Logistics transportation management and real-time data sharing method and system

Abstract

The invention discloses a logistics transportation management and real-time data sharing method and system, and relates to the technical field of logistics transportation, and the method comprises the steps: S1, collecting logistics transportation data on a logistics transportation vehicle in real time; s2, preprocessing the collected real-time data by using an edge computing technology, encrypting the data through a wireless network, and transmitting the data to a cloud computing platform; s3, integrating and storing the real-time data by the cloud computing platform, and performing intelligent analysis by using a machine learning algorithm; s4, giving an optimization suggestion according to an analysis result, generating a report and updating the report in real time; and S5, providing a self-service platform to inquire the cargo state. According to the invention, the transparency of logistics transportation information can be enhanced, all parties can know the logistics transportation condition in time, the user experience is improved, the user credibility is enhanced, the enterprise cost of a carrier is reduced, and the benefit of enterprise management is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a logistics transportation management and real-time data sharing method and system thereof. Background Art

[0002] In the modern economy, logistics and transportation have become a vital link connecting production and consumption. With the rapid development of globalization and e-commerce, the demand for logistics and transportation is increasing, driving continuous innovation in the logistics industry.

[0003] Existing logistics and transportation systems have significant shortcomings in real-time data updates. Many traditional logistics management systems rely on manual record-keeping and periodic updates, resulting in untimely data updates. This delayed data update prevents shippers, consignees, and carriers from obtaining real-time information on the status of goods during transportation. For example, in the transportation of cold chain foods, it is crucial to understand the refrigeration status of cold chain foods during transportation and to determine whether any refrigeration failures could cause spoilage or damage.

[0004] Secondly, the existing logistics and transportation systems are often unable to provide comprehensive and accurate transportation data. During the transportation process, due to the lack of an effective data sharing mechanism, shippers and consignees are unable to timely understand the details of the logistics and transportation vehicles. The receiving warehouses and the receiving parties are unable to timely understand the delivery status of the goods and any emergencies encountered by the goods on the transportation route. They are unable to take timely response measures, which can easily aggravate the losses of all parties.

[0005] At the same time, due to the inability to grasp the transportation status in a timely manner, carrier companies often need to invest more manpower and material resources in tracking and management to deal with potential delays and risks. This not only increases management and operating costs, but may also lead to customer dissatisfaction with logistics services, thereby causing an increase in complaint rates.

[0006] Therefore, there is an urgent need for a new method of logistics and transportation management and real-time data sharing to improve the timeliness and comprehensiveness of data updates and enhance the visualization and control capabilities of all parties involved in the transportation process. Summary of the Invention

[0007] The purpose of the present invention is to provide a logistics transportation management and real-time data sharing method and system thereof to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solutions: a logistics and transportation management and real-time data sharing method and system thereof, comprising:

[0009] S1. Install IoT sensors on logistics transport vehicles to collect logistics transport data in real time;

[0010] S2. Use edge computing technology to pre-process the collected real-time data, and encrypt the processed real-time data and transmit it to the cloud computing platform via a wireless network;

[0011] S3, the cloud computing platform, integrates and stores the received real-time data and uses machine learning algorithms for intelligent analysis;

[0012] S4. Provide optimization suggestions based on the analysis results, generate reports and update them in real time;

[0013] S5. Provide a self-service platform that allows customers to check the status of goods in real time and supports SMS and mobile application push notifications.

[0014] Preferably, the logistics and transportation data collected in real time in S1 includes:

[0015] Vehicle location data: real-time monitoring of transport vehicle locations, understanding of cargo transportation progress, identifying transportation delays and timely adjustments to transportation plans;

[0016] Temperature and humidity data, real-time monitoring of changes in environmental conditions during cold chain transportation, understanding of the freshness of goods during transportation, identifying abnormal temperature and humidity during cold chain transportation and implementing timely pneumatic emergency plans;

[0017] Vibration status data: real-time monitoring of cargo vibration during transportation to identify potential damage risks;

[0018] Transportation time data, recording the entire transportation time from vehicle departure to arrival, and analyzing transportation efficiency;

[0019] Vehicle status data: real-time monitoring of the operating status of transport vehicles and timely understanding of backup plans for transport vehicle failures;

[0020] Traffic data: obtain real-time traffic conditions on transportation routes, predict possible delays, and optimize transportation routes in a timely manner.

