A method and system for managing road freight transport based on electronic waybills
Patent Information
- Application Number
- CN202611057783.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-16
- Publication Date
- 2026-10-16
AI Technical Summary
[0003]然而，现有基于电子运单的道路货物运输管理方法仍存在以下不足：第一，电子运单承载的信息局限于托运人、收货人、货物名称、起讫地等常规静态数据，未将货物自身的环境敏感性（如不同货物对温度、湿度、振动、光照等环境因素的耐受能力差异）作为运单的必要组成部分进行记录和管理，导致运输管理过程中缺乏针对不同货物个性化环境需求的精准判定依据；第二，现有物流环境监测方法多是利用特定种类的传感器采集特定的、单一的环境数据，而后通过设定简单的阈值对某一环境变量进行异常判断和识别，普遍存在环境监测缺乏系统性的缺点；同时，现有方法未能充分考虑温度、湿度、振动、倾斜等多环境因素对货物的耦合影响，单一因素阈值判断难以准确反映多因素协同作用下的真实风险；第三，现有电子运单与环境监测数据之间缺乏深度的关联融合机制，环境监测数据未能与运单中的货物信息建立实时映射关系并进行动态风险研判，运输过程中的环境异常预警也多停留在被动提示层面，缺乏基于风险等级的分级预警、主动干预和全流程追溯管理能力
[0031] This invention discloses a road freight transport management method and system based on electronic waybills. During the electronic waybill generation stage, it requires the configuration of complete environmental sensitivity parameters for the cargo, including temperature tolerance range, humidity tolerance range, maximum permissible vibration acceleration, maximum permissible tilt angle, and light sensitivity level. This provides a clear judgment benchmark for accurate environmental monitoring during subsequent transportation, overcoming the deficiency of existing electronic waybills that do not distinguish between the environmental sensitivity of the cargo. This invention not only performs threshold judgments on single environmental data but also constructs a multi-factor coupled risk assessment model. It weights and couples multiple environmental factors such as temperature deviation, humidity deviation, vibration intensity, and tilt angle to calculate a comprehensive risk coefficient. When the comprehensive risk coefficient exceeds a preset coupled risk threshold, it determines that there is a risk of multi-factor coupled influence, effectively overcoming the problem of missed or false alarms that may be caused by single-factor threshold judgment, and significantly improving the accuracy of risk identification. This invention can deeply bind electronic waybills with the environmental sensitivity of the cargo during road freight transport management, realizing multi-factor coupled risk assessment, hierarchical early warning, and proactive management.
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Figure CN122928159A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of cargo transportation management technology, and in particular to a road cargo transportation management method and system based on electronic waybills. Background Technology
[0002] The application of electronic waybills in road freight transport management has become increasingly widespread. With the development and popularization of online freight platforms, more and more actual carriers are using these platforms to accept orders. These platforms integrate cargo sources, vehicles, and drivers, significantly improving logistics efficiency. Currently, there are existing technical solutions for using electronic waybills in transport process management. For example, electronic waybills can be used to generate estimated freight rates, allocate fuel and gas quotas, track fuel and gas costs, and record loading and unloading documents and cash flows to form a complete data chain. Furthermore, some existing technologies have begun to combine electronic waybills with IoT sensors for logistics monitoring. For instance, by obtaining waybill information, the type of goods to be transported and associated sensors can be determined, and the status information of the sensors during transport can be obtained, generating alerts when alert criteria are met. Another example is the use of smart waybill circuits to collect environmental data such as temperature, humidity, and air pressure of the goods and to detect collisions and drops.
[0003] However, existing road freight transport management methods based on electronic waybills still have the following shortcomings: First, the information carried by electronic waybills is limited to conventional static data such as consignor, consignee, cargo name, and origin and destination. They fail to record and manage the environmental sensitivity of the cargo itself (such as the differences in the tolerance of different cargo to environmental factors such as temperature, humidity, vibration, and light) as a necessary component of the waybill, resulting in a lack of accurate judgment criteria for the personalized environmental needs of different cargo during transport management. Second, existing logistics environment monitoring methods mostly utilize specific types of sensors to collect specific, single environmental data, and then set simple thresholds to measure certain environmental conditions. The identification and judgment of anomalies in environmental variables generally suffers from the lack of systematic environmental monitoring. At the same time, existing methods fail to fully consider the coupled effects of multiple environmental factors such as temperature, humidity, vibration, and tilt on goods, and the threshold judgment of a single factor is difficult to accurately reflect the real risks under the synergistic effect of multiple factors. Third, there is a lack of deep correlation and integration mechanism between existing electronic waybills and environmental monitoring data. Environmental monitoring data has not established a real-time mapping relationship with the cargo information in the waybill and has not been able to conduct dynamic risk assessment. Environmental anomaly warnings during transportation are mostly at the level of passive prompts, lacking the ability to conduct graded warnings based on risk levels, proactive intervention, and full-process traceability management.
