Goods transportation dynamic supervision system based on Internet of Things and block chain

Abstract

The invention discloses a cargo transportation dynamic supervision system based on the Internet of Things and a block chain, relates to the field of transportation dynamic supervision, realizes dynamic supervision and intelligent cooperation of a cargo transportation whole process through fusion of the Internet of Things and the block chain, and obtains multi-dimensional data such as a position, an environment and a vehicle state in real time through a sensor and a vehicle platform. A complete data set is constructed in combination with task information, a cargo storage risk model and a fuel early warning model are established based on environmental parameters, vibration data and historical fuel consumption, the cargo damage risk and the fuel shortage probability are quantitatively evaluated, the early warning reliability is improved, cooperative supervision is enabled through a block chain, and the early warning efficiency is improved. Real-time sharing and credible evidence storage of data of a shipper, a logistics party and a receiver are realized by adopting an alliance chain architecture, an information island is broken, a multi-party dynamic decision and dynamic optimization mechanism is supported, and transportation adjustment suggestions are automatically generated by analyzing residual routes, timeliness requirements and risk coefficients, so that efficient allocation of resources is promoted.

Description

Technical Field

[0001] This application relates to the field of dynamic monitoring of transportation, and in particular to a dynamic monitoring system for cargo transportation based on the Internet of Things and blockchain. Background Technology

[0002] In logistics and transportation, cargo transportation is characterized by high frequency, multiple batches, complex transportation routes, and high cargo value. Currently, logistics and transportation supervision mainly relies on traditional information management systems, which have many shortcomings.

[0003] Existing technologies, particularly the application of IoT technology, are not deep or comprehensive enough. Although simple positioning devices have been introduced in some stages, the ability to collect and integrate environmental data (such as temperature, humidity, vibration, etc.) and vehicle status data (such as speed, fuel consumption, fault warnings, etc.) during cargo transportation is limited, making it impossible to achieve comprehensive real-time monitoring of cargo transportation. On the other hand, the data sharing mechanism is imperfect. Data often exists in silos among the various participants involved in logistics transportation, such as shippers, logistics companies, and consignees. Information transmission is untimely and inaccurate, and the security and reliability of the data are difficult to guarantee, resulting in low regulatory efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic monitoring system for cargo transportation based on the Internet of Things and blockchain to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dynamic monitoring system for cargo transportation based on the Internet of Things and blockchain, comprising: Multidimensional data acquisition module: used to monitor and collect multidimensional data corresponding to the target vehicle, and obtain a multidimensional dataset corresponding to the target vehicle; Data analysis module: used to perform data analysis on the multi-dimensional dataset corresponding to the target vehicle to obtain the warning dataset of the target vehicle; Data communication and transmission module: used to transmit data between the multi-dimensional dataset and the early warning dataset of the target vehicle, and obtain the data transmission results corresponding to the blockchain; Collaborative supervision module: Used to conduct collaborative supervision based on the data transmission results corresponding to the blockchain, and obtain the collaborative supervision results corresponding to the blockchain.

[0006] In the preferred embodiment of this solution, the multidimensional data acquisition module is executed as follows: Data is monitored by preset sensors in the target vehicle to obtain the corresponding sensor dataset. The sensor dataset includes location data, environmental data and vehicle data. The location data refers to the vehicle's position coordinates. The environmental data includes the ambient temperature and humidity inside the vehicle. The vehicle data includes the vehicle's driving speed and vehicle vibration information. The vehicle vibration information includes the vehicle vibration amplitude and vehicle vibration frequency. The vehicle status information of the target vehicle is obtained through the vehicle control platform, including fuel consumption rate and remaining fuel. Based on the vehicle status information and sensor dataset corresponding to the target vehicle, a multi-dimensional dataset corresponding to the target vehicle is established. The establishment time point of the multi-dimensional dataset corresponding to the target vehicle is obtained and recorded as the data analysis time point corresponding to the target vehicle.