[0021] Preferably, in S2, the preprocessing of real-time data includes:

[0022] Filter invalid data based on preset rule base;

[0023] Identify sensor device failures through sensor cross-checking;

[0024] Lightweight compression after adding timestamp and sensor device digital signature.

[0025] Preferably, in S2, the real-time data is encrypted and transmitted to the cloud computing platform via the wireless network as follows:

[0026] Select a symmetric encryption algorithm to generate a security key, encrypt the processed real-time data, and convert the data into an encrypted format;

[0027] The encrypted real-time data is sent to the cloud computing platform using a secure wireless network protocol.

[0028] Preferably, in S3, the cloud computing platform integrates and stores the received real-time data, including:

[0029] The cloud computing platform configuration API receives the encrypted data packet sent from the edge computing device;

[0030] After receiving the data packet, the cloud computing platform uses the pre-shared key to decrypt the encrypted data and obtain the original pre-processed data;

[0031] Convert the decrypted data into a unified format to meet the storage requirements of the cloud database, and perform structured processing to ensure that the data fields match the database table structure;

[0032] After validating, cleaning and deduplicating the data, it is stored in a cloud database;

[0033] Create indexes for stored data and differentiate data based on data volume and usage scenarios;

[0034] Integrate newly received data with existing data to update real-time data.

[0035] Preferably, in S3, the cloud computing platform integrates and stores the received real-time data and further includes:

[0036] Back up stored data regularly to prevent data loss;

[0037] Factual data access control and encryption storage strategies ensure the security of sensitive data.

[0038] Preferably, in S3, using a machine learning algorithm to perform intelligent analysis specifically includes:

[0039] Divide historical data into training sets and test sets, select the random forest regression algorithm to train the model on the selected training set, and adjust the hyperparameters to optimize the model performance;

[0040] Set evaluation metrics and evaluate the performance of the model on the test set;

[0041] Deploy the trained model to the cloud computing platform and receive new data in real time for prediction.

[0042] Preferably, in said S4, optimization suggestions are given according to the analysis results, and a report is generated and updated in real time, specifically: the intelligent analysis results of the machine learning algorithm are visualized, and a transportation status report, risk assessment and resource optimization suggestions are generated.

[0043] Preferably, in said S5, a self-service platform is provided to allow customers to check the status of goods in real time and support SMS and mobile application push notifications. Specifically:

[0044] Provide real-time query services to customers through mobile applications or web platforms, showing cargo status, congestion arrival time, and abnormal conditions;

[0045] Establish a customer feedback mechanism to collect customers' opinions and suggestions on logistics and transportation services, and regularly evaluate and optimize management processes.

[0046] In another aspect, the present invention further provides a logistics and transportation management and real-time data sharing system, which implements any one of the above-mentioned logistics and transportation management and real-time data sharing methods, including:

[0047] Data collection module, real-time collection of logistics and transportation data from logistics and transportation vehicles;

[0048] The edge computing module receives the real-time logistics and transportation data collected by the data acquisition module, pre-processes the collected real-time logistics and transportation data using edge computing technology, and then encrypts and transmits the processed real-time data through a wireless network;

[0049] The cloud computing platform module receives the real-time data encrypted by the edge computing module, decrypts, integrates and stores it, and uses machine learning algorithms for intelligent analysis;

[0050] The data analysis and reporting module generates optimization suggestions and reports based on the analysis results obtained by the machine learning algorithm in the cloud computing platform module, updates them in real time, and displays visual data;

[0051] The user service platform provides a self-service platform that allows customers to check the status of goods in real time and supports SMS and mobile application push notifications.

