Full-link logistics intelligent collaborative management and tracing system based on data processing
By generating and comparing dynamic fingerprints, combined with real-time status and inherent attributes, the problems of easy tampering of static identifiers and lagging link monitoring in logistics systems are solved, thereby improving the security and efficiency of logistics management.
Patent Information
- Application Number
- CN202511749827.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-26
- Publication Date
- 2026-02-27
AI Technical Summary
In existing logistics systems, static identifiers are easily copied and tampered with, lack independent calculation and automatic triggering mechanisms between the parties, cannot ensure the authenticity of handover information, and link monitoring is lagging, making it difficult to quickly identify cargo delays and node mismatches.
By combining the real-time status and inherent attributes of goods with a dynamic fingerprint generation module, a dynamic fingerprint is generated and compared at node handover to build a fingerprint chain for real-time monitoring. Combined with physical contact sensing to initiate cross-verification, automatic triggering and two-way verification are achieved.
It effectively prevents static identifier tampering, ensures the authenticity of handover information, quickly identifies link anomalies, and improves the security and efficiency of logistics management.
Smart Images
Figure CN121581745A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of logistics management technology, specifically to a data-processing-based end-to-end intelligent collaborative management and traceability system for logistics. Background Technology
[0002] Intelligent collaborative management and traceability of the entire logistics chain are crucial for ensuring the safety of cargo transportation and improving circulation efficiency, especially in the transportation of sensitive goods such as fresh produce and pharmaceuticals. However, existing technologies still have the following shortcomings: Firstly, existing systems mostly use static identification for goods, but such identification cannot be dynamically linked to the real-time status of the goods, making them easy to copy, tamper with, or reuse, resulting in weak anti-counterfeiting capabilities. Secondly, existing solutions mostly rely on one party generating fingerprints or manually initiating verification, lacking independent calculation and automatic triggering mechanisms for both parties, thus failing to ensure the authenticity of the handover information; Third, the existing system simply sorts fingerprints by time, without establishing a complete link between nodes, and the anomaly detection is lagging, making it difficult to quickly identify problems such as cargo delays and node mismatches. Therefore, there is an urgent need for a data-driven, end-to-end intelligent collaborative management and traceability system for logistics. Summary of the Invention
[0003] To address the shortcomings of existing technologies, this invention provides a data-processing-based intelligent collaborative management and traceability system for the entire logistics chain, which solves the problems of weak anti-counterfeiting of static identification, unreliable node verification, and lagging link monitoring in existing solutions.
[0004] To achieve the above objectives, the present invention provides the following technical solution: a data processing-based end-to-end intelligent collaborative management and traceability system for logistics, comprising: The dynamic fingerprint generation module obtains the current location of the goods, environmental parameters, and timestamp, and performs mixed calculations on them using a hash algorithm to generate a dynamic fingerprint for each batch of goods. The current location of the goods is the latitude and longitude of the current goods, and the environmental parameters include the temperature and humidity of the current environment of the goods. The node fingerprint verification module calculates dynamic fingerprints for each adjacent node when they are connected and compares them. If they match, the connection is passed; otherwise, manual verification is triggered and the subsequent process is frozen. The adjacent nodes refer to two operation units that are directly connected in the entire logistics chain without any intermediate nodes. The fingerprint chain construction module connects the dynamic fingerprints of the entire chain in chronological order into a fingerprint chain. The generation rhythm of the fingerprint chain is monitored in real time by the scheduling system. At the same time, the system can perform reverse matching through the fingerprint chain to locate the node, time and operator corresponding to the dynamic fingerprint when the user inputs any dynamic fingerprint.
[0005] As a further aspect of the present invention, the specific steps for generating dynamic fingerprints for each batch of goods are as follows: The current location, environmental parameters, and timestamp of the goods are structured and encoded. The structured encoding is a combination of type identifier and original value: the current location, i.e. the latitude and longitude of the current goods, is prefixed with the type identifier "L-", and encoded as "L-original latitude and longitude string"; the environmental parameters, i.e. the temperature and humidity of the current environment of the goods, are prefixed with the type identifier "H-", and encoded as "H-original temperature and humidity string"; the timestamp is prefixed with the type identifier "T-", and encoded as "T-original timestamp string". A static confusion factor is generated based on the inherent attributes of the goods and then appended to the end of the structured coding result obtained above. Perform a single hash calculation on each of the concatenated sub-items to obtain a sub-hash value, and then perform an overall hash calculation on all sub-hash values in the concatenation order to generate an intermediate fingerprint. The intermediate fingerprint is concatenated with the last N bits of the dynamic fingerprint from the previous moment, and the hash calculation is performed again to obtain the dynamic fingerprint at the current moment.
