Alcohol detection data analysis method based on cloud computing

Abstract

The alcohol detection data analysis method based on cloud computing comprises the following steps: constructing a cloud information management platform, and carrying out alcohol concentration judgment on collected alcohol detection data by a detection point; an error correction model is constructed on the cloud information management platform, and error correction feedback, individual portrait predictive risk intervention and block chain evidence storage are carried out on alcohol detection data uploaded by each detection point; the method comprises the following steps: constructing a spatial twinborn visual graph and a simulation model on a cloud information management platform, constructing a digital twinborn model based on the spatial twinborn visual graph and the simulation model, carrying out analogue simulation according to the digital twinborn model, and judging whether to carry out model updating and strategy optimization and generate an optimal alcohol management strategy based on an analogue simulation result. According to the invention, through the closed-loop design of cloud collaboration, edge processing, data correction, risk prediction, space twinning and strategy optimization, the intelligent, precise and scientific level of alcohol management is significantly improved.

Description

Technical Field

[0001] This invention relates to the intersection of cloud computing, the Internet of Things, and intelligent detection technology, specifically a cloud-based method for analyzing alcohol detection data. Background Technology

[0002] Alcohol testing, as a key technology for preventing drunk driving and other alcohol-related safety hazards, has been widely applied in traffic enforcement, community supervision, and corporate safety. Traditional alcohol testing systems typically rely on a single testing terminal to collect data, which is then aggregated locally or simply uploaded to a central server, followed by manual or basic algorithm analysis. However, with the increasing demand for testing and the growing complexity of scenarios, existing technologies have gradually revealed the following core problems: First, the data credibility and traceability are insufficient. Traditional systems mostly adopt a centralized storage architecture, and the detection data is easily distorted due to human tampering, equipment failure, or transmission loss. In addition, they lack a full-process recording mechanism, making it difficult to meet the strict requirements for the originality and integrity of data in scenarios such as judicial evidence collection and liability determination.

[0003] Secondly, there is an imbalance between data processing efficiency and resource consumption. Existing solutions typically adopt a "one-size-fits-all" approach to real-time uploading of all detection data (regardless of whether it exceeds the standard), without differentiating processing based on risk levels. On the one hand, redundant uploading of a large amount of data that does not exceed the standard consumes network bandwidth and cloud storage resources; on the other hand, the uploading delay of high-risk data such as data that exceeds the standard may delay early warning response and reduce the system's efficiency in handling sudden risks.

[0004] Third, the accuracy of detection is significantly affected by environmental interference. The measurement results of alcohol testing equipment are easily affected by factors such as ambient temperature and humidity, equipment usage time, and calibration status. Traditional systems lack the ability to quantitatively correct for multi-dimensional interference factors, resulting in systematic errors in the test data, which may lead to misjudgments or missed judgments, affecting the scientific nature of management decisions.

[0005] Fourth, risk intervention is passive and lacks specificity. Existing technologies mostly focus on post-event recording and punishment of events that have already exceeded the limits, without deeply mining the historical behavior, physiological characteristics, and environmental data of the tested individuals. This makes it difficult to predict the individual's future alcohol-related risks and implement proactive interventions, resulting in management measures remaining at the level of "post-event accountability" and failing to form a closed-loop prevention and control system of "monitoring-early warning-intervention".

[0006] Fifth, the system lacks spatial scene and dynamic situation analysis capabilities. The distribution of alcohol testing data is closely related to spatial elements such as traffic flow, population activity, and road structure. However, traditional systems lack the ability to perform spatial modeling by fusing multi-source data, making it difficult to intuitively present the hotspot distribution, risk clusters, and dynamic evolution patterns of alcohol testing within a region. This makes it impossible to provide spatial decision support for law enforcement resource allocation and key area deployment.

[0007] Sixth, management strategy optimization relies on experience-driven approaches. Existing systems often adjust strategies based on human experience or simple statistical indicators, lacking the ability to dynamically simulate and verify different detection point configurations, enforcement rules, and resource allocations. This makes it difficult to quantify and evaluate the actual effects of different strategies, resulting in management strategy optimization lagging behind changes in actual needs and failing to achieve global optimization.

[0008] In summary, traditional alcohol testing data analysis methods have significant shortcomings in terms of data reliability, processing efficiency, detection accuracy, risk foresight, spatial analysis depth, and strategy optimization capabilities. There is an urgent need for a new approach that integrates cloud computing, blockchain, edge computing, artificial intelligence, and digital twin technologies to build a full-chain intelligent management system covering data collection, correction, analysis, prediction, and strategy optimization, in order to improve the application value of alcohol testing data and the scientific nature of management decisions. Summary of the Invention

[0009] To address the aforementioned technical problems, the present invention aims to provide a cloud-based method for analyzing alcohol test data, comprising the following steps: Step s1: Build a cloud-based information management platform. The testing points determine the alcohol concentration of the collected alcohol test data and intelligently upload the alcohol test data to the cloud-based information management platform based on the alcohol concentration determination results. Step s2: Construct an error correction model on the cloud information management platform to provide error correction feedback, predictive risk intervention based on individual profiles, and blockchain evidence storage for the alcohol test data uploaded by each testing point; Step s3: Construct a spatial twin visualization and simulation model on the cloud-based information management platform, build a digital twin model based on the spatial twin visualization and simulation model, conduct simulation based on the digital twin model, and determine whether to update the model and optimize the strategy based on the simulation results, and generate the best alcohol management strategy.

