High-quality and high-efficiency logistics order management system

Abstract

The invention relates to the technical field of logistics information processing, and particularly discloses a high-quality and high-efficiency logistics order management system. The system comprises a registration and login terminal, an intelligent review terminal, an order processing terminal and a block chain evidence storage terminal, and is modularly deployed based on a distributed architecture. The intelligent review terminal introduces an image recognition and rule engine to realize automatic order splitting and suggestion merging; the order processing terminal combines a scoring model and a path optimization algorithm to realize dynamic payment routing and multi-target path planning; the block chain evidence storage terminal generates a non-tampering credible receipt based on hash and zero knowledge verification, and supports cross-chain auditing. The system supports GeoHash fragmentation, Flink parallel computing, high-availability container scheduling and hot and cold data hierarchical storage, and the order processing efficiency, the system elasticity and the data security are remarkably improved. The logistics order full-life-cycle management method is suitable for logistics order full-life-cycle management in a high-concurrency and high-credibility scene.

Description

Technical Field

[0001] This invention relates to the field of logistics information processing technology, and in particular to a high-quality and efficient logistics order management system, which is applicable to order lifecycle management and processing optimization in various logistics business scenarios such as e-commerce logistics, cross-border delivery, and same-city express delivery. Background Technology

[0002] With the rapid development of e-commerce, the logistics industry has placed higher demands on the performance, intelligence, and security of order management systems. However, the mainstream logistics order management systems currently on the market generally have significant shortcomings in terms of architecture design, data processing capabilities, intelligent decision-making levels, and system scalability, making it difficult to meet the actual needs of modern high-concurrency and ever-changing scenarios.

[0003] In terms of technical architecture, traditional systems often adopt a monolithic architecture and outdated technology stacks, such as using Oracle databases and WebLogic application servers, and rendering pages through JSP+Servlet. This architecture cannot effectively support high-concurrency requests during order processing, especially during major promotional periods (such as "618" or "Double 11") when millions of orders occur. The system performance bottleneck is obvious, page response latency is high, and user experience is severely affected.

[0004] In terms of intelligentization, existing systems still heavily rely on manual judgment during order processing. For example, order splitting, merging, and route selection are mostly decided by operators based on experience, lacking the ability to automatically identify and intelligently analyze order characteristics, resulting in low processing efficiency. In addition, route planning mostly uses static algorithms based on fixed rules, failing to incorporate dynamic factors such as real-time traffic information, capacity conditions, and driver ratings, resulting in limited optimization effects on logistics routes and low resource utilization.

[0005] In terms of scalability, existing systems typically use hard-coded methods to fix business logic, lacking plug-in and modular design, making it difficult to quickly adapt to changes in business needs. For example, payment methods and order filtering conditions are mostly statically configured and do not support hot-swappable expansion; the overall system is a monolithic architecture with high module coupling, making it difficult to achieve elastic scaling and failing to meet the high availability and high reliability requirements of a cloud-native environment.

[0006] Regarding data security, some systems suffer from the problem of transmitting sensitive information in plaintext, lacking effective data encryption and privacy protection mechanisms, which can easily lead to user data leaks. Furthermore, the order data processing lacks a reliable evidence storage mechanism, making it difficult to meet the auditability requirements of scenarios such as financial-grade risk control or cross-border e-commerce.

[0007] In summary, existing logistics order management systems generally suffer from outdated technical architecture, insufficient intelligence, inadequate scalability, and high data security risks, making it difficult to support the diverse and complex logistics order management needs of today's e-commerce environment. Therefore, there is an urgent need for a novel logistics order management solution that integrates distributed system architecture, artificial intelligence decision-making mechanisms, and blockchain-based trusted evidence storage technology to improve order processing efficiency, enhance system resilience, and ensure data security and reliability. Summary of the Invention

[0008] Existing logistics order management systems generally suffer from rigid architecture, limited processing performance, insufficient intelligence, and lack of data security, making it difficult to meet the demands of massive concurrent order processing under the rapid development of e-commerce and the requirements of reliable evidence storage in cross-border compliance scenarios. Therefore, this invention proposes a high-quality and efficient logistics order management system and method. By introducing a distributed computing architecture, an artificial intelligence decision-making model, and a blockchain-based reliable evidence storage mechanism, it constructs a comprehensive solution with high concurrency processing capabilities, intelligent scheduling capabilities, modular scalability, and end-to-end reliable assurance capabilities. This effectively addresses the shortcomings of existing technologies and improves the overall efficiency and reliability of logistics order management.

[0009] In one embodiment of the present invention, a high-quality and efficient logistics order management system is provided, comprising the following modular terminals, each interacting with data via a distributed service bus. The system includes: a registration and login terminal for multi-role authentication of users, merchants, and logistics providers, supporting two-factor authentication using biometrics and dynamic tokens; an intelligent review terminal, including an integrity detection module, a rule decision engine, and a resource optimization module, automatically generating order splitting / merging suggestions and processing risk assessments based on computer vision models and the rule engine; an order processing terminal, including a credit scoring module, a dynamic payment routing engine, and a route planning module, respectively used for credit determination, channel selection, and optimal delivery route calculation; and a blockchain evidence storage terminal for generating immutable evidence of the order lifecycle, supporting cross-chain auditing and privacy protection verification.

[0010] Furthermore, the rule decision engine in the intelligent review terminal determines the splitability of orders based on the following rules: ```prolog can_split(Order) :- has_multiple_destinations(Order), not(contains_fragile_items(Order)), total_weight(Order) > 20kg. ``` Preferably, the integrity detection module identifies order image defects based on the ResNet50 model, with an accuracy of not less than 95%; the resource optimization module predicts the processing time gain after splitting or merging based on the LSTM model, with a prediction error of less than 8%.

[0011] In one embodiment of the present invention, the route planning module of the order processing terminal is used to perform delivery route optimization by executing the following objective function: in, For road segment distance, For vehicle speed, This represents the real-time congestion coefficient. For the weight of the goods, This is the maximum load capacity of the vehicle. , , These are the weight parameters.

[0012] Furthermore, the dynamic payment routing engine selects the payment channel based on the following scoring function: Preferably, the payment plugin uses the SPI mechanism for hot-swappable loading, and the channel health monitoring module is used to dynamically adjust the weight of the payment channel based on the response time and success rate, thereby achieving optimized selection of the payment path.

[0013] In one embodiment of the present invention, the blockchain evidence storage terminal includes: a data uploading module for generating SHA-256 hash values ​​for key fields of an order and writing them into the blockchain ledger; a zero-knowledge verification module for generating irreversible verification proofs for sensitive fields using the zk-SNARKs protocol, wherein the verification logic includes Merkle path verification and zero-knowledge constraint verification; and a cross-chain auditing module for synchronizing and reconciling order data with an external custodian chain based on a relay bridge protocol.

