Logistics order management system and method

Abstract

The invention discloses a logistics order management system and method, and belongs to the technical field of logistics information management. The method comprises the following steps: step 1, generating a unique digital fingerprint for each logistics order; step 2, performing multi-level security encryption and state synchronization management on order information; 3, constructing a digital twinborn model corresponding to the order, collecting and preprocessing key data in real time and visually displaying the key data in the logistics process, and realizing dynamic monitoring of the whole process; step 4, based on an order state consensus mechanism, automatically matching optimal logistics resources and driving an order processing flow, and generating a verifiable order execution proof chain at the same time; and step 5, performing intelligent analysis and adaptive optimization on the full-life-cycle data of the order, and continuously improving the overall efficiency and precision of logistics management.

Description

Technical Field

[0001] The present application relates to the technical field of logistics information management, and more specifically, to a logistics order management system and method. Background Art

[0002] With the rapid development of e-commerce and modern logistics, the number of logistics orders has increased dramatically, and the complexity of order management has also increased. Traditional logistics order management methods rely heavily on manual operations and simple information systems, which are unable to meet the current high demands for order processing speed, accuracy, and security. Order data is often stored in a decentralized manner, resulting in serious information silos, making it difficult to share and synchronize data in real time, affecting the transparency and controllability of the logistics process. Furthermore, the allocation of logistics resources is often based on experience and static rules, lacking the ability to adjust dynamically. This can easily lead to resource waste and scheduling conflicts, affecting overall transportation efficiency.

[0003] At the same time, the logistics process presents security risks such as order fraud and data tampering. Traditional security measures are unable to effectively protect the entire order lifecycle. Furthermore, limited logistics status monitoring methods cannot fully reflect the true status of orders in warehousing, transportation, and distribution. The lack of effective forecasting and early warning mechanisms makes it difficult to respond to emergencies in a timely manner. Logistics companies urgently need a new system and method that can achieve secure order information management, dynamic monitoring of the entire process, and intelligent resource scheduling to improve overall logistics service quality.

[0004] To sum up, how to achieve unique identification of logistics orders, information security protection, real-time monitoring of the entire process, and intelligent resource optimization and allocation has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the present application aims to provide a logistics order management method for the above-mentioned problems, comprising the following steps: Step 1: Generate a unique digital fingerprint for each logistics order; Step 2: Implement multi-level security encryption and status synchronization management on order information; Step 3: Build a digital twin model corresponding to the order, collect, pre-process, and visualize key data from the logistics process in real time, and achieve dynamic monitoring of the entire process; Step 4: Based on the order status consensus mechanism, automatically match the optimal logistics resources and drive the order processing process, while generating a verifiable order execution proof chain; Step 5: Conduct intelligent analysis and adaptive optimization of order data throughout its life cycle to continuously improve the overall efficiency and accuracy of logistics management.

[0006] Furthermore, step 1 includes the following steps: Convert logistics order data into a unified standardized format and establish mapping relationships between core fields; Standardize order address information into latitude and longitude coordinates and address components, and perform matching verification with a standard geographic database; Perform integrity checks, consistency checks, and business rule validation on standardized order data, and identify and process abnormal data; Based on the key attributes of the order and the salting mechanism, an irreversible and globally unique digital fingerprint of the order is generated, and timestamp and version information are added to realize order identity identification and modification tracking; Add system-level metadata and business-related information to standardized orders to build a complete order information model.

[0007] Furthermore, the generation process of the order digital fingerprint specifically includes: first determining a set of core fields for uniquely identifying the order, including the order number, the hash summary of the consignee and consignor information, the cargo category code, and the expected delivery time period identifier; then generating a random salt value, and combining it with the current timestamp and the processing node identifier to form a salting factor; calculating the hash value for each key field separately, and splicing and performing a secondary hash in a preset order, and then merging it with the salting factor to perform the final hash calculation to generate a fingerprint string with a high entropy value and irreversible; then establishing a mapping relationship between the fingerprint string and the original order and storing it in a distributed index service; finally, combining the order version information with the digital fingerprint to construct a fingerprint chain for version tracking.