[0010] Furthermore, a cloud-based information management platform is built based on blockchain technology. The cloud-based information management platform has communication connections with several blockchain nodes, and the blockchain nodes are interconnected to form a blockchain network. Each testing point deployed within the target area is connected to a blockchain node, and the blockchain node is used to perform data uploading to the blockchain for alcohol testing data.

[0011] Furthermore, the process of determining alcohol concentration and intelligently uploading data includes: The testing site includes testing terminals and edge computing nodes. The testing terminals are used to collect alcohol test data. Edge computing nodes are used to extract alcohol concentration values ​​from alcohol test data for judgment. If the alcohol concentration value is greater than or equal to a preset threshold, an audible and visual alarm signal is immediately generated, the alcohol test data is marked as exceeding the limit, and the exceeding data is uploaded to the blockchain node for error correction. If the alcohol concentration value is less than the preset threshold, the alcohol test data is marked as not exceeding the limit, and the not exceeding data is stored in the edge computing node. At the same time, a preset batch upload interval K is set, and every batch upload interval K, the not exceeding data stored in the edge computing node is uploaded to the blockchain node.

[0012] Furthermore, the process of constructing the error correction model includes: An error correction model is constructed by obtaining historical data on excessive levels stored in each blockchain node in the blockchain network. The alcohol concentration value, ambient temperature and humidity, equipment usage time, and standard alcohol concentration value in the historical data on excessive levels are extracted as training data to train the error correction model and obtain the trained error correction model.

[0013] Furthermore, the process of correcting errors in alcohol test data includes: The system extracts the alcohol concentration value, ambient temperature and humidity, and equipment usage time from the excessive data uploaded to the blockchain node and inputs them into an error correction model. Based on the error correction model, it outputs correction coefficients and corrects the alcohol concentration value in the excessive data uploaded to the blockchain node according to the correction coefficients. The corrected alcohol concentration value is then judged. If the corrected alcohol concentration value is less than a preset threshold, the excessive data uploaded to the blockchain node is marked as normal data, and an audible and visual alarm cancellation signal is sent to the edge computing node connected to the blockchain node.

[0014] Furthermore, the process of predictive risk intervention based on individual profiling includes: Extract the examinee identifier from the data exceeding the standard uploaded to the blockchain node, use the examinee identifier as the cluster center to cluster the blockchain network, obtain several blockchain nodes containing the same cluster center, and extract the feature dimensions from the historical data of several blockchain nodes. Construct an LSTM time series prediction model, use the feature dimensions of historical data from several blockchain nodes as training data to train the LSTM time series prediction model, and output the predicted features for the next time period based on the trained LSTM time series prediction model. Three levels of risk labels are set based on feature dimensions. The predicted features are matched with the three levels of risk labels to obtain the risk labels corresponding to the predicted features. Predictive intervention measures for the examinee are generated based on the risk labels.

[0015] Furthermore, the process of constructing a spatial twin view includes: Acquire GIS road data, real-time traffic flow data, and population activity data within the target area, and simultaneously extract historical alcohol test data stored in each blockchain node of the blockchain network; Construct a unified coordinate system within the target area, convert the original coordinate systems of GIS road data, real-time traffic flow data, historical alcohol test data, and population activity data into a unified coordinate system, obtain road vectors, intersection nodes, and attribute tables in the unified coordinate system from the GIS road data, use road vectors as line elements of the regional topology map, use intersection nodes as point elements of the regional topology map, construct the regional topology map, and assign attribute values ​​to the line elements and point elements in the regional topology map according to the attribute table; Time-aligned preprocessing is performed on real-time traffic flow data under a unified coordinate system, and the preprocessed real-time traffic flow data is mapped to a regional topology map. Historical alcohol detection data under a unified coordinate system is also mapped to a regional topology map. At the same time, spatial granularity is set, and several grids are divided in the regional topology map according to the spatial granularity. Population activity data under a unified coordinate system is mapped to each grid to generate a spatial twin view.

[0016] Furthermore, the process of constructing a simulation model, based on the spatial twin visualization and the simulation model, includes: Based on historical alcohol testing data, the probability of exceeding the standard in different time periods in the spatial twin view is obtained; based on real-time traffic flow data, the average vehicle speed of different line elements in the spatial twin view in different time periods is obtained; and based on population activity data, the population density, population attributes, and population flow direction of each grid in the spatial twin view are obtained. Based on the probability of exceeding the standard at different time periods in the spatial twin view, the average vehicle speed of different line elements at different time periods, and the population density, population attributes, and population flow direction of each grid, several driver models with different numbers and attributes are configured for each line element in the spatial twin view. Configure the detection point model and enforcement rules of the detection point model in the spatial twin view for each detection point deployed within the target area; Based on the attribute assignment of line and point elements in the regional topology map, configure the traffic environment model and the congestion evolution rules of the traffic environment model in the spatial twin view. A digital twin model is generated based on the spatial twin view and the driver model, detection point model, and traffic environment model configured in the spatial twin view.

