Coal transportation and management method combined with Internet of Things technology

Abstract

The invention discloses a coal transportation and management method combined with an internet of things technology, which collects key data in a coal transportation process in real time through a multi-layer sensor network, and enhances the stability and continuity of data transmission by using edge calculation and an adaptive network technology. Meanwhile, intelligent data processing and prediction are carried out in combination with big data analysis and a machine learning model, graded early warning feedback and control measures are provided, and the transparency of transportation management is improved. Besides, the scheme also supports environmental protection monitoring and low-carbon management, data tracing and management are realized, the real-time monitoring capability, the data transmission stability, the emergency response speed and the environmental protection management level of coal transportation are remarkably improved, and the problems existing in traditional coal transportation management are effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of coal transportation and management, and in particular to a method for coal transportation and management combined with Internet of Things technology. Background Art

[0002] Coal is an important global energy resource and is widely used in many fields such as electricity, metallurgy and chemical industry. However, the coal transportation management process faces multiple challenges, which affect the efficiency, safety and environmental protection of transportation. Traditional coal transportation management methods mainly rely on manual monitoring and regular records, which have the following problems:

[0003] Data lag and lack of real-time monitoring: Traditional coal transportation management relies on manual inspection and timed recording, which makes it difficult to grasp the real-time status of the transportation process and cannot effectively monitor the real-time temperature, humidity, location, vibration and other data of the coal. In the event of an emergency (such as abnormal temperature or excessive turbulence during transportation), there is a lack of timely response measures.

[0004] Low transparency in the transportation process: There is a lack of transparent management methods in the coal transportation process, and it is impossible to know the specific location and transportation status of the transportation vehicles at any time, which makes it difficult to share transportation status information in various links of the transportation process, increasing the difficulty of transportation management.

[0005] Slow emergency response: When sudden abnormal situations occur during transportation (such as abnormal temperature or risk of spontaneous combustion of coal), traditional coal transportation management needs to rely on manual intervention or manual notification, and lacks automatic warning and control methods of the system. Therefore, the emergency response is not fast enough and there are safety hazards.

[0006] Lack of environmental monitoring and low-carbon management: With the increasing demand for environmental protection, carbon emission monitoring and management of coal transportation has become particularly important. However, the traditional coal transportation method lacks real-time monitoring of carbon emissions, which makes it impossible to optimize the transportation process according to carbon emissions, affecting the environmental protection and low-carbon nature of transportation.

[0007] The existence of the above problems limits the efficiency and safety of traditional coal transportation management, and also increases management costs and environmental burdens. Therefore, an innovative coal transportation and management method is urgently needed to achieve efficient and visual management of the transportation process, improve safety and response speed, and meet modern environmental protection requirements. Summary of the invention

[0008] The present application provides a coal transportation and management method combined with Internet of Things technology to achieve efficient and visual management of the transportation process, improve safety and response speed, and meet modern environmental protection requirements.

[0009] A method for coal transportation and management in combination with Internet of Things technology, the method comprising:

[0010] Data collection: Real-time location information, temperature, humidity, vibration and load distribution data during coal transportation are obtained through a multi-level sensor network;

[0011] Data transmission: Use edge computing to clean, deduplicate and compress collected data, and transmit key data to the central management platform through adaptive network switching;

[0012] Data processing: Use big data analysis and machine learning models to analyze and predict historical and real-time data and generate abnormal warning information;

[0013] Early warning feedback: Generate graded early warning information based on the analysis results of data processing, and provide graded feedback and control measures based on the severity;

[0014] Visual management: The status, location and environmental information of the coal transportation process can be displayed in real time through the three-dimensional visualization system.

[0015] In the above solution, optionally, the data collection includes:

[0016] GPS positioning modules, temperature and humidity sensors, vibration sensors and infrared or laser scanning equipment are installed on the surfaces of transport vehicles, coal bins and coal piles to collect real-time information on the location of coal, the temperature, humidity and vibration status of the vehicle and coal pile environment. Infrared or laser scanning equipment is used to collect coal stacking density and volume data.

[0017] In the above solution, optionally, the data transmission includes:

[0018] Edge computing is used to clean, deduplicate and compress sensor data, and only critical data and abnormal data are transmitted to the central management platform. Adaptive switching between LoRa, NB-IoT or 5G networks is also used to ensure the stability of data transmission.

