Digital intelligence park supply chain warehouse distribution integrated service system

Abstract

The invention, which relates to the technical field of the logistics supply chain, discloses a digital intelligence park supply chain warehouse distribution integrated service system comprising a dynamic warehouse resource collaborative scheduling module, a multi-modal intelligent path planning module and a block chain federal learning security center module. Data are collected in real time through 5G-IoT, storage locations and equipment are dynamically allocated in combination with a quantum genetic algorithm, and the storage proportion of cold and hot regions is optimized; planning a path in real time by using an improved ant colony algorithm and a dynamic cost function, and supporting multi-carrier collaborative distribution of unmanned vehicles and unmanned aerial vehicles; a hierarchical block chain and federated learning technology is adopted to ensure data encryption storage and cross-enterprise security sharing; the system improves the storage efficiency and the inventory turnover rate, shortens the distribution time, reduces the energy consumption, guarantees the data privacy and compliance, and is suitable for the supply chain management of a modern park.

Description

Technical Field

[0001] The present invention relates to the field of logistics supply chain technology, and specifically to a digital park supply chain warehousing and distribution integrated service system. Background Art

[0002] With the rapid development of the logistics industry, industrial park supply chain warehousing and distribution services face numerous challenges. Traditional warehousing and distribution systems typically operate independently, resulting in inefficient resource allocation and low inventory turnover in the warehousing phase, while distribution faces challenges such as illogical route planning and difficulties in coordinating multiple vehicles. Furthermore, data security and privacy protection are becoming increasingly important in cross-enterprise collaboration. Existing technologies struggle to effectively ensure encrypted data storage and secure sharing, leading to numerous concerns among enterprises regarding supply chain collaboration.

[0003] When it comes to warehouse resource management, existing systems mostly rely on manual experience or simple automated algorithms for storage allocation and equipment scheduling, making them unable to cope with complex and changing order demands and dynamic environments. For example, during promotional events, the ratio of hot and cold storage areas cannot be flexibly adjusted, resulting in low storage space utilization. Simultaneously, improper equipment load distribution can lead to equipment failure or reduced efficiency. Furthermore, the lack of an effective dynamic priority adjustment mechanism prevents urgent orders from being prioritized, impacting customer satisfaction.

[0004] In terms of delivery route planning, traditional route planning methods are unable to integrate multi-source data (such as GIS geographic information, traffic flow data, drone images, etc.) in real time, making it difficult to accurately reflect real-time road conditions and environmental information within the park, resulting in inaccurate route planning, long delivery times, and high energy consumption. At the same time, when multiple vehicles such as unmanned vehicles and drones collaborate in delivery, there is a lack of effective conflict resolution mechanisms and dynamic time window adjustment strategies, which makes route conflicts and resource waste prone to occur. In addition, existing technologies have deficiencies in data security and privacy protection, making it difficult to meet the needs of encrypted data storage and secure sharing in cross-enterprise supply chain collaboration, which can easily lead to data leakage risks and affect the cooperative trust between enterprises.

[0005] In order to solve the above-mentioned defects, a technical solution is now provided. Summary of the Invention

[0006] The purpose of this invention is to solve the problems of inefficient warehousing resource allocation, unreasonable distribution route planning, and insufficient data security and privacy protection in traditional park supply chains, and to propose a digital park supply chain warehousing and distribution integrated service system.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A digital and intelligent park supply chain warehousing and distribution integrated service system, including:

[0009] A dynamic warehouse resource collaborative scheduling module collects data in real time through 5G-IoT, builds a three-dimensional digital twin model, and uses quantum genetic algorithms to dynamically allocate storage locations and equipment, optimize the storage ratio between hot and cold areas, and improve warehouse efficiency and inventory turnover.

[0010] A multimodal intelligent route planning module integrates GIS, traffic data, and drone imagery, using an improved ant colony algorithm and a dynamic cost function to plan routes in real time. It supports collaboration between unmanned vehicles and drones, shortening delivery times and reducing energy consumption.

[0011] The blockchain federated learning security hub module uses layered blockchain and federated learning technologies to ensure encrypted data storage and secure cross-enterprise sharing. It also automatically audits warehousing and distribution services through smart contracts to protect data privacy and compliance.