[0007] In the preferred embodiment of this solution, the data analysis module is executed as follows: Obtain the task information corresponding to the target vehicle, including the types of goods transported by the target vehicle, the quantity of each type of goods, and the standard transportation arrival time of the target vehicle. Obtain the transportation route, route distance, and destination corresponding to the target vehicle; The remaining route distance of the target vehicle at the time of data analysis is obtained by comparing and matching the location coordinates of the vehicle at the time of data analysis with the transportation route corresponding to the target vehicle. The remaining transportation time for the target vehicle is calculated by comparing the data analysis time point with the standard transportation arrival time point. The minimum average travel speed of the target vehicle on the remaining route is calculated based on the remaining transport time and remaining route distance of the target vehicle. Extract the storage temperature range, storage humidity range, safe vibration frequency range, and safe vibration amplitude range corresponding to various types of goods stored in the database, and match them with the various types of goods corresponding to the target vehicle to obtain the storage temperature range, storage humidity range, safe vibration frequency range, and safe vibration amplitude range corresponding to the various types of goods corresponding to the target vehicle. Extract the reference duration corresponding to each combination of driving speed and vehicle vibration information stored in the database, and filter according to the driving speed and vehicle vibration information of the target vehicle at the data analysis time point to obtain the reference duration corresponding to the target vehicle at the data analysis time point. Based on the reference duration and data analysis time point of the target vehicle, the reference time period of the target vehicle is obtained. Based on the reference time period of the target vehicle, the environmental data and vehicle vibration information of the target vehicle are divided and extracted to obtain the environmental temperature change curve, the environmental temperature change curve, the vehicle vibration amplitude change curve and the vehicle vibration frequency change curve of the reference time period of the target vehicle. Based on the ambient temperature change curve, vehicle vibration amplitude change curve and vehicle vibration frequency change curve of the reference time period corresponding to the target vehicle, the data is matched with the storage temperature range, storage humidity range, safe vibration frequency range and safe vibration amplitude range corresponding to the various types of goods of the target vehicle to obtain the abnormal proportion of ambient temperature, abnormal proportion of ambient temperature, abnormal proportion of vehicle vibration amplitude and abnormal proportion of vehicle vibration frequency of the reference time period corresponding to the various types of goods of the target vehicle. A cargo storage risk model is established based on the proportion of abnormal ambient temperature, abnormal vehicle vibration amplitude, and abnormal vehicle vibration frequency of various types of goods corresponding to the target vehicle during the reference time period. Data analysis is then performed based on the cargo storage risk model to obtain the cargo storage risk coefficients corresponding to various types of goods for the target vehicle. Based on the minimum average driving speed of the target vehicle on the remaining route, the historical vehicle data and historical vehicle status information corresponding to the target vehicle are filtered to obtain the historical time points that match the minimum average driving speed of the target vehicle on the remaining route and the corresponding fuel consumption rate of each historical time point. A time decay analysis model is established based on the historical time points that match the minimum average driving speed and the corresponding fuel consumption rate at each historical time point. Data analysis is performed through the time decay analysis model to obtain the effective fuel consumption rate corresponding to the minimum average driving speed. The minimum total fuel consumption for the target vehicle on the remaining route is calculated based on the effective fuel consumption rate corresponding to the minimum average driving speed and the remaining transportation time of the target vehicle. Fuel warning coefficient for target vehicle = minimum total fuel consumption for the remaining route for target vehicle ÷ remaining fuel for target vehicle at the data analysis time point; The fuel warning coefficient and cargo storage risk coefficient corresponding to the target vehicle are recorded as the warning dataset for the target vehicle.

