A logistics information tracking method based on whole-process transparent management

Abstract

The present application relates to the technical field of wisdom logistics, realizes the whole process credible closed loop of logistics pieces from "collection-encryption-on-chain-records-tracing", provides the authoritative data basis for the logistics participants, solves the problems such as information island, data fraud and tracing difficulty in traditional logistics, especially relates to a logistics information tracking method based on whole process transparent management; the present application first establishes the corresponding relationship between logistics pieces and unique digital identification code, ensures that the physical piece and the digital identification are matched one by one, simultaneously accompanies the information verification process, avoids the deviation of subsequent twin body data caused by information error, and "creates digital twin" for logistics pieces based on the corresponding relationship on the block chain, realizes the mapping of physical data and digital data, realizes dynamic correlation whole process multidimensional data, monitors the logistics pieces, does not need manual sampling inspection, and when inquiring, verifies the data accuracy through the hash value, ensures that the tracing result is real and reliable, and improves the inquiry efficiency.

Description

Technical Field

[0001] This invention relates to the field of smart logistics technology, and in particular to a logistics information tracking method based on full-process transparent management. Background Technology

[0002] With the rapid development of computers and the Internet of Things, people's daily activities have become more and more convenient. On various online shopping platforms, suppliers can upload their product information (such as electronic accessories, food, etc.) to the online sales platform, and users (including individuals and enterprises) can place orders on the online sales platform to realize the online purchase of goods. Among these, logistics is an indispensable and crucial link in connecting suppliers and users. Against the backdrop of the rapid development of the modern logistics industry, the end-to-end management of goods from outbound to receipt faces numerous technical bottlenecks. Traditional logistics information tracking models are no longer sufficient to meet the demands for efficient, reliable, and transparent management. Currently, the industry generally suffers from the following pain points: First, data from various logistics stages is typically scattered across centralized systems in different enterprises or departments, leading to prominent information silos. Furthermore, under the traditional centralized storage architecture, logistics data is susceptible to human tampering, increasing the risk to logistics information. Second, existing tracking methods largely rely on manual reporting of information from nodes, lacking real-time monitoring mechanisms for anomalies, resulting in delayed and passive responses to anomalies. Third, a precise correspondence between physical goods and digital models has not been established. Goods status monitoring mainly relies on manual sampling, making it impossible to achieve end-to-end visual control through digital terminals. Monitoring efficiency is low and coverage is limited. Data retrieval requires coordination among multiple parties, making it difficult to meet rapid verification needs and verify data accuracy. To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0003] The purpose of this invention is to provide a logistics information tracking method based on full-process transparent management to solve the aforementioned technical defects. This invention realizes a closed-loop management of the entire process, including data collection, digital modeling, dynamic monitoring, reliable evidence storage, and efficient traceability, and solves problems such as poor data correlation, untimely anomaly warnings, and low traceability efficiency.

[0004] The objective of this invention can be achieved through the following technical solution: a logistics information tracking method based on full-process transparent management, comprising the following steps: Step 1: The process of constructing the correspondence table between logistics shipments and digital identification codes based on information consistency comparison; Step 2: Based on the uploaded correspondence table between logistics shipments and digital identification codes, complete the initial construction of the digital twin of the logistics shipment; Step 3: Collect multi-dimensional raw data of the entire logistics process of the logistics shipment and dynamically associate it with the digital twin, and obtain an anomaly feedback list through multi-dimensional comparison and analysis of the state attributes of the digital twin. Step 4: Construction of the logistics evidence dataset and analysis of the formation of the logistics event chain; Step 5: Establish an index module based on the logistics event chain, output the query results of the query information through the index module, and perform a data accuracy verification process in the query results.

[0005] Preferably, the process for obtaining the correspondence table between the logistics item and the digital identification code is as follows: S1: Collect core information about logistics items manually or through equipment when packaging or shipping them out of the warehouse; S2: Verify the consistency between the core information of the collected logistics shipments and the order data to obtain a consistency signal or an inconsistency signal. When an inconsistency signal is generated, immediately output and display the preset warning text corresponding to the inconsistency signal: "Information Deviation". S3: When a consistent signal is generated, a unique digital identifier is assigned to each logistics item using a QR code / barcode or RFID chip; S4: After the identification is generated, the device reads the assigned unique digital identification code and associates it with the core information based on the logistics item to form a correspondence table between the logistics item and the digital identification code.