[0008] Furthermore, the implementation process of step 2 is as follows: verifying the uniqueness of the order based on the digital fingerprint generated in step 1 to prevent duplicate and fraudulent orders; protecting key information through asymmetric and symmetric encryption, and generating a verification certificate; using distributed storage and blockchain technology to store encrypted data, and ensuring the privacy protection and efficient verification of order information through smart sharding and zero-knowledge proof; realizing real-time synchronization and consistency management of order processing status in a distributed node network to ensure complete recording and traceability of status change operations.

[0009] Furthermore, step 3 includes the following steps: Based on the order data structure, a digital twin basic model containing physical characteristics and business attributes is created, key parameters are defined, and a mapping relationship with the physical order is established; Build a data collection network covering the entire warehousing, transportation, and distribution process to automatically collect order location, environmental conditions, and status information, and perform real-time cleaning, normalization, and structuring. Achieve intuitive monitoring of order operation status and efficient communication of key information; Perform dynamic forecasts of order status, including estimated arrival time, potential delay risks, and resource consumption estimates; Realize cross-system data synchronization and automatic triggering mechanism to form a collaborative decision-making network with digital twins as the core.

[0010] Furthermore, the dynamic prediction of order status specifically includes: first, building a statistical baseline model based on historical data to characterize the time distribution characteristics of each processing link, including average time consumption, standard deviation and quantiles; second, comprehensively considering the node processing capacity, path congestion and transportation resource allocation, simulating the propagation path and residence time of orders in the logistics network; then, introducing external factors to adjust the basic prediction results to improve the adaptability to abnormal situations; generating multiple possible processing paths and their corresponding probability distributions, calculating the possibility of order completion on time, and identifying potential delay risk points; finally, based on order attributes and historical similar cases, estimating its resource consumption characteristics during the processing process, including manpower input, required vehicle type, storage capacity and energy consumption level.

[0011] Furthermore, step 4 includes the following steps: Obtain the latest status data of the order from the distributed network and perform consensus verification to determine the current status and processing priority of the order; By collecting and analyzing multi-dimensional logistics resource data in real time, we can calculate the matching degree between orders and resources and allocate the optimal logistics resource combination for orders; Automatically generate a detailed execution plan based on the selected logistics resources, push the plan to all participants, and form a smart contract after confirmation; Monitor the status and trajectory of logistics resources in real time, promptly identify deviations from the execution plan, automatically adjust subsequent execution steps, and push update notifications to relevant parties in the event of major delays or environmental changes; Collect multimodal proof data at key order execution nodes, and add the data to the distributed ledger after node verification, forming an unalterable execution proof chain; By automatically comparing order requirements with actual execution results, evaluating service completion and quality, triggering the settlement process, and generating an execution summary report, all data is archived in the order digital archive.

[0012] Furthermore, the process of analyzing multi-dimensional logistics resource data and calculating the matching degree between orders and resources includes: First, a multi-dimensional resource feature vector covering static attributes and dynamic states is constructed to comprehensively describe the current status of resources; Secondly, quantify the order's space requirements, timeliness requirements, special handling conditions, and priority parameters; Subsequently, resources are preliminarily screened based on hard constraints, eliminating candidates that do not meet basic conditions; Finally, combining spatial utilization efficiency, time coordination, path matching, energy consumption performance and cost factors, the weighted cosine similarity method is used to calculate the matching score between order requirements and resource characteristics.

[0013] Furthermore, step 5 includes the following steps: Integrate data from the entire lifecycle of an order, from creation to completion. By slicing and drilling into data cubes, quickly discover key distribution characteristics, resource consumption patterns, and abnormal correlations during order processing, and build a multi-dimensional panoramic data view. Establish a baseline for normal order processing, automatically identify abnormal orders that deviate from the path, and trace the source of the abnormality through root cause analysis. Build an extensible knowledge base of abnormality types to automatically classify and identify new types of abnormalities. Optimize order allocation, route planning, and resource scheduling strategies based on deep reinforcement learning, and achieve real-time adaptive adjustment of scheduling parameters; Infer the order processing flow from actual execution data, identify bottlenecks and redundant operations, and optimize resource allocation and processing strategies; Through stress testing and fault injection, various abnormal scenarios are simulated, and redundancy and resource reservation strategies are automatically adjusted to implement intelligent flow control and degradation mechanisms.