[0017] Furthermore, the process of determining whether to update the model and optimize the strategy based on the simulation results includes: The target area is simulated using a digital twin model. The simulation process of the digital twin model is statistically analyzed to obtain the average daily detection volume, exceedance rate, congestion rate and resource cost of each detection point. The average daily detection volume, exceedance rate, and congestion rate of each detection point are marked as the judgment criteria. A judgment period and error upper limit are set. Real-time traffic flow data and alcohol test data uploaded to the blockchain node within the current judgment period are obtained. The real-time traffic flow data and alcohol test data are statistically analyzed to obtain the real-time detection volume, real-time exceedance rate, and real-time congestion rate of each detection point. The real-time detection volume, real-time exceedance rate, and real-time congestion rate of each detection point are compared with the judgment criteria to obtain the deviation of real-time detection volume, real-time exceedance rate, and real-time congestion rate of each detection point. If the absolute value of the deviation of real-time detection volume, real-time exceedance rate, or real-time congestion rate of any detection point is greater than the error upper limit, the model is updated and the strategy is optimized.

[0018] Furthermore, the process of updating the model and optimizing the strategy to generate the best alcohol management strategy includes: Obtain GIS road data, real-time traffic flow data, population activity data, and all alcohol test data stored in each blockchain node of the blockchain network within the current judgment period. Regenerate a digital twin model based on the GIS road data, real-time traffic flow data, population activity data, and all alcohol test data stored in each blockchain node of the blockchain network within the current judgment period. Based on the detection point model in the digital twin model, several alternative strategies are generated, chromosome encoding and population initialization are performed on several production allocation schemes, an initial population is generated, and the simulation operation process of the digital twin model under different alternative strategy conditions is statistically analyzed to obtain the average daily detection volume, over-limit rate, congestion rate and resource cost of each detection point in the digital twin model under different alternative strategy conditions. Based on the average daily detection volume, over-limit rate, congestion rate and resource cost of each detection point, the fitness function corresponding to each alternative strategy is constructed. The optimal alcohol management strategy is obtained through a multi-objective genetic algorithm based on the initial population and fitness function.

[0019] Compared with the prior art, the beneficial effects of the present invention are: The cloud computing-based alcohol testing data analysis method of this invention has the following significant advantages: 1. A cloud-based information management platform is built using blockchain technology. Leveraging the distributed storage and immutability of blockchain nodes, the platform ensures end-to-end traceability and security of alcohol testing data, preventing data forgery or tampering and providing a highly reliable data foundation for subsequent analysis and decision-making. Simultaneously, testing sites utilize edge computing nodes for local data preprocessing. Data exceeding the limit triggers alarms and is uploaded in real time, while data within the limit is transmitted in batches. This balances cloud computing load with network transmission efficiency, ensuring timely response to high-risk data while reducing system resource consumption.

[0020] 2. The error correction model, trained on historical multi-dimensional data, can accurately correct for environmental interference and equipment errors in excessive data, effectively improving the accuracy of alcohol concentration detection results, reducing misjudgments caused by external factors, and providing a reliable basis for subsequent risk assessment. Individual profiling and predictive risk intervention, through cluster analysis and LSTM time-series models, uncover the behavioral patterns and risk trends of those being tested, achieving a shift from passive detection to proactive prevention. Personalized intervention measures are generated for different risk levels, significantly improving the precision and foresight of alcohol management.

[0021] 3. Spatial twin visualization integrates multi-source data such as geographic information, traffic flow, and population activity to construct a dynamic regional profile under a unified coordinate system. This intuitively presents the spatial distribution patterns of alcohol testing data, providing visualization support for scenario-based analysis. The digital twin model integrates the dynamic configuration of elements such as drivers, testing points, and traffic environment. It can simulate testing situations under different scenarios and statistically analyze key indicators through simulation, providing an efficient virtual verification platform for evaluating the effectiveness of management strategies and identifying potential problems.

[0022] 4. The model update and strategy optimization mechanism based on simulation results triggers the optimization process by comparing the deviation between real-time and standard indicators, combines multi-source data to iteratively generate a digital twin model, and uses a multi-objective genetic algorithm to generate a comprehensive optimal strategy, thereby realizing the adaptive evolution of the model and the continuous improvement of the management strategy, ensuring the adaptability of the system and the scientific nature of decision-making in different scenarios.

[0023] This invention forms a complete link from data collection to decision support through a closed-loop design of cloud collaboration, edge processing, data correction, risk prediction, spatial twinning, and strategy optimization. It significantly improves the intelligence, precision, and scientific level of alcohol management and provides efficient and reliable technical support for refined management in the field of public safety. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the cloud-based alcohol detection data analysis method according to an embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] like Figure 1 As shown, the cloud-based alcohol test data analysis method includes the following steps: Step s1: Build a cloud-based information management platform. The testing points determine the alcohol concentration of the collected alcohol test data and intelligently upload the alcohol test data to the cloud-based information management platform based on the alcohol concentration determination results. Step s2: Construct an error correction model on the cloud information management platform to provide error correction feedback, predictive risk intervention based on individual profiles, and blockchain evidence storage for the alcohol test data uploaded by each testing point; Step s3: Construct a spatial twin visualization and simulation model on the cloud-based information management platform, build a digital twin model based on the spatial twin visualization and simulation model, conduct simulation based on the digital twin model, and determine whether to update the model and optimize the strategy based on the simulation results, and generate the best alcohol management strategy.

[0027] It should be further explained that, in the specific implementation process, a cloud-based information management platform is built based on blockchain technology. The cloud-based information management platform has communication connections with several blockchain nodes, and the blockchain nodes are interconnected to form a blockchain network. Each testing point deployed within the target area is connected to a blockchain node, and the blockchain node is used to perform data uploading to the blockchain for alcohol testing data.

