Digital twinning-driven intelligent factory AI intelligent decision-making system

Abstract

The invention relates to the technical field of smart factory decision-making, and discloses a digital twin-driven smart factory AI intelligent decision-making system, which comprises a data acquisition and preprocessing module used for acquiring and preprocessing real-time and historical data of a production line; the twinborn modeling identification module is used for constructing a twinborn body and carrying out parameter identification and uncertainty quantification; the alignment evaluation credible module is used for comparing twin prediction data with real data and generating availability marks and trust scores; the strategy simulation optimization module is used for generating candidate strategies and risk evidences based on the constraints and the key performance indicators; the grayscale online publishing module is used for screening and grayscale publishing a strategy according to the trust score and the performance result; the operation evaluation auditing module is used for collecting operation data and generating an auditing packet; and the drifting detection updating module is used for detecting distribution drifting and recalibrating and updating the twinborn body and the strategy library. According to the invention, the reliability, robustness and continuous optimization of the production decision of the smart factory are realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent factory decision-making, and particularly relates to an intelligent factory AI intelligent decision-making system driven by digital twinning. BACKGROUND

[0002] With the development of digitalization and intelligentization of manufacturing industry, intelligent factory gradually becomes an important way to improve production efficiency, flexibility and resource utilization. In traditional production management, although production line operation data can be collected through sensors and information systems, there are often problems of data missing, noise interference and difficulty in timely removing abnormal points, resulting in lack of unified and reliable input for subsequent modeling and analysis. At the same time, the data formats and statistical indicators between different processes and links are inconsistent, which also limits the comprehensive optimization of the overall production line.

[0003] The existing decision-making methods mostly rely on static models or single prediction results, and it is usually difficult to reflect the advantages of AI intelligent decision-making, and the dynamic evolution and uncertainty of the production process cannot be described. For example, when the order demand fluctuates, the device state is abnormal or external environmental disturbance occurs, the existing method often cannot adjust the parameters in time, resulting in deviation of the strategy from the actual demand. Although digital twinning technology has been applied to device health monitoring, energy consumption prediction and other scenes in recent years, most of them are limited to local links and cannot fully combine the autonomous learning and autonomous decision-making ability of AI, lacking end-to-end modeling, optimization and feedback loop. In addition, the existing production line optimization means also has shortcomings in the execution level, and often directly applies the offline optimization results to the real production line, lacking the gray release and compliance verification mechanism based on AI evaluation. Once there is deviation between the online strategy and the actual production line environment, it may cause production interruption or efficiency decline, lacking effective rollback means and risk control ability. SUMMARY

[0004] The application provides an intelligent factory AI intelligent decision-making system driven by digital twinning, which solves the technical problems of inconsistent production line operation data quality, lack of uncertainty description in model prediction, and lack of gray verification in strategy online in related technologies.

[0005] The application provides an intelligent factory AI intelligent decision-making system driven by digital twinning, which includes:

[0006] A data acquisition and preprocessing module is configured to acquire real-time data and historical statistical data of the production line, standardize, suppress noise and detect abnormalities of the real-time data of the production line, and obtain a production line state vector, real observation data, noise parameters and abnormal markers;

[0007] A twinning modeling and identification module is configured to construct a twin body based on the production line state vector and the real observation data, and perform parameter identification and uncertainty quantification, and output twin body prediction data;

[0008] An alignment evaluation trusted module is configured to align and evaluate the twin prediction data and the real observation data, and obtain an availability label and a trust score according to a preset alignment threshold and a preset trust threshold;

[0009] A strategy simulation optimization module is configured to, when the availability label is available, solve a candidate strategy set in the counterfactual scenario set according to the key performance indicators and constraint conditions, and give a key performance indicator prediction result and risk evidence; wherein the constraint conditions include: physical constraints, capacity constraints and risk constraints.

[0010] A gray release module is configured to apply a preset online threshold to the candidate strategy set according to the trust score and the key performance indicator prediction result, and issue the candidate strategy set to generate an online strategy version in a gray release range.

[0011] A running evaluation audit module is configured to collect online strategy running data corresponding to the online strategy version, form a contrast evaluation, and generate an audit package.

[0012] A drift detection and update module is configured to detect drift based on a feature distribution stability indicator and an online error threshold, and recalibrate and update the twin and the strategy library.

[0013] Further, the process of standardizing, noise suppressing and anomaly detecting the production line real-time data includes:

[0014] Step 11, time alignment and integrity check are performed on the production line real-time data, wherein the time alignment generates a unified timestamp using a preset resampling period, and the integrity check removes continuous missing data segments according to the upper limit of the missing rate in the historical statistical data to obtain real observation data.

[0015] Step 12, the real observation data is standardized based on the mean and standard deviation in the historical statistical data, and a standby standard deviation is used to replace the standard deviation when the standard deviation is zero, to obtain a standardized sequence.

[0016] Step 13, Kalman filtering is applied to the standardized sequence channel by channel to generate a noise reduction sequence, and the residual error is used to estimate the observation noise variance and the process noise variance to form noise parameters.

[0017] Step 14, according to the noise parameters, determine the double-sided threshold, and perform preliminary threshold judgment on the noise reduction sequence, use the graph neural network model to analyze the multi-channel correlation of the noise reduction sequence, identify the abnormal points across channels and generate an abnormal label, and after compensation processing of the time stamp covered by the abnormal label using the historical mean, aggregate it into a production line state vector.

