Logistics supply chain transparent tracking and early warning method, system, device and medium

Abstract

The present invention relates to a transparent tracking and early warning method, system, device and medium for logistics supply chain, and the method includes: obtaining real-time location data, cargo status data and unique identification information of goods; storing the real-time location data, cargo status data and unique identification information in a distributed database; obtaining the planned path information of goods from the distributed database according to the unique identification information, performing real-time route tracking analysis on the real-time location data based on the planned path information, and obtaining cargo route information; performing status detection on the cargo status data in real time according to preset status early warning rules, generating corresponding initial early warning information, visualizing the cargo route information and the initial early warning information, and generating an early warning report; encrypting and transmitting the early warning report to the corresponding monitoring collaborative platform through a preset encrypted communication channel. The present invention can intuitively display the real-time status and potential risks of goods, and provide more reliable data support for monitoring and decision-making.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics supply chain, and in particular to a logistics supply chain transparent tracking and early warning method, system, device and medium. Background Art

[0002] As a key technology in modern logistics management, transparent tracking and early warning methods for logistics supply chains aim to improve visibility and safety during cargo transportation. With the rapid development of global trade and the increasing demand of consumers for fast and reliable delivery, how to achieve real-time monitoring and early warning of cargo flow has become one of the important research directions in the field of supply chain management. Existing logistics tracking and early warning systems usually rely on a single data source, such as GPS location data or cargo status sensor data, while ignoring the comprehensive analysis of multiple data sources. Such a single data processing method may lead to inaccurate cargo positioning or failure to detect abnormal status in time, thus affecting the efficiency and safety of the entire logistics supply chain. Summary of the invention

[0003] The main purpose of the present invention is to provide a logistics supply chain transparent tracking and early warning method, system, device and medium, which can intuitively display the real-time status and potential risks of goods, and provide more reliable data support for monitoring and decision-making.

[0004] To achieve the above objectives, the present invention provides a logistics supply chain transparent tracking and early warning method, comprising: Obtain real-time location data, cargo status data and unique identification information of the cargo; storing the real-time location data, the cargo status data and the unique identification information in a distributed database; Acquire the planned path information of the goods from the distributed database according to the unique identification information, and perform real-time route tracking analysis on the real-time location data based on the planned path information to obtain the route information of the goods; Perform status detection on the cargo status data in real time according to preset status warning rules, generate corresponding initial warning information, visualize the cargo route information and the initial warning information, and generate a warning report; The early warning report is encrypted and transmitted to the corresponding monitoring collaboration platform through a preset encrypted communication channel.

[0005] Furthermore, the acquisition of the real-time location data, cargo status data and unique identification information of the cargo includes: Obtaining the real-time geographic coordinates of all goods, and time-stamping the real-time geographic coordinates to obtain the real-time location data with time information; Perform label matching on the goods to obtain corresponding electronic label information, and obtain corresponding status data of the goods according to the electronic label information; Obtaining basic information of the goods according to the electronic tag information, and performing identification processing on the basic information to obtain the unique identification information; The real-time location data, the cargo status data and the unique identification information are categorized according to the electronic tags to obtain the real-time location coordinates, the cargo status data and the unique identification information of the same electronic tag.

[0006] Furthermore, storing the real-time location data, the cargo status data and the unique identification information in a distributed database includes: Performing data compression processing on the real-time location data, the cargo status data and the unique identification information to obtain compressed cargo information data; Cargo information data is processed by data sharding to obtain multiple data shards, and storage nodes are allocated to the multiple data shards according to a preset distributed storage strategy to obtain a mapping relationship between the data shards and the storage nodes; According to the mapping relationship, multiple data shards are sent to the corresponding storage nodes respectively to obtain distributed node data, generate index information based on the distributed node data, and integrate all index information to obtain a data index table; Storing the data index table in the master control node of the distributed database to obtain a distributed data structure that can be quickly retrieved; Data backup analysis is performed according to the distributed data structure to obtain a data backup plan, redundant backup is performed on the cargo information data according to the data backup plan to obtain cargo backup data, and the cargo backup data is stored in the distributed database.

[0007] Further, acquiring the planned path information of the goods from the distributed database according to the unique identification information, and performing real-time route tracking analysis on the real-time location data based on the planned path information to obtain the goods route information includes: Extracting the planned path information of the cargo from the distributed database according to the unique identification information, obtaining a planned path sequence including a plurality of preset path nodes, performing spatiotemporal coordinate mapping on the preset path nodes in the planned path sequence, and obtaining the geographic coordinates and estimated arrival time of each preset path node; Calculating the current geographic coordinates and timestamp of the cargo according to the real-time location data to obtain the real-time location information of the cargo; Perform nearest neighbor matching between the real-time location information and the geographic coordinates of the preset path nodes to obtain the nearest path node of the cargo in the planned path sequence; Calculate the actual progress of the goods according to the nearest path node position and obtain the progress percentage; Compare and analyze the progress percentage with the estimated arrival time, and calculate the deviation between the actual travel speed of the goods and the expected speed to obtain a speed deviation value; Perform trajectory deviation analysis on the real-time location information and the planned path sequence to obtain the path deviation degree; Classifying the transportation status of the goods according to the degree of path deviation to obtain a transportation status category; Performing data fusion on the transport status category, the progress percentage, the speed deviation value and the path deviation degree to generate a comprehensive route evaluation index; According to the comprehensive route evaluation index, the transportation efficiency of the goods is quantitatively evaluated to obtain the transportation efficiency score, and the transportation efficiency score, real-time location information and planned path sequence are integrated to generate the cargo route information; The calculation formula of the speed deviation value includes: Speed ​​deviation value = (actual speed - expected speed) / expected speed * 100%; Wherein: Actual travel speed: the current actual travel speed calculated based on the real-time location information of the goods, obtained by dividing the distance between two consecutive time points by the time interval; Expected speed: The ideal travel speed calculated based on the planned path and estimated arrival time, obtained by dividing the total distance by the estimated total time.