[0006] Preferably, the process of obtaining the digital twin is as follows: Logistics companies can submit identity verification applications by accessing the network through a pre-set blockchain node client. Once the verification is successful, they will obtain the authority to create a twin of a logistics shipment. Upload the mapping table of logistics shipments and digital identification codes on the client side, and initiate a request to create a digital twin; After receiving a request, the blockchain network verifies the request through a pre-set consensus mechanism. Once the verification is successful, an encrypted digital twin is automatically generated.

[0007] Preferably, the process for obtaining the anomaly feedback list is as follows: T1: Deploy authorized equipment at every node of the logistics process for logistics shipments; T2: All authorized devices deployed at every node of the logistics process complete identity registration in the blockchain network and generate a unique device ID; T3: Obtain multi-dimensional raw data of logistics items collected by the corresponding authorized device based on the unique device ID; T4: Preprocess the collected multidimensional raw data to obtain standard raw data; T5: Data fields for logistics shipments composed of standard raw data, device ID, and digital identification code.

[0008] Preferably, T5 includes T51: signing the data field using a pre-set private key, i.e. generating a digital signature; T52: Simultaneously employs symmetric encryption on data fields; T53: The data field is uploaded to the blockchain node closest to the current location, and the signature is decrypted by the node's device public key; T54: When the decryption verification is successful, the data field is written into the digital twin of the corresponding logistics item as the status attribute of the twin; T55: Based on the state attributes in the digital twin, a comparative analysis is performed from the time dimension, numerical dimension and behavioral dimension to obtain the comparison results of the time dimension, numerical dimension and behavioral dimension. The comparison results include compliance or abnormal alarms. T56: Write the comparison results into the digital twin, analyze the digital twin, and if there is an abnormal alarm in the digital twin, obtain the abnormal information corresponding to the abnormal alarm and build an abnormal feedback list based on the abnormal information corresponding to the abnormal alarm. If no abnormal alarm is detected in the comparison result in the digital twin, the logistics shipment will be continuously monitored.

[0009] Preferably, the process of constructing the logistics event chain is as follows: Obtain multi-dimensional raw data and anomaly feedback list of the entire logistics process of the logistics shipment, and form a logistics evidence dataset based on the multi-dimensional raw data and anomaly feedback list; The obtained logistics evidence dataset is standardized in format and a unique association identifier is added. The standardized logistics evidence dataset is encrypted using an existing asymmetric encryption algorithm. Calculate the hash value of each standardized data entry in the logistics evidence dataset, which serves as the unique digital fingerprint of that single standardized data entry; Establish a time-series association, meaning that the hash value of each new single standardized data item must contain the hash value of the previous single standardized data item, forming a logistics event chain.

[0010] Preferably, the analysis process of the logistics event chain indexing module is as follows: After the logistics event chain is packaged and distributed for storage, the core fields in the logistics event chain are obtained and an index module is established. The index module is then connected to the front-end query tool. The system retrieves the input query information, obtains the query results based on the query information, recalculates the new hash value of the original data retrieved in the query results using existing tools, and compares it with the stored hash value. If the new hash value matches the original hash value, the data is determined not to have been tampered with; if the new hash value does not match the original hash value, the data is determined to have been tampered with, a tampering signal is generated, and the preset warning text corresponding to the tampering signal is immediately output: "Information has been tampered with".

[0011] The beneficial effects of this invention are as follows: (1) This invention first establishes a correspondence between logistics items and unique digital identifiers to ensure that physical items and digital identifiers match one by one. At the same time, the information verification process is carried out to avoid deviations in the subsequent twin data due to information errors. Based on the correspondence, a "digital clone" is created for the logistics item on the blockchain to realize the mapping between physical data and digital data. This enables dynamic association of multi-dimensional data throughout the process to monitor the logistics item. No manual sampling is required. The real-time status of the goods can be intuitively grasped through the digital terminal, improving monitoring efficiency. At the same time, the blockchain is used to generate encrypted hash values ​​for the logistics evidence dataset and store them on the chain to ensure that the data cannot be tampered with from collection to evidence storage, thus improving credibility. (2) The present invention establishes an index module based on the logistics event chain. When querying, there is no need to traverse the entire blockchain. The block position can be quickly located by logistics item ID, abnormal type, etc. At the same time, the accuracy of the data is verified by hash value to ensure that the traceability results are true and reliable, and improve the query efficiency. That is, it realizes a reliable closed loop of the entire process of logistics items from "collection-encryption-on-chain-evidence storage-traceability", providing logistics participants with authoritative data evidence that cannot be tampered with, and solving the problems of information silos, data fraud and traceability difficulties in traditional logistics. Attached Figure Description