[0006] As a further aspect of the present invention, the specific content of generating a static confusion factor based on the inherent properties of goods is as follows: Extract the inherent attribute descriptions of the goods from the production system, including the specific material of the goods, the specific packaging form at the time of leaving the factory, and the weight characteristics of a single batch of goods. Convert the text descriptions of the three types of attributes into character identifiers according to the following rules: Get the first letter of the pinyin of the core noun in the attribute text description and convert it to uppercase, denoted as F; calculate the length of the attribute text description, take the modulo of a fixed base, and use it as the feature value, denoted as Q; take the sum of the ASCII code value of the first letter and the feature value, take the modulo of 10, and use it as the check bit, denoted as Y; the character identifier of the attribute text description is FQY; After obtaining the character identifiers of the three types of attribute text descriptions, they are assembled into a static confusion factor in the order of the specific material of the main body of the goods, the specific packaging form at the time of manufacture, and the weight characteristics of a single batch of goods.
[0007] As a further aspect of the present invention, when the first dynamic fingerprint is generated, the system automatically extracts the unique identifier of the goods, performs a hash calculation on it, and then extracts the last N bits as the initial anchor fragment to replace the last N bits of the dynamic fingerprint at the previous moment. Then, it is concatenated with the intermediate fingerprint and hashed again to generate the first dynamic fingerprint. The unique identifier of the goods includes the production batch number and the factory unique code.
[0008] As a further aspect of the present invention, the dynamic fingerprint is automatically updated every 5 minutes.
[0009] As a further aspect of the present invention, the specific operation of calculating and comparing dynamic fingerprints at the two nodes is as follows: When goods are transferred from node A to adjacent node B, physical contact sensing is used to determine whether to initiate the cross-validation process. After cross-validation is initiated, node A and node B generate dynamic fingerprints F_A and F_B respectively based on the current real-time status of the goods. They exchange fingerprints F_A and F_B and directly compare whether they are completely consistent. If they are consistent, the verification is deemed to have passed; otherwise, the verification is deemed to have failed. If the verification passes, node A marks the goods as "outbound" and node B as "received," and the subsequent process proceeds normally. If the verification fails, both systems immediately perform the following operations: ① freeze the subsequent operations of their own nodes; ② send an anomaly warning to the administrators of both systems, including the comparison results of F_A and F_B and the current status data of the goods.
[0010] As a further aspect of the present invention, the specific operation of determining whether to initiate the cross-validation process through physical contact sensing is as follows: Each node has a unique identifier embedded in its sensing device. When the physical distance between the two nodes approaches the sensing range, the infrared transmitter of node A first sends an encrypted signal containing its own identifier to node B. After the infrared receiver of node B decrypts the signal, it verifies whether the identifier is in the preset trusted node whitelist. If it is, it returns its own identifier and a confirmation signal to node A. The unique identifier is a hardware-coded device ID. After identity authentication is successful, physical contact verification continues. Specifically, the contact panel of node A and the contact probe of node B make physical contact to form a closed circuit. At this time, node A generates a random code C_A and sends it to node B through the contact circuit. After receiving it, node B generates a random code C_B, concatenates it with C_A, and returns it to node A. If node A verifies that the concatenation result is consistent with the local preset rules, then the physical contact verification is considered successful. If the physical contact verification passes, Node A and Node B simultaneously generate a cross-validation start command. The command contains a handover time marker confirmed by both parties. At the same time, both parties write the start command to their local temporary cache and send a "ready" signal to each other. After both parties receive the signal, they synchronously start the cross-validation process. The handover time marker is a timestamp jointly recorded by the two nodes.