[0004] Therefore, there is an urgent need for a road freight transport management method that can deeply bind electronic waybills with the environmental sensitivity of goods, realize multi-factor coupled risk assessment, and have hierarchical early warning and proactive management functions, in order to overcome the above-mentioned defects of existing technologies. Summary of the Invention
[0005] The purpose of this invention is to provide a road freight transport management method and system based on electronic waybills, which can deeply bind electronic waybills with the environmental sensitivity of goods when conducting road freight transport management, and realize multi-factor coupled risk assessment, hierarchical early warning and proactive management.
[0006] To achieve the above objectives, in a first aspect, the present invention provides a road freight transport management method based on electronic waybills, comprising:
[0007] Acquire freight data for cargo transportation and generate electronic waybills, which contain cargo information and environmental sensitivity parameters corresponding to the cargo.
[0008] Real-time collection of environmental data during cargo transportation;
[0009] By correlating and matching environmental data with environmental sensitivity parameters in electronic waybills, it can be determined whether the current environment poses a risk to the goods.
[0010] When a risk is identified, corresponding early warning information is generated based on the risk level, and management measures are implemented.
[0011] The environmental sensitivity parameters include at least one of the following: temperature tolerance range, humidity tolerance range, maximum permissible vibration acceleration, maximum permissible tilt angle, and light sensitivity level.
[0012] Specifically, the process of correlating and matching environmental data with environmental sensitivity parameters in electronic waybills to determine whether the current environment poses a risk to the goods includes:
[0013] The real-time collected environmental data is correlated and matched with the environmental sensitivity parameters bound in the electronic waybill;
[0014] The real-time collected environmental data is compared with the corresponding threshold in the environmental sensitivity parameters. If any environmental data exceeds the corresponding tolerance threshold, it is determined that there is a risk of single-factor impact.
[0015] The comprehensive risk coefficient is calculated by weighted coupling of at least two environmental factors among temperature deviation, humidity deviation, vibration intensity and tilt angle. When the comprehensive risk coefficient exceeds the preset coupling risk threshold, it is determined that there is a risk of multi-factor coupling influence.
[0016] By obtaining the vehicle's current location and weather forecast data along the driving route through the meteorological data interface, the risk of severe weather that the vehicle is about to encounter can be predicted as an auxiliary basis for judgment.
[0017] By combining the results of single-factor threshold judgment, multi-factor coupled risk assessment, and external meteorological risk prediction, the risk level of the current environment's impact on goods is determined.
[0018] This involves comparing each piece of real-time collected environmental data with the corresponding threshold in the environmental sensitivity parameters. If any environmental data exceeds the corresponding tolerance threshold, a single-factor impact risk is determined to exist. Specifically, this includes:
[0019] The real-time temperature data is compared one by one with the upper and lower limits of the temperature tolerance range; the real-time humidity data is compared one by one with the upper and lower limits of the humidity tolerance range; the real-time vibration acceleration data is compared with the maximum permissible vibration acceleration; the real-time tilt angle data is compared with the maximum permissible tilt angle; and the real-time light intensity data is compared with the light sensitivity level. If any environmental data exceeds the corresponding tolerance threshold, it is determined that there is a risk of single-factor influence.
[0020] Among the steps, when a risk is determined to exist, generating corresponding early warning information based on the risk level and implementing management measures,
[0021] The risk levels are divided into three levels: Level 3, Level 2, and Level 1. Level 3 risk is a warning level triggered when environmental data is close to but does not exceed the tolerance threshold; Level 2 risk is an alarm level triggered when a single environmental data exceeds the tolerance threshold; and Level 1 risk is an emergency level triggered when multiple environmental factors exceed the standard simultaneously or the comprehensive risk coefficient reaches the high-risk level.
