Intelligent perception and active early warning system and method for long-distance cargo multimodal transport safety risk based on multi-source data fusion

Abstract

This invention discloses an intelligent perception and proactive early warning system for safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion, relating to the fields of logistics transportation and intelligent transportation technology. It includes: a raw data input module; a business scenario intelligent perception module; a data collection and analysis module; a safety risk early warning module; an operation guidance module; and a network information receiving module. This invention also discloses a method for intelligent perception and proactive early warning of safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion. This invention solves the complex safety challenges faced by long and heavy cargo in multimodal transport, improving the safety and efficiency of the transportation process.

Description

Technical Field

[0001] This invention relates to the fields of logistics and intelligent transportation technology, and in particular to an intelligent perception and proactive early warning system and method for safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion. Background Technology

[0002] Oversized and long cargo refers to heavy-duty road freight, oversized and overweight rail freight, and bulky and long-duty waterway freight, characterized by its weight, size, and high value. These cargoes mainly include large power equipment (transformers, generator stators, wind turbine blades, towers, etc.), large manufacturing equipment (machining equipment, production line components, molds, etc.), and large construction machinery (excavators, loaders, bulldozers, road rollers, cranes, etc.). They are typically enormous in size and weight, placing extremely stringent requirements on transportation conditions. With the rapid development of industrialization and modernization, the demand for transporting these oversized and long-duty goods is constantly increasing.

[0003] However, transporting long and heavy goods faces numerous challenges. Firstly, due to limitations in equipment size and weight, special attention must be paid to road width, height, and load-bearing capacity, as well as the passability of bridges and tunnels. Complex transport scenarios, such as bends in city streets, low-ceilinged spaces within tunnels, narrow passages in culverts, and the load-bearing limitations of bridges, can all become obstacles. These scenarios are highly susceptible to collisions, overloading, and other safety accidents, which can lead to significant property damage and threaten personnel safety. Furthermore, the transport of long and heavy goods is also affected by weather conditions. For example, strong winds, heavy rain, snow, and fog can all impact transport safety and increase risks during transport. Therefore, real-time monitoring of weather changes is necessary, and transport plans and strategies must be adjusted promptly based on weather conditions.

[0004] Existing transportation monitoring systems often fail to meet these complex needs. They can only provide limited data, such as vehicle location and speed, lacking the ability to comprehensively perceive the transportation environment and detect the entire road segment. Furthermore, these systems typically have insufficient data analysis capabilities, making it difficult to identify potential safety risks from large amounts of data. The untimely nature of early warning systems is also a problem, often issuing alerts only after an accident has occurred, by which time it is too late to prevent losses.

[0005] Therefore, developing a novel intelligent safety risk perception and early warning system is crucial for the transportation of long and heavy cargo. This system needs to be able to perceive the transportation environment in real time, including the operational status of transport vehicles, the fixed status of long and heavy cargo, the operational behavior of relevant personnel, road conditions, and climate changes, and intelligently analyze this data to identify potential risk factors. More importantly, the system needs to provide early warnings, giving transport personnel sufficient time to take measures to prevent accidents. This will not only improve transportation safety but also increase transportation efficiency and reduce transportation costs. Summary of the Invention

[0006] To address the problems existing in the prior art, the purpose of this invention is to provide an intelligent perception and proactive early warning system and method for safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion. This invention solves the complex safety challenges faced by long and heavy cargo in multimodal transport and improves the safety and efficiency of the transportation process.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: an intelligent perception and proactive early warning system for safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion, comprising:

[0008] Raw data input module: used to receive detailed dimensions, weight, transportation route, personnel information, and transportation vehicle information of long and heavy cargo input by users, and to build an initial data model to provide a foundation for intelligent analysis; and to import historical transportation data and external data sources;

[0009] Intelligent perception module for business scenarios: Integrates high-definition cameras, Beidou positioning, tilt sensors, acceleration monitors, tension monitors, tire pressure monitors, vehicle level monitors, and clearance laser rangefinders to perceive the real-time operating status of transportation vehicles, the fixed status of goods, personnel operation behavior, and dynamic information of the road environment; and to interface with other intelligent devices to obtain transportation environment information;

[0010] Data Acquisition and Analysis Module: Utilizing big data processing technology and artificial intelligence algorithms, this module performs in-depth analysis of data collected by the intelligent perception module for business scenarios. This includes the matching degree between the vehicle's location, speed, direction, and preset route; the adaptability of the vehicle's current condition to the road environment; the stability of the cargo's reinforcement status; and the compliance of personnel's operational behavior. Simultaneously, it continuously optimizes the analysis model and early warning strategies based on historical data and real-time feedback.

[0011] Safety risk early warning module: used to issue alarms to the on-site commander and drivers when potential safety hazards are discovered; and for remote monitoring and emergency communication, to contact relevant departments and personnel in a timely manner in emergency situations;

[0012] Operation guidance module: Used to provide operation guidance to the on-site commander and drivers in conjunction with early warning information; the guidance includes specific emergency measures and operating procedures, as well as explanations of potential risks and instructions on preventive measures; it has a voice prompt function to provide timely guidance when the driver's line of sight is obstructed or attention is distracted;

[0013] Networked information receiving module: Connects to external systems to receive and process the latest traffic and weather information in real time. Based on the received information, it can automatically adjust early warning strategies and operational guidance plans to cope with emergencies and complex environments.

[0014] This invention also provides a method for intelligent perception and proactive early warning of safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion. This method is implemented using the aforementioned intelligent perception and proactive early warning system for safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion. The method includes the following steps:

[0015] Step 1: System Initialization and Data Input: After logging into the system, users first fill in the detailed dimensions, precise weight, and specific transportation route information of long and heavy goods through the user interface. Then, the built-in verification algorithm automatically verifies the input information to ensure the integrity and rationality of the data.

[0016] Step 2, Real-time Data Sensing and Collection: During transportation, the business scenario intelligent sensing module continuously senses the vehicle's operating status in real time.

[0017] Step 3, Data Analysis and Risk Assessment: After receiving the real-time data collected by the sensing module, the data collection and analysis module uses big data processing technology and artificial intelligence algorithms to conduct in-depth analysis.