[0011] As a further aspect of the present invention, the specific steps for connecting the dynamic fingerprints of the entire chain into a fingerprint chain in chronological order are as follows: After each node completes cross-validation, it automatically packages and stores the dynamic fingerprint generated by the node in the format of "timestamp + previous fingerprint identifier + node type" and uploads it to the distributed node cache, ultimately forming a chain structure of "dynamic fingerprint 1 → dynamic fingerprint 2 → dynamic fingerprint 3 → ...". Each dynamic fingerprint is accompanied by the type information of the generating node. The specific format of the previous fingerprint identifier is: dynamic fingerprint generated at the previous timestamp - the ID of the node to which it belongs. After each node handover is completed, the scheduling system performs a time-series continuity scan on the generated fingerprint chain: ① Checks whether the timestamp of each dynamic fingerprint is later than the dynamic fingerprint corresponding to the previous timestamp; ② Checks whether the node type conforms to the preset logistics link logic. If both conditions are met, the fingerprint chain is considered to be complete in timing; otherwise, it is marked as having a timing anomaly.
[0012] As a further aspect of the present invention, real-time monitoring of the fingerprint chain generation rhythm specifically includes: The scheduling system automatically retrieves the node flow time range of similar scenarios from the historical database based on the current node type and cargo type, and sets this range as the expected generation window for the next fingerprint. Once the current node completes dynamic fingerprint generation and confirms the handover, the system automatically records the handover completion time and starts countdown monitoring based on the expected generation window: tracking the remaining time until the end of the window in real time, while listening for the dynamic fingerprint generation signal of the next node. When the window ends, the system makes a binary judgment on the dynamic fingerprint generation status of the next node: ① If a dynamic fingerprint has been generated, and the preceding node identifier of the dynamic fingerprint is consistent with the current node's dynamic fingerprint and the node type conforms to the preset logic, then the generation rhythm is judged to be normal; ② If no dynamic fingerprint is generated when the window ends, or the generated dynamic fingerprint has "preceding identifier mismatch" or "node type logic error", then the generation rhythm is judged to be abnormal.
[0013] As a further aspect of the present invention, the specific operation of the system performing reverse fingerprint chain matching is as follows: After the user scans the code to enter the traceability interface and enters the dynamic fingerprint, the system first extracts the feature anchor points of the input dynamic fingerprint: it extracts two feature segments, the first M and the last M characters of the dynamic fingerprint letter string, and uses them as anchor point identifiers for fast retrieval; at the same time, it checks whether the dynamic fingerprint format conforms to the dynamic fingerprint generation rules. If the format does not conform, it is directly judged as an invalid fingerprint, where M is the configurable length. The system pre-divides the entire fingerprint chain into several independent partitions according to node type. Each partition can only store dynamic fingerprints generated by the same type of node. A "feature anchor point-fingerprint index table" is established for each partition. After extracting the feature anchor point from the dynamic fingerprint input by the user, the system searches the corresponding partition according to the feature anchor point and quickly locates the complete dynamic fingerprint that perfectly matches the feature anchor point through the index table. After locating the complete dynamic fingerprint, the system retrieves the node attribute package associated with the dynamic fingerprint and traces back the dynamic fingerprints and attribute packages of all upstream nodes through the preceding fingerprint identifier built into the dynamic fingerprint. The node attribute package specifically includes the type of the generating node, timestamp, operator ID, and device number. Finally, all the backtracked node information is arranged in reverse chronological order to generate a visual reverse correlation graph.
[0014] This invention provides a data processing-based end-to-end intelligent collaborative management and traceability system for logistics, which has the following advantages compared with existing technologies: (1) The present invention combines real-time status parameters of goods with inherent attribute static confusion factors through a dynamic fingerprint generation module to generate fingerprints that are dynamically updated with the status, and ensures temporal uniqueness through prior fingerprint association, effectively solving the defects of existing static identifiers that are easy to copy and tamper with. (2) This invention associates the preceding fingerprint identifier with the node type to form a complete and orderly fingerprint chain, and with the help of dynamic rhythm monitoring, it can quickly identify anomalies such as link interruption and node mismatch, thus solving the problem of lagging monitoring in the existing system. Attached Figure Description
[0015] Figure 1 This is the system principle block diagram of the present invention; Figure 2 A flowchart illustrating the steps involved in generating a dynamic fingerprint of goods according to this invention; Figure 3 This is a flowchart illustrating the steps of dynamic fingerprint comparison between two nodes in this invention. Detailed Implementation
[0016] 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.