[0008] Furthermore, the status detection of the cargo status data is performed in real time according to the preset status warning rules to generate corresponding initial warning information, including: Performing multimodal data fusion processing on the cargo status data to obtain a fusion status feature matrix; Perform self-attention calculation on the time dimension of the fusion state feature matrix to obtain the time attention weight, and perform self-attention calculation on the space dimension of the fusion state feature matrix to obtain the space attention weight; The temporal attention weight and the spatial attention weight are weighted together to obtain the spatiotemporal joint attention weight; The fusion state feature matrix is ​​weighted analyzed according to the spatiotemporal joint attention weight to obtain the associated state representation, and anomaly detection is performed on the associated state representation to obtain the abnormality degree score of the cargo state; According to the abnormality degree score of the cargo status, relevant warning rules are retrieved from the preset warning rule library to obtain a warning rule set, and the cargo status data is analyzed based on the warning rule set to obtain a multi-dimensional abnormal status description; Perform early warning analysis on the multi-dimensional abnormal state description to obtain the corresponding initial warning information.

[0009] Furthermore, the visualizing of the cargo route information and the initial warning information to generate a warning report includes: Performing geographic information coding processing on the cargo route information to obtain a geographic coordinate sequence, marking a preset electronic map according to the geographic coordinate sequence to obtain a marked route map; Extracting keywords and warning levels from the initial warning information to obtain structured warning data; Generate corresponding warning icons based on structured warning data, map the warning icons onto the marked route map, and obtain a warning route map; Perform text processing on structured warning data to obtain formatted warning text; Combine the warning route map and the warning text to obtain an initial report; The initial report is converted into a transmission data format to obtain the early warning report.

[0010] Furthermore, the encrypting and transmitting the early warning report to the corresponding monitoring collaboration platform through a preset encrypted communication channel includes: Segmenting the early warning report into data segments to obtain a plurality of data segments, and encoding each of the data segments to obtain corresponding encoding information; Perform protocol matching based on the coded information to obtain the corresponding key distribution protocol, generate a shared key based on the key distribution protocol, obtain the key, and pre-send the key to the monitoring collaboration platform; Applying an encryption operation to the coded information according to the key to obtain initial encrypted coded information, and performing a rotation operation on the initial encrypted coded information to obtain rotated coded information; Generate noise using a random number generator, mask the rotational coded information according to the noise, and obtain encrypted coded information; Entangling the encrypted coded information with the auxiliary bit to obtain entangled state information, and performing error correction coding on the entangled state information to obtain coded information with error correction capability; Converting the coded information to obtain a remote coded state, transmitting the remote coded state to the monitoring collaborative platform through the encrypted communication channel, and receiving feedback information sent by the monitoring collaborative platform; The process of monitoring the feedback information sent by the collaborative platform includes: When the monitoring collaboration platform receives the remote coding state, the monitoring collaboration platform performs error correction decoding on the remote coding state to obtain decoded coding information; The monitoring collaboration platform decrypts the encoded information according to the pre-shared key to obtain the original data segments, and recombines all the decrypted data segments to obtain the complete early warning report. The monitoring collaboration platform generates corresponding feedback information based on the early warning report, and feeds back the feedback information through the encrypted communication channel.

[0011] The present invention also provides a logistics supply chain transparent tracking and early warning system, which is applied to any of the above logistics supply chain transparent tracking and early warning methods, including: A collection module, which is used for real-time location data, cargo status data and unique identification information of the cargo; An analysis module, the analysis module is used to store the real-time location data, the cargo status data and the unique identification information in a distributed database; An association module, the association module is used to obtain the planned path information of the goods from the distributed database according to the unique identification information, and perform real-time route tracking analysis on the real-time location data based on the planned path information to obtain the goods route information; A processing module, the processing module is used to perform status detection on the cargo status data in real time according to a preset status warning rule, generate corresponding initial warning information, visualize the cargo route information and the initial warning information, and generate a warning report; A control module, wherein the control module is used to encrypt and transmit the early warning report to the corresponding monitoring collaboration platform through a preset encrypted communication channel.