[0012] The invention will now be further described with reference to the accompanying drawings; Figure 1 This is a reference diagram of the method of the present invention; Figure 2 This is a reference analysis diagram of Embodiment 2 of the present invention. Detailed Implementation

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

[0014] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments; Example 1: Please see Figure 1 As shown, this invention is a logistics information tracking method based on full-process transparent management, comprising the following steps: Step 1: The process of constructing the correspondence table between logistics shipments and digital identification codes based on information consistency comparison; Step 2: Based on the uploaded correspondence table between logistics shipments and digital identification codes, complete the initial construction of the digital twin of the logistics shipment; Step 3: Collect multi-dimensional raw data of the entire logistics process of the logistics shipment and dynamically associate it with the digital twin, and obtain an anomaly feedback list through multi-dimensional comparison and analysis of the state attributes of the digital twin. Step 4: Construction of the logistics evidence dataset and analysis of the formation of the logistics event chain; Step 5: Establish an index module based on the logistics event chain, output the query results of the query information through the index module, and perform a data accuracy verification process in the query results; Step one, the process of constructing the correspondence table between logistics shipments and digital identification codes based on information consistency comparison, is as follows: S1: When packing or shipping logistics items (such as parcels, containers, etc.), collect the core information of the logistics items manually or by equipment. The core information includes the category (such as fresh food / electronic products), specifications (size / weight), etc. S2: Verify the consistency between the core information of the collected logistics shipments and the order data. If the information is consistent, a consistency signal is generated. If the information is inconsistent, an inconsistency signal is generated. When an inconsistency signal is generated, the preset warning text corresponding to the inconsistency signal is immediately output and displayed: "Information Deviation". This is to remind the operation and management personnel to verify the information of the logistics shipments in a timely manner and avoid subsequent twin data deviations due to information errors. S3: When a consistent signal is generated, a unique digital identifier is assigned to each logistics item using a QR code / barcode or RFID chip; S4: After the identification is generated, the unique digital identification code is read by the device (barcode scanner / RFID reader) and associated with the core information based on the logistics item to form a correspondence table between the logistics item and the digital identification code, which prepares for the subsequent association of the twin. Step 2: Based on the uploaded correspondence table between logistics shipments and digital identification codes, complete the initial construction of the digital twin of the logistics shipment; The specific process is as follows: Logistics companies access the network through a pre-set blockchain node client to perform initialization operations, that is, submit an identity verification application. Once the verification is successful, they obtain the authority to create a twin of the logistics shipment, ensuring that only authorized parties can initiate the operation and preventing malicious creation. Upload the mapping table of logistics shipments and digital identification codes on the client and initiate a request to create a digital twin. The request must include key parameters such as the twin name (which can be associated with the digital identification code), data storage permission scope, and initial status (such as "pending shipment"). After receiving a request, the blockchain network verifies the request through a pre-set consensus mechanism (such as PBFT or Raft). Once the verification is successful, an encrypted digital twin is automatically generated. This digital twin is bound to a unique digital identifier by default, and core information is written into the digital twin. At the same time, an immutable twin creation timestamp is generated to complete the initialization. This means creating a "digital clone" of a logistics item on the blockchain, enabling the mapping between physical and digital data.