[0014] Optionally, the system is deployed in a containerized environment and further includes: an elastic scaling module, which enables automatic scaling of service instances based on Kubernetes HPA strategy; a fault recovery module, which enables automatic migration of service nodes through a health check mechanism; and a log replay module, which is used to automatically rebuild the order processing flow after system fault recovery.

[0015] In one embodiment of the present invention, the system includes a high-concurrency order processing module, the processing flow of which includes: spatially partitioning orders based on GeoHash and scheduling them to multiple parallel computing nodes; each node running stream processing tasks to realize credit assessment, payment routing and path planning of orders; and finally aggregating the calculation results through a consistent hashing mechanism to ensure data consistency and system processing throughput.

[0016] Furthermore, the stream processing engine is preferably built on Apache Flink, which has the ability to save task state, fault tolerance and recovery and load balancing, and supports order-level low-latency processing (TP99<200ms).

[0017] In one embodiment of the present invention, the system supports a hierarchical data storage mechanism, including: hot data is stored using a columnar database (such as Doris) with a response time of less than 50ms; warm data is stored using a distributed relational database (such as TiDB); and cold data is archived through an object storage service (such as JuiceFS) with an archiving latency of less than 2 seconds.

[0018] In one embodiment of the present invention, a logistics order management method is provided, comprising the following steps: obtaining an order and generating structured processing suggestions and risk scores through an intelligent review terminal; dividing the order into multiple processing nodes based on GeoHash and executing parallel processing flow; selecting the optimal payment channel based on the scoring function and completing the transaction; executing a path optimization model to generate a delivery route; and storing key order data in a reliable manner through blockchain and generating a verifiable electronic receipt.

[0019] Based on the above technical solutions, the logistics order management system of the present invention constructs an integrated platform with high concurrency processing capabilities, intelligent decision-making capabilities, modular expansion capabilities, and end-to-end reliable assurance capabilities. In terms of structural design, the system adopts a distributed architecture of "terminal + middle platform + data foundation," with each module decoupled through a service bus and microservice containers, enabling it to adapt to the complex and ever-changing e-commerce logistics environment.

[0020] By introducing image recognition and knowledge rule engines into the intelligent review terminal, this invention achieves automatic parsing of order structures and generation of processing suggestions. Compared with traditional systems that rely on manual operation, the processing efficiency is improved by more than 5 times, and the rate of manual intervention is reduced to below 20%. At the same time, the use of an LSTM model to predict the timeliness of splitting / merging behavior makes the decision results more quantifiable and forward-looking, significantly reducing the scheduling error rate.

[0021] In the order processing terminal, by constructing a multi-objective path optimization model and a payment routing scoring mechanism, the system can dynamically generate the optimal delivery and payment routes by combining real-time traffic conditions, order attributes, and user preferences. Compared with traditional systems that rely on static rules, delivery efficiency is improved by an average of 33.3%, and the payment failure rate is reduced by more than 40%, effectively improving order fulfillment quality and customer satisfaction.

[0022] In terms of data security and traceability, this invention constructs a blockchain evidence storage terminal, combining the Merkle hash structure and the zk-SNARKs privacy verification protocol to ensure that order data is verifiable and tamper-proof throughout its lifecycle. It also enhances the credibility of the system in cross-border and compliance scenarios by connecting with external regulatory systems (such as customs chains and tax chains) through a cross-chain bridge protocol.

[0023] At the system operation level, combining Kubernetes container orchestration and HPA elastic scaling strategies, the system can dynamically scale according to real-time load, possessing self-healing and high availability capabilities, with a system SLA availability index exceeding 99.99%. Meanwhile, order data employs a three-tiered "hot-warm-cold" storage strategy, balancing access performance and storage costs, maintaining high throughput and low latency response even with order volumes reaching tens of millions.

[0024] Overall, this invention, through the deep integration of algorithms and system architecture, significantly improves the performance and applicability of existing logistics order management systems in the following aspects: In high-concurrency scenarios, it supports a processing capacity of over 100,000 TPS to meet the extreme business pressures of events such as Double 11. In terms of intelligent decision-making, the path optimization rate is >90%, the payment success rate is >98%, and the scheduling error rate is reduced by more than 30%. In terms of system operation and maintenance, it enables module-level hot-swappable expansion and zero-interruption upgrades; In terms of data security, it meets the evidence storage and auditing requirements for financial, e-commerce, and cross-border compliance. In terms of promotion and adaptation, it supports private deployment and public cloud SaaS applications, and is compatible with various logistics business models such as B2C, B2B, cross-border e-commerce, and same-city delivery.

[0025] Therefore, this invention has achieved substantial technological breakthroughs over existing technologies in terms of overall performance, security, scalability, and intelligence, and has strong industrial application value and promotion prospects. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the embodiments of the present invention will be further described below with reference to the accompanying drawings, in which the same reference numerals denote the same or similar components. The accompanying drawings are only used to illustrate the principles of the present invention and do not constitute any limitation on the present invention.

[0027] Figure 1 This is a schematic diagram of the overall architecture of the logistics order management system described in this invention, showing the functional composition of each terminal module and the data interaction path between them, including the registration and login terminal, the intelligent review terminal, the order processing terminal and the blockchain evidence storage terminal, as well as their connection methods with the intelligent middle platform and the data base.

[0028] Figure 2 This is a data flow sequence diagram of the blockchain evidence storage terminal in the system described in this invention, which shows the entire process of an order from generation to hashing on the blockchain, including steps such as user submitting an order, system generating a hash, on-chain evidence storage, and receipt return.

[0029] Figure 3 This is a flowchart of the order route optimization module described in this invention, which shows the input variables (including road segment distance, real-time congestion coefficient, cargo weight, etc.) and the execution flow of the optimization objective function of the multi-objective optimization algorithm used in order route calculation.

[0030] Figure 4 This is a functional module structure diagram of the intelligent review terminal described in this invention, showing the logical relationship and information flow of the integrity detection module, rule decision engine, and resource optimization module.

[0031] Figure 5 This is a flowchart of the dynamic scoring and plugin loading process of the payment routing engine described in this invention, illustrating how to select the optimal payment path based on parameters such as channel health, user preferences, and rates, and how to achieve hot-swappable loading of payment plugins through the SPI mechanism.

[0032] Figure 6 This is a distributed scheduling flowchart of the high-concurrency order processing module described in this invention, which shows the complete processing flow of order GeoHash sharding, task assignment to multiple computing nodes, Flink stream processing task execution, and consistent hash aggregation.

[0033] Figure 7 This is a schematic diagram of the hot and cold data hierarchical storage structure described in this invention, which shows the distribution and access paths of hot data, warm data and cold data in different storage systems, reflecting the trade-off between system response speed and storage cost. Detailed Implementation

[0034] To make the technical solution of this invention clearer and more complete, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the following embodiments are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Equivalent substitutions or modifications made by those skilled in the art without departing from the concept of this invention should be included within the scope of protection of this invention.