[0012] The present invention also provides a logistics supply chain transparent tracking and early warning device, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of the logistics supply chain transparent tracking and early warning method described in any one of the above.

[0013] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0014] The present invention provides the following beneficial effects: By storing the real-time location data, cargo status data and unique identification information of the goods in a distributed database, and combining the cargo planning path information for real-time route tracking analysis, the actual route and status of the cargo flow can be more accurately grasped, thereby improving the accuracy and reliability of cargo tracking. Through real-time status detection and initial warning information generation, it is possible to timely discover and respond to abnormal situations that may occur during cargo transportation, avoiding monitoring blind spots caused by single data. The cargo route information and initial warning information are visualized to generate warning reports, which intuitively display the real-time status and potential risks of the goods, providing more reliable data support for monitoring and decision-making. The warning report is securely transmitted to the monitoring collaboration platform through an encrypted communication channel, ensuring the security and privacy of data transmission, and effectively preventing data leakage and tampering. Combining the above technical means, transparent tracking and warning of the logistics supply chain are realized, and the overall efficiency and security of supply chain management are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention provides a flow chart of a transparent tracking and early warning method for a logistics supply chain; Figure 2 It is a structural diagram of a logistics supply chain transparent tracking and early warning system provided by the present invention; Figure 3 The present invention provides a structural diagram of a logistics supply chain transparent tracking and early warning device.

[0016] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0019] Reference Figure 1 As shown, the present invention provides a logistics supply chain transparent tracking and early warning method, comprising: Step S1: Obtaining real-time location data, cargo status data and unique identification information of the cargo; Step S2: storing the real-time location data, cargo status data and unique identification information in a distributed database; Step S3: acquiring the planned route information of the goods from the distributed database according to the unique identification information, and performing real-time route tracking analysis on the real-time location data based on the planned route information to obtain the route information of the goods; Step S4: Perform status detection on the cargo status data in real time according to the preset status warning rules, generate corresponding initial warning information, visualize the cargo route information and the initial warning information, and generate a warning report; Step S5: Encrypt and transmit the early warning report to the corresponding monitoring collaboration platform through a preset encrypted communication channel.

[0020] Based on the above steps, the detailed process is as follows: Step S1: Collect key information of the goods through multiple channels and devices. Real-time location data is obtained through GPS positioning devices, mobile communication networks or IoT sensors to accurately record the geographic coordinates of the goods during transportation.

[0021] The cargo status data is collected in real time through various sensors, including environmental parameters such as temperature, humidity, vibration, and packaging integrity. The unique identification information can be a barcode, RFID tag or other unique identification code, which is used to accurately track each item throughout the supply chain.

[0022] Step S2: First, the collected data is preliminarily processed and formatted to ensure a unified data structure for subsequent analysis and retrieval. Then, the processed data is dispersed and stored in database nodes in multiple physical locations. During the storage process, the data version control and synchronization mechanism are used to ensure data consistency between different nodes.

[0023] Step S3: The planned transportation route of the goods is retrieved from the distributed database through the unique identification information of the goods. The planned route includes the expected transportation route, transfer points, estimated arrival time and other information. After obtaining the planned route, it is compared and analyzed with the collected real-time location data. The analysis process is dynamic and continuous. It will calculate the deviation between the current location of the goods and the planned route in real time, evaluate the transportation progress, and predict possible delays or early arrival. At the same time, the route will be dynamically optimized based on the impact of external factors such as actual road conditions and weather conditions on transportation.

[0024] Step S4: According to the characteristics and transportation requirements of different types of goods, a series of status warning rules are preset, such as temperature exceeding the limit, abnormal humidity, excessive vibration, etc.

[0025] These rules are applied to the collected cargo status data for real-time monitoring and analysis. When any anomaly or potential risk is detected, initial warning information is generated, including the type of anomaly, severity, time and location of occurrence, etc.

[0026] The initial warning information is integrated and visualized with the cargo route information. The visualization process includes marking abnormal points on the electronic map, generating charts to show the trend of status changes, using different colors and icons to represent different levels of alarms, etc. Finally, a comprehensive warning report is generated.

[0027] Step S5: According to the pre-set security policy, select the appropriate encryption algorithm and key management scheme. The encryption process ensures that even if the data is intercepted during transmission, unauthorized personnel cannot read its content.

[0028] The encrypted early warning report is transmitted to the designated monitoring and coordination platform through the preset secure communication channel. The monitoring and coordination platform includes the management systems of all parties in the supply chain, the dispatch center of the logistics company, the customer's tracking system, etc. During the transmission process, identity authentication and access control are implemented. At the same time, the log of each transmission is recorded for subsequent audit and tracking.