[0022] Among them, in the step of generating corresponding early warning information and implementing management measures according to the risk level when an impact risk is determined, the management measures include:
[0023] For Level 3 risks, send voice prompts to the vehicle terminal;
[0024] For level 2 risks, a voice alarm is sent to the vehicle terminal, and alarm information is pushed to the dispatch center;
[0025] For Level 1 risks, an emergency voice alarm is sent to the vehicle terminal, emergency response instructions are pushed to the dispatch center, and alternative transportation routes are automatically generated.
[0026] Secondly, the present invention also provides a road freight transport management system based on electronic waybills, comprising: a cargo sensitive feature calibration module, a transport environment perception module, a transport risk simulation module, and an early warning and handling module; wherein the cargo sensitive feature calibration module, the transport environment perception module, the transport risk simulation module, and the early warning and handling module are connected in sequence;
[0027] The cargo sensitive feature calibration module is used to acquire cargo transportation data and generate electronic waybills. The electronic waybill contains cargo information and environmental sensitivity parameters corresponding to the cargo.
[0028] The transportation environment sensing module is used to collect environmental data in real time during the cargo transportation process.
[0029] The transportation risk simulation module is used to correlate and match environmental data with environmental sensitivity parameters in the electronic waybill to determine whether the current environment poses a risk to the goods.
[0030] The early warning and handling module is used to generate corresponding early warning information and implement management measures based on the risk level when it is determined that there is a risk.
[0031] This invention discloses a road freight transport management method and system based on electronic waybills. During the electronic waybill generation stage, it requires the configuration of complete environmental sensitivity parameters for the cargo, including temperature tolerance range, humidity tolerance range, maximum permissible vibration acceleration, maximum permissible tilt angle, and light sensitivity level. This provides a clear judgment benchmark for accurate environmental monitoring during subsequent transportation, overcoming the deficiency of existing electronic waybills that do not distinguish between the environmental sensitivity of the cargo. This invention not only performs threshold judgments on single environmental data but also constructs a multi-factor coupled risk assessment model. It weights and couples multiple environmental factors such as temperature deviation, humidity deviation, vibration intensity, and tilt angle to calculate a comprehensive risk coefficient. When the comprehensive risk coefficient exceeds a preset coupled risk threshold, it determines that there is a risk of multi-factor coupled influence, effectively overcoming the problem of missed or false alarms that may be caused by single-factor threshold judgment, and significantly improving the accuracy of risk identification. This invention can deeply bind electronic waybills with the environmental sensitivity of the cargo during road freight transport management, realizing multi-factor coupled risk assessment, hierarchical early warning, and proactive management. Attached Figure Description
[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.
[0033] Figure 1 This is a flowchart of a road freight transport management method based on electronic waybills according to the present invention.
[0034] Figure 2 This is a schematic diagram of the structure of a road freight transport management system based on electronic waybills according to the present invention.
[0035] 101-Cargo Sensitive Feature Identification Module, 102-Transportation Environment Perception Module, 103-Transportation Risk Simulation Module, 104-Early Warning and Response Module. Detailed Implementation
[0036] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.
[0037] Firstly, please refer to Figure 1 This invention provides a road freight transport management method based on electronic waybills, comprising:
[0038] S1 acquires freight data for cargo transportation and generates an electronic waybill, which contains cargo information and environmental sensitivity parameters corresponding to the cargo.
[0039] The environmental sensitivity parameters include at least one of the following: temperature tolerance range, humidity tolerance range, maximum permissible vibration acceleration, maximum permissible tilt angle, and light sensitivity level.
[0040] In this embodiment of the invention, freight data for cargo transportation is acquired, and an electronic waybill is generated. The freight data includes at least consignor information, consignee information, cargo name, cargo weight, and origin and destination information. When generating the electronic waybill, environmental sensitivity parameters for the cargo are automatically matched based on the cargo name or manually set by the consignor. The environmental sensitivity parameters include temperature tolerance range and its upper and lower limits, humidity tolerance range and its upper and lower limits, maximum permissible vibration acceleration, maximum permissible tilt angle, and light sensitivity level. The environmental sensitivity parameters are written into the electronic waybill along with the regular freight data and bound to the unique identification code of the electronic waybill. The electronic waybill containing complete environmental sensitivity parameters is stored in a cloud database, forming the environmental constraint set for this transportation task.
