A bulk commodity transportation information management method and system based on cloud computing

Abstract

The present invention relates to the field of big data mining technology, and in particular to a method and system for managing bulk commodity transportation information based on cloud computing. The method comprises the following steps: obtaining bulk commodity transportation data, and modeling to obtain a bulk commodity carrier structure model; evaluating the bulk commodity carrier structure model to obtain carrier loading stability data; obtaining road environment monitoring data, and screening to obtain a bulk commodity transportation path data set; modeling the transportation environment monitoring data and the bulk commodity transportation path data set to obtain a transportation environment digital network; selecting the bulk commodity transportation path data set to obtain low-risk bulk commodity transportation path data; integrating the transportation environment digital network and the low-risk bulk commodity transportation path data to obtain a bulk commodity transportation strategy; and performing correction based on the transportation environment digital network to obtain actual estimated transportation time data. The present invention can improve the efficiency of bulk commodity transportation management.

Description

Technical Field

[0001] The present invention relates to the technical field of big data mining, and in particular to a method and system for managing bulk commodity transportation information based on cloud computing. Background Art

[0002] Traditional methods of information management for bulk commodity transportation mainly rely on manual operations and local information systems, and there are many problems, such as information fragmentation, decision-making lags, and insufficient data security. In the past, the management of bulk commodity transportation mainly relied on paper documents and local computer systems. The process of information collection and updating is usually manual, which leads to problems with inaccurate and time-effective data. Decision makers often lack real-time data support, making it difficult to carry out accurate transportation planning and resource scheduling, thus affecting transportation efficiency and cost control. In addition, another problem in traditional methods is the fragmentation and isolation of information. Different transportation links and departments may use different information systems, and data cannot be effectively integrated and shared. This leads to obstacles to information flow and difficulties in decision-making coordination, increasing uncertainty and risks in the transportation process. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a bulk commodity transportation information management method and system based on cloud computing to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for managing commodity transportation information based on cloud computing includes the following steps:

[0005] Step S1: Obtain bulk commodity transportation data, and extract the structural features of the transportation vehicle from the bulk commodity transportation data, thereby obtaining bulk commodity transportation vehicle structural data; perform bulk commodity vehicle structural three-dimensional modeling based on the bulk commodity transportation vehicle structural data, thereby obtaining a bulk commodity vehicle structural model;

[0006] Step S2: performing a carrier transportation simulation on the bulk commodity carrier structure model to obtain bulk commodity carrier transportation simulation data, and performing a commodity loading stability assessment based on the bulk commodity carrier transportation simulation data to obtain carrier loading stability data;

[0007] Step S3: Obtain road environment monitoring data, and screen the road environment monitoring data for bulk commodity transportation paths according to bulk commodity transportation data and vehicle loading stability data, thereby obtaining a bulk commodity transportation path data set; perform digital modeling of the transportation environment according to the transportation environment monitoring data and the bulk commodity transportation path data set, thereby obtaining a transportation environment digital network;

[0008] Step S4: Perform a transportation transfer commodity leakage risk assessment based on the transportation environment digital network and the bulk commodity carrier structure model, thereby obtaining transportation transfer leakage risk assessment data; select a low-risk transportation path for the bulk commodity transportation path data set based on the transportation transfer leakage risk assessment data, thereby obtaining low-risk bulk commodity transportation path data;

[0009] Step S5: forecasting the transportation time of bulk commodities based on the digital network of the transportation environment and the low-risk bulk commodity transportation route data, thereby obtaining the predicted transportation time data of bulk commodities; integrating the bulk commodity transportation strategy based on the low-risk bulk commodity transportation route data and the predicted transportation time data of bulk commodities, thereby obtaining the bulk commodity transportation strategy, and uploading it to the bulk commodity transportation management cloud platform to execute the bulk commodity transportation task;

[0010] Step S6: Acquire real-time bulk commodity transportation environment data based on the transportation environment digital network; perform adaptive correction on the predicted transportation time data of bulk commodities according to the real-time bulk commodity transportation environment data, so as to obtain the actual estimated transportation time data, and upload it to the bulk commodity transportation management cloud platform to execute the bulk commodity transportation information update task.

[0011] The present invention realizes digital modeling of the structure of the transport vehicle, and improves the accurate understanding and recording of the structural characteristics of the vehicle. This helps to reduce the data inaccuracy and timeliness problems caused by information fragmentation and manual operation. Through digital modeling, the transport vehicle can be managed and maintained more effectively. Through transportation simulation and stability assessment, the stability of the vehicle under different transportation conditions can be predicted. This provides accurate data support for decision makers to help them optimize transportation planning and resource scheduling, thereby improving transportation efficiency and reducing costs. Based on road environment monitoring data and transportation path screening, a digital network of the transportation environment is created. Such a digital model not only helps to fully understand and analyze the transportation environment, but also provides a reliable basis for the selection of bulk commodity transportation paths, reducing unnecessary risks and uncertainties. Through the risk assessment of leakage of transport-transfer commodities, potential leakage risks are identified and evaluated. According to the evaluation results, a low-risk transportation path is selected, which further reduces the possible safety incidents and item losses during transportation, and enhances the reliability and safety of transportation. By predicting transportation time and real-time data adaptive correction, the real-time monitoring and management capabilities of the transportation process are improved. These data provide decision makers with more accurate transportation time prediction and the ability to adjust strategies in real time, thereby improving transportation efficiency and responsiveness. In summary, these steps not only improve the efficiency and safety of commodity transportation management, but also optimize resource utilization and cost control. Through modern technical means such as digitization, simulation and evaluation, the problems of information fragmentation and data timeliness existing in traditional management methods are effectively addressed.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: acquiring bulk commodity transportation data, and extracting the structural features of the transportation vehicle from the bulk commodity transportation data, thereby obtaining bulk commodity transportation vehicle structural data;

[0014] Step S12: classifying the structural data of the bulk commodity transport vehicle by structural type, thereby obtaining the vehicle structural data and the loading tool structural data;

[0015] Step S13: performing loading tool topology analysis based on the loading tool structure data, thereby obtaining a loading tool topology model;

[0016] Step S14: extracting vehicle connection structure features according to the vehicle structure data, thereby obtaining vehicle connection structure data;

[0017] Step S15: Perform three-dimensional modeling according to the carrier structure data to obtain a three-dimensional carrier structure model, and perform three-dimensional connection relationship mapping on the loading tool topological structure model and the three-dimensional carrier structure model according to the carrier connection structure data to obtain a bulk commodity carrier structure model.

[0018] The present invention can accumulate a large amount of actual transportation information by acquiring bulk commodity transportation data. These data include not only the basic information of the transport vehicle, but also various conditions and environmental factors in the transportation process. The analysis of these data helps to identify key problems and bottlenecks in the transportation process. Extracting the structural characteristics of the transport vehicle can help understand the design and use of different transport vehicles. For example, by analyzing the characteristics of the vehicle such as size, load capacity, durability, etc., its applicability and efficiency under different conditions can be evaluated. Classifying the structural types of transport vehicles helps to establish standardized data models and evaluation criteria. For example, different types of transport vehicles (such as trucks, containers, ships, etc.) can be classified so that their performance and efficiency can be accurately compared in subsequent analysis. Through classification analysis, it can help transportation managers and designers optimize the design and selection of vehicles to meet different transportation needs and environmental conditions. Analyzing the topological structure of the loading tool can help optimize the loading scheme and improve loading efficiency and safety. Based on the topological structure analysis, a mathematical model of the loading tool can be established, and simulation can be performed to evaluate the feasibility and effect of different loading schemes. Extracting the characteristics of the vehicle connection structure helps to evaluate the reliability and safety of the connection between vehicles, thereby ensuring that no structural problems or failures occur during transportation. By analyzing the connection structure of the vehicles, the transportation layout can be optimized and the stability and efficiency of the transportation process can be improved. By performing three-dimensional modeling, the structure and connection relationship of the vehicles used to transport bulk commodities can be intuitively displayed. This visualization helps transportation managers and designers better understand and optimize the transportation system. The three-dimensional model can be used to verify the rationality and feasibility of the design solution, and can also be used for subsequent design improvements and optimization.

[0019] Optionally, step S13 is specifically:

[0020] Step S131: classifying the loading tool structure data into component structures, thereby obtaining connection component structure data and functional component structure data;

[0021] Step S132: extracting structural material features from the loading tool structural data to obtain the loading tool structural material data, and performing structural material property analysis based on the loading tool structural material data to obtain structural material property data;

[0022] Step S133: integrating the tool component connection relationship according to the connection component structure data, thereby obtaining component connection data;

[0023] Step S134: performing loading component topology analysis on the functional component structure data according to the component connection data, thereby obtaining a loading component topology model;

[0024] Step S135: mapping the component structure material properties of the loading component topological structure model according to the structural material characteristic data, thereby obtaining the loading tool topological structure model.

[0025] The present invention can systematically understand and manage the structural relationship of different components by classifying the component structure of the loading tool structure data. This classification helps to establish the association between the connection component structure data and the functional component structure data. Specifically, this classification can help to better organize and manage data, making it easier to analyze and use. By classifying the different component structures, possible optimization and improvement points can be identified, thereby improving the performance and efficiency of the loading tool. According to the extracted structural material characteristic data, further material selection and analysis can be performed. This helps to ensure that the selected materials can meet the requirements of the loading tool design, such as strength, durability and weight. Understanding the material properties can guide engineers to optimize during the design and manufacturing process to improve the overall performance and reliability of the loading tool. The process of integrating the connection component structure data into the component connection data helps to ensure that the connection between the various components of the loading tool has the necessary strength and stability to cope with the forces and pressures in the working environment. Optimizing the connection relationship can improve the operating efficiency and safety of the loading tool and ensure that the various components work in coordination. Performing topological structure analysis on the functional component structure data helps to understand the structural relationship between the internal components of the loading tool, and can optimize its functional distribution and layout to improve overall efficiency and operability. Analyzing the topology helps predict and diagnose possible faults and problems, and perform maintenance and repair in a timely manner. Mapping the structural material property data to the loading component topology model can comprehensively consider the material properties and topology of the structure and evaluate the performance of the loading tool in actual use. Based on the comprehensive analysis results, further design optimization and improvement can be carried out to meet the requirements under different working conditions.