[0018] Further, the construction of the twin and the generation of the twin prediction data include:

[0019] Step 21, construct a twin taking the in-process state vector as input and taking the predicted value of each channel as output, the twin including mechanism terms and data terms, and set the initial value of the model parameter according to historical statistical data;

[0020] Step 22, remove the corresponding sample using the abnormality label, determine the sample weight based on the observation noise variance and the process noise variance in the noise parameter, and combine the remaining samples to form a weighted training set;

[0021] Step 23, establish a target function of the prediction error sum of squares based on the weighted training set, update the twin parameters using an iterative optimization algorithm, stop iteration when the target function decreases by less than a first preset threshold, and obtain an identified twin;

[0022] Step 24, use the identified twin to infer the in-process state vector, output the twin prediction data, and synthesize the prediction variance based on the model parameter covariance and the noise parameter to obtain the uncertainty quantification result.

[0023] Further, the generation process of the availability label and the trust score includes:

[0024] Step 31, remove abnormal points based on the abnormality label, calculate the error between the twin prediction data and the true observation data for each channel for the remaining data, and obtain an alignment error sequence;

[0025] Step 32, generate a sample weight based on the sum of the observation noise variance and the process noise variance in the noise parameter, and square and normalize the alignment error sequence according to the sample weight to obtain an overall error indicator as a weighted root mean square error;

[0026] Step 33, compare the overall error indicator with a second preset threshold set according to historical statistical data, and compare the uncertainty quantification result with a third preset threshold, and generate an availability label as available when both are satisfied, otherwise generate an unavailable label;

[0027] Step 34, set a normalization constant according to historical statistical data, map the overall error indicator to a score through an exponential monotone decreasing function, and multiply the score by the availability label to obtain a trust score.

[0028] Further, the key performance indicators include: throughput, work-in-process, energy consumption, and overdue rate.

[0029] Further, the strategy simulation optimization module specifically includes:

[0030] Step 41, construct a counterfactual scenario set according to the in-process state vector and a preset disturbance, and weight and normalize the scenario probability using the trust score to form a weighted scenario set;

[0031] Step 42, taking the weighted sum of key performance indicators as the optimization target, and integrating physical constraints, capacity constraints, and risk constraints defined by a preset risk threshold into the constraint conditions;

[0032] Step 43, calling the twin to simulate the control parameter sequence under the weighted scenario set, eliminating solutions that do not meet the constraint conditions or are inferior to other solutions in all key performance indicators, and selecting a plurality of candidate strategies with high scores to form a candidate strategy set;

[0033] Step 44, outputting key performance indicator prediction results and risk evidence for the candidate strategy, and packaging the candidate strategy identifier, control parameter sequence, evaluation time window, key performance indicator prediction result, risk evidence, and twin version number into a set, wherein the risk evidence includes the most adverse scenario result and risk threshold determination.

[0034] Further, the step 43 further comprises: obtaining a prediction variance by synthesizing model parameter covariance and noise parameters, tightening physical constraints and capacity constraints at a preset confidence level, so that they are converted into deterministic constraints at the preset confidence level; and adaptively adjusting the length and precision of the control parameter sequence using the prediction variance; obtaining candidate strategies according to the tightened constraint conditions and the adaptively set control parameter sequence and performing screening.

[0035] Further, the gray release module specifically comprises:

[0036] Step 51, setting a trust score threshold, a key performance indicator boundary, and a preset risk threshold based on historical statistical data, comparing the candidate strategy set one by one, and if the threshold set is met, including it in the online candidate; if the screening result is empty, relaxing a single threshold according to a preset de-escalation strategy, and only when the key performance indicator improvement of the candidate strategy relative to the baseline strategy reaches a preset improvement amplitude, determining it as an effective candidate;

[0037] Step 52, setting a gray ratio based on the trust score and key performance indicator margin of the effective candidate strategy through a monotonically increasing function, setting a gray time domain window based on the risk level through a monotonically decreasing function, and preferentially selecting units that have less impact on bottleneck processes within the coverage range;

[0038] Step 53, generating an online strategy version for the gray release candidate strategy, and recording the rollback trigger condition and the baseline strategy binding relationship, and switching to the baseline strategy when triggered.

[0039] Further, collect online strategy running data corresponding to the online strategy version, form a comparative evaluation, and generate an audit package, including:

[0040] Step 61, collect running data within the coverage and validity period window of the online policy version, and resample and align according to the data snapshot identifier, fill in the missing data and remove the abnormal points to form a consistent running data set;

[0041] Step 62, construct a control set based on the running data, preferentially use the true control of the unit without intervention, supplement the insufficient part with the corresponding twin version, and form the counterfactual key performance indicator by weighted fusion;

[0042] Step 63, calculate the observed key performance indicator using the unified caliber, generate the improvement amount by comparing with the counterfactual key performance indicator, multiply the improvement amount of each key performance indicator by the preset business weight one by one, sum to get the comprehensive effect score, and output the confidence interval at the significance level;

[0043] Step 64, within the validity period window, calculate the proportion of time when the actual execution value falls within the tolerance range of the preset control parameter sequence to obtain the compliance rate, and when the compliance rate is lower than the preset compliance rate threshold, mark it as low compliance, and detect the distribution distance of the key exogenous variable to generate the drift flag;

[0044] Step 65, encapsulate the policy identifier, control parameter sequence, twin version number, data snapshot identifier, validity period window, running data result, control result, effect score, compliance rate and drift flag field to generate an audit package.