[0041] S2 collects environmental data in real time during cargo transportation;
[0042] In this embodiment of the invention, temperature and humidity sensors and light sensors are deployed inside the cargo compartment of the transport vehicle, and three-axis accelerometers and gyroscopes are deployed on the cargo packaging or carrier brackets. GPS or Beidou positioning modules are installed on the transport vehicle. During loading, the driver displays the verification QR code generated by the electronic waybill through the vehicle terminal or mobile terminal. After the loading party scans the verification QR code, the consistency of the personnel, vehicle, cargo, equipment and waybill information is verified online. After the verification is passed, all sensor terminals are activated to start collecting data. The sensor terminals continuously collect temperature data, humidity data, light intensity data, and vibration acceleration data and tilt angle data of the cargo in the compartment at a sampling frequency of seconds or minutes. At the same time, the GPS or Beidou positioning module continuously records the real-time location data and timestamp data of the vehicle. All collected environmental data are uploaded to the cloud platform in real time through 4G, 5G or NB-IoT communication networks.
[0043] S3 correlates and matches environmental data with environmental sensitivity parameters in the electronic waybill to determine whether the current environment poses a risk to the goods.
[0044] Specifically, it includes:
[0045] S31 will associate and match the real-time collected environmental data with the environmental sensitivity parameters bound in the electronic waybill;
[0046] S32 compares the real-time collected environmental data item by item with the corresponding threshold in the environmental sensitivity parameters. If any environmental data exceeds the corresponding tolerance threshold, it is determined that there is a risk of single-factor influence.
[0047] Specifically, this includes: comparing real-time temperature data with the upper and lower limits of the temperature tolerance range one by one; comparing real-time humidity data with the upper and lower limits of the humidity tolerance range one by one; comparing real-time vibration acceleration data with the maximum permissible vibration acceleration; comparing real-time tilt angle data with the maximum permissible tilt angle; and comparing real-time light intensity data with the light sensitivity level. If any environmental data exceeds the corresponding tolerance threshold, it is determined that there is a risk of single-factor influence.
[0048] S33 calculates a comprehensive risk coefficient by weighted coupling of at least two environmental factors among temperature deviation, humidity deviation, vibration intensity, and tilt angle. When the comprehensive risk coefficient exceeds the preset coupling risk threshold, it is determined that there is a risk of multi-factor coupling influence.
[0049] S34 obtains the vehicle's current location and weather forecast data along the driving route through the meteorological data interface, and uses the prediction of the severe weather risks that the vehicle will encounter as an auxiliary basis for judgment.
[0050] Based on the combined results of single-factor threshold judgment, multi-factor coupled risk assessment, and external meteorological risk prediction, S35 determines the risk level of the current environment's impact on goods.
[0051] In this embodiment of the invention, after the cloud platform receives and stores various types of uploaded environmental data, it associates and matches the real-time collected environmental data with the environmental sensitivity parameters corresponding to the goods bound in the electronic waybill. First, it compares the real-time temperature data with the upper and lower limits of the temperature tolerance range; then, it compares the real-time humidity data with the upper and lower limits of the humidity tolerance range; finally, it compares the real-time vibration acceleration data with the maximum permissible vibration acceleration; then, it compares the real-time tilt angle data with the maximum permissible tilt angle; and finally, it compares the real-time light intensity data with the light sensitivity level. If any environmental data exceeds the corresponding tolerance threshold, it is determined that there is an impact. To assess the impact of environmental factors, a multi-factor coupled risk assessment model is constructed. This model weights and couples at least two environmental factors, including temperature deviation, humidity deviation, vibration intensity, and tilt angle, to calculate a comprehensive risk coefficient. When the comprehensive risk coefficient exceeds a preset coupled risk threshold, a risk of multi-factor coupled impact is determined. In addition, weather forecast data along the vehicle's current location and route is obtained through a meteorological data interface to predict the risk of severe weather that the vehicle will encounter, serving as an auxiliary basis for judgment. By combining the results of single-factor threshold judgment, multi-factor coupled risk assessment, and external meteorological risk prediction, the risk level of the current environment's impact on the cargo is determined.