[0019] In the above solution, optionally, the data processing includes:

[0020] Through machine learning, big data analysis of historical and real-time data is performed to predict future transportation route optimization, schedule and potential risks, and generate early warning information when the set threshold is reached.

[0021] In the above solution, optionally, the coal state analysis includes:

[0022] A dynamic state analysis model is built based on the temperature, humidity and vibration data of coal, and the analysis model is dynamically adjusted based on sensor data to evaluate the risks of coal spontaneous combustion and quality changes in real time, and automatically generate early warning information when the set risk threshold is reached.

[0023] In the above solution, optionally, the early warning feedback includes:

[0024] Different levels of warning information are generated according to the severity of the abnormal situation, and remote control measures are automatically initiated when a high-level warning is triggered.

[0025] In the above solution, optionally, the remote control includes:

[0026] When an abnormality is detected, the deceleration or parking control of the transport vehicle is remotely initiated to ensure the safety of coal transportation.

[0027] In the above solution, optionally, the path optimization includes:

[0028] Based on external real-time traffic and weather data, the optimal transportation route is generated through artificial intelligence algorithms to reduce transportation time and improve transportation efficiency.

[0029] In the above solution, optionally, the carbon emission monitoring includes:

[0030] The vehicle's carbon emissions are monitored in real time during transportation, and route optimization or vehicle replacement recommendations are generated when the preset carbon emission threshold is exceeded to reduce carbon emissions during transportation.

[0031] In the above solution, optionally, the data tracing includes:

[0032] By embedding RFID chips into coal batches and recording key transportation data through blockchain, the whole life cycle traceability management of coal can be realized, ensuring the accessibility and non-tamperability of data.

[0033] Compared with the prior art, this application has at least the following beneficial effects:

[0034] This application is based on further analysis and research of existing technical problems. It recognizes that existing solutions limit the efficiency and safety of traditional coal transportation management, and also increases management costs and environmental burdens. Through a multi-level sensor network, key data in the coal transportation process is collected in real time, and edge computing and adaptive network technologies are used to enhance the stability and continuity of data transmission. At the same time, it combines big data analysis and machine learning models for intelligent data processing and prediction, provides graded early warning feedback and control measures, and improves the transparency of transportation management. In addition, the solution also supports environmental monitoring and low-carbon management, realizes data traceability and management, significantly improves the real-time monitoring capabilities of coal transportation, data transmission stability, emergency response speed and environmental management level, and effectively solves the problems existing in traditional coal transportation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flow chart of a method for coal transportation and management combined with Internet of Things technology provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0037] In one embodiment, Figure 1 As shown, a method for coal transportation and management combined with Internet of Things technology is provided, comprising the following steps:

[0038] Data collection: Real-time location information, temperature, humidity, vibration and load distribution data during coal transportation are obtained through a multi-level sensor network;

[0039] Data transmission: Use edge computing to clean, deduplicate and compress collected data, and transmit key data to the central management platform through adaptive network switching;

[0040] Data processing: Use big data analysis and machine learning models to analyze and predict historical and real-time data and generate abnormal warning information;

[0041] Early warning feedback: Generate graded early warning information based on the analysis results of data processing, and provide graded feedback and control measures based on the severity;

[0042] Visual management: The status, location and environmental information of the coal transportation process can be displayed in real time through the three-dimensional visualization system.

[0043] This embodiment proposes a coal transportation and management method combined with Internet of Things technology, which can significantly improve the level of intelligent management of coal transportation.