[0018] Step 4, Early Warning and Operation Guidance: Through multi-channel early warning, ensure that the driver and operator can receive the early warning information in a timely manner even when they are not in the cab;

[0019] Step 5: Network Information Reception and Processing: Receive official information from traffic management departments and obtain relevant data through interfaces with other traffic information systems;

[0020] Step 6: System Maintenance and Upgrades: Take various measures to maintain continuous optimization of its performance and functionality.

[0021] As a further improvement to the present invention, step 1 is specifically as follows:

[0022] Based on the verified data, the system constructs an initial data model, including a model describing the shape and size of the equipment using a three-dimensional spatial geometric algorithm, and a model generating the best or feasible transportation route based on a GIS geographic information system and route planning algorithm. At the same time, the system automatically connects to and initializes various sensor devices through a built-in communication protocol, including vehicle operation status sensors, environmental perception sensors, cargo reinforcement status monitoring sensors, and personnel operation behavior monitoring sensors.

[0023] As a further improvement of the present invention, in step 2, for the monitoring of key vehicle parameters, a sensor data acquisition algorithm is used to ensure the accuracy and real-time nature of the data; Beidou positioning information is obtained according to the satellite positioning algorithm, and the vehicle's position, speed, and direction information are calculated in real time.

[0024] High-definition cameras and radar capture surrounding environmental information in real time through image processing algorithms and radar detection algorithms, providing the system with data on road width, height restrictions, and turning angles. By comparing vehicle operating status with road environment information, an adaptive evaluation algorithm is used to assess the vehicle's adaptability to the environment in real time, ensuring the safe and smooth transportation process.

[0025] During cargo reinforcement monitoring, the lashing tension monitoring and impact vibration detectors use mechanical models and signal processing algorithms to monitor the cargo's stress and vibration status in real time. When an abnormality is detected, an early warning mechanism is immediately triggered. At the same time, facial recognition monitoring, fatigue recognition, and distraction recognition devices use deep learning algorithms to continuously monitor personnel's operating behavior to ensure that drivers and operators are in good condition.

[0026] As a further improvement of the present invention, in step 3, the big data processing technology includes data cleaning, data aggregation, and data visualization; the artificial intelligence algorithm includes machine learning algorithm and deep learning algorithm; in terms of the matching degree analysis of vehicle position, speed, direction and preset route, a path matching algorithm is used to evaluate the degree of deviation between the current position of the vehicle and the preset route, and adjustment suggestions are given in combination with road condition information; the adaptability assessment of vehicle condition and road environment is carried out by a comprehensive evaluation algorithm, using fuzzy logic or Bayesian network method to calculate the adaptability score.

[0027] During the stability assessment of cargo reinforcement status, a cargo status model is constructed using real-time monitoring data, and a status prediction algorithm is used to predict the future status of the cargo. An early warning mechanism is triggered when an anomaly is detected. The compliance assessment of personnel operation behavior is based on the behavior recognition results of deep learning algorithms, combined with the operation specification library to make compliance judgments. When a violation is detected, an alarm is immediately issued and operation guidance suggestions are provided.

[0028] By employing collision risk assessment algorithms, which comprehensively consider multiple factors such as vehicle status, road environment, cargo reinforcement status, and personnel operation behavior, the system can accurately determine whether there are potential safety threats during the current transportation process and provide important basis for subsequent early warning and operational guidance.

[0029] As a further improvement of the present invention, in step 4, the multi-channel early warning includes, in addition to audible and visual alarms, SMS, email, or mobile application push notifications; and:

[0030] Interactive display screen: The display screen in the driver's cab is not only used to display alarm information, but also to provide detailed map views and animated simulations, intuitively showing potential safety hazards and their possible impact range, helping personnel to better understand and take appropriate measures;

[0031] Voice commands: The system supports voice command functionality, automatically playing preset voice warnings or operation instructions for use when the driver's line of sight is limited or both hands are busy operating the system;

[0032] The operation guidance module provides personalized risk avoidance suggestions or operation guidance based on the specific content of the warning, based on the prediction results of big data analysis and machine learning models, and taking into account current traffic conditions, vehicle status, cargo status and personnel status factors.

[0033] Dynamic route planning: When it is detected that the vehicle is about to deviate from the preset route, it is recommended that the driver slow down or adjust the driving direction, and a new optimal route is dynamically planned based on real-time traffic information and displayed on the driver's cab display screen in real time.

[0034] Cargo reinforcement recommendations: For cargo with abnormal reinforcement status, specific reinforcement measures will be recommended based on the nature, size, weight of the cargo and the stress conditions during transportation;

[0035] Personnel operation guidance: Issue alarms for violations or unsafe behaviors of drivers and operators, and provide operation guidance videos or graphic instructions to help personnel correct errors and improve operational standardization;

[0036] After each warning and operational guidance, user feedback and actual processing results are collected. Machine learning algorithms are then used to optimize the warning model and operational guidance strategy to improve the accuracy and effectiveness of the system.

[0037] The intelligent sensing and early warning system based on multi-source data fusion for multimodal transport of long and heavy cargo of the present invention has brought about significant technical and economic benefits and has had a profound and positive impact on the transportation of long and heavy cargo in the power industry.

[0038] This system, by integrating advanced sensor technology, big data processing capabilities, and artificial intelligence algorithms, significantly improves the safety and efficiency of long and heavy cargo transportation. Its real-time monitoring capabilities ensure that even the slightest changes during transport are monitored, providing timely warnings of potential risks such as collisions, exceeding size limits, and overloading. This real-time perception and intelligent analysis not only reduces the probability of accidents but also enhances adaptability to complex transportation environments, ensuring the smooth operation of the transportation process.

[0039] The implementation of this invention helps reduce transportation costs and improve transportation efficiency. By reducing the occurrence of accidents, it directly reduces maintenance costs, cargo loss costs, and potential downtime costs caused by accidents. Simultaneously, the system's early warning function helps optimize transportation routes and times, reducing unnecessary delays and thus improving transportation efficiency. Furthermore, the intelligent system reduces reliance on manual monitoring, further lowering labor costs.

[0040] In conclusion, this invention not only improves the safety and efficiency of long-haul freight transportation at the technical level, but also brings significant cost savings and efficiency improvements to transportation companies at the economic level. These beneficial effects not only enhance the competitiveness of transportation companies but also provide solid logistical support for the infrastructure construction of the entire power industry.