[0014] In addition, this application also provides a logistics order management system, including: Fingerprint generation module, responsible for standardizing order data and generating unique digital fingerprints; The security synchronization module is responsible for the encryption protection, secure storage and status synchronization management of order information; Twin visualization module, builds a digital twin model of orders to achieve real-time monitoring, prediction, and visualization of the logistics process; The resource scheduling module, based on the order status consensus mechanism, realizes intelligent resource matching, execution process management and proof chain generation; Intelligent optimization module, which conducts intelligent analysis of order life cycle data and continuously optimizes logistics management processes and strategies; The interface integration module is responsible for interaction with the external environment and provides unified interfaces and services.

[0015] Compared with the prior art, this application has the following beneficial effects: This application generates a unique digital fingerprint for each logistics order, and combines multi-level encryption mechanism, digital twin modeling, consensus-driven resource matching and execution proof chain generation to achieve visual monitoring and adaptive optimization of the entire life cycle of the order, thereby improving the security, transparency and execution efficiency of logistics management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1This is a flow chart of a logistics order management method disclosed in an embodiment of the present application.

[0017] Figure 2 This is a structural diagram of a logistics order management system disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Throughout the drawings, identical or similar reference numerals represent identical or similar elements or elements having identical or similar functions. The described embodiments are only some, not all, of the embodiments of the present invention.

[0019] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0020] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0021] like Figure 1 As shown in the figure, a logistics order management method mainly includes the following five steps: Step 1: Generate a unique digital fingerprint for each logistics order. The specific steps are as follows: Convert logistics order data into a unified standardized format, establish mapping relationships for core fields, and ensure consistency in data types and formats; Through structured parsing and geocoding processing, order address information is standardized into latitude and longitude coordinates and address components, and matched and verified with a standard geographic database to resolve address ambiguity and expression differences. Perform integrity checks, consistency checks, and business rule validation on standardized order data, identify and process abnormal data, ensure that data entering subsequent processes meets quality requirements, and trigger automatic repairs or flag manual intervention when necessary; Based on the key attributes of the order and the salting mechanism, an irreversible and globally unique digital fingerprint of the order is generated, and timestamp and version information are added to realize order identity identification and modification tracking; Add system-level metadata and business-related information to standardized orders to build a complete order information model. System-level metadata includes processing timestamp, source identifier, and processing node ID; business-related information includes customer account ID, freight level, and service SLA.

[0022] The process of converting logistics order data into a unified standardized format includes the following: first, building a dynamic mapping rule library based on the format characteristics of different data sources, and using a configured conversion engine to automatically identify the format type of the input order; then, extracting the product information, consignee and consignor information, timeliness requirements, and special processing instructions from the order, and completing the structured reorganization; for unstructured text fields, extracting key information and mapping it to standard parameters, while performing unit conversion and precision standardization on numerical fields; finally, generating a version identifier for the standardized result, and recording the original data source and conversion parameters in the metadata to form a complete data traceability chain.

[0023] In the above, in the standardization processing of address information, the original address text is first segmented and entity recognized to extract the core address elements; then, the extracted results are normalized and converted to map the abbreviations, aliases and abbreviations to authoritative standard names; for the structured address content, it is parsed into accurate coordinate information and administrative division codes through multi-level geocoding services; then it is matched and verified with the national standard address database, the matching confidence score is calculated, and low-confidence addresses are marked for manual review. At the same time, multilingual address conversion and international postal code verification are performed for cross-border logistics orders; finally, a complete address data packet is generated containing standardized text address, geographic coordinates, administrative division code, matching confidence and verification status.

[0024] In the above, the process of performing integrity verification on order data specifically includes: building a multi-level verification rule library containing a list of required fields, association rules between fields, and business constraints; for the structural integrity of order data, checking whether required fields exist and are in the correct format; for the logical consistency between data, verifying the association constraints between fields within the order; for compliance with business rules, checking whether order parameters comply with current business policies and service capabilities; for abnormal data found, hierarchical processing is carried out according to the preset fault tolerance threshold.

[0025] In the above, the process of generating the digital fingerprint of an order specifically includes: first, determining a set of core fields for uniquely identifying the order, including the order number, the hash summary of the consignee and consignor information, the cargo category code, and the expected delivery time period identifier; then generating a random salt value, and combining it with the current timestamp and the processing node identifier to form a salting factor; calculating the hash value for each key field separately, and splicing and hashing it twice in a preset order, and then merging it with the salting factor to perform the final hash calculation to generate a fingerprint string with a high entropy value and is irreversible; then establishing a mapping relationship between the fingerprint string and the original order and storing it in a distributed index service; finally, combining the order version information with the digital fingerprint to construct a fingerprint chain for version tracking.