[0044] Through a multi-level sensor network, this method can collect data such as location, temperature and humidity, vibration, and load distribution during coal transportation in real time, monitor the transportation status in real time, ensure dynamic visualization management of the transportation process, and effectively overcome the problem of real-time data lag in traditional coal transportation. This method uses edge computing to clean, deduplicate, and compress the collected data, and combines adaptive network switching technology to transmit key data and abnormal data to the central management platform in a timely manner. This ensures the continuity and stability of data transmission and effectively reduces the impact of network fluctuations on transportation monitoring. The system uses big data analysis and machine learning models to conduct a comprehensive analysis of historical and real-time data. Through operations such as path optimization, time scheduling, and risk prediction, the system can identify potential risks and generate abnormal warning information, thereby improving the safety of coal transportation and enhancing prevention capabilities. According to the data analysis results, the system generates different levels of warning information according to the severity of the abnormal situation, and provides remote control measures when necessary, including safety operations such as deceleration or parking. This design ensures the timeliness of emergency handling and overcomes the problem of insufficient emergency response in traditional transportation management. The three-dimensional visualization system displays the coal status, location, and environmental information during transportation in real time, enabling managers to clearly understand the transportation situation and improve the transparency and efficiency of management. This method monitors the carbon emissions of vehicles in real time during transportation and generates optimization suggestions when the preset carbon emission threshold is reached to reduce carbon emissions during transportation, meet environmental protection needs, and adapt to the requirements of modern transportation management for low-carbon and environmental protection. The traceability management of coal transportation data through RFID chips and blockchain technology ensures the integrity and non-tamperability of the data, realizes the data recording and traceability of the entire life cycle of coal, and facilitates the query and management of historical data during the transportation process.

[0045] The technical solution proposed in this embodiment significantly improves the real-time monitoring, data transmission stability, emergency response speed and environmental protection management level of coal transportation, and solves the problems of poor real-time performance, slow response speed, information opacity and insufficient environmental protection monitoring in traditional coal transportation management.

[0046] In this embodiment, the data collection includes:

[0047] GPS positioning modules, temperature and humidity sensors, vibration sensors and infrared or laser scanning equipment are installed on the surfaces of transport vehicles, coal bins and coal piles to collect real-time information on the location of coal, the temperature, humidity and vibration status of the vehicle and coal pile environment. Infrared or laser scanning equipment is used to collect coal stacking density and volume data.

[0048] This embodiment involves data collection during coal transportation, including configuring GPS positioning modules, temperature and humidity sensors, vibration sensors, and infrared or laser scanning equipment on the surface of transportation vehicles, coal bunkers, and coal piles to collect location, ambient temperature and humidity, vibration status, stacking density, and volume data during transportation. The technical effect of this embodiment is to provide comprehensive real-time data collection capabilities, thereby improving the intelligent monitoring and refined management level of coal transportation.

[0049] In this embodiment, through the cooperation of GPS positioning module, temperature and humidity sensor, vibration sensor and infrared or laser scanning equipment, the system can collect key data of multiple dimensions in real time during transportation. These data cover various factors that have a direct impact on the safety and quality of coal transportation during transportation, including the real-time position of the transport vehicle, the temperature and humidity changes of coal, the vibration during transportation, the stacking density and volume of coal, etc. Comprehensive data collection makes up for the problems of incomplete information collection and single collection method in the traditional coal transportation management method, and ensures the multi-directional monitoring of coal transportation. By configuring sensors on the surface of the transport vehicle and the coal pile, real-time monitoring of the coal state during transportation is realized. For example, the temperature and humidity sensor can monitor the temperature and humidity changes of the coal transportation environment to prevent the danger of spontaneous combustion of coal caused by abnormal environment. At the same time, the vibration sensor can monitor the vibration of coal in real time during transportation to prevent the scattering or quality damage of coal due to severe bumps during transportation. The GPS positioning module can provide the location information of the transport vehicle in real time, so that the management personnel can grasp the dynamics of the vehicle at any time during the coal transportation process, and improve the safety of transportation and the transparency of management. The introduction of infrared or laser scanning equipment enables the system to collect the stacking density and volume data of coal in real time. Traditional coal transportation management methods usually make it difficult to accurately determine the coal accumulation during transportation. Infrared or laser scanning equipment can generate density and volume information by scanning the surface of coal accumulation, which can more accurately reflect the load changes during transportation. The acquisition of this real-time accumulation data helps managers to promptly discover uneven load problems during transportation, thereby adjusting the transportation method and ensuring the stability of coal transportation.