[0012] Furthermore, the specific execution process of the dynamic warehouse resource collaborative scheduling module is as follows:

[0013] S101, Real-time collection and fusion of multi-source heterogeneous data: 5G-IoT devices deployed in various areas of the warehouse collect data of different dimensions in real time at millisecond frequency;

[0014] The Kalman filter algorithm is used in data preprocessing to eliminate noise interference. The formula is expressed as: Among them, F k is the state transfer matrix, H k is the observation matrix, w k 、v k are process noise and observation noise respectively; is the estimated value of the state at the kth moment; B k is the control input matrix; u k is the control input at the kth moment; z k is the observed value;

[0015] S102, 3D digital twin modeling and dynamic profiling: Build a 3D digital twin warehouse model based on collected data, and use a spatiotemporal convolutional network to dynamically profile warehouse resources;

[0016] The network structure includes spatiotemporal convolutional layers and gated recurrent units. The spatiotemporal feature extraction formula is: g(X) = σ(W s (W t ×X+b t )+b s ), where g(X) is the spatiotemporal convolution output, b t 、b s is the bias term; W s 、W t are spatial and temporal convolution kernels respectively, X is the input tensor, and σ is the activation function;

[0017] S103, Quantum Genetic Algorithm-driven Flexible Resource Allocation: When an order arrives, a multi-objective matching of storage location, equipment, and personnel is achieved based on an improved quantum genetic algorithm;

[0018] Algorithm definition fitness function: f = α × T 效率 +β×C 成本 +γ×S 安全 , where the dynamic weight coefficient satisfies α+β+γ=1, T 效率 、C 成本 、S 安全 They are order processing efficiency index, resource usage cost standardization value and equipment load safety factor respectively; quantum chromosome encoding adopts quantum revolving door update strategy, the formula is: The algorithm outputs the Pareto optimal solution within 100ms;

[0019] A dynamic priority weight adjustment mechanism is introduced to adaptively update α, β, and γ according to the real-time order urgency.

[0020] Furthermore, the execution process of the dynamic warehouse resource collaborative scheduling module also includes:

[0021] S104, elastic resource scaling engine and dynamic control of hot and cold zones: The elastic resource scaling engine dynamically adjusts the storage ratio of hot and cold zones based on the LSTM prediction model;

[0022] The hot and cold zones are divided based on the product access frequency λ, and the hot zone threshold is defined as: λ 热 =μ 访问 +2σ 访问 , automatically expand the temporary storage area by 30% during the promotion period, and optimize the cold storage compressor power through the energy consumption model: Where V 冷藏 、V 常温 are the volume of the refrigerated area and the volume of the room temperature area, μ 访问 is the average frequency of product visits, 2σ 访问 is the standard deviation of the frequency of commodity visits; κ is the heat transfer coefficient, ΔT is the temperature difference between the cold storage and the outside world;

[0023] S105, Digital Twin Simulation and Reinforcement Learning Optimization: A dynamic simulation is launched every 5 minutes to optimize inventory turnover based on deep reinforcement learning.

[0024] Construct a Markov decision process, where the state space S includes the storage location status, order queue, etc., and the action space A is the resource adjustment strategy;

[0025] The Q-learning update rule is: The optimal warehousing strategy is generated through thousands of simulations; where η is the learning rate and ξ is the discount factor.

[0026] Furthermore, the dynamic warehouse resource collaborative scheduling module introduces a dynamic priority weight adjustment mechanism. The specific operation steps are as follows:

[0027] The initial weights are set as efficiency α0 = 0.5, cost β0 = 0.3, and safety γ0 = 0.2;

[0028] According to the proportion of real-time urgent orders R 紧急 , increase the efficiency weight α in proportion, the formula is: α=α0+η×R 紧急 (η=0.3,α≤0.8);

[0029] The remaining weights are dynamically distributed according to the initial proportion to ensure normalization:

[0030] In order to avoid algorithm oscillation caused by weight mutation, exponential smoothing filter is introduced: α(t)=λ×α(t-1)+(1-λ)α 新 (λ=0.7);

[0031] Fitness function reconstruction: Inject dynamic weights into the fitness function of the quantum genetic algorithm and define the multi-objective optimization problem: f = α(t) × T 效率 +β(t)×C 成本 +γ(t)×S 安全 , where T 效率 、C 成本 、S 安全 They are the inverse of order processing time, the normalized value of resource usage cost, and the equipment load safety factor;

[0032] Using the quantum bit encoding storage allocation scheme, the quantum revolving door update strategy is adjusted to: The optimization direction of urgent orders is amplified by the dynamic weight α(t).