[0026] Step 2: Perform multi-level security encryption and status synchronization management on the order information. The implementation process is as follows: verify the uniqueness of the order based on the digital fingerprint generated in step 1 to prevent duplicate and fraudulent orders; protect key information through asymmetric and symmetric encryption, and generate a verification certificate; use distributed storage and blockchain technology to store encrypted data, and ensure the privacy protection and efficient verification of order information through smart sharding and zero-knowledge proof; realize real-time synchronization and consistency management of order processing status in the distributed node network to ensure complete recording and traceability of status change operations.

[0027] Among them, the specific implementation of real-time synchronization and consistency management of order processing status includes: encapsulating order status changes into event messages containing operation type, state transition parameters and timestamps, and pushing them asynchronously to subscribing nodes; after receiving the event, each node performs legitimacy verification according to the local state machine rules and completes the status update under the final consistency protocol; for conflicting concurrent state changes, a globally consistent state order is determined through a consensus mechanism; in high-concurrency or large-scale processing scenarios, an adaptive partitioning strategy is adopted to group orders by dimensions such as geographic location and business stage to reduce the complexity of cross-node synchronization; at the same time, a persistent log record is established for each state change, including the operation initiator, execution content, verification node signature and precise timestamp, to form a complete audit chain; through regular consistency checks and automatic repair mechanisms, state inconsistencies caused by network partitions or node failures are identified and resolved.

[0028] Step 3: Build a digital twin model corresponding to the order, collect, pre-process, and visualize key data from the logistics process in real time, and achieve dynamic monitoring of the entire process. The specific steps are as follows: Based on the order data structure, a digital twin basic model containing physical characteristics and business attributes is created, key parameters are defined, and a mapping relationship with the physical order is established; Build a data collection network covering the entire warehousing, transportation, and distribution process to automatically collect order location, environmental conditions, and status information, and perform real-time cleaning, normalization, and structuring. Build a multi-dimensional visual interface to achieve intuitive monitoring of order operation status and efficient communication of key information; Leverage historical and real-time data to dynamically predict order status, including estimated arrival time, potential delay risks, and resource consumption estimates; Build a two-way integrated interface between the digital twin model and the external system to achieve cross-system data synchronization and automatic triggering mechanism, form a collaborative decision-making network with digital twin as the core, and improve the responsiveness and execution efficiency of the overall logistics system.

[0029] Preferably, the process of creating a digital twin basic model specifically includes: first, based on the logistics business domain ontology model, defining the core object types of the digital twin and its static and dynamic attribute sets; then building a multi-level model structure: the bottom layer is the data model, which is used to describe the format and storage method of the original data; the middle layer is the business model, which is used to express the order processing process and related business rules; the top layer is the interaction model, which is used to define the external system access interface and event response logic; then establish an attribute dependency diagram to clarify the causal relationship and calculation logic between attributes to support automatic derivation and consistency verification of values; then define the model state transition rules, clarify the trigger conditions and their transition logic, and form a complete state mechanism; finally, generate a model configuration file containing attribute mapping tables, data conversion rules and verification constraints.

[0030] Preferably, the dynamic prediction of order status specifically includes: first, constructing a statistical baseline model based on historical data to characterize the time distribution characteristics of each processing link, including average time consumption, standard deviation and quantile; second, comprehensively considering the node processing capacity, path congestion and transportation resource allocation, simulating the propagation path and residence time of the order in the logistics network; then, introducing external factors to adjust the basic prediction results to improve the adaptability to abnormal situations; generating multiple possible processing paths and their corresponding probability distributions, calculating the possibility of order completion on time, and identifying potential delay risk points; finally, based on the order attributes and historical similar cases, estimating its resource consumption characteristics during the processing process, including manpower input, required vehicle type, storage capacity and energy consumption level.