[0041] The beneficial effects of this invention are:

[0042] 1. Significantly improve transportation safety: Through real-time sensing, data analysis and early warning mechanisms, this invention can promptly detect and warn of potential safety hazards, effectively reducing the accident rate during the transportation of long and heavy cargo.

[0043] 2. Improve transportation efficiency: Intelligent monitoring and early warning systems can optimize transportation routes and speed control strategies, reducing transportation delays caused by avoiding obstacles or waiting for road conditions to improve.

[0044] 3. Reduce operating costs: By reducing the occurrence of accidents and delays, this invention helps to reduce the operating costs of transportation companies and improve economic efficiency.

[0045] 4. Enhance user experience: Provide detailed operation guidance and risk avoidance suggestions to enable drivers to cope with complex transportation environments more easily, thereby improving user experience and satisfaction.

[0046] 5. Promote the intelligent development of the logistics industry: The successful application of this invention will set a benchmark for the intelligent transformation of the logistics industry and promote the entire industry to develop in a more efficient, safe and intelligent direction. Attached Figure Description

[0047] Figure 1 This is a system framework diagram of an embodiment of the present invention;

[0048] Figure 2 This is a flowchart of a method according to an embodiment of the present invention.

[0049] Figure 3 This is an intended representation of the data analysis and risk assessment results in the embodiments of the present invention;

[0050] Figure 4 This is a flowchart of data analysis and risk assessment in an embodiment of the present invention. Detailed Implementation

[0051] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0052] Example 1

[0053] like Figure 1 As shown, a smart perception and proactive early warning system for safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion includes:

[0054] The raw data input module receives key information from users, including detailed dimensions, weight, transportation routes, personnel information, and transportation vehicle information for long and heavy cargo. It then establishes an initial data model to provide a foundation for subsequent intelligent analysis. This module also supports importing historical transportation data and external data sources to improve the accuracy and comprehensiveness of the analysis.

[0055] The intelligent perception module for business scenarios integrates multiple sensors, including high-definition cameras, BeiDou positioning, tilt sensors, accelerometers, tension sensors, tire pressure sensors, vehicle level sensors, and clearance laser rangefinders, to perceive real-time dynamic information such as the operating status of transportation vehicles, the fixed status of goods, personnel operating behaviors, and the road environment. Furthermore, this module supports seamless integration with other intelligent devices, such as drone inspections and mobile ground monitoring stations, to obtain more comprehensive transportation environment information.

[0056] Data Acquisition and Analysis Module: Utilizing big data processing technology and artificial intelligence algorithms, this module performs in-depth analysis of the data collected by the sensing module. The analysis covers multiple dimensions, including the matching degree of the vehicle's location, speed, and direction with the preset route; the adaptability of the vehicle's current condition to the road environment; the stability of the cargo's securing status; and the compliance of personnel operational behaviors. Furthermore, this module possesses self-learning capabilities, continuously optimizing the analysis model and early warning strategies based on historical data and real-time feedback.

[0057] Safety Risk Early Warning Module: This module will immediately trigger upon detecting potential safety hazards, such as potential collision risks, failure to meet clearance requirements, improper personnel operation, or cargo status data deviations exceeding thresholds. It will issue alerts to the on-site commander and driver through multiple means, including sound, lights, and displays. Furthermore, this module supports remote monitoring and emergency communication functions to facilitate timely contact with relevant departments and personnel in emergency situations.

[0058] Operational Guidance Module: This module provides operational guidance to on-site supervisors and drivers, in conjunction with early warning information. The guidance includes not only specific emergency measures and operational procedures, but also explanations of potential risks and instructions for prevention. The module also features voice prompts to provide timely guidance when the driver's view is obstructed or their attention is distracted.

[0059] Networked information receiving module: Connects to external systems such as traffic management departments and meteorological departments to receive and process the latest traffic information (such as changes in bridge load-bearing limits and temporary road closures) and weather information (such as warnings of severe weather such as rain, snow, and fog) in real time. Based on the received information, the system can automatically adjust warning strategies and operational guidance plans to cope with emergencies and complex environments.

[0060] like Figure 2 As shown in the figure, this embodiment also provides a method for intelligent perception and proactive early warning of safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion, including the following steps:

[0061] Step 1: System Initialization and Data Input

[0062] In this stage, after logging into the system, users first need to fill in the detailed dimensions (such as length, width, and height), precise weight, and specific transportation route information (including origin, destination, transit points, and specific road condition requirements) of the oversized cargo through the user interface (UI). The system receives this data using structured data formats such as JSON or XML to ensure data accuracy and parsability. Subsequently, the system uses built-in validation algorithms (such as regular expression matching and range checking) to automatically validate the input information, ensuring data integrity and reasonableness.

[0063] Based on the validated data, the system constructs an initial data model. These models include, but are not limited to, equipment size models (describing equipment shape and size using 3D spatial geometric algorithms) and transportation route models (generating optimal or feasible routes based on GIS geographic information systems and path planning algorithms). Simultaneously, the system automatically connects to and initializes various sensor devices via built-in communication protocols (such as CAN bus and MQTT), including vehicle operation status sensors (monitoring acceleration, tire pressure, vehicle level, etc., extracting key parameters using sensor data parsing algorithms), environmental perception sensors (combining high-definition cameras and radar, using image processing and radar detection algorithms to measure road width, height restrictions, etc.), cargo reinforcement status monitoring sensors (monitoring lashing tension and impact vibration, assessing cargo status through mechanical models and signal processing algorithms), and personnel operation behavior monitoring sensors (facial recognition, fatigue recognition, and distraction recognition, performing real-time monitoring and analysis based on deep learning algorithms).

[0064] Step 2: Real-time data sensing and acquisition:

[0065] During transportation, the intelligent sensing module continuously monitors the vehicle's operating status in real time. For monitoring key vehicle parameters, such as acceleration and tire pressure, the system employs sensor data acquisition algorithms (such as ADC analog-to-digital conversion and noise removal algorithms) to ensure data accuracy and real-time performance. The acquisition of BeiDou positioning information relies on satellite positioning algorithms to calculate vehicle position, speed, and direction in real time.