[0015] Example 2: Please see Figure 2 As shown, step three involves the collection of multi-dimensional raw data from the entire logistics process of a shipment and the dynamic association of the digital twin. This is accompanied by a multi-dimensional comparison and analysis of the digital twin's state attributes, resulting in an anomaly feedback list. The specific analysis process is as follows: T1: Deploy authorized equipment at every node of the logistics process, such as infrared sensors in the warehousing process, temperature and humidity sensors and GPS positioning equipment in the transportation process; T2: All authorized devices deployed at every node of the logistics process complete identity registration in the blockchain network, generating a unique device ID to ensure that only authorized devices can upload data; T3: Based on the unique device ID, obtain multi-dimensional raw data of the logistics items collected by the corresponding authorized device. The multi-dimensional raw data includes GPS latitude and longitude, temperature value, vibration acceleration, etc. T4: Preprocess the collected multidimensional raw data to obtain standard raw data. Preprocessing includes cleaning, standardization, etc. T5: Data fields for logistics shipments composed of standard raw data, device ID, and digital identification code; For example, data fields include: {Logistics shipment digital identifier, device ID, data type: location, value, timestamp, etc.}; T51: Further use a pre-set private key (such as the device's own private key) to sign the data field, i.e. generate a digital signature, to ensure that the data source is traceable; T52: Simultaneously, symmetric encryption (such as AES-256) is used on the data fields, which can only be decrypted by blockchain nodes that possess the corresponding public key; T53: The data field is uploaded to the blockchain node closest to the current location, and the node verifies the device's public key to decrypt the signature, confirming that the data has not been tampered with; T54: When the decryption verification is successful, the data fields are written into the digital twin of the corresponding logistics item as the state attribute of the twin. At the same time, a transaction record containing complete data fields (with timestamp, node signature, etc.) is generated in the blockchain ledger to achieve tamper-proof data storage. T55: Based on the state attributes in the digital twin, a comparative analysis is performed from the time dimension, numerical dimension and behavioral dimension to obtain the comparison results of the time dimension, numerical dimension and behavioral dimension. The comparison results include compliance or abnormal alarms. T56: Write the comparison results into the digital twin and generate a timestamp to ensure traceability. Analyze the digital twin. If there is an abnormal alarm in the digital twin, obtain the abnormal information corresponding to the abnormal alarm. The abnormal information includes the abnormal type (such as time dimension, numerical dimension and behavioral dimension), the time of occurrence, the current location and other information. Construct an abnormal feedback list based on the abnormal information corresponding to the abnormal alarm. Send the abnormal feedback list to the operations staff so that they can take timely emergency response measures to ensure the safety of the logistics shipments; If there is no abnormal alarm in the comparison result in the digital twin, the logistics shipment will be continuously monitored; Among them, the time-dimensional comparison analysis is as follows: the time spent in the current stage (such as warehousing stage, transportation stage, etc.) is calculated and compared with the preset time threshold to determine whether it has exceeded the time limit. If it has exceeded the time limit, it is determined to be an abnormal alarm. If it has not exceeded the time limit, it is determined to meet the requirements. Numerical dimension comparison and analysis: The real-time collected temperature, vibration and other values ​​are compared with the preset threshold range to determine whether they exceed the standard. If there are values ​​that exceed the standard, it is determined to be an abnormal alarm. If there are no values ​​that exceed the standard, it is determined to meet the requirements. Behavioral dimension comparison analysis: The real-time location is compared with the preset transportation route to determine whether there is a deviation. The door sensor status is compared with the setting that it can only be opened at a specified node to determine whether there is an anomaly. If there is a deviation or anomaly, it is determined to be an anomaly alarm. If there is no deviation or anomaly, it is determined to meet the requirements.