[0017] Example 1
[0018] like Figure 1 This invention provides a data processing-based end-to-end intelligent collaborative management and traceability system for logistics, comprising: The dynamic fingerprint generation module obtains the current location of the goods, environmental parameters, and timestamp, and performs mixed calculations on them using a hash algorithm to generate a dynamic fingerprint for each batch of goods. The current location of the goods is the latitude and longitude of the current goods, and the environmental parameters include the temperature and humidity of the current environment of the goods. In the entire logistics chain, the spatial location of goods is the core basis for determining whether they are flowing according to the predetermined process. If the fingerprint does not contain location information, attackers may switch the goods during transportation and use other parameters of the original fingerprint to deceive the system. For sensitive goods such as fresh produce, medicines, and precision instruments, the ambient temperature and humidity directly determine their quality. If fingerprints only contain location and time, they cannot reflect the quality status of the goods during transportation, which may lead to the concealment of abnormal quality problems. Although the same goods may be in the same location and environment at different times, from the perspective of the logistics process, they belong to different time nodes. If the fingerprint does not contain a timestamp, an attacker may copy the fingerprint at a certain moment for verification at other time points.
[0019] The node fingerprint verification module calculates dynamic fingerprints for each adjacent node when they are handed over. If the fingerprints match, the transaction is considered successful. Otherwise, manual verification is triggered, and the subsequent process is frozen. The adjacent nodes refer to two directly connected operation units in the entire logistics chain without intermediate nodes. They are the two operation points that directly handle the goods when they are transferred from one link to the next. They are responsible for the direct handover of the goods.
[0020] The fingerprint chain construction module connects the dynamic fingerprints of the entire chain in chronological order into a fingerprint chain. The generation rhythm of the fingerprint chain is monitored in real time by the scheduling system. At the same time, the system can perform reverse matching through the fingerprint chain to locate the node, time and operator corresponding to the dynamic fingerprint when the user inputs any dynamic fingerprint. The specific steps to connect the dynamic fingerprints of the entire chain into a fingerprint chain in chronological order are as follows: After each node completes cross-validation, it automatically packages and stores the dynamic fingerprint generated by the node in the format of "timestamp + previous fingerprint identifier + node type" and uploads it to the distributed node cache, ultimately forming a chain structure of "dynamic fingerprint 1 → dynamic fingerprint 2 → dynamic fingerprint 3 → ...". Each dynamic fingerprint is accompanied by the type information of the generating node, such as "warehouse", "transport vehicle", and "delivery point". The preceding fingerprint identifier is directly derived from the dynamic fingerprint generated at the previous timestamp, and its specific format is: dynamic fingerprint generated at the previous timestamp - the ID of the node to which it belongs; The scheduling system periodically (e.g., after each node handover) performs a temporal continuity scan on the generated fingerprint chain: ① Checks whether the timestamp of each dynamic fingerprint is strictly later than the dynamic fingerprint corresponding to the previous timestamp to ensure that the time is not reversed; ② Checks whether the node type conforms to the preset logistics link logic, such as "warehouse" must be followed by "transport vehicle" or "transfer station", rather than directly by "delivery point". If both of the above are met, the fingerprint chain is judged to be complete in time sequence; otherwise, it is marked as time sequence abnormal. Real-time monitoring of the fingerprint chain generation rhythm specifically includes: The scheduling system automatically retrieves the node flow time range of similar scenarios in the historical database based on the current node type and cargo type, and sets this range as the expected generation window for the next fingerprint, such as the historical flow time range of fresh goods and the historical flow time range of general goods in the "warehouse → transport vehicle" process. Once the current node completes dynamic fingerprint generation and confirms the handover, the system automatically records the handover completion time and starts countdown monitoring based on the expected generation window: tracking the remaining time until the end of the window in real time, while listening for the dynamic fingerprint generation signal of the next node. When the window ends, the system makes a binary judgment on the dynamic fingerprint generation status of the next node: ① If a dynamic fingerprint has been generated, and the preceding node identifier of the dynamic fingerprint is consistent with the current node's dynamic fingerprint and the node type conforms to the preset logic, then the generation rhythm is judged to be normal; ② If no dynamic fingerprint is generated when the window ends, or the generated dynamic fingerprint has "preceding identifier mismatch" or "node type logic error", then the generation rhythm is judged to be abnormal, that is, the fingerprint chain is interrupted or mismatched. When an abnormal generation rhythm is detected, the scheduling system automatically performs the following operations: extracts the type information of the abnormal node, such as the interruption occurring in the "warehouse → transportation" link, and matches suitable resources from the preset backup resource pool; sends a collaborative scheduling instruction to the abnormal node and upstream nodes, including the location, identifier and handover priority of the backup resources, and updates the expected nodes of the fingerprint chain, such as replacing the originally planned transportation vehicle with a backup vehicle. For any input dynamic fingerprint, the specific operation of the system performing reverse fingerprint chain matching is as follows: After a user scans a code to enter the traceability interface and inputs a dynamic