[0008] In the preferred embodiment of this scheme, the data communication transmission module is executed as follows: A consortium blockchain architecture is established, comprising a shipper, a logistics company, a consignee, and a target vehicle. The multi-dimensional dataset and early warning dataset corresponding to the target vehicle are transmitted to the consortium. The shipper, logistics company, and consignee receive the multi-dimensional dataset and early warning dataset corresponding to the target vehicle. The multi-dimensional dataset and early warning dataset received by the shipper, logistics company, and consignee for each transport vehicle are statistically analyzed and recorded as the data transmission result corresponding to the blockchain.

[0009] In the preferred embodiment of this solution, the collaborative supervision module is implemented as follows: Obtain the delivery address of each recipient, match each recipient's delivery address with each destination, and obtain the recipient corresponding to each destination; Extract the cargo loss ratio corresponding to the storage risk coefficient of each cargo stored in the database. Filter the cargo based on the storage risk coefficient of each type of cargo corresponding to the target vehicle to obtain the cargo loss ratio of each type of cargo corresponding to the target vehicle. Calculate the cargo damage and intact quantity of each type of cargo corresponding to each transport vehicle based on the cargo type and quantity of each type of cargo corresponding to each transport vehicle. Statistically calculate the total cargo damage and intact quantity of each type of cargo corresponding to each consignee based on the destination of each transport vehicle. The fuel warning coefficient corresponding to each transport vehicle is compared with the preset fuel warning coefficient threshold. If the fuel warning coefficient corresponding to the transport vehicle is greater than or equal to the preset fuel warning coefficient threshold, it means that the transport vehicle cannot arrive on time and needs to be refueled. If the fuel warning coefficient corresponding to the transport vehicle is less than the preset fuel warning coefficient threshold, it means that the transport vehicle can arrive on time. Based on the destination corresponding to each transport vehicle, the transport vehicles that cannot arrive on time and the transport vehicles that can arrive are obtained for each consignee. Based on the damage and integrity of each type of goods corresponding to each transport vehicle for each consignee, the total integrity of each type of goods that can arrive on time and the total integrity of each type of goods that cannot arrive on time are obtained for each consignee. The total integrity of each type of goods that can arrive on time and the total integrity of each type of goods that cannot arrive on time are recorded as the receiving result of each consignee. The shipper resends the total damage amount corresponding to each type of goods to each consignee. The total damage amount corresponding to each type of goods sent to each consignee is recorded as the shipper's shipping demand. The transporter reallocates transport vehicles to meet the shipper's shipping demand. The transporter's reallocation of transport vehicles to meet the shipper's shipping demand is recorded as the transporter's transport coordination result. The shipper's shipping requirements, the receiving results of each recipient, and the transportation coordination results of the transporter are recorded to obtain the corresponding collaborative supervision results on the blockchain.

[0010] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves dynamic monitoring and intelligent collaboration of the entire cargo transportation process through the integration of the Internet of Things and blockchain. It acquires multi-dimensional data such as location, environment, and vehicle status in real time through sensors and vehicle platforms, and constructs a complete dataset by combining task information. At the same time, it establishes cargo storage risk model and fuel early warning model based on environmental parameters, vibration data and historical fuel consumption, quantitatively assesses cargo damage risk and fuel shortage probability, and improves early warning reliability. This invention facilitates collaborative supervision enabled by blockchain. It adopts a consortium blockchain architecture to achieve real-time data sharing and trusted evidence storage among shippers, logistics providers, and consignees, breaking down information silos, supporting dynamic decision-making by multiple parties, and dynamic optimization mechanisms. Furthermore, by analyzing remaining routes, time requirements, and risk coefficients, it automatically generates transportation adjustment suggestions to promote efficient resource allocation. Attached Figure Description