[0050] The multi-level sensor network of this embodiment realizes real-time monitoring of the coal transportation status through multi-angle and multi-dimensional data collection methods, and provides support for the visualization of data. This not only meets the needs of managers for real-time monitoring of transportation data, but also provides a rich data basis for subsequent data analysis and risk prediction. The data collection system with strong real-time performance can detect abnormal situations more quickly, providing a guarantee for timely early warning of coal transportation. The multi-sensor coordination design improves the detection and analysis capabilities of multiple environmental factors in the coal transportation process, can provide early warning under potential dangerous conditions, and take corresponding management measures at the first time of abnormal environmental changes. This method brings the safety of coal transportation to a new level. At the same time, it also allows managers to make scientific and accurate management decisions based on multi-dimensional real-time data, thereby further improving the efficiency and management level of coal transportation.

[0051] This embodiment achieves comprehensive monitoring of the coal transportation process through the integration of multi-dimensional sensors, providing highly reliable real-time data support. This data acquisition system overcomes the defects of incomplete information and delayed monitoring in traditional coal transportation, and provides high-quality data input for subsequent data transmission, processing and analysis.

[0052] In this embodiment, the data transmission includes:

[0053] Edge computing is used to clean, deduplicate and compress sensor data, and only critical data and abnormal data are transmitted to the central management platform. Adaptive switching between LoRa, NB-IoT or 5G networks is also used to ensure the stability of data transmission.

[0054] This embodiment uses edge computing to achieve data cleaning, deduplication and compression, which effectively reduces the amount of data and improves transmission efficiency. During coal transportation, a large amount of raw data collected by sensors includes a variety of information, some of which may not change in a short period of time or may be redundant data. Edge computing technology pre-processes the data and only transmits key data and abnormal data, reducing unnecessary data transmission, thereby improving the utilization efficiency of network bandwidth. Through the adaptive network switching function, LoRa, NB-IoT or 5G network can be automatically selected according to network conditions to ensure the stability and timeliness of data transmission in different environments. During coal transportation, signal strength and network coverage will fluctuate due to vehicle movement or changes in geographical environment. Adaptive network switching enables the system to automatically select a suitable communication method when the network signal is poor, ensuring uninterrupted data transmission, and effectively solving the common delays and interruptions in traditional coal transportation data transmission.

[0055] The edge computing of this embodiment only retains important information and abnormal data after data processing at the sensor end, which greatly reduces the total amount of data transmission. The reduction in the amount of data not only improves the transmission speed, but also reduces the network energy consumption, provides a guarantee for low-power and long-distance data transmission in coal transportation, and adapts to the demand for continuous and stable low-power transmission in long-distance coal transportation. Through the preprocessing and compression of edge computing, the transmitted data is more streamlined and accurate, reducing the time the data is exposed to the external network and improving the security of transmission. In addition, the adaptive network switching technology can not only adapt to the complex transportation environment, but also transmit key data in a safer network as needed, further enhancing the security protection of data during transmission. Under the data cleaning and compression of edge computing, only abnormal data and key data are transmitted to the central management platform, so that the system will not generate too high a load when facing a large amount of data. This not only ensures the operating efficiency of the central management platform, but also provides efficient support for the system to perform real-time data analysis and processing. In this way, the central management platform can focus on processing high-value data, reduce the waste of resources, and improve the accuracy and timeliness of data analysis.

[0056] The edge computing and adaptive network switching design of this embodiment significantly improves the stability, efficiency and security of data transmission during coal transportation, solves the problems of interruption, delay and data redundancy that may occur in coal transportation data transmission, and makes the management of coal transportation more efficient and real-time.

[0057] In this embodiment, the data processing includes:

[0058] Through machine learning, big data analysis of historical and real-time data is performed to predict future transportation route optimization, schedule and potential risks, and generate early warning information when the set threshold is reached.

[0059] The machine learning model in this embodiment analyzes historical and real-time data, and realizes in-depth mining and prediction of complex factors in the coal transportation process. This data processing method can not only identify the regular patterns in the transportation process, but also learn and adapt to the emerging data features, improve the accuracy of data analysis, provide more comprehensive analysis results, and help managers discover potential problems that traditional manual analysis cannot detect.

[0060] Through the integration and analysis of historical data and real-time data, the system can predict possible risks in advance according to the dynamic changes in transportation, such as abnormal temperature and humidity during transportation, risk of spontaneous combustion of coal, excessive vibration, etc. When the analysis results reach the set risk threshold, the system will automatically generate early warning information to remind managers to take timely measures. This design can effectively improve the safety of transportation and ensure the stability of the quality of coal during transportation.