[0028] The data upload process includes: The cloud-based information management platform extracts the alcohol test data received by the blockchain nodes, applies a hash function to the alcohol test data to generate a SHA-256 hash value, packages the SHA-256 hash value and the node signature (the private key signature of the testing point) into a transaction, and broadcasts it to the consortium blockchain. Each blockchain node in the consortium blockchain verifies the transaction (such as comparing the device compliance information of the device manufacturer's node), and after successful verification, it is written into a block (block generation time ≤ 10 seconds), and the block is added to the end of the blockchain. Source tracing application: Law enforcement officers enter the alcohol test data number through the cloud information management platform to obtain the hash value on the blockchain and the original data. If the two match, it proves that the data has not been tampered with and can be used as judicial evidence (such as in court hearings of drunk driving cases).

[0029] It should be further explained that, in the specific implementation process, the process of determining alcohol concentration and intelligently uploading data includes: The testing site includes testing terminals (portable alcohol detectors, checkpoint-type testing equipment, environmental sensors, etc.) and edge computing nodes. The testing terminals are used to collect alcohol testing data, which includes alcohol concentration value, time, subject identification (ID number hash value), testing site address, ambient temperature and humidity, equipment usage time (time since last calibration), and number. Edge computing nodes are used to extract alcohol concentration values ​​from alcohol test data for judgment. If the alcohol concentration value is greater than or equal to a preset threshold (20mg / 100ml), an audible and visual alarm signal is immediately generated, marking the alcohol test data as exceeding the limit. The exceeding data is then uploaded to the blockchain node for error correction. After correction, if the cloud information management platform does not send an audible and visual alarm cancellation signal to the edge computing node, the person being tested is arranged to go to a fixed testing point (such as a hospital) for alcohol concentration standard value testing (such as a more precise test like blood alcohol content testing). The standard alcohol concentration value is obtained and uploaded to the blockchain node where the person's alcohol test data is stored. If the alcohol concentration value is less than the preset threshold (20mg / 100ml), the alcohol test data is marked as not exceeding the limit, and the not exceeding data is stored in the edge computing node. At the same time, a preset batch upload interval K is set, and every batch upload interval K, the not exceeding data stored in the edge computing node is uploaded to the blockchain node.

[0030] It should be further explained that, in the specific implementation process, the process of constructing the error correction model includes: An error correction model (using the XGBoost model) is built on a cloud-based information management platform. Historical data on exceeding limits stored in each blockchain node of the blockchain network is obtained. The alcohol concentration value, ambient temperature and humidity, equipment usage time, and standard alcohol concentration value in the historical data on exceeding limits are extracted as training data (100,000+ sets of comparisons between alcohol concentration values ​​and standard values ​​under different temperatures, humidity, and equipment usage time) to train the error correction model and obtain the completed error correction model.

[0031] It should be further explained that, in the specific implementation process, the process of correcting errors in alcohol test data includes: The system extracts alcohol concentration values, ambient temperature and humidity, and equipment usage time from the excessive data uploaded to the blockchain node and inputs them into an error correction model. Based on this model, a correction coefficient is output. This coefficient is then used to correct the alcohol concentration values ​​in the excessive data uploaded to the blockchain node (original alcohol concentration value × correction coefficient = corrected alcohol concentration value; for example, if historical data shows a 10% higher detection value at 80% humidity, the corresponding correction coefficient is 0.9, so the current value is calibrated by multiplying by 0.9). The corrected alcohol concentration value is then evaluated. If the corrected alcohol concentration value is less than a preset threshold, the excessive data uploaded to the blockchain node is marked as normal, and an audible and visual alarm cancellation signal is sent to the edge computing node connected to the blockchain node.

[0032] It should be further explained that, in the specific implementation process, the individual profiling predictive risk intervention process includes: Extract the examinee identifier from the data exceeding the standard uploaded to the blockchain node, use the examinee identifier as the cluster center to cluster the blockchain network, obtain several blockchain nodes containing the same cluster center, and extract the feature dimensions from the historical data of several blockchain nodes, including: the number of times exceeding the standard in the past 3 months (feature 1), the distribution of the time of exceeding the standard (feature 2, such as the proportion of 8 pm to 12 am), the frequency of nighttime travel (feature 3), and the number of times of travel around the bar gathering area (feature 4). Construct an LSTM time series prediction model, use the feature dimensions of historical data from several blockchain nodes as training data to train the LSTM time series prediction model, and output the predicted features for the next time period (the next 7 days) based on the trained LSTM time series prediction model. Three levels of risk labels are set according to feature dimensions. The predicted features are matched with the three levels of risk labels (Euclidean clustering matching) to obtain the risk labels corresponding to the predicted features. Based on the risk labels, predictive intervention measures are generated for the examinee. The predictive intervention measures include: for employees of high-risk enterprises (such as logistics drivers), the cloud information management platform pushes a "mandatory pre-job testing reminder" to the enterprise management terminal; for ordinary drivers, a "no driving under the influence" warning is pushed through the navigation APP, and their emergency contact is reminded at the same time.