[0045] Further, based on the feature distribution stability index and the online error threshold to detect drift, recalibrate and update the twin and policy library, including:

[0046] Step 71, extract the feature distribution of the running data within the sliding time window, and align with the baseline snapshot, and calculate the stability index using the binning method;

[0047] Step 72, double compare the stability index with the preset upper threshold and the online error threshold of the key performance indicator, trigger when multiple windows exceed the threshold continuously, and generate a drift determination flag;

[0048] Step 73, when the drift determination triggers, select samples that meet the compliance rate requirement and have no abnormal marks, generate weights according to the inverse of noise variance, and update the twin mechanism item and data item parameters using weighted regularized least squares iteration, stop when the parameter change is less than the preset change threshold, and get the updated twin parameters;

[0049] Step 74, re-simulate the policy library based on the updated twin in the weighted scenario, retain the candidate policies that meet the preset risk threshold and improve the baseline policy by the fourth preset threshold, and remove the schemes that are dominated on all key performance indicators to generate the updated policy library.

[0050] The beneficial effects of the present application are: the present application realizes accurate simulation and dynamic updating of the operation state of the production line by introducing digital twin modeling, parameter identification and uncertainty quantification, ensuring the reliability of the prediction data; through the availability mark and trust score generation method based on error alignment and threshold determination, the credibility of the twin can be quantified and fed back to the strategy optimization link; by combining the counter-factual scenario set to carry out strategy simulation and screening under multiple constraints, both physical and production capacity constraints are considered, and risk constraints are also included, making the candidate strategy more robust; through the trust score and key performance indicator driven online threshold control, as well as the dynamic setting of the gray scale ratio and gray time domain window, the gradual landing of the strategy in the real production line environment is ensured, and the online risk is reduced; through the drift detection based on stability indicators and online errors, the twin and the strategy library are recalibrated and updated, ensuring the long-term adaptability and continuous optimization capability of the system. Overall, the reliability, stability and continuous optimization capability of the intelligent factory production decision are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is a module schematic diagram of the digital twin driven intelligent factory AI intelligent decision system of the present application. DETAILED DESCRIPTION

[0052] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is merely meant to provide a better understanding of the subject matter described herein and can be changed in function and arrangement without departing from the scope of the present specification. Various processes or components can be omitted, substituted, or added according to desired implementations. Additionally, features described with respect to some examples can be combined in other examples.

[0053] As shown in Figure 1 The digital twin driven intelligent factory AI intelligent decision system includes:

[0054] The data acquisition and preprocessing module 1 is used to obtain real-time data and historical statistical data of the production line, standardize, suppress noise and detect abnormalities of the real-time data of the production line, and obtain a production line state vector, real observation data, noise parameters and abnormal markers;

[0055] The twin modeling and identification module 2 is used to construct a twin based on the production line state vector and real observation data, and perform parameter identification and uncertainty quantification, and output twin prediction data;

[0056] The alignment evaluation and credibility module 3 is used to align and evaluate the twin prediction data and the real observation data, and obtain an availability mark and a trust score according to a preset alignment threshold and a preset trust threshold;

[0057] The strategy simulation optimization module 4 is configured to solve a candidate strategy set in the counterfactual scenario set according to the key performance indicators and constraint conditions when the availability is marked as available, and give a key performance indicator prediction result and risk evidence; wherein the constraint conditions include: physical constraints, capacity constraints and risk constraints.

[0058] The gray release module 5 is configured to apply a preset online threshold to the candidate strategy set according to the trust score and the key performance indicator prediction result, and issue the candidate strategy set in the gray release range to generate an online strategy version.

[0059] The running evaluation audit module 6 is configured to collect online strategy running data corresponding to the online strategy version, form a contrast evaluation and generate an audit package.

[0060] The drift detection update module 7 is configured to detect drift based on a feature distribution stability indicator and an online error threshold, and recalibrate and update the twin and the strategy library.

[0061] In an embodiment of the present application, the production line real-time data refers to the running data such as process parameters, equipment state quantities and material information continuously collected by sensors or information systems in the production line during the production process, which reflects the instant running situation of the production line in the form of time series. The historical statistical data refers to the results obtained by archiving and statistically analyzing the production line running data in a long time period, including the mean, standard deviation, missing rate and distribution characteristics of each channel, which are used as a reference for data processing and model initialization.

[0062] The process of standardizing, noise suppressing and anomaly detecting the production line real-time data includes:

[0063] Step 11, time alignment and integrity check are performed on the production line real-time data, wherein the time alignment generates a unified timestamp by using a preset resampling period, so as to ensure the correspondence between the data of different channels in the time dimension, and the integrity check removes the continuously missing data segment according to the upper limit of the missing rate in the historical statistical data, so as to avoid the data quality not meeting the requirements and obtain real observation data;

[0064] Step 12, the real observation data is standardized based on the mean and standard deviation in the historical statistical data, and a standby standard deviation is used to replace the standard deviation when the standard deviation is zero, to obtain a standardized sequence; specifically, the standardization refers to subtracting the historical mean from the original data and dividing by the historical standard deviation, so that the data sequence has consistent dimension and scale between different channels;

[0065] Step 13, Kalman filtering is applied to the standardized sequence channel by channel to generate a noise reduction sequence, and the residual error is used to estimate the observation noise variance and process noise variance to form noise parameters;

[0066] Step 14, determine the double-sided threshold according to the noise parameter, and perform preliminary threshold judgment on the noise reduction sequence to quickly identify data points that exceed the reasonable range; the double-sided threshold refers to the limits set in the upper and lower directions of the data distribution; on this basis, further multi-channel correlation analysis of the noise reduction sequence is performed by using a graph neural network model, so as to identify abnormal points across channels and generate abnormal markers.