[0016] Example 3: Step 4: The construction of the logistics evidence dataset and the analysis of the logistics event chain are as follows: Obtain multi-dimensional raw data and anomaly feedback list of the entire logistics process of the logistics shipment, and form a logistics evidence dataset based on the multi-dimensional raw data and anomaly feedback list; The obtained logistics evidence dataset is standardized in format and a unique association identifier (such as logistics shipment ID + timestamp) is added. The standardized logistics evidence dataset is encrypted using existing asymmetric encryption algorithms (such as RSA-2048). Each data collection node (such as a sensor or IoT gateway) holds a unique private key. After the data is encrypted, a digital signature of the private key is attached. When the data is uploaded to the blockchain node, the digital signature must be verified with the corresponding public key (to prevent unauthorized devices from forging data). Calculate the hash value (such as SHA-256 hash value) for each standardized data entry in the logistics evidence dataset, which serves as the unique digital fingerprint of that standardized data entry; Establish a time-series association, meaning that the hash value of each new single standardized data item must contain the hash value of the previous single standardized data item, forming a logistics event chain (e.g., hash of data N = SHA-256(content of data N + hash of data N-1)), to ensure the continuity and traceability of the logistics process; Step 5: Establish an index module based on the logistics event chain. The index module outputs the query results, along with a data accuracy verification process. The specific analysis process is as follows: After the logistics event chain is packaged and distributed for storage, the core fields in the logistics event chain (such as logistics unit ID, timestamp, node location, exception type, etc.) are obtained and an index module is built, which is associated with the block location (block height, hash value). By connecting the index module with front-end query tools (such as enterprise management platforms, regulatory systems, user apps, etc.), it is possible to quickly locate the block where the target logistics event chain is located through the index, and then extract the complete data chain information from the block (without traversing the entire blockchain, thus improving query efficiency). The system retrieves the input query information, which includes the shipment ID, exception type, etc. Based on the query information, the query results are obtained. A new hash value is recalculated from the original data retrieved in the query results using existing tools (such as integrating the SHA-256 algorithm), and then compared with the stored hash value. If the new hash value matches the original hash value, then the data has not been tampered with. If the new hash value is inconsistent with the original hash value, it is determined that the data has been tampered with, and a tampering signal is generated. The preset warning text corresponding to the tampering signal is immediately output: "Information has been tampered with" to remind the query personnel and intuitively understand whether the information of the logistics shipment has been tampered with throughout the logistics process, thereby improving the traceability of the logistics shipment. Based on the above analysis, a reliable closed loop for logistics data can be achieved from "collection-encryption-on-chain-existence-traceability", providing logistics participants with authoritative data that cannot be tampered with, and solving problems such as information silos, data fraud, and traceability difficulties in traditional logistics. In summary, this invention first establishes a correspondence between logistics shipments and unique digital identifiers, ensuring a one-to-one match between physical shipments and digital identifiers. Simultaneously, an information verification process is implemented to prevent subsequent deviations in twin data due to information errors. Based on this correspondence, a "digital clone" is created for each logistics shipment on the blockchain, achieving a mapping between physical and digital data. This enables dynamic association of multi-dimensional data throughout the entire process, allowing for monitoring of logistics shipments. No manual sampling is required; the real-time status of goods can be intuitively grasped through digital terminals, improving monitoring efficiency. Furthermore, the blockchain generates encrypted hash values ​​for all logistics evidence datasets and stores them on the chain, ensuring the data is tamper-proof from collection to evidence storage, enhancing credibility. An index module is established based on the logistics event chain, allowing for quick block location via logistics shipment ID, anomaly type, etc., without traversing the entire blockchain during queries. The hash value verifies data accuracy, ensuring the authenticity and reliability of traceability results, improving query efficiency. In short, this invention achieves a trusted closed loop for the entire logistics shipment process from "collection-encryption-on-chain-evidence storage-traceability," providing tamper-proof and authoritative data evidence for logistics participants and solving problems such as information silos, data fraud, and traceability difficulties in traditional logistics.

[0017] The threshold is set for comparative analysis of results to determine whether they are good or bad. The value of the threshold is determined by a combination of large-scale model analysis of sample data and human experience. It can also be adjusted appropriately based on seasonal or common-sense influencing factors. The size of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The size of the coefficient depends on the amount of sample data and the corresponding operating coefficient initially set by those skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0018] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A logistics information tracking method based on full-process transparent management, characterized in that, Includes the following steps: Step 1: The process of constructing the correspondence table between logistics shipments and digital identification codes based on information consistency comparison; Step 2: Based on the uploaded correspondence table between logistics shipments and digital identification codes, complete the initial construction of the digital twin of the logistics shipment; Step 3: Collect multi-dimensional raw data of the entire logistics process of the logistics shipment and dynamically associate it with the digital twin, and obtain an anomaly feedback list through multi-dimensional comparison and analysis of the state attributes of the digital twin. Step 4: Construction of the logistics evidence dataset and analysis of the formation of the logistics event chain; Step 5: Establish an index module based on the logistics event chain, output the query results of the query information through the index module, and perform a data accuracy verification process in the query results.