[0033] The three-level risk labels include high risk, medium risk, and low risk. Matching the predicted features with the three-level risk labels is achieved by setting central feature values ​​for high, medium, and low risk. For example: High risk: Central characteristic value is [0.7±0.1, 0.8±0.1, 0.6±0.1, 0.7±0.1] (frequent exceedances, frequent nighttime travel, frequent visits to bar areas); Medium risk: The central characteristic value is [0.3±0.1, 0.4±0.1, 0.3±0.1, 0.3±0.1] (occasionally exceeding the standard, moderate risk during nighttime travel); Low risk: The central characteristic value is [0.05±0.05, 0.05±0.05, 0.1±0.05, 0.1±0.05] (no exceedances in the past 3 months or only 1 time, few nighttime trips).

[0034] It should be further explained that, in the specific implementation process, the process of constructing a spatial twin visualization includes: Acquire GIS road data (source: urban GIS system, including attribute tables (number of lanes, speed limit, road grade, turning restrictions), road vector layers and intersection nodes) within the target area, real-time traffic flow data (source: traffic cameras (captured every 5 minutes), ground loop coils (real-time counting), floating car GPS trajectories (such as taxis and ride-hailing vehicles, sampling frequency 1 time / 30 seconds)) and population activity data (source: population census data (regional resident population density), business district POS machine consumption records (distribution of restaurant / bar consumption time periods)), and simultaneously extract historical alcohol test data stored in each blockchain node of the blockchain network; Construct a unified coordinate system (WGS84 coordinate system) within the target area. Convert the original coordinate systems of GIS road data, real-time traffic flow data, historical alcohol test data, and population activity data into a unified coordinate system. For example, convert road coordinates from local coordinate systems (such as Beijing 54) to the WGS84 coordinate system (unified spatial reference). Obtain road vectors, intersection nodes, and attribute tables in the unified coordinate system from the GIS road data. Use road vectors as line elements of the regional topology map and intersection nodes as point elements of the regional topology map. Construct the regional topology map and assign attribute values ​​to the line elements and point elements in the regional topology map according to the attribute table. Real-time traffic flow data under a unified coordinate system undergoes time-aligned preprocessing. The collection frequency of different devices is unified to "5 minutes / time" (data with a frequency lower than this is supplemented by linear interpolation, and data with a frequency higher is averaged). The preprocessed real-time traffic flow data is then mapped to a regional topology map (obtaining the coordinates of the real-time traffic flow data under the unified coordinate system (obtained from the source of the real-time traffic flow data), obtaining the line or point features corresponding to the coordinates of the real-time traffic flow data in the regional topology map, and assigning traffic data attribute values ​​to the line or point features). Historical alcohol test data under the unified coordinate system is also mapped to the regional topology map (based on historical alcohol test data). The coordinates of the measured data (obtained from the test point addresses in historical alcohol test data) are obtained. The line or point features corresponding to the coordinates of the historical alcohol test data are then located on the regional topology map. The historical alcohol test data attributes are assigned to the line or point features. At the same time, the spatial granularity (1 square kilometer) is set. Based on the spatial granularity, several grids are divided within the regional topology map. Population activity data under a unified coordinate system is mapped to each grid. Specifically, this includes: dividing the region into 1 square kilometer grids, calculating the population density of each grid at different times (e.g., "the population density of business district grid G-023 at night on weekends is 5000 people / km²"), and generating a spatial twin view.

[0035] Spatial twin visualization forms a "spatiotemporal cube" data structure (spatial dimension: road / grid; time dimension: 5-minute unit; attribute dimension: traffic flow, over-limit rate, population density), stored on a cloud-based information management platform, supporting millisecond-level queries.

[0036] It should be further explained that, in the specific implementation process, the process of constructing a simulation model and building a digital twin model based on the spatial twin visualization and the simulation model includes: Based on historical alcohol testing data, the probability of exceeding the standard in different time periods in the spatial twin view is obtained; based on real-time traffic flow data, the average vehicle speed of different line elements in the spatial twin view in different time periods is obtained; and based on population activity data, the population density, population attributes, and population flow direction of each grid in the spatial twin view are obtained. Based on the probability of exceeding the standard at different time periods in the spatial twin view, the average vehicle speed of different line elements at different time periods, and the population density, population attributes, and population flow direction of each grid, several driver models with different numbers and attributes are configured for each line element in the spatial twin view. Configure the detection point model and enforcement rules of the detection point model in the spatial twin view for each detection point deployed within the target area; Based on the attribute assignment of line and point elements in the regional topology map, configure the traffic environment model and the congestion evolution rules of the traffic environment model in the spatial twin view. A digital twin model is generated based on the spatial twin view and the driver model, detection point model, and traffic environment model configured in the spatial twin view.

[0037] It should be further explained that the attributes of the driver model include: vehicle type (car / truck / bus, for example, if the population attribute shows that truck drivers account for 30% in a certain grid, then in the driver models generated by the line elements contained in that area, 30% are "truck drivers"), average speed, alcohol status (sober / drunk, set based on the probability of exceeding the limit at different times, for example, if the probability of exceeding the limit on a certain road is 15% on a weekend night, then in the several driver models set for that road, the number of drivers in a drunk driving state accounts for 15%), and origin / destination (randomly generated, the route selection of the driver model ("from where to where") depends entirely on the population). For example, if the population attribute shows that "70% of the consumption in a certain grid A on weekend nights is in restaurants / bars", then 70% of the driver models start at the parking lot in a certain grid A (corresponding to the scenario of "preparing to drive away after a meal"), 20% start at the surrounding residential area ("residents traveling at night"), and 10% start at the office building ("returning home after working overtime"). At the same time, if the population flow shows that "60% of the flow of people in a certain grid A on weekend nights flows to the eastern residential area and 30% flows to the western suburbs", then in the driver models starting at a certain grid A, 60% of the destination is set to the eastern residential area and 30% to the western suburbs. The number of driver models for different line elements is set according to the population density of each grid. For example, if the "resident population / floating population density of different grids" is extracted, and the "weekend nighttime population density of the business district grid is 5000 people / km²", then the number of driver models (corresponding to the number of vehicles) generated by the line elements in this grid is 10 times that of "suburban grid G-056 (population density 500 people / km²)". It should be further explained that the attributes of the detection point model include: location, number of devices (e.g., 2 detectors), number of law enforcement personnel (3 people), working hours (e.g., 19:00-22:00), detection time per device (30 seconds / vehicle, including air blowing + result judgment), queuing rules (first come, first served), and interception rate (the proportion of passing vehicles intercepted, such as 20% random interception, or 100% interception of suspicious vehicles).