[0033] Furthermore, the specific operation steps of the multimodal intelligent path planning module are as follows:

[0034] S201. Multi-source data fusion and 3D path network construction: Integrate park GIS geographic information, real-time traffic flow data, and drone aerial images to construct a 3D path network;

[0035] The Bayesian data fusion algorithm is used to eliminate multi-source data conflicts. The formula is: Where Path is the path feasibility event; D1 and D2 are GIS data and UAV image data respectively; P(·) is the probability distribution function;

[0036] The network nodes include ground roads, air routes and indoor passages, and the node connection weights are initialized as travel time;

[0037] S202, Dynamic cost function modeling and weight adaptation: Establish a dynamic cost function C total , comprehensive time cost C time , energy consumption cost C energy , safety cost C safety :C total =w1×C time +w2×C energy +w3×C safety ;

[0038] Time weight w1: Based on the order timeliness requirement, if the remaining time T 剩余 <T 阈值 , then w1←w1+0.1;

[0039] Safety weight w3: Through the road condition monitoring unit, if the risk level R≥2, then w3←w3+0.15;

[0040] Constraints: w1+w2+w3=1, initial values ​​w1=0.45, w2=0.3, w3=0.25.

[0041] Furthermore, the multimodal intelligent path planning module execution step further includes:

[0042] S203, staged path optimization engine: In the processing stage, R-tree index is used to compress the path search space to reduce computational complexity;

[0043] The coverage area of ​​R-tree node N is defined as: MBB(N)=[min(x i ),max(x i )]×min[(y i ),max(y i )] ， Where (x i ,y i ) are child node coordinates, excluding invalid path branches;

[0044] The improved ant colony algorithm is applied in the core planning stage, and the pheromone update rule is: in Q is the pheromone intensity constant, ρ is the volatility coefficient, is the pheromone concentration of path (i, j), is the pheromone increment released by the kth ant on the path (i, j);

[0045] Introducing the elite ant strategy to retain the top 10% of paths to accelerate convergence;

[0046] S204, real-time dynamic adjustment mechanism: The vehicle OBU data is synchronized and uploaded to the vehicle terminal every 10 seconds, with the position (x, y) and speed v, to update the real-time traffic matrix M.traffic ;

[0047] Sudden obstacle handling: When a roadblock is detected, the incremental Dijkstra algorithm is triggered to replan the path, reducing the time complexity to O((E+N)logN);

[0048] Multi-objective conflict resolution: If there is a conflict in paths, the Nash equilibrium game model is used to assign priorities: where s i is the path strategy of vehicle i, s -i is the path strategy set of other vehicles, u i The utility function of vehicle i ensures that all parties maximize their benefits;

[0049] Introducing a dynamic time window adjustment strategy to dynamically shorten the route replanning interval based on traffic flow forecasts;

[0050] S204, Multi-vehicle Collaborative Channel Management: Differentiated access strategies include:

[0051] Unmanned vehicles: limited to ground roads, maximum speed v max =30km / h;

[0052] UAV: air route height h ≥ 50m, obstacle avoidance distance d safe =10m;

[0053] Manual forklift: Indoor aisles have the highest priority, and right of way is dynamically allocated;

[0054] Conflict detection model: Detect potential collisions based on the space-time cube and define conflict conditions: where p i (t) is the coordinate of vehicle i at time t, d min For a safe distance.

[0055] Furthermore, the specific operation steps of introducing the dynamic time window adjustment strategy in S204 are as follows:

[0056] Use long short-term memory network to predict traffic flow in the park:

[0057] The model inputs include historical traffic data, real-time sensor data, and weather information. Through training, the LSTM predicts traffic flow trends for the next 5-15 minutes and identifies upcoming peak periods.