[0026] Optionally, step S2 specifically includes:

[0027] Step S21: extracting historical bulk commodity transportation condition characteristics from bulk commodity transportation data, thereby obtaining historical bulk commodity transportation condition data;

[0028] Step S22: performing Monte Carlo random operation condition generation based on historical bulk commodity transportation operation condition data, thereby obtaining random transportation operation condition data;

[0029] Step S23: performing a carrier transportation simulation on the random transportation condition data through the bulk commodity carrier structure model, thereby obtaining bulk commodity carrier transportation simulation data;

[0030] Step S24: extracting the characteristics of commodity loading structure changes and the characteristics of commodity loading weight distribution from the bulk commodity carrier transportation simulation data, thereby obtaining commodity loading structure change data and commodity loading weight distribution data;

[0031] Step S25: Evaluate the stability of commodity loading according to the commodity loading structure data and the commodity loading weight distribution data, so as to obtain the carrier loading stability data.

[0032] The present invention understands the historical transportation conditions and can provide an important data basis for subsequent simulation and evaluation. These features include transportation routes, transportation conditions (such as weather, road conditions, etc.), loading methods, etc., which can be used to verify the rationality and authenticity of the subsequent randomly generated transportation condition data, thereby enhancing the credibility of the simulation. Analysis of historical data can reveal common trends and changes in transportation conditions, which helps to optimize future transportation strategies and loading designs. The Monte Carlo method can generate a variety of possible random conditions, covering a variety of different transportation scenarios and conditions, which helps to evaluate the performance and stability of the vehicle in different scenarios, and can evaluate the loading stability and safety in extreme or unusual situations, help design more robust loading solutions, supplement special situations that may not be covered in historical data, and ensure the comprehensiveness and applicability of the simulation. Using a commodity vehicle structure model to simulate randomly generated transportation conditions can evaluate the dynamic response and structural load of the vehicle under various transportation conditions, and ensure the safety and stability of loading. The loading structure and design parameters can be optimized based on the simulation results to improve loading efficiency and reduce unstable factors during loading. Discover potential problems in advance to avoid additional costs and time waste caused by poor design. Extracting loading structure changes and weight distribution characteristics from bulk commodity carrier transportation simulation data helps to understand loading structure changes and weight distribution, and can optimize loading plans and structural designs to improve loading stability and safety. Identify possible stability issues and risks in advance based on different loading characteristics, and take necessary preventive measures and adjustments. Provide decision support based on actual simulation data to ensure that the loading process and structural design meet expected requirements and standards. Perform stability assessment based on loading structure data and weight distribution data to determine the safety margin and reliability level of loading. Ensure that loading meets relevant technical standards and safety regulations to avoid potential loading instability and safety hazards. Verify the reliability and stability of the loading plan to provide guarantee and support for actual transportation operations.

[0033] Optionally, step S25 is specifically:

[0034] Step S251: Calculating the height of the center of gravity of the loaded goods based on the goods loading weight distribution data, thereby obtaining the height data of the center of gravity of the loaded goods;

[0035] Step S252: classifying and calculating the commodity loading weight distribution data based on the center of gravity height data of the loaded commodities, thereby obtaining the loading data of commodities with high center of gravity height and the loading data of commodities with low center of gravity height;

[0036] Step S253: calculating the commodity transportation loading structure deformation amount based on the commodity loading structure change data, thereby obtaining the commodity transportation loading structure deformation amount data;

[0037] Step S254: classifying and calculating the commodity loading structure change data according to the commodity transportation loading structure deformation data, thereby obtaining high deformation transportation commodity loading data and low deformation transportation commodity loading data;

[0038] Step S255: performing a commodity loading intersection operation on the high center of gravity height commodity loading data and the high deformation amount transport commodity loading data, thereby obtaining low stability commodity loading data; performing a commodity loading intersection operation on the low center of gravity height commodity loading data and the low deformation amount transport commodity loading data, thereby obtaining high stability commodity loading data;

[0039] Step S256: Merging the low-stability commodity loading data and the high-stability commodity loading data to obtain vehicle loading stability data.

[0040] In the truck or loading equipment of the present invention, the center of gravity height of the commodity is crucial to the stability of loading. By calculating the center of gravity height of the commodity, the center of gravity position during loading can be ensured to be appropriate, which helps to reduce the risk of overturning or unbalanced loading, and can help determine the center of gravity position of each commodity during loading, providing basic data for subsequent optimization. By classifying the commodities according to their center of gravity height, the stability and balance under different loading conditions can be understood more accurately. High-center-of-gravity commodities and low-center-of-gravity commodities require different treatment methods during loading, which helps to carry out subsequent optimization and safety measures in a targeted manner. Commodities will deform due to various factors during transportation, such as stacking method, vehicle operation status, etc. Calculating the deformation amount of the commodity transportation loading structure can help evaluate the changes in the loading structure, adjust or optimize the loading method in time, and ensure the structural stability and safety during the transportation of goods. The degree of change of the classification of the commodity loading structure helps to distinguish which commodities have larger or smaller deformations during transportation. This classification can provide an important basis for subsequent safety measures and transportation plans, ensuring the stability and reliability of the cargo structure during transportation. An intersection operation is performed based on the calculated data to further refine the loading strategy. Low-stability goods loading data refers to goods with a high center of gravity and large deformation, which may cause unstable loading and safety hazards during transportation. On the contrary, high-stability goods loading data refers to goods with a low center of gravity and small deformation, which are more stable and reliable during loading and transportation. Combining low-stability and high-stability loading data to comprehensively evaluate the stability of the entire loading plan can formulate the optimal loading strategy and safety measures to ensure the stability and safety of the goods during transportation.

[0041] Optionally, step S3 specifically includes:

[0042] Step S31: Acquire road environment monitoring data, and classify the road environment monitoring data, thereby obtaining construction road environment monitoring data and normal road environment monitoring data;

[0043] Step S32: digitally modeling the roads in the monitoring area according to the normal road environment monitoring data, thereby obtaining a digital model of the roads in the monitoring area, and performing construction road coding on the digital model of the roads in the monitoring area according to the construction road environment monitoring data, thereby obtaining a coded model of the roads in the monitoring area;

[0044] Step S33: extracting real-time bulk commodity transportation orders from the bulk commodity transportation data to obtain real-time bulk commodity transportation order data, and extracting transportation point features from the real-time bulk commodity transportation order data to obtain order transportation point data;

[0045] Step S34: planning the transportation route for the order transportation point data and the vehicle loading stability data according to the road coding model of the monitoring area, thereby obtaining a bulk commodity transportation route data set;

[0046] Step S35: extracting the transportation path environment monitoring data from the road environment monitoring data according to the bulk commodity transportation path data set, thereby obtaining the transportation path environment monitoring data;

[0047] Step S36: Digital modeling of the transportation environment is performed based on the transportation path environment monitoring data, thereby obtaining a digital network of the transportation environment.

[0048] The acquisition and classification of the road environment monitoring data of the present invention is a key step to ensure transportation safety and efficiency. By monitoring the road environment, different types of construction roads and normal roads can be identified. This classification helps to predict and avoid potential safety hazards during transportation, such as obstacles, traffic restrictions or poor road conditions that may exist on construction roads. Normal road environment monitoring data is used to establish a digital model of the road in the monitoring area, which helps to accurately describe key features such as the topological structure, curvature, and slope of the road. The construction road environment monitoring data is used to mark the specific location and conditions of the construction area in the digital model. Through digital modeling and coding, a basis can be provided for subsequent path planning and transportation arrangements. Extracting real-time bulk commodity transportation order data and order transportation point data is to accurately capture and manage key information in the transportation process. By extracting the characteristics of the transportation point, such as location, destination requirements, and cargo types, a basis can be provided for subsequent path planning and decision-making during transportation. Based on the road coding model of the monitoring area, the order transportation point data is routed to ensure that the goods choose the optimal path during transportation. This path planning takes into account the real-time conditions of the road and environmental factors, such as construction areas, traffic conditions, and safety, to maximize transportation efficiency and reduce transportation costs. Based on the commodity transportation path dataset, environmental monitoring data of the transportation path are extracted from the road environmental monitoring data. These data include various environmental factors on the path, such as traffic density, weather conditions, road conditions, etc. This information is very important for real-time adjustments and safety decisions during transportation. Based on the environmental monitoring data of the transportation path, digital modeling of the transportation environment is carried out. This model can help analyze and predict the environmental characteristics of different transportation paths, and provide support for intelligent decision-making and risk management during transportation. Through digital modeling, effective identification and prevention of potential risks in the transportation process can be achieved.

[0049] Optionally, step S4 is specifically:

[0050] Step S41: collecting the transport point environment from the transport environment digital network to obtain transport point environment data, and extracting the transfer point environment features from the transport point environment data to obtain transfer point environment data;

[0051] Step S42: Modeling the switching point environment according to the switching point environment data to obtain the switching point environment model, and calculating the inclination of the horizontal plane of the switching point on the switching point environment model to obtain the inclination data of the horizontal plane of the switching point;

[0052] Step S43: estimating the commodity inclination based on the horizontal plane inclination data of the transfer point according to the bulk commodity carrier structure model, thereby obtaining the commodity inclination data;

[0053] Step S44: extracting commodity type features from the real-time commodity transportation order data to obtain commodity type data, and performing volatility characteristic analysis on the commodity type data to obtain commodity chemical volatility data;

[0054] Step S45: evaluating the commodity loading tightness of the bulk commodity carrier structure model, thereby obtaining commodity loading tightness data;

[0055] Step S46: estimating the commodity volatility based on the commodity loading sealing data and the commodity chemical volatility data according to the transfer point environment model, thereby obtaining commodity volatility data;

[0056] Step S47: performing a transportation and loading transfer leakage risk assessment based on the commodity volatility data and the commodity inclination data, thereby obtaining transportation and loading transfer leakage risk assessment data;

[0057] Step S48: selecting a low-risk transportation path for the bulk commodity transportation path data set according to the transportation transfer leakage risk assessment data, thereby obtaining low-risk bulk commodity transportation path data.