[0066] Advanced equipment such as high-definition cameras and radar captures real-time environmental information through image processing algorithms (e.g., edge detection, feature extraction, target recognition) and radar detection algorithms (e.g., distance measurement, speed estimation, target tracking), providing the system with key data such as road width, height restrictions, and turning angles. The system compares vehicle operating status (e.g., vehicle size, driving trajectory) with road environment information and uses adaptive evaluation algorithms (e.g., collision warning models, route feasibility analysis) to assess the vehicle's adaptability to the environment in real time, ensuring a safe and smooth transportation process.

[0067] In terms of cargo reinforcement status monitoring, lashing tension monitoring and impact vibration detectors use mechanical models and signal processing algorithms (such as FFT fast Fourier transform to analyze vibration frequency and amplitude, and stress-strain analysis to assess tension status) to monitor the stress and vibration status of the cargo in real time. An early warning mechanism is triggered immediately upon detecting any abnormalities. Simultaneously, facial recognition monitoring, fatigue detection, and distraction detection devices continuously monitor personnel's operational behavior using deep learning algorithms (such as convolutional neural networks (CNN) for facial feature extraction and recurrent neural networks (RNN) for behavioral pattern analysis), ensuring the good condition of drivers and operators.

[0068] Step 3: Data Analysis and Risk Assessment

[0069] After receiving real-time data collected by the perception module, the data analysis module performs in-depth analysis using big data processing technologies and artificial intelligence algorithms. Big data processing technologies include data cleaning (removing outliers and filling in missing values), data aggregation (summarizing data by time, space, category, etc.), and data visualization (generating charts, heatmaps, etc., to visually display the data). Artificial intelligence algorithms include machine learning algorithms (such as classification, regression, and clustering) and deep learning algorithms (such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). These algorithms can automatically identify key features and patterns in the data, providing the system with comprehensive data insights.

[0070] In terms of analyzing the matching degree between vehicle position, speed, direction, and the preset route, the system uses path matching algorithms (such as Hausdorff distance, Frechet distance, etc.) to evaluate the deviation of the vehicle's current position from the preset route and provides adjustment suggestions in conjunction with road condition information. The adaptability assessment of vehicle condition and road environment is carried out through a comprehensive evaluation algorithm (considering vehicle performance parameters, road conditions, weather factors, etc.) and uses methods such as fuzzy logic or Bayesian networks to calculate the adaptability score.

[0071] Regarding the stability assessment of cargo reinforcement status, the system constructs cargo status models (such as finite element analysis models and dynamic mechanical models) through real-time monitoring data, uses status prediction algorithms (such as Kalman filtering and particle filtering) to predict the future state of the cargo, and triggers an early warning mechanism when anomalies are detected. The compliance assessment of personnel operation behavior is based on the behavior recognition results of deep learning algorithms, combined with an operation specification library to make compliance judgments. When violations are detected, an alarm is immediately issued and operation guidance suggestions are provided.

[0072] Based on the above analysis results, the system uses a collision risk assessment algorithm (Monte Carlo simulation) to comprehensively consider multiple factors such as vehicle status, road environment, cargo reinforcement status, and personnel operation behavior, to accurately determine whether there are potential safety threats in the current transportation process, and to provide an important basis for subsequent early warning and operation guidance.

[0073] Specific steps:

[0074] (1) Data cleaning: First, the collected data is cleaned to remove outliers and fill in missing values. For example, the mean is used to fill in missing values ​​in numerical data, and outliers are identified and removed or replaced with the median. Suppose there are 1000 data points, of which 5 speed data points are missing and 3 are outliers. The missing values ​​are filled by calculating the mean, and the speed outliers are replaced with the median.

[0075] (2) Data Aggregation: The cleaned data is aggregated. For example, according to the time dimension, data within each 10-minute interval is aggregated into a group to analyze the changing trends of vehicle driving status. In terms of the spatial dimension, the data can be classified and summarized according to different road segments. For the category dimension, the data can be divided into categories such as vehicle status, road environment, and cargo status for analysis.

[0076] (3) Data visualization: Generate charts and heat maps to visually display data. For example, draw a line graph of vehicle position changes over time, generate a histogram of vehicle speed distribution, and a heat map of road accident frequency.

[0077] (4) Path matching algorithm: The deviation of the vehicle's current position from the preset route is calculated using Hausdorff distance or Frechet distance. For example, if the Hausdorff distance threshold is set to 0.1 km, and the calculated deviation distance is greater than this threshold, it is considered a significant deviation.

[0078] Formula example:

[0079]

[0080] (5) Comprehensive evaluation algorithm: Considering vehicle performance parameters, road conditions, weather factors, etc., fuzzy logic or Bayesian network is used to calculate the adaptability score. For example, fuzzy logic is used to evaluate vehicle performance, weight factors are set, and a comprehensive score is calculated.

[0081] Formula example:

[0082] S = w1·P1 + w2·P2 + ... + w n ·P n

[0083] Where S is the adaptability score, w i It is a weighting factor, P i These are performance parameters.

[0084] Specifically, considering vehicle performance parameters, road conditions, weather factors, etc., this embodiment uses fuzzy logic to evaluate vehicle performance, sets weighting factors, and calculates a comprehensive score:

[0085] a. Input parameters and weighting factors:

[0086] 1. Vehicle performance parameters (P) i P1: Maximum load capacity; P2: Maximum operating speed; P3: Fuel efficiency; P4: Maintenance cost; Weighting factor (w) i ): w1=0.3; w2=0.2; w3=0.2; w4=0.1;

[0087] 2. Road condition parameters (P)i P5: Road type (highway, urban road, rural road, converted into a quantitative score); P6: Road condition (good, average, poor, converted into a quantitative score); Weighting factors (w) i ): w5 = 0.05; w6 = 0.05;

[0088] 3. Weather factor parameters (P) i P7: Current weather conditions (sunny, cloudy, rain / snow, fog, converted into a quantitative score); weighting factors (w) i ): w7 = 0.1;

[0089] b. The input parameter quantification scoring table is shown in the table below:

[0090]

[0091] According to the formula S=w1·P1+w2·P2+…+w n ·P n Calculate the adaptability score for each vehicle under the current route and weather conditions. For example, if the quantitative scores of various parameters of the vehicle under the current route and weather conditions are shown in the table above, then the adaptability score S is calculated as follows:

[0092] S=0.380+0.290+0.275+0.160+0.0585+0.0590+0.1*70=24+18+15+6+4.25+4.5+7=84.75;

[0093] Based on the adaptability score, the vehicle with the highest score is selected and assigned to a specific route. If multiple vehicles have similar scores, other factors, such as the vehicle's current location and the urgency of the mission, are considered before a final decision is made.