[0058] Combine LSTM prediction results with real-time data to comprehensively determine peak hours:

[0059] Traffic threshold: When the predicted traffic exceeds 80% of the road capacity, peak determination is triggered;

[0060] Congestion index: Real-time monitoring of road congestion index, a continuous increase is considered a peak;

[0061] Event-driven: sudden activity is directly marked as a temporary peak;

[0062] Dynamically adjust the path replanning interval:

[0063] During off-peak hours: By default, path replanning is performed every 10 seconds;

[0064] Moderate peak: interval shortened to 7 seconds;

[0065] During severe peak hours: the interval is shortened again to 5 seconds, and the routes are updated in real time across the entire area;

[0066] Path replanning execution process:

[0067] Data synchronization: collect the latest traffic data;

[0068] Path calculation: Uses an improved ant colony algorithm to quickly generate new paths, prioritizing bypassing congested areas;

[0069] Conflict detection: Analyze potential conflicts between the new path and drones and forklifts using a space-time cube model;

[0070] Path distribution: Push the optimized path to the vehicle terminal in real time to ensure execution consistency;

[0071] Exception handling: If an extreme event is detected, immediate replanning is triggered, skipping the fixed interval limit.

[0072] Furthermore, the specific operation steps of the blockchain federated learning security hub module are as follows:

[0073] S301. Layered blockchain architecture and data sharding storage: Design a layered blockchain architecture, divided into application layer and transport layer:

[0074] Application layer: Based on Hyperledger Fabric to record warehouse and distribution operation logs, the PBFT consensus algorithm is used to ensure transaction consistency;

[0075] Block generation time T block satisfy: Where f is the number of fault nodes, N node is the total number of nodes, t prop , t verify They are proposal propagation time and verification time respectively;

[0076] Transport layer: Use IPFS to store the time series data generated by IoT devices, and the data shard hash value H shard Recorded on the chain to ensure that it cannot be tampered with: H shard =SHA-3(Dshard ||Nonce);

[0077] S302, Dynamic Data Encryption and Quantum Security Enhancement: Implementing differentiated encryption strategies includes:

[0078] Warehousing data: Using the national secret SM9 algorithm, identity-based public key encryption, the encryption process is: C SM9 =E pk (M,ID warehouse ), where ID warehouse It is the unique identifier of the database, and the private key is distributed and generated by the key generation center;

[0079] Data delivery: Using quantum-resistant NTRU algorithm, polynomial ring encryption: Where c(x) is the ciphertext polynomial, m(x) is the plaintext polynomial, h(x) is the public key polynomial, and e(x) is the small coefficient noise. is the modulus;

[0080] S303, Cross-enterprise federated learning and security aggregation: Building a federated learning platform, participants P i Local training LSTM demand forecasting model, model parameter θ i Aggregation via secure multi-party computation:

[0081] Local training: Where L is the loss function, D i For local datasets;

[0082] Secure aggregation: Adopting additive secret sharing, each participant splits the parameters Aggregate global model: where Θ global is the global model parameter, w i，j is the jth parameter shard of participant i, where p is a large prime number to ensure privacy;

[0083] Develop a Trusted Execution Environment (TEE) to process sensitive order information in the Intel SGX enclave, ensuring data availability but not visibility. Utilize dynamic trust metrics to periodically monitor the enclave memory status, triggering a fuse mechanism when anomalies occur to prevent data leaks.

[0084] S304. Establish an audit mechanism driven by smart contracts to automatically verify the achievement of SLAs for warehousing and distribution services, and trigger an automatic compensation process in the event of a violation.

[0085] Furthermore, in S303, dynamic trust metrics are used to periodically detect the Enclave memory status. When an abnormality occurs, the fuse mechanism is triggered. The specific operation steps are as follows:

[0086] During the Enclave initialization phase, the encrypted hash value of its memory area is calculated and stored in the secure storage area as a baseline fingerprint;

[0087] Recalculate the real-time hash value of the enclave memory every 30 seconds and compare it with the baseline fingerprint;

[0088] In addition to memory contents, it also monitors the code execution flow of the enclave to detect abnormal instruction jumps or unauthorized API calls;

[0089] Anomaly determination is based on the following multi-dimensional indicators:

[0090] Hash value mismatch: The difference between the real-time hash value and the benchmark value exceeds the threshold;

[0091] Abnormal memory access pattern: Unusual memory read and write operations are detected;

[0092] Abnormal resource usage: Enclave CPU or memory usage suddenly increases;

[0093] Meeting any of the conditions triggers an abnormal alarm;

[0094] When an anomaly is detected, the circuit breaker mechanism is activated immediately:

[0095] Immediately suspend Enclave operation: terminate all currently processing sensitive operations and freeze the Enclave process;

[0096] Memory data erasure: Use secure erasure algorithms to clear sensitive data in the Enclave memory;

[0097] Network isolation: Cut off the connection between the Enclave and the external network to prevent data leakage;

[0098] Log forensics: Encrypt and store the timestamps, memory snapshots, and operation logs of abnormal events for subsequent audit analysis;

[0099] Alarm notification: Send real-time alarms to administrators, prompting manual intervention and investigation;

[0100] System recovery and subsequent processing include:

[0101] Automatic restart: After ensuring the safety of the environment, the enclave is automatically restarted and the baseline hash is reinitialized;

[0102] Vulnerability repair: If the exception is caused by a software vulnerability, the patch automatic update process is triggered;

[0103] Manual review: The security team analyzes log data to identify the source of the attack and strengthen defense strategies.

[0104] Compared with the prior art, the present invention has the following beneficial effects:

[0105] (1) The present invention realizes the efficient allocation and optimized management of warehousing resources through a dynamic warehousing resource collaborative scheduling module. First, by using 5G-IoT to collect data in real time and combining it with quantum genetic algorithms, it can quickly and accurately allocate storage locations and equipment to orders, significantly improving warehousing efficiency and inventory turnover. Secondly, the elastic resource scaling engine can dynamically adjust the storage ratio of hot and cold areas according to the frequency of commodity access, and automatically expand the temporary storage area during promotions to further optimize storage space utilization. The digital twin deduction and reinforcement learning optimization mechanism can generate long-term optimal warehousing strategies based on simulation deduction, further improving the intelligent level of warehousing management;

[0106] (2) In terms of delivery route planning, the multimodal intelligent route planning module of the present invention integrates GIS, traffic data and drone images, and uses an improved ant colony algorithm and dynamic cost function to plan routes in real time, supporting collaborative delivery of multiple vehicles such as unmanned vehicles and drones. It can dynamically adjust the route replanning interval according to real-time traffic flow, and resolve route conflicts through the Nash equilibrium game model, effectively shortening delivery time and reducing energy consumption. Differentiated traffic strategies and space-time cube conflict detection models further ensure the safety and reliability of multi-vehicle collaborative delivery;

[0107] (3) The blockchain federated learning security hub module of the present invention provides a strong guarantee for data security and privacy protection; through the layered blockchain architecture and data sharding storage, combined with encryption algorithms such as the national secret SM9 and quantum-resistant NTRU, it ensures the encrypted storage and cross-enterprise secure sharing of warehousing and distribution data. The federated learning platform and secure multi-party computing technology realize the secure aggregation of model parameters and protect the data privacy of all participants. The trusted execution environment (TEE) and dynamic trust measurement mechanism can detect and block data leakage risks in real time, and the smart contract-driven audit mechanism realizes the automatic verification and violation compensation of warehousing and distribution services, further improving the security and credibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0109] Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0110] The technical solutions of the present invention will be described clearly and completely below with reference to the embodiments. It is obvious that the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0111] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0112] It should also be understood that the terminology used in this disclosure is for the purpose of describing specific embodiments only and is not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should be further understood that the term "and / or" as used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0113] like Figure 1 As shown, a digital park supply chain warehousing and distribution integrated service system includes:

[0114] The dynamic warehouse resource collaborative scheduling module collects data in real time through 5G-IoT, builds a three-dimensional digital twin model, and combines quantum genetic algorithms to dynamically allocate storage locations and equipment, optimize the storage ratio of hot and cold areas, and improve warehouse efficiency and inventory turnover.