[0035] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings to better understand the technical solution of the present invention. However, those skilled in the art should understand that these embodiments do not limit the scope of protection of the present invention.

[0036] I. System Overall Architecture like Figure 1 As shown, this invention provides a high-quality and efficient logistics order management system, which adopts a three-layer distributed architecture design, consisting of a front-end access layer, an intelligent middleware platform, and a data foundation. The system achieves decoupled communication between modules through a service bus, supporting dynamic expansion, elastic deployment, and high-concurrency processing capabilities.

[0037] The system mainly includes the following four core business terminal modules: 1. Register and log in to the terminal The registration and login terminal serves as the system's front-end entry module, providing access services for multiple roles, including customers, merchants, and logistics companies. It supports identity authentication and access control based on the following methods: Biometric identification (such as facial recognition and fingerprint authentication); Dynamic tokens (such as OTP passwords and SMS verification codes); JWT+OAuth2.0 protocol authentication mechanism.

[0038] The system can grant different access permissions and module operation capabilities to different user types to ensure system security and data isolation.

[0039] 2. Intelligent review terminal The intelligent review terminal is a key module of this invention for improving order processing efficiency and intelligence. Its main functions include: Order integrity detection: By integrating computer vision models (such as ResNet50, YOLOv5), it identifies whether uploaded order images have problems such as blurriness, missing parts, or information occlusion; Rule-driven decision-making: The system has a built-in business rule engine based on Drools or Prolog, which combines information such as order dimensions, location, weight, and attributes to determine whether an order needs to be split or merged; Risk identification and scoring: Based on data such as historical order anomaly rate and customer credit score, output order execution risk score for reference in subsequent processing decisions; Resource optimization recommendations: By combining historical delivery data, warehouse location, and operational efficiency models (such as LSTM), predict the changes in timeliness and cost caused by order splitting and consolidation, and output optimal strategy recommendations.

[0040] 3. Order processing terminal The order processing terminal receives the reviewed orders and executes the complete processing logic, including: Credit scoring module: Based on the FICO scoring model, customer transaction history and other dimensions, dynamically assess the credit risk of orders; Dynamic payment routing engine: Obtains available payment channels in the system (such as Alipay, WeChat Pay, UnionPay, etc.), scores them based on factors such as response speed, fee rate, and user preference, and selects the optimal payment path using the following weighted scoring model: Route planning module: Based on a multi-objective path optimization algorithm, it comprehensively considers factors such as road segment distance, real-time congestion, cargo weight, and vehicle load limits to calculate the optimal delivery route. The system supports calling third-party real-time map APIs (such as Gaode and Baidu) to obtain dynamic traffic conditions and achieve dynamic route updates.

[0041] 4. Blockchain Evidence Storage Terminal The blockchain-based evidence storage terminal is a key guarantee for this system to achieve reliable recording of the entire order lifecycle. Its main functions include: Hash generation and on-chain: Generate SHA-256 hashes for key order fields (such as order number, amount, timestamp, and handler) and write them to the Hyperledger Fabric blockchain ledger; Zero-knowledge verification: Generate zk-SNARKs privacy proofs for sensitive information (such as recipient address and contact information) in orders, enabling verifiable, non-disclosure evidence storage; Cross-chain audit interface: Supports inter-chain communication with external compliance systems such as tax chain and customs chain, and realizes cross-chain operations such as data synchronization and receipt confirmation through relay bridge; Verifiable receipt output: Users can obtain electronic receipts containing block height, hash value and transaction ID, which support third-party verification and legal validity determination.

[0042] System Communication and Architecture Coordination Description: The four business terminal modules mentioned above are connected to the system through a unified API gateway. The API gateway is responsible for authentication, rate limiting, and log collection, and interfaces with a service bus (such as Service Mesh) to decouple the modules and discover services. Each terminal uses an intelligent middleware platform for model scheduling and rule management, and a data foundation for unified data reading and writing.

[0043] Data base components: like Figure 1 As shown, the data infrastructure adopts a tiered cold and hot storage architecture based on data access frequency and business value, specifically including: Hot data storage (such as Apache Doris): used to store frequently accessed data, with a response latency of less than 50ms, supporting real-time order queries; Warm data storage (such as TiDB): Stores historical orders and analysis data, with TP99 latency of less than 200ms, supporting report statistics and modeling; Cold data storage (such as JuiceFS): used for scenarios such as order archiving and compliance auditing, with archiving latency controlled within 2 seconds.

[0044] Data scheduling is managed by a unified storage interface layer, which supports automatic migration and policy adjustment.

[0045] In summary, this system architecture fully integrates multiple advanced technologies such as microservice decoupling, artificial intelligence decision-making, blockchain trusted evidence storage, and hierarchical data storage. It possesses high concurrency processing capabilities, intelligent decision-making capabilities, elastic scalability capabilities, and data trust assurance capabilities, making it suitable for various business scenarios such as e-commerce logistics, cross-border transportation, and government and enterprise procurement.

[0046] II. Blockchain Evidence Preservation Process like Figure 2 As shown, the logistics order management system of this invention integrates a blockchain evidence storage terminal to achieve reliable data recording and traceable verification throughout the entire order processing process, thereby enhancing the system's security, credibility, and compliance capabilities.

[0047] The system uses the Hyperledger Fabric consortium blockchain architecture to build an order evidence storage chain. It performs on-chain hash recording and zero-knowledge verification for key operation nodes of the order to ensure that the data is "verifiable, immutable, and auditable".

[0048] 1. Overview of the Evidence Preservation Process In one embodiment of the present invention, when a user submits an order through a registered login terminal, the system first calls the authentication and authorization center to verify the identity. After the verification is successful, the order request enters the intelligent review terminal through the API gateway.

[0049] The review terminal uses the business rules engine to determine whether the order can be split / merged and assesses its timeliness risk and processing priority. Subsequently, the order processing terminal calls the payment routing engine to complete the channel selection and transaction request, and generates payment vouchers.

[0050] After the payment is completed, the system will immediately trigger the blockchain notarization process, which mainly includes the following steps: 1.1. Extract key fields from the order (such as order number, user ID, amount, creation time, and transaction signature); 1.2. Use the SHA-256 algorithm to generate an irreversible hash digest; 1.3. Construct a Fabric transaction request, submitting the hash value as the data payload to the sorting node; 1.4. Fabric consensus nodes complete endorsement, sorting, and block production; 1.5. After successful notarization, the system receives metadata such as transaction ID, block height, and block hash returned from the blockchain; 1.6. Package the above metadata into a verifiable electronic receipt and return it to the user or regulatory authority.