[0094] (6) Cargo status model construction: finite element analysis model or dynamic mechanical model is constructed by real-time monitoring data. For example, the finite element analysis model is used to simulate the cargo reinforcement status.

[0095] (7) State Prediction Algorithm: Use Kalman filtering or particle filtering algorithms to predict the future state of the goods. For example, assume the current state of the goods is X. k The state X at the next moment is predicted using the Kalman filter algorithm. k+1 .

[0096] Formula example:

[0097] X k+1 =F k X k +B k U k +W k

[0098] Among them, F k It is the state transition matrix, B k It is the control matrix, U k It is the control input, W k It is process noise.

[0099] (8) Collision risk assessment algorithm: Use Monte Carlo simulation or fault tree analysis to assess collision risk. For example, use Monte Carlo simulation to simulate the vehicle's driving trajectory under different road conditions and calculate the probability of a collision.

[0100] Algorithm: Monte Carlo simulation, Formula:

[0101]

[0102] Where: is P 碰撞 The probability of a collision occurring, where N is the total number of simulations, and f i It is the collision function of the i-th simulation. If a collision occurs, then I(f) i ) = 1, otherwise I(f) i ) = 0.

[0103] Data parameters:

[0104] Vehicle trajectory data: including vehicle speed, acceleration, direction, etc.

[0105] Traffic data includes road conditions, traffic flow, and traffic signals.

[0106] Environmental data includes weather conditions, visibility, and road surface wetness.

[0107] (9) Safety Hazard Identification Algorithm: The Support Vector Machine (SVM) algorithm is used to identify safety hazards. For example, historical accident data is analyzed to train an SVM model and identify potential hazard factors that may lead to accidents.

[0108] Through the above embodiments, the system comprehensively analyzes vehicle status, road environment, cargo reinforcement status, and personnel operation behavior to accurately determine potential safety threats during transportation, providing a basis for early warning and operational guidance.

[0109] Algorithm: Support Vector Machine (SVM), Formula:

[0110]

[0111] Where: f(x) is the decision function, used to determine whether something is a safety hazard. x is the input feature vector. i These are training samples. i These are the labels of the training samples, indicating whether there are any security risks. α iThese are Lagrange multipliers. K is the kernel function used to calculate the similarity between samples. b is the bias term.

[0112] Data parameters:

[0113] Historical accident data includes the environment, time, vehicle condition, and operational behavior of the accident.

[0114] Feature vectors may include vehicle speed, acceleration, driving trajectory, environmental conditions, cargo securing status, personnel operation behavior, etc.

[0115] Tags: Indicate whether there are potential safety hazards in historical accident data.

[0116] Imagine a large truck transporting a batch of 120-ton power transformers, each measuring 15 meters long, 4 meters wide, and 4.5 meters high, from Chengdu to a power station in Chongqing. The intelligent sensing and early warning system in this solution will be fully integrated to ensure the safety and efficiency of the transportation.

[0117] During the data cleaning phase, the system collected 1000 speed data points through onboard sensors, identifying 5 missing data points and 3 outliers. To address these issues, the system calculated the average speed of the non-missing data to be 29 km / h and used this average to fill in the missing data. For the outliers, the system calculated the median of all valid speed data and replaced the outliers with the median, ensuring the accuracy and consistency of the dataset.

[0118] During the data aggregation phase, the system aggregates the cleaned data in 10-minute intervals to analyze trends in vehicle driving status. For example, the system might detect a decrease in average vehicle speed from 30 km / h to 28 km / h within a 10-minute period, potentially indicating traffic congestion or deteriorating road conditions. Simultaneously, the system categorizes and summarizes the data according to different road segments, such as calculating average speed and traffic flow on highways and urban roads separately. Furthermore, the system analyzes data by classifying it into categories such as vehicle status, road environment, and cargo status to provide a more comprehensive understanding of various factors in the transportation process.

[0119] During the data visualization phase, the system generated various charts and heatmaps to visually represent the data. For example, the system plotted a line graph showing the vehicle's position over time, displaying the vehicle's location at different points in time and reflecting its driving trajectory. The system also generated a histogram of vehicle speed distribution, showing the frequency of vehicle occurrences within different speed ranges and analyzing the distribution of vehicle speeds. Furthermore, the system created a heatmap of road accident frequency, using different colors on the map to represent accident frequency; darker colors indicate more frequent accidents, providing drivers with intuitive risk warnings.

[0120] During the path matching algorithm phase, the system uses Hausdorff distance or Frechet distance to calculate the degree of deviation between the vehicle's current position and the preset route. For example, the system sets the Hausdorff distance threshold to 0.1 kilometers. If the calculated deviation distance is greater than this threshold, it is considered a significant deviation and may trigger a warning. This step helps ensure that the vehicle travels along the predetermined route, reducing the risks associated with deviating from the route.

[0121] In the comprehensive evaluation algorithm phase, the system considers vehicle performance parameters, road conditions, weather factors, etc., and uses fuzzy logic or Bayesian networks to calculate an adaptability score. For example, the system uses fuzzy logic to evaluate vehicle performance, sets weighting factors, and calculates a comprehensive score. This score helps assess the vehicle's performance and safety under current conditions.

[0122] During the cargo condition model construction phase, the system builds finite element analysis models or dynamic mechanical models using real-time monitoring data. For example, the finite element analysis model can be used to simulate the reinforced state of the cargo. This model helps to assess the stability of the cargo during transportation, ensuring that the cargo arrives at its destination safely.

[0123] In the state prediction algorithm stage, the system uses Kalman filtering or particle filtering algorithms to predict the future state of the goods. For example, suppose the current state of the goods is X. k The system predicts the state X at the next moment using the Kalman filter algorithm. k+1 .

[0124] In the collision risk assessment algorithm phase, the system uses Monte Carlo simulation or fault tree analysis to evaluate collision risk. For example, the system uses Monte Carlo simulation to simulate the vehicle's trajectory under different road conditions and calculate the probability of a collision. This assessment helps identify high-risk road sections and provide early risk warnings.