fingerprint, the system first extracts the feature anchor points of the input dynamic fingerprint: it extracts two feature segments, the first M and the last M characters of the dynamic fingerprint letter string, and uses them as anchor point identifiers for fast retrieval; at the same time, it verifies whether the dynamic fingerprint format conforms to the dynamic fingerprint generation rules, such as whether the length matches and whether it contains the character distribution characteristics unique to the hash algorithm. If the format does not match, it is directly judged as an invalid fingerprint, where M is the configurable length; The main purpose of extracting feature anchors is to avoid the high computational cost caused by full dynamic fingerprint string comparison. By using segments at fixed positions, the search range can be quickly narrowed down. At the same time, format verification is used to further filter out forged and invalid fingerprints. The system pre-divides the entire fingerprint chain into several independent partitions according to node type (such as warehouse, transport vehicle, and delivery point). Each partition can only store dynamic fingerprints generated by the same type of node. A "feature anchor point-fingerprint index table" is established for each partition to record the mapping relationship between the anchor point and the corresponding complete dynamic fingerprint. After extracting the feature anchor point from the dynamic fingerprint input by the user, the system searches the corresponding partition according to the feature anchor point and quickly locates the complete dynamic fingerprint that completely matches the feature anchor point through the index table. Partitioning by node type can significantly reduce the search scope. For example, the entire link may contain 1,000 dynamic fingerprints, while the transportation node partition may only contain 300. Combined with the feature anchor index table, invalid searches across partitions can be further avoided. After locating the complete dynamic fingerprint, the system retrieves the node attribute package associated with the dynamic fingerprint. The node attribute package specifically includes the type of the generating node, timestamp, operator ID, and device number. At the same time, through the preceding fingerprint identifier built into the dynamic fingerprint, the system traces back the dynamic fingerprints and attribute packages of all upstream nodes level by level. Finally, all the backtracked node information is arranged in reverse chronological order to generate a visual reverse correlation graph, and the operator, time and key status parameters of each node are labeled.
[0021] Example 2
[0022] This embodiment further discloses a method for generating dynamic fingerprints for each batch of goods, based on embodiment 1. Figure 2 As shown, the specific content includes: The real-time status parameters (location parameters, environmental parameters, timestamps) of the goods are structured and encoded, and the structured encoding is a combination of type identifier and original value; For example, the location parameter, i.e. the latitude and longitude of the current cargo, is preceded by the type identifier "L-", which is encoded as "L-original latitude and longitude string"; the environmental parameter, i.e. the temperature and humidity of the current cargo's environment, is preceded by the type identifier "H-", which is encoded as "H-original temperature and humidity string"; and the timestamp is preceded by the type identifier "T-", which is encoded as "T-original timestamp string". Type identifiers can clearly distinguish the categories of parameters and avoid hash collisions caused by different types of parameters having the same original value. If the original value is used, "123.45" may represent both temperature and longitude, but after adding the identifier, "L-123.45" and "H-123.45" can be clearly distinguished as the two. A static confusion factor is generated based on the inherent attributes of the goods and then appended to the end of the structured coding result obtained above. The specific content of generating a static confusion factor based on the inherent properties of goods is as follows: Extract the inherent attribute descriptions of the goods from the production system, including the specific material of the goods, the specific packaging form at the time of leaving the factory, and the weight characteristics of a single batch of goods. Convert the text descriptions of the three types of attributes into character identifiers according to the following rules: Get the first letter of the pinyin of the core noun in the attribute text description and convert it to uppercase, denoted as F; calculate the length of the attribute text description, take the modulo of a fixed base, and use it as the feature value, denoted as Q; take the sum of the ASCII code value of the first letter and the feature value, take the modulo of 10, and use it as the check bit, denoted as Y; the character identifier of the attribute text description is FQY; After obtaining the character identifiers of the three types of attribute text descriptions, they are assembled into a static confusion factor in the order of the specific material of the main body of the goods, the specific packaging form at the time of manufacture, and the weight characteristics of a single batch of goods. Perform a single hash calculation on each of the concatenated sub-items to obtain a sub-hash value, and then perform an overall hash calculation on all sub-hash values in the concatenation order to generate an intermediate fingerprint. The intermediate fingerprint is concatenated with the last N bits of the dynamic fingerprint from the previous moment, and the hash calculation is performed again to obtain the dynamic fingerprint at the current moment. When the first dynamic fingerprint is generated (without a preceding fingerprint), the system automatically extracts the unique identifier of the goods (such as the production batch number and the factory unique code, which cannot be tampered with), performs a hash calculation on it, and then extracts the last N bits as the initial anchor fragment to replace the last N bits of the fingerprint at the previous moment. Then it is concatenated with the intermediate fingerprint and hashed again to generate the first dynamic fingerprint. The dynamic fingerprint is automatically updated every 5 minutes to avoid long update intervals that could cause the fingerprint to become out of touch with the current status of the goods.