[0067] Specifically, the graph neural network model is constructed based on graph structure data, the nodes in the graph structure data represent the monitoring data of different channels in the production line, and the edges in the graph structure data represent the logical or physical connection between the channels, such as the sequence of processes, the material transfer relationship between devices, etc. By iteratively transmitting information on the graph structure, the graph neural network model can automatically capture high-order dependency relationships between different channels, thereby effectively identifying coupled abnormalities that are difficult to find by single-channel threshold methods. If the features of a node show obvious inconsistency with the neighbor nodes, the timestamp data corresponding to the node is marked as an abnormal point; the system generates an abnormal marker for the corresponding timestamp, and compensates the abnormal data using the historical mean value to ensure data continuity and stability; this process embodies the intelligent perception and abnormal cognition ability of AI, enabling the system to not only handle local abnormalities, but also maintain data quality under complex process coupling conditions.

[0068] Through the above process, data objects that meet the modeling requirements can be extracted from the original real-time production line data, i.e., production line state vectors, real observation data, noise parameters, and abnormal markers. Among them, the production line state vector is used to show the running state of the production line under a unified time scale, the real observation data provides the original input corresponding to the actual working condition, the noise parameter provides the basis for subsequent uncertainty modeling and weight allocation, and the abnormal marker is used to ensure that abnormal points do not interfere with the modeling accuracy.

[0069] In an embodiment of the present application, the construction of the twin and the generation of the twin prediction data include:

[0070] Step 21, construct a twin with the production line state vector as input and the prediction value of each channel as output, the twin includes a mechanism term and a data term, and the initial value of the model parameter is set according to historical statistical data; specifically, the twin refers to a hybrid model based on the real production line running mechanism, which combines mechanism modeling and data-driven modeling, wherein the mechanism term is a function relationship established based on the known physical laws and process constraints of the production line, including but not limited to material balance model, energy consumption model, dynamic transfer function model, used to describe the causal relationship of physical processes, and the data term is used to fit nonlinear or disturbance effects that are not fully covered by the mechanism;

[0071] Step 22, when training the twin, the corresponding samples are removed by using the anomaly label to avoid the interference of noise and anomaly on the training accuracy, and the sample weight is determined based on the observation noise variance and the process noise variance in the noise parameter, and the reserved samples are combined to form a weighted training set;

[0072] Step 23, a target function of prediction error square sum is established based on the weighted training set, an iterative optimization algorithm is used to update the twin parameters, and the iteration is stopped when the descending amplitude of the target function is lower than a first preset threshold, and an identified twin is obtained; specifically, the calculation formula of the target function is: , , represents the value of the target function, T represents the time index number, t represents the time index, C represents the total number of channels, c represents the channel index, , represents the sample weight of channel c at time t, and the weight can be determined by the reciprocal of the observation noise variance and the process noise variance, , represents the real observation data, , represents the line state vector at time t, , represents the prediction value of channel c under the twin parameters , the target function is used to measure the difference between the twin prediction value and the real observation data;

[0073] Step 24, the line state vector is inferred by using the identified twin, the twin prediction data is output, the prediction variance is synthesized based on the model parameter covariance and the noise parameter, and the uncertainty quantization result is obtained. Wherein, the model parameter covariance refers to the covariance matrix of parameter estimation in the iterative optimization process, which is used to describe the uncertainty of the model parameters; the uncertainty quantization result refers to the variance or interval estimation given on the basis of the prediction output, which is used to reflect the confidence range of the twin prediction result.

[0074] Through the above steps, the twin reflecting the actual running state of the production line can be constructed under the premise of ensuring the data quality, and the stability and reliability of the twin are improved through weighted identification and uncertainty quantization; the twin prediction data obtained in this way not only can provide accurate numerical prediction for subsequent decision-making, but also can provide a measure of prediction reliability, avoiding the complete dependence of the decision-making process on single-point prediction values; embodies the learning and modeling ability of AI, can identify the law from historical and real-time data, dynamically adapt to unknown disturbances, and thus provide more intelligent and adaptive input for subsequent decision-making.

[0075] In an embodiment of the present application, the generation process of the availability label and the trust score includes:

[0076] Step 31, based on the abnormal marker, the abnormal points are removed, the error between the twin prediction data and the real observation data is calculated for the remaining data channel by channel, and the alignment error sequence is obtained, which reflects the prediction deviation of the twin in the current period;

[0077] Step 32, based on the sum of the observation noise variance and the process noise variance in the noise parameter, a sample weight is generated, and the alignment error sequence is squared and normalized according to the sample weight to obtain an overall error indicator as a weighted root mean square error, which considers the error size and noise level, thereby avoiding the dominance of the channel with high noise in the overall result;

[0078] Step 33, compare the overall error indicator with the second preset threshold set according to historical statistical data, and compare the uncertainty quantification result with the third preset threshold, when both meet, generate an availability marker as available, otherwise generate unavailable, and represent them with 1 and 0 respectively;

[0079] Step 34, set a normalization constant according to historical statistical data, map the overall error indicator to a score through an exponential monotone decreasing function, and multiply it by the availability marker to obtain a trust score; specifically, the calculation formula of the trust score is: S represents the trust score, represents the availability marker, exp represents the exponential function, represents the overall error indicator, represents the normalization constant.

