Blood alcohol detection data intelligent analysis management system
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- KEJIAN GRP CO LTD
- Filing Date
- 2026-04-21
- Publication Date
- 2026-07-14
Smart Images
Figure CN122392679A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of blood alcohol concentration detection and analysis technology, and in particular to an intelligent analysis and management system for blood alcohol detection data. Background Technology
[0002] In many fields, such as traffic safety, law enforcement, and healthcare, blood alcohol concentration (BAC) is an important indicator for determining whether someone is driving under the influence of alcohol or suffering from alcohol poisoning. With the development of data acquisition technology, the automation and intelligence of blood alcohol testing equipment are gradually becoming the trend. Intelligent analysis and management systems can improve the accuracy, timeliness, and reliability of blood alcohol concentration testing through automated data acquisition, real-time monitoring, and data analysis.
[0003] For example, Chinese Patent Publication No. CN119150217A discloses an intelligent alcohol detection method and system based on sensor fusion, which achieves high-precision, low-noise alcohol concentration detection, improves the system's intelligence level, ensures data accuracy, and enhances the overall system efficiency and management efficiency. This results in significant improvements in the accuracy, stability, and data management of the alcohol detection system, meeting the modern society's demand for efficient and intelligent alcohol detection.
[0004] In existing technologies, due to individual metabolic differences and sampling time delays, it is difficult to directly deduce the actual end time of drinking from the blood alcohol concentration decay curve. It is also difficult to distinguish between normal metabolism and post-intervention behavior. Moreover, existing systems cannot identify the deceptive pattern of avoiding peaks formed by multiple small doses of alcohol consumed at different times from the residual metabolic residuals. Consequently, it is difficult to determine whether the individual is truly in a dangerous range that affects driving ability at the current enforcement moment, rather than mechanically comparing it to the legal threshold. Therefore, an intelligent analysis and management system for blood alcohol detection data is proposed to solve the above-mentioned problems. Summary of the Invention
[0005] To overcome the shortcomings of the prior art, the present invention provides an intelligent analysis and management system for blood alcohol test data, which can effectively solve the problems involved in the prior art.
[0006] The objective of this invention can be achieved through the following technical solution: This invention provides an intelligent analysis and management system for blood alcohol test data, comprising the following modules: The reverse time metabolism inversion device, based on reverse temporal reinforcement learning and Bayesian structural change point detection, backfits the metabolic inflection point from the temporal decay curve of blood alcohol concentration, corrects the time window and extracts the metabolic residual sequence, thereby accurately locating the individual's true drinking end time and effectively eliminating the positioning bias caused by sampling delay and metabolic drift. The residual trace searcher is used to perform segmented path mining on metabolic residual sequences within a corrected time window using ant colony segmented path search and hidden semi-Markov model. It identifies the hidden repeated intake pattern of multiple small doses of alcohol consumed at different times, and reliably cracks the deceptive behavior of drinking at different times to avoid peaks. The dual-mode fusion device is used to align and resolve conflicts between the actual end time of drinking and multiple intake patterns in time and space, forming a unified temporal profile of individual drinking behavior and fully constructing the entire temporal behavioral chain of an individual from intake to metabolism. The game risk assessor, based on a knowledge graph rule engine and a game adversarial network, simulates the dynamic risk equilibrium point in law enforcement scenarios and outputs an interpretable danger judgment that actually affects driving ability, realizing the integrated output of risk assessment and interpretable attribution in adversarial scenarios. The dynamic threshold calibrator is used to dynamically adjust the personalized danger assessment threshold at the time of law enforcement based on the fused individual drinking behavior structure and game risk assessment results, and output the final law enforcement recommendation and confidence interval, generating probabilistic law enforcement recommendations and avoiding mechanical comparison with statutory thresholds.
[0007] Preferably, the reverse-time metabolism inversion device specifically includes: Collect individual blood alcohol concentration measurements in continuous time series, construct initial metabolic decay curves, and perform reverse scanning of the time axis based on reverse temporal reinforcement learning to identify potential metabolic inflection point regions where the concentration decrease rate drifts significantly, effectively locating the time interval of metabolic rate mutation. A Bayesian structural change point detection model is embedded in the potential metabolic inflection point region. The confidence of each time point as the actual end point of drinking is calculated by posterior probability. The time window boundary with the highest change point probability is selected and a high-confidence change point probability sequence is output to accurately select the most likely drinking end time window. The original time window is dynamically corrected based on the filtered boundaries to eliminate the offset caused by sampling delay and individual metabolic drift. The actual end time of drinking for each individual is then output after inverse fitting, eliminating time offset and outputting an accurate end time.
[0008] Preferably, the reverse-time metabolism inversion device further includes: On the constructed initial metabolic decay curve, a reward function of time-series attention mechanism weighted inverse reinforcement learning is further introduced to enhance the sensitivity to capture small inflection points in the early metabolic stage and significantly improve the sensitivity to identify weak metabolic inflection points in the early stage. By inputting the high-confidence change point probability sequence into the hierarchical Bayesian mixture effect model, the coupled influence of individual intrinsic metabolic rate and random environmental disturbance on inflection point localization is decomposed, effectively separating intrinsic metabolic differences and environmental disturbances, and improving localization accuracy. Based on the corrected time window, the fitting error is rebalanced by superimposing a posterior prediction check, the metabolic residual sequence is extracted and the refined real drinking end timestamp after metabolic heterogeneity correction is output, the metabolic heterogeneity is corrected and the high-precision real end time point is output.
[0009] Preferably, the residual trace finder specifically includes: Within the corrected time window, metabolic residual sequences are extracted as the search space. Multiple artificial ants are initialized using an ant colony segmented path search algorithm, and pheromones are released in segments to mark residual fluctuation paths. This effectively marks residual fluctuation paths and improves the coverage of covert pattern search. The segmented path nodes are input into the hidden semi-Markov model. The residence time distribution and hidden state transition matrix are set to perform state segmentation on the metabolic residual sequence, identify the residual recovery segment corresponding to repeated intake behavior, accurately segment the metabolic state, and enhance the sensitivity of repeated intake behavior identification. Based on the state segmentation results, the hidden intake pattern of multiple small doses consumed at different times is output, including the temporal distribution of each intake time point, relative dose intensity, and interval between adjacent intakes. The complete hidden intake pattern is output, providing a structured basis for subsequent fusion and judgment.
[0010] Preferably, the residual trace finder further includes: In the segmented path search of ant colonies, an adaptive pheromone volatile factor and a heuristic guiding function are introduced to enable ants to prioritize exploring path branches related to the abnormal rebound of residual differences after the metabolic inflection point, thereby enhancing the ability to focus on abnormal rebound paths and improving the detection rate of covert ingestion patterns. The hidden semi-Markov model is extended into a hierarchical hidden semi-Markov model. The upper layer identifies the macro-level drinking segment, and the lower layer analyzes the sub-state transition path of micro-dose intake within the segment, realizing hierarchical analysis of macro- and micro-level intake behavior and refining the temporal structure of multiple micro-dose intakes. By integrating hidden ingestion patterns and employing a Bayesian model to quantify the uncertainty of multiple ingestion time points, segmented path mining results with confidence scores are output. This quantifies the uncertainty of ingestion time points and improves the reliability and interpretability of the mining results.
