Logistics transportation optimization method and system based on Internet of Things

Abstract

The invention relates to the technical field, and discloses a logistics transportation optimization method and system based on the Internet of Things, and the method comprises the steps: collecting data during transportation in real time through Internet of Things equipment; integrating the collected data by using a deep learning algorithm, constructing a multi-dimensional prediction model, and predicting future transportation demands; according to a data analysis result, optimizing a transportation route and arrangement in real time; an RFID tag and a sensor are used for tracking the whole transportation process of goods in real time, and a state report is automatically generated and fed back to a client and a management center in time; a collaborative distribution network based on the Internet of Things is established, information and resources are shared among logistics companies, and reasonable distribution of transportation tasks and efficient utilization of the resources are realized. According to the invention, the purposes of intelligence and comprehensive optimization can be realized in the logistics transportation industry, the problems of information islands and resource waste in traditional logistics management are effectively solved, and powerful support is provided for promoting sustainable development of the logistics industry.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics and transportation management and optimization, and in particular to a logistics and transportation optimization method and system based on the Internet of Things. Background Art

[0002] With the rapid development of the global economy, the logistics and transportation industry has gradually evolved into an indispensable part of the modern economic system. The rise of Internet of Things (IoT) technology has provided new possibilities for the optimization of logistics and transportation. In recent years, the widespread use of IoT devices has made data collection in the transportation process more efficient. Through the GPS positioning system and various sensors installed on the transport vehicles, logistics companies can obtain key information in the transportation process in real time, including cargo location, environmental conditions, vehicle performance, etc. The real-time transmission and analysis of this data has greatly improved the transparency and controllability of the transportation process. In addition, the introduction of advanced technologies such as deep learning and big data analysis has also provided technical support for logistics demand forecasting and transportation scheduling optimization, and promoted the development of intelligent logistics.

[0003] However, the existing Internet of Things technology still has many shortcomings in logistics and transportation optimization. First, although the timeliness of data collection has been improved, the lack of effective data integration and analysis mechanisms has led to low data utilization efficiency in many cases. For example, when using traditional analysis methods, logistics companies often find it difficult to fully consider the interactive effects of climate, traffic conditions and cargo characteristics, which limits the accuracy of demand forecasting and the effectiveness of transportation route optimization. In addition, in the case of collaborative distribution by multiple logistics companies, the lack of standardized information sharing protocols and smart contract mechanisms leads to opacity and inefficiency in resource utilization and information transmission among all parties. In summary, the existing technologies have certain limitations in dynamic route optimization, accurate demand forecasting, and collaborative operations between logistics companies, which is obviously insufficient for achieving an efficient logistics and transportation system. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for optimizing logistics and transportation based on the Internet of Things, which can improve the efficiency and transparency of the entire transportation process through real-time data collection, in-depth analysis models and collaborative distribution networks. Compared with the prior art, the present invention effectively integrates various types of relevant data by building a multi-dimensional prediction model, significantly improves the accuracy of demand prediction, and thus lays the foundation for real-time optimization of transportation routes.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a logistics transportation optimization method based on the Internet of Things, comprising: collecting data in transportation in real time through Internet of Things devices; integrating the collected data using deep learning algorithms, building a multi-dimensional prediction model, and predicting future transportation needs; optimizing transportation routes and arrangements in real time based on data analysis results; using RFID tags and sensors to track the entire transportation process of goods in real time, and automatically generating status reports, and providing timely feedback to customers and management centers; establishing a collaborative distribution network based on the Internet of Things, sharing information and resources among logistics companies, and realizing reasonable allocation of transportation tasks and efficient use of resources.

[0007] As a preferred solution of the method for optimizing logistics and transportation based on the Internet of Things described in the present invention, the real-time collection of data during transportation includes installing a GPS positioning device and an environmental sensor on the transportation vehicle, interconnecting the sensor with the engine control system of the vehicle, and transmitting the data to the cloud in real time;

[0008] The GPS module obtains vehicle location information, driving speed, and road condition information in real time; the environmental sensor collects climate data, including real-time weather information from a third-party weather service API; and the sensor monitors the status of the cargo and the performance of the vehicle.