[0058] The present invention collects environmental data of the transport point through a digital network, including factors such as temperature, humidity, and air pressure. These data can help understand the actual environmental conditions of the transport point and provide basic data for subsequent analysis and model building. Based on the collected environmental data, an environmental model of the transfer point is established, and the inclination of the horizontal plane is calculated. These data are crucial to the stability and safety of cargo loading, and possible risks can be predicted and adjusted in advance. According to the inclination data of the transfer point, combined with the structural model of bulk commodities, the possible inclination of the cargo during transportation is estimated. This helps to optimize the loading plan and reduce the risk of damage to the cargo during transportation. Real-time bulk commodity order data is analyzed, commodity types are extracted, and volatility characteristics are analyzed. These data can help evaluate the volatility of the cargo in different environments, thereby determining appropriate packaging and transportation conditions and reducing the safety and environmental risks caused by volatility. The loading sealing of the cargo is evaluated using the carrier structure model to ensure that the cargo is in a suitable closed environment during transportation. This helps to protect the cargo from the external environment and reduce possible damage and safety risks. Combined with the environmental model of the transfer point and the volatility data of the cargo, the volatility of the cargo during transportation is estimated. This estimation can help develop appropriate transportation and storage strategies to reduce the potential impact of volatile substances on the environment and personnel. Based on the volatility and inclination data of the commodity, the leakage risk that may occur during transportation and loading can be assessed. This assessment can help the transportation team predict and reduce safety and environmental accidents that may be caused by leakage. Based on the leakage risk assessment data, low-risk commodity transportation routes can be selected. This can minimize the probability of unexpected events and ensure the safety and reliability of cargo transportation.

[0059] Optionally, step S47 is specifically:

[0060] Conduct statistical analysis on the volatility of goods at the transfer point based on the commodity volatility data, thereby obtaining high volatility transfer point data and low volatility transfer point data;

[0061] Based on the commodity inclination data, statistical analysis of the commodity inclination at the transfer point is performed to obtain high-inclination transfer point data and low-inclination transfer point data;

[0062] Performing a switching point intersection operation on the high volatility switching point data and the high inclination switching point data, thereby obtaining the first high leakage risk switching point data;

[0063] Performing a switching point intersection operation on the high volatility switching point data and the low inclination switching point data, thereby obtaining the second highest leakage risk switching point data;

[0064] Performing a switching point intersection operation on the low volatility switching point data and the high inclination switching point data, thereby obtaining the third highest leakage risk switching point data;

[0065] Performing a switching point intersection operation on the low volatility switching point data and the low inclination switching point data, thereby obtaining the low leakage risk switching point data;

[0066] The first high leakage risk transfer point data, the second high leakage risk transfer point data, the third high leakage risk transfer point data and the low leakage risk transfer point data are spatially merged to obtain transport transfer leakage risk assessment data.

[0067] The present invention divides the transshipment points into two categories, high volatility and low volatility, based on the volatility data of the goods. The goods with higher and lower volatility during transportation are distinguished, thereby providing basic data for subsequent risk assessment. According to the inclination data of the goods, the transshipment points are divided into two categories, high inclination and low inclination. This helps to identify transshipment points that may have problems with unstable loading and predict potential risks of cargo tilting and damage. The transshipment point data with high volatility and high inclination are intersected to identify transshipment points with both high volatility and high inclination, which have a higher risk of leakage. The transshipment point data with high volatility and low inclination are intersected to identify transshipment points with high volatility but low inclination. In this case, the goods themselves have high volatility, but the transshipment point environment is relatively stable and has a low inclination. Despite the relatively stable environment, high volatility still leads to an increased risk of leakage, especially during loading and unloading. Perform an intersection operation on the transshipment point data with low volatility and high inclination to identify transshipment points with low volatility but high inclination. In this case, even if the volatility of the goods is low, if the transshipment point is in a high-inclination environment, it may still face leakage risks. High inclination increases the possibility of movement of items, which may cause container damage or leakage. Perform an intersection operation on the transshipment point data with low volatility and low inclination to identify transshipment points with low volatility and inclination, which are relatively safe in terms of leakage risk. Spatially merge the transshipment point data with different leakage risk levels obtained through the intersection operation. This can integrate transshipment points of different levels into a final leakage risk assessment data set, providing decision makers with a clear perspective to help them choose the most appropriate transportation route or take necessary measures to reduce leakage risks.

[0068] Optionally, step S48 is specifically:

[0069] Step S481: extracting low leakage risk loading point according to the transport loading leakage risk assessment data, thereby obtaining low leakage risk loading point data;

[0070] Step S482: Associating the low leakage risk transfer point data with the bulk commodity transportation path data set with a transportation path, thereby obtaining a low leakage risk transfer point associated path data set;

[0071] Step S483: Select the shortest transportation path for the low leakage risk transfer point associated path data set, so as to obtain low risk bulk commodity transportation path data.

[0072] The present invention can reduce the possibility of accidents by selecting a transfer point with a low risk of leakage. The selection of transfer points may consider factors such as the safety of facilities and the strictness of operating procedures to ensure safety in the logistics process. Reducing the risk of leakage helps to protect the surrounding environment and reduce potential impacts on public health. Selecting a transfer point with a low risk of leakage helps to minimize possible damage in the event of an accident. Although safety is the primary consideration, selecting a transfer point with a low risk of leakage also indirectly reduces maintenance and cleanup costs, thereby saving costs in the long run. By associating transfer point and transportation path data, the selection of transportation paths can be optimized. This involves selecting a path that is not only safe but also efficient in transportation to improve overall transportation benefits. Associating transfer point data with path data sets helps to form a comprehensive transportation plan. Such a plan can better integrate various factors involved in the transportation process, such as traffic, environment and safety requirements. Selecting the shortest transportation path usually means shorter transportation time and lower transportation costs. This is particularly important for the transportation of bulk commodities, because time and cost directly affect the efficiency and profit of the overall operation. Selecting the shortest path also helps to further reduce potential accident risks. Shorter routes mean fewer transport nodes and potential problem points, reducing the risk of leakage during transportation.

[0073] Optionally, the present invention further provides a bulk commodity transportation information management system based on cloud computing, which is used to execute the bulk commodity transportation information management method based on cloud computing as described above, and the bulk commodity transportation information management system based on cloud computing includes:

[0074] The structural three-dimensional modeling module is used to obtain bulk commodity transportation data and extract the structural features of the transport vehicle from the bulk commodity transportation data, thereby obtaining the bulk commodity transportation vehicle structural data; and perform three-dimensional structural modeling of the bulk commodity vehicle based on the bulk commodity transportation vehicle structural data, thereby obtaining the bulk commodity vehicle structural model;

[0075] A loading stability assessment module is used to perform a carrier transportation simulation on a bulk commodity carrier structure model, thereby obtaining bulk commodity carrier transportation simulation data, and to perform a commodity loading stability assessment based on the bulk commodity carrier transportation simulation data, thereby obtaining carrier loading stability data;

[0076] The transportation environment digital modeling module is used to obtain road environment monitoring data, and screen the road environment monitoring data for bulk commodity transportation paths based on bulk commodity transportation data and vehicle loading stability data, thereby obtaining a bulk commodity transportation path data set; digital modeling of the transportation environment is performed based on the transportation environment monitoring data and the bulk commodity transportation path data set, thereby obtaining a transportation environment digital network;

[0077] The low-risk transport path selection module is used to evaluate the risk of leakage of transport-transfer commodities based on the digital network of the transport environment and the structural model of the bulk commodity carrier, thereby obtaining the transport-transfer leakage risk assessment data; and to select low-risk transport paths for the bulk commodity transport path data set based on the transport-transfer leakage risk assessment data, thereby obtaining low-risk bulk commodity transport path data;

[0078] The transportation strategy integration module is used to predict the transportation time of bulk commodities based on the digital network of the transportation environment and the transportation route data of low-risk bulk commodities, so as to obtain the predicted transportation time data of bulk commodities; integrate the transportation strategy of bulk commodities based on the transportation route data of low-risk bulk commodities and the predicted transportation time data of bulk commodities, so as to obtain the transportation strategy of bulk commodities, and upload it to the bulk commodity transportation management cloud platform to execute the transportation task of bulk commodities;

[0079] The transportation information update module is used to obtain real-time bulk commodity transportation environment data based on the transportation environment digital network; according to the real-time bulk commodity transportation environment data, the predicted transportation time data of the bulk commodity is adaptively corrected to obtain the actual estimated transportation time data, and uploaded to the bulk commodity transportation management cloud platform to execute the bulk commodity transportation information update task.

[0080] The cloud computing-based bulk commodity transportation information management system of the present invention can implement any one of the cloud computing-based bulk commodity transportation information management methods of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the cloud computing-based bulk commodity transportation information management method. The internal modules of the system cooperate with each other to improve the efficiency of bulk commodity transportation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0082] Figure 1 It is a schematic diagram of the steps of the bulk commodity transportation information management method based on cloud computing of the present invention;

[0083] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0084] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0085] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0086] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0087] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0088] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0089] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for managing bulk commodity transportation information based on cloud computing, the method comprising the following steps:

[0090] Step S1: Obtain bulk commodity transportation data, and extract the structural features of the transportation vehicle from the bulk commodity transportation data, thereby obtaining bulk commodity transportation vehicle structural data; perform bulk commodity vehicle structural three-dimensional modeling based on the bulk commodity transportation vehicle structural data, thereby obtaining a bulk commodity vehicle structural model;

[0091] In this embodiment, the acquisition of bulk commodity transportation data can be achieved through the database of the logistics company or the data interface of the industry partner to ensure the accuracy and completeness of the data. For the extraction of structural features of the transport vehicle, computer vision technology and image processing algorithms can be used to extract the size, shape, load capacity and other features of the transport vehicle from the image or video data obtained by the sensor. For example, the transport vehicle in the image is separated from the background using edge detection algorithms and feature extraction algorithms, and relevant parameters such as length, height, width, etc. are extracted. Then, these feature data are used for three-dimensional modeling, and CAD software such as SolidWorks or AutoCAD is used to establish an accurate structural model of the bulk commodity transport vehicle for subsequent simulation and evaluation.