[0029] The present invention provides a transparent tracking and early warning method for a logistics supply chain. By storing the real-time location data, cargo status data and unique identification information of the goods in a distributed database, and combining the cargo planning path information for real-time route tracking analysis, the actual route and status of the cargo flow can be more accurately grasped, thereby improving the accuracy and reliability of cargo tracking. Through real-time status detection and initial early warning information generation, it is possible to timely discover and respond to abnormal situations that may occur during cargo transportation, avoiding monitoring blind spots caused by single data. The cargo route information and initial early warning information are visualized to generate an early warning report, which intuitively displays the real-time status and potential risks of the goods, providing more reliable data support for monitoring and decision-making. The early warning report is securely transmitted to the monitoring collaborative platform through an encrypted communication channel, ensuring the security and privacy of data transmission, and effectively preventing data leakage and tampering. Combining the above technical means, transparent tracking and early warning of the logistics supply chain are realized, and the overall efficiency and security of supply chain management are improved.

[0030] In one embodiment, obtaining the real-time location data, cargo status data and unique identification information of the cargo includes: The real-time geographic coordinates of all goods are obtained through other positioning technologies, and these coordinate data are time-stamped to generate real-time location data with time information. The goods are matched with labels, the electronic tag information corresponding to each goods is identified, and the corresponding goods status data, such as temperature, humidity, vibration and other environmental parameters, are obtained according to the electronic tag information.

[0031] The basic information of the goods, such as the name, specification, production date, etc. of the goods, is extracted based on the electronic tag information, and the basic information is identified and processed to generate unique identification information. This unique identification information can be a unique string or code, which is used to uniquely identify each piece of goods in the entire logistics process.

[0032] Using electronic tags as association keys, real-time location data, cargo status data and unique identification information are categorized by tags. The real-time location coordinates, cargo status data and unique identification information corresponding to the same electronic tag are associated together to obtain the real-time location coordinates, cargo status data and unique identification information of the same electronic tag, and also form a complete cargo information set.

[0033] This embodiment realizes the comprehensive and dynamic accurate tracking and management of goods by integrating real-time location data, cargo status data and unique identification information. By timestamping the real-time geographic coordinates, the system can accurately record the movement trajectory of the goods, which is convenient for analyzing logistics efficiency and optimizing transportation routes. The use of electronic tag matching technology to obtain cargo status data not only improves the accuracy and real-time performance of data collection, but also can timely discover and respond to abnormal situations that may occur during transportation, effectively reducing the risk of cargo damage. By identifying and processing basic information to generate unique identification information, the system ensures the uniqueness and traceability of each piece of cargo, greatly improving the accuracy and security of logistics management. Finally, by classifying various types of information according to electronic tags, a high degree of data integration and association is achieved, providing comprehensive and accurate information support for logistics decision-making, and helping to improve overall logistics efficiency and customer satisfaction. This comprehensive cargo information management method not only simplifies the logistics operation process, but also improves the reliability and practicality of data, laying a solid foundation for logistics companies to achieve refined management and intelligent decision-making.

[0034] In one embodiment, real-time location data, cargo status data, and unique identification information are stored in a distributed database, including: The real-time location data, cargo status data and unique identification information are compressed to obtain compressed cargo information data.

[0035] The compressed cargo information data is sharded and divided into multiple smaller data shards. According to the pre-set distributed storage strategy, appropriate storage nodes are allocated to the data shards, thereby establishing a mapping relationship between the data shards and the storage nodes.

[0036] According to the established mapping relationship, each data shard is sent to the corresponding storage node to form distributed node data. Based on the distributed node data, the corresponding index information is generated, and all the index information is integrated into a complete data index table, which contains the key information required for rapid positioning and retrieval of data.

[0037] The integrated data index table is stored in the master node of the distributed database, thus building a distributed data structure that can be quickly retrieved. This structure not only supports efficient data retrieval, but also provides central control and management capabilities.

[0038] Based on the constructed distributed data structure, data backup analysis is performed and an appropriate data backup plan is formulated. According to this plan, redundant backup of cargo information data is performed, cargo backup data is generated, and the backup data is stored in other nodes in the distributed database.

[0039] This embodiment significantly reduces the data storage space and transmission bandwidth requirements by compressing the cargo information data, thereby improving the overall system efficiency. By adopting data sharding and distributed storage strategies, the balanced distribution of data is achieved, and the scalability and parallel processing capabilities of the system are enhanced. By constructing a data index table and storing it in the master control node, an efficient distributed data structure is formed, which greatly improves the data retrieval speed and system responsiveness. In addition, this embodiment introduces a data backup mechanism based on a distributed data structure, which effectively prevents data loss through redundant backup and significantly improves the fault tolerance and reliability of the system. This comprehensive and optimized data management solution not only ensures the integrity and security of cargo information, but also provides strong data support for real-time tracking and rapid decision-making. This embodiment achieves efficient storage, rapid retrieval and reliable backup of cargo information through multi-level data optimization and management strategies, and provides a flexible, efficient and reliable data processing solution for cargo tracking and management systems.

[0040] In one embodiment, the planned path information of the goods is obtained from the distributed database according to the unique identification information, and the real-time route tracking analysis is performed on the real-time location data based on the planned path information to obtain the route information of the goods, including: The planned path information of the goods is extracted from the distributed database according to the unique identification information, and a planned path sequence including multiple preset path nodes is obtained. The preset path nodes in the planned path sequence are mapped with time and space coordinates to obtain the geographic coordinates and estimated arrival time of each preset path node.