[0125] Finally, in the safety hazard identification algorithm stage, the system utilizes the Support Vector Machine (SVM) algorithm to identify safety hazards. For example, the system analyzes historical accident data to train an SVM model and identify potential hazard factors that may lead to accidents. This identification helps the system comprehensively analyze vehicle status, road environment, cargo securing status, and personnel operational behavior to accurately determine potential safety threats during transportation, providing a scientific basis for early warning and operational guidance.

[0126] Intelligent sensing and early warning systems enable comprehensive monitoring of the transportation process, timely detection and warning of potential safety hazards, thereby improving transportation safety and efficiency. The application of this system not only reduces the probability of accidents and lowers transportation costs but also enhances transportation efficiency, providing solid logistical support for the infrastructure construction of the power industry. The data analysis and risk assessment results are intended to illustrate... Figure 3 As shown in the diagram; the workflow for data analysis and risk assessment is as follows. Figure 4 As shown.

[0127] Step 4: Early Warning and Operation Instructions:

[0128] During the warning and operational guidance phase, the system's response mechanism is designed to be both rapid and detailed to ensure swift and effective action in emergency situations. In addition to alerting drivers and operators via sound, light, and displays, the system offers various interactive methods to enhance the effectiveness of the warnings.

[0129] Multi-channel early warning: In addition to basic audible and visual alarms, the system also pushes notifications via SMS, email, or mobile applications to ensure that drivers and operators can receive early warning information in a timely manner even when they are not in the cab.

[0130] Interactive display screen: The display screen in the driver's cab not only displays alarm information, but also provides detailed map views and animated simulations, intuitively showing potential safety hazards and their possible impact range, helping personnel to better understand and take appropriate measures.

[0131] Voice commands: The system supports voice command functionality and can automatically play preset voice warnings or operating instructions, which is especially suitable for use when the driver's line of sight is limited or when both hands are busy operating.

[0132] The operation guidance module provides personalized risk avoidance suggestions or operation guidance based on the specific content of the warning. These suggestions and guidance are based on the prediction results of big data analysis and machine learning models, and take into account various factors such as current traffic conditions, vehicle status, cargo status, and personnel status.

[0133] Dynamic route planning: When the system detects that the vehicle is about to deviate from the preset route, it not only suggests that the driver slow down or adjust the driving direction, but also dynamically plans a new optimal route based on real-time road conditions and displays it on the driver's cab display screen in real time.

[0134] Cargo reinforcement recommendations: If the cargo reinforcement status is abnormal, the system will provide specific reinforcement measures based on the nature, size, weight of the cargo and the stress conditions during transportation, such as adding binding points or adjusting binding strength.

[0135] Personnel operation guidance: For violations or unsafe acts by drivers and operators, the system not only issues alarms, but also provides detailed operation guidance videos or graphic instructions to help personnel correct errors and improve operational standardization.

[0136] In addition, the system has self-learning and optimization capabilities. After each warning and operation guidance, the system collects user feedback and actual processing results, and continuously optimizes the warning model and operation guidance strategy through machine learning algorithms to improve the system's accuracy and effectiveness.

[0137] Step 5: Receiving and Processing Networked Information

[0138] The network information receiving module serves as a bridge between the system and the external traffic management system, undertaking the important task of acquiring and processing the latest traffic information in real time.

[0139] The system not only receives official information from traffic management departments, but also obtains relevant data through interfaces with other traffic information systems (such as road condition monitoring systems and weather forecasting systems). These diverse information sources ensure that the system can comprehensively grasp the latest traffic dynamics and weather changes.

[0140] Real-time processing: The system employs efficient data processing algorithms and concurrent processing technology to ensure that new information can be quickly parsed and processed after it is received, and to update the internal data model and early warning rules in the shortest possible time.

[0141] Dynamic adaptability: Based on updated information, the system can automatically adjust various parameters and strategies during transportation, such as adjusting driving speed and selecting alternative routes, to adapt to the ever-changing traffic environment.

[0142] In addition, the system also has data backup and recovery functions to ensure that data can be quickly recovered and normal operation can continue in the event of unexpected situations such as network failure or data loss.

[0143] Step 6: System Maintenance and Upgrade

[0144] System maintenance and upgrades are crucial for ensuring long-term stable operation and continuous improvement of the system's intelligence level. During this phase, the system employs various measures to maintain continuous optimization of its performance and functionality.

[0145] In summary, this embodiment, through systematic design and refined implementation, achieves comprehensive monitoring and intelligent management of the transportation process of long and heavy cargo. From system initialization and data input to early warning and operational guidance, to network information reception and processing, and system maintenance and upgrades, all aspects are closely interconnected and mutually supportive, forming an efficient, reliable, and intelligent transportation management system.

[0146] Example 2

[0147] In the transportation of long and heavy goods in the power industry, consider the scenario of transporting a batch of large power transformers, each weighing 120 tons, from their manufacturing location in Chengdu to a power station in Chongqing. This involves not only complex logistics arrangements but also ensuring the safety and integrity of the equipment during transport. Therefore, the application of an intelligent sensing and early warning system based on multi-source data fusion for long and heavy cargo multimodal transport becomes particularly important in this scenario.

[0148] System initialization and data input:

[0149] Before transportation began, the user input the transformer's detailed dimensions (15 meters long, 4 meters wide, and 4.5 meters high), precise weight (120 tons), and specific transportation route (starting from the manufacturing plant in Chengdu, via the Chengdu-Chongqing Expressway, and finally arriving at the power station in Chongqing) through the system interface. The system received this information in JSON format and used a built-in validation algorithm (regular expression matching) to verify the accuracy of the data. Next, based on the input data, the system constructed a three-dimensional spatial geometric model describing the equipment's shape and, combined with a GIS geographic information system, generated the optimal transportation route.