[0051] 2. Evidence Data Structure and Content In a preferred embodiment, each order corresponds to a data structure for evidence storage, and its on-chain data fields include, but are not limited to: Table 1. Blockchain Evidence Storage Terminal - Explanation of Evidence Storage Data Structure Fields Fields illustrate OrderHash Order hash value (calculated from the order field using SHA-256) TxID Fabric Transaction ID BlockHeight Block height Timestamp On-chain timestamp Signature System private key signature value (used for post-verification) The system can be configured to store sensitive fields (such as delivery address, payment method, etc.) based on order type and business sensitivity. For data involving privacy, zero-knowledge proofs are generated using the zk-SNARKs protocol, enabling authenticity verification without exposing the data content.

[0052] 3. Zero-knowledge verification module (optional feature) In an optional embodiment of the present invention, to meet the higher requirements for privacy protection and compliance in scenarios such as cross-border e-commerce and financial logistics, the system introduces a zero-knowledge verification module, the core process of which is as follows: (1) Use algorithms such as Pedersen Commitment to generate ciphertext for sensitive fields (such as mailing address and ID number); (2) Construct zk-SNARKs circuits and generate constraint proofs; (3) Upload the proof along with the order hash onto the blockchain; (4) External verifiers can call the smart contract and verify the legality relationship between the hash and the original order using the `verifyProof()` method; (5) No user privacy data needs to be exposed throughout the process, which meets the requirements of regulations such as GDPR.

[0053] 4. Cross-chain auditing mechanism (optional feature) In another preferred embodiment, the system supports data interoperability with external regulatory chains (such as tax chains, customs chains, and financial regulatory chains). This can be achieved by integrating the Polkadot relay chain or Fabric Chaincode bridging mechanism. Block header synchronization and mapping; Data summary cross-validation; Receipt information is shared and linked.

[0054] This mechanism ensures that key actions in the order processing chain are traceable in the regulatory system, and is particularly suitable for scenarios that require compliance with laws and regulations, invoice verification, customs clearance and evidence storage.

[0055] 5. Receipt generation and verifiability After the order is successfully registered, the system encapsulates the information returned on the blockchain into an electronic receipt with a digital signature, the contents of which include: OrderHash TxID (Transaction Hash) BlockHeight (height of the block) Timestamp (on-chain time) Signature (System Signature) This receipt can be verified on the blockchain by any third-party verifier to ensure that the order is genuine, has not been tampered with, and the record is traceable.

[0056] In summary, the blockchain evidence storage process described in this invention constructs a trusted recording mechanism for the entire process from order generation to payment completion and then to tamper-proof on-chain recording. By combining encrypted hashing, zero-knowledge proofs, and blockchain smart contracts, it effectively solves the technical challenges of traditional logistics systems in terms of order data tamper-proofing, auditability, and trusted cross-system transmission.

[0057] III. Path Optimization Module Design like Figure 3 As shown, the route optimization module proposed in this invention generates the most efficient and resource-efficient delivery route for each order during the order execution phase. This module is deployed within the order processing terminal and uses an algorithm engine to analyze and model multi-dimensional input features, combining real-time traffic data, transportation resources, and order physical attributes to make route decisions.

[0058] 1. Feature extraction stage In a preferred embodiment, the system automatically triggers the path optimization module after the order passes through the intelligent review terminal and payment confirmation is completed.

[0059] The path optimization module first extracts features from the path dimensions involved in the order and system environment, mainly including: Distance between road sections ( ): represents the physical length of the i-th segment in the order delivery route, in kilometers, usually obtained through Gaode, Baidu or self-built map services; Real-time congestion coefficient ( ): Calculated based on real-time traffic flow and historical averages along the target route, reflecting the degree of traffic congestion; Cargo weight ( ): The actual weight (in kg) of each item in the order; Vehicle load limit ( ): The maximum load capacity of the delivery vehicles selected during the dispatching process; Average vehicle speed ( ): Dynamically estimated based on vehicle type, current road segment type (highway / urban / rural) and real-time traffic data.

[0060] These variables are all organized into feature vectors before scheduling and used as model inputs.

[0061] 2. Multi-objective optimization model The route optimization module uses the following weighted multi-objective function to comprehensively evaluate order routes, with the objective of minimizing overall cost while considering delivery timeliness, traffic conditions, and loading efficiency: in: Indicates the priority of timeliness. Indicates the weight of traffic conditions. This represents the resource load efficiency weight.

[0062] This model takes into account the following three aspects: Table 2. Description of each item in the path optimization module - multi-objective function project meaning Control Target Single segment time Minimize the total transportation time Congestion level Try to avoid highly congested road sections load ratio Ensure high vehicle loading rate and avoid overloading. 3. Optimize algorithms and deployment methods In terms of implementation, the path optimization module can choose the following strategies: Static strategy priority + dynamic fine-tuning: First, use historical data to train an initial path model, and then fine-tune the model results using real-time traffic conditions; Heuristic search methods such as Ant Colony Optimization (ACO) and Genetic Algorithm (GA) are used for complex path combination problems. A hybrid model of Graph Neural Network (GNN) + XGBoost is used to handle path scoring and ranking tasks in large-scale urban networks. Supports concurrent execution (>5000 QPS), suitable for high-concurrency order scheduling systems.

[0063] This module can be deployed on Flink stream processing nodes or containerized AI inference services, and supports asynchronous execution of path decisions in different geographical partitions.

[0064] 4. Output and Business Integration The model calculation results include: Optimal path node sequence (GPS coordinates or road number); Estimated time for each segment of the journey; Total estimated delivery time and shipping window; Path credibility score (used for manual review or two-way verification).

[0065] This route information will be included as part of the delivery plan, working in conjunction with the warehouse scheduling module, and ultimately written into the order execution plan and sent to the delivery execution end.

[0066] 5. Implementation Results and Comparative Analysis After the system of this invention was deployed, the actual test results compared with the traditional static path planning system (which only considers the shortest path) are shown in Table 3: Table 3 Comparison of the effects of traditional methods and the system of this invention in the path optimization module. index Traditional methods This invention system Increase ratio Average delivery time 48 hours 32 hours ↓33.3% Road congestion avoidance rate 62% 91% ↑46.8% Average vehicle loading rate 68% 89% ↑30.9% Path change response time 15 minutes Real-time (<30 seconds) Real-time This path optimization module is particularly suitable for the following scenarios: Consolidated delivery of multiple orders (consolidation route calculation); Emergency replanning for sudden traffic congestion during peak hours; Complex business scenarios such as mixed loading of light and heavy goods / multi-vehicle scheduling optimization.

[0067] In summary, the path optimization module proposed in this invention, which combines structured feature extraction, weighted multi-objective functions, intelligent algorithm scheduling, and business linkage output, significantly improves the system's responsiveness and path scheduling accuracy, making it an indispensable intelligent core component in modern logistics order management systems.

[0068] IV. Intelligent Review Terminal Structure like Figure 4 As shown, the intelligent review terminal of this invention is used to perform structured intelligent analysis on order information submitted by users, and to determine whether the order has any abnormalities, incompleteness, conflicts, or optimizable delivery conditions. It is the core component of the system for realizing intelligent order preprocessing. The terminal includes the following three main functional modules: 1. Integrity Detection Module The integrity detection module is used to determine the structural integrity of order images or electronic order data, ensuring that subsequent system processing is based on reliable basic information.