[0092] Step S2: performing a carrier transportation simulation on the bulk commodity carrier structure model to obtain bulk commodity carrier transportation simulation data, and performing a commodity loading stability assessment based on the bulk commodity carrier transportation simulation data to obtain carrier loading stability data;

[0093] In this embodiment, after the bulk commodity carrier structure model is established, the carrier transportation simulation can be performed using virtual simulation software, such as ANSYS or COMSOL Multiphysics. These software can simulate the carrier transportation under different road conditions, taking into account factors such as load distribution, vibration, and stress conditions. The data obtained through simulation can be used to evaluate the stability of commodity loading, such as judging the stability of commodities under different road conditions, and ensuring that the goods will not be damaged or tilted due to carrier vibration or sharp turns during transportation.

[0094] Step S3: Obtain road environment monitoring data, and screen the road environment monitoring data for bulk commodity transportation paths according to bulk commodity transportation data and vehicle loading stability data, thereby obtaining a bulk commodity transportation path data set; perform digital modeling of the transportation environment according to the transportation environment monitoring data and the bulk commodity transportation path data set, thereby obtaining a transportation environment digital network;

[0095] In this embodiment, the acquisition of road environment monitoring data can be achieved through GPS, drones or sensor networks to obtain information such as road quality, road conditions, and weather. Data mining and geographic information system (GIS) technology are used to combine the transportation needs of bulk commodities and road characteristics to screen out suitable transportation routes. Through digital modeling of the transportation environment, road characteristics are digitally represented, such as modeling the road in sections, taking into account factors such as slope and curvature, to provide accurate data support for subsequent path selection.

[0096] Step S4: Perform a transportation transfer commodity leakage risk assessment based on the transportation environment digital network and the bulk commodity carrier structure model, thereby obtaining transportation transfer leakage risk assessment data; select a low-risk transportation path for the bulk commodity transportation path data set based on the transportation transfer leakage risk assessment data, thereby obtaining low-risk bulk commodity transportation path data;

[0097] In this embodiment, based on the digital network of the transportation environment and the vehicle structure model, the leakage risk of the transported goods is evaluated using numerical calculation methods and risk assessment models. For example, a finite element analysis model is used to simulate the collision or external force impact that the vehicle may encounter during transportation to evaluate the possibility of cargo leakage. Based on the evaluation results, a low-risk transportation route is selected, such as avoiding densely populated areas or areas prone to geological disasters, to ensure transportation safety and cargo integrity.

[0098] Step S5: forecasting the transportation time of bulk commodities based on the digital network of the transportation environment and the low-risk bulk commodity transportation route data, thereby obtaining the predicted transportation time data of bulk commodities; integrating the bulk commodity transportation strategy based on the low-risk bulk commodity transportation route data and the predicted transportation time data of bulk commodities, thereby obtaining the bulk commodity transportation strategy, and uploading it to the bulk commodity transportation management cloud platform to execute the bulk commodity transportation task;

[0099] In this embodiment, based on the digital network of the transportation environment and the low-risk transportation path data, data analysis and machine learning algorithms are used to predict the transportation time of bulk commodities. For example, by combining historical transportation data and real-time traffic information, a time series model or regression model is established to predict the time it takes for goods to arrive at the destination under different paths. According to the predicted transportation time data, a reasonable transportation strategy is formulated, such as adjusting the loading time or route selection to optimize transportation efficiency and reduce costs. Finally, the bulk commodity transportation strategy is uploaded to the bulk commodity transportation management cloud platform through network communication means such as wireless networks to manage the transportation status of bulk commodities, that is, to execute bulk commodity transportation tasks.

[0100] Step S6: Acquire real-time bulk commodity transportation environment data based on the transportation environment digital network; perform adaptive correction on the predicted transportation time data of bulk commodities according to the real-time bulk commodity transportation environment data, so as to obtain the actual estimated transportation time data, and upload it to the bulk commodity transportation management cloud platform to execute the bulk commodity transportation information update task.

[0101] In this embodiment, sensor networks and Internet of Things technologies are used to obtain real-time road environment monitoring data, and the real-time road environment monitoring data, such as road conditions, traffic flow, weather conditions and other commodity transportation environment characteristics, are extracted based on the established transportation environment digital network to obtain real-time commodity transportation environment data. The real-time data is compared and corrected with the predicted transportation time data, and the predicted transportation time is adjusted through an algorithm to ensure the timeliness and accuracy of the information. The updated and corrected data is uploaded to the transportation management cloud platform to realize real-time updating and adjustment of transportation information to support real-time transportation decision-making and management.

[0102] Optionally, step S1 specifically includes:

[0103] Step S11: acquiring bulk commodity transportation data, and extracting the structural features of the transportation vehicle from the bulk commodity transportation data, thereby obtaining bulk commodity transportation vehicle structural data;

[0104] In this embodiment, the acquisition of bulk commodity transportation data can be achieved through the database of the logistics company or the data interface of the industry partner to ensure the accuracy and completeness of the data. These data may include the type of goods, transportation distance, type of transportation tool, etc. For example, for coal transportation, the freight volume, origin and destination, type of transportation tool (such as truck, train, etc.) and transportation time of each transportation can be obtained. The data can be collected through the records of the logistics company or the reports of the industry research institutions.

[0105] Step S12: classifying the structural data of the bulk commodity transport vehicle by structural type, thereby obtaining the vehicle structural data and the loading tool structural data;

[0106] In this embodiment, the collected structural data of bulk commodity transport vehicles are classified and analyzed. Taking railway trains as an example, their structural characteristics can be classified according to different models (such as freight trains, container trains, etc.). This includes detailed descriptions and classifications of vehicle size, load capacity, loading methods, etc. For example, for freight trains, data can be classified and sorted according to different types of carriages (such as closed trucks, open trucks) and loading methods (such as bulk loading, container loading). By classifying the structural characteristics of different types of vehicles in detail, the necessary basic data can be provided for subsequent topological structure analysis and three-dimensional modeling.

[0107] Step S13: performing loading tool topology analysis based on the loading tool structure data, thereby obtaining a loading tool topology model;

[0108] In this embodiment, the structural data of the loading tool (such as a truck, a container, etc.) is subjected to a topological structure analysis. For example, for a container, its standard size, structural features (such as the connection method of each panel), the location of the lifting point, etc. can be analyzed. These data are crucial for the subsequent three-dimensional modeling and the generation of the structural model. For example, for a standard container, the connection method of its six panels (such as the connection method of the angle iron), the location of the lifting ring (usually at the four upper corners), the structure of the bottom support, etc. can be analyzed according to the ISO standard. These structural features are of great significance to the safety and stability of the loading tool.

[0109] Step S14: extracting vehicle connection structure features according to the vehicle structure data, thereby obtaining vehicle connection structure data;

[0110] In this embodiment, the characteristics of the connection structure are extracted from the collected vehicle structure data. For example, in a railway freight car, the structural characteristics of the vehicle's connecting hook and hook, as well as their role and restrictions in the vehicle connection process can be analyzed. These characteristics are crucial for the safe transportation of bulk commodities and the smooth progress of the loading and unloading process. For example, the connection hook structure of different types of railway freight cars is analyzed, including its shape, size, load capacity, and connection method (such as automatic connection or manual connection). Through a detailed analysis of these characteristics, the compatibility between different types of railway vehicles can be determined, and a basis for safe transportation can be provided.

[0111] Step S15: Perform three-dimensional modeling according to the carrier structure data to obtain a three-dimensional carrier structure model, and perform three-dimensional connection relationship mapping on the loading tool topological structure model and the three-dimensional carrier structure model according to the carrier connection structure data to obtain a bulk commodity carrier structure model.

[0112] In this embodiment, three-dimensional modeling is performed based on the collected carrier structure data. For example, for different types of trucks and containers, CAD software can be used to perform three-dimensional modeling to accurately represent their appearance and internal structure. These models can be used to map the three-dimensional connection relationship between the loading tool topological structure model and the three-dimensional carrier structure model. For example, CAD software is used to model different types of trucks (such as flatbed trucks, vans) and containers, including the vehicle's size, structural details (such as support structures, internal partitions, etc.), and the location of the lifting point. Through these three-dimensional models, various situations in the loading and connection process can be simulated to optimize the transportation plan and improve efficiency.

[0113] Optionally, step S13 is specifically:

[0114] Step S131: classifying the loading tool structure data into component structures, thereby obtaining connection component structure data and functional component structure data;

[0115] In this embodiment, in the process of classifying the structural data of bulk transport loading tools, it is first necessary to accurately classify the structures of the various components of the loading tools. For example, for an iron ore bulk carrier, the main components such as the hull structure, cabin structure, and unloading equipment structure can be distinguished. The structural classification of these components helps to accurately understand the role and connection method of each component in the overall loading tool. For example, the hull structure can be subdivided into hull shell plates, ship decks, ship beams, etc. This classification not only helps to obtain the subsequent structural data of the connected components, but also provides a basis for the subsequent material feature extraction and topological structure analysis.

[0116] Step S132: extracting structural material features from the loading tool structural data to obtain the loading tool structural material data, and performing structural material property analysis based on the loading tool structural material data to obtain structural material property data;

[0117] In this embodiment, in the structural material feature extraction stage, for the hull structure of the iron ore bulk carrier, the structural material features of each component can be extracted through the methods of material science and engineering technology. For example, the hull plate may be made of high-strength steel plate, and its tensile strength, corrosion resistance and weldability and other characteristics need to be considered. By analyzing these characteristics, specific structural material characteristic data can be obtained, such as the material, thickness, tensile strength, etc. of the steel plate.

[0118] Step S133: integrating the tool component connection relationship according to the connection component structure data, thereby obtaining component connection data;

[0119] In this embodiment, the integration of the connection component structure data involves the integration of the connection relationship and connection mode between different components. Taking an iron ore bulk carrier as an example, the connection components may include various bolt connections, welding connections, etc. By analyzing and integrating various connection modes, detailed component connection data may be obtained, such as bolt specifications, tightening torque requirements, etc.