[0080] Through the above steps, the embodiment can not only generate the availability marker of the twin based on the dual standards of error and uncertainty, but also further generate the trust score as a measure; on the one hand, it can eliminate unreliable twin prediction to avoid its entering the subsequent decision; on the other hand, it can establish ranking and priority among multiple candidate schemes through the trust score, enhancing the decision stability and interpretability of the system in the intelligent factory scenario; the above process is equivalent to AI self-cognition, which does not blindly rely on model output, but makes a judgment on whether to adopt it through the availability marker and the trust score.

[0081] In an embodiment of the present application, the key performance indicators include: throughput, work-in-process quantity, energy consumption and overdue rate. Among them, the throughput refers to the number of qualified products completed by the production line within a preset time window; the work-in-process quantity refers to the number of semi-finished products or materials being processed in each process of the production line; the energy consumption refers to the total energy consumption of the production line within a preset time window; and the overdue rate refers to the proportion of the number of production orders that cannot be completed on time within a preset time window to the total number of orders.

[0082] In an embodiment of the present application, the strategy simulation optimization module specifically includes:

[0083] Step 41, according to the production line state vector and the preset disturbance, a counterfactual scenario set is constructed, and the scenario probability is weighted and normalized by using the trust score to form a weighted scenario set; specifically, the preset disturbance refers to order demand fluctuation, equipment failure simulation, or external supply delay; the weighted scenario probability is used to measure the importance of different scenarios;

[0084] Step 42, taking the weighted sum of key performance indicators as the optimization target, wherein the weights are set according to historical statistical data, and physical constraints, capacity constraints, and risk constraints defined by a preset risk threshold are uniformly included in the constraint conditions; specifically, the physical constraints define the safety limits of the equipment, and the capacity constraints define the maximum load of the process;

[0085] Step 43, under the weighted scenario set, the twin body is called to simulate the control parameter sequence, the schemes that do not meet the constraint conditions or are inferior to other solutions in all key performance indicators are removed, and a plurality of candidate strategies with high scores are selected to form a candidate strategy set; the control parameter sequence refers to the timing setting of adjustable variables in the production line operation process within a preset time window, including production rate, equipment switching, resource allocation, and scheduling window, etc., and the control parameter sequence is the core input of the candidate strategy set for twin body simulation and evaluation;

[0086] Step 44, the key performance indicator prediction results and risk evidence are output for the candidate strategies, and the candidate strategy identifier, control parameter sequence, evaluation time window, key performance indicator prediction results, risk evidence, and twin body version number are packaged into a set; the risk evidence refers to the results extracted in the evaluation process of the candidate strategy to prove the robustness of the strategy, including the most adverse scenario result and risk threshold determination.

[0087] Through the above steps, the embodiment not only can generate candidate strategies that meet physical and capacity constraints under multiple counterfactual scenarios, but also can ensure the robustness and explainability of the candidate strategies through trust score weighting and risk evidence output; further improving the reliability of the strategy optimization process driven by digital twin, so that the generated strategy not only has high expected return, but also has strong anti-risk ability in uncertain environment.

[0088] In an embodiment of the present application, the step 43 further comprises: obtaining a prediction variance by synthesizing the model parameter covariance and the noise parameter, tightening the physical constraints and the capacity constraints at a preset confidence level to convert them into deterministic constraints at the preset confidence level, and adaptively adjusting the length and precision of the control parameter sequence according to the prediction variance; and obtaining a candidate strategy according to the tightened constraints and the adaptively set control parameter sequence and performing screening. Specifically, the prediction variance is obtained by synthesis, which can quantify the uncertainty level of the prediction result; the constraint boundary is tightened at the preset confidence level, so that the constraint can still be met under high probability conditions; and when the prediction variance is large, the length of the control parameter sequence is shortened and the step interval is increased to reduce the uncertainty risk of long-term prediction; and when the prediction variance is small, the control length is extended and the step interval is reduced to fully exploit the optimization space.

[0089] In an embodiment of the present application, the gray release module specifically comprises:

[0090] Step 51, according to historical statistical data, set a trust score threshold, a key performance indicator boundary and a preset risk threshold, compare the candidate strategy set one by one, if the threshold set is met, it is included in the online candidate; if the screening result is empty, relax the single threshold according to the preset de-escalation strategy, and only when the key performance indicator improvement amplitude of the candidate strategy relative to the baseline strategy reaches the preset improvement amplitude, it is determined as an effective candidate; this way can avoid the strategy from being unable to go online due to too strict screening under the premise of ensuring safety and stability; wherein the preset de-escalation strategy includes but is not limited to: appropriately relaxing the throughput improvement amplitude or the energy consumption boundary, but keeping other indicators and risk thresholds unchanged;

[0091] Step 52, set the gray ratio based on the trust score and the key performance indicator margin of the effective candidate strategy through a monotonically increasing function, that is, initially only cover a part of the production units during the system online process, the higher the trust score and the indicator margin, the larger the gray ratio; set the gray time domain window based on the risk level through a monotonically decreasing function, that is, gradually release the time length within the online period, the higher the risk level, the shorter the time domain window; and preferentially select units that have less impact on the bottleneck process within the coverage range to reduce the impact of gray release on the overall stability of the production line; the gray ratio refers to the proportion of the candidate strategy covering the production line units in the early stage of online; the gray time domain window refers to the execution length allowed by the gray strategy during online;

[0092] Step 53, generate an online strategy version for the gray release candidate strategy, the minimum fields encapsulated include strategy identification, control parameter sequence, key performance indicator prediction result, risk evidence, trust score, gray ratio, gray time domain window, coverage range, twin version number, data snapshot identification and valid period window, and record the rollback trigger condition and the baseline strategy binding relationship, and switch to the baseline strategy when triggered.