[0052] Compared with the prior art, the technical effects of the present invention are:

[0053] The present invention uses IoT sensors to collect a variety of transportation data from logistics and transportation vehicles in real time, and uses edge computing for real-time data processing and cloud computing platforms for integration, storage and intelligent analysis. It can provide optimization suggestions and generate real-time reports based on the logistics and transportation conditions, enhance information transparency, enable all parties to understand the logistics and transportation conditions in a timely manner, improve user experience, enhance user trust, while reducing the carrier's corporate costs and improving the efficiency of corporate management. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The figure is a flow chart of a method for logistics transportation management and real-time data sharing according to the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] The present invention provides Figure 1 A logistics transportation management and real-time data sharing method is shown, comprising:

[0057] S1. Install IoT sensors on logistics transport vehicles to collect logistics transport data in real time. The logistics transport data collected in real time by various on-board IoT sensors include vehicle location data, temperature and humidity data, vibration status data, transportation time data, vehicle status data and traffic data. Vehicle location data is used to monitor the location of transport vehicles in real time to ensure that the transportation route and status of the goods can be tracked, so as to identify transportation delays in a timely manner and adjust the transportation plan to improve transportation efficiency. At the same time, it can enhance the transparency of users to the goods and improve user satisfaction. Transportation time data records the entire transportation time from departure to arrival of the vehicle, which is convenient for enterprises to evaluate the efficiency of the transportation process and provide data support for subsequent enterprises to optimize transportation routes and resource allocation. At the same time, it is convenient for users to understand the transportation time and improve user satisfaction. Vehicle status data can monitor the operating status of the vehicle to ensure that the vehicle is in good condition, which is convenient for drivers and enterprises to carry out maintenance in a timely manner, extend the service life of the vehicle, and facilitate the judgment of whether the cargo delay is caused by vehicle failure, so that enterprises can respond to the corresponding emergency plan in time to remedy the situation, improve enterprise management efficiency, and help improve logistics efficiency and further. Improve customer trust in the company; traffic data can be used to understand the traffic conditions on the transportation route in real time based on navigation satellites, so as to timely adjust the transportation route to avoid congested sections, further improving the accuracy of logistics arrival time prediction, and also allowing users to know the transportation status of the goods and the reasons for delays, reducing user suspicion, gaining user understanding, and improving trust; other temperature, humidity and vibration status data can be selected and used according to the type of goods being transported. For cold chain foods and goods that require suitable temperature storage or fragile goods, temperature and humidity data can monitor changes in environmental conditions of cold chain foods during transportation, providing real-time data support for the cargo transportation environment, and at the same time facilitate enterprises to promptly understand changes in temperature and humidity in the cargo storage environment. When a fault causes changes in environmental conditions, emergency plans can be promptly activated to handle them (such as activating the nearest cold storage for temporary storage, calling the nearest cold chain transportation vehicle for relay, or promptly arranging for emergency repairs by the logistics maintenance department, etc.), thus ensuring the safety of cold chain foods to the greatest extent. At the same time, data support is provided to determine whether the goods are damaged due to changes in environmental conditions during transportation, avoiding "black box operations" and making the entire logistics transportation process more transparent;

[0058] S2. Use edge computing technology to pre-process the collected real-time data, and encrypt the processed real-time data through a wireless network and transmit it to the cloud computing platform; use edge computing devices deployed on the vehicle (such as edge gateways, small servers, etc.) to filter invalid data collected by sensors based on a preset rule base (such as GPS drift data in tunnels, etc.), and identify sensor failures through sensor cross-checking (for example, when a sensor uses three simultaneous monitoring, a self-check is triggered when the deviation of the monitoring data of the three sensors is greater than a preset value, and the sensor data with larger deviation is eliminated to ensure data accuracy), and add a timestamp and a digital signature of the sensor device for lightweight compression, thereby reducing data bandwidth occupancy and preventing data tampering to meet judicial evidence requirements; select a symmetric encryption algorithm to generate a security key for the lightweight compressed data, encrypt the processed real-time data, convert the data into an encrypted format, and use a secure wireless network protocol (such as LoRa, NB-IoT, 4G / 5G, etc.) to send the encrypted real-time data to the cloud computing platform;

[0059] S3. The cloud computing platform integrates and stores the received real-time data and uses machine learning algorithms for intelligent analysis. Specifically, the cloud computing platform configures an API to receive encrypted data packets sent from edge computing devices. After receiving the data packets, the cloud computing platform uses a pre-shared key to decrypt the encrypted data to obtain the original pre-processed data. The decrypted data is converted into a unified format to meet the storage requirements of the cloud database and structured to ensure that the data fields match the database table structure. The data is verified, cleaned, and deduplicated before being stored in the cloud database. An index is created for the stored data, and data is differentiated according to data volume and usage scenarios. Newly received data is integrated with existing data to update real-time data. In addition, the stored data can be backed up regularly and data access control and encrypted storage strategies can be implemented to prevent data loss while ensuring that only authorized personnel can access sensitive data.