[0120] Step S134: performing loading component topology analysis on the functional component structure data according to the component connection data, thereby obtaining a loading component topology model;

[0121] In this embodiment, in the loading component topology analysis stage, a topology analysis can be performed on the unloading equipment structure of the iron ore bulk carrier. For example, the layout and connection mode of the transmission system, unloading channel, support structure and other components of the unloading equipment are analyzed to determine the topology model of the overall loading component. This analysis helps to understand the overall working principle and safety performance of the loading tool.

[0122] Step S135: mapping the component structure material properties of the loading component topological structure model according to the structural material characteristic data, thereby obtaining the loading tool topological structure model.

[0123] In this embodiment, in the component structure material property mapping stage, the structural material property data extracted previously can be mapped to the topological structure model of the loading tool. For example, in the cabin structure of an iron ore bulk carrier, the material property data of different parts can be mapped through the spatial relationship of the component structure to determine the material type and properties required for each part, such as wear resistance, seawater corrosion resistance, etc. Such a mapping process ensures the safety and reliability of the loading tool during actual transportation.

[0124] Optionally, step S2 specifically includes:

[0125] Step S21: extracting historical bulk commodity transportation condition characteristics from bulk commodity transportation data, thereby obtaining historical bulk commodity transportation condition data;

[0126] In this embodiment, data mining and statistical analysis methods can be used to extract the characteristics of historical bulk commodity transportation conditions from bulk commodity transportation data. First, a data set is established by collecting historical bulk commodity transportation data, such as cargo type, transportation distance, transportation time, weather conditions, etc. Then, key features between different transportation conditions are identified using machine learning algorithms such as cluster analysis or regression analysis. For example, typical transportation patterns of different types of cargo under different weather conditions can be found through cluster analysis, or the degree of influence of transportation distance on loading capacity can be determined through regression analysis.

[0127] Step S22: performing Monte Carlo random operation condition generation based on historical bulk commodity transportation operation condition data, thereby obtaining random transportation operation condition data;

[0128] In this embodiment, Monte Carlo random operating conditions are generated based on historical commodity transportation operating condition data. This step is intended to simulate possible random transportation operating conditions in the future to increase the robustness of the model. Through the Monte Carlo method, various possible transportation operating conditions can be randomly generated, such as different weather conditions, traffic conditions, and cargo type combinations. For example, simulated random weather conditions and traffic conditions can be generated based on the statistical distribution of different weather and traffic events in historical data to simulate the diverse transportation environments that may be faced in the future.

[0129] Step S23: performing a carrier transportation simulation on the random transportation condition data through the bulk commodity carrier structure model, thereby obtaining bulk commodity carrier transportation simulation data;

[0130] In this embodiment, the random transport condition data is simulated by the bulk commodity carrier structure model. This step involves establishing the carrier structure model and physical simulation of the transport process. For example, the computational fluid dynamics (CFD) model can be used to simulate and analyze the aerodynamic characteristics of the truck under different speeds and load conditions to evaluate the stability and durability of the vehicle under various random transport conditions.

[0131] Step S24: extracting the characteristics of commodity loading structure changes and the characteristics of commodity loading weight distribution from the bulk commodity carrier transportation simulation data, thereby obtaining commodity loading structure change data and commodity loading weight distribution data;

[0132] In this embodiment, the commodity loading structure change feature extraction and the commodity loading weight distribution feature extraction are performed on the bulk commodity carrier transportation simulation data. This step involves extracting key loading structure and weight distribution information from the simulation data. For example, the distribution of goods under different transportation conditions is obtained through simulation analysis, and then the stability and distribution characteristics of the goods under different loading structures are extracted. These data can be used to optimize the cargo loading plan and improve transportation efficiency and safety.

[0133] Step S25: Evaluate the stability of commodity loading according to the commodity loading structure data and the commodity loading weight distribution data, so as to obtain the carrier loading stability data.

[0134] In this embodiment, the stability of commodity loading is evaluated based on the commodity loading structure data and the commodity loading weight distribution data, and finally the loading stability of the carrier is evaluated. For example, the structural mechanics analysis method can be used to calculate the center of gravity position, force distribution and stability boundary of the carrier under different cargo loading structures, so as to determine the optimal loading method and safe transportation restrictions. These evaluations can help formulate reasonable loading standards and safety measures to ensure the stability and safety of bulk commodities during transportation.

[0135] Optionally, step S25 is specifically:

[0136] Step S251: Calculating the height of the center of gravity of the loaded goods based on the goods loading weight distribution data, thereby obtaining the height data of the center of gravity of the loaded goods;

[0137] In this embodiment, when calculating the center of gravity height of the loaded goods based on the goods loading weight distribution data, the center of gravity position of each commodity is usually determined first. This involves calculating the position distribution and weight distribution of the goods in the carrier, as well as the mass of each part. For example, for long goods, the center of gravity position may be calculated based on the length and mass distribution, and for multiple goods, the center of gravity position of each goods can be combined to calculate the overall center of gravity position.

[0138] Step S252: classifying and calculating the commodity loading weight distribution data based on the center of gravity height data of the loaded commodities, thereby obtaining the loading data of commodities with high center of gravity height and the loading data of commodities with low center of gravity height;

[0139] In this embodiment, when classifying and calculating the commodity loading weight distribution data based on the center of gravity height data of the loaded commodities, mathematical statistical methods and data analysis techniques can be used. By setting a threshold value of the center of gravity height, the loaded commodities are divided into two categories: high center of gravity height and low center of gravity height. For example, the classification can be performed based on the distance between the center of gravity height and the center of the vehicle. If the distance exceeds a certain preset value, it is classified as high center of gravity height loading, and vice versa.

[0140] Step S253: calculating the commodity transportation loading structure deformation amount based on the commodity loading structure change data, thereby obtaining the commodity transportation loading structure deformation amount data;

[0141] In this embodiment, when calculating the deformation of the commodity loading structure during transportation based on the commodity loading structure change data, it is necessary to analyze and quantify the structural deformation during the loading process. This includes calculating the deformation of the loaded object, such as the shape change of the object after loading, the change in the force position, etc. Through mathematical modeling and mechanical simulation, the deformation of the object under different loading conditions can be accurately calculated.

[0142] Step S254: classifying and calculating the commodity loading structure change data according to the commodity transportation loading structure deformation data, thereby obtaining high deformation transportation commodity loading data and low deformation transportation commodity loading data;

[0143] In this embodiment, when classifying and calculating the commodity loading structure change data according to the commodity transportation loading structure deformation data, different loading conditions can be classified according to the size and degree of change of the deformation. For example, by setting a threshold of the deformation, the commodity loading data is divided into two categories: high deformation and low deformation. A high deformation usually indicates that the shape or structure of the object has changed significantly during the loading process, which may affect stability and safety.

[0144] Step S255: performing a commodity loading intersection operation on the high center of gravity height commodity loading data and the high deformation amount transport commodity loading data, thereby obtaining low stability commodity loading data; performing a commodity loading intersection operation on the low center of gravity height commodity loading data and the low deformation amount transport commodity loading data, thereby obtaining high stability commodity loading data;

[0145] In this embodiment, when performing the commodity loading intersection operation on the loading data of commodities with high center of gravity height and the loading data of commodities transported with high deformation, the purpose is to identify the loading conditions with both high center of gravity height and high deformation. These conditions pose a challenge to the stability of the vehicle and require special attention and improvement. On the contrary, the intersection operation is performed on the loading data of commodities with low center of gravity height and the loading data of commodities transported with low deformation, with the purpose of determining the most stable loading condition to ensure safety and stability during transportation.

[0146] Step S256: Merging the low-stability commodity loading data and the high-stability commodity loading data to obtain vehicle loading stability data.

[0147] In this embodiment, when merging the loading data of low-stability goods and high-stability goods, it is necessary to comprehensively consider the stability evaluation results of different loading conditions. This involves further analyzing and improving the low-stability loading conditions to improve the stability of the loading. Through data merging, the stability data of the vehicle loading is finally obtained, which can be used to guide the loading operation and the formulation of safety management strategies in actual transportation.

[0148] Optionally, step S3 specifically includes:

[0149] Step S31: Acquire road environment monitoring data, and classify the road environment monitoring data, thereby obtaining construction road environment monitoring data and normal road environment monitoring data;

[0150] In this embodiment, it is first necessary to deploy road environment monitoring sensors, such as cameras, lidars, and temperature sensors, to obtain real-time data on the road environment. This data may include information such as traffic flow, road surface conditions (such as wet or flooded), and weather conditions. The collected data needs to be processed and analyzed by computer vision and machine learning algorithms, such as using convolutional neural networks (CNNs) to detect and classify different road environment conditions. For example, CNNs can be used to identify vehicles, pedestrians, and obstacles on the road from camera images, and to analyze lidar data to measure the height and shape of the road surface.

[0151] Step S32: digitally modeling the roads in the monitoring area according to the normal road environment monitoring data, thereby obtaining a digital model of the roads in the monitoring area, and performing construction road coding on the digital model of the roads in the monitoring area according to the construction road environment monitoring data, thereby obtaining a coded model of the roads in the monitoring area;

[0152] In this embodiment, using the normal road environment monitoring data obtained and classified above, a digital model of the road in the monitoring area can be established. This involves combining information such as the geometric features of the road, traffic signs, and street light locations to create an accurate virtual road map. This process can use geographic information system (GIS) tools and 3D modeling software, such as ArcGIS or Blender. For example, a high-precision road map is generated based on lidar data, or traffic signs are identified using camera data and marked in the digital model. According to the construction road environment monitoring data, the digital model of the road in the monitoring area needs to be coded for construction roads. This process involves matching and mapping the road environment features in actual construction with the digital model. For example, for road conditions (such as cracks, potholes), traffic signs, restrictions in the construction area, etc., this information is encoded into the digital model.