[0069] In one embodiment of the present invention, the module includes: Image acquisition unit: used to acquire images of order vouchers uploaded by users, such as bills of lading, delivery receipts, invoices, QR code screenshots, etc. Image recognition model: The ResNet50 deep convolutional neural network model is preferred, which can extract features from images and identify multiple defects, such as image blurring, missing content, watermark occlusion, truncation, and angle deviation. Detection logic processor: performs threshold judgment on the model output (e.g., if the confidence level is <0.85, it is considered as defective) and generates an order integrity score (0~100); Output interface: Feeds the integrity identification results back to the rule decision engine, which serves as the basis for whether to allow the order to proceed to the next process.

[0070] Preferably, the module has an accuracy rate of over 95% and an identification time of less than 300ms, meeting the requirements for large-scale real-time processing.

[0071] 2. Rule-based decision engine The rule-based decision engine is a key module in the intelligent review terminal that enables order splitting, merging, and process routing suggestions. It has the following functional structure: Order rule base: Contains a set of rules extracted based on business experience, such as: When the number of destinations in an order is greater than 1 and the total weight is greater than 20kg, splitting the order is recommended. When multiple orders have the same payment method and the same shipping address, a merger is recommended. Rule execution engine: Supports conditional logic reasoning based on rule languages ​​such as Drools and Prolog, and supports forward chaining and backward chaining inference methods; Hot rule update interface: Business users can edit and publish new rules in real time through the rule configuration panel. The system will automatically load and apply the rules, supporting seamless switching. Rule interpreter: The decision-making process is output in a structured form as "rule hit path + decision output" for subsequent modules and user interface to call.

[0072] In a preferred embodiment, the system supports rule priority configuration and condition weight setting, which is suitable for differentiated review strategies for different order types (B2B / B2C) or industry scenarios.

[0073] 3. Resource Optimization Module The resource optimization module is used to evaluate the historical efficiency and cost-benefit of order execution paths and to perform "benefit prediction" on splitting / merging suggestions, thereby helping to form better operational recommendations.

[0074] This module mainly includes: Historical data analysis unit: Statistically analyzes similar order processing time, delivery cost, and anomaly rate to generate decision reference vectors; LSTM prediction model: This model uses a Long Short-Term Memory (LSTM) neural network to predict order processing time trends. For example, if the system predicts that the current path's execution time is >48 hours and can be shortened to <36 hours after merging, it outputs a "merge recommended" signal. Timeliness and benefit evaluator: Scores each operation path (such as direct processing / merged processing / split processing) and quantifies its expected benefits (such as reduced time, decreased error rate, reduced number of trips, etc.). The results are passed to the rule engine for final decision reinforcement.

[0075] In one embodiment, the prediction error of this module is controlled within ±8%, supporting dynamic learning and continuous training updates.

[0076] Module Collaboration Description: The three modules communicate with each other via an event-driven mechanism. For example: 1. The integrity detection module outputs "incomplete" → blocks the rule engine from executing and prompts the user; 2. The rules engine determines that the path can be merged → it calls the resource optimization module to compare the benefits of "merged paths vs. independent paths"; 3. The resource optimization module predicts that the timeliness will be improved by more than 30% after merging. → The recommended enhancement is "automatic merging" and it is marked as green.

[0077] This collaborative mechanism ensures that intelligent review not only has the ability to make formal judgments, but also has business-oriented prediction and optimization capabilities.

[0078] The application effects and performance indicators (actual measurements) are shown in Table 4: Table 4 Comparison of the effects of intelligent review terminal - traditional manual review and the system of this invention index Traditional manual review This invention is an intelligent review terminal. Improvement ratio Average review time 12~15 minutes / order <1 second / single ↑>95% Split suggestion hit rate 60% 91% ↑>50% Abnormal order identification rate <70% >92% ↑>30% Real-time response of rules Hot update is not supported. Supports millisecond-level hot reloading Real-time In summary, the intelligent review terminal, through multi-module collaboration and the integration of image recognition, rule reasoning, and predictive analysis, effectively improves the automation level and intelligent decision-making capabilities of order preprocessing, laying a reliable data foundation for subsequent order processing, payment routing, and path planning. It is an indispensable core component for building an intelligent logistics management system.

[0079] V. Payment Routing Engine Process like Figure 5As shown, the payment routing engine described in this invention is one of the key sub-modules in the order processing terminal, used to realize intelligent channel selection, plug-in invocation, and dynamic routing scheduling during the order payment stage. This module fully considers the real-time health status, cost efficiency, and user preferences of different payment channels to achieve automated matching of the optimal payment path, effectively improving the order payment success rate and user satisfaction.

[0080] 1. Obtain available payment channels After order confirmation and submission, the system first calls the payment channel registration center to obtain information on all available payment channels supported by the current merchant, including but not limited to: Third-party payment (such as Alipay, WeChat Pay, PayPal); Direct bank connection channels (such as online banking payment, quick payment); Internal settlement channels within enterprises (such as pre-deposits and credit periods).

[0081] Each channel information includes metadata such as channel ID, rate structure, channel status, plugin version number, supported currencies, and supported terminals (PC / mobile).

[0082] 2. Channel health monitoring and scoring calculation The system has a built-in payment channel health monitoring module, which is used to collect the operating indicators of each channel in real time within the historical and current time windows, as shown in Table 5: Table 5 Payment Routing Engine - Channel Health Monitoring Indicators Indicator Item meaning Data source Success Rate Percentage of requests successfully returned in the last 10 minutes Log statistics system Average response latency (Latency) Average time to a successful return (in milliseconds) APM / Link Tracing System Available status Is the channel reachable / stable? Monitoring probe / heartbeat mechanism After obtaining the above indicators, the system executes the following scoring function for each channel to calculate its overall payment feasibility score: in: This indicates sensitivity to payment costs (e.g., setting it to higher for small orders). This indicates the importance attached to the speed of payment arrival; This indicates the weight of user-defined priority channels (such as setting WeChat as the preferred channel and avoiding overseas payments). The higher the Score, the more suitable the payment channel is for use under the current conditions.

[0083] The scoring results will be used as the basis for channel routing ranking, and the channel ranked first will be the current preferred channel.

[0084] 3. SPI Plugin Hot Loading and Dynamic Binding To achieve modularity and dynamic expansion of the payment function, this system adopts the Java SPI (Service Provider Interface) mechanism for the registration, invocation, and dynamic loading of payment plugins. Its core mechanism includes: Each payment channel corresponds to a plugin class that implements a unified `PaymentPlugin` interface; The plugin supports registering metadata using annotations (e.g., @Plugin(name="Alipay", version="3.2")). The routing engine dynamically loads the corresponding plugin based on the channel ID and binds it to the current payment task; The plugin hot update mechanism supports version upgrades and parameter adjustments without requiring a system restart.