[0031] Step 4: Based on the order status consensus mechanism, automatically match the optimal logistics resources and drive the order processing process, while generating a verifiable order execution proof chain. The specific steps are as follows: Obtain the latest status data of the order from the distributed network and perform consensus verification to determine the current status and processing priority of the order; By collecting and analyzing multi-dimensional logistics resource data in real time, we can calculate the matching degree between orders and resources and allocate the optimal logistics resource combination for orders; Automatically generate a detailed execution plan based on the selected logistics resources, push the plan to all participants, and form a smart contract after confirmation; Based on the digital twin model established in step 3, the status and trajectory of logistics resources are monitored in real time, deviations from the execution plan are promptly detected, subsequent execution steps are automatically adjusted, and update notifications are pushed to relevant parties in the event of major delays or environmental changes; Collect multimodal proof data at key order execution nodes, and add the data to the distributed ledger after node verification, forming an unalterable execution proof chain; By automatically comparing order requirements with actual execution results, evaluating service completion and quality, triggering the settlement process, and generating an execution summary report, all data is archived in the order digital archive, completing the proof chain closed loop of the entire process.

[0032] Preferably, the process of analyzing multi-dimensional logistics resource data and calculating the matching degree between orders and resources includes: First, a multi-dimensional resource feature vector covering static attributes and dynamic states is constructed to comprehensively describe the current status of resources; Secondly, quantify the order's space requirements, timeliness requirements, special handling conditions, and priority parameters; Subsequently, resources are preliminarily screened based on hard constraints, eliminating candidates that do not meet basic conditions; Finally, combining spatial utilization efficiency, time coordination, path matching, energy consumption performance and cost factors, the weighted cosine similarity method is used to calculate the matching score between order requirements and resource characteristics.

[0033] Preferably, the specific process of automatically generating a detailed execution plan includes: First, we build a multi-layered execution framework based on the order's service level and resource characteristics, covering network flow planning at the strategic level, path optimization at the tactical level, and specific task decomposition at the operational level. Secondly, calculate the optimal flow of orders in the logistics network and identify key transit nodes and processing time windows; Then, a detailed driving route and stop plan is generated based on real-time traffic conditions, time window constraints, and load balancing requirements; Next, break down the execution plan into specific operational tasks, clearly defining the execution subject, start and end time, and preconditions; Then, the required documents, operation manuals and emergency plans are integrated into a complete execution package and pushed to the task management systems of all relevant parties; Finally, a multi-party smart contract based on blockchain is constructed to clarify the rights and responsibilities, service standards and incentive mechanisms of each participant. The contract is automatically activated after confirmation by all parties, and execution progress tracking and settlement are achieved to ensure efficient collaboration and contract fulfillment.

[0034] Preferably, the multimodal proof data collection process for key order execution nodes includes: First, key node events are predefined, covering collection confirmation, handover verification, transshipment loading and unloading, and final delivery. The corresponding evidence data type and collection specifications for each type of event are clearly defined. Secondly, when each key node is triggered, the system intelligently coordinates available collection equipment to automatically collect multi-source evidence data. For the physical handover process, it collects multimodal data sets including operator biometrics, electronic signatures, high-definition cargo images, and environmental parameters. Then, the collected raw data is pre-processed locally, including image compression, privacy information desensitization, and anomaly detection, to reduce the transmission burden and detect problems in advance. Finally, the preprocessed data is hashed to generate a fingerprint, which is then signed by the node’s private key to form a verifiable digital proof.

[0035] Step 5: Conduct intelligent analysis and adaptive optimization of order lifecycle data to continuously improve the overall efficiency and accuracy of logistics management. The specific steps are as follows: Integrate data from the entire lifecycle of an order, from creation to completion. By slicing and drilling into data cubes, quickly discover key distribution characteristics, resource consumption patterns, and abnormal correlations during order processing, and build a multi-dimensional panoramic data view. Establish a baseline for normal order processing, automatically identify abnormal orders that deviate from the path, and trace the source of the abnormality through root cause analysis. Build an extensible knowledge base of abnormality types to automatically classify and identify new types of abnormalities. Optimize order allocation, route planning, and resource scheduling strategies based on deep reinforcement learning, achieve real-time adaptive adjustment of scheduling parameters, and promote the evolution from static rules to dynamic intelligent scheduling mechanisms; Infer the order processing flow from actual execution data, identify bottlenecks and redundant operations, and optimize resource allocation and processing strategies; Through stress testing and fault injection, various abnormal scenarios are simulated, and redundancy and resource reservation strategies are automatically adjusted to implement intelligent flow control and degradation mechanisms.

[0036] The above-mentioned logistics order management method covers multiple dimensions such as data standardization and unique identification, security improvement, process transparency, intelligent resource scheduling, and full-process verifiability, thereby building an efficient, secure, and traceable intelligent logistics management process.