[0009] As a preferred solution of the method for optimizing logistics and transportation based on the Internet of Things described in the present invention, the method of integrating the collected data using a deep learning algorithm includes storing the data in a data warehouse, using an ETL tool to clean and organize the data, adding a weight factor during the integration process, defining the importance of different data sources, and giving a higher weight to the transportation data of important customers based on the priority of historical orders;

[0010] Use the real-time weather API to obtain weather forecast data for the area, and combine it with historical weather data to form a complete time series weather data set, including variables such as temperature, precipitation, humidity and wind speed; introduce weather influencing factors to consider the impact of weather factors on different types of goods. For perishable goods, when the temperature exceeds the threshold, a specific adjustment coefficient is added;

[0011] By analyzing traffic flow, historical accident data and peak-hour traffic information, and using spatiotemporal data analysis methods, we can obtain traffic flow characteristics for specific time periods and areas.

[0012] As a preferred solution of the method for optimizing logistics and transportation based on the Internet of Things described in the present invention, wherein: the construction of the multi-dimensional prediction model includes using an improved recurrent neural network to capture time series patterns, the input layer receives feature vectors, the hidden layer includes LSTM units for capturing time dependencies, and the output layer generates future transportation demand forecasts;

[0013] Use the labeled dataset for training to minimize the loss function between predicted demand and actual demand:

[0014]

[0015] Where N is the total number of samples, is the demand predicted by the model, Y t+1 Forecasting future transportation demand.

[0016] As a preferred solution of the method for optimizing logistics and transportation based on the Internet of Things described in the present invention, the real-time optimization of transportation routes and arrangements includes predicting future demand based on the above model, determining upcoming transportation tasks, and using a dynamic programming algorithm to optimize transportation routes based on real-time traffic and weather data:

[0017]

[0018] Among them, C opt is the optimized total transportation cost, D ij is the distance from point i to point j, T ij is the estimated transportation time from point i to point j, C T is the weight of time cost, and n is the total number of transportation tasks.

[0019] As a preferred solution of the method for optimizing logistics and transportation based on the Internet of Things described in the present invention, the real-time tracking of the entire transportation process of the goods includes that each of the goods is affixed with a unique RFID tag before leaving the warehouse, which contains basic information of the goods, and the environmental conditions of the goods are monitored in real time through the installed sensors;

[0020] RFID readers read RFID tag information in real time, and sensors send environmental data to the data processing platform through wireless networks. Edge computing devices are set up at each transportation node to perform preliminary data processing. At each stage of transportation, edge computing devices will regularly send RFID and sensor data to the central processing platform, and automatically generate status reports based on real-time data during transportation;

[0021] Based on the status report, relevant personnel are automatically notified when anomalies are detected, via SMS, email or instant push notification. The management center's control panel visualizes the real-time status of all goods, including map views and environmental monitoring data, to facilitate instant decision-making.

[0022] As a preferred solution of the method for optimizing logistics and transportation based on the Internet of Things described in the present invention, the establishment of a collaborative distribution network based on the Internet of Things includes that each participating logistics company needs to install Internet of Things equipment, and through a standardized data interface, the systems of different companies can communicate with each other, and through an open API, the consistency of data format and protocol is ensured;

[0023] Establish smart contracts to stipulate the rights and obligations of all parties in collaborative distribution. Whenever a transportation task is generated or the status is updated, the logistics information will be uploaded to the cloud platform in real time through IoT devices and automatically broadcast to all participating logistics companies to dynamically allocate transportation tasks;

[0024] After the logistics company receives an order from a customer, it publishes the transportation task information through the platform and automatically recommends the logistics company or resource that is most suitable for performing the task. The execution status of each delivery task needs to be recorded and fed back to the cloud platform, including the actual arrival time, transportation conditions, and abnormal situations; after the transportation is completed, the performance of all parties is evaluated, including on-time delivery rate, loss rate and customer satisfaction.

[0025] As a preferred solution of the logistics and transportation optimization system based on the Internet of Things described in the present invention, it includes:

[0026] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of a logistics transportation optimization method based on the Internet of Things.

[0027] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the computer program implements the steps of a logistics transportation optimization method based on the Internet of Things.