[0011] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of module connections in an embodiment of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Please see Figure 1 This invention provides a dynamic monitoring system for cargo transportation based on the Internet of Things and blockchain. The system includes a multi-dimensional data acquisition module, a data analysis module, a data communication and transmission module, and a collaborative monitoring module. The multi-dimensional data acquisition module is connected to the data analysis module, the data analysis module is connected to the data communication and transmission module, and the data communication and transmission module is connected to the collaborative supervision module. The multidimensional data acquisition module is used to monitor and collect multidimensional data corresponding to the target vehicle, and obtain the multidimensional dataset corresponding to the target vehicle. Furthermore, the specific execution method of the multidimensional data acquisition module is as follows: Data is monitored by preset sensors in the target vehicle to obtain the corresponding sensor dataset. The sensor dataset includes location data, environmental data and vehicle data. The location data refers to the vehicle's position coordinates. The environmental data includes the ambient temperature and humidity inside the vehicle. The vehicle data includes the vehicle's driving speed and vehicle vibration information. The vehicle vibration information includes the vehicle vibration amplitude and vehicle vibration frequency. The vehicle status information of the target vehicle is obtained through the vehicle control platform, including fuel consumption rate and remaining fuel. Based on the vehicle status information and sensor dataset corresponding to the target vehicle, a multi-dimensional dataset corresponding to the target vehicle is established. The establishment time point of the multi-dimensional dataset corresponding to the target vehicle is obtained and recorded as the data analysis time point corresponding to the target vehicle.