[0052] When S4 determines that there is a risk, it generates corresponding early warning information based on the risk level and implements management measures.
[0053] In this step, the risk levels include Level 3, Level 2, and Level 1. Level 3 risk is a warning level triggered when environmental data is close to but does not exceed the tolerance threshold; Level 2 risk is an alarm level triggered when a single environmental data exceeds the tolerance threshold; and Level 1 risk is an emergency level triggered when multiple environmental factors exceed the standard simultaneously or the comprehensive risk coefficient reaches a high-risk level.
[0054] The management measures include:
[0055] For Level 3 risks, send voice prompts to the vehicle terminal;
[0056] For level 2 risks, a voice alarm is sent to the vehicle terminal, and alarm information is pushed to the dispatch center;
[0057] For Level 1 risks, an emergency voice alarm is sent to the vehicle terminal, emergency response instructions are pushed to the dispatch center, and alternative transportation routes are automatically generated.
[0058] In this embodiment of the invention, based on the risk level determined in step S3, graded early warning and management measures corresponding to that risk level are implemented. The risk levels include Level 3 risk, Level 2 risk, and Level 1 risk. Level 3 risk is a warning level triggered when environmental data approaches but has not yet exceeded the tolerance threshold. Level 2 risk is an alarm level triggered when a single environmental data point exceeds the tolerance threshold or when low-level coupled risks occur. Level 1 risk is an emergency level triggered when multiple environmental factors simultaneously exceed the standard, the comprehensive risk coefficient reaches a high-risk level, or extreme weather is imminent. For Level 3 risk, a warning message is sent to the driver via the voice broadcast module of the vehicle terminal, reminding the driver to pay attention to changes in the cabin environment and recording the warning event on the cloud platform. For Level 2 risk, a voice alarm is sent to the driver via the vehicle terminal, and simultaneously, an alarm message containing the type of exceeding environmental parameter, its current value, and the tolerance threshold is pushed to the dispatch center. If the exceeding environmental... If the parameter is temperature and the onboard temperature control device has an automatic adjustment function, the onboard temperature control device will automatically adjust the temperature inside the vehicle. The cloud platform records the alarm event and the handling measures. For Level 1 risks, an emergency voice alarm is sent to the driver through the onboard terminal, and emergency handling suggestions are pushed. At the same time, an emergency handling instruction is pushed to the dispatch center. The system automatically generates one or more alternative transportation routes to avoid adverse environmental factors based on the vehicle's current location, the origin and destination information in the electronic waybill, and the weather data along the route for dispatchers or drivers to choose from. If the transported goods are hazardous chemicals, the emergency response mechanism is automatically triggered, and the vehicle location data, cargo information, and emergency handling plan are shared with the fire and emergency management departments. The cloud platform records the emergency event, the handling instruction, and the final management measures taken. All risk level warning information, handling instructions, and management measures are written to the data record corresponding to the electronic waybill in real time to form a complete management closed loop.
[0059] This invention provides a road freight transport management method based on electronic waybills. During the electronic waybill generation stage, it requires configuring complete environmental sensitivity parameters for the cargo, including temperature tolerance range, humidity tolerance range, maximum permissible vibration acceleration, maximum permissible tilt angle, and light sensitivity level. This provides a clear judgment benchmark for accurate environmental monitoring during subsequent transportation, overcoming the deficiency of existing electronic waybills that do not distinguish between the environmental sensitivity of the cargo. This invention not only performs threshold judgments on single environmental data but also constructs a multi-factor coupled risk assessment model. It weights and couples multiple environmental factors such as temperature deviation, humidity deviation, vibration intensity, and tilt angle to calculate a comprehensive risk coefficient. When the comprehensive risk coefficient exceeds a preset coupled risk threshold, it is determined that there is a risk of multi-factor coupled influence, effectively overcoming the problem of missed or false alarms that may be caused by single-factor threshold judgment, and significantly improving the accuracy of risk identification. This invention can deeply bind electronic waybills with the environmental sensitivity of the cargo during road freight transport management, realizing multi-factor coupled risk assessment, hierarchical early warning, and proactive management.