[0037] First, this method generates an irreversible, globally unique digital fingerprint for each logistics order, enabling unique identification and data tracking. Through standardized order processing, including structuring and geocoding address information, field mapping and conversion, unstructured data extraction, unit conversion, and data quality verification, it not only improves data consistency and integrity but also enhances adaptability to heterogeneous data sources.

[0038] Secondly, the digital fingerprint generated by the salting mechanism is highly secure and tamper-proof. Combined with timestamps and version information, it can achieve a complete record of order change history, effectively supporting version tracking and audit management. It not only enhances the anti-counterfeiting capabilities of order management, but also provides technical support for subsequent compliance analysis, dispute tracing and other scenarios.

[0039] In terms of security management, a multi-layered data encryption strategy is employed, including symmetric encryption, asymmetric encryption, and blockchain-encrypted storage. This, combined with zero-knowledge proof technology and intelligent sharding, ensures that critical order information is protected from leakage or tampering during transmission and storage across the distributed network. The data storage structure and state synchronization mechanisms established with blockchain technology also ensure consistency across distributed nodes and traceability of operations.

[0040] In terms of visualization and real-time monitoring, by building a digital twin model of the order, each link in the logistics process is presented in a digitized and visual manner, and key data such as order status, location, and environment are automatically collected, cleaned in real time, and displayed in a structured manner. This not only improves managers' controllability and responsiveness over the entire logistics chain, but also provides support for intelligent analysis such as delay prediction and resource consumption estimation based on historical and real-time data.

[0041] At the same time, the dynamic prediction function of order status uses statistical models and simulation algorithms. Based on factors such as traffic congestion, processing node load and resource allocation, it can more accurately assess the completion probability and potential risks of orders. This not only helps to identify problems in advance, but also provides data basis for resource scheduling and process optimization, thereby improving the operating efficiency of the entire logistics network.

[0042] In terms of intelligent scheduling, an order status consensus mechanism automatically matches orders to optimal logistics resources. This system, combined with a multi-dimensional matching algorithm and a cosine similarity scoring system, achieves precise matching of resources and demand. The resulting execution plan is then pushed to relevant nodes via smart contracts, ensuring consistent execution and automated process response. Furthermore, information on key nodes during the execution process is collected in a multimodal manner and stored in the blockchain ledger, creating an immutable proof-of-execution chain.

[0043] In summary, this logistics order management method effectively solves the problems of inconsistent order information, opaque processes, inefficient scheduling, and lack of traceability in traditional logistics through the architecture of "standardization + unique fingerprint + encrypted storage + digital twin + intelligent matching + closed execution chain", greatly improving the intelligence level, operational efficiency, and service quality of the logistics system.

[0044] Figure 2 A schematic diagram of the structure of a logistics order management system disclosed in an embodiment of the present application is shown. Figure 2 As shown, the system includes: Fingerprint generation module, responsible for standardizing order data and generating unique digital fingerprints; The security synchronization module is responsible for the encryption protection, secure storage and status synchronization management of order information; Twin visualization module, builds a digital twin model of orders to achieve real-time monitoring, prediction, and visualization of the logistics process; The resource scheduling module, based on the order status consensus mechanism, realizes intelligent resource matching, execution process management and proof chain generation; Intelligent optimization module, which conducts intelligent analysis of order life cycle data and continuously optimizes logistics management processes and strategies; The interface integration module is responsible for interaction with the external environment and provides unified interfaces and services.

[0045] The above-mentioned logistics order management system effectively integrates a number of key technologies through the design of a modular architecture, significantly improving the efficiency, security and intelligence level of order processing, thereby realizing the digitalization, transparency and intelligent management of the entire logistics process.

[0046] First, the fingerprint generation module generates a unique digital fingerprint by standardizing order data, giving each order a unique and verifiable identity. This mechanism technically improves data traceability and consistency, avoids data redundancy and tampering, and provides a technical foundation for subsequent order tracking, comparison, and verification.

[0047] Secondly, the security synchronization module implements the security protection of order information. At the same time, combined with the status synchronization mechanism, it realizes the consistency management of order data among multiple nodes, effectively preventing the leakage and tampering of order information during transmission and storage.

