of collaborative control between the two processes further includes:

[0078] The collaborative control participation, phase control deviation factor, and control constraint weights are fused together, and a collaborative control value representing the degree of chaos in collaborative control between each pair of processes is calculated. Specifically, the collaborative control participation of all processes is summed to obtain the total participation. The phase control deviation factor is multiplied by the collaborative control participation of each process and then divided by the total participation. The calculation result is normalized to the 0-1 interval to obtain the individual phase deviation index of the process. The individual phase deviation index represents the contribution of the process to the overall phase deviation. The higher the participation of the process, the greater its impact on the overall phase deviation.

[0079] For any two processes forming a process pair, calculate the absolute value of the difference between their individual phase deviation indices as the initial phase incoordination degree of the process pair. The larger the initial phase incoordination degree, the more significant the difference in the phase deviation contribution of the two processes, and the more unstable the basis of collaborative control.

[0080] Multiply the initial phase inconsistency by 1 and sum it with the control constraint weights, and normalize the calculation result to the 0-1 range to obtain the collaborative control value representing the degree of chaos in the collaborative control between each pair of processes. The closer the collaborative control value is to 1, the more chaotic the collaborative control state of the pair of processes is. The closer the collaborative control value is to 0, the more orderly the collaborative control of the two processes is.

[0081] The collaborative control values ​​are analyzed to generate a process control degree representing the degree of collaborative control between two processes. Specifically, for each pair of processes, the collaborative control values ​​of twenty consecutive sampling points before the current time are collected to form the entropy time series window of the process pair. The collaborative control values ​​within the entropy time series window are divided into three levels: below 0.3 is marked as low level, between 0.3 and 0.7 is marked as medium level, and above 0.7 is marked as high level. Thus, the entropy time series is transformed into a sequence composed of low, medium, and high levels. For the frequency of occurrence of each adjacent symbol pair, its frequency is divided by the total number of adjacent level pairs to obtain the probability of the symbol pair. Then, each probability is multiplied by the logarithm of the probability to the base 2, the negative value is taken, and the sum is obtained to obtain the collaborative control binary value of the process pair.

[0082] The initial control degree is obtained by multiplying the current collaborative control value of the process pair with the collaborative control binary value and taking the square root. If the initial control degree is greater than 0.8, it indicates that the collaborative control is extremely chaotic and disordered, and the process control degree is set to 0.1. If the initial control degree is less than 0.2, it indicates that the collaborative control is highly ordered and stable, and the process control degree is set to 0.9. In other cases, the initial control degree is converted into a process control degree between 0.2 and 0.8 through linear mapping. The mapping rule is that for every 0.1 increase in the initial control degree, the process control degree decreases by 0.1 accordingly. Finally, the process control degree representing the degree of collaborative control between the two processes is obtained, and the process control degree is between 0 and 1.

[0083] By calculating the degree of correlation between processes in the multi-process synchronization timing matrix to generate process control degree, the collaborative control participation degree can be obtained from the correlation concentration and state fluctuation contribution degree, and the core control process can be identified. At the same time, the coordination of control between processes is reflected by the phase control deviation factor. The control constraint weight is generated by combining the phase drift accumulation and the spectral radius change rate. Then, the degree of collaborative control disorder is accurately reflected by the collaborative control value and the collaborative control binary. This effectively overcomes the defects of traditional fixed-sequence open-loop control, which can only handle single anomalies independently and destroy the collaborative relationship between processes. It realizes the global assessment of the collaborative state of multiple processes in the At-211 separation system, identifies the risk of timing disorder in advance, and ensures the synchronization and collaborative stability of the control actions of related processes.