[0028] Beneficial effects of the present invention: The present invention effectively integrates various types of relevant data by constructing a multi-dimensional prediction model, significantly improving the accuracy of demand prediction, thereby laying the foundation for real-time optimization of transportation routes. At the same time, the use of RFID technology and data processing platform for full-process cargo tracking helps to achieve remote monitoring and rapid response to abnormalities. In addition, the establishment of a collaborative network of logistics companies based on the Internet of Things ensures information sharing and reasonable allocation of resources among all parties, which can effectively improve overall transportation efficiency, reduce empty driving rate, and thus improve customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0030] Figure 1 A schematic flow chart of a method for optimizing logistics and transportation based on the Internet of Things is provided for one embodiment of the present invention.

[0031] Figure 2 A schematic diagram of working modules of a logistics and transportation optimization system based on the Internet of Things is provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0035] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0036] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0037] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0038] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a logistics transportation optimization method based on the Internet of Things, comprising:

[0039] S1: Collect data in real time during transportation through IoT devices, use deep learning algorithms to integrate the collected data, build a multi-dimensional prediction model, and predict future transportation needs.

[0040] Going a step further, GPS positioning devices and environmental sensors are installed on transport vehicles, so that the sensors are interconnected with the vehicle’s engine control system and transmit data to the cloud in real time;

[0041] The GPS module obtains vehicle location information, driving speed, and road condition information in real time; the environmental sensor collects climate data, including real-time weather information from a third-party weather service API; and the sensor monitors the status of the cargo and the performance of the vehicle.

[0042] It should be noted that the data is stored in a data warehouse, and ETL tools are used to clean and organize the data. During the integration process, weight factors are added to define the importance of different data sources. Based on the priority of historical orders, higher weight is given to the shipping data of important customers;

[0043] Use the real-time weather API to obtain weather forecast data for the area, and combine it with historical weather data to form a complete time series weather data set, including variables such as temperature, precipitation, humidity and wind speed; introduce weather influencing factors to consider the impact of weather factors on different types of goods. For perishable goods, when the temperature exceeds the threshold, a specific adjustment coefficient is added;

[0044] By analyzing traffic flow, historical accident data and peak-hour traffic information, and using spatiotemporal data analysis methods, we can obtain traffic flow characteristics for specific time periods and areas.

[0045] Furthermore, the constructing of the multi-dimensional forecasting model includes using an improved recurrent neural network to capture time series patterns, wherein an input layer receives feature vectors, a hidden layer includes LSTM units for capturing time dependencies, and an output layer generates future transportation demand forecasts;

[0046] Use the labeled dataset for training to minimize the loss function between predicted demand and actual demand:

[0047]

[0048] Where N is the total number of samples, is the demand predicted by the model, Y t+1 Forecasting future transportation demand.

[0049] S2: Optimize transportation routes and arrangements in real time based on data analysis results.

[0050] Furthermore, the above model is used to predict future demand, determine upcoming transportation tasks, and use dynamic programming algorithms to optimize transportation routes based on real-time traffic and weather data:

[0051]

[0052] Among them, C opt is the optimized total transportation cost, D ij is the distance from point i to point j, T ij is the estimated transportation time from point i to point j, C T is the weight of time cost, and n is the total number of transportation tasks.

[0053] According to the optimized routes and real-time available vehicles, the intelligent dispatching system can reasonably allocate transportation tasks:

[0054]

[0055] Among them, S ij is the assignment score of task i to vehicle j, C min is the minimum transportation cost benchmark, C vij is the availability score of vehicle j, β is the priority importance weight, A i is the priority of task i,

[0056] S3: Use RFID tags and sensors to track the entire transportation process of goods in real time, automatically generate status reports, and provide timely feedback to customers and management centers.

[0057] Furthermore, each cargo will be affixed with a unique RFID tag before leaving the warehouse, which contains basic information about the cargo and monitors the environmental conditions of the cargo in real time through installed sensors;

[0058] RFID readers read RFID tag information in real time, and sensors send environmental data to the data processing platform through wireless networks. Edge computing devices are set up at each transportation node to perform preliminary data processing. At each stage of transportation, edge computing devices will regularly send RFID and sensor data to the central processing platform, and automatically generate status reports based on real-time data during transportation;

[0059] Based on the status report, relevant personnel are automatically notified when anomalies are detected, via SMS, email or instant push notification. The management center's control panel visualizes the real-time status of all goods, including map views and environmental monitoring data, to facilitate instant decision-making.