[0150] Real-time data sensing and acquisition:

[0151] During transportation, the intelligent sensing module begins to function. For example, the system uses an integrated high-definition camera to detect a height restriction of 4.2 meters ahead and, through image processing algorithms, determines the distance between the vehicle's top and the bottom of the bridge to be 0.3 meters, close to the preset safety threshold of 0.5 meters. At this point, the system uses a collision warning model and Monte Carlo simulation to predict the probability of a collision when the vehicle passes through this section of road at different speeds. Assuming the current vehicle speed is 25 km / h, the system calculates through simulation that if the vehicle continues at its current speed, the probability of a collision is 5%, exceeding the preset safety threshold of 3%. Therefore, the system will display a warning message on the display screen in the driver's cabin, accompanied by a voice prompt, "Height restriction ahead is too low, please slow down," and simultaneously notify transportation management personnel via SMS.

[0152] Data analysis and risk assessment:

[0153] After receiving real-time data from the perception module, the data analysis module assesses collision risk using Monte Carlo simulation. This algorithm calculates the probability of a collision by simulating the vehicle's trajectory under different road conditions. For example, it simulates a vehicle passing through a height-restricted area at different speeds (20 km / h, 25 km / h, 30 km / h) and different road inclination angles (2 degrees, 3 degrees, 4 degrees), calculating the corresponding collision probabilities. Assuming the collision probability is 5% at 25 km / h and decreases to 2% at 20 km / h, the system advises the driver to slow down to reduce the risk of a collision.

[0154] During the real-time data perception and acquisition phase, the intelligent perception module, through an integrated high-definition camera, captures the height limit of the road section ahead as 4.2 meters. Using image processing algorithms, the system determines the distance between the top of the vehicle and the bottom of the bridge to be 0.3 meters, close to the preset safety threshold of 0.5 meters. In this process, the system utilizes image processing techniques, such as edge detection algorithms (Canny edge detection), to determine the distance between the vehicle and the bottom of the bridge. Assuming the image resolution captured by the camera is 1920x1080 pixels, the system can use the Canny edge detection algorithm to identify the boundary between the vehicle's top outline and the bottom of the bridge, and then convert the pixel distance in the image into the actual distance. The key here is the pixel-to-actual-distance conversion factor, which can be obtained by calibration using an object of known size (such as a calibration board).

[0155] In this embodiment, it is necessary to simulate the situation of a vehicle passing through a height-restricted area at different speeds V and different road inclination angles (θ) to calculate the corresponding collision probability.

[0156] First, define a few key variables:

[0157] The minimum vertical distance Hmin from the top of the vehicle to the ground.

[0158] The height limit for the road ahead is Hlimit = 4.2m.

[0159] Safety threshold Hsafe = 0.5m

[0160] The goal of collision risk assessment is to calculate, at a given speed V and road inclination angle θ, whether the vertical distance between the top of the vehicle and the bottom of the bridge is greater than or equal to Hsafe, i.e.:

[0161] Htop ≥ Hlimit - Hsafe

[0162] Where Htop is the vertical distance between the top of the vehicle and the ground. On a straight road, Htop can be directly obtained from the vehicle's design parameters; however, on an inclined road, the change in the vehicle's center of gravity needs to be considered. The following formula can be used to estimate the change of Htop at the angle of inclination:

[0163] Htop(θ) = Hmin + Hadjust(θ)

[0164] Hadjust(θ) = Hmin·sin(θ)

[0165] Hadjust(θ) represents the change in height caused by the tilt angle.

[0166] Improved algorithm: To improve the algorithm and make it more accurately reflect the actual situation, a vehicle dynamic model and an uncertainty distribution can be introduced. The vehicle dynamic model takes into account the impact of factors such as vehicle acceleration and braking on height, while the uncertainty distribution is the collision probability distribution under different speeds and tilt angles based on historical data statistics.

[0167] In the Monte Carlo simulation, a large number of samples are randomly selected, and each sample represents a combination of a set of speed and tilt angle. For each group of samples, calculate Htop(θ), and compare it with Hlimit - Hsafe. If Htop(θ) < Hlimit - Hsafe, it is considered that a collision will occur. By repeating this process multiple times, the collision probability under given speed and tilt angle can be estimated.

[0168] For example, simulate the situation of a vehicle passing through a height restriction area at different speeds (20 km / h, 25 km / h, 30 km / h) and different road tilt angles (2 degrees, 3 degrees, 4 degrees). Assume that at a speed of 20 km / h, the collision probability is 5%, while at a speed of 25 km / h, the collision probability is reduced to 2%. Therefore, the system recommends that the driver decelerate to reduce the collision risk.

[0169] Early warning and operation guidance:

[0170] In the early warning and operation guidance stage, the system not only issues alarms to the driver through sound, light, and display screen, but also pushes notifications through text messages, emails, or mobile applications to ensure that early warning information can be received in a timely manner even when the driver and operator are not in the cab. In addition, the system also provides a detailed map view and animation simulation to intuitively display potential safety hazards and their possible impact ranges, helping personnel better understand and take corresponding measures. For example, in the above scenario, the system shows the height restriction of the front road section through the display screen in the cockpit and simulates the situation of the vehicle passing through this section at different speeds, enabling the driver to clearly understand the current dangerous situation and take deceleration measures according to the system's recommendations.

[0171] The above-described embodiments only represent the specific implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention.