[0153] Step S33: extracting real-time bulk commodity transportation orders from the bulk commodity transportation data to obtain real-time bulk commodity transportation order data, and extracting transportation point features from the real-time bulk commodity transportation order data to obtain order transportation point data;

[0154] In this embodiment, based on the real-time bulk commodity transportation data, such as order information in the warehousing system or supply chain management system, it is necessary to extract the transportation order data related to the monitoring area. This involves using data mining and integration tools, such as ETL (extraction, transformation, loading) processes, to extract valid information from various data sources. For example, the cargo distribution demand for a specific area retrieved from the order system and the changes in customer demand can be retrieved using a database query.

[0155] Step S34: planning the transportation route for the order transportation point data and the vehicle loading stability data according to the road coding model of the monitoring area, thereby obtaining a bulk commodity transportation route data set;

[0156] In this embodiment, the generated monitoring area road coding model is used to combine the extracted transportation order data and vehicle loading stability data to perform transportation route planning. This can be achieved through route optimization algorithms, such as Dijkstra algorithm or A* search algorithm, taking into account road conditions, transportation distance and loading requirements. For example, the shortest path, the path with the least traffic lights, and the path with the least construction sections are calculated based on the road coding model, while taking into account the load limit and the time window between transportation points, and these paths are integrated to obtain a bulk commodity transportation path dataset.

[0157] Step S35: extracting the transportation path environment monitoring data from the road environment monitoring data according to the bulk commodity transportation path data set, thereby obtaining the transportation path environment monitoring data;

[0158] In this embodiment, the generated commodity transportation route dataset is used to extract and match the acquired road environment monitoring data accordingly. This includes associating the real-time monitored road environment data with a specific transportation route, such as checking the road surface conditions and weather changes during a predetermined transportation time period. This can be achieved through database queries or real-time data stream analysis to ensure that the environmental data on the transportation route is consistent with expectations.

[0159] Step S36: Digital modeling of the transportation environment is performed based on the transportation path environment monitoring data, thereby obtaining a digital network of the transportation environment.

[0160] In this embodiment, the extracted environmental monitoring data of the transport path is used to perform digital modeling of the transport environment. Geographic Information System (GIS) and 3D modeling software are used to convert environmental data into visual digital models. These models can display environmental features along the transport path, such as topography, vegetation coverage, water distribution, etc., to help decision makers understand and analyze environmental impacts. Based on the established digital environmental model, the impact of different transport strategies and route selections on the environment is evaluated. For example, route planning can be optimized to reduce carbon emissions, reduce traffic congestion, or reduce ecological damage.

[0161] Optionally, step S4 is specifically:

[0162] Step S41: collecting the transport point environment from the transport environment digital network to obtain transport point environment data, and extracting the transfer point environment features from the transport point environment data to obtain transfer point environment data;

[0163] In this embodiment, it is first necessary to establish a digital network to monitor and collect environmental data at transportation points. This involves the use of sensor networks or monitoring devices to collect data on environmental parameters such as temperature, humidity, air quality, etc. For example, sensors can be installed at transportation hubs, warehouses, or traffic nodes to monitor environmental conditions in real time. IoT (Internet of Things) devices such as temperature sensors, humidity sensors, and air quality sensors can be deployed to transmit data to a central database via a wireless network. These sensors can regularly sample environmental data to ensure the real-time and accuracy of the data.

[0164] Step S42: Modeling the switching point environment according to the switching point environment data to obtain the switching point environment model, and calculating the inclination of the horizontal plane of the switching point on the switching point environment model to obtain the inclination data of the horizontal plane of the switching point;

[0165] In this embodiment, the environment of the transfer point is modeled based on the collected environmental data. This may involve using a geographic information system (GIS) or three-dimensional modeling software to create a virtual environment model of the transfer point. The model may include information such as ground elevation, building structure, road layout, etc. It may be possible to use laser scanning technology to obtain field data of the transfer point and combine it with map data for modeling. The horizontal plane inclination of the transfer point is evaluated by calculating the ground inclination, which is crucial for the subsequent estimation of the inclination of the goods.

[0166] Step S43: estimating the commodity inclination based on the horizontal plane inclination data of the transfer point according to the bulk commodity carrier structure model, thereby obtaining the commodity inclination data;

[0167] In this embodiment, when estimating the inclination of the commodity, the inclination data of the horizontal plane of the transfer point obtained in the transfer point environment model is first calculated in combination with the commodity carrier structure model. For example, for liquid bulk commodities in transportation, such as oil or chemicals, the structure and liquid level of the container, as well as the vibration and inclination that may occur during the vehicle transfer process, are considered. Through mathematical modeling and simulation, the inclination of the liquid under different transfer environment conditions can be estimated, and then the degree of inclination and impact that may occur during its transportation can be predicted.

[0168] Step S44: extracting commodity type features from the real-time commodity transportation order data to obtain commodity type data, and performing volatility characteristic analysis on the commodity type data to obtain commodity chemical volatility data;

[0169] In this embodiment, the commodity type feature extraction and volatility characteristics analysis are performed for real-time commodity transportation order data. For example, for chemicals or volatile substances, their volatility characteristics are analyzed based on their chemical composition and physical state (solid, liquid or gas). Through experimental data or historical records, their volatility under different temperature and environmental conditions is evaluated, and these data are combined with the transfer point environment model to predict the volatility of the commodity under different environments.

[0170] Step S45: evaluating the commodity loading tightness of the bulk commodity carrier structure model, thereby obtaining commodity loading tightness data;

[0171] In this embodiment, the sealing degree and volatility of commodity loading are evaluated and estimated. For example, for flammable, explosive or chemically sensitive bulk commodities, the sealing performance of their loading containers needs to be evaluated. The loading sealing degree is determined by checking the sealing interface, material strength and pressure change. At the same time, combined with the chemical volatility data of the commodity, the volatility and potential safety risks that may occur during transportation are predicted. The commodity loading sealing degree can also be estimated based on the sealing degree of the commodity loading components included in the bulk commodity carrier structure model.

[0172] Step S46: estimating the commodity volatility based on the commodity loading sealing data and the commodity chemical volatility data according to the transfer point environment model, thereby obtaining commodity volatility data;

[0173] In this embodiment, the goal of this step is to evaluate whether the commodity will release volatile chemicals under specific environmental conditions and to estimate the extent and rate of release. For example, if a certain commodity may release volatile substances at higher temperatures and less at lower temperatures, the transfer point environmental model will take these factors into account. Through the ambient temperature and humidity data of the transfer point, combined with the sealing and volatility data of the commodity, mathematical modeling and predictive analysis can be performed to estimate the volatility of the commodity in this environment. Mathematical modeling techniques, such as reaction rate equations or diffusion models, are used to combine environmental conditions and chemical volatility data, and numerical simulations or machine learning methods are used to predict the numerical results of commodity volatility. For example, a machine learning algorithm can be used to train a model to accurately predict volatility based on historical data and real-time environmental parameters.

[0174] Step S47: performing a transportation and loading transfer leakage risk assessment based on the commodity volatility data and the commodity inclination data, thereby obtaining transportation and loading transfer leakage risk assessment data;

[0175] In this embodiment, the risk assessment of leakage during transportation and loading is performed based on the volatility and inclination data of the commodity. For example, by simulating possible accidents under the environmental conditions of different loading points, the probability and impact of commodity leakage during the loading and unloading of bulk commodities are assessed. Taking into account environmental factors and vehicle status, the safety risk level of the commodity during transportation is determined.

[0176] Step S48: selecting a low-risk transportation path for the bulk commodity transportation path data set according to the transportation transfer leakage risk assessment data, thereby obtaining low-risk bulk commodity transportation path data.

[0177] In this embodiment, a low-risk commodity transportation route is selected based on the transport transfer leakage risk assessment data. For example, the transportation route is optimized by combining the aforementioned assessment results with geographic information system (GIS) data to select a route that minimizes the leakage risk. This may involve route planning that avoids densely populated areas and reduces traffic congestion to ensure safety and efficiency during the transportation of bulk commodities.

[0178] Optionally, step S47 is specifically:

[0179] Conduct statistical analysis on the volatility of goods at the transfer point based on the commodity volatility data, thereby obtaining high volatility transfer point data and low volatility transfer point data;

[0180] In this embodiment, the volatility data is analyzed and classified using statistical methods such as mean value, standard deviation, or machine learning algorithms such as cluster analysis, decision tree, etc. For example, a clustering algorithm can be used to classify the transfer points into two categories: high volatility and low volatility. A threshold can also be set to distinguish between high volatility and low volatility commodity transfer points. For example, a commodity transfer point with a volatility greater than a certain percentile (such as the 75% percentile) belongs to high volatility, and a commodity transfer point with a volatility less than a certain percentile belongs to low volatility.

[0181] Based on the commodity inclination data, statistical analysis of the commodity inclination at the transfer point is performed to obtain high-inclination transfer point data and low-inclination transfer point data;

[0182] In this embodiment, statistical methods such as data distribution analysis, skewness and kurtosis analysis, or machine learning methods such as regression analysis, principal component analysis, etc. are used to classify and analyze the inclination data. For example, a clustering algorithm can be used to divide the transfer points into two categories: high inclination and low inclination. A threshold can also be set to distinguish between high-inclination and low-inclination commodity transfer points. For example, a commodity transfer point with an inclination greater than a certain percentile (such as the 75% percentile) belongs to a high inclination, and a commodity transfer point with an inclination less than a certain percentile belongs to a low inclination.

[0183] Performing a switching point intersection operation on the high volatility switching point data and the high inclination switching point data, thereby obtaining the first high leakage risk switching point data;

[0184] In this embodiment, the data of transfer points with high volatility and high inclination are mainly processed. These transfer points usually represent the highest leakage risk points during transportation, because substances with high volatility are more likely to leak, and high inclination increases the possibility of accidental tilting or leakage. Use professional geographic information system (GIS) software to load the data set of high volatility and high inclination transfer points. Volatility can be determined based on the physical properties of chemical substances (such as flash point, vapor pressure, etc.), and a threshold value of volatility index is usually set, for example, volatility index is greater than 80. The inclination angle is measured according to the loading equipment and transportation conditions. In the GIS software, spatial analysis tools are used to perform intersection operations. These tools can accurately calculate the overlap between two data sets and output transfer point data with high volatility and high inclination.