[0041] The current geographic coordinates and timestamp of the goods are calculated based on the real-time location data to obtain the real-time location information of the goods.

[0042] Perform nearest neighbor matching between the real-time location information and the geographic coordinates of the preset path nodes to obtain the nearest path node of the goods in the planned path sequence.

[0043] The actual progress of the goods is calculated based on the position of the nearest path node to obtain the progress percentage.

[0044] The progress percentage is compared with the estimated arrival time, and the deviation between the actual speed of the goods and the expected speed is calculated to obtain the speed deviation value.

[0045] The trajectory deviation analysis is performed on the real-time position information and the planned path sequence to obtain the path deviation degree.

[0046] The transportation status of the goods is classified according to the degree of path deviation to obtain the transportation status category.

[0047] The transport status category, progress percentage, speed deviation value and path deviation degree are fused to generate a comprehensive route evaluation index.

[0048] Based on the comprehensive route evaluation indicators, the transportation efficiency of goods is quantitatively evaluated to obtain the transportation efficiency score. The transportation efficiency score, real-time location information and planned path sequence are integrated to generate cargo route information.

[0049] The calculation formula of the speed deviation value includes: Speed ​​deviation value = (actual travel speed - expected speed) / expected speed * 100%.

[0050] Among them: Actual travel speed: the current actual travel speed calculated based on the real-time location information of the goods, which is obtained by calculating the distance between two consecutive time points and dividing it by the time interval.

[0051] Expected speed: The ideal travel speed calculated based on the planned path and estimated arrival time, obtained by dividing the total distance by the estimated total time.

[0052] This embodiment achieves comprehensive monitoring and evaluation of the cargo transportation process by obtaining the planned path information of the cargo from a distributed database and performing route tracking analysis based on real-time location data. By mapping the time-space coordinates of the preset path nodes, the position of the cargo on the planned path can be accurately located, so as to accurately calculate the actual progress and speed deviation. By analyzing the trajectory deviation between the real-time location information and the planned path, abnormal situations in the cargo transportation process can be discovered in time, and the transportation status can be classified according to the degree of path deviation, thereby improving the safety and controllability of the transportation process. Data fusion of multiple evaluation indicators to generate comprehensive route evaluation indicators can fully reflect the efficiency and quality of cargo transportation. Finally, by calculating the transportation efficiency score and integrating the real-time location information and the planned path sequence, detailed cargo route information is generated, which provides comprehensive and accurate data support for logistics management, helps to optimize transportation strategies, improve logistics efficiency, and reduce operating costs.

[0053] In one embodiment, the cargo status data is detected in real time according to the preset status warning rules, and corresponding initial warning information is generated, including: Multimodal data fusion processing is performed on the cargo status data, and different types of multimodal data (such as temperature, humidity, acceleration, etc.) are integrated into a fusion state feature matrix.

[0054] The self-attention of the fusion state feature matrix is ​​calculated in the time dimension and the space dimension. In the time dimension, the time series characteristics of the data are analyzed to obtain the time attention weight; in the space dimension, the correlation between different sensor data is analyzed to obtain the space attention weight. Then, these two attention weights are weighted and combined to form the spatiotemporal joint attention weight. This step can consider the time and space characteristics of the data at the same time and improve the accuracy of the analysis.

[0055] Based on the obtained spatiotemporal joint attention weights, the fusion state feature matrix is ​​weighted analyzed to obtain the associated state representation. This representation can better reflect the key characteristics of the cargo state. Subsequently, anomaly detection is performed on this associated state representation to calculate the cargo state abnormality score, which quantifies the degree to which the cargo state deviates from the normal range.

[0056] According to the calculated abnormality score, the relevant warning rules are retrieved from the pre-set warning rule library to form a warning rule set for the current situation. Then, based on this rule set, the cargo status data is deeply analyzed to generate a multi-dimensional abnormal status description.

[0057] Finally, the multi-dimensional abnormal state description is analyzed for early warning, and the severity of the abnormality, potential impact and other factors are comprehensively considered to generate the corresponding initial early warning information. This early warning information includes an overview of the abnormal state, possible risks and recommended measures.

[0058] This embodiment achieves comprehensive and accurate monitoring of the status of goods through multimodal data fusion and spatiotemporal joint attention mechanism. It can simultaneously consider the time series characteristics of the data and the spatial correlation between different sensors, greatly improving the accuracy and sensitivity of anomaly detection. By introducing adaptive spatiotemporal attention weights, the system can automatically identify and focus on the most relevant status features, effectively filter out noise information, and improve the reliability of early warning. The early warning rule retrieval mechanism based on the abnormality degree score enables the system to flexibly select the most suitable early warning strategy for abnormal conditions of different degrees and types, improving the pertinence and practicality of the early warning. The generation of multi-dimensional abnormal state descriptions provides managers with a more comprehensive and in-depth analysis of abnormal situations, which helps to quickly locate the source of the problem and formulate countermeasures.