[0048] By building a digital twin of the order, the twin visual module can present key information such as the order's location, status, environmental parameters, etc. in the entire logistics chain in real time, realizing dynamic monitoring and visual management of the entire logistics process. It not only improves the user's perception of the logistics process, but also can detect potential risks in advance, realize early warning response and decision-making assistance.

[0049] In terms of resource allocation, the resource scheduling module, through the introduction of an order status consensus mechanism, can dynamically perceive order changes and intelligently match and schedule resources. Its built-in execution process management and proof chain mechanism further ensure the orderly, controllable, and auditable operation process, effectively reducing delays and cost waste caused by improper resource allocation.

[0050] The intelligent optimization module leverages data analysis and machine learning algorithms to conduct in-depth data mining and model building across the entire order lifecycle, enabling continuous optimization of logistics strategies, process nodes, and service efficiency. By integrating historical data with real-time data analysis, it can adaptively adjust scheduling strategies, optimize transportation routes, reduce operating costs, and improve customer satisfaction.

[0051] Finally, the interface integration module provides unified data interaction standards and API interfaces to ensure that it can seamlessly connect with external environments such as third-party platforms, enterprise ERP systems, warehouse management systems, and transportation platforms, providing guarantees for data integration and business collaboration in multi-party collaboration scenarios.

[0052] Overall, the logistics order management system realizes full-chain technical support from order generation, tracking, management to optimization through multi-module collaborative operation, greatly enhancing the intelligence level, response speed and security capabilities of the logistics system.

[0053] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art will appreciate that modifications may be made to the technical solutions described in the above embodiments, or that some of the technical features may be replaced with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A logistics order management method, characterized in that: The following steps are involved: Step 1: Generate a unique digital fingerprint for each logistics order; Step 2: Implement multi-level security encryption and status synchronization management on order information; Step 3: Build a digital twin model corresponding to the order, collect, pre-process, and visualize key data from the logistics process in real time, and achieve dynamic monitoring of the entire process; Step 4: Based on the order status consensus mechanism, automatically match the optimal logistics resources and drive the order processing process, while generating a verifiable order execution proof chain; Step 5: Conduct intelligent analysis and adaptive optimization of order data throughout its life cycle to continuously improve the overall efficiency and accuracy of logistics management.

2. A logistics order management method according to claim 1, characterized in that: Step 1 includes the following steps: Convert logistics order data into a unified standardized format and establish mapping relationships between core fields; Standardize order address information into latitude and longitude coordinates and address components, and perform matching verification with a standard geographic database; Perform integrity checks, consistency checks, and business rule validation on standardized order data, and identify and process abnormal data; Based on the key attributes of the order and the salting mechanism, an irreversible and globally unique digital fingerprint of the order is generated, and timestamp and version information are added to realize order identity identification and modification tracking; Add system-level metadata and business-related information to standardized orders to build a complete order information model.

3. A logistics order management method according to claim 2, characterized in that: The process of generating the order digital fingerprint specifically includes: first determining a set of core fields for uniquely identifying the order, including the order number, the hash summary of the consignee and consignor information, the cargo category code, and the expected delivery time period identifier; then generating a random salt value, and combining it with the current timestamp and the processing node identifier to form a salting factor; calculating the hash value for each key field separately, splicing and performing a secondary hash in a preset order, and then combining it with the salting factor to perform a final hash calculation to generate a fingerprint string with a high entropy value and is irreversible; then establishing a mapping relationship between the fingerprint string and the original order and storing it in a distributed index service; finally, combining the order version information with the digital fingerprint to construct a fingerprint chain for version tracking.

4. A logistics order management method according to claim 1, characterized in that: The implementation process of step 2 is as follows: verifying the uniqueness of the order based on the digital fingerprint generated in step 1 to prevent duplicate and fraudulent orders; protecting key information through asymmetric and symmetric encryption, and generating a verification certificate; Use distributed storage and blockchain technology to store encrypted data, and ensure privacy protection and efficient verification of order information through intelligent sharding and zero-knowledge proof; Realize real-time synchronization and consistency management of order processing status in a distributed node network, ensuring complete recording and traceability of status change operations.

5. A logistics order management method according to claim 1, characterized in that: Step 3 includes the following steps: Based on the order data structure, a digital twin basic model containing physical characteristics and business attributes is created, key parameters are defined, and a mapping relationship with the physical order is established; Build a data collection network covering the entire warehousing, transportation, and distribution process to automatically collect order location, environmental conditions, and status information, and perform real-time cleaning, normalization, and structuring. Achieve intuitive monitoring of order operation status and efficient communication of key information; Perform dynamic forecasts of order status, including estimated arrival time, potential delay risks, and resource consumption estimates; Realize cross-system data synchronization and automatic triggering mechanism to form a collaborative decision-making network with digital twins as the core.