[0185] Performing a switching point intersection operation on the high volatility switching point data and the low inclination switching point data, thereby obtaining the second highest leakage risk switching point data;

[0186] In this embodiment, for the high volatility but low inclination switching point data, these data usually have a relatively high leakage risk, but lower than the first high leakage risk switching point data. Load the high volatility and low inclination switching point data set, the volatility screening condition remains unchanged (volatility index greater than 80), and low inclination is defined as an inclination angle less than 5 degrees. Use GIS software to perform intersection operations to obtain high volatility and low inclination switching point data.

[0187] Performing a switching point intersection operation on the low volatility switching point data and the high inclination switching point data, thereby obtaining the third highest leakage risk switching point data;

[0188] In this embodiment, the purpose is to find the data of the transfer point with low volatility but high inclination, which increases the risk of leakage due to the inclination during the loading process. The low volatility and high inclination transfer point data set is loaded, and the screening condition of volatility can be set to volatility index less than 80, and high inclination is set to inclination angle greater than 5 degrees. The intersection operation is performed using GIS software to obtain the transfer point data with low volatility and high inclination.

[0189] Performing a switching point intersection operation on the low volatility switching point data and the low inclination switching point data, thereby obtaining the low leakage risk switching point data;

[0190] In this embodiment, the data of the loading point with low volatility and low inclination are sought. These data usually represent the loading points with relatively stable and low leakage risk during the loading process. The low volatility and low inclination loading point data set is loaded, and the screening conditions of volatility and inclination are set as volatility index less than 80 and inclination angle less than 5 degrees respectively. The intersection operation is performed using GIS software to obtain the loading point data with low volatility and low inclination.

[0191] The first high leakage risk transfer point data, the second high leakage risk transfer point data, the third high leakage risk transfer point data and the low leakage risk transfer point data are spatially merged to obtain transport transfer leakage risk assessment data.

[0192] In this embodiment, the data of all four transfer points with leakage risk levels (first, second, third highest leakage risk and low leakage risk) are spatially merged to obtain comprehensive transportation transfer leakage risk assessment data. The data of the transfer points with the first, second, and third highest leakage risk and the data of the transfer points with low leakage risk can be loaded into the GIS software. Using the merge operation in the GIS tool, the spatial range of the four data sets are merged to ensure that all transfer point data are correctly covered and recorded. A merged data set is generated, which can be further used to analyze and evaluate the leakage risk during transportation.

[0193] Optionally, step S48 is specifically:

[0194] Step S481: extracting low leakage risk loading point according to the transport loading leakage risk assessment data, thereby obtaining low leakage risk loading point data;

[0195] In this embodiment, the extraction of low leakage risk transfer points based on the transport transfer leakage risk assessment data means that in the process of bulk commodity transportation, detailed data analysis and geographic information system (GIS) technology are needed to identify the risk points where leakage is most likely to occur during transfer. For example, historical accident data, topographic analysis, and traffic condition assessment are used to determine potential danger areas and optimal transfer points. For example, in the transportation of hazardous chemicals, the optimal safe transfer point is calculated through a mathematical model to ensure that the risk of leakage of goods is minimized during transportation. Of course, the low leakage risk transfer points that have been defined in the previous steps can also be extracted.

[0196] Step S482: Associating the low leakage risk transfer point data with the bulk commodity transportation path data set with a transportation path, thereby obtaining a low leakage risk transfer point associated path data set;

[0197] In this embodiment, the low leakage risk transfer point data and the bulk commodity transportation path data set are associated with the transportation path, which means that after the transfer point is determined, it needs to be associated and analyzed with the existing bulk commodity transportation path data set. This involves the application of transportation network modeling and path planning algorithms to ensure the safe transportation of goods from the departure point to the destination. For example, using network analysis tools such as the Dijkstra algorithm, the transfer point is associated with the transportation path data set to find the bulk commodity transportation path with the low leakage risk transfer point.

[0198] Step S483: Select the shortest transportation path for the low leakage risk transfer point associated path data set, so as to obtain low risk bulk commodity transportation path data.

[0199] In this embodiment, the shortest transportation path selection is performed for the low leakage risk transfer point associated path data set, which means that the optimization algorithm is used to determine the shortest and safest transportation path based on the obtained transfer point associated path data set. For example, combined with a heuristic search algorithm such as the A* algorithm, taking into account the characteristics and safety requirements of the goods, the path that can transport the goods to the destination in the fastest and most economical way is selected. This method can select the most suitable path among multiple transportation paths to minimize the risk of leakage and ensure the safe transportation of bulk commodities.

[0200] Optionally, the present invention further provides a bulk commodity transportation information management system based on cloud computing, which is used to execute the bulk commodity transportation information management method based on cloud computing as described above, and the bulk commodity transportation information management system based on cloud computing includes:

[0201] The structural three-dimensional modeling module is used to obtain bulk commodity transportation data and extract the structural features of the transport vehicle from the bulk commodity transportation data, thereby obtaining the bulk commodity transportation vehicle structural data; and perform three-dimensional structural modeling of the bulk commodity vehicle based on the bulk commodity transportation vehicle structural data, thereby obtaining the bulk commodity vehicle structural model;

[0202] A loading stability assessment module is used to perform a carrier transportation simulation on a bulk commodity carrier structure model, thereby obtaining bulk commodity carrier transportation simulation data, and to perform a commodity loading stability assessment based on the bulk commodity carrier transportation simulation data, thereby obtaining carrier loading stability data;

[0203] The transportation environment digital modeling module is used to obtain road environment monitoring data, and screen the road environment monitoring data for bulk commodity transportation paths based on bulk commodity transportation data and vehicle loading stability data, thereby obtaining a bulk commodity transportation path data set; digital modeling of the transportation environment is performed based on the transportation environment monitoring data and the bulk commodity transportation path data set, thereby obtaining a transportation environment digital network;

[0204] The low-risk transport path selection module is used to evaluate the risk of leakage of transport-transfer commodities based on the digital network of the transport environment and the structural model of the bulk commodity carrier, thereby obtaining the transport-transfer leakage risk assessment data; and to select low-risk transport paths for the bulk commodity transport path data set based on the transport-transfer leakage risk assessment data, thereby obtaining low-risk bulk commodity transport path data;

[0205] The transportation strategy integration module is used to predict the transportation time of bulk commodities based on the digital network of the transportation environment and the transportation route data of low-risk bulk commodities, so as to obtain the predicted transportation time data of bulk commodities; integrate the transportation strategy of bulk commodities based on the transportation route data of low-risk bulk commodities and the predicted transportation time data of bulk commodities, so as to obtain the transportation strategy of bulk commodities, and upload it to the bulk commodity transportation management cloud platform to execute the transportation task of bulk commodities;

[0206] The transportation information update module is used to obtain real-time bulk commodity transportation environment data based on the transportation environment digital network; according to the real-time bulk commodity transportation environment data, the predicted transportation time data of the bulk commodity is adaptively corrected to obtain the actual estimated transportation time data, and uploaded to the bulk commodity transportation management cloud platform to execute the bulk commodity transportation information update task.

[0207] The cloud computing-based bulk commodity transportation information management system of the present invention can implement any one of the cloud computing-based bulk commodity transportation information management methods of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the cloud computing-based bulk commodity transportation information management method. The internal modules of the system cooperate with each other to improve the efficiency of bulk commodity transportation management.

[0208] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0209] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for managing bulk commodity transportation information based on cloud computing, characterized in that: The following steps are involved: Step S1: Obtain bulk commodity transportation data, and extract the structural features of the transportation vehicle from the bulk commodity transportation data, thereby obtaining bulk commodity transportation vehicle structural data; perform bulk commodity vehicle structural three-dimensional modeling based on the bulk commodity transportation vehicle structural data, thereby obtaining a bulk commodity vehicle structural model; Step S2: performing a carrier transportation simulation on the bulk commodity carrier structure model to obtain bulk commodity carrier transportation simulation data, and performing a commodity loading stability assessment based on the bulk commodity carrier transportation simulation data to obtain carrier loading stability data; Step S3: Obtain road environment monitoring data, and screen the road environment monitoring data for bulk commodity transportation paths according to bulk commodity transportation data and vehicle loading stability data, thereby obtaining a bulk commodity transportation path data set; perform digital modeling of the transportation environment according to the transportation environment monitoring data and the bulk commodity transportation path data set, thereby obtaining a transportation environment digital network; Step S4: Perform a transportation transfer commodity leakage risk assessment based on the transportation environment digital network and the bulk commodity carrier structure model, thereby obtaining transportation transfer leakage risk assessment data; select a low-risk transportation path for the bulk commodity transportation path data set based on the transportation transfer leakage risk assessment data, thereby obtaining low-risk bulk commodity transportation path data; Step S5: forecasting the transportation time of bulk commodities based on the digital network of the transportation environment and the low-risk bulk commodity transportation route data, thereby obtaining the predicted transportation time data of bulk commodities; integrating the bulk commodity transportation strategy based on the low-risk bulk commodity transportation route data and the predicted transportation time data of bulk commodities, thereby obtaining the bulk commodity transportation strategy, and uploading it to the bulk commodity transportation management cloud platform to execute the bulk commodity transportation task; Step S6: Acquire real-time bulk commodity transportation environment data based on the transportation environment digital network; perform adaptive correction on the predicted transportation time data of bulk commodities according to the real-time bulk commodity transportation environment data, so as to obtain the actual estimated transportation time data, and upload it to the bulk commodity transportation management cloud platform to execute the bulk commodity transportation information update task.