[0115] Real-time collection and fusion of multi-source heterogeneous data:

[0116] 5G-IoT devices deployed in various areas of the warehouse (including weight sensors, AGV status monitors, temperature and humidity sensors, etc.) collect data on 20+ dimensions, including shelf weight distribution, AGV operating speed, and ambient temperature and humidity, in real time at millisecond frequency. Data preprocessing uses the Kalman filter algorithm to eliminate noise interference. The formula is expressed as: Among them, F k is the state transfer matrix, H k is the observation matrix, w k 、v k are process noise and observation noise respectively; is the estimated value of the state at the kth moment; B k is the control input matrix; u k is the control input at the kth moment; z k is the observed value;

[0117] 3D digital twin modeling and dynamic portraits:

[0118] A 3D digital twin warehouse model is constructed based on the collected data, and a spatiotemporal convolutional network (ST-ConvNet) is used to dynamically profile the warehouse resources. The network structure includes a spatiotemporal convolutional layer and a gated recurrent unit (GRU). The spatiotemporal feature extraction formula is: g(X) = σ(W s (W t ×X+b t )+b s ), where g(X) is the spatiotemporal convolution output, b t 、b s is the bias term; W s 、W t where X is the input tensor and σ is the activation function. The model is updated every 5 seconds, accurately mapping key indicators such as storage occupancy rate and equipment load rate.

[0119] Quantum genetic algorithm driven elastic resource allocation:

[0120] When an order arrives, the improved quantum genetic algorithm (QGA) is used to achieve multi-objective matching of storage location, equipment and personnel. The algorithm defines the fitness function: f = α × T 效率 +β×C 成本 +γ×S 安全 , where the dynamic weight coefficient satisfies α+β+γ=1, T 效率 、C 成本 、S 安全 They are order processing efficiency index, resource usage cost standardization value and equipment load safety factor respectively; quantum chromosome encoding adopts quantum revolving door update strategy, the formula is: The algorithm outputs the Pareto optimal solution within 100ms and supports the scheduling of thousands of concurrent orders.

[0121] A dynamic priority weight adjustment mechanism is introduced to adaptively update α, β, and γ based on the real-time order urgency (for example, the priority of cold chain products is increased by 20%). The process is as follows:

[0122] The initial weights are set as efficiency α0 = 0.5, cost β0 = 0.3, and safety γ0 = 0.2; according to the proportion of real-time emergency orders R 紧急 , increase the efficiency weight α in proportion, the formula is: α=α0+η×R 紧急 (η=0.3,α≤0.8); the remaining weights are dynamically distributed according to the initial proportion to ensure normalization:

[0123] In order to avoid algorithm oscillation caused by weight mutation, exponential smoothing filter is introduced: α(t)=λ×α(t-1)+(1-λ)α 新 (λ=0.7);

[0124] Fitness function reconstruction: Inject dynamic weights into the fitness function of the quantum genetic algorithm (QGA) and define the multi-objective optimization problem: f = α(t) × T 效率 +β(t)×C 成本 +γ(t)×S 安全 , where T 效率 、C 成本 、S 安全 They are the inverse of order processing time (1 / average time), the normalized value of resource usage cost, and the equipment load safety factor (1-load rate / 100);

[0125] Using the quantum bit encoding storage allocation scheme, the quantum revolving door update strategy is adjusted to: The optimization direction of urgent orders is amplified by the dynamic weight α(t).

[0126] Elastic resource scaling engine and dynamic control of hot and cold zones:

[0127] The resource elastic scaling engine dynamically adjusts the storage ratio of hot and cold zones based on the LSTM prediction model. The hot and cold zones are divided based on the product access frequency λ, and the hot zone threshold is defined as: λ 热 =μ 访问 +2σ 访问 , automatically expand the temporary storage area by 30% during the promotion period, and optimize the cold storage compressor power through the energy consumption model: Where V 冷藏 、V 常温 are the volume of the refrigerated area and the volume of the room temperature area, μ 访问 is the average frequency of product visits, 2σ 访问 is the standard deviation of the frequency of commodity visits; κ is the heat transfer coefficient, ΔT is the temperature difference between the cold storage and the outside world;

[0128] Digital twin deduction and reinforcement learning optimization:

[0129] A dynamic simulation is launched every 5 minutes to optimize inventory turnover based on deep reinforcement learning (DRL). A Markov decision process (MDP) is constructed, where the state space S includes the inventory location status, order queue, etc., and the action space A is the resource adjustment strategy. The Q-learning update rule is: Through thousands of simulations, we generated the optimal long-term warehousing strategy, which increased inventory turnover by 25%. Here, η is the learning rate and ξ is the discount factor.