[0085] This design allows the system to add new payment methods or switch payment service providers without modifying the main program logic, enabling "plug and play, on-demand loading" flexible calls.

[0086] 4. Execute payment requests and process results After the plugin is loaded, the system encapsulates the payment parameters of the current order (such as order ID, amount, currency, payment callback address, etc.) into a payment request object and calls the plugin's `process(order)` method to initiate the transaction.

[0087] The plugin performs the following operations internally: 1. Perform necessary signatures or encryption on the requested data (such as using the national standard SM4 encryption protocol). 2. Call payment gateway interfaces (such as Alipay Open Platform, WeChat Merchant API); 3. Listen for payment callbacks (synchronous / asynchronous) and parse the results; 4. Returns payment status, third-party transaction number, error message, etc.

[0088] The results will be written to the order processing database and a payment voucher will be generated and transmitted to the blockchain evidence storage module.

[0089] 5. Payment Failure Handling and Degradation Strategies In a preferred embodiment, the system supports the following exception handling strategy: If the main channel request fails or times out, the system can automatically switch to the channel ranked second in the score. Set a circuit breaker flag for channels that fail more than 3 times consecutively, and stop routing calls; Automatically trigger plugin alerts and version rollback for payment response anomalies (such as signature errors or interface changes); For high-priority orders, manual intervention or the offline payment mechanism can be invoked.

[0090] 6. Application effects and performance indicators are shown in Table 6: Table 6 Comparison of the effects of the payment routing engine - traditional process and the system of this invention project Traditional payment process This invention is a payment routing engine. Improved results Average payment success rate 88% 98.6% ↑ 10.6% Average payment time 3.8 seconds 2.2 seconds ↓ 42% Channel access period Several days Instantly online Real-time Payment plugin extension methods Hard-coded integration SPI hot reloading Modular and flexible In summary, the payment routing engine provided by this invention achieves intelligent, dynamic, and highly available payment processes through mechanisms such as "scoring selection + hot plug-in loading + anomaly degradation," significantly improving the system's payment processing efficiency and stability in high-concurrency scenarios.

[0091] VI. High-Concurrency Order Processing Flow like Figure 6 As shown, the logistics order management system of the present invention supports a processing flow that supports concurrent access to large-scale orders, distributed parallel computing and unified aggregation of results. It has strong task isolation, execution throughput and stability assurance capabilities, and can be adapted to extremely high-concurrency scenarios such as "Double 11" promotion, flash sales, and centralized delivery.

[0092] 1. Order Receiving and Pre-processing The system receives order requests from various channels (PC, e-commerce platform, mobile terminal) through the API gateway. First, the authentication and authorization center performs security verification, including JWT token verification, source verification, and duplicate submission judgment.

[0093] Subsequently, the order enters the processing queue, and is read by the scheduling controller to trigger a high-concurrency processing flow.

[0094] 2. GeoHash spatial partitioning In a preferred embodiment of the present invention, the system uses the GeoHash spatial encoding algorithm to divide the destination address (delivery location coordinates) of the order into regions.

[0095] GeoHash can encode two-dimensional latitude and longitude information into strings with similar prefixes, enabling rapid grouping of geographical regions; Depending on the number of prefix digits, the spatial partitioning granularity can be flexibly adjusted (e.g., 6 digits correspond to approximately 1.2km × 0.6km). Orders within the same shard will be routed to the same computing node, thereby maximizing "data locality" and improving processing efficiency.

[0096] For example, orders in Beijing's urban area might be divided into two computing sub-regions, "wx4g0a" and "wx4g0b", with each sub-region bound to one or more concurrent processing nodes.

[0097] 3. Scheduled to the compute node cluster The system distributes orders from different GeoHash regions to ready compute nodes (which can be containers, Pods, or Function instances) based on a service registry and scheduling strategies. The scheduling supports the following strategies: Least Loaded First; Zone-Aware priority; Prioritize scheduling of cold-start nodes (to improve resource utilization).

[0098] Node services can be hosted by processing clusters composed of Kubernetes, Flink, Kafka Streams, etc.

[0099] 4. Parallel subtask execution Within each compute node, orders are broken down into multiple subtasks that are executed asynchronously, including but not limited to: Credit scoring subtask: Use the FICO model or historical transaction database to conduct credit assessments on users / merchants who place orders; Payment routing subtask: Invoke Figure 5 The payment routing engine completes channel selection and plugin loading; Path optimization subtask: call Figure 3 The route planning module calculates the optimal delivery route; Warehouse scheduling subtask (optional): Match the optimal shipping warehouse, wave, etc.

[0100] Each subtask is independent of the others and is executed using a non-blocking asynchronous mechanism. It supports asynchronous return, priority queues, and event callback processing to maximize the parallel utilization of computing resources.

[0101] 5. Result aggregation and consistent hashing integration After each subtask is completed, the node encapsulates the intermediate results into a task response object and sends it to the result aggregation module. This module performs the following operations based on the consistent hashing algorithm: Ensure that the same user / the same order is always aggregated into the same processing slot (to avoid dispersion); To avoid the problem of "high redistribution rate" in traditional sharded hash tables; Supports hot-swapping of nodes / migrating of only a small portion of data after a failure, improving system resilience; After aggregation, the final results are used to construct a complete order execution plan object, which includes information such as payment status, credit score, delivery route, estimated delivery time, and execution instructions.

[0102] 6. Performance Indicators and Technical Support Through the above structured concurrency processing scheme, this system can still maintain stable response performance under large-scale stress scenarios. The measured indicators are shown in Table 7. Table 7 High-Concurrency Order Processing Flow - Performance Indicators index value illustrate TPS (Throughput Capacity) >100,000 Orders processed per second by a single cluster TP99 latency <200ms 99% of requests return within 200ms Task failure rate <0.1% Abnormal rollback and retry protection Extended time <10 seconds Instance horizontal scaling time In addition, the system also has the following enhanced capabilities: Rate limiting mechanism: Supports QPS rate limiting based on user / merchant / IP; Idempotent processing: All processing tasks generate idempotent keys to prevent duplicate submissions; Priority scheduling: Weighted scheduling can be set for large customer orders and targeted order pushes; Elastic scaling: Automatic scaling up / down is triggered based on HPA or custom metrics.

[0103] In summary, the high-concurrency order processing flow proposed in this invention effectively achieves rapid order processing, reasonable resource scheduling, and output stability control in extremely high-concurrency environments by combining GeoHash partitioning, asynchronous task execution, and consistent hash aggregation. It is widely applicable to complex business scenarios such as e-commerce promotions, peak logistics, and real-time order integration.

[0104] VII. Hot and Cold Data Layered Storage Mechanism like Figure 7 As shown, in order to address the problems of large data volume, uneven access frequency, and large differences in read and write performance requirements in logistics order systems, this invention introduces a three-level data tiered storage architecture based on access popularity. Combined with data lifecycle management and intelligent scheduling strategies, it achieves an efficient balance between system performance, storage cost, and data availability.