2. The method for managing bulk commodity transportation information based on cloud computing according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring bulk commodity transportation data, and extracting the structural features of the transportation vehicle from the bulk commodity transportation data, thereby obtaining bulk commodity transportation vehicle structural data; Step S12: classifying the structural data of the bulk commodity transport vehicle by structural type, thereby obtaining the vehicle structural data and the loading tool structural data; Step S13: performing loading tool topology analysis based on the loading tool structure data, thereby obtaining a loading tool topology model; Step S14: extracting vehicle connection structure features according to the vehicle structure data, thereby obtaining vehicle connection structure data; Step S15: Perform three-dimensional modeling according to the carrier structure data to obtain a three-dimensional carrier structure model, and perform three-dimensional connection relationship mapping on the loading tool topological structure model and the three-dimensional carrier structure model according to the carrier connection structure data to obtain a bulk commodity carrier structure model.

3. The method for managing bulk commodity transportation information based on cloud computing according to claim 2, characterized in that: Step S13 is specifically as follows: Step S131: classifying the loading tool structure data into component structures, thereby obtaining connection component structure data and functional component structure data; Step S132: extracting structural material features from the loading tool structural data to obtain the loading tool structural material data, and performing structural material property analysis based on the loading tool structural material data to obtain structural material property data; Step S133: integrating the tool component connection relationship according to the connection component structure data, thereby obtaining component connection data; Step S134: performing loading component topology analysis on the functional component structure data according to the component connection data, thereby obtaining a loading component topology model; Step S135: mapping the component structure material properties of the loading component topological structure model according to the structural material characteristic data, thereby obtaining the loading tool topological structure model.

4. The method for managing bulk commodity transportation information based on cloud computing according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: extracting historical bulk commodity transportation condition characteristics from bulk commodity transportation data, thereby obtaining historical bulk commodity transportation condition data; Step S22: performing Monte Carlo random operation condition generation based on historical bulk commodity transportation operation condition data, thereby obtaining random transportation operation condition data; Step S23: performing a carrier transportation simulation on the random transportation condition data through the bulk commodity carrier structure model, thereby obtaining bulk commodity carrier transportation simulation data; Step S24: extracting the characteristics of commodity loading structure changes and the characteristics of commodity loading weight distribution from the bulk commodity carrier transportation simulation data, thereby obtaining commodity loading structure change data and commodity loading weight distribution data; Step S25: Evaluate the stability of commodity loading according to the commodity loading structure data and the commodity loading weight distribution data, so as to obtain the carrier loading stability data.

5. The method for managing bulk commodity transportation information based on cloud computing according to claim 4, characterized in that: Step S25 is specifically as follows: Step S251: Calculating the height of the center of gravity of the loaded goods based on the goods loading weight distribution data, thereby obtaining the height data of the center of gravity of the loaded goods; Step S252: classifying and calculating the commodity loading weight distribution data based on the center of gravity height data of the loaded commodities, thereby obtaining the loading data of commodities with high center of gravity height and the loading data of commodities with low center of gravity height; Step S253: calculating the commodity transportation loading structure deformation amount based on the commodity loading structure change data, thereby obtaining the commodity transportation loading structure deformation amount data; Step S254: classifying and calculating the commodity loading structure change data according to the commodity transportation loading structure deformation data, thereby obtaining high deformation transportation commodity loading data and low deformation transportation commodity loading data; Step S255: performing a commodity loading intersection operation on the high center of gravity height commodity loading data and the high deformation amount transport commodity loading data, thereby obtaining low stability commodity loading data; performing a commodity loading intersection operation on the low center of gravity height commodity loading data and the low deformation amount transport commodity loading data, thereby obtaining high stability commodity loading data; Step S256: Merging the low-stability commodity loading data and the high-stability commodity loading data to obtain vehicle loading stability data.

6. The method for managing bulk commodity transportation information based on cloud computing according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: Acquire road environment monitoring data, and classify the road environment monitoring data, thereby obtaining construction road environment monitoring data and normal road environment monitoring data; Step S32: digitally modeling the roads in the monitoring area according to the normal road environment monitoring data, thereby obtaining a digital model of the roads in the monitoring area, and performing construction road coding on the digital model of the roads in the monitoring area according to the construction road environment monitoring data, thereby obtaining a coded model of the roads in the monitoring area; Step S33: extracting real-time bulk commodity transportation orders from the bulk commodity transportation data to obtain real-time bulk commodity transportation order data, and extracting transportation point features from the real-time bulk commodity transportation order data to obtain order transportation point data; Step S34: planning the transportation route for the order transportation point data and the vehicle loading stability data according to the road coding model of the monitoring area, thereby obtaining a bulk commodity transportation route data set; Step S35: extracting the transportation path environment monitoring data from the road environment monitoring data according to the bulk commodity transportation path data set, thereby obtaining the transportation path environment monitoring data; Step S36: Digital modeling of the transportation environment is performed based on the transportation path environment monitoring data, thereby obtaining a digital network of the transportation environment.

7. The method for managing bulk commodity transportation information based on cloud computing according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: collecting the transport point environment from the transport environment digital network to obtain transport point environment data, and extracting the transfer point environment features from the transport point environment data to obtain transfer point environment data; Step S42: Modeling the switching point environment according to the switching point environment data to obtain the switching point environment model, and calculating the inclination of the horizontal plane of the switching point on the switching point environment model to obtain the inclination data of the horizontal plane of the switching point; Step S43: estimating the commodity inclination based on the horizontal plane inclination data of the transfer point according to the bulk commodity carrier structure model, thereby obtaining the commodity inclination data; Step S44: extracting commodity type features from the real-time commodity transportation order data to obtain commodity type data, and performing volatility characteristic analysis on the commodity type data to obtain commodity chemical volatility data; Step S45: evaluating the commodity loading tightness of the bulk commodity carrier structure model, thereby obtaining commodity loading tightness data; Step S46: estimating the commodity volatility based on the commodity loading sealing data and the commodity chemical volatility data according to the transfer point environment model, thereby obtaining commodity volatility data; Step S47: performing a transportation and loading transfer leakage risk assessment based on the commodity volatility data and the commodity inclination data, thereby obtaining transportation and loading transfer leakage risk assessment data; Step S48: selecting a low-risk transportation path for the bulk commodity transportation path data set according to the transportation transfer leakage risk assessment data, thereby obtaining low-risk bulk commodity transportation path data.

8. The method for managing bulk commodity transportation information based on cloud computing according to claim 7, characterized in that: Step S47 is specifically as follows: Conduct statistical analysis on the volatility of goods at the transfer point based on the commodity volatility data, thereby obtaining high volatility transfer point data and low volatility transfer point data; Based on the commodity inclination data, statistical analysis of the commodity inclination at the transfer point is performed to obtain high-inclination transfer point data and low-inclination transfer point data; Performing a switching point intersection operation on the high volatility switching point data and the high inclination switching point data, thereby obtaining the first high leakage risk switching point data; Performing a switching point intersection operation on the high volatility switching point data and the low inclination switching point data, thereby obtaining the second highest leakage risk switching point data; Performing a switching point intersection operation on the low volatility switching point data and the high inclination switching point data, thereby obtaining the third highest leakage risk switching point data; Performing a switching point intersection operation on the low volatility switching point data and the low inclination switching point data, thereby obtaining the low leakage risk switching point data; The first high leakage risk transfer point data, the second high leakage risk transfer point data, the third high leakage risk transfer point data and the low leakage risk transfer point data are spatially merged to obtain transport transfer leakage risk assessment data.

9. The method for managing bulk commodity transportation information based on cloud computing according to claim 7, characterized in that: Step S48 is specifically as follows: Step S481: extracting low leakage risk loading point according to the transport loading leakage risk assessment data, thereby obtaining low leakage risk loading point data; Step S482: Associating the low leakage risk transfer point data with the bulk commodity transportation path data set with a transportation path, thereby obtaining a low leakage risk transfer point associated path data set; Step S483: Select the shortest transportation path for the low leakage risk transfer point associated path data set, so as to obtain low risk bulk commodity transportation path data.

10. A bulk commodity transportation information management system based on cloud computing, characterized in that: Used to execute the bulk commodity transportation information management method based on cloud computing as claimed in claim 1, the bulk commodity transportation information management system based on cloud computing comprises: The structural three-dimensional modeling module is used to obtain bulk commodity transportation data and extract the structural features of the transport vehicle from the bulk commodity transportation data, thereby obtaining the bulk commodity transportation vehicle structural data; and perform three-dimensional structural modeling of the bulk commodity vehicle based on the bulk commodity transportation vehicle structural data, thereby obtaining the bulk commodity vehicle structural model; A loading stability assessment module is used to perform a carrier transportation simulation on a bulk commodity carrier structure model, thereby obtaining bulk commodity carrier transportation simulation data, and to perform a commodity loading stability assessment based on the bulk commodity carrier transportation simulation data, thereby obtaining carrier loading stability data; The transportation environment digital modeling module is used to obtain road environment monitoring data, and screen the road environment monitoring data for bulk commodity transportation paths based on bulk commodity transportation data and vehicle loading stability data, thereby obtaining a bulk commodity transportation path data set; digital modeling of the transportation environment is performed based on the transportation environment monitoring data and the bulk commodity transportation path data set, thereby obtaining a transportation environment digital network; The low-risk transport path selection module is used to evaluate the risk of leakage of transport-transfer commodities based on the digital network of the transport environment and the structural model of the bulk commodity carrier, thereby obtaining the transport-transfer leakage risk assessment data; and to select low-risk transport paths for the bulk commodity transport path data set based on the transport-transfer leakage risk assessment data, thereby obtaining low-risk bulk commodity transport path data; The transportation strategy integration module is used to predict the transportation time of bulk commodities based on the digital network of the transportation environment and the transportation route data of low-risk bulk commodities, so as to obtain the predicted transportation time data of bulk commodities; integrate the transportation strategy of bulk commodities based on the transportation route data of low-risk bulk commodities and the predicted transportation time data of bulk commodities, so as to obtain the transportation strategy of bulk commodities, and upload it to the bulk commodity transportation management cloud platform to execute the transportation task of bulk commodities; The transportation information update module is used to obtain real-time bulk commodity transportation environment data based on the transportation environment digital network; according to the real-time bulk commodity transportation environment data, the predicted transportation time data of the bulk commodity is adaptively corrected to obtain the actual estimated transportation time data, and uploaded to the bulk commodity transportation management cloud platform to execute the bulk commodity transportation information update task.

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