[0038] Enforcement rules for the detection point module: Triggering condition: a vehicle enters a 50-meter range of the detection point; Decision function: if a vehicle is randomly selected (e.g., with a 20% probability), then interception is triggered; Output result: intercepted vehicles enter the queue and are detected sequentially according to detection efficiency. Vehicles exceeding the standard are recorded (included in the exceeding rate statistics), while vehicles not exceeding the standard are released (delays may occur due to queuing).

[0039] The attributes of the traffic environment model include: road capacity (e.g., a maximum of 1,000 vehicles per hour in one direction) and traffic light timing (60 seconds for red light and 40 seconds for green light at intersections).

[0040] Congestion Evolution Rules: Triggering Condition: Real-time traffic flow on the road segment > 70% of road capacity; Decision Function: Congestion Coefficient = 1 + 0.01 × (Actual Traffic Flow - 70% Capacity), Vehicle Speed ​​= Base Speed ​​ / Congestion Coefficient; Output Result: Vehicle speed on the road segment decreases, affecting the route time cost of subsequent vehicles.

[0041] Users can zoom and pan the digital twin model through the interactive interface of the cloud information management platform (a browser developed with WebGL), click on any vehicle to view driver attributes (such as "blood concentration 0.05mg / 100ml"), click on the detection point to view the real-time queue length (such as "currently 5 vehicles in the queue, estimated wait time 2.5 minutes"), and "speed up playback" (1 minute to simulate 1 hour of real time) or "pause analysis" to easily observe key nodes (such as the peak congestion at the detection point at 8 pm).

[0042] It should be further explained that, in the specific implementation process, the process of determining whether to update the model and optimize the strategy based on the simulation results includes: The target area was simulated using a digital twin model, where 1 second in the digital twin model corresponds to 1 minute in the real world (i.e., 1 time step = 1 minute). Parallel simulation was performed using a GPU cluster (8 NVIDIA A100 GPUs) on a cloud computing platform. The area was divided into 4 sub-grids, with each sub-grid allocated one GPU core for independent computation. The results were then aggregated (reducing the simulation time from 8 hours to 1.5 hours for a month of real-world time). Statistical analysis was performed on the simulation process of the digital twin model to obtain the average daily detection volume at each detection point (simulation data). The calculation includes: the total number of vehicles detected within the period divided by the number of simulation days; the exceedance rate (exceedance rate = (number of vehicles detected exceeding the standard) / (total number of vehicles detected) × 100%); the congestion rate (congestion rate = (congestion time of roads within 500 meters of the detection point) / (total simulation time) × 100%, congestion time: the cumulative time when the road speed is less than 50% of the average speed); and resource costs. Resource costs = equipment costs (80,000 yuan per unit × quantity) + labor costs (200 yuan per person per day × number of enforcement days × number of people) + congestion losses (hourly congestion losses = traffic flow × congestion time). The average daily detection volume, exceedance rate, and congestion rate of each detection point are marked as the judgment criteria. A judgment period (5 minutes) and an error limit (5%) are set. Real-time traffic flow data and alcohol detection data uploaded to the blockchain node by each detection point within the current judgment period are obtained (alcohol detection data error correction has been completed). The real-time traffic flow data and alcohol detection data are statistically analyzed to obtain the real-time detection volume, real-time exceedance rate, and real-time congestion rate of each detection point. The real-time detection volume, real-time exceedance rate, and real-time congestion rate of each detection point are compared with the judgment criteria to obtain the real-time detection volume deviation, real-time exceedance rate deviation, and real-time congestion rate deviation of each detection point (the real-time detection volume, real-time exceedance rate, and real-time congestion rate are obtained by dividing the real-time detection volume, real-time exceedance rate, and real-time congestion rate by the corresponding judgment criteria, respectively). If the absolute value of the real-time detection volume deviation, the absolute value of the real-time exceedance rate deviation, or the absolute value of the real-time congestion rate deviation of any detection point is greater than the error limit, the model is updated and the strategy is optimized.

[0043] It should be further explained that, in the specific implementation process, the process of updating the model and optimizing the strategy to generate the best alcohol management strategy includes: Obtain GIS road data, real-time traffic flow data, population activity data, and all alcohol test data stored in each blockchain node of the blockchain network within the current judgment period. Regenerate a digital twin model based on the GIS road data, real-time traffic flow data, population activity data, and all alcohol test data stored in each blockchain node of the blockchain network within the current judgment period. Based on the detection point model in the digital twin model, several alternative strategies are generated (based on the existing detection point model, several detection point models at different positions are randomly added to the digital twin model). Chromosome encoding and population initialization are performed on several production allocation schemes to generate an initial population. Statistical analysis is performed on the simulation operation of the digital twin model under different alternative strategy conditions to obtain the average daily detection volume, over-limit rate, congestion rate and resource cost of each detection point in the digital twin model under different alternative strategy conditions. Based on the average daily detection volume, over-limit rate, congestion rate and resource cost of each detection point, the fitness function corresponding to each alternative strategy is constructed. The optimal alcohol management strategy is obtained through a multi-objective genetic algorithm based on the initial population and fitness function.