[0023] Example 3
[0024] This embodiment further discloses a method for dynamic fingerprint comparison between two nodes, based on Embodiments 1 and 2, such as... Figure 3 As shown, the specific process is as follows: When goods are transferred from node A (e.g., a warehouse) to adjacent node B (e.g., a transport vehicle), the system uses physical contact sensing to determine whether to initiate a cross-validation process, which specifically includes: Each node has a unique identifier embedded in its sensing device. When the physical distance between the two nodes approaches the sensing range, the infrared transmitter of node A first sends an encrypted signal containing its own identifier to node B. After the infrared receiver of node B decrypts the signal, it verifies whether the identifier is in the preset trusted node whitelist. If it is, it returns its own identifier and a confirmation signal to node A. The unique identifier is a hardware-coded device ID. After identity authentication is successful, physical contact verification continues. Specifically, the contact panel of node A and the contact probe of node B make physical contact to form a closed circuit. At this time, node A generates a random code C_A and sends it to node B through the contact circuit. After receiving it, node B generates a random code C_B, concatenates it with C_A, and returns it to node A. If node A verifies that the concatenation result is consistent with the local preset rules, then the physical contact verification is considered successful. After the physical contact verification is passed, Node A and Node B simultaneously generate a verification start command. The command contains a handover time marker confirmed by both parties (such as the timestamp currently recorded jointly). At the same time, both parties write the start command to their local temporary cache and send a "ready" signal to each other. When both parties receive the signal, they synchronously start the cross-verification process. After the verification is initiated, Node A and Node B generate dynamic fingerprints F_A (calculated by Node A) and F_B (calculated by Node B) respectively based on the current real-time status of the goods. The two exchange fingerprints F_A and F_B and directly compare whether they are completely consistent. If they are consistent, the verification is deemed to have passed; otherwise, the verification is deemed to have failed. If the verification passes, node A marks the goods as "outbound" and node B as "received," and the subsequent process proceeds normally. If the verification fails, both systems immediately perform the following operations: ① Freeze the subsequent operations of their own nodes. For example, node A cannot continue to outbound other goods, and node B cannot start transportation. ② Send an anomaly warning to the administrators of both systems, including the comparison results of F_A and F_B and the current status data of the goods.
[0025] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.
[0026] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.
Claims
1. A data-processing-based end-to-end intelligent collaborative management and traceability system for logistics, characterized in that: include: The dynamic fingerprint generation module obtains the current location of the goods, environmental parameters, and timestamp, and performs mixed calculations on them using a hash algorithm to generate a dynamic fingerprint for each batch of goods. The current location of the goods is the latitude and longitude of the current goods, and the environmental parameters include the temperature and humidity of the current environment of the goods. The node fingerprint verification module calculates dynamic fingerprints for each adjacent node when they are connected and compares them. If they match, the connection is passed; otherwise, manual verification is triggered and the subsequent process is frozen. The adjacent nodes refer to two operation units that are directly connected in the entire logistics chain without any intermediate nodes. The fingerprint chain construction module connects the dynamic fingerprints of the entire chain in chronological order into a fingerprint chain. The generation rhythm of the fingerprint chain is monitored in real time by the scheduling system. At the same time, the system can perform reverse matching through the fingerprint chain to locate the node, time and operator corresponding to the dynamic fingerprint when the user inputs any dynamic fingerprint.