[0060] Secondly, please refer to Figure 2 The present invention also provides a road freight transport management system based on electronic waybills, comprising: a cargo sensitive feature calibration module 101, a transport environment perception module 102, a transport risk simulation module 103, and an early warning and handling module 104; the cargo sensitive feature calibration module 101, the transport environment perception module 102, the transport risk simulation module 103, and the early warning and handling module 104 are connected in sequence.
[0061] The cargo sensitive feature calibration module 101 is used to acquire cargo transportation data and generate electronic waybills. The electronic waybill contains cargo information and environmental sensitivity parameters corresponding to the cargo.
[0062] The transportation environment sensing module 102 is used to collect environmental data in real time during the cargo transportation process.
[0063] The transportation risk simulation module 103 is used to correlate and match environmental data with environmental sensitivity parameters in the electronic waybill to determine whether the current environment poses a risk to the goods.
[0064] The early warning and handling module 104 is used to generate corresponding early warning information and implement management measures according to the risk level when it is determined that there is a risk of impact.
[0065] This invention discloses a road freight transport management system based on electronic waybills. The system acquires freight transport data through the freight sensitive feature calibration module 101 and generates electronic waybills. The freight data includes at least consignor information, consignee information, freight name, freight weight, and origin and destination information. When generating the electronic waybill, environmental sensitivity parameters are automatically matched based on the freight name or manually set by the consignor. These environmental sensitivity parameters include temperature tolerance range and its upper and lower limits, humidity tolerance range and its upper and lower limits, maximum permissible vibration acceleration, maximum permissible tilt angle, and light sensitivity level. These environmental sensitivity parameters are written into the electronic waybill along with the regular freight data and bound to the electronic waybill's unique identification code. The electronic waybill containing complete environmental sensitivity parameters is stored in a cloud database, forming the environmental constraint set for this transport task.
[0066] The transportation environment sensing module 102 collects environmental data in real time during the cargo transportation process, including temperature data, humidity data, light intensity data inside the carriage, vibration acceleration data and tilt angle data of the cargo, as well as real-time location data and timestamp data of the vehicle.
[0067] The transportation risk simulation module 103 correlates and matches the real-time collected environmental data with the environmental sensitivity parameters corresponding to the goods bound in the electronic waybill. First, it compares the real-time temperature data with the upper and lower limits of the temperature tolerance range; then it compares the real-time humidity data with the upper and lower limits of the humidity tolerance range; finally, it compares the real-time vibration acceleration data with the maximum permissible vibration acceleration; then, it compares the real-time tilt angle data with the maximum permissible tilt angle; and finally, it compares the real-time light intensity data with the light sensitivity level. If any environmental data exceeds the corresponding tolerance threshold, it is determined that there is an impact risk. Simultaneously, through... The constructed multi-factor coupled risk assessment model calculates a comprehensive risk coefficient by weighting and coupling at least two environmental factors from temperature deviation, humidity deviation, vibration intensity, and tilt angle. When the comprehensive risk coefficient exceeds a preset coupled risk threshold, a risk of multi-factor coupled impact is determined. Furthermore, weather forecast data along the vehicle's current location and route is obtained through a meteorological data interface to predict the risk of severe weather the vehicle will encounter, serving as an auxiliary basis for judgment. The system combines the single-factor threshold judgment results, the multi-factor coupled risk assessment results, and the external meteorological risk prediction results to determine the risk level of the current environment's impact on the cargo. Based on the determined risk level, the early warning and handling module 104 executes tiered early warning and management measures corresponding to that risk level. This system can deeply bind electronic waybills with the environmental sensitivity of cargo during road freight transport management, achieving multi-factor coupled risk assessment, tiered early warning, and proactive management.
[0068] The above-disclosed embodiments are merely one or more preferred embodiments of this application and should not be construed as limiting the scope of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments and equivalent changes made in accordance with the claims of this application still fall within the scope of this application.
Claims
1. A method for road freight transport management based on electronic waybills, characterized in that, include: Acquire freight data for cargo transportation and generate electronic waybills, which contain cargo information and environmental sensitivity parameters corresponding to the cargo. Real-time collection of environmental data during cargo transportation; By correlating and matching environmental data with environmental sensitivity parameters in electronic waybills, it can be determined whether the current environment poses a risk to the goods. When a risk is identified, corresponding early warning information is generated based on the risk level, and management measures are implemented.