[0084] In one embodiment, based on the process control degree, the operating status of each process at the current moment is jointly evaluated to obtain a deviation control vector that determines the overall impact of deviations in the status of each process, including:

[0085] Based on the process control degree, the influence weight and propagation path of each control loop on other loops are calculated to generate control loop weights that represent the interdependence of control actions between processes. Specifically, this includes filling the process control degree of all process pairs into a matrix according to the process number order, with rows representing source process control loops, columns representing target process control loops, and diagonal elements fixed at 1, to form a process control degree matrix. Each element in the process control degree matrix represents the degree of collaborative control between the corresponding two process control loops.

[0086] For each process control loop, sum all off-diagonal elements in the corresponding row of the process control degree matrix to obtain the total external influence of the process control loop. Divide the total external influence by the total number of processes to obtain the average external influence degree of the process control loop. At the same time, sum all off-diagonal elements in the corresponding column of the process control loop to obtain the total received influence of other loops on the loop. Divide this by the total number of processes to obtain the average received influence degree of the loop. Multiply the average external influence degree by the average received influence degree and take the square root to obtain the control source strength of the process control loop. The control source strength reflects the overall activity level of the process control loop in the control action network.

[0087] For any control loop between processes A and B, if the process control degree of A and B is greater than 0.5, it is determined that there is a direct control path from A to B with a path length of 1 and the path weight is the process control degree.

[0088] If no direct control path exists, then check if there is at least one intermediate process control loop C such that the process control degree of A to C is greater than 0.5 and the process control degree of C to B is greater than 0.5. If there are two or more intermediate loops C that meet the conditions, then select the path with the largest product of the process control degree of A to C and the process control degree of C to B as the indirect propagation path from A to B, with a path length of 2 and a path weight equal to the product value. If there is only one intermediate loop C that meets the conditions, then use this unique path as the indirect propagation path from A to B, with a path length of 2 and a path weight equal to the product of the process control degree of A to C and the process control degree of C to B. If there are no intermediate loops C that meet the conditions, then it is determined that there is no effective propagation path between A and B, and the path weight is set to 0.

[0089] For the control loops of processes A and B, if a propagation path exists, the path weight is multiplied by the process control degree of A and B, and then divided by the path length to obtain the influence weight of A on B. This influence weight is the control loop weight representing the interdependence of process A on process B.

[0090] The real-time operation data of each process is analyzed to generate a control deviation degree representing the control quality of each process. Specifically, for a single process, the measured values ​​of medium temperature, cavity pressure, fluid flow rate, and tank liquid level at the current moment are collected, and the historical measured values ​​of three consecutive sampling points before the current moment are collected. For each parameter, the absolute value of the difference between the measured value at the current moment and the mean value of the measured values ​​of the previous three sampling points of the parameter is calculated, and then divided by the range of the maximum and minimum values ​​of the previous three sampling points of the parameter to obtain the instantaneous deviation coefficient of the parameter.

[0091] For each process, collect four operating parameters from fifteen consecutive sampling points before the current time, calculate the range of each parameter in that time period, i.e. the difference between the maximum and minimum values, and divide the range of each parameter by the sum of the ranges of the four parameters to obtain the fluctuation sensitivity weight of each parameter in that process.

[0092] Multiply the instantaneous deviation coefficients of the four parameters at the current moment by the corresponding fluctuation sensitivity weights, and then sum the four products to obtain the comprehensive deviation of the process at the current moment.

[0093] The overall deviation of the four consecutive sampling points before the current moment is collected to form a trend sequence. If the overall deviation at the current moment is greater than the average of the previous three moments in the sequence, the trend coefficient is 1.2; if the overall deviation at the current moment is less than the average of the previous three moments in the sequence, the trend coefficient is 0.8; otherwise, the trend coefficient is 1.

[0094] Finally, the overall deviation is multiplied by the trend coefficient, and the product is normalized to the range of 0 to 1 to obtain the control deviation representing the quality control of each process.

[0095] In one embodiment, based on the process control degree, the operating status of each process at the current moment is jointly evaluated to obtain a deviation control vector that measures the overall impact caused by deviations in the status of each process. This also includes:

[0096] Based on the control loop weights and control deviation, the transmission response of the deviation between each loop is calculated to obtain the loop disturbance response degree, which reflects the impact of disturbance on each process under the current control action. Specifically, the control deviation degree of each process at the current moment is taken as the initial disturbance source of the process. The larger the value, the worse the control quality of the process itself and the stronger the energy as the disturbance source.