[0044] The fitness function is: ; in, Indicates fitness. This represents the exceedance rate at the i-th detection point. This represents the average daily detection volume at the i-th detection point. This represents the congestion rate at the i-th detection point. Let represent the resource cost of the i-th detection point, and n be the total number of detection points.

[0045] The specific process of obtaining the optimal alcohol management strategy using a multi-objective genetic algorithm includes: The alternative strategies are encoded as chromosomes, for example, using a string of numbers to represent information such as the location and number of additional detection points. A certain number of chromosomes are randomly generated to form an initial population, with each chromosome representing a possible alternative strategy. The fitness function corresponding to each alternative strategy is obtained. Then, a tournament selection method is used to select chromosomes with higher fitness from the current population as parents. The parent chromosomes are crossovered, exchanging some genes to generate new offspring chromosomes, simulating the exchange of genes in biological genetics to generate new alternative strategies. The offspring chromosomes are mutated, randomly changing some genes to increase population diversity and avoid getting trapped in local optima. The above steps are repeated iteratively until the termination condition is met, such as reaching the maximum number of iterations or the fitness no longer significantly improving. The alternative strategy with the highest fitness is output and marked as the optimal alcohol management strategy.

[0046] Traditional alcohol testing management strategies (such as "adding testing sites" and "adjusting enforcement hours") rely heavily on experience and lack scientific validation (e.g., does adding testing sites actually improve the efficiency of detecting excessive levels?), easily leading to resource waste (e.g., adding testing sites in low-risk areas). This embodiment uses a real area (e.g., a certain urban area) as a prototype and builds a digital twin model in the cloud, integrating alcohol testing data, traffic flow data, road network data, and population activity data of that area. By simulating different management strategies (e.g., "adding 3 testing sites"), it predicts the "change in excessive level rate," "resource consumption," and "traffic impact" after the strategy is implemented, thereby generating the optimal strategy and making decision-makers' decisions more scientific.

[0047] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A cloud computing-based method for analyzing alcohol test data, characterized in that, Includes the following steps: Step s1: Build a cloud-based information management platform. The testing points determine the alcohol concentration of the collected alcohol test data and intelligently upload the alcohol test data to the cloud-based information management platform based on the alcohol concentration determination results. Step s2: Construct an error correction model on the cloud information management platform to provide error correction feedback, predictive risk intervention based on individual profiles, and blockchain evidence storage for the alcohol test data uploaded by each testing point; Step s3: Construct a spatial twin visualization and simulation model on the cloud-based information management platform, build a digital twin model based on the spatial twin visualization and simulation model, conduct simulation based on the digital twin model, and determine whether to update the model and optimize the strategy based on the simulation results, and generate the best alcohol management strategy.

2. The alcohol detection data analysis method based on cloud computing according to claim 1, characterized in that, A cloud-based information management platform is built based on blockchain technology. The platform has communication connections with several blockchain nodes, which are interconnected to form a blockchain network. Each testing point deployed within the target area is connected to a blockchain node, which is used to perform data uploading to the blockchain for alcohol testing data.

3. The alcohol detection data analysis method based on cloud computing according to claim 2, characterized in that, The process of determining alcohol concentration and intelligently uploading data includes: The testing site includes testing terminals and edge computing nodes. The testing terminals are used to collect alcohol test data. Edge computing nodes are used to extract alcohol concentration values ​​from alcohol test data for judgment. If the alcohol concentration value is greater than or equal to a preset threshold, an audible and visual alarm signal is immediately generated, the alcohol test data is marked as exceeding the limit, and the exceeding data is uploaded to the blockchain node for error correction. If the alcohol concentration value is less than the preset threshold, the alcohol test data is marked as not exceeding the limit, and the not exceeding data is stored in the edge computing node. At the same time, a preset batch upload interval K is set, and every batch upload interval K, the not exceeding data stored in the edge computing node is uploaded to the blockchain node.

4. The cloud computing-based alcohol detection data analysis method according to claim 3, characterized in that, The process of constructing an error correction model includes: An error correction model is constructed by obtaining historical data on excessive levels stored in each blockchain node in the blockchain network. The alcohol concentration value, ambient temperature and humidity, equipment usage time, and standard alcohol concentration value in the historical data on excessive levels are extracted as training data to train the error correction model and obtain the trained error correction model.

5. The alcohol detection data analysis method based on cloud computing according to claim 4, characterized in that, The process of correcting errors in alcohol test data includes: The system extracts the alcohol concentration value, ambient temperature and humidity, and equipment usage time from the excessive data uploaded to the blockchain node and inputs them into an error correction model. Based on the error correction model, it outputs correction coefficients and corrects the alcohol concentration value in the excessive data uploaded to the blockchain node according to the correction coefficients. The corrected alcohol concentration value is then judged. If the corrected alcohol concentration value is less than a preset threshold, the excessive data uploaded to the blockchain node is marked as normal data, and an audible and visual alarm cancellation signal is sent to the edge computing node connected to the blockchain node.