[0061] Based on the analysis of historical transportation data by machine learning, the system can identify the optimal transportation route and schedule. Combined with real-time traffic and weather data, the system can dynamically optimize future transportation routes, automatically generate more time-saving and energy-saving route plans, reduce transportation costs, and improve transportation efficiency. In addition, the system's time scheduling prediction function can also help managers optimize scheduling resources, reasonably allocate transportation capacity, and avoid transportation delays. Through the real-time data analysis system of machine learning, the system can quickly generate response strategies when sudden abnormalities are detected during transportation. It has a high degree of automation and reduces the time consumption of manual intervention. At the same time, the system's prediction function can be continuously optimized, and the model can gradually "self-perfect" by continuously learning new data, improving the response speed and accuracy of future abnormal situations. Through functions such as route optimization and risk prediction, machine learning models provide unprecedented accuracy and timeliness in data processing and analysis. The introduction of prediction capabilities helps prevent potential safety risks, helping managers identify risk factors and take measures in advance before and during transportation, and reducing the probability of accidents during transportation.

[0062] This embodiment significantly improves the efficiency and safety of coal transportation through intelligent data processing and risk prediction. It not only solves the problem of lagging data analysis in traditional transportation management methods, but also provides the ability to take proactive precautions and optimize, providing strong support for the sustainable management of the transportation process.

[0063] In this embodiment, the coal state analysis includes:

[0064] A dynamic state analysis model is built based on the temperature, humidity and vibration data of coal, and the analysis model is dynamically adjusted based on sensor data to evaluate the risks of coal spontaneous combustion and quality changes in real time, and automatically generate early warning information when the set risk threshold is reached.

[0065] The dynamic state analysis model in this embodiment is based on multi-dimensional data such as temperature, humidity, and vibration, and can promptly detect quality changes and safety hazards that may occur during coal transportation, such as spontaneous combustion risks and coal deterioration. The risk of spontaneous combustion of coal is usually related to increased temperature, frequent vibration, and changes in environmental humidity. By monitoring these key indicators in real time, the dynamic state analysis model can identify abnormal changes at the early stage of spontaneous combustion risk, thereby triggering early warnings or corresponding safety measures to avoid safety accidents caused by spontaneous combustion or combustion of coal.

[0066] The dynamic state analysis model can adjust parameters according to real-time sensor data, thereby dynamically optimizing the analysis results. Compared with the static analysis model, the dynamic model can more accurately adapt to data fluctuations under different environmental conditions during coal transportation, thereby providing accurate detection results. Dynamic state analysis enables the model to adapt to data changes under different transportation road conditions and weather conditions, significantly improving the accuracy and real-time performance of detection, and being able to respond to any potential abnormalities in a timely manner.

[0067] Temperature, humidity and vibration frequency during transportation have a significant impact on coal quality. The dynamic state analysis model can effectively monitor these environmental parameters, helping managers to take timely protective measures to reduce the quality loss of coal caused by environmental changes. For types of coal that are susceptible to moisture or are highly sensitive, the model can automatically generate a suitable environmental parameter range based on the actual transportation environment, and optimize transportation conditions to maintain the stability and quality of the coal.

[0068] With the support of the dynamic state analysis model, the system can achieve automatic management based on preset thresholds. When environmental indicators such as temperature, humidity or vibration are detected to be beyond the normal range, the model can trigger automatic control or early warning systems, thereby reducing the need for manual monitoring and improving the intelligence level of the system. This function eliminates the need for managers to monitor the transportation status all the time, simplifies the management process, and ensures the accuracy of management.

[0069] The dynamic state analysis model will continue to accumulate data in practical applications. These data can further optimize and adjust the model parameters, making the model have "self-learning" characteristics. With the accumulation of coal transportation data, the model's analysis capabilities and risk identification accuracy will continue to improve, and managers can make more scientific management decisions based on the historical data and analysis results provided by the model. This adaptive optimization capability not only enhances the adaptability of the model, but also enables the model to better cope with risks in complex transportation scenarios in the future.