[0097] For any control loop of process A and B, the influence weight of A on B is used as the basic coefficient for disturbance transmission. The process control degree of A and B is subtracted from 1 to obtain the path damping coefficient. The larger the path damping coefficient, the worse the coordination of the two process control loops and the more severe the attenuation during disturbance transmission. The influence weight is multiplied by the path damping coefficient to obtain the disturbance transmission coefficient from A to B.

[0098] For the target process B, the initial disturbance sources of all other processes A are multiplied by the corresponding disturbance transmission coefficient from A to B, and all product results are summed to obtain the total amount of externally transmitted disturbances received by B. The total amount of externally transmitted disturbances reflects the cumulative effect of deviations from other processes transmitted to B through the control action network.

[0099] Multiply the initial disturbance source of B by its control source strength to obtain the self-disturbance amplification value of B; add the total externally transmitted disturbance of B to the self-disturbance amplification value, and normalize the calculation result to the 0-1 range to obtain the loop disturbance response degree, which reflects the disturbance impact on process B under the current control action. The closer the loop disturbance response degree is to 1, the more severe the overall disturbance impact on the process at the current moment, that is, the poor its own control quality and the strong transmission impact of deviations from other processes; the closer the loop disturbance response degree is to 0, the more stable the process is under disturbance.

[0100] The loop disturbance response is analyzed to obtain the deviation control vector of the overall impact caused by the state deviation of each process. Specifically, for each process A, the control loop weights from it to all other processes B are extracted. The loop disturbance response of A is multiplied by the control loop weights from A to each B, and all products are summed. The summation result is added to the loop disturbance response of A to obtain the global impact accumulation of A. The global impact accumulation reflects the total potential impact of the disturbance currently experienced by process A on the whole after it radiates outward through the network.

[0101] The cumulative global impact of each process is normalized to the 0-1 range, which is the deviation control intensity of that process. The deviation control intensities of all processes are arranged in order of process number to obtain the deviation control vector of the overall impact caused by the state deviation of each process.

[0102] By calculating the control loop weights, the interdependencies and disturbance propagation paths between processes are clarified. Combined with parameters such as medium temperature and cavity pressure, control deviation is generated to reflect the quality of process control. Then, through the loop disturbance response and deviation control vector, the overall impact of deviations in each process is accurately assessed. This solves the defects of traditional open-loop control, where associated anomalies are handled independently, disrupting the cooperative relationship. It can accurately track the deviation transmission path, confirm the degree of disturbance impact, and judge the risk of timing disorder, avoiding chain problems caused by handling a single anomaly. This ensures the multi-process cooperative stability of the At-211 separation system and improves the pertinence and effectiveness of anomaly handling.

[0103] In one embodiment, based on the deviation control vector, process combinations with timing disorder risk are identified, and anomaly control identifiers indicating the existence of timing disorder risk among the processes are generated, including:

[0104] Based on the deviation control vector, the control deviation of adjacent processes is analyzed, and the process control deviation gradient that reflects the deviation change trend between adjacent control loops is generated. Specifically, according to the flow of the At-211 separation process, each process is arranged according to the material transfer direction to form a process control link. For any two processes that are adjacent on the link, they are denoted as process X and process Y, respectively, where X is the upstream process and Y is the downstream process.

[0105] Obtain the deviation control intensity of process X and process Y at the current moment from the deviation control vector, and calculate the absolute value of the difference between the two as the basic deviation gradient of the adjacent process pair.

[0106] Calculate the ratio of the control source strength of process X to the control source strength of process Y, multiply the basic deviation gradient by the square of the control degree of processes X and Y, and then multiply by the ratio of control source strength to obtain the initial deviation gradient.