[0130] The multimodal intelligent route planning module integrates GIS, traffic data, and drone imagery, using an improved ant colony algorithm and a dynamic cost function to plan routes in real time. It supports multi-vehicle collaboration between unmanned vehicles and drones, shortening delivery time and reducing energy consumption.

[0131] Multi-source data fusion and 3D path network construction:

[0132] Integrate park GIS geographic information, real-time traffic flow data (from roadside sensors), and drone aerial images to build a three-dimensional path network. Use the Bayesian data fusion algorithm to eliminate multi-source data conflicts. The formula is: Where Path is a feasible path event; D1 and D2 are GIS data and drone image data, respectively; P(·) is a probability distribution function; network nodes include ground roads, air routes, and indoor passages, and the node connection weights are initialized to travel time;

[0133] Dynamic cost function modeling and weight adaptation:

[0134] Establish a dynamic cost function C total , comprehensive time cost C time , energy consumption cost C energy , safety cost C safety :C total =w1×C time +w2×C energy +w3×C safety ; Dynamic adjustment of weight coefficient: Time weight w1: Based on the timeliness requirement of the order, if the remaining time T 剩余 <T 阈值 , then w1←w1+0.1; safety weight w3: through the road condition monitoring unit (detection of water accumulation / icing), if the risk level R≥2, then w3←w3+0.15; constraint conditions: w1+w2+w3=1, initial values ​​w1=0.45, w2=0.3, w3=0.25;

[0135] Phased Path Optimization Engine:

[0136] Processing stage: Use R-tree index to compress the path search space and reduce the computational complexity. Define the coverage area of ​​R-tree node N as: MBB(N) = [min(x i ),max(x i )]×min[(y i ),max(y i )], where (x i ,y i ) is the child node coordinate, quickly eliminating invalid path branches;

[0137] Core planning stage: Apply the improved ant colony algorithm (IACO) and the pheromone update rule is: in Q is the pheromone intensity constant, ρ is the volatility coefficient, is the pheromone concentration of path (i, j), is the pheromone increment released by the kth ant on the path (i, j); the elite ant strategy is introduced to retain the top 10% paths to accelerate convergence;

[0138] Real-time dynamic adjustment mechanism:

[0139] On-board OBU data synchronization: upload the position (x, y) and speed v every 10 seconds through the on-board terminal to update the real-time traffic matrix M traffic .

[0140] Sudden obstacle handling: When a roadblock is detected, the incremental Dijkstra algorithm is triggered to replan the path, reducing the time complexity to O((E+N)logN), meeting the 200ms response requirement;

[0141] Multi-objective conflict resolution: If there is a path conflict (e.g., the routes of a drone and a self-driving car overlap), a Nash equilibrium game model is used to assign priorities: where s i is the path strategy of vehicle i, s -i is the path strategy set of other vehicles, u i The utility function of vehicle i ensures that all parties maximize their benefits;

[0142] A dynamic time window adjustment strategy is introduced to dynamically shorten the route replanning interval based on traffic flow prediction (LSTM model). The process is as follows:

[0143] A long short-term memory (LSTM) network is used to predict traffic flow within the park. Model inputs include historical traffic data (such as hourly traffic volume and average speed), real-time sensor data (roadside cameras, onboard OBU devices), and weather information. Through training, the LSTM can predict traffic flow trends for the next 5-15 minutes and identify upcoming peak periods (such as morning and evening rush hours and promotional events).

[0144] Combine LSTM prediction results with real-time data to comprehensively determine peak hours:

[0145] Traffic threshold: When the predicted traffic exceeds 80% of the road capacity, peak determination is triggered;

[0146] Congestion index: Real-time monitoring of road congestion index (e.g. average vehicle speed below 20km / h), and a continuous increase is considered a peak;

[0147] Event-driven: Sudden activities (such as the concentrated entry and exit of logistics vehicles) are directly marked as temporary peaks.