[0070] This embodiment improves the safety, real-time and accuracy of the coal transportation process by introducing a dynamic state analysis model, provides strong technical support for efficient management and safety assurance of coal transportation, and effectively solves the problems of lag and lack of flexibility in traditional static monitoring.

[0071] In this embodiment, the early warning feedback includes:

[0072] Different levels of warning information are generated according to the severity of the abnormal situation, and remote control measures are automatically initiated when a high-level warning is triggered.

[0073] In the coal transportation process of this embodiment, the abnormal situation that occurs may involve multiple factors, and the degree of impact is also different. Through the hierarchical design of early warning, the system can classify the early warning into different levels according to the severity of the abnormality, such as minor abnormality, moderate abnormality and severe abnormality. The hierarchical early warning design can accurately identify and classify abnormal situations, help managers quickly judge the urgency of abnormal situations, and take corresponding measures in a targeted manner. This can not only prevent overreaction to minor abnormalities, but also ensure that major safety hazards are responded to quickly, enhancing the adaptability and efficiency of the system. In the case of high-level early warning triggering, the system can automatically start remote control measures, such as vehicle deceleration or parking. This function ensures that the system does not need to rely on manual intervention in the event of an emergency, and can take measures in the first time to ensure the safety of coal transportation. For example, when the temperature, humidity or vibration abnormality reaches a dangerous level, the system can automatically suspend the transport vehicle to prevent coal from spontaneous combustion or quality damage. The automated remote control response mechanism significantly shortens the emergency response time and provides a strong guarantee for the safety of coal transportation. The hierarchical early warning and automatic response mechanism eliminates the need for managers to monitor all details of the coal transportation process around the clock, and only needs to pay attention to higher-level early warning information. For minor abnormal situations, the system will automatically record and monitor without triggering alarms or remote control, thus reducing unnecessary manual intervention and improving the degree of automation of management. Managers can focus on handling important events more efficiently and improve the efficiency of coal transportation management.

[0074] In traditional coal transportation, abnormal situations often require manual operation to handle, which delays the processing time and causes human errors. The automated hierarchical warning and remote control measures of this embodiment can effectively eliminate the uncertainty of human intervention and ensure the safety and stability of coal transportation. For example, the automatic deceleration and parking functions of the system in an emergency can effectively reduce risks and prevent major accidents.

[0075] This embodiment implements a hierarchical response strategy for early warning and remote control, providing technical support for flexible handling of diverse abnormal situations during coal transportation. The hierarchical response mechanism can generate corresponding early warning levels for different types of abnormal situations, ensuring a higher degree of intelligence in the system. At the same time, the automated response of remote control can provide effective safety protection under high-level early warning, effectively preventing risk events in coal transportation.

[0076] This embodiment provides efficient and reliable technical means for emergency response in coal transportation through hierarchical early warning and automated remote control measures. The hierarchical management design improves the accuracy, automation and emergency response speed of the system, and provides effective technical guarantee for the safety of coal transportation.

[0077] In this embodiment, the remote control includes:

[0078] When an abnormality is detected, the deceleration or parking control of the transport vehicle is remotely initiated to ensure the safety of coal transportation.

[0079] In this embodiment, the path optimization includes:

[0080] Based on external real-time traffic and weather data, the optimal transportation route is generated through artificial intelligence algorithms to reduce transportation time and improve transportation efficiency.

[0081] This embodiment achieves rapid response to extreme abnormal situations in coal transportation through the application of remote control measures, providing safe and efficient technical guarantees for coal transportation. The automated deceleration and parking control functions improve the intelligence and safety of coal transportation, ensure the stability and reliability of the transportation process under abnormal conditions, and provide important technical support for coal transportation management.

[0082] The path optimization scheme of this embodiment achieves efficiency improvement, cost reduction, environmental protection and energy saving, and transportation safety guarantee for coal transportation through real-time calculation and dynamic path selection of AI algorithms. The path optimization scheme provides more intelligent and efficient scheduling support for coal transportation, making coal transportation management more flexible and adaptable.

[0083] In this embodiment, the carbon emission monitoring includes:

[0084] The vehicle's carbon emissions are monitored in real time during transportation, and route optimization or vehicle replacement recommendations are generated when the preset carbon emission threshold is exceeded to reduce carbon emissions during transportation.