[0093] Through the above steps, the embodiment can convert the candidate strategy into an online strategy version with an online execution condition; through multi-dimensional threshold screening and degrading strategy mechanism, the usability and improvement of the candidate strategy are ensured; through dynamic adjustment of the gray scale ratio and the gray time domain window, the online risk is reduced and the strategy is gradually expanded; through the rollback mechanism and the baseline binding, it is ensured that the system can quickly recover to a safe running state in abnormal conditions; it embodies the risk perception and adaptive online decision-making ability of AI, and ensures the stable landing of the strategy in the smart factory environment; and provides reliable execution guarantee for digital twin AI intelligent decision-making in the smart factory environment.

[0094] In an embodiment of the present application, the online strategy version corresponding online strategy running data is collected, a contrast evaluation is formed, and an audit package is generated, including:

[0095] Step 61, collecting running data within the coverage range and the effective period window of the online strategy version, and resampling and aligning according to the data snapshot identifier, using missing data completion and outlier rejection to form a consistent running data set; the coverage range refers to the actual application production unit set when the strategy is released in gray scale, and the effective period window refers to the effective time interval set for the strategy version;

[0096] Step 62, constructing a contrast set based on the running data, preferentially using the real contrast of the non-strategy unit, supplementing the insufficient part by the corresponding twin version, and weighted fusion to form counterfactual key performance indicators; wherein the non-strategy unit refers to the equipment or time slice in the same production environment that has not applied the candidate strategy, which is used as a real contrast; the counterfactual key performance indicator refers to the expected performance of the production line if the candidate strategy is not used in the current production environment, which is used for differential comparison with the actual observation result;

[0097] Step 63, calculating the observed key performance indicators using a unified scale, comparing them with the counterfactual key performance indicators to generate improvement amounts, multiplying each key performance indicator improvement amount by the preset business weight one by one, summing to obtain a comprehensive effect score, and outputting a confidence interval at a significance level; preferably, the significance level is 0.05; the comprehensive effect score is used to show the overall effect of the candidate strategy;

[0098] Step 64, within the effective period window, calculate the proportion of the period when the actual execution value falls within the tolerance range of the preset control parameter sequence to obtain the compliance rate, and when the compliance rate is lower than the preset compliance rate threshold, mark it as low compliance, and detect the distribution distance of the key exogenous variables to generate a drift flag; the key exogenous variable values include order demand, supply delay, and environment temperature; specifically, the difference between the distribution of the key exogenous variable and the historical distribution is detected, the distribution distance is calculated, and if the distance exceeds the corresponding threshold, a drift flag is generated to prompt that the external conditions significantly deviate from the training or evaluation environment;

[0099] Step 65, encapsulate the policy identification, control parameter sequence, twin version number, data snapshot identification, validity period window, running data result, control result, effect score, compliance rate and drift flag field to generate an audit package, and write fingerprint information to support reproduction and audit.

[0100] Through the above steps, the embodiment realizes the full-link closed loop from running data collection, control construction, effect evaluation, compliance and drift detection to audit encapsulation; the process not only provides quantitative evidence for the effect of the candidate strategy, but also provides guarantee for subsequent reproduction and external audit through the generation and fingerprint storage of the audit package, thereby enhancing the transparency and controllability of the system in the intelligent factory environment.

[0101] In an embodiment of the present application, based on the feature distribution stability index and the online error threshold value to detect drift, the twin and the strategy library are recalibrated and updated, including:

[0102] Step 71, extract the feature distribution of the running data in the sliding time window, and align it with the baseline snapshot, and calculate the stability index by using the binning method; specifically, the baseline snapshot refers to the running data distribution saved at the time of the last update of the twin, which is used as a comparison benchmark; to calculate the feature distribution stability index, the feature variable is divided into multiple intervals, and the difference between the current distribution and the baseline distribution is compared to obtain the stability index value; the index is used to quantify the statistical consistency between the running data and the historical benchmark;

[0103] Step 72, compare the stability index with the preset upper threshold value and the online error threshold value of the key performance indicator, and trigger when the threshold is exceeded for a plurality of windows in succession, and generate a drift determination flag; the drift determination flag is a binary signal, which is used to indicate whether the prediction performance of the twin has decreased significantly or the input feature distribution has deviated significantly, and recalibration is needed;

[0104] Step 73, when the drift determination is triggered, select samples that meet the compliance rate requirement and have no abnormal marks, generate weights according to the inverse of the noise variance, that is, the smaller the noise variance, the higher the weight of the sample in training, update the mechanism term and data term parameters of the twin by using weighted regularized least squares iteration, stop when the parameter change is less than the preset change threshold, obtain the updated twin parameters and record the version number;

[0105] Step 74, re-simulate the strategy library based on the updated twin in the weighted scenario, retain the candidate strategies that meet the preset risk threshold and improve the baseline strategy by the fourth preset threshold, eliminate the schemes that are dominated in all key performance indicators, and generate an updated strategy library. The domination refers to if a certain candidate strategy is not superior to another strategy in all key performance indicators, it is determined that the former is dominated and is eliminated.