[0015] The data analysis module is used to perform data analysis on the multi-dimensional dataset corresponding to the target vehicle to obtain the warning dataset of the target vehicle; Furthermore, the specific execution method of the data analysis module is as follows: Obtain the task information corresponding to the target vehicle, including the types of goods transported by the target vehicle, the quantity of each type of goods, and the standard transportation arrival time of the target vehicle. Obtain the transportation route, route distance, and destination corresponding to the target vehicle; The remaining route distance of the target vehicle at the time of data analysis is obtained by comparing and matching the location coordinates of the vehicle at the time of data analysis with the transportation route corresponding to the target vehicle. The remaining transportation time for the target vehicle is calculated by comparing the data analysis time point with the standard transportation arrival time point. The minimum average travel speed of the target vehicle on the remaining route is calculated based on the remaining transport time and remaining route distance of the target vehicle. Extract the storage temperature range, storage humidity range, safe vibration frequency range, and safe vibration amplitude range corresponding to various types of goods stored in the database, and match them with the various types of goods corresponding to the target vehicle to obtain the storage temperature range, storage humidity range, safe vibration frequency range, and safe vibration amplitude range corresponding to the various types of goods corresponding to the target vehicle. Extract the reference duration corresponding to each combination of driving speed and vehicle vibration information stored in the database, and filter according to the driving speed and vehicle vibration information of the target vehicle at the data analysis time point to obtain the reference duration corresponding to the target vehicle at the data analysis time point. Based on the reference duration and data analysis time point of the target vehicle, the reference time period of the target vehicle is obtained. Based on the reference time period of the target vehicle, the environmental data and vehicle vibration information of the target vehicle are divided and extracted to obtain the environmental temperature change curve, the environmental temperature change curve, the vehicle vibration amplitude change curve and the vehicle vibration frequency change curve of the reference time period of the target vehicle. Based on the ambient temperature change curve, vehicle vibration amplitude change curve and vehicle vibration frequency change curve of the reference time period corresponding to the target vehicle, the data is matched with the storage temperature range, storage humidity range, safe vibration frequency range and safe vibration amplitude range corresponding to the various types of goods of the target vehicle to obtain the abnormal proportion of ambient temperature, abnormal proportion of ambient temperature, abnormal proportion of vehicle vibration amplitude and abnormal proportion of vehicle vibration frequency of the reference time period corresponding to the various types of goods of the target vehicle. A cargo storage risk model is established based on the proportion of abnormal ambient temperature, abnormal vehicle vibration amplitude, and abnormal vehicle vibration frequency of various types of goods corresponding to the target vehicle during the reference time period. Data analysis is then performed based on the cargo storage risk model to obtain the cargo storage risk coefficients corresponding to various types of goods for the target vehicle. It should be noted that the specific analysis process for obtaining the cargo storage risk coefficients for various types of goods corresponding to the target vehicle based on the cargo storage risk model is as follows: Take ambient temperature as an example; The overlap between ambient temperature and storage temperature at each time point in the ambient temperature change curve is analyzed. If the ambient temperature falls within the overlap range of storage temperature, the time point is recorded as the temperature overlap time point. If the ambient temperature does not fall within the overlap range of storage temperature, the time point is recorded as the temperature non-overlap time point. The proportion of abnormal ambient temperature = the total number of temperature non-overlap time points ÷ the total number of time points in the ambient temperature change curve. Similarly, the analysis yielded the proportion of abnormal ambient temperature, abnormal vehicle vibration amplitude, and abnormal vehicle vibration frequency for the target vehicle and the corresponding reference time period for this type of cargo. The statistics were obtained for the proportion of abnormal ambient temperature, abnormal vehicle vibration amplitude, and abnormal vehicle vibration frequency for the target vehicle and the corresponding types of goods during the reference time period. Take a single item as an example; Extract the influence weights of the environmental temperature abnormality ratio, environmental temperature abnormality ratio, vehicle vibration amplitude abnormality ratio, and vehicle vibration frequency abnormality ratio of the cargo in the cargo storage risk model, and label them as a, b, c, and d, respectively. The weights of the impact of the proportion of abnormal ambient temperature, the proportion of abnormal vehicle vibration amplitude, and the proportion of abnormal vehicle vibration frequency on the risk coefficient of cargo storage were obtained through the analysis of the results of multiple actual transportation tasks. The percentages of abnormal ambient temperature, abnormal vehicle vibration amplitude, and abnormal vehicle vibration frequency for the target vehicle corresponding to the same type of goods during the reference time period are marked as A, B, C, and D, respectively. The cargo storage risk coefficient for the target vehicle corresponding to this type of goods = A×a + B×b + C×c + D×d; Statistically obtain the cargo storage risk coefficients for various types of goods corresponding to the target vehicle; Based on the minimum average driving speed of the target vehicle on the remaining route, the historical vehicle data and historical vehicle status information corresponding to the target vehicle are filtered to obtain the historical time points that match the minimum average driving speed of the target vehicle on the remaining route and the corresponding fuel consumption rate of each historical time point. A time decay analysis model is established based on the historical time points that match the minimum average driving speed and the corresponding fuel consumption rate at each historical time point. Data analysis is performed through the time decay analysis model to obtain the effective fuel consumption rate corresponding to the minimum average driving speed. It should be noted that the specific analysis process for obtaining the effective fuel consumption rate corresponding to the lowest average driving speed through data analysis using the time decay analysis model is as follows: Extract the data influence weight set corresponding to each combination of time intervals stored in the time decay analysis model. The data influence weight set refers to the data influence weight corresponding to each interval length in the combination of time intervals. The longer the interval length, the lower the influence weight of the corresponding data. Time calculations are performed based on historical time points and data analysis time points that match the minimum average driving speed to obtain the interval duration corresponding to each historical time point. The interval duration corresponding to each historical time point is matched with the combination of interval time periods stored in the time decay analysis model to obtain the data influence weight set corresponding to the target vehicle. The data influence weight corresponding to each historical time point is obtained by matching the interval duration corresponding to each historical time point. The effective fuel consumption rate corresponding to the minimum average driving speed = the sum of the products of the data influence weights at each historical time point and the fuel consumption rate; The minimum total fuel consumption for the target vehicle on the remaining route is calculated based on the effective fuel consumption rate corresponding to the minimum average driving speed and the remaining transportation time of the target vehicle. Fuel warning coefficient for target vehicle = minimum total fuel consumption for the remaining route for target vehicle ÷ remaining fuel for target vehicle at the data analysis time point; The fuel warning coefficient and cargo storage risk coefficient corresponding to the target vehicle are recorded as the warning dataset for the target vehicle.