[0085] The carbon emission monitoring solution of this embodiment realizes the environmental management of coal transportation through real-time monitoring, dynamic optimization and data support, which meets the requirements of low-carbon economy and environmental protection policies. Carbon emission monitoring not only reduces operating costs, but also enhances the environmental competitiveness of enterprises, providing important technical support for the sustainable development of coal transportation.

[0086] In this embodiment, the data tracing includes:

[0087] By embedding RFID chips into coal batches and recording key transportation data through blockchain, the whole life cycle traceability management of coal can be realized, ensuring the accessibility and non-tamperability of data.

[0088] The data traceability management solution of this embodiment realizes transparent data management of the entire life cycle of coal through the combination of RFID chips and blockchain technology, and improves data security, traceability and decision-making support capabilities. Data traceability not only ensures the compliance and safety of the coal transportation process, but also provides important technical support for the efficient management and market competitiveness of enterprises.

[0089] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A coal transportation and management method combining Internet of Things technology, characterized in that: The method comprises: Data collection: Real-time location information, temperature, humidity, vibration and load distribution data during coal transportation are obtained through a multi-level sensor network; Data transmission: Use edge computing to clean, deduplicate and compress collected data, and transmit key data to the central management platform through adaptive network switching; Data processing: Use big data analysis and machine learning models to analyze and predict historical and real-time data and generate abnormal warning information; Early warning feedback: Generate graded early warning information based on the analysis results of data processing, and provide graded feedback and control measures based on the severity; Visual management: The status, location and environmental information of the coal transportation process can be displayed in real time through the three-dimensional visualization system.

2. The method for coal transportation and management according to claim 1, characterized in that: The data collection includes: GPS positioning modules, temperature and humidity sensors, vibration sensors and infrared or laser scanning equipment are installed on the surfaces of transport vehicles, coal bins and coal piles to collect real-time information on the location of coal, the temperature, humidity and vibration status of the vehicle and coal pile environment. Infrared or laser scanning equipment is used to collect coal stacking density and volume data.

3. The method for coal transportation and management according to claim 1, characterized in that: The data transmission includes: Edge computing is used to clean, deduplicate and compress sensor data, and only critical data and abnormal data are transmitted to the central management platform. Adaptive switching between LoRa, NB-IoT or 5G networks is also used to ensure the stability of data transmission.

4. The method for coal transportation and management according to claim 1, characterized in that: The data processing includes: Through machine learning, big data analysis of historical and real-time data is performed to predict future transportation route optimization, schedule and potential risks, and generate early warning information when the set threshold is reached.

5. The method for coal transportation and management according to claim 1, characterized in that: The coal status analysis includes: A dynamic state analysis model is built based on the temperature, humidity and vibration data of coal, and the analysis model is dynamically adjusted based on sensor data to evaluate the risks of coal spontaneous combustion and quality changes in real time, and automatically generate early warning information when the set risk threshold is reached.

6. The method for coal transportation and management according to claim 1, characterized in that: The early warning feedback includes: Different levels of warning information are generated according to the severity of the abnormal situation, and remote control measures are automatically initiated when a high-level warning is triggered.

7. The method for coal transportation and management according to claim 6, characterized in that: The remote control comprises: When an abnormality is detected, the deceleration or parking control of the transport vehicle is remotely initiated to ensure the safety of coal transportation.

8. The method for coal transportation and management according to claim 4, characterized in that: The path optimization includes: Based on external real-time traffic and weather data, the optimal transportation route is generated through artificial intelligence algorithms to reduce transportation time and improve transportation efficiency.

9. The method for coal transportation and management according to claim 1, characterized in that: The method also includes carbon emissions monitoring: The vehicle's carbon emissions are monitored in real time during transportation, and route optimization or vehicle replacement recommendations are generated when the preset carbon emission threshold is exceeded to reduce carbon emissions during transportation.

10. The method for coal transportation and management according to claim 1, characterized in that: The data tracing includes: By embedding RFID chips into coal batches and recording key transportation data through blockchain, the whole life cycle traceability management of coal can be realized, ensuring the accessibility and non-tamperability of data.

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