[0106] Through the above steps, when the running data distribution or model error is detected to drift, the embodiment can automatically trigger the recalibration of the twin parameters, and update the strategy library on this basis, so that the system keeps synchronized with the actual state of the production line. This mechanism not only improves the long-term effectiveness of the twin prediction and optimization results, but also ensures that the candidate strategies retained in the strategy library have high stability and improvement value through the dominant screening and improvement amplitude constraint, which means that the AI has the ability of continuous learning and self-correction, and can automatically evolve when the environment changes or the data distribution deviates, thereby enhancing the adaptability and reliability of AI intelligent decision-making in the intelligent factory environment.

[0107] It should be noted that the setting of the interval and the threshold size is for easy comparison, and the size of the threshold depends on the number of sample data and the base number set by the person skilled in the art for each group of sample data, as long as it does not affect the proportional relationship of the parameters and the quantized values. And the above formula is a calculation of the dimensionless value, and the formula is obtained by software simulation of a large amount of data to obtain a formula of the nearest true situation, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation.

[0108] The above describes the embodiments of the present application, but the present application is not limited to the above specific embodiments, and the above specific embodiments are only illustrative and not limiting. Those skilled in the art can make many forms under the inspiration of the present embodiment, which are all within the protection scope of the present embodiment.

Claims

1. A digital twin driven smart factory AI intelligent decision system, characterized in that, The method comprises the following steps: A data acquisition preprocessing module is used to acquire real-time data and historical statistical data of a production line, standardize the real-time data, suppress noise, and detect abnormalities to obtain a production line state vector, real observation data, noise parameters, and an abnormality label; A twin modeling and identification module is used to construct a twin based on the production line state vector and the real observation data, and to perform parameter identification and uncertainty quantification, and to output twin prediction data; An alignment evaluation and credibility module is used to align the twin prediction data with the real observation data, and to obtain an availability label and a trust score based on a preset alignment threshold and a preset credibility threshold; A strategy simulation and optimization module is used to solve a candidate strategy set in a counterfactual scenario set based on key performance indicators and constraint conditions when the availability label is available, and to provide key performance indicator prediction results and risk evidence; wherein the constraint conditions include physical constraints, capacity constraints, and risk constraints; A gray release module is used to apply a preset online threshold to the candidate strategy set according to the trust score and the key performance indicator prediction results, and to issue the candidate strategy set within a gray release range to generate an online strategy version; A running evaluation and audit module is used to collect online strategy running data corresponding to the online strategy version, perform a contrast evaluation, and generate an audit package; A drift detection and update module is used to detect drift based on a feature distribution stability indicator and an online error threshold, and to recalibrate and update the twin and the strategy library.

2. The digital twin driven smart factory AI intelligent decision system of claim 1, wherein, The process of standardizing, suppressing noise, and detecting abnormalities of the real-time data of the production line comprises the following steps: Step 11: Time alignment and integrity verification are performed on the real-time data of the production line, wherein a uniform timestamp is generated using a preset resampling period for time alignment, and data segments with continuous missing data are removed according to the upper limit of the missing rate in the historical statistical data for integrity verification to obtain real observation data; Step 12: The real observation data is standardized based on the mean and standard deviation in the historical statistical data, and a backup standard deviation is used to replace the standard deviation when the standard deviation is zero to obtain a standardized sequence; Step 13: Kalman filtering is applied to the standardized sequence to generate a noise-reduced sequence, and the residual error is used to estimate the observation noise variance and the process noise variance to form noise parameters; Step 14: The noise parameters are used to determine the double-sided threshold, the noise-reduced sequence is subjected to preliminary threshold determination, a graph neural network model is used to analyze the multi-channel correlation of the noise-reduced sequence, abnormal points across channels are identified, and an abnormality label is generated; the timestamps covered by the abnormality label are compensated using the historical mean and aggregated into a production line state vector.

3. The digital twin driven smart factory AI intelligent decision system of claim 1, wherein, The construction of the twin and the generation of the twin prediction data comprise the following steps: Step 21: A twin is constructed with the production line state vector as input and the predicted values of each channel as output, the twin includes mechanism terms and data terms, and the initial values of the model parameters are set based on the historical statistical data; Step 22: The corresponding samples are removed using the abnormality label, and the observation noise variance and the process noise variance in the noise parameters are used to determine the sample weights, and the remaining samples are combined to form a weighted training set; Step 23, a target function of prediction error square sum is established based on the weighted training set, an iterative optimization algorithm is used to update the twin parameters, and the iteration is stopped when the descending amplitude of the target function is lower than a first preset threshold value, so as to obtain the identified twin; Step 24, the production line state vector is inferred by using the identified twin, twin prediction data is output, and prediction variance is synthesized based on the model parameter covariance and the noise parameter, so as to obtain the uncertainty quantification result.

4. The digital twin driven smart factory Al intelligent decision system of claim 1, wherein, The generation process of the availability mark and the trust score includes: Step 31, abnormal points are removed based on the abnormality mark, the error between the twin prediction data and the real observation data is calculated for the remaining data channel by channel, and the alignment error sequence is obtained; Step 32, sample weights are generated based on the sum of the observation noise variance and the process noise variance in the noise parameter, and the alignment error sequence is squared and normalized according to the sample weights, so as to obtain the overall error index as the weighted root mean square error; Step 33, the overall error index is compared with a second preset threshold value set according to historical statistical data, and the uncertainty quantification result is compared with a third preset threshold value, and when both satisfy, the availability mark is generated as available, otherwise, it is generated as unavailable; Step 34, a normalization constant is set according to historical statistical data, the overall error index is mapped to a score by an exponential monotone decreasing function, and the trust score is obtained by multiplying the availability mark.