6. A logistics order management method according to claim 5, characterized in that: The dynamic prediction of order status specifically includes: first, building a statistical baseline model based on historical data to characterize the time distribution characteristics of each processing link, including average time consumption, standard deviation and quantiles; second, comprehensively considering node processing capabilities, path congestion and transportation resource allocation, simulating the transmission path and residence time of orders in the logistics network; then, introducing external factors to adjust the basic prediction results to improve the ability to adapt to abnormal situations; generating multiple possible processing paths and their corresponding probability distributions, calculating the possibility of order completion on time, and identifying potential delay risk points; finally, based on order attributes and historical similar cases, estimating its resource consumption characteristics during the processing process, including manpower input, required vehicle type, storage capacity and energy consumption level.

7. A logistics order management method according to claim 1, characterized in that: Step 4 includes the following steps: Obtain the latest status data of the order from the distributed network and perform consensus verification to determine the current status and processing priority of the order; By collecting and analyzing multi-dimensional logistics resource data in real time, we can calculate the matching degree between orders and resources and allocate the optimal logistics resource combination for orders; Automatically generate a detailed execution plan based on the selected logistics resources, push the plan to all participants, and form a smart contract after confirmation; Monitor the status and trajectory of logistics resources in real time, promptly identify deviations from the execution plan, automatically adjust subsequent execution steps, and push update notifications to relevant parties in the event of major delays or environmental changes; Collect multimodal proof data at key order execution nodes, and add the data to the distributed ledger after node verification, forming an unalterable execution proof chain; By automatically comparing order requirements with actual execution results, evaluating service completion and quality, triggering the settlement process, and generating an execution summary report, all data is archived in the order digital archive.

8. A logistics order management method according to claim 7, characterized in that: The process of analyzing multi-dimensional logistics resource data and calculating the matching degree between orders and resources includes: First, a multi-dimensional resource feature vector covering static attributes and dynamic states is constructed to comprehensively describe the current status of resources; Secondly, quantify the order's space requirements, timeliness requirements, special handling conditions, and priority parameters; Subsequently, resources are preliminarily screened based on hard constraints, eliminating candidates that do not meet basic conditions; Finally, combining spatial utilization efficiency, time coordination, path matching, energy consumption performance and cost factors, the weighted cosine similarity method is used to calculate the matching score between order requirements and resource characteristics.

9. A logistics order management method according to claim 1, characterized in that: Step 5 includes the following steps: Integrate data from the entire lifecycle of an order, from creation to completion. By slicing and drilling into data cubes, quickly discover key distribution characteristics, resource consumption patterns, and abnormal correlations during order processing, and build a multi-dimensional panoramic data view. Establish a baseline for normal order processing, automatically identify abnormal orders that deviate from the path, and trace the source of the abnormality through root cause analysis. Build an extensible knowledge base of abnormality types to automatically classify and identify new types of abnormalities. Optimize order allocation, route planning, and resource scheduling strategies based on deep reinforcement learning, and achieve real-time adaptive adjustment of scheduling parameters; Infer the order processing flow from actual execution data, identify bottlenecks and redundant operations, and optimize resource allocation and processing strategies; Through stress testing and fault injection, various abnormal scenarios are simulated, and redundancy and resource reservation strategies are automatically adjusted to implement intelligent flow control and degradation mechanisms.

10. A logistics order management system, used to implement a logistics order management method according to any one of claims 1 to 9, characterized in that: include: Fingerprint generation module, responsible for standardizing order data and generating unique digital fingerprints; The security synchronization module is responsible for the encryption protection, secure storage and status synchronization management of order information; Twin visualization module, builds a digital twin model of orders to achieve real-time monitoring, prediction, and visualization of the logistics process; The resource scheduling module, based on the order status consensus mechanism, realizes intelligent resource matching, execution process management and proof chain generation; Intelligent optimization module, which conducts intelligent analysis of order life cycle data and continuously optimizes logistics management processes and strategies; The interface integration module is responsible for interaction with the external environment and provides unified interfaces and services.

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