[0107] If the deviation control strength of process Y is greater than that of process X, it indicates that the overall impact of downstream processes is higher than that of upstream processes, and the deviation may be amplified during the downstream transmission process. In this case, the initial deviation gradient is multiplied by 1.2 to obtain the corrected deviation gradient. If the deviation control strength of process Y is less than that of process X, it indicates that the deviation is mainly concentrated upstream. The initial deviation gradient is multiplied by 0.8 to obtain the corrected deviation gradient. If the deviation control strength of process Y is equal to that of process X, the corrected deviation gradient is the same as the initial deviation gradient.

[0108] The correction deviation gradients of adjacent process pairs are normalized to the 0-1 range to generate process control deviation gradients that reflect the deviation change trends between adjacent control loops. The closer the process control deviation gradient is to 1, the more drastic the deviation change trend between adjacent processes is, that is, there are significant differences in the overall impact of upstream and downstream processes and the deviation is amplified along the control link, which poses a high risk of timing disorder. The closer the process control deviation gradient is to 0, the more gradual the deviation change between adjacent processes is, and the lower the risk is.

[0109] In one embodiment, based on the deviation control vector, the process combination with timing disorder risk is identified, and an anomaly control flag indicating the existence of timing disorder risk among the processes is generated. The method further includes:

[0110] Based on the process control deviation gradient, analyze the cumulative effect and attenuation characteristics of deviation propagation along the process chain, and generate a control propagation coefficient representing the deviation propagation trend. Specifically, this includes arranging the process control deviation gradients of each adjacent process pair in sequence along the process control chain to form a gradient propagation sequence. The sequence length is equal to the total number of processes minus 1.

[0111] Starting from the beginning of the gradient propagation sequence, the control deviation gradients of each process are multiplied sequentially to obtain the cumulative amplification factor of each position relative to the starting point. When 0.8 ≤ cumulative amplification factor ≤ 0.9, it indicates that the deviation propagates along the link with a weak amplification trend; when the cumulative amplification factor > 0.9, it indicates that the deviation propagates along the link with a strong amplification trend; when the cumulative amplification factor < 0.8, it indicates that the deviation propagates with a decaying trend.

[0112] Using the cumulative amplification factor at the end of the link at the current moment as the cumulative effect coefficient, a sign analysis is performed on the gradient propagation sequence to mark the direction of increase or decrease of the control deviation gradient of each process relative to the control deviation gradient of the previous process. The length of the segment where the control deviation gradients of three or more consecutive processes increase or decrease in the same direction is counted. The length of the longest consecutive segment in the same direction is divided by the total length of the gradient propagation sequence to obtain the propagation inertia coefficient. The larger the propagation inertia coefficient, the more stable the propagation path is, indicating that the deviation change has a continuous trend in the same direction.

[0113] Extract the process control degree of each adjacent process pair from the process control degree matrix, subtract the process control degree from 1 to get the damping potential value of the adjacent process pair, sum the damping potential values ​​of all adjacent process pairs and divide by the total number of process pairs to get the link average damping coefficient. The link average damping coefficient reflects the overall suppression capability of the deviation propagation of the entire link.

[0114] Multiply the cumulative effect coefficient by the propagation inertia coefficient, then divide by the average damping coefficient, and normalize the calculation result to the 0-1 range to obtain the control propagation coefficient, which represents the propagation trend of the deviation. The closer the control propagation coefficient is to 1, the more significant the amplification effect of the deviation propagation along the link, the stronger the inertia, and the weaker the link damping capability, and the stronger the overall propagation trend. The closer the control propagation coefficient is to 0, the more the deviation propagation tends to decay.

[0115] The control propagation coefficient is calculated to generate anomaly control indicators, specifically including: dividing the control propagation coefficient into three risk level ranges: below 0.25 is the low-risk range, between 0.25 and 0.55 is the medium-risk range, and above 0.55 is the high-risk range;

[0116] Process control links with a cumulative effect coefficient greater than 0.9 and a propagation inertia coefficient greater than 0.6 are extracted and marked as active propagation paths. For each active propagation path, adjacent process pairs with a process control deviation gradient exceeding 0.5 on the path are identified as potential risk nodes.