[0105] 1. Tiered storage architecture design In a preferred embodiment of the present invention, data is dynamically divided into three storage layers based on multi-dimensional indicators such as time, access frequency, and tag attributes: (1) Hot Tier Storage system: Apache Doris columnar database (or similar databases such as ClickHouse and StarRocks); Data content: Active order data within the past 7 days, including: basic order information, payment status, delivery progress, customer reviews, etc. Access characteristics: high-frequency read / write, high real-time requirements; Typical operations include: order details inquiry, customer service processing, and real-time risk control API calls. Performance metrics: Single-point query latency <50ms, supports high-concurrency read and write (QPS>10,000); Compression and Indexing: Improve query efficiency by using columnar storage compression and Bitmap indexes.

[0106] (2) Warm Tier Storage system: TiDB distributed relational database (or a NewSQL-compatible architecture, such as CockroachDB or PolarDB); Data content: Orders, scheduling records, settlement records, and some historical transaction logs from the past 1-3 months; Access characteristics: Medium-frequency access, mainly used for monthly queries, business analysis, and model training; Typical operations include: operational dashboard analysis, report generation, refund auditing, and invoice retrieval. Performance metrics: TP99 query latency is controlled within 200ms, and it supports complex table joins and time-series aggregations; Structural advantages: Compatible with MySQL syntax, supports horizontal scaling and OLAP operations.

[0107] (3) Cold data layer Storage system: JuiceFS distributed object storage (compatible with object protocols such as S3, OSS, and COS); Data content: Data older than 3 months or marked as archived, such as historical order archives, raw logs, blockchain receipts, compliance audit records, etc. Access characteristics: low-frequency access, primarily write-based, high latency tolerance; Typical operations include: government audits, cross-border compliance, extraction of arbitration evidence, and batch export of evidence. Performance metrics: Read latency is within 1-2 seconds; supports large file retrieval (logs, invoice PDFs); Redundant backup: Supports multi-region disaster recovery, multi-copy data storage, and encrypted compression.

[0108] 2. Data scheduling mechanism The system includes a data scheduling controller module to enable automatic data migration and lifecycle management between hot, warm, and cold storage. The scheduling criteria are detailed in Table 8. Table 8 Data Scheduling Mechanism - Data Hierarchical Migration Conditions Table condition illustrate Time rules Data generation / update time exceeds thresholds such as 7 days or 90 days. Access frequency Data that has not been accessed for more than 30 days is marked as warm / cold. Tag attributes Labels such as "High-Value Customers" and "Disputed Orders" maintain high-volume data over extended periods. Audit Mark Data subject to random inspections or regulatory scrutiny can be locked and frozen, and prioritized for migration to the cold data layer. The scheduling module supports automated execution based on CRON tasks, stream processing engines (such as Flink), or AI access prediction models. Data migration processes support: Asynchronous migration does not block the main business; Incremental synchronization avoids redundant data transfer; Storage format conversion (e.g., Row → Column → Object); Data encryption and signature verification.

[0109] 3. Multi-source read / write and transparent access layer design To maintain consistency in business system access, the system introduces a transparent data access layer (DAL) to uniformly encapsulate data sources at each layer. The application layer does not need to concern itself with data location, and it supports: Heat / temperature data is read directly according to routing rules; Cold data supports retroactive reloading; Automatically rollback to lower-level data if query times out; Data tracking and caching preheating mechanism.

[0110] 4. Practical application effects and performance improvement After deploying this hot and cold tiered architecture, the system's performance in a real operating environment is shown in Table 9: Table 9 Comparison of the effects of the traditional architecture and the system of this invention in the hierarchical storage mechanism for hot and cold data. index Unlayered architecture This invention system Improved results Average latency for hot data queries 320ms 41ms ↓87% Storage resource costs (same as data volume) 100% 63% ↓37% Reliability of cold data retrieval middle high ↑Reliability Improvement Peak daily active user order processing ~200,000 orders >600,000 orders ↑3x expansion capability This mechanism is particularly suitable for application scenarios in e-commerce platforms, government and enterprise procurement, cross-border logistics, and financial clearing and settlement systems where order data has a complex lifecycle and uneven distribution of hot and cold data.

[0111] In summary, the cold and hot data hierarchical storage mechanism proposed in this invention combines access frequency, business attributes, and data sensitivity, and performs intelligent classification management based on multiple heterogeneous storage systems. This significantly improves the system's response performance, data availability, and operational cost control capabilities, making it a key foundational capability in modern logistics order management systems.

[0112] VIII. System Deployment and Elasticity To ensure the stable operation of the logistics order management system under high concurrency, high volatility, and high reliability scenarios, this invention constructs the entire system as a distributed service architecture based on cloud-native container architecture, deploys it in a Kubernetes (K8s) cluster environment that supports elastic scaling and self-healing capabilities, and combines it with service mesh, automated operation and maintenance platform and asynchronous fault tolerance mechanism to achieve high availability and business continuity assurance of the platform.

[0113] 1. Kubernetes containerized deployment architecture Each functional module of the system (such as the registration and login terminal, intelligent review terminal, order processing terminal, blockchain evidence storage terminal, data scheduling module, etc.) is encapsulated as a stateless container service and deployed in a Kubernetes cluster.

[0114] Each service is deployed as a separate Deployment + Service; Supports horizontal scaling (scale-out) and rolling upgrades; State services (such as databases and caches) are deployed as StatefulSets and bound to persistent volumes (PVCs). Configuration items are managed through ConfigMap / Secret and support runtime hot reloading; Network connectivity is achieved through ClusterIP + Ingress gateway or Service Mesh; Container images are automatically built and deployed through CI / CD pipelines (such as GitLab CI and ArgoCD), enabling integrated DevOps operations and maintenance.

[0115] 2. HPA Flexible Expansion / Shrinkage Mechanism The system supports Kubernetes-based HPA (Horizontal Pod Autoscaler) for automatic horizontal scaling, enabling elastic responses to fluctuations in order traffic.

[0116] The scaling metrics can be set to CPU utilization, memory, QPS, latency, or custom Prometheus metrics; For example, when the CPU utilization of the order processing terminal container is consistently >80% and QPS >5000, the number of Pods is expanded from 5 to 15. The shrinkage strategy introduces cooldown time and minimum replica count control to avoid frequent jitter.

[0117] In addition, it supports serverless scaling based on tools such as KEDA and Virtual Kubelet, which can adapt to sudden extreme peak scenarios (such as flash sales / snap-ups).