[0016] The data communication and transmission module is used to transmit data between the multi-dimensional dataset and the early warning dataset of the target vehicle, and obtain the data transmission results corresponding to the blockchain. Furthermore, the specific execution method of the data communication transmission module is as follows: A consortium blockchain architecture is established, comprising a shipper, a logistics company, a consignee, and a target vehicle. The multi-dimensional dataset and early warning dataset corresponding to the target vehicle are transmitted to the consortium. The shipper, logistics company, and consignee receive the multi-dimensional dataset and early warning dataset corresponding to the target vehicle. The multi-dimensional dataset and early warning dataset received by the shipper, logistics company, and consignee for each transport vehicle are statistically analyzed and recorded as the data transmission result corresponding to the blockchain.

[0017] The collaborative supervision module is used to conduct collaborative supervision based on the data transmission results corresponding to the blockchain, and obtain the collaborative supervision results corresponding to the blockchain.

[0018] Furthermore, the specific implementation method of the collaborative supervision module is as follows: Obtain the delivery address of each recipient, match each recipient's delivery address with each destination, and obtain the recipient corresponding to each destination; Extract the cargo loss ratio corresponding to the storage risk coefficient of each cargo stored in the database. Filter the cargo based on the storage risk coefficient of each type of cargo corresponding to the target vehicle to obtain the cargo loss ratio of each type of cargo corresponding to the target vehicle. Calculate the cargo damage and intact quantity of each type of cargo corresponding to each transport vehicle based on the cargo type and quantity of each type of cargo corresponding to each transport vehicle. Statistically calculate the total cargo damage and intact quantity of each type of cargo corresponding to each consignee based on the destination of each transport vehicle. The fuel warning coefficient corresponding to each transport vehicle is compared with the preset fuel warning coefficient threshold. If the fuel warning coefficient corresponding to the transport vehicle is greater than or equal to the preset fuel warning coefficient threshold, it means that the transport vehicle cannot arrive on time and needs to be refueled. If the fuel warning coefficient corresponding to the transport vehicle is less than the preset fuel warning coefficient threshold, it means that the transport vehicle can arrive on time. Based on the destination corresponding to each transport vehicle, the transport vehicles that cannot arrive on time and the transport vehicles that can arrive are obtained for each consignee. Based on the damage and integrity of each type of goods corresponding to each transport vehicle for each consignee, the total integrity of each type of goods that can arrive on time and the total integrity of each type of goods that cannot arrive on time are obtained for each consignee. The total integrity of each type of goods that can arrive on time and the total integrity of each type of goods that cannot arrive on time are recorded as the receiving result of each consignee. The shipper resends the total damage amount corresponding to each type of goods to each consignee. The total damage amount corresponding to each type of goods sent to each consignee is recorded as the shipper's shipping demand. The transporter reallocates transport vehicles to meet the shipper's shipping demand. The transporter's reallocation of transport vehicles to meet the shipper's shipping demand is recorded as the transporter's transport coordination result. The shipper's shipping requirements, the receiving results of each recipient, and the transportation coordination results of the transporter are recorded to obtain the corresponding collaborative supervision results on the blockchain.

[0019] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A dynamic monitoring system for cargo transportation based on the Internet of Things and blockchain, characterized in that: include: Multidimensional data acquisition module: used to monitor and collect multidimensional data corresponding to the target vehicle, and obtain a multidimensional dataset corresponding to the target vehicle; Data analysis module: used to perform data analysis on the multi-dimensional dataset corresponding to the target vehicle to obtain the warning dataset of the target vehicle; Data communication and transmission module: used to transmit data between the multi-dimensional dataset and the early warning dataset of the target vehicle, and obtain the data transmission results corresponding to the blockchain; Collaborative supervision module: Used to conduct collaborative supervision based on the data transmission results corresponding to the blockchain, and obtain the collaborative supervision results corresponding to the blockchain.