2. The logistics information tracking method based on full-process transparent management according to claim 1, characterized in that, The process for obtaining the correspondence table between the logistics item and the digital identification code is as follows: S1: Collect core information about logistics items manually or through equipment when packaging or shipping them out of the warehouse; S2: Verify the consistency between the core information of the collected logistics shipments and the order data to obtain a consistency signal or an inconsistency signal. When an inconsistency signal is generated, immediately output and display the preset warning text corresponding to the inconsistency signal: "Information Deviation"; S3: When a consistent signal is generated, a unique digital identifier is assigned to each logistics item using a QR code / barcode or RFID chip; S4: After the identification is generated, the device reads the assigned unique digital identification code and associates it with the core information based on the logistics item to form a correspondence table between the logistics item and the digital identification code.

3. The logistics information tracking method based on full-process transparent management according to claim 1, characterized in that, The process of obtaining the digital twin is as follows: Logistics companies can submit identity verification applications by accessing the network through a pre-set blockchain node client. Once the verification is successful, they will obtain the authority to create a twin of a logistics shipment. Upload the mapping table of logistics shipments and digital identification codes on the client side, and initiate a request to create a digital twin; After receiving a request, the blockchain network verifies the request through a pre-set consensus mechanism. Once the verification is successful, an encrypted digital twin is automatically generated.

4. The logistics information tracking method based on full-process transparent management according to claim 1, characterized in that, The process for obtaining the anomaly feedback list is as follows: T1: Deploy authorized equipment at every node of the logistics process for logistics shipments; T2: All authorized devices deployed at every node of the logistics process complete identity registration in the blockchain network and generate a unique device ID; T3: Obtain multi-dimensional raw data of logistics items collected by the corresponding authorized device based on the unique device ID; T4: Preprocess the collected multidimensional raw data to obtain standard raw data; T5: Data fields for logistics shipments composed of standard raw data, device ID, and digital identification code.

5. A logistics information tracking method based on full-process transparent management according to claim 4, characterized in that, The T5 includes T51: signing the data field using a pre-set private key, i.e., generating a digital signature; T52: Simultaneously employs symmetric encryption on data fields; T53: The data field is uploaded to the blockchain node closest to the current location, and the signature is decrypted by the node's device public key; T54: When the decryption verification is successful, the data field is written into the digital twin of the corresponding logistics item as the status attribute of the twin; T55: Based on the state attributes in the digital twin, a comparative analysis is performed from the time dimension, numerical dimension and behavioral dimension to obtain the comparison results of the time dimension, numerical dimension and behavioral dimension. The comparison results include compliance or abnormal alarms. T56: Write the comparison results into the digital twin, analyze the digital twin, and if there is an abnormal alarm in the digital twin, obtain the abnormal information corresponding to the abnormal alarm and build an abnormal feedback list based on the abnormal information corresponding to the abnormal alarm. If no abnormal alarm is detected in the comparison result in the digital twin, the logistics shipment will be continuously monitored.

6. The logistics information tracking method based on full-process transparent management according to claim 1, characterized in that, The process of constructing the logistics event chain is as follows: Obtain multi-dimensional raw data and anomaly feedback list of the entire logistics process of the logistics shipment, and form a logistics evidence dataset based on the multi-dimensional raw data and anomaly feedback list; The obtained logistics evidence dataset is standardized in format and a unique association identifier is added. The standardized logistics evidence dataset is encrypted using an existing asymmetric encryption algorithm. Calculate the hash value of each standardized data entry in the logistics evidence dataset, which serves as the unique digital fingerprint of that single standardized data entry; Establish a time-series association, meaning that the hash value of each new single standardized data item must contain the hash value of the previous single standardized data item, forming a logistics event chain.

7. A logistics information tracking method based on full-process transparent management according to claim 1, characterized in that, The analysis process of the logistics event chain indexing module is as follows: After the logistics event chain is packaged and distributed for storage, the core fields in the logistics event chain are obtained and an index module is established. The index module is then connected to the front-end query tool. The system retrieves the input query information, obtains the query results based on the query information, recalculates the new hash value of the original data retrieved in the query results using existing tools, and compares it with the stored hash value. If the new hash value matches the original hash value, the data is determined not to have been tampered with; if the new hash value does not match the original hash value, the data is determined to have been tampered with, a tampering signal is generated, and the preset warning text corresponding to the tampering signal is immediately output: "Information has been tampered with".

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