2. The road freight transport management method based on electronic waybills as described in claim 1, characterized in that, The environmental sensitivity parameters include at least one of the following: temperature tolerance range, humidity tolerance range, maximum permissible vibration acceleration, maximum permissible tilt angle, and light sensitivity level.
3. The road freight transport management method based on electronic waybills as described in claim 2, characterized in that, The process involves correlating and matching environmental data with environmental sensitivity parameters in electronic waybills to determine whether the current environment poses a risk to the goods. Specifically, this includes: The real-time collected environmental data is correlated and matched with the environmental sensitivity parameters bound in the electronic waybill; The real-time collected environmental data is compared with the corresponding threshold in the environmental sensitivity parameters. If any environmental data exceeds the corresponding tolerance threshold, it is determined that there is a risk of single-factor impact. The comprehensive risk coefficient is calculated by weighted coupling of at least two environmental factors among temperature deviation, humidity deviation, vibration intensity and tilt angle. When the comprehensive risk coefficient exceeds the preset coupling risk threshold, it is determined that there is a risk of multi-factor coupling influence. By obtaining the vehicle's current location and weather forecast data along the driving route through the meteorological data interface, the risk of severe weather that the vehicle is about to encounter can be predicted as an auxiliary basis for judgment. By combining the results of single-factor threshold judgment, multi-factor coupled risk assessment, and external meteorological risk prediction, the risk level of the current environment's impact on goods is determined.
4. The road freight transport management method based on electronic waybills as described in claim 3, characterized in that, The real-time collected environmental data is compared item by item with the corresponding threshold in the environmental sensitivity parameters. If any environmental data exceeds the corresponding tolerance threshold, it is determined that there is a single-factor impact risk. Specifically, this includes: The real-time temperature data is compared one by one with the upper and lower limits of the temperature tolerance range; the real-time humidity data is compared one by one with the upper and lower limits of the humidity tolerance range; the real-time vibration acceleration data is compared with the maximum permissible vibration acceleration; the real-time tilt angle data is compared with the maximum permissible tilt angle; and the real-time light intensity data is compared with the light sensitivity level. If any environmental data exceeds the corresponding tolerance threshold, it is determined that there is a risk of single-factor influence.
5. The road freight transport management method based on electronic waybills as described in claim 4, characterized in that, When a risk is identified, the steps include generating corresponding early warning information based on the risk level and implementing management measures. The risk levels are divided into three levels: Level 3, Level 2, and Level 1. Level 3 risk is a warning level triggered when environmental data is close to but does not exceed the tolerance threshold; Level 2 risk is an alarm level triggered when a single environmental data exceeds the tolerance threshold; and Level 1 risk is an emergency level triggered when multiple environmental factors exceed the standard simultaneously or the comprehensive risk coefficient reaches the high-risk level.
6. The road freight transport management method based on electronic waybills as described in claim 5, characterized in that, When a risk is determined to exist, the steps of generating corresponding early warning information based on the risk level and implementing management measures include: For Level 3 risks, send voice prompts to the vehicle terminal; For level 2 risks, a voice alarm is sent to the vehicle terminal, and alarm information is pushed to the dispatch center; For Level 1 risks, an emergency voice alarm is sent to the vehicle terminal, emergency response instructions are pushed to the dispatch center, and alternative transportation routes are automatically generated.
7. A road freight transport management system based on electronic waybills, employing the road freight transport management method based on electronic waybills as described in any one of claims 1-6, characterized in that, It includes a cargo sensitive feature identification module, a transportation environment perception module, a transportation risk simulation module, and an early warning and handling module; the cargo sensitive feature identification module, the transportation environment perception module, the transportation risk simulation module, and the early warning and handling module are connected in sequence; The cargo sensitive feature calibration module is used to acquire cargo transportation data and generate electronic waybills. The electronic waybill contains cargo information and environmental sensitivity parameters corresponding to the cargo. The transportation environment sensing module is used to collect environmental data in real time during the cargo transportation process. The transportation risk simulation module is used to correlate and match environmental data with environmental sensitivity parameters in the electronic waybill to determine whether the current environment poses a risk to the goods. The early warning and handling module is used to generate corresponding early warning information and implement management measures based on the risk level when it is determined that there is a risk.