2. The end-to-end intelligent collaborative management and traceability system for logistics based on data processing as described in claim 1, characterized in that, The specific steps for generating dynamic fingerprints for each batch of goods are as follows: The current location, environmental parameters, and timestamp of the goods are structured and encoded. The structured encoding is a combination of type identifier and original value: the current location, i.e. the latitude and longitude of the current goods, is prefixed with the type identifier "L-", and encoded as "L-original latitude and longitude string"; the environmental parameters, i.e. the temperature and humidity of the current environment of the goods, are prefixed with the type identifier "H-", and encoded as "H-original temperature and humidity string"; the timestamp is prefixed with the type identifier "T-", and encoded as "T-original timestamp string". A static confusion factor is generated based on the inherent attributes of the goods and then appended to the end of the structured coding result obtained above. Perform a single hash calculation on each of the concatenated sub-items to obtain a sub-hash value, and then perform an overall hash calculation on all sub-hash values in the concatenation order to generate an intermediate fingerprint. The intermediate fingerprint is concatenated with the last N bits of the dynamic fingerprint from the previous moment, and the hash calculation is performed again to obtain the dynamic fingerprint at the current moment.
3. The end-to-end intelligent collaborative management and traceability system for logistics based on data processing as described in claim 2, characterized in that, The specific content of generating a static confusion factor based on the inherent properties of goods is as follows: Extract the inherent attribute descriptions of the goods from the production system, including the specific material of the goods, the specific packaging form at the time of leaving the factory, and the weight characteristics of a single batch of goods. Convert the text descriptions of the three types of attributes into character identifiers according to the following rules: Get the first letter of the pinyin of the core noun in the attribute text description and convert it to uppercase, denoted as F; calculate the length of the attribute text description, take the modulo of a fixed base, and use it as the feature value, denoted as Q; take the sum of the ASCII code value of the first letter and the feature value, take the modulo of 10, and use it as the check bit, denoted as Y; the character identifier of the attribute text description is FQY; After obtaining the character identifiers of the three types of attribute text descriptions, they are assembled into a static confusion factor in the order of the specific material of the main body of the goods, the specific packaging form at the time of manufacture, and the weight characteristics of a single batch of goods.
4. The end-to-end intelligent collaborative management and traceability system for logistics based on data processing according to claim 2, characterized in that, When the first dynamic fingerprint is generated, the system automatically extracts the unique identifier of the goods, performs a hash calculation on it, and then extracts the last N bits as the initial anchor fragment to replace the last N bits of the dynamic fingerprint at the previous moment. Then it is concatenated with the intermediate fingerprint and hashed again to generate the first dynamic fingerprint. The unique identifier of the goods includes the production batch number and the factory unique code.
5. The end-to-end intelligent collaborative management and traceability system for logistics based on data processing according to claim 1, characterized in that, The dynamic fingerprint updates automatically every 5 minutes.
6. The end-to-end intelligent collaborative management and traceability system for logistics based on data processing according to claim 1, characterized in that, The specific operations for calculating and comparing dynamic fingerprints at the two nodes are as follows: When goods are transferred from node A to adjacent node B, physical contact sensing is used to determine whether to initiate the cross-validation process. After cross-validation is initiated, node A and node B generate dynamic fingerprints F_A and F_B respectively based on the current real-time status of the goods. They exchange fingerprints F_A and F_B and directly compare whether they are completely consistent. If they are consistent, the verification is deemed to have passed; otherwise, the verification is deemed to have failed. If the verification passes, node A marks the goods as "outbound" and node B as "received," and the subsequent process proceeds normally. If the verification fails, both systems immediately perform the following operations: ① freeze the subsequent operations of their own nodes; ② send an anomaly warning to the administrators of both systems, including the comparison results of F_A and F_B and the current status data of the goods.