[0148] Dynamically adjust the path replanning interval:

[0149] During off-peak hours: By default, path replanning is performed every 10 seconds;

[0150] Moderate peak hours: The interval is shortened to 7 seconds, with some key sections being updated first;

[0151] During severe peak hours: the interval is further shortened to 5 seconds, and the routes are updated in real time across the entire area.

[0152] The adjustment process takes effect in real time, and the system continuously monitors traffic conditions. If it predicts a decrease in traffic volume or an alleviation of congestion, it gradually returns to normal intervals.

[0153] Path replanning execution process:

[0154] Data synchronization: collect the latest traffic data (such as roadblocks, accidents, and vehicle locations);

[0155] Path calculation: Uses an improved ant colony algorithm to quickly generate new paths, prioritizing bypassing congested areas;

[0156] Conflict detection: Analyze potential conflicts between new paths and other vehicles such as drones and forklifts using a space-time cube model;

[0157] Path distribution: Push the optimized path to the vehicle terminal in real time to ensure execution consistency;

[0158] Exception handling: If an extreme event (such as a traffic accident) is detected, immediate replanning is triggered immediately, skipping the fixed interval limit.

[0159] Multi-vehicle collaborative channel management:

[0160] Differentiated traffic strategies:

[0161] Unmanned vehicles: limited to ground roads, maximum speed v max =30km / h;

[0162] UAV: air route height h ≥ 50m, obstacle avoidance distance d safe =10m;

[0163] Manual forklift: Indoor aisles have the highest priority, and right of way is dynamically allocated.

[0164] Conflict detection model: Detect potential collisions based on the space-time cube (3D space + time axis) and define conflict conditions: where p i (t) is the coordinate of vehicle i at time t, d min For a safe distance.

[0165] The blockchain federated learning security hub module uses layered blockchain and federated learning technologies to ensure encrypted data storage and secure cross-enterprise sharing. It also automatically audits warehousing and distribution services through smart contracts to protect data privacy and compliance.

[0166] Layered blockchain architecture and data sharding storage:

[0167] Design a layered blockchain architecture, divided into application layer and transport layer:

[0168] Application layer: Based on Hyperledger Fabric to record warehouse and distribution operation logs, using PBFT consensus algorithm to ensure transaction consistency. Block generation time T block satisfy: Where f is the number of fault nodes, N node is the total number of nodes, t prop , t verify They are proposal propagation time and verification time respectively;

[0169] Transport layer: Use IPFS to store the time series data generated by IoT devices, and the data shard hash value H shard Recorded on the chain to ensure that it cannot be tampered with: H shard =SHA-3(D shard ||Nonce);

[0170] Dynamic data encryption with quantum security enhancements:

[0171] Implementing a differentiated encryption strategy:

[0172] Warehousing data: Using the national secret SM9 algorithm, identity-based public key encryption, the encryption process is: C SM9 =E pk (M,ID warehouse ), where ID warehouse It is the unique identifier of the database. The private key is distributed and generated by the Key Generation Center (KGC);

[0173] Data delivery: Using quantum-resistant NTRU algorithm, polynomial ring encryption: Where c(x) is the ciphertext polynomial, m(x) is the plaintext polynomial, h(x) is the public key polynomial, and e(x) is the small coefficient noise. is the modulus.

[0174] Cross-enterprise federated learning and secure aggregation:

[0175] Build a federated learning platform, with participants P i Local training LSTM demand forecasting model, model parameter θ i Aggregation via Secure Multi-Party Computation (SMPC):

[0176] Local training: Where L is the loss function, D i For local datasets;

[0177] Secure aggregation: Adopting additive secret sharing, each participant splits the parameters Aggregate global model: where Θ global is the global model parameter, w i，jis the jth parameter shard of participant i, where p is a large prime number to ensure privacy;

[0178] Develop a Trusted Execution Environment (TEE) to process sensitive order information in the Intel SGX enclave, ensuring data availability but not visibility. Introduce dynamic trust metrics to periodically monitor the enclave memory status. Trigger a fuse mechanism when anomalies occur, preventing data leakage. The specific process is as follows:

[0179] During the enclave initialization phase, a cryptographic hash value (e.g., SHA-256) is calculated