[0011] Preferably, the dual-mode fusion unit specifically includes: The system receives the actual drinking end time output by the reverse metabolism inversion device and the multiple intake patterns output by the residual trace search device, and establishes a spatiotemporal alignment matrix with time as the horizontal axis and intake events as the vertical axis to ensure that multi-source data are uniformly expressed under the same time reference and to eliminate time axis bias. Conflict resolution is performed on conflict regions in the spatiotemporal alignment matrix, including temporal logical contradictions between the end time and subsequent intake time, as well as ambiguity in the attribution of the source of metabolic residual recovery, to avoid event attribution confusion and improve the accuracy of intake behavior identification. The alignment results after decomposition and the classification and attribution are encoded into a unified temporal profile of individual drinking behavior, which includes a joint representation structure of continuous metabolic segments and discrete intake events, fully reconstructing the entire process of individual drinking and providing a structured data foundation for risk assessment.
[0012] Preferably, the game risk determiner specifically includes: A knowledge graph rule engine is constructed that includes alcohol metabolism dynamics rules, law enforcement precedents, and individual defense strategies. This engine transforms the temporal profile of individual drinking behavior into a set of fact triples, ensuring that the facts are expressed in a structured manner and improving reasoning efficiency and rule interpretability. The fact triple is input into the game adversarial network, where the law enforcement discriminator and individual behavior simulator are alternately optimized to simulate the risk adversarial evolution process under different law enforcement strategies. By adversarial game, the boundary of real disputes is approached, and the law enforcement adaptability of the judgment result is enhanced. The system extracts dynamic risk equilibrium points from adversarial networks, outputs an interpretable hazard determination that actually affects driving ability, and includes key characteristic attribution paths on which the determination is based, providing a traceable chain of determination criteria to support the credibility and transparency of law enforcement decisions.
[0013] Preferably, the game risk determiner further includes: By embedding temporal logic operators into the knowledge graph rule engine, dynamic rule constraints are applied to the metabolic interval between the end of drinking and the time of law enforcement, enhancing the sensitivity of the rules to the time sequence, ensuring the rigor of the temporal judgment logic, and avoiding misjudgments due to disordered time sequence. The game adversarial network is extended into a multi-agent game architecture, introducing three intelligent agents: the prosecutor, the defense, and the expert system. In the adversarial process, risk distributions that closely resemble real law enforcement scenarios are generated, improving the realism of risk assessment adversarial and enhancing the adaptability of law enforcement decision-making scenarios. Based on the interpretable hazard assessment output, adversarial examples are generated by superimposing counterfactual reasoning paths to verify the robustness of the assessment results. The hazard assessment explanation text with causal chain annotations is output to improve the robustness of the assessment results and provide verifiable and traceable explanatory evidence.
[0014] Preferably, the dynamic threshold calibrator specifically includes: The individual drinking behavior time-series profile output by the dual-mode fusion device and the danger judgment result output by the game risk judge are obtained. A personalized risk assessment feature vector based on the enforcement time is constructed so that the judgment standard deviates from the uniform template and fits the actual metabolic and intake characteristics of individuals. The personalized risk assessment feature vector is input into the dynamic threshold generation network. The statutory threshold is used as the prior anchor point. The anchor point is nonlinearly offset and calibrated according to the individual intake structure and game risk weight to achieve dynamic matching between the threshold and individual risk characteristics, thereby improving the scientific nature of the judgment. It outputs a personalized danger assessment threshold at the moment of law enforcement and simultaneously calculates the offset and direction of this threshold relative to the statutory threshold, forming a dynamic calibration record. This provides a traceable adjustment basis for law enforcement decisions and enhances the transparency of the assessment process.
[0015] Preferably, the dynamic threshold calibrator further includes: Based on the personalized risk assessment feature vector, time-series uncertainty interval estimation is introduced to quantify the predicted interval of metabolic residuals before and after the enforcement moment, which serves as a confidence boundary constraint for threshold calibration, thereby improving the anti-interference capability and stability of threshold calibration. The dynamic threshold generation network is extended to a Bayesian deep regression architecture, which outputs the posterior distribution of the threshold instead of single-point values, supports the probabilistic interpretation of calibration results, and provides quantifiable confidence and risk boundaries for the judgment results. Based on dynamic calibration records and threshold posterior distribution, the final law enforcement recommendation and its confidence interval are output. The law enforcement recommendation includes three categories of labels: taking coercive measures, allowing release but requiring observation, or requiring supplementary testing. This achieves tiered law enforcement output and clearly defines the range of credibility.
[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This intelligent analysis and management system for blood alcohol test data, through a collaborative mechanism of reverse time axis scanning and change point detection, can accurately identify metabolic inflection points from the blood alcohol concentration decay curve, effectively overcome the positioning deviation caused by individual metabolic rate differences and sampling time delays, correct the time window to output a high-confidence true drinking end timestamp, accurately restore the true drinking end time, and eliminate the interference of individual metabolic differences and sampling delays.
[0017] 2. This intelligent analysis and management system for blood alcohol test data can uncover hidden intake patterns formed by multiple small-dose drinking sessions at different times from residual fluctuations by segmenting the path search and state segmentation of the metabolic residual sequence. It does not rely on a single concentration threshold and identifies repeated intake behavior by the morphological characteristics of residual recovery fragments. It effectively cracks the deceptive strategy of avoiding concentration peaks by drinking in a dispersed manner and makes up for the blind spots of traditional detection methods.
[0018] 3. This intelligent analysis and management system for blood alcohol test data constructs a unified temporal profile of individual drinking behavior by integrating the actual end time of drinking with multiple intake patterns. Based on knowledge graphs and game-theoretic mechanisms, it simulates dynamic risk balance in law enforcement scenarios, distinguishes between normal metabolism and post-intervention behavior, and outputs interpretable risk judgments based on the individual's actual intake structure and metabolic characteristics, significantly improving the fairness of the judgment.
[0019] 4. This intelligent analysis and management system for blood alcohol test data introduces a multi-agent game architecture to simulate the interactive confrontation process between the prosecution, the defense, and the expert system in law enforcement scenarios. It can effectively deal with the defense strategies that individuals may raise, such as intake time deviation or metabolic rate differences. Through counterfactual reasoning and adversarial sample verification, it can maintain the consistency of judgment within the reasonable defense range, ensure that the output results are not unreliably reversed due to the defense behavior, and enhance the stability of law enforcement decision-making. Attached Figure Description
[0020] Figure 1 This is a schematic diagram of the workflow of an intelligent analysis and management system for blood alcohol test data according to the present invention; Figure 2 This is a system architecture diagram of an intelligent analysis and management system for blood alcohol test data according to the present invention. Detailed Implementation
[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.