[0118] 3. Container anomaly handling and self-healing capabilities The system incorporates a complete container self-healing mechanism, which can automatically detect and recover when containers, services, or nodes experience anomalies. Pod-level fault tolerance: Heartbeat detection is implemented through `livenessProbe` and `readinessProbe`, and the system restarts upon exception; Node-level fault tolerance: When a node fails, Kubernetes automatically migrates the service to a healthy node through the scheduler; Distributed rate limiting and circuit breaking: Implement service degradation, rate limiting, and circuit breaking protection through Service Mesh such as Istio; Health monitoring and recovery: Works with Prometheus + Grafana + AlertManager to monitor metrics in real time and trigger container rebuilds or traffic switching.

[0119] 4. Fault recovery and log replay mechanism In one embodiment of the present invention, the system is designed with a Log Replay Engine to restore the business context in case of abnormal situations such as system crashes or data interruptions, and to realize breakpoint resumption and idempotent repair of order processing.

[0120] Key actions during order processing (such as payment, route calculation, and sorting scheduling) are written to message middleware such as Kafka and Pulsar in the form of events; After the system restarts following a failure, it automatically scans for orders that are not yet completed and traces the task status back based on the log stream. Consistency can be verified by combining transaction logs, database binlogs, and on-chain receipts; The idempotency guarantee mechanism ensures that each order processing logic will not cause errors even if it is executed repeatedly.

[0121] For example, when an order payment is completed but route planning has not yet been executed, the system will read the "payment completed" event from the log and re-trigger route calculation after restarting, thus avoiding data loss and business interruption.

[0122] 5. System Availability and SLA Assurance Indicators The deployment architecture and resilience strategy of this invention enable the system to maintain stable operation even under high loads. Its key performance indicators are shown in Table 10. Table 10 System Deployment and Resilience Capabilities - Key SLA Assurance Indicators index describe numerical values System Availability Annual unavailable time <52 minutes ≥99.99% Data durability The probability of data storage errors is extremely low. ≥11 nines (99.999999999%) Order processing recovery time (RTO) Time required to restore orders after system crash <30 seconds Data Recovery Point Objective (RPO) Maximum data loss window 0 (Real-time log synchronization) These safeguards provide solid support for processing high-value, time-sensitive orders.

[0123] In summary, this invention deploys the order management system on a Kubernetes container platform and combines it with mechanisms such as HPA automatic scaling, service self-healing, log replay, rate limiting, and circuit breaking to build a modern logistics order processing platform with high reliability, strong elasticity, and continuous availability. It can be widely used in complex business environments such as e-commerce, logistics, and financial clearing.

[0124] The high-quality and efficient logistics order management system and method proposed in this invention integrates a distributed computing architecture, an artificial intelligence decision-making model, and a blockchain trusted evidence storage mechanism. It realizes the automation, intelligence, security, and trustworthiness of the entire order processing process, significantly improving the system's concurrent processing capability, path optimization efficiency, payment routing success rate, and data audit reliability. It can be widely applied to various business scenarios such as e-commerce logistics, cross-border transportation, same-city express delivery, and financial clearing, and has good industrial application value and promotion prospects.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the accompanying drawings and specific embodiments, equivalent substitutions, modifications, or variations made by those skilled in the art without departing from the essential spirit and principles of the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A high quality and efficient logistics order management system, characterized in that, The system comprises the following modular terminals, each of which interacts with data through a distributed service bus: A registration and login terminal: used to implement multi-role identity authentication of users, merchants and logistics parties, and supports biometric identification and dynamic token two-factor authentication; An intelligent review terminal: including an integrity detection module, a rule decision engine and a resource optimization module, which automatically generates order splitting / merging suggestions and risk assessment based on computer vision models and rule engines; An order processing terminal: including a credit scoring module, a dynamic payment routing engine and a path planning module, which are used for credit determination, channel selection and optimal delivery path calculation, respectively; A blockchain storage terminal: used to generate tamper-proof storage of the order lifecycle, supporting cross-chain audit and privacy protection verification.

2. The system of claim 1, wherein, The rule decision engine in the intelligent review terminal makes order splittability judgments based on the following rules: ```prolog can_split(Order) :- has_multiple_destinations(Order), not(contains_fragile_items(Order)), total_weight(Order) > 20kg. ``` The integrity detection module identifies order image defects based on the ResNet50 model, with an accuracy of not less than 95%; the resource optimization module predicts the processing time efficiency gain after splitting / merging based on the LSTM model, with a prediction error of less than 8%.

3. The system of claim 1, wherein, The path planning module of the order processing terminal is used to perform the following objective function for delivery path optimization: ； wherein, is a road segment distance, is a vehicle speed, is a real-time congestion coefficient, is a cargo weight, is a vehicle load upper limit, , , is a weight parameter.

4. The system of claim 1, wherein, The dynamic payment routing engine selects payment channels based on the following scoring function: ； The payment plug-in is hot-loaded based on the SPI mechanism, and the channel health monitoring module is used to dynamically adjust the availability weight of the channel according to the response time and success rate.

5. The system of claim 1, wherein, The blockchain storage terminal includes: A data chaining module for generating SHA-256 hash of order key fields and writing to the blockchain ledger; A zero-knowledge verification module that uses the zk-SNARKs protocol to generate irreversible verification proofs of private fields, with verification logic including Merkle path verification and zero-knowledge constraint checking; A cross-chain audit module based on Polkadot or relay chain bridging protocol for data synchronization and reconciliation verification with external supervision chain.

6. The system of claim 1, wherein, The system is deployed in a containerized platform environment, further including: An elastic expansion module that automatically adjusts computing resources based on Kubernetes HPA strategy; A fault recovery module that implements node disaster recovery through service health checks and Pod migration mechanisms; A log replay module for recovering order processing context after system failure or restart.

7. The system of claim 1, wherein, The system includes a high-concurrency order processing module, whose processing flow includes: Order is split based on GeoHash encoding and dispatched to multiple parallel computing nodes by region; Each node runs a stream processing job to perform order credit assessment, payment routing and path planning; Finally, the multi-node results are aggregated based on consistent hashing mechanism to ensure data consistency.

8. The order processing module of claim 7, wherein, The parallel stream processing engine is based on Apache Flink implementation, with task state reservation and fault tolerance processing capability, supporting order-level processing delay control and throughput monitoring (TP99 < 200ms).

9. The system of claim 1, wherein, The system supports "hot and cold data" hierarchical storage strategy, including: Hot data is stored using columnar databases (such as Apache Doris), with response time less than 50ms; Warm data is stored based on distributed relational databases such as TiDB; Cold data is archived based on object storage services (such as JuiceFS), with an archive delay of less than 2 seconds.

10. A logistics order management method applied to the system of any one of claims 1-9, characterized in that, Including the following steps: S101. Obtain orders and generate structured processing suggestions and risk ratings through intelligent review terminals; S102. Divide orders into multiple GeoHash area nodes and execute parallel processing procedures; S103. Select payment channels based on intelligent payment scoring models and initiate transactions; S104. Perform path optimization calculations to generate delivery plans; S105. Store key operations through blockchain and generate verifiable electronic receipts.

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