[0117] For each potential risk node, extract the process control deviation gradient at its location, the average gradient of one adjacent process pair upstream and downstream of the potential risk node, and the process control degree of the corresponding process pair. Divide the process control deviation gradient of the node by the average gradient of the upstream and downstream process control deviations to obtain the local mutation coefficient. Subtract the process control degree from 1 to obtain the collaborative vulnerability coefficient. Multiply the local mutation coefficient and the collaborative vulnerability coefficient, and then multiply by the control propagation coefficient corresponding to the potential risk node to obtain the risk confidence of the potential risk node.

[0118] For process pairs with a risk confidence level > 0.6, the anomaly control flag is Level 1, indicating a serious risk of time sequence disorder, and an early warning is issued. For process pairs with a risk confidence level ≤ 0.3 and ≤ 0.6, the anomaly control flag is Level 2, indicating a potential risk of time sequence disorder, which requires constant monitoring. For process pairs with a risk confidence level < 0.3, no flag is generated.

[0119] By calculating the process control deviation gradient to reflect the trend of deviation changes in adjacent processes, and combining it with the control propagation coefficient to reflect the propagation characteristics of deviations along the process link, and then classifying the level of anomalies and generating anomaly control identifiers through risk confidence, this effectively solves the defects of traditional open-loop control in handling associated anomalies independently and destroying the cooperative relationship. It can accurately identify process combinations and potential risk nodes with time sequence disorder risks, avoid deviation propagation amplification that causes time sequence disorder, ensure the cooperativeness of the process link of the At-211 separation system, improve the timeliness of anomaly handling, and enhance the accuracy of time sequence control.

[0120] In one embodiment, a timing logic exception handling system for fully automated At-211 separation is applied to the above-described processing method, including:

[0121] The correlation analysis unit is used to collect real-time operation data of multiple related processes during the At-211 separation process, and to calculate the pre-processed real-time operation data to generate a multi-process synchronization time series matrix representing the degree of correlation between the operation of each process.

[0122] The collaborative control unit is used to calculate the degree of correlation between each two processes in the multi-process synchronization timing matrix and generate a process control degree that represents the degree of collaborative control between the two processes.

[0123] The deviation control unit is used to jointly evaluate the operating status of each process at the current moment based on the process control degree, and obtain the deviation control vector of the overall impact caused by the deviation of each process status.

[0124] An anomaly control unit is used to identify process combinations with a risk of timing disorder based on the deviation control vector, and generate anomaly control flags indicating that there is a risk of timing disorder between the processes.

[0125] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for handling sequential logic exceptions in fully automated At-211 separation, characterized in that, include: Step S1: Collect real-time operational data of multiple related processes during the At-211 separation process, and calculate the preprocessed real-time operational data to generate a multi-process synchronization time series matrix representing the degree of correlation between the operations of each process, including: Collaborative analysis is performed on the preprocessed real-time operational data to generate dynamic feature vectors that reflect the instantaneous changing trends of each process. Based on the dynamic feature vectors of each process, calculate the consistency of control deviation direction and the difference in response time lag between the dynamic feature vectors of different processes, and generate a process coordination matrix representing the operational coordination between processes. A time-series recursive analysis is performed on the process coordination matrix to generate a dynamic coordination weight matrix representing the evolution trend; Based on the real-time operation data and dynamic collaborative weight matrix of each process at the current moment, the correlation status of each process is analyzed, and a multi-process synchronization time sequence matrix representing the degree of correlation of each process is generated. Step S2: Calculate the correlation degree between every two processes in the multi-process synchronization timing matrix, and generate a process control degree representing the degree of collaborative control between the two processes, including: The synchronization time matrix of multiple processes is decomposed to generate the collaborative control participation degree of each process; Based on the multi-process synchronization timing matrix, the phase deviation of the control loop of each process is calculated to obtain the phase control deviation factor representing the coordination of control actions between processes; A control analysis is performed on the phase control deviation factor to generate control constraint weights that indicate the need for control intervention. Step S3: Based on the process control system, jointly evaluate the operating status of each process at the current moment to obtain the deviation control vector of the overall impact caused by the deviation of each process status. Step S4: Based on the deviation control vector, identify the process combinations with time sequence disorder risk and generate an anomaly control identifier indicating that there is a time sequence disorder risk between the processes.