2. The dynamic monitoring system for cargo transportation based on the Internet of Things and blockchain as described in claim 1, characterized in that: The specific execution method of the multidimensional data acquisition module is as follows: Data is monitored by preset sensors in the target vehicle to obtain the corresponding sensor dataset. The sensor dataset includes location data, environmental data and vehicle data. The location data refers to the vehicle's position coordinates. The environmental data includes the ambient temperature and humidity inside the vehicle. The vehicle data includes the vehicle's driving speed and vehicle vibration information. The vehicle vibration information includes the vehicle vibration amplitude and vehicle vibration frequency. The vehicle status information of the target vehicle is obtained through the vehicle control platform, including fuel consumption rate and remaining fuel. Based on the vehicle status information and sensor dataset corresponding to the target vehicle, a multi-dimensional dataset corresponding to the target vehicle is established. The establishment time point of the multi-dimensional dataset corresponding to the target vehicle is obtained and recorded as the data analysis time point corresponding to the target vehicle.

3. The dynamic monitoring system for cargo transportation based on the Internet of Things and blockchain according to claim 2, characterized in that: The specific execution method of the data analysis module is as follows: Obtain the task information corresponding to the target vehicle, including the types of goods transported by the target vehicle, the quantity of each type of goods, and the standard transportation arrival time of the target vehicle. Obtain the transportation route, route distance, and destination corresponding to the target vehicle; The remaining route distance of the target vehicle at the time of data analysis is obtained by comparing and matching the location coordinates of the vehicle at the time of data analysis with the transportation route corresponding to the target vehicle. The remaining transportation time for the target vehicle is calculated by comparing the data analysis time point with the standard transportation arrival time point. The minimum average travel speed of the target vehicle on the remaining route is calculated based on the remaining transport time and remaining route distance of the target vehicle. Extract the storage temperature range, storage humidity range, safe vibration frequency range, and safe vibration amplitude range corresponding to various types of goods stored in the database, and match them with the various types of goods corresponding to the target vehicle to obtain the storage temperature range, storage humidity range, safe vibration frequency range, and safe vibration amplitude range corresponding to the various types of goods corresponding to the target vehicle. Extract the reference duration corresponding to each combination of driving speed and vehicle vibration information stored in the database, and filter according to the driving speed and vehicle vibration information of the target vehicle at the data analysis time point to obtain the reference duration corresponding to the target vehicle at the data analysis time point. Based on the reference duration and data analysis time point of the target vehicle, the reference time period of the target vehicle is obtained. Based on the reference time period of the target vehicle, the environmental data and vehicle vibration information of the target vehicle are divided and extracted to obtain the environmental temperature change curve, the environmental temperature change curve, the vehicle vibration amplitude change curve and the vehicle vibration frequency change curve of the reference time period of the target vehicle. Based on the ambient temperature change curve, vehicle vibration amplitude change curve and vehicle vibration frequency change curve of the reference time period corresponding to the target vehicle, the data is matched with the storage temperature range, storage humidity range, safe vibration frequency range and safe vibration amplitude range corresponding to the various types of goods of the target vehicle to obtain the abnormal proportion of ambient temperature, abnormal proportion of ambient temperature, abnormal proportion of vehicle vibration amplitude and abnormal proportion of vehicle vibration frequency of the reference time period corresponding to the various types of goods of the target vehicle. A cargo storage risk model is established based on the proportion of abnormal ambient temperature, abnormal vehicle vibration amplitude, and abnormal vehicle vibration frequency of various types of goods corresponding to the target vehicle during the reference time period. Data analysis is then performed based on the cargo storage risk model to obtain the cargo storage risk coefficients corresponding to various types of goods for the target vehicle. Based on the minimum average driving speed of the target vehicle on the remaining route, the historical vehicle data and historical vehicle status information corresponding to the target vehicle are filtered to obtain the historical time points that match the minimum average driving speed of the target vehicle on the remaining route and the corresponding fuel consumption rate of each historical time point. A time decay analysis model is established based on the historical time points that match the minimum average driving speed and the corresponding fuel consumption rate at each historical time point. Data analysis is performed through the time decay analysis model to obtain the effective fuel consumption rate corresponding to the minimum average driving speed. The minimum total fuel consumption for the target vehicle on the remaining route is calculated based on the effective fuel consumption rate corresponding to the minimum average driving speed and the remaining transportation time of the target vehicle. Fuel warning coefficient for target vehicle = minimum total fuel consumption for the remaining route for target vehicle ÷ remaining fuel for target vehicle at the data analysis time point; The fuel warning coefficient and cargo storage risk coefficient corresponding to the target vehicle are recorded as the warning dataset for the target vehicle.