[0060] S4. Provide optimization suggestions based on the analysis results, generate reports and update them in real time. Specifically, based on the analysis results obtained by the machine learning algorithm, generate transportation status reports, risk assessments and resource optimization suggestions, and display them visually (in the form of charts) to facilitate all parties to understand the logistics and transportation status in a comprehensive and easy-to-understand manner. This helps companies optimize transportation routes and allocate resources in a timely manner (for example, warehouse unloaders and couriers can promptly understand the time of unloading and pickup and delivery), helping company managers make more accurate decisions, improve corporate management efficiency, and reduce costs. At the same time, it also provides shippers and consignees with more transparent logistics and transportation, enhances customer trust, and reduces unnecessary disputes and complaints.

[0061] S5. Provide a self-service platform that allows customers to query the status of goods in real time and supports SMS and mobile application push notifications. Specifically, it can provide real-time query services to customers through mobile applications or web platforms, displaying the status of goods, congestion arrival time and abnormal conditions, and push messages in the form of SMS and mobile applications, so that users can understand them in a timely manner. By providing real-time information, it can reduce customers' anxiety about transportation status and improve customer satisfaction. At the same time, it can establish a customer feedback mechanism to collect customers' opinions and suggestions on logistics and transportation services, regularly evaluate and optimize management processes, sustainably improve the quality of logistics services, enhance customer satisfaction and loyalty, provide data support for future transportation management strategies, and promote the intelligent development of logistics systems.

[0062] In a preferred embodiment, in S3, the use of machine learning algorithms for intelligent analysis specifically includes: dividing historical data into training sets and test sets, selecting a random forest regression algorithm to train the model on the selected training set, adjusting hyperparameters to optimize model performance, and through model training, enabling it to learn patterns in the data, thereby making more accurate predictions on new data; setting evaluation indicators, evaluating the performance of the model on the test set, ensuring that the model's performance meets business requirements, and providing reliable prediction results; deploying the trained model to a cloud computing platform, receiving new data in real time for prediction, implementing data analysis, and providing timely decision support (such as adjusting transportation routes, warning of potential delays, etc.).

[0063] This embodiment also provides a logistics and transportation management and real-time data sharing system, which implements a logistics and transportation management and real-time data sharing method in the above embodiment, including a data acquisition module, an edge computing module, a cloud computing platform module, a data analysis and reporting module and a user service platform; the data acquisition module collects logistics and transportation data on logistics and transportation vehicles in real time; the edge computing module receives the real-time logistics and transportation data collected by the data acquisition module, uses edge computing technology to pre-process the collected real-time logistics and transportation data, and then encrypts and transmits the processed real-time data through a wireless network; the cloud computing platform module receives the real-time data encrypted by the edge computing module, decrypts, integrates and stores it, and uses a machine learning algorithm for intelligent analysis; the data analysis and reporting module generates optimization suggestions and reports based on the analysis results obtained by the machine learning algorithm in the cloud computing platform module, updates them in real time, and performs visual data display; the user service platform provides a self-service platform allowing customers to query the status of goods in real time, and supports SMS and mobile application push notifications.

[0064] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A logistics transportation management and real-time data sharing method, characterized in that: include: S1. Install IoT sensors on logistics transport vehicles to collect logistics transport data in real time; S2. Use edge computing technology to pre-process the collected real-time data, and encrypt the processed real-time data and transmit it to the cloud computing platform via a wireless network; S3, the cloud computing platform, integrates and stores the received real-time data and uses machine learning algorithms for intelligent analysis; S4. Provide optimization suggestions based on the analysis results, generate reports and update them in real time; S5. Provide a self-service platform that allows customers to check the status of goods in real time and supports SMS and mobile application push notifications.