[0059] In one embodiment, the cargo route information and the initial warning information are visualized to generate a warning report, including: Cargo route information is processed by geographic information coding, and key points on the route are converted into geographic coordinate sequences. Then, the geographic coordinate sequences are used to mark the pre-prepared electronic map, thereby generating a map marking the cargo transportation route.

[0060] At the same time, keywords and warning levels are extracted from the initial warning information, and this information is organized into structured warning data. Based on this warning data, corresponding warning icons are generated and mapped to the previously marked route map, thus forming a complete warning route map.

[0061] Perform text processing on structured warning data to generate formatted warning text. This text will contain detailed information about the warning, such as warning type, level, possible impact, etc. Combine the warning roadmap and warning text into a graphic to generate an initial warning report.

[0062] Finally, to ensure that the report can be correctly displayed and transmitted on different systems and platforms, the system will convert the data format of the initial report to generate the final early warning report. The format of this early warning report includes PDF, HTML or other formats suitable for transmission and display.

[0063] This embodiment performs geographic information coding on the cargo route information and marks it on the electronic map, so that the warning information is intuitively associated with the actual geographical location, greatly improving the readability and practicality of the information. Secondly, keywords and warning levels are extracted from the initial warning information, and corresponding warning icons are generated, so that important information can be quickly identified and understood, improving decision-making efficiency. The warning route map and the formatted warning text are combined in a graphic and text manner, which not only ensures the integrity of the information, but also enhances the visualization of the report, making complex warning information easier to understand and analyze. Finally, by converting the transmission data format of the initial report, the compatibility of the warning report on different systems and platforms is ensured, and the efficiency and accuracy of information transmission are improved.

[0064] In one embodiment, encrypting and transmitting the early warning report to the corresponding monitoring collaboration platform through a preset encrypted communication channel includes: The early warning report is segmented to obtain a plurality of data segments, and each data segment is encoded to obtain corresponding encoding information.

[0065] Protocol matching is performed based on the coded information to obtain the corresponding key distribution protocol, shared keys are generated based on the key distribution protocol to obtain the keys, and the keys are pre-sent to the monitoring collaboration platform.

[0066] An encryption operation is applied to the coded information according to the key to obtain initial encrypted coded information, and a rotation operation is performed on the initial encrypted coded information to obtain rotated coded information.

[0067] A random number generator is used to generate noise, and the rotational coded information is masked according to the noise to obtain the encrypted coded information.

[0068] The encrypted coded information is entangled with the auxiliary bit to obtain entangled state information, and the entangled state information is error-corrected to obtain coded information with error correction capability.

[0069] The coded information is converted to obtain a remote coded state, the remote coded state is transmitted to the monitoring collaboration platform through an encrypted communication channel, and feedback information sent by the monitoring collaboration platform is received.

[0070] The process of monitoring the feedback information sent by the collaborative platform includes: When the monitoring collaboration platform receives the remote coding state, the monitoring collaboration platform performs error correction decoding on the remote coding state to obtain decoded coding information.

[0071] The coded information is decrypted by the monitoring collaboration platform according to the pre-shared key to obtain the original data segments, and all the decrypted data segments are recombined to obtain a complete early warning report. The corresponding feedback information is generated by the monitoring collaboration platform according to the early warning report, and the feedback information is fed back through an encrypted communication channel.

[0072] This embodiment achieves efficient segmented encryption processing by segmenting and encoding data and generating shared keys in combination with a key distribution protocol. The use of rotation operations and random noise masks further enhances the encryption strength and effectively prevents information leakage. The encrypted coded information is entangled with auxiliary bits, and error correction coding is introduced, which significantly improves the anti-interference ability and data integrity during transmission. The use of remote coding states ensures transmission security, while the decryption and reorganization mechanism of the monitoring collaboration platform ensures the accurate restoration of early warning reports. The feedback mechanism of the monitoring collaboration platform realizes the two-way flow of information and improves the response efficiency and collaboration capabilities of the entire system. This multi-level, multi-technology integrated encryption transmission solution not only ensures the security of sensitive information, but also improves the reliability and real-time performance of the entire early warning system, providing strong support for secure communications in complex environments.

[0073] Reference Figure 2 As shown, the present invention also provides a logistics supply chain transparent tracking and early warning system, which is applied to any of the above logistics supply chain transparent tracking and early warning methods, including: A collection module, which is used for real-time location data, cargo status data and unique identification information of the cargo; An analysis module, the analysis module is used to store real-time location data, cargo status data and unique identification information in a distributed database; The association module is used to obtain the planned path information of the goods from the distributed database according to the unique identification information, and perform real-time route tracking analysis on the real-time location data based on the planned path information to obtain the route information of the goods; The processing module is used to perform status detection on the cargo status data in real time according to the preset status warning rules, generate corresponding initial warning information, visualize the cargo route information and the initial warning information, and generate a warning report; The control module is used to encrypt and transmit the early warning report to the corresponding monitoring collaboration platform through a preset encrypted communication channel.