2. The timing logic exception handling method for fully automated At-211 separation according to claim 1, characterized in that, Calculating the correlation between every two processes in the multi-process synchronization timing matrix, generating a process control degree representing the degree of collaborative control between the two processes, also includes: The collaborative control participation, phase control deviation factor and control constraint weight are fused, and a collaborative control value representing the degree of chaos in collaborative control between each pair of processes is calculated. The collaborative control values ​​are analyzed to generate a process control degree that represents the degree of collaborative control between two processes.

3. The timing logic exception handling method for fully automated At-211 separation according to claim 2, characterized in that, Based on the process control system, the operating status of each process at the current moment is jointly evaluated to obtain the deviation control vector of the overall impact caused by the deviation of each process status, including: Based on the process control degree, calculate the influence weight and propagation path of each control loop on other loops, and generate control loop weights that represent the interdependence of inter-process control actions. Analyze the real-time operating data of each process to generate a control deviation that represents the quality control of each process.

4. The timing logic exception handling method for fully automated At-211 separation according to claim 3, characterized in that, Based on the process control system, the operating status of each process at the current moment is jointly evaluated to obtain the deviation control vector of the overall impact caused by the deviation of each process status, which also includes: Based on the control loop weights and control deviation, the transmission response of the deviation between each loop is calculated to obtain the loop disturbance response degree, which reflects the impact of disturbance on each process under the current control action. By analyzing the loop disturbance response, the deviation control vector of the overall impact caused by the state deviation of each process is obtained.

5. The timing logic exception handling method for fully automated At-211 separation according to claim 4, characterized in that, Based on the deviation control vector, process combinations with time-sequence disorder risk are identified, and abnormal control indicators representing the existence of time-sequence disorder risk among processes are generated, including: Based on the deviation control vector, the control deviation of adjacent processes is analyzed, and the process control deviation gradient, which reflects the trend of deviation change between adjacent control loops, is generated.

6. The timing logic exception handling method for fully automated At-211 separation according to claim 5, characterized in that, Based on the deviation control vector, process combinations with time-sequence disorder risk are identified, and anomaly control identifiers indicating the existence of time-sequence disorder risk among processes are generated. This also includes: Based on the process control deviation gradient, the cumulative effect and attenuation characteristics of deviation propagation along the process chain are analyzed, and a control propagation coefficient representing the deviation propagation trend is generated. The control propagation coefficient is calculated to generate anomaly control indicators.

7. A timing logic exception handling system for fully automated At-211 separation, applied in the processing method described in any one of claims 1-6, characterized in that, include: The correlation analysis unit is used to collect real-time operation data of multiple related processes during the At-211 separation process, and to calculate the pre-processed real-time operation data to generate a multi-process synchronization time series matrix representing the degree of correlation between the operation of each process. The collaborative control unit is used to calculate the degree of correlation between each two processes in the multi-process synchronization timing matrix and generate a process control degree that represents the degree of collaborative control between the two processes. The deviation control unit is used to jointly evaluate the operating status of each process at the current moment based on the process control degree, and obtain the deviation control vector of the overall impact caused by the deviation of each process status. An anomaly control unit is used to identify process combinations with a risk of timing disorder based on the deviation control vector, and generate anomaly control flags indicating that there is a risk of timing disorder between the processes.

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