4. The dynamic monitoring system for cargo transportation based on the Internet of Things and blockchain according to claim 3, characterized in that: The specific execution method of the data communication transmission module is as follows: A consortium blockchain architecture is established, comprising a shipper, a logistics company, a consignee, and a target vehicle. The multi-dimensional dataset and early warning dataset corresponding to the target vehicle are transmitted to the consortium. The shipper, logistics company, and consignee receive the multi-dimensional dataset and early warning dataset corresponding to the target vehicle. The multi-dimensional dataset and early warning dataset received by the shipper, logistics company, and consignee for each transport vehicle are statistically analyzed and recorded as the data transmission result corresponding to the blockchain.

5. The dynamic monitoring system for cargo transportation based on the Internet of Things and blockchain according to claim 4, characterized in that: The specific execution method of the collaborative supervision module is as follows: Obtain the delivery address of each recipient, match each recipient's delivery address with each destination, and obtain the recipient corresponding to each destination; Extract the cargo loss ratio corresponding to the storage risk coefficient of each cargo stored in the database. Filter the cargo based on the storage risk coefficient of each type of cargo corresponding to the target vehicle to obtain the cargo loss ratio of each type of cargo corresponding to the target vehicle. Calculate the cargo damage and intact quantity of each type of cargo corresponding to each transport vehicle based on the cargo type and quantity of each type of cargo corresponding to each transport vehicle. Statistically calculate the total cargo damage and intact quantity of each type of cargo corresponding to each consignee based on the destination of each transport vehicle. The fuel warning coefficient corresponding to each transport vehicle is compared with the preset fuel warning coefficient threshold. If the fuel warning coefficient corresponding to the transport vehicle is greater than or equal to the preset fuel warning coefficient threshold, it means that the transport vehicle cannot arrive on time and needs to be refueled. If the fuel warning coefficient corresponding to the transport vehicle is less than the preset fuel warning coefficient threshold, it means that the transport vehicle can arrive on time. Based on the destination corresponding to each transport vehicle, the transport vehicles that cannot arrive on time and the transport vehicles that can arrive are obtained for each consignee. Based on the damage and integrity of each type of goods corresponding to each transport vehicle for each consignee, the total integrity of each type of goods that can arrive on time and the total integrity of each type of goods that cannot arrive on time are obtained for each consignee. The total integrity of each type of goods that can arrive on time and the total integrity of each type of goods that cannot arrive on time are recorded as the receiving result of each consignee. The shipper resends the total damage amount corresponding to each type of goods to each consignee. The total damage amount corresponding to each type of goods sent to each consignee is recorded as the shipper's shipping demand. The transporter reallocates transport vehicles to meet the shipper's shipping demand. The transporter's reallocation of transport vehicles to meet the shipper's shipping demand is recorded as the transporter's transport coordination result. The shipper's shipping requirements, the receiving results of each recipient, and the transportation coordination results of the transporter are recorded to obtain the corresponding collaborative supervision results on the blockchain.

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