[0074] The transparent tracking and early warning system for the logistics supply chain provided by the present invention can more accurately grasp the actual route and status of the flow of goods by storing the real-time location data, cargo status data and unique identification information of the goods in a distributed database, and combining the cargo planning path information for real-time route tracking analysis, thereby improving the accuracy and reliability of cargo tracking. Through real-time status detection and initial early warning information generation, it is possible to timely discover and respond to abnormal situations that may occur during the transportation of goods, avoiding monitoring blind spots caused by single data. The cargo route information and the initial early warning information are visualized to generate an early warning report, which intuitively displays the real-time status and potential risks of the goods, providing more reliable data support for monitoring and decision-making. The early warning report is securely transmitted to the monitoring collaboration platform through an encrypted communication channel, ensuring the security and privacy of data transmission, and effectively preventing data leakage and tampering. Combining the above technical means, transparent tracking and early warning of the logistics supply chain are realized, and the overall efficiency and security of supply chain management are improved.

[0075] Reference Figure 3 As shown, the present invention also provides a logistics supply chain transparent tracking and early warning device, including: Memory, used to store programs; The processor is used to execute the program to implement each step of the logistics supply chain transparent tracking and early warning method described in any one of the above.

[0076] In this embodiment, the processor and the memory may be connected via a bus or other means. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0077] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0078] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0079] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A logistics supply chain transparent tracking and early warning method, characterized in that: include: Obtain real-time location data, cargo status data and unique identification information of the cargo; storing the real-time location data, the cargo status data and the unique identification information in a distributed database; Acquire the planned path information of the goods from the distributed database according to the unique identification information, and perform real-time route tracking analysis on the real-time location data based on the planned path information to obtain the route information of the goods; Perform status detection on the cargo status data in real time according to preset status warning rules, generate corresponding initial warning information, visualize the cargo route information and the initial warning information, and generate a warning report; The early warning report is encrypted and transmitted to the corresponding monitoring collaboration platform through a preset encrypted communication channel.

2. The logistics supply chain transparent tracking and early warning method according to claim 1 is characterized in that: The acquisition of the real-time location data, cargo status data and unique identification information of the cargo includes: Obtaining the real-time geographic coordinates of all goods, and time-stamping the real-time geographic coordinates to obtain the real-time location data with time information; Perform label matching on the goods to obtain corresponding electronic label information, and obtain corresponding status data of the goods according to the electronic label information; Obtaining basic information of the goods according to the electronic tag information, and performing identification processing on the basic information to obtain the unique identification information; The real-time location data, the cargo status data and the unique identification information are categorized according to the electronic tags to obtain the real-time location coordinates, the cargo status data and the unique identification information of the same electronic tag.

3. The transparent tracking and early warning method for logistics supply chain according to claim 1 is characterized in that: The storing the real-time location data, the cargo status data and the unique identification information in a distributed database comprises: Performing data compression processing on the real-time location data, the cargo status data and the unique identification information to obtain compressed cargo information data; Cargo information data is processed by data sharding to obtain multiple data shards, and storage nodes are allocated to the multiple data shards according to a preset distributed storage strategy to obtain a mapping relationship between the data shards and the storage nodes; According to the mapping relationship, multiple data shards are sent to the corresponding storage nodes respectively to obtain distributed node data, generate index information based on the distributed node data, and integrate all index information to obtain a data index table; Storing the data index table in the master control node of the distributed database to obtain a distributed data structure that can be quickly retrieved; Data backup analysis is performed according to the distributed data structure to obtain a data backup plan, redundant backup is performed on the cargo information data according to the data backup plan to obtain cargo backup data, and the cargo backup data is stored in the distributed database.

4. The logistics supply chain transparent tracking and early warning method according to claim 1 is characterized in that: The step of acquiring the planned path information of the goods from the distributed database according to the unique identification information, and performing real-time route tracking analysis on the real-time location data based on the planned path information to obtain the route information of the goods includes: Extracting the planned path information of the cargo from the distributed database according to the unique identification information, obtaining a planned path sequence including a plurality of preset path nodes, performing spatiotemporal coordinate mapping on the preset path nodes in the planned path sequence, and obtaining the geographic coordinates and estimated arrival time of each preset path node; Calculating the current geographic coordinates and timestamp of the cargo according to the real-time location data to obtain the real-time location information of the cargo; Perform nearest neighbor matching between the real-time location information and the geographic coordinates of the preset path nodes to obtain the nearest path node of the cargo in the planned path sequence; Calculate the actual progress of the goods according to the nearest path node position and obtain the progress percentage; Compare and analyze the progress percentage with the estimated arrival time, and calculate the deviation between the actual travel speed of the goods and the expected speed to obtain a speed deviation value; Perform trajectory deviation analysis on the real-time location information and the planned path sequence to obtain the path deviation degree; Classifying the transportation status of the goods according to the degree of path deviation to obtain a transportation status category; Performing data fusion on the transport status category, the progress percentage, the speed deviation value and the path deviation degree to generate a comprehensive route evaluation index; According to the comprehensive route evaluation index, the transportation efficiency of the goods is quantitatively evaluated to obtain the transportation efficiency score, and the transportation efficiency score, real-time location information and planned path sequence are integrated to generate the cargo route information; The calculation formula of the speed deviation value includes: Speed ​​deviation value = (actual speed - expected speed) / expected speed * 100%; Wherein: Actual travel speed: the current actual travel speed calculated based on the real-time location information of the goods, obtained by dividing the distance between two consecutive time points by the time interval; Expected speed: The ideal travel speed calculated based on the planned path and estimated arrival time, obtained by dividing the total distance by the estimated total time.