5. The digital twin driven smart factory Al intelligent decision system of claim 1, wherein, The key performance indicators include: throughput, work-in-process quantity, energy consumption and overdue rate.

6. The digital twin driven smart factory Al intelligent decision system of claim 1, wherein, The strategy simulation optimization module specifically includes: Step 41, counterfactual scenario sets are constructed according to the production line state vector and the preset disturbance, and the trust score is used to weight and normalize the scenario probability to form a weighted scenario set; Step 42, the weighted sum of the key performance indicators is taken as the optimization target, and the physical constraints, the capacity constraints and the risk constraints defined by the preset risk threshold are uniformly included in the constraint conditions; Step 43, the twin is called under the weighted scenario set to simulate the control parameter sequence, schemes that do not satisfy the constraint conditions or are inferior to other solutions in all key performance indicators are removed, and a plurality of candidate strategies with high scores are selected to form a candidate strategy set; Step 44, the key performance indicator prediction results and the risk evidence are output for the candidate strategies, and the candidate strategy identifier, the control parameter sequence, the evaluation time window, the key performance indicator prediction results, the risk evidence and the twin version number are packaged into a set, and the risk evidence includes the most adverse scenario result and the risk threshold determination.

7. The digital twin driven smart factory AI intelligent decision system of claim 6, wherein, The step 43 further includes: the prediction variance is obtained by synthesizing the model parameter covariance and the noise parameter, and the physical constraints and the capacity constraints are tightened at a preset confidence level, so that they are converted into deterministic constraints at the preset confidence level; and the length and precision of the control parameter sequence are adaptively adjusted by using the prediction variance; the candidate strategies are obtained according to the tightened constraints and the adaptively set control parameter sequence, and are screened.

8. The digital twin driven smart factory Al intelligent decision system of claim 1, wherein, The gray online release module specifically includes: Step 51, set the trust score threshold, key performance indicator boundary and preset risk threshold according to historical statistical data, compare the candidate strategy set one by one, if it meets the threshold set, it is included in the online candidate; if the screening result is empty, relax the single threshold according to the preset de-escalation strategy, and only when the improvement amplitude of the key performance indicator of the candidate strategy relative to the baseline strategy reaches the preset improvement amplitude, it is determined as an effective candidate; Step 52, based on the trust score and key performance indicator margin of the effective candidate strategy, set the gray scale ratio by a monotonically increasing function, set the gray time domain window based on the risk level by a monotonically decreasing function, and preferentially select the unit that has less impact on the bottleneck process within the coverage range; Step 53, generate an online strategy version for the gray release candidate strategy, and record the rollback trigger condition and the baseline strategy binding relationship, and switch to the baseline strategy when triggered.

9. The digital twin driven smart factory Al intelligent decision system of claim 1, wherein, Collect the online strategy running data corresponding to the online strategy version, form a contrast evaluation and generate an audit package, including: Step 61, collect running data within the coverage range and effective period window of the online strategy version, and resample and align according to the data snapshot identifier, use missing data completion and outlier rejection to form a consistent running data set; Step 62, based on the running data, construct a contrast set, preferentially use the true contrast of the unit without strategy, supplement the insufficient part by the corresponding twin version, and weighted fusion to form the counterfactual key performance indicator; Step 63, calculate the observed key performance indicator using the unified caliber, compare it with the counterfactual key performance indicator to generate the improvement amount, multiply each key performance indicator improvement amount by the preset business weight one by one, sum to get the comprehensive effect score, and output the confidence interval at the significance level; Step 64, within the effective period window, calculate the proportion of the period when the actual execution value falls within the tolerance range of the preset control parameter sequence to get the compliance rate, and mark it as low compliance when the compliance rate is lower than the preset compliance rate threshold, and detect the key exogenous variable distribution distance to generate the drift flag; Step 65, encapsulate the strategy identifier, control parameter sequence, twin version number, data snapshot identifier, effective period window, running data result, contrast result, effect score, compliance rate and drift flag field to generate an audit package.

10. The digital twin driven smart factory Al intelligent decision system of claim 1, wherein, Based on the feature distribution stability index and online error threshold to detect drift, recalibrate and update the twin and strategy library, including: Step 71, extract the feature distribution of the running data within the sliding time window, and align with the baseline snapshot, calculate the stability index using the binning method; Step 72, double compare the stability index with the preset upper threshold and the key performance indicator online error threshold, trigger when multiple windows exceed the threshold continuously, and generate a drift determination flag; Step 73, when the drift determination is triggered, select samples that meet the compliance rate requirement and have no abnormal mark, generate weights according to the inverse of noise variance, and update the twin mechanism item and data item parameters using weighted regularized least squares iteration, stop when the parameter change is less than the preset change threshold, get the updated twin parameters; At step 74, the strategy library is re-simulated based on the updated twin in the weighted scenario, candidate strategies that meet the preset risk threshold and improve relative to the baseline strategy to reach a fourth preset threshold are retained, and solutions that are dominated in all key performance indicators are eliminated, generating an updated strategy library.

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