6. The alcohol detection data analysis method based on cloud computing according to claim 5, characterized in that, The process of predictive risk intervention based on individual profiling includes: Extract the examinee identifier from the data exceeding the standard uploaded to the blockchain node, use the examinee identifier as the cluster center to cluster the blockchain network, obtain several blockchain nodes containing the same cluster center, and extract the feature dimensions from the historical data of several blockchain nodes. Construct an LSTM time series prediction model, use the feature dimensions of historical data from several blockchain nodes as training data to train the LSTM time series prediction model, and output the predicted features for the next time period based on the trained LSTM time series prediction model. Three levels of risk labels are set based on feature dimensions. The predicted features are matched with the three levels of risk labels to obtain the risk labels corresponding to the predicted features. Predictive intervention measures for the examinee are generated based on the risk labels.

7. The alcohol detection data analysis method based on cloud computing according to claim 6, characterized in that, The process of constructing a spatial twin view includes: Acquire GIS road data, real-time traffic flow data, and population activity data within the target area, and simultaneously extract historical alcohol test data stored in each blockchain node of the blockchain network; Construct a unified coordinate system within the target area, convert the original coordinate systems of GIS road data, real-time traffic flow data, historical alcohol test data, and population activity data into a unified coordinate system, obtain road vectors, intersection nodes, and attribute tables in the unified coordinate system from the GIS road data, use road vectors as line elements of the regional topology map, use intersection nodes as point elements of the regional topology map, construct the regional topology map, and assign attribute values ​​to the line elements and point elements in the regional topology map according to the attribute table; Time-aligned preprocessing is performed on real-time traffic flow data under a unified coordinate system, and the preprocessed real-time traffic flow data is mapped to a regional topology map. Historical alcohol detection data under a unified coordinate system is also mapped to a regional topology map. At the same time, spatial granularity is set, and several grids are divided in the regional topology map according to the spatial granularity. Population activity data under a unified coordinate system is mapped to each grid to generate a spatial twin view.

8. The alcohol detection data analysis method based on cloud computing according to claim 7, characterized in that, The process of constructing a digital twin model based on a spatial twin visualization and a simulation model includes: Based on historical alcohol testing data, the probability of exceeding the standard in different time periods in the spatial twin view is obtained; based on real-time traffic flow data, the average vehicle speed of different line elements in the spatial twin view in different time periods is obtained; and based on population activity data, the population density, population attributes, and population flow direction of each grid in the spatial twin view are obtained. Based on the probability of exceeding the standard at different time periods in the spatial twin view, the average vehicle speed of different line elements at different time periods, and the population density, population attributes, and population flow direction of each grid, several driver models with different numbers and attributes are configured for each line element in the spatial twin view. Configure the detection point model and enforcement rules of the detection point model in the spatial twin view for each detection point deployed within the target area; Based on the attribute assignment of line and point elements in the regional topology map, configure the traffic environment model and the congestion evolution rules of the traffic environment model in the spatial twin view. A digital twin model is generated based on the spatial twin view and the driver model, detection point model, and traffic environment model configured in the spatial twin view.

9. The alcohol detection data analysis method based on cloud computing according to claim 8, characterized in that, The process of determining whether to update the model and optimize the strategy based on simulation results includes: The target area is simulated using a digital twin model. The simulation process of the digital twin model is statistically analyzed to obtain the average daily detection volume, exceedance rate, congestion rate and resource cost of each detection point. The average daily detection volume, exceedance rate, and congestion rate of each detection point are marked as the judgment criteria. A judgment period and error upper limit are set. Real-time traffic flow data and alcohol test data uploaded to the blockchain node within the current judgment period are obtained. The real-time traffic flow data and alcohol test data are statistically analyzed to obtain the real-time detection volume, real-time exceedance rate, and real-time congestion rate of each detection point. The real-time detection volume, real-time exceedance rate, and real-time congestion rate of each detection point are compared with the judgment criteria to obtain the deviation of real-time detection volume, real-time exceedance rate, and real-time congestion rate of each detection point. If the absolute value of the deviation of real-time detection volume, real-time exceedance rate, or real-time congestion rate of any detection point is greater than the error upper limit, the model is updated and the strategy is optimized.

10. The cloud computing-based alcohol detection data analysis method according to claim 9, characterized in that, The process of updating the model and optimizing the strategy to generate the best alcohol management strategy includes: Obtain GIS road data, real-time traffic flow data, population activity data, and all alcohol test data stored in each blockchain node of the blockchain network within the current judgment period. Regenerate a digital twin model based on the GIS road data, real-time traffic flow data, population activity data, and all alcohol test data stored in each blockchain node of the blockchain network within the current judgment period. Based on the detection point model in the digital twin model, several alternative strategies are generated, chromosome encoding and population initialization are performed on several production allocation schemes, an initial population is generated, and the simulation operation process of the digital twin model under different alternative strategy conditions is statistically analyzed to obtain the average daily detection volume, over-limit rate, congestion rate and resource cost of each detection point in the digital twin model under different alternative strategy conditions. Based on the average daily detection volume, over-limit rate, congestion rate and resource cost of each detection point, the fitness function corresponding to each alternative strategy is constructed. The optimal alcohol management strategy is obtained through a multi-objective genetic algorithm based on the initial population and fitness function.

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