Claims

1. A method for intelligent perception and proactive early warning of safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion, characterized in that, This is achieved using an intelligent perception and proactive early warning system for safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion. The system includes: Raw data input module: used to receive detailed dimensions, weight, transportation route, personnel information, and transportation vehicle information of long and heavy cargo input by users, and to build an initial data model to provide a foundation for intelligent analysis; and to import historical transportation data and external data sources; Intelligent perception module for business scenarios: Integrates high-definition cameras, Beidou positioning, tilt sensors, acceleration monitors, tension monitors, tire pressure monitors, vehicle level monitors, and clearance laser rangefinders to perceive the real-time operating status of transportation vehicles, the fixed status of goods, personnel operation behavior, and dynamic information of the road environment; and to interface with other intelligent devices to obtain transportation environment information; Data Acquisition and Analysis Module: Utilizing big data processing technology and artificial intelligence algorithms, this module performs in-depth analysis of data collected by the intelligent perception module for business scenarios. This includes the matching degree between the vehicle's location, speed, direction, and preset route; the adaptability of the vehicle's current condition to the road environment; the stability of the cargo's reinforcement status; and the compliance of personnel's operational behavior. Simultaneously, it continuously optimizes the analysis model and early warning strategies based on historical data and real-time feedback. Safety risk early warning module: used to issue alarms to the on-site commander and drivers when potential safety hazards are discovered; and for remote monitoring and emergency communication, to contact relevant departments and personnel in a timely manner in emergency situations; Operation guidance module: Used to provide operation guidance to the on-site commander and drivers in conjunction with early warning information; the guidance includes specific emergency measures and operating procedures, as well as explanations of potential risks and instructions on preventive measures; it has a voice prompt function to provide timely guidance when the driver's line of sight is obstructed or attention is distracted; Network information receiving module: Connects to external systems to receive and process the latest traffic and weather information in real time. Based on the received information, it can automatically adjust early warning strategies and operational guidance plans to cope with emergencies and complex environments. The method includes the following steps: Step 1: System Initialization and Data Input: After logging into the system, users first fill in the detailed dimensions, precise weight, and specific transportation route information of long and heavy goods through the user interface. Then, the built-in verification algorithm automatically verifies the input information to ensure the integrity and rationality of the data. Step 2, Real-time Data Sensing and Collection: During transportation, the business scenario intelligent sensing module continuously senses the vehicle's operating status in real time. Step 3, Data Analysis and Risk Assessment: After receiving the real-time data collected by the sensing module, the data collection and analysis module uses big data processing technology and artificial intelligence algorithms to conduct in-depth analysis. Step 4, Early Warning and Operation Guidance: Through multi-channel early warning, ensure that the driver and operator can receive the early warning information in a timely manner even when they are not in the cab; Step 5, Network Information Reception and Processing: Receive official information from traffic management departments and obtain relevant data through interfaces with other traffic information systems; Step 6, System Maintenance and Upgrades: Take various measures to maintain continuous optimization of its performance and functionality.

2. The method for intelligent perception and proactive early warning of safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion as described in claim 1, characterized in that, Step 1 is described in detail as follows: Based on the verified data, the system constructs an initial data model, including a model describing the shape and size of the equipment using a three-dimensional spatial geometric algorithm, and a model generating the best or feasible transportation route based on a GIS geographic information system and route planning algorithm. At the same time, the system automatically connects to and initializes various sensor devices through a built-in communication protocol, including vehicle operation status sensors, environmental perception sensors, cargo reinforcement status monitoring sensors, and personnel operation behavior monitoring sensors.

3. The method for intelligent perception and proactive early warning of safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion as described in claim 1, characterized in that, In step 2, for the monitoring of key vehicle parameters, sensor data acquisition algorithms are used to ensure the accuracy and real-time performance of the data; BeiDou positioning information is obtained based on satellite positioning algorithms to calculate the vehicle's position, speed, and direction information in real time. High-definition cameras and radar capture surrounding environmental information in real time through image processing algorithms and radar detection algorithms, providing the system with data on road width, height restrictions, and turning angles. By comparing vehicle operating status with road environment information, an adaptive evaluation algorithm is used to assess the vehicle's adaptability to the environment in real time, ensuring the safe and smooth transportation process. During cargo reinforcement monitoring, the lashing tension monitoring and impact vibration detectors use mechanical models and signal processing algorithms to monitor the cargo's stress and vibration status in real time. When an abnormality is detected, an early warning mechanism is immediately triggered. At the same time, facial recognition monitoring, fatigue recognition, and distraction recognition devices use deep learning algorithms to continuously monitor personnel's operating behavior to ensure that drivers and operators are in good condition.

4. The method for intelligent perception and proactive early warning of safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion as described in claim 3, characterized in that, In step 3, big data processing technologies include data cleaning, data aggregation, and data visualization; artificial intelligence algorithms include machine learning algorithms and deep learning algorithms; in terms of the matching degree analysis between vehicle position, speed, direction and preset route, a path matching algorithm is used to evaluate the degree of deviation between the vehicle's current position and the preset route, and adjustment suggestions are given in combination with road condition information; the adaptability assessment of vehicle condition and road environment is carried out through a comprehensive evaluation algorithm, using fuzzy logic or Bayesian network methods to calculate the adaptability score. During the stability assessment of cargo reinforcement status, a cargo status model is constructed using real-time monitoring data, and a status prediction algorithm is used to predict the future status of the cargo. An early warning mechanism is triggered when an anomaly is detected. The compliance assessment of personnel operation behavior is based on the behavior recognition results of deep learning algorithms, combined with the operation specification library to make compliance judgments. When a violation is detected, an alarm is immediately issued and operation guidance suggestions are provided. By employing collision risk assessment algorithms, which comprehensively consider multiple factors such as vehicle status, road environment, cargo reinforcement status, and personnel operation behavior, the system can accurately determine whether there are potential safety threats during the current transportation process and provide important basis for subsequent early warning and operational guidance.

5. The method for intelligent perception and proactive early warning of safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion as described in claim 1, characterized in that, In step 4, multi-channel alerts include audible and visual alarms, SMS, email, or mobile application push notifications; as well as: Interactive display screen: The display screen in the driver's cab is not only used to display alarm information, but also to provide detailed map views and animated simulations, intuitively showing potential safety hazards and their possible impact range, helping personnel to better understand and take appropriate measures; Voice commands: The system supports voice command functionality, automatically playing preset voice warnings or operation instructions for use when the driver's line of sight is limited or both hands are busy operating the system; The operation guidance module provides personalized risk avoidance suggestions or operation guidance based on the specific content of the warning, based on the prediction results of big data analysis and machine learning models, and taking into account current traffic conditions, vehicle status, cargo status and personnel status factors. Dynamic route planning: When it is detected that the vehicle is about to deviate from the preset route, it is recommended that the driver slow down or adjust the driving direction, and a new optimal route is dynamically planned based on real-time traffic information and displayed on the driver's cab display screen in real time. Cargo reinforcement recommendations: For cargo with abnormal reinforcement status, specific reinforcement measures will be recommended based on the nature, size, weight of the cargo and the stress conditions during transportation; Personnel operation guidance: Issue alarms for violations or unsafe behaviors of drivers and operators, and provide operation guidance videos or graphic instructions to help personnel correct errors and improve operational standardization; After each warning and operational guidance, user feedback and actual processing results are collected. Machine learning algorithms are then used to optimize the warning model and operational guidance strategy to improve the accuracy and effectiveness of the system.

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