5. The logistics supply chain transparent tracking and early warning method according to claim 1 is characterized in that: The method of performing status detection on the cargo status data in real time according to the preset status warning rules and generating corresponding initial warning information includes: Performing multimodal data fusion processing on the cargo status data to obtain a fusion status feature matrix; Perform self-attention calculation on the time dimension of the fusion state feature matrix to obtain the time attention weight, and perform self-attention calculation on the space dimension of the fusion state feature matrix to obtain the space attention weight; The temporal attention weight and the spatial attention weight are weighted together to obtain the spatiotemporal joint attention weight; The fusion state feature matrix is ​​weighted analyzed according to the spatiotemporal joint attention weight to obtain the associated state representation, and anomaly detection is performed on the associated state representation to obtain the abnormality degree score of the cargo state; According to the abnormality score of the cargo status, relevant warning rules are retrieved from the preset warning rule library to obtain a warning rule set, and the cargo status data is analyzed based on the warning rule set to obtain a multi-dimensional abnormal status description; Perform early warning analysis on the multi-dimensional abnormal state description to obtain the corresponding initial warning information.

6. The logistics supply chain transparent tracking and early warning method according to claim 1 is characterized in that: The visualizing of the cargo route information and the initial warning information to generate a warning report includes: Performing geographic information coding processing on the cargo route information to obtain a geographic coordinate sequence, marking a preset electronic map according to the geographic coordinate sequence to obtain a marked route map; Extracting keywords and warning levels from the initial warning information to obtain structured warning data; Generate corresponding warning icons based on structured warning data, map the warning icons onto the marked route map, and obtain a warning route map; Perform text processing on structured warning data to obtain formatted warning text; Combine the warning route map and the warning text to obtain an initial report; The initial report is converted into a transmission data format to obtain the early warning report.

7. The logistics supply chain transparent tracking and early warning method according to claim 1 is characterized in that: The step of encrypting and transmitting the early warning report to the corresponding monitoring collaboration platform through a preset encrypted communication channel includes: Segmenting the early warning report into data segments to obtain a plurality of data segments, and encoding each of the data segments to obtain corresponding encoding information; Perform protocol matching based on the coded information to obtain the corresponding key distribution protocol, generate a shared key based on the key distribution protocol, obtain the key, and pre-send the key to the monitoring collaboration platform; Applying an encryption operation to the coded information according to the key to obtain initial encrypted coded information, and performing a rotation operation on the initial encrypted coded information to obtain rotated coded information; Generate noise using a random number generator, mask the rotational coded information according to the noise, and obtain encrypted coded information; Entangling the encrypted coded information with the auxiliary bit to obtain entangled state information, and performing error correction coding on the entangled state information to obtain coded information with error correction capability; Converting the coded information to obtain a remote coded state, transmitting the remote coded state to the monitoring collaborative platform through the encrypted communication channel, and receiving feedback information sent by the monitoring collaborative platform; The process of monitoring the feedback information sent by the collaborative platform includes: When the monitoring collaboration platform receives the remote coding state, the monitoring collaboration platform performs error correction decoding on the remote coding state to obtain decoded coding information; The monitoring collaboration platform decrypts the encoded information according to the pre-shared key to obtain the original data segments, and recombines all the decrypted data segments to obtain the complete early warning report. The monitoring collaboration platform generates corresponding feedback information based on the early warning report, and feeds back the feedback information through the encrypted communication channel.

8. A transparent tracking and early warning system for logistics supply chain, characterized in that: The logistics supply chain transparent tracking and early warning method applied to any one of claims 1 to 7 above comprises: A collection module, which is used for real-time location data, cargo status data and unique identification information of the cargo; An analysis module, the analysis module is used to store the real-time location data, the cargo status data and the unique identification information in a distributed database; An association module, the association module is used to obtain the planned path information of the goods from the distributed database according to the unique identification information, and perform real-time route tracking analysis on the real-time location data based on the planned path information to obtain the goods route information; A processing module, the processing module is used to perform status detection on the cargo status data in real time according to a preset status warning rule, generate corresponding initial warning information, visualize the cargo route information and the initial warning information, and generate a warning report; A control module, wherein the control module is used to encrypt and transmit the early warning report to the corresponding monitoring collaboration platform through a preset encrypted communication channel.

9. A transparent tracking and early warning device for logistics supply chain, characterized in that: include: Memory, used to store programs; The processor is used to execute the program to implement the various steps of a logistics supply chain transparent tracking and early warning method as described in any one of claims 1-7.

10. A storage medium, characterized in that: Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 7.

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