[0060] It should be noted that according to changes in transportation tasks, the inventory level and the order of goods being put on the shelves are automatically adjusted to ensure the efficiency of warehouse management.

[0061] The inventory adjustment formula is:

[0062]

[0063] Among them, I new and I old are the adjusted and original inventory levels, δ is the adjustment factor for demand fluctuations, and D forecast is the demand forecast value, D actual is the actual consumption demand, γ is the weight of asset impact, Asset k The asset status of the goods, and m is the number of goods types.

[0064] S4: Establish a collaborative distribution network based on the Internet of Things to share information and resources among logistics companies to achieve reasonable allocation of transportation tasks and efficient use of resources.

[0065] Furthermore, each participating logistics company needs to install IoT devices, enable different companies’ systems to communicate with each other through standardized data interfaces, and ensure consistency of data formats and protocols through open APIs;

[0066] Establish smart contracts to stipulate the rights and obligations of all parties in collaborative distribution. Whenever a transportation task is generated or the status is updated, the logistics information will be uploaded to the cloud platform in real time through IoT devices and automatically broadcast to all participating logistics companies to dynamically allocate transportation tasks;

[0067] After the logistics company receives an order from a customer, it publishes the transportation task information through the platform and automatically recommends the logistics company or resource that is most suitable for performing the task. The execution status of each delivery task needs to be recorded and fed back to the cloud platform, including the actual arrival time, transportation conditions, and abnormal situations; after the transportation is completed, the performance of all parties is evaluated, including on-time delivery rate, loss rate and customer satisfaction.

[0068] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that:

[0069] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0070] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0071] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0072] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0073] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a logistics transportation optimization system based on the Internet of Things, characterized by: comprising a data collection and transmission module 1, a data analysis and prediction module 2, a transportation optimization and scheduling module 3, a real-time tracking and feedback module 4, and a collaborative distribution and resource sharing module 5;

[0074] The data collection and transmission module 1 is responsible for collecting various data in the transportation process in real time through the Internet of Things devices;

[0075] The data analysis and prediction module 2 uses a deep learning algorithm to process and analyze the collected data, build a multi-dimensional prediction model, and predict future transportation demand and potential transportation bottlenecks;

[0076] The transport optimization and scheduling module 3 optimizes the transport routes and arrangements in real time based on the prediction results using dynamic programming and other optimization algorithms to ensure efficient transport;

[0077] The real-time tracking and feedback module 4 is responsible for real-time monitoring of the transportation process of the goods, providing accurate status reports through RFID and sensors to ensure the transparency and security of the transportation process;

[0078] The collaborative distribution and resource sharing module 5 establishes a collaborative distribution network based on the Internet of Things, and realizes efficient cooperation among logistics companies through information sharing and resource pooling.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A logistics transportation optimization method based on the Internet of Things, characterized by: include, Collect data in transit in real time through IoT devices; Use deep learning algorithms to integrate collected data and build multi-dimensional prediction models to predict future transportation demand; Optimize transportation routes and arrangements in real time based on data analysis results; Use RFID tags and sensors to track the entire transportation process of goods in real time, and automatically generate status reports and provide timely feedback to customers and management centers; Establish a collaborative distribution network based on the Internet of Things to share information and resources among logistics companies, and achieve rational allocation of transportation tasks and efficient use of resources.

2. The method for optimizing logistics and transportation based on the Internet of Things according to claim 1, characterized in that: The real-time collection of data during transportation includes installing GPS positioning equipment and environmental sensors on the transport vehicle, interconnecting the sensors with the vehicle's engine control system, and transmitting data to the cloud in real time; The GPS module obtains vehicle location information, driving speed, and road condition information in real time; the environmental sensor collects climate data, including real-time weather information from a third-party weather service API; and the sensor monitors the status of the cargo and the performance of the vehicle.

3. The method for optimizing logistics and transportation based on the Internet of Things as claimed in claim 2, characterized in that: The use of deep learning algorithms to integrate the collected data includes storing the data in a data warehouse, using ETL tools to clean and organize the data, adding weight factors during the integration process, defining the importance of different data sources, and giving higher weights to the shipping data of important customers based on the priority of historical orders; Use the real-time weather API to obtain weather forecast data for the area, and combine it with historical weather data to form a complete time series weather data set, including variables such as temperature, precipitation, humidity and wind speed; introduce weather influencing factors to consider the impact of weather factors on different types of goods. For perishable goods, when the temperature exceeds the threshold, a specific adjustment coefficient is added; By analyzing traffic flow, historical accident data and peak-hour traffic information, and using spatiotemporal data analysis methods, we can obtain traffic flow characteristics for specific time periods and areas.