[0022] Example 1, please refer to Figure 1 , Figure 2 This invention provides a technical solution: an intelligent analysis and management system for blood alcohol test data, comprising the following modules: The reverse-time metabolism inversion device, based on reverse temporal reinforcement learning and Bayesian structural change point detection, inversely fits metabolic inflection points from the temporal decay curve of blood alcohol concentration, corrects the time window, and extracts the metabolic residual sequence to accurately locate the individual's true drinking end time. It effectively eliminates the positioning bias caused by sampling delay and metabolic drift. It collects individual blood alcohol concentration measurements under continuous time series, constructs an initial metabolic decay curve, and performs reverse scanning of the time axis based on reverse temporal reinforcement learning to identify potential metabolic inflection point regions where the concentration decay rate drifts significantly. It effectively locates the time interval of metabolic rate mutation, embeds a Bayesian structural change point detection model in the potential metabolic inflection point region, calculates the confidence of each time point as the true drinking end point through posterior probability, filters out the time window boundary with the highest change point probability, and outputs a high-confidence change point probability sequence to accurately select the most likely drinking end time window. Based on the filtered boundary, it dynamically corrects the original time window to eliminate the offset caused by sampling delay and individual metabolic drift, and outputs the inversely fitted individual's true drinking end time point, eliminating time offset and outputting an accurate end time point. It should be noted that the sampling interval was set to 5 minutes, and the continuous monitoring duration was no less than 180 minutes to construct an initial metabolic decay curve. Based on reverse temporal reinforcement learning, the system scanned backward along the time axis from the last sampling point. The state space was defined as the concentration value at the current time point and the concentration difference vector of the previous three time points. The action space was set as inflection point markers and non-inflection point markers. The reward function was the negative log-likelihood of the concentration decrease rate, and the discount factor γ was set to 0.95 to enhance the sensitivity to regions with significant rate drift. During the reverse scanning process, for each time step forward, the current point was calculated relative to the subsequent... The slope differences of local polynomial fitting at four time points were analyzed. When the absolute value of the difference exceeded 0.008 mmol / L / min, the region was marked as a potential metabolic inflection point region, and the start and end timestamps and drift amplitude of the region were recorded. Within each potential metabolic inflection point region, a Bayesian structural change point detection model was embedded, with a prior probability of 0.05 and observation noise following a Gaussian distribution with a mean of 0 and a standard deviation of 0.015 mmol / L. For each time point within the region, the posterior probability of it being the actual end point of alcohol consumption was calculated. The likelihood function was based on the difference in the mean concentration of six sampling points before and after the inflection point. The system is constructed with a difference threshold of 0.03 mmol / L. After traversing all time points within the region, a posterior probability sequence is output. Continuous intervals with a probability value exceeding 0.75 and a duration of at least 3 sampling points (i.e., 15 minutes) are selected as the boundaries of a high-confidence time window. The time point corresponding to the maximum posterior probability within this window is taken as a candidate variable point. Finally, a high-confidence variable point probability sequence is output, with a sequence length consistent with the original number of sampling points. The probability of non-variable points is assigned a value of 0. Based on the selected high-confidence variable point probability sequence boundaries, the original time window is dynamically corrected. The correction rule is: move the candidate variable points forward... The correction time window starts 20 minutes back and ends 10 minutes later. Data outside this range within the original time window is truncated to eliminate the bias caused by sampling delay and individual metabolic drift. Based on this, local weighted regression is re-executed on the concentration data within the correction window with a bandwidth parameter of 15 minutes and the regression residual threshold controlled within 0.01 mmol / L. The turning point where the first derivative of the regression curve changes from negative to zero is taken as the actual end time of individual drinking after inverse fitting. This time point is output with accuracy down to the second, and the time window offset before and after correction is recorded simultaneously. Furthermore, the reverse metabolic inversion device also includes: on the constructed initial metabolic decay curve, a reward function of time-series attention mechanism weighted inverse reinforcement learning is introduced to enhance the sensitivity capture ability of small inflection points in the early metabolic stage, significantly improve the sensitivity of identifying weak metabolic inflection points in the early stage, input the high-confidence change point probability sequence into the hierarchical Bayesian mixture effect model, decompose the coupling effect of individual inherent metabolic rate and random environmental interference on inflection point localization, effectively separate inherent metabolic differences and environmental interference, improve localization accuracy, and based on the corrected time window, superimpose posterior prediction check to perform residual rebalancing on fitting error, extract metabolic residual sequence and output refined real drinking end timestamp after metabolic heterogeneity correction, correct metabolic heterogeneity, and output high-precision real end time point; It should be noted that, on the initial metabolic decay curve, the local state composed of its three preceding difference vectors is calculated for each sampling point, and reinforcement learning scans are performed point by point along the reverse time axis. A temporal attention mechanism is introduced, assigning attention weights of 1.5 times higher to state-action pairs in the early metabolic phase (within 60 minutes before the actual end of drinking) than in the later phase. The weighted reward function uses the negative log-likelihood of the concentration decrease rate. The attention weights enable the model to generate non-zero rewards in regions where the decrease rate is flat but has small drift (absolute slope difference ≥ 0.004 mmol / L / min). This signal allows for the capture of potential metabolic inflection point regions even when concentration changes are less than the conventional inflection point threshold of 0.008 mmol / L / min, enhancing the sensitivity to subtle early metabolic changes after small-dose alcohol consumption at different time intervals. High-confidence change point probability sequences (continuous intervals with posterior probability > 0.75 and duration ≥ 15 minutes) are used as observation data and input into a hierarchical Bayesian mixed-effects model. The model sets the individual intrinsic metabolic rate as a random effect term, following a normal distribution with a mean of 0.018 mmol / L / min and a standard deviation of 0.004. Sampling operation delay and environmental factors are also considered. Random environmental disturbances due to temperature fluctuations were treated as a fixed-effect term with a prior variance of 0.002. Markov chain Monte Carlo sampling (5000 iterations, the first 2000 being a warm-up) was used to decompose the coupled effects of the two types of factors on inflection point localization, outputting a posterior shrinkage estimate of the individual metabolic rate. The localization bias caused by environmental disturbances was controlled within ±2.5 minutes, thus obtaining a pure inflection point localization result independent of external disturbances. Based on the corrected time window (20 minutes before to 10 minutes after the candidate change point), locally weighted regression (15-minute bandwidth, residual threshold 0.01 mmol) was employed. The concentration curve was fitted by / L, the fitted residual sequence was calculated, and a posterior prediction check was introduced: 500 sets of parameters were extracted from the posterior distribution of metabolic rate generated by the hierarchical Bayesian model, and the residual interval was simulated and predicted for each time point. If the measured residual exceeded 90% of the prediction interval, a decay factor of 0.7 was applied to the point and the regression was reweighted. After three rounds of iteration, the root mean square error of the overall residual converged to within 0.008 mmol / L. The final residual sequence was extracted, and the turning point where the first derivative of the regression fitted curve changed from negative to zero was taken as the refined real drinking end time stamp. The time window offset before and after correction was output simultaneously. The residual trace searcher employs ant colony segmented path search and