2. A method for logistics and transportation management and real-time data sharing according to claim 1, characterized in that: The logistics and transportation data collected in real time in S1 include: Vehicle location data: real-time monitoring of transport vehicle locations, understanding of cargo transportation progress, identifying transportation delays and adjusting transportation plans in a timely manner; Temperature and humidity data, real-time monitoring of changes in environmental conditions during cold chain transportation, understanding of the freshness of goods during transportation, identifying abnormal temperature and humidity during cold chain transportation and implementing timely pneumatic emergency plans; Vibration status data: real-time monitoring of cargo vibration during transportation to identify potential damage risks; Transportation time data, recording the entire transportation time from vehicle departure to arrival, and analyzing transportation efficiency; Vehicle status data: real-time monitoring of the operating status of transport vehicles and timely understanding of backup plans for transport vehicle failures; Traffic data: obtain real-time traffic conditions on transportation routes, predict possible delays, and optimize transportation routes in a timely manner.

3. A method for logistics and transportation management and real-time data sharing according to claim 1, characterized in that: In S2, the preprocessing of real-time data includes: Filter invalid data based on preset rule base; Identify sensor device failures through sensor cross-checking; Lightweight compression after adding timestamp and sensor device digital signature.

4. A method for logistics and transportation management and real-time data sharing according to claim 1, characterized in that: In S2, the real-time data is encrypted and transmitted to the cloud computing platform via the wireless network as follows: Select a symmetric encryption algorithm to generate a security key, encrypt the processed real-time data, and convert the data into an encrypted format; The encrypted real-time data is sent to the cloud computing platform using a secure wireless network protocol.

5. A method for logistics and transportation management and real-time data sharing according to claim 1, characterized in that: In S3, the cloud computing platform integrates and stores the received real-time data, including: The cloud computing platform configuration API receives the encrypted data packet sent from the edge computing device; After receiving the data packet, the cloud computing platform uses the pre-shared key to decrypt the encrypted data and obtain the original pre-processed data; Convert the decrypted data into a unified format to meet the storage requirements of the cloud database, and perform structured processing to ensure that the data fields match the database table structure; After validating, cleaning and deduplicating the data, it is stored in a cloud database; Create indexes for stored data and differentiate data based on data volume and usage scenarios; Integrate newly received data with existing data to update real-time data.

6. A method for logistics and transportation management and real-time data sharing according to claim 5, characterized in that: In the S3, the cloud computing platform integrates and stores the received real-time data and further includes: Back up stored data regularly to prevent data loss; Factual data access control and encryption storage strategies ensure the security of sensitive data.

7. A method for logistics and transportation management and real-time data sharing according to claim 2, characterized in that: In S3, intelligent analysis using machine learning algorithms specifically includes: Divide historical data into training sets and test sets, select the random forest regression algorithm to train the model on the selected training set, and adjust the hyperparameters to optimize the model performance; Set evaluation metrics and evaluate the performance of the model on the test set; Deploy the trained model to the cloud computing platform and receive new data in real time for prediction.

8. A method for logistics and transportation management and real-time data sharing according to claim 1, characterized in that: In said S4, optimization suggestions are given according to the analysis results, and a report is generated and updated in real time, specifically: the intelligent analysis results of the machine learning algorithm are visualized, and a transportation status report, risk assessment and resource optimization suggestions are generated.

9. A method for logistics and transportation management and real-time data sharing according to claim 4, characterized in that: In the S5, a self-service platform is provided, allowing customers to check the status of goods in real time and supporting SMS and mobile application push notifications. Specifically: Provide real-time query services to customers through mobile applications or web platforms, showing cargo status, arrival time of congestion, and abnormal conditions; Establish a customer feedback mechanism to collect customers' opinions and suggestions on logistics and transportation services, and regularly evaluate and optimize management processes.

10. A logistics and transportation management and real-time data sharing system according to claim 5, implementing a logistics and transportation management and real-time data sharing method according to any one of claims 1 to 9, characterized in that: include: Data collection module, real-time collection of logistics and transportation data from logistics and transportation vehicles; The edge computing module receives the real-time logistics and transportation data collected by the data acquisition module, pre-processes the collected real-time logistics and transportation data using edge computing technology, and then encrypts and transmits the processed real-time data through a wireless network; The cloud computing platform module receives the real-time data encrypted by the edge computing module, decrypts, integrates and stores it, and uses machine learning algorithms for intelligent analysis; The data analysis and reporting module generates optimization suggestions and reports based on the analysis results obtained by the machine learning algorithm in the cloud computing platform module, updates them in real time, and displays visual data; The user service platform provides a self-service platform that allows customers to check the status of goods in real time and supports SMS and mobile application push notifications.

Images