4. The method for optimizing logistics and transportation based on the Internet of Things as claimed in claim 3, characterized in that: The multi-dimensional forecasting model is constructed by using an improved recurrent neural network to capture time series patterns, wherein the input layer receives feature vectors, the hidden layer includes LSTM units for capturing time dependencies, and the output layer generates future transportation demand forecasts; Use the labeled dataset for training to minimize the loss function between predicted demand and actual demand: Where N is the total number of samples, is the demand predicted by the model, Y t+1 Forecasting of future transportation demand.

5. The method for optimizing logistics and transportation based on the Internet of Things according to claim 4, characterized in that: The real-time optimization of transportation routes and arrangements includes predicting future demand based on the above model, determining upcoming transportation tasks, and using dynamic programming algorithms to optimize transportation routes based on real-time traffic and weather data: Among them, C opt is the optimized total transportation cost, D ij is the distance from point i to point j, T ij is the estimated transportation time from point i to point j, C T is the weight of time cost, and n is the total number of transportation tasks.

6. The method for optimizing logistics and transportation based on the Internet of Things as claimed in claim 5, characterized in that: The real-time tracking of the entire transportation process of goods includes that each item will be affixed with a unique RFID tag before leaving the warehouse, which contains basic information of the goods and monitors the environmental conditions of the goods in real time through installed sensors; RFID readers read RFID tag information in real time, and sensors send environmental data to the data processing platform through wireless networks. Edge computing devices are set up at each transportation node to perform preliminary data processing. At each stage of transportation, edge computing devices will regularly send RFID and sensor data to the central processing platform, and automatically generate status reports based on real-time data during transportation; Based on the status report, relevant personnel are automatically notified when anomalies are detected, via SMS, email or push notification. The management center's control panel visualizes the real-time status of all goods, including map views and environmental monitoring data, to facilitate instant decision-making.

7. The method for optimizing logistics and transportation based on the Internet of Things according to claim 6, characterized in that: The establishment of a collaborative distribution network based on the Internet of Things includes that each participating logistics company needs to install Internet of Things devices, enable the systems of different companies to communicate with each other through standardized data interfaces, and ensure the consistency of data formats and protocols through open APIs; Establish smart contracts to stipulate the rights and obligations of all parties in collaborative distribution. Whenever a transportation task is generated or the status is updated, the logistics information will be uploaded to the cloud platform in real time through IoT devices and automatically broadcast to all participating logistics companies to dynamically allocate transportation tasks; After the logistics company receives an order from a customer, it publishes the transportation task information through the platform and automatically recommends the logistics company or resource that is most suitable for performing the task. The execution status of each delivery task needs to be recorded and fed back to the cloud platform, including the actual arrival time, transportation conditions, and abnormal situations; after the transportation is completed, the performance of all parties is evaluated, including on-time delivery rate, loss rate and customer satisfaction.

8. A system using a logistics transportation optimization method based on the Internet of Things as claimed in any one of claims 1 to 7, characterized in that: It includes data collection and transmission module, data analysis and prediction module, transportation optimization and scheduling module, real-time tracking and feedback module, collaborative distribution and resource sharing module; The data collection and transmission module is responsible for collecting various data in the transportation process in real time through IoT devices; The data analysis and prediction module uses deep learning algorithms to process and analyze the collected data, build a multi-dimensional prediction model, and predict future transportation demand and potential transportation bottlenecks; The transportation optimization and scheduling module uses dynamic programming and other optimization algorithms to optimize transportation routes and arrangements in real time based on the prediction results to ensure efficient transportation; The real-time tracking and feedback module is responsible for real-time monitoring of the transportation process of goods, providing accurate status reports through RFID and sensors to ensure the transparency and security of the transportation process; The collaborative distribution and resource sharing module establishes a collaborative distribution network based on the Internet of Things, and realizes efficient cooperation among logistics companies through information sharing and resource pooling.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images