a hidden semi-Markov model to segment and mine metabolic residual sequences within a corrected time window. This identifies hidden repeated intake patterns of multiple small-dose, time-segmented drinking, reliably cracking the deceptive behavior of avoiding peaks through time-segmented drinking. Within the corrected time window, the metabolic residual sequence is extracted as the search space. Multiple artificial ants are initialized using an ant colony segmented path search algorithm, releasing pheromones in segments to mark residual fluctuation paths, effectively marking these paths and improving the coverage of the hidden pattern search. The segmented path nodes are input into the hidden semi-Markov model, setting the residence time distribution and hidden state transition matrix to segment the metabolic residual sequence. This identifies residual recovery segments corresponding to repeated intake behaviors, accurately segmenting metabolic states and enhancing the sensitivity of identifying repeated intake behaviors. Based on the state segmentation results, the hidden intake patterns of multiple small-dose, time-segmented drinking are output, including the temporal distribution of each intake time point, relative dose intensity, and adjacent intake intervals. This complete output of the hidden intake patterns provides a structured basis for subsequent fusion and judgment. It should be noted that the length of the metabolic residual sequence is the number of sampling points within the correction window, the sampling interval is 5 minutes, and the unit of each residual value is mmol / L. The ant colony segmented path search algorithm initializes the number of artificial ants to 30. Each ant starts from the starting point of the correction window and decides whether to release pheromones based on the magnitude of the absolute value of the residual (the threshold is set to 0.003 mmol / L). The pheromone volatile factor is initially set to 0.1 and is updated once every time step forward. During the ant's movement, if the absolute value of the residual at the current point exceeds 0.003 mmol / L and the rate of change of the residual between two adjacent points is greater than 0.002 mmol / L / minute, then a pheromone deposition is marked at that node. After 30 ants have completed all traversals, the cumulative pheromone concentration of each node is calculated. The nodes with the highest concentrations (top 20%) are output as key nodes in the residual fluctuation path. Simultaneously, the original timestamp and residual amplitude of each node are recorded. When the segmented path nodes are input into the hidden semi-Markov model, the hidden states are set to three types: normal metabolic decay state, residual recovery initiation state, and residual recovery persistence state. The initial values of the state transition matrix are set as follows: the transition probability from the normal metabolic decay state to the residual recovery initiation state is 0.08, the transition probability from the residual recovery initiation state to the residual recovery persistence state is 0.85, and the transition probability from the residual recovery persistence state back to the normal metabolic decay state is 0.20. The residence time distribution adopts Poisson's Law. The system uses a loose distribution, with the average dwell time in the normal state set at 25 minutes (i.e., 5 sampling points) and the average dwell time in the sustained recovery state set at 10 minutes (i.e., 2 sampling points). The observation probability matrix is constructed based on the residual amplitude. When the residual value is greater than 0.004 mmol / L and two consecutive sampling points are positive, it is determined to be the initial recovery state; when the residual value fluctuates between 0.002 mmol / L and 0.008 mmol / L and lasts for at least 10 minutes, it is determined to be the sustained recovery state. After decoding using the Viterbi algorithm, the hidden state sequence corresponding to each sampling point is output. Based on the state segmentation results, the specific parameters of the concealed intake mode are output as follows: the intake time point is the first sampling point of the initial recovery state. Timestamp; The relative dose intensity is calculated by the ratio of the area under the residual curve within the sustained recovery period to the individual's average metabolic rate. The area is calculated using the trapezoidal integral method, and the integration interval is between the start of the recovery and the return to normal. The interval between adjacent intakes is the time difference between the end of the previous recovery start state and the start of the next recovery start state. For two recovery periods with an interval of less than 15 minutes, they are merged into a single intake event. The relative dose intensity of the merged event is the sum of the two events, and the time point is the weighted average of the two events. Finally, a list of multiple intake patterns is output. Each element includes the intake time, normalized dose intensity, and the interval between the previous intake and the previous intake. At the same time, the start and end timestamps of the residual recovery segment corresponding to the pattern are recorded. Furthermore, the residual hidden trace searcher also includes: in the segmented path search of ant colonies, an adaptive pheromone evaporation factor and a heuristic guiding function are introduced to enable ants to prioritize exploring path branches related to abnormal recovery of residuals after metabolic inflection points, thereby enhancing the ability to focus on abnormal recovery paths and improving the detection rate of hidden intake patterns. The hidden semi-Markov model is extended to a hierarchical hidden semi-Markov model, with the upper layer identifying macro-level drinking segments and the lower layer analyzing sub-state transition paths of micro-dose intake within segments, achieving hierarchical analysis of macro- and micro-level intake behaviors, refining the temporal structure of multiple micro-dose intakes, integrating hidden intake patterns, and using a Bayesian model to quantify the uncertainty of multiple intake time points, outputting segmented path mining results with confidence, quantifying the uncertainty of intake time, and improving the reliability and interpretability of the mining results. It should be noted that the volatile factor ρ is initially set to 0.1. When the absolute value of the residuals at three consecutive sampling points exceeds 0.003 mmol / L and the rate of change is greater than 0.002 mmol / L / min, ρ dynamically decays to 0.05 to enhance pheromone accumulation in abnormal pathways. Conversely, in stable segments without abnormal fluctuations, ρ is restored to 0.1 to avoid pathway solidification. The heuristic guidance function η is defined as the ratio of the residual amplitude at the current point to the average residual baseline after the metabolic inflection point (taken as 1.5 times the average residual of the first four sampling points after the start of the correction window). When η is greater than 1.2, the probability of this node being selected is multiplied by a coefficient of 2.0. Thirty ants were used to prioritize exploring path branches related to the abnormal rebound of residuals after the metabolic inflection point, ultimately outputting the key nodes with the top 15% pheromone concentrations and recording the initial confidence level of residual rebound for each node. The hidden semi-Markov model was extended to a hierarchical hidden semi-Markov model. The upper layer set two macro states: macro-drinking segment and macro-non-drinking segment, with the macro state transition probability set to 0.12 and the average residence time being 35 minutes and 20 minutes, respectively. The Viterbi algorithm was used to decode the macro sequence. The lower layer further analyzed the sub-state transition paths within each macro-drinking segment. The sub-states included: normal decay substate and micro-dose intake. The initial substate and the sustained microdose substate, with residence times following Poisson distributions of 25 minutes (mean), 10 minutes (single-point), and 10 minutes, respectively, were analyzed. The observation probability was determined using residual amplitude thresholds (initial rise greater than 0.004 mmol / L with two consecutive positive values, sustained state between 0.002 and 0.008 mmol / L). After hierarchical decoding, the substate transition paths of multiple microdose intakes within the same macro-drinking period were distinguished, and the time window and residual amplitude range corresponding to each substate were output. After fusing the concealed intake pattern, a Bayesian model was used to quantify the uncertainty of multiple intake time points, and the posterior variance of the hierarchical hidden semi-Markov model was analyzed. 