7. The end-to-end intelligent collaborative management and traceability system for logistics based on data processing according to claim 6, characterized in that, The specific steps for determining whether to initiate the cross-validation process using physical contact sensing are as follows: Each node has a unique identifier embedded in its sensing device. When the physical distance between the two nodes approaches the sensing range, the infrared transmitter of node A first sends an encrypted signal containing its own identifier to node B. After the infrared receiver of node B decrypts the signal, it verifies whether the identifier is in the preset trusted node whitelist. If it is, it returns its own identifier and a confirmation signal to node A. The unique identifier is a hardware-coded device ID. After identity authentication is successful, physical contact verification continues. Specifically, the contact panel of node A and the contact probe of node B make physical contact to form a closed circuit. At this time, node A generates a random code C_A and sends it to node B through the contact circuit. After receiving it, node B generates a random code C_B, concatenates it with C_A, and returns it to node A. If node A verifies that the concatenation result is consistent with the local preset rules, then the physical contact verification is considered successful. If the physical contact verification passes, Node A and Node B simultaneously generate a cross-validation start command. The command contains a handover time marker confirmed by both parties. At the same time, both parties write the start command to their local temporary cache and send a "ready" signal to each other. After both parties receive the signal, they synchronously start the cross-validation process. The handover time marker is a timestamp jointly recorded by the two nodes.
8. The end-to-end intelligent collaborative management and traceability system for logistics based on data processing according to claim 1, characterized in that, The specific steps to connect the dynamic fingerprints of the entire chain into a fingerprint chain in chronological order are as follows: After each node completes cross-validation, it automatically packages and stores the dynamic fingerprint generated by the node in the format of "timestamp + previous fingerprint identifier + node type" and uploads it to the distributed node cache, ultimately forming a chain structure of "dynamic fingerprint 1 → dynamic fingerprint 2 → dynamic fingerprint 3 → ...". Each dynamic fingerprint is accompanied by the type information of the generating node. The specific format of the previous fingerprint identifier is: dynamic fingerprint generated at the previous timestamp - the ID of the node to which it belongs. After each node handover is completed, the scheduling system performs a time-series continuity scan on the generated fingerprint chain: ① Check whether the timestamp of each dynamic fingerprint is later than the dynamic fingerprint corresponding to the previous timestamp; ② Check whether the node type conforms to the preset logistics link logic; If both conditions are met, the fingerprint chain is considered to be complete in timing; otherwise, it is marked as having a timing anomaly.
9. The end-to-end intelligent collaborative management and traceability system for logistics based on data processing according to claim 1, characterized in that, Real-time monitoring of the fingerprint chain generation rhythm specifically includes: The scheduling system automatically retrieves the node flow time range of similar scenarios from the historical database based on the current node type and cargo type, and sets this range as the expected generation window for the next fingerprint. Once the current node completes dynamic fingerprint generation and confirms the handover, the system automatically records the handover completion time and starts countdown monitoring based on the expected generation window: tracking the remaining time until the end of the window in real time, while listening for the dynamic fingerprint generation signal of the next node. When the window ends, the system makes a binary judgment on the dynamic fingerprint generation status of the next node: ① If a dynamic fingerprint has been generated, and the preceding node identifier of the dynamic fingerprint is consistent with the current node's dynamic fingerprint and the node type conforms to the preset logic, then the generation rhythm is judged to be normal; ② If no dynamic fingerprint is generated when the window ends, or the generated dynamic fingerprint has "preceding identifier mismatch" or "node type logic error", then the generation rhythm is judged to be abnormal.
10. The end-to-end intelligent collaborative management and traceability system for logistics based on data processing according to claim 1, characterized in that, The specific operation of the system for reverse fingerprint chain matching is as follows: After the user scans the code to enter the traceability interface and enters the dynamic fingerprint, the system first extracts the feature anchor points of the input dynamic fingerprint: it extracts two feature segments, the first M and the last M characters of the dynamic fingerprint letter string, and uses them as anchor point identifiers for fast retrieval; at the same time, it checks whether the dynamic fingerprint format conforms to the dynamic fingerprint generation rules. If the format does not conform, it is directly judged as an invalid fingerprint, where M is the configurable length. The system pre-divides the entire fingerprint chain into several independent partitions according to node type. Each partition can only store dynamic fingerprints generated by the same type of node. A "feature anchor point-fingerprint index table" is established for each partition. After extracting the feature anchor point from the dynamic fingerprint input by the user, the system searches the corresponding partition according to the feature anchor point and quickly locates the complete dynamic fingerprint that perfectly matches the feature anchor point through the index table. After locating the complete dynamic fingerprint, the system retrieves the node attribute package associated with the dynamic fingerprint and traces back the dynamic fingerprints and attribute packages of all upstream nodes through the preceding fingerprint identifier built into the dynamic fingerprint. The node attribute package specifically includes the type of the generating node, timestamp, operator ID, and device number. Finally, all the backtracked node information is arranged in reverse chronological order to generate a visual reverse correlation graph.