200 sets of parameters are extracted from the data. Each set of parameters is independently decoded to obtain a sequence of intake time points. The posterior frequency of each time point being identified as the start of intake is calculated. Time points with a posterior frequency greater than 0.70 are included in the candidate intake set. Mean clustering (with a cluster radius of 7.5 minutes) is used to merge neighboring candidate points. Finally, the confidence level (i.e., the posterior frequency of that point) and the 95% confidence interval (taking the 2.5% and 97.5% quantiles of the posterior distribution) of each intake time point are output. At the same time, the median and interquartile range of the relative dose intensity corresponding to each intake time point are recorded to form a segmented path mining result with probabilistic uncertainty measure. A dual-mode fusion unit is used to spatiotemporally align and resolve conflicts between the actual drinking end time and multiple intake patterns, forming a unified temporal profile of individual drinking behavior. This fully constructs the entire temporal behavioral chain of an individual from intake to metabolism. It receives the actual drinking end time output by the reverse time metabolism inversion unit and the multiple intake patterns output by the residual trace search unit. It establishes a spatiotemporal alignment matrix with time as the horizontal axis and intake events as the vertical axis, ensuring that multi-source data are uniformly expressed under the same time reference, eliminating time axis deviation, and resolving conflicts in conflict areas in the spatiotemporal alignment matrix, including temporal logical contradictions between the end time and subsequent intake time, as well as ambiguities in the attribution of metabolic residual recovery sources. This avoids event attribution confusion and improves the accuracy of intake behavior discrimination. The resolved alignment results and attribution are encoded into a unified temporal profile of individual drinking behavior, including a joint representation structure of continuous metabolic segments and discrete intake events, fully restoring the entire process of individual drinking and providing a structured data foundation for risk assessment. It should be noted that the system receives the actual alcohol consumption end timestamp output by the reverse metabolism inversion unit and simultaneously receives a list of multiple intake patterns output by the residual trace search unit. Each intake pattern includes the intake time, normalized dose intensity, and interval between adjacent intakes. A two-dimensional spatiotemporal alignment matrix is constructed with time as the horizontal axis and each intake event as the vertical axis. The matrix row index corresponds to each sampling time point within the correction window (the sampling interval is fixed at 5 minutes), and the column index corresponds to the number of each intake event. For each time point, the matrix element records whether the point belongs to the residual recovery segment of a certain intake event, and the... The time difference between the point and the actual end time of drinking is used to automatically align the start and end time windows and correction window boundaries of each intake event during matrix construction, ensuring that all timestamps are compared and calculated under the same time base. For conflict areas appearing in the spatiotemporal alignment matrix, a two-level conflict resolution mechanism is implemented. The first level handles temporal logic contradictions: when the intake time of a certain intake pattern is later than the actual end time of drinking but earlier than the end point of the correction window, it is determined that the intake is a secondary intake behavior after the end, the event is retained, and its time difference with the end time is marked as a positive interval; if the intake time is earlier than the actual end time of drinking... If the alcohol consumption ends at a certain time, it is considered an intake before the end of the consumption period. This is marked as a negative interval and triggers an ambiguous judgment on the source of metabolic residual rise. The second level of residual rise attribution is as follows: when the same metabolic residual rise segment may belong to two adjacent intake events, the ratio of the residual amplitude at each point within the segment to the intensity of the two intake doses is calculated. If the ratio to the intensity of the earlier intake exceeds 0.7, it is attributed to the earlier event; otherwise, it is attributed to the later event. Segments with an attribution probability lower than 0.3 are marked as independent noise events. The temporal profile of an individual's drinking behavior contains a joint representation structure of continuous metabolic segments and discrete intake events. The continuous metabolic segments are represented by piecewise linear interpolation, with the starting point of the correction window as the starting point, the actual end time of drinking as the inflection point, and the ending point of the correction window as the ending point. The slope of each segment is determined by the posterior contraction estimate of the individual's intrinsic metabolic rate. Discrete intake events are embedded in the continuous metabolic segments as labeled points. Each event records the intake time, normalized dose intensity, interval with the previous intake, and attribution confidence. The joint characterization structure supports traversing all metabolic segments and intake events in chronological order and outputs a unified data object for subsequent game risk determiner to call, while retaining the original residual sequence as a backtracking basis. The game risk assessor, based on a knowledge graph rule engine and a game adversarial network, simulates the dynamic risk equilibrium point in law enforcement scenarios and outputs an interpretable danger judgment that actually affects driving ability, realizing the integrated output of risk assessment and interpretable attribution in adversarial scenarios. The dynamic threshold calibrator is used to dynamically adjust the personalized danger assessment threshold at the time of law enforcement based on the fused individual drinking behavior structure and game risk assessment results, and output the final law enforcement recommendation and confidence interval, generating probabilistic law enforcement recommendations and avoiding mechanical comparison with statutory thresholds.
[0023] Example 2, as Figure 1 , Figure 2 As shown, based on Embodiment 1, the present invention provides a technical solution: the game risk determiner specifically includes: constructing a knowledge graph rule engine containing alcohol metabolism dynamics rules, law enforcement judgment precedents and individual defense strategies, converting the temporal profile of individual drinking behavior into a set of fact triples, ensuring the structured expression of facts, improving reasoning efficiency and rule interpretability, inputting the fact triples into a game adversarial network, in which the law enforcement discriminator and individual behavior simulator alternately optimize, simulating the risk adversarial evolution process under different law enforcement strategies, approaching the real dispute boundary through adversarial game, enhancing the law enforcement adaptability of the judgment result, extracting the dynamic risk balance point from the adversarial network, outputting an interpretable danger judgment that actually affects driving ability, and attaching the key feature attribution path on which the judgment is based, providing a traceable judgment basis chain, and supporting the credibility and transparency of law enforcement decisions; It should be noted that the alcohol metabolism kinetics rule sets the posterior contraction estimate of the individual's intrinsic metabolic rate as the baseline descent slope, and defines the threshold for the initial judgment of residual recovery in the metabolic residual sequence as 0.004 mmol / L with two consecutive positive values. The fluctuation range of the residual recovery duration is 0.002 mmol / L to 0.008 mmol / L. The law enforcement precedent includes previous cases based on the statutory threshold of 0.02 mmol / L and their corresponding metabolic feature vectors. The individual defense strategy records the common intake time defense intervals and dosage dispute ranges. The continuous metabolic segment slope, discrete intake event time point, normalized dose intensity, and attribution confidence in the individual drinking behavior time series profile output by the dual-mode fusion machine are mapped one by one to fact triples. Each triplet adopts a subject-predicate-object structure, where the subject is a time point or event number, the predicate is the relation type, and the object is the corresponding attribute value. All triplets are stored uniformly in a graph database. The game-playing adversarial network consists of two adversarial modules: a law enforcement discriminator and an individual behavior simulator. The two modules optimize alternately. The law enforcement discriminator calculates the deviation between the metabolic residual prediction interval and the statutory threshold based on the intake time point, dose intensity, and time difference from the law enforcement time in the current fact triplet, and outputs a preliminary hazard probability value. The individual behavior simulator uses the defense strategies in the knowledge graph as its action space to simulate possible intake time offset defenses or metabolic rate individual difference defenses that individuals may raise. In each round of simulation, the behavior simulator attempts to adjust the hazard probability value downwards. The two modules alternate. In iterative training, the discriminator updates its discrimination boundary based on the simulator's defense actions in each iteration. The simulator adjusts its defense strategy parameters based on the discriminator's output until the fluctuation range of the danger probability output by the discriminator is below 0.02 for three consecutive rounds. At this point, a dynamic risk equilibrium point is reached. This dynamic risk equilibrium point reflects the minimum irreducible divergence between law enforcement judgment and reasonable defense under a given time-series profile of an individual's drinking behavior. The final danger probability value is extracted from the adversarial network that has reached the dynamic risk equilibrium point. If the value is greater than 0.65, the individual is determined to be in a dangerous range due to the actual impact on driving ability at the current law enforcement moment. If it is between 0.35 and 0.65, it is determined to be in a marginal state and requires further testing. If it is below 0.35, it is determined to be in a safe range and the danger can be explained. The risk assessment is accompanied by a key feature attribution path, which is generated by backtracking the attention weights in the game adversarial network. The three features with the greatest impact on risk assessment are listed in descending order of contribution: the first is the normalized dose intensity of the most recent intake event before the enforcement time and its time difference from the enforcement time; the second is whether the predicted interval of metabolic residuals between the actual end time of drinking and the enforcement time exceeds the personalized boundary after calibration of the statutory threshold; and the third is the degree of deviation between the posterior contraction estimate of the individual's intrinsic metabolic rate and the mean metabolic rate of the standard population. Each feature is labeled with its original value, contribution weight and the corresponding knowledge graph rule source, forming a complete chain of judgment criteria. Finally, the risk assessment result, attribution path and confidence interval are encapsulated as an output object. Furthermore, the game risk assessor also includes: embedding temporal logic operators into the knowledge graph rule engine to apply dynamic rule constraints to the metabolic interval between the end of drinking and the time of law enforcement, enhancing the sensitivity of the rules to the time sequence, ensuring the rigor of the temporal judgment logic, and avoiding misjudgments due to disordered time sequence; expanding the game adversarial network into a multi-agent game architecture, introducing three parties of intelligent agents: the prosecutor, the defense, and the expert system, generating risk distributions that closely resemble real law enforcement scenarios in the adversarial process, improving the realism of risk assessment adversarial, enhancing the adaptability of law enforcement decision-making scenarios, generating adversarial samples based on the output interpretable danger assessment, superimposing counterfactual reasoning paths, verifying the robustness of the assessment results, and outputting danger assessment explanation text with causal chain annotations to improve the robustness of the assessment results and provide verifiable and traceable explanatory basis; It should be noted that in the actual deployment of the knowledge graph rule engine, temporal logic operators are embedded to enhance the sensitivity to dynamic constraints on metabolic intervals. For the metabolic interval between the end of drinking time and the enforcement time, temporal operators are set to include three types of logical relationships: "before," "after," and "interval." Constraint verification is performed with minutes as the smallest time granularity. Simultaneously, based on a 5-minute sampling interval time axis, temporal order dependency rules are applied to the upper and lower boundaries of the metabolic residual prediction interval: if the enforcement time falls within the 2nd to 4th sampling point window after an intake event, the risk probability weight within that window is increased by 1.2 times. The logic operator is implemented through the path query interface of the graph database, ensuring that each fact triple is accompanied by a timestamp validity check during the reasoning process; the prosecution agent uses the statutory threshold of 0.02 mmol / L as the prior anchor point and outputs the probability of charge based on the slope of the metabolic segment and the residual recovery segment in the time-series profile of individual drinking behavior; the defense agent uses the intake time offset interval of ±15 minutes and the dose dispute range of ±20% in the individual defense strategy library as the action space to generate adversarial parameter adjustments; the expert system agent outputs the posterior contraction estimate of the individual metabolic rate (mean 0.018 mmol / L) based on the hierarchical Bayesian mixture effect model. (l / L, standard deviation 0.004) to balance the outputs of the prosecution and defense. The three intelligent agents alternately iterate, with the prosecution minimizing the judgment error and the defense maximizing the adversarial benefit in each round. The expert system adjusts the weights of both parties based on metabolic kinetic rules until the fluctuation range of the danger probability output by the three parties is less than 0.015 for five consecutive rounds. Based on the current individual drinking behavior time-series profile, three sets of counterfactual scenarios are constructed: the first set increases or decreases the normalized dose intensity of the most recent intake event before the enforcement time by ±20%; the second set shifts the actual drinking end time by ±10 minutes; the third set accepts the individual's inherent metabolic rate afterward. The estimated value increases or decreases by ±0.004 mmol / L / min. Each counterfactual scenario is input into the converged multi-agent game architecture, the danger probability value is recalculated, and the judgment result is observed to see if it is reversed. If the reversal rate exceeds 25%, the model parameters are fine-tuned until the judgment consistency rate reaches more than 90% on 500 randomly sampled adversarial samples. The final output danger judgment explanation text is accompanied by causal chain annotations, listing the direct influence path of key features on the judgment result in the form of cause-effect-evidence triples, and marking the confidence level of each causal chain (taking the median of the posterior distribution), forming a complete and verifiable judgment basis. The dynamic threshold calibrator specifically includes: acquiring the time-series profile of individual drinking behavior output by the dual-mode fusion device and the danger judgment result output by the game risk judge; constructing a personalized risk assessment feature vector based on the enforcement moment; making the judgment standard deviate from the uniform template and fit the individual's actual metabolic and intake characteristics; inputting the personalized risk assessment feature vector into the dynamic threshold generation network; using the legal threshold as a priori anchor point; performing nonlinear offset calibration on the anchor point according to the individual's intake structure and game risk weights; realizing dynamic matching between the threshold and individual risk characteristics; improving the scientific nature of the judgment; outputting the personalized danger judgment threshold at the enforcement moment; and simultaneously calculating the offset amount and offset direction of this threshold relative to the legal threshold, forming a dynamic calibration record; providing a traceable adjustment basis for law enforcement decisions; and enhancing the transparency of the judgment process. It should be noted that the individual drinking behavior time-series profile output by the dual-mode fusion unit is obtained. This profile includes the slope of the continuous metabolic segment starting from the correction window start point (the value is the posterior contraction estimate of the individual's intrinsic metabolic rate) and the normalized dose intensity and attribution confidence of each discrete intake event. At the same time, the final hazard probability value and dynamic risk equilibrium point parameters output by the game risk determiner are read. Using the enforcement time as the time reference, the following features are extracted to form a personalized risk assessment feature vector: the dose intensity of the most recent intake event before the enforcement time and its time difference from the enforcement time, the upper and lower limits of the metabolic residual prediction interval at the enforcement time, and the individual's metabolic rate and standard... The deviation of the population mean (0.018 mmol / L / min) (expressed as a multiple of standard deviation), and the risk probability weight factor after weighting processing following time-series logic operator verification, are all verified for the validity of timestamps in the graph database to ensure that each feature is accompanied by a corresponding time label and confidence level. The constructed personalized risk assessment feature vector is input into a dynamic threshold generation network. This network adopts a three-layer fully connected structure, using the statutory threshold of 0.02 mmol / L as the prior anchor point. The network applies a first-level nonlinear shift to the anchor point based on the normalized dose intensity in the individual intake structure, with the shift amplitude being positively correlated with the dose intensity. Then, based on game theory... Risk weights (final weighting coefficients for the prosecution and defense after expert system adjudication, ranging from 0.30 to 0.70) apply a second-level offset to the anchor point. These two offsets, normalized by a sigmoid function, are then superimposed onto the prior anchor point to generate a personalized risk assessment threshold. The offset direction is determined by whether the upper limit of the metabolic residual prediction interval exceeds the statutory threshold: if it does, a positive offset (threshold increases); otherwise, a negative offset (threshold decreases). The offset is calculated precisely to 0.001 mmol / L and recorded synchronously in the dynamic calibration log. After the dynamic threshold generates the personalized risk assessment threshold at the time of law enforcement output by the network, it is simultaneously calculated relative to the statutory threshold of 0.0. The absolute offset and relative offset percentage of 2 mmol / L, if the offset direction is positive, indicate that the individual requires a more stringent judgment standard to be judged as dangerous; if it is negative, it indicates that the metabolic characteristics or intake structure make it more sensitive to the statutory threshold. The calibration record includes the following fields: enforcement time stamp, personalized threshold, statutory threshold, absolute offset, relative offset percentage, dose intensity value and game risk weight value involved in the offset calculation, and the respective contribution ratio of the two offsets. This record is stored in the local database and uploaded to the cloud simultaneously. At the same time, the calibrated threshold will be sent back to the game risk determiner as the reference boundary for the next round of interaction, forming a closed-loop calibration mechanism. Furthermore, the dynamic threshold calibrator also includes: based on the personalized risk assessment feature vector, it introduces temporal uncertainty interval estimation to quantify the predicted interval of metabolic residuals before and after the enforcement moment, which serves as a confidence boundary constraint for threshold calibration, thereby improving the anti-interference capability and stability of threshold calibration; it extends the dynamic threshold generation network to a Bayesian deep regression architecture, outputting the posterior distribution of the threshold instead of a single point value, supporting probabilistic interpretation of the calibration results, providing quantifiable confidence and risk boundaries of the judgment results, and outputting the final enforcement recommendation and its confidence interval based on the dynamic calibration record and the posterior distribution of the threshold. The enforcement recommendation includes three categories of labels: taking coercive measures, allowing release but requiring observation, or requiring supplementary testing, thereby realizing hierarchical enforcement output and clarifying the range of credible judgments. It should be noted that the temporal uncertainty of the metabolic residual prediction interval before and after the enforcement moment was quantified. Based on the metabolic decay curve constructed by sampling at 5-minute intervals and continuous monitoring for 180 minutes, the posterior distribution of the residuals at six time points—four sampling points before and two sampling points after the enforcement moment—was extracted. The residual observation noise at each time point followed a Gaussian distribution with a mean of 0 and a standard deviation of 0.015 mmol / L. The posterior contraction estimate of the individual metabolic rate output by the hierarchical Bayesian mixed effects model (mean of 0.018 mmol / L and standard deviation of 0.004) was used as the baseline descent slope to calculate the 90% prediction interval of the residuals at each time point, i.e., taking the 2.5% quantile and 97th percentile of the posterior distribution. The 0.5% quantile is used as a confidence boundary, which serves as a hard constraint for threshold calibration. When the upper limit of the predicted metabolic residual at the enforcement time exceeds the statutory threshold of 0.02 mmol / L and the lower limit is below the threshold, it is considered a boundary state, and the adjustment step size of subsequent offsets needs to be reduced to within 0.002 mmol / L to avoid calibration overshoot. The posterior distribution of the output threshold is used to replace the single-point threshold. The network retains a three-layer fully connected structure, and a standard normal prior distribution is applied to the weight parameters of each layer. Monte Carlo dropout is used to achieve approximate Bayesian inference. During forward propagation, neurons with a random dropout ratio of 0.2 are randomly dropped, and 50 random forward computations are performed to obtain 50 sets of candidate thresholds. The mean of these 50 sets of thresholds is... The value is used as a point estimate of the personalized hazard assessment threshold, and the standard deviation is used as a measure of calibration uncertainty. The two-stage nonlinear offset mechanism remains unchanged: the first stage calculates the offset based on the dose intensity of the most recent intake event and the time difference from the enforcement moment, and the second stage calculates the offset based on the game risk weight coefficient. The offset direction is determined by whether the upper limit of the metabolic residual prediction interval at the enforcement moment exceeds 0.02 mmol / L. The posterior distribution of the final output threshold can be represented as a Gaussian distribution with a mean of μ and a standard deviation of σ. The actual blood alcohol concentration measurement at the enforcement moment is compared with the posterior distribution of the personalized hazard assessment threshold: if the actual concentration is greater than the 95% confidence upper limit of the threshold posterior distribution (i.e., μ + 1.96σ), the offset is calculated as follows: The output suggests mandatory measures, with a confidence interval of the upper limit ± 0.002 mmol / L. If the actual concentration is less than the 95% lower confidence limit of the threshold posterior distribution (i.e., μ-1.96σ), the output suggests release but requires observation, with a confidence interval of the lower limit ± 0.002 mmol / L. If the actual concentration falls between the two, the output suggests supplementary testing, with a prompt for retesting after 15 minutes. The three types of labels for the final enforcement recommendations all include corresponding confidence intervals. These intervals are calculated by comparing the threshold posterior distribution output by the Bayesian deep regression architecture with the actual concentration. At the same time, the absolute offset and the contribution ratio of the two-level offset in the dynamic calibration record are encapsulated together for display and review by the enforcement terminal.
[0024] The following is a detailed explanation of the workflow of this intelligent analysis and management system for blood alcohol test data.
[0025] First, individual blood alcohol concentration measurements are continuously collected at fixed sampling intervals to construct an initial metabolic decay curve. A reverse-time metabolism inversion device scans this curve along the reverse time axis to identify potential metabolic inflection point regions where the concentration decay rate drifts significantly. A Bayesian change point detection model is embedded to calculate the posterior probability of each time point as the actual end point of drinking. Based on this, high-confidence time window boundaries are selected, and the original time window is dynamically corrected to eliminate the offset caused by sampling delay and individual metabolic drift. The actual end point of drinking and metabolic residual sequence after inverse fitting are output. Subsequently, the residual hidden trace searcher initializes multiple artificial ants to perform segmented path search within the corrected time window, using the metabolic residual sequence as the search space. The residual fluctuation path is marked by pheromone deposition. The segmented path nodes are input into a hidden semi-Markov model for state segmentation to identify residual recovery segments corresponding to repeated intake behaviors and output the hidden intake pattern of multiple small doses of drinking at different time periods. The dual-mode fusion unit receives the actual drinking end time and multiple intake patterns, establishes a spatiotemporal alignment matrix, performs two-level conflict resolution, and encodes the resolution results into a unified temporal profile of individual drinking behavior containing continuous metabolic segments and discrete intake events. The game-theoretic risk assessor transforms this profile into fact triples based on a knowledge graph rule engine. The input is a game-theoretic adversarial network simulating the alternating optimization process of law enforcement judgment and individual defense, extracting the dynamic risk equilibrium point, and outputting an interpretable risk assessment and its key feature attribution path. Finally, the dynamic threshold calibrator constructs a personalized risk assessment feature vector based on the law enforcement moment, inputs it into the dynamic threshold generation network, performs nonlinear offset calibration with the statutory threshold as a priori anchor point, and outputs a personalized risk assessment threshold and final law enforcement recommendation, along with a confidence interval, completing the entire intelligent analysis process from data collection to law enforcement decision-making.
[0026] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A smart analysis and management system for blood alcohol test data, characterized in that, Includes the following modules: The reverse time metabolism inversion device, based on reverse temporal reinforcement learning and Bayesian structural change point detection, backfits the metabolic inflection point from the temporal decay curve of blood alcohol concentration, corrects the time window and extracts the metabolic residual sequence, thereby accurately locating the individual's true drinking end time. The residual trace searcher is used to perform segmented path mining on metabolic residual sequences within a corrected time window using ant colony segmented path search and hidden semi-Markov models to identify hidden repeated intake patterns of multiple small doses of alcohol consumed at different times. A dual-mode fusion device is used to spatiotemporally align and resolve conflicts between the actual end time of drinking and multiple intake patterns, forming a unified temporal profile of individual drinking behavior. The game risk assessor, based on a knowledge graph rule engine superimposed with a game adversarial network, simulates the dynamic risk equilibrium point in law enforcement scenarios and outputs an interpretable risk assessment of whether the situation actually affects driving ability. The dynamic threshold calibrator is used to dynamically adjust the personalized danger assessment threshold at the time of law enforcement based on the fused individual drinking behavior structure and game risk assessment results, and output the final law enforcement recommendation and confidence interval.
2. The intelligent analysis and management system for blood alcohol test data according to claim 1, characterized in that: The reverse metabolism inversion device specifically includes: Individual blood alcohol concentration measurements were collected over a continuous time series to construct an initial metabolic decay curve. The time axis was then scanned in reverse based on inverse temporal reinforcement learning to identify potential metabolic inflection point regions where the rate of concentration decline significantly shifted. A Bayesian structural change point detection model is embedded in the potential metabolic inflection point region. The confidence of each time point as the actual end point of drinking is calculated by posterior probability. The time window boundary with the highest change point probability is selected and a high-confidence change point probability sequence is output. The original time window is dynamically corrected based on the filtered boundaries to eliminate the offset caused by sampling delay and individual metabolic drift, and the actual end time of drinking for the individual is output after inverse fitting.
3. The intelligent analysis and management system for blood alcohol test data according to claim 2, characterized in that: The reverse metabolism inversion device also includes: On the constructed initial metabolic decay curve, a reward function of time-series attention mechanism weighted inverse reinforcement learning is further introduced to enhance the sensitivity to capture small inflection points in the early metabolic stage. The high-confidence change point probability sequence is input into the hierarchical Bayesian mixture effect model to decompose the coupled influence of individual intrinsic metabolic rate and random environmental disturbance on inflection point localization. Based on the corrected time window, the fitting error is rebalanced by superimposing a posterior prediction check, the metabolic residual sequence is extracted, and the refined real drinking end timestamp after metabolic heterogeneity correction is output.
4. The intelligent analysis and management system for blood alcohol test data according to claim 1, characterized in that: The residual trace finder specifically includes: Within the corrected time window, the metabolic residual sequence is extracted as the search space. Multiple artificial ants are initialized using the ant colony segmented path search algorithm, and pheromones are released in segments to mark the residual fluctuation path. The segmented path nodes are input into the hidden semi-Markov model. The residence time distribution and hidden state transition matrix are set to perform state segmentation on the metabolic residual sequence and identify the residual recovery segment corresponding to repeated intake behavior. Based on the state segmentation results, the hidden intake pattern of multiple small doses of alcohol consumed at different times is output, including the temporal distribution of each intake time point, the relative intensity of the dose, and the interval between adjacent intakes.
5. The intelligent analysis and management system for blood alcohol test data according to claim 4, characterized in that: The residual trace finder also includes: In the segmented path search of ant colonies, an adaptive pheromone evaporation factor and a heuristic guiding function are introduced to enable ants to prioritize exploring path branches that are related to the abnormal recovery of residuals after the metabolic inflection point. The hidden semi-Markov model is extended to a hierarchical hidden semi-Markov model, with the upper layer identifying the macro-drinking segment and the lower layer analyzing the sub-state transition path of micro-dose intake within the segment. By integrating the hidden ingestion pattern, a Bayesian model is used to quantify the uncertainty of multiple ingestion time points, and the segmented path mining results with confidence are output.
6. The intelligent analysis and management system for blood alcohol test data according to claim 1, characterized in that: The dual-mode fusion unit specifically includes: The actual drinking end time output by the reverse metabolism inversion device and the multiple intake patterns output by the residual trace search device are used to establish a spatiotemporal alignment matrix with time as the horizontal axis and intake events as the vertical axis. Conflict resolution is performed on conflict regions in the spatiotemporal alignment matrix, including temporal logical contradictions between the end time and subsequent intake time, and ambiguity in the attribution of the source of metabolic residual recovery. The alignment results after resolution and the discrimination attribution code are encoded into a unified temporal profile of individual drinking behavior, which includes a joint representation structure of continuous metabolic segments and discrete intake events.
7. The intelligent analysis and management system for blood alcohol test data according to claim 1, characterized in that: The game risk assessor specifically includes: Construct a knowledge graph rule engine that includes alcohol metabolism dynamics rules, law enforcement precedents, and individual defense strategies, and transform the temporal profile of individual drinking behavior into a set of fact triples; The fact triples are input into the game adversarial network, where the law enforcement discriminator and the individual behavior simulator are alternately optimized to simulate the risk adversarial evolution process under different law enforcement strategies. Extract the dynamic risk balance point from the adversarial network, output whether it is at an explainable danger that actually affects driving ability, and attach the key feature attribution path on which the judgment is based.
8. The intelligent analysis and management system for blood alcohol test data according to claim 7, characterized in that: The game risk assessor also includes: By embedding temporal logic operators into the knowledge graph rule engine, dynamic rule constraints are applied to the metabolic interval between the end of drinking and the time of law enforcement, thereby enhancing the sensitivity of the rules to the temporal order. The adversarial network is extended into a multi-agent game architecture, introducing three intelligent agents: the prosecutor, the defense, and the expert system, to generate risk distributions that closely resemble real law enforcement scenarios during the adversarial process. Based on the interpretable danger assessment of the output, adversarial examples are generated by superimposing counterfactual reasoning paths to verify the robustness of the assessment results, and an explanatory text of the danger assessment with causal chain annotations is output.
9. The intelligent analysis and management system for blood alcohol test data according to claim 1, characterized in that: The dynamic threshold calibrator specifically includes: The individual drinking behavior time-series profile output by the dual-mode fusion device and the danger judgment result output by the game risk judge are obtained to construct a personalized risk assessment feature vector based on the law enforcement moment. The personalized risk assessment feature vector is input into the dynamic threshold generation network. The statutory threshold is used as the prior anchor point, and the anchor point is nonlinearly offset and calibrated according to the individual intake structure and game risk weight. Output the personalized danger assessment threshold at the moment of law enforcement, and simultaneously calculate the offset and direction of the threshold relative to the statutory threshold to form a dynamic calibration record.
10. The intelligent analysis and management system for blood alcohol test data according to claim 9, characterized in that: The dynamic threshold calibrator also includes: Based on the personalized risk assessment feature vector, a temporal uncertainty interval estimation is introduced to quantify the predicted interval of metabolic residuals before and after the enforcement moment, which serves as a confidence boundary constraint for threshold calibration. The dynamic threshold generation network is extended to a Bayesian deep regression architecture, which outputs the posterior distribution of the threshold instead of single-point values, and supports the probabilistic interpretation of the calibration results. Based on dynamic calibration records and threshold posterior distribution, the final enforcement recommendation and its confidence interval are output. The enforcement recommendation includes three categories of labels: taking coercive measures, allowing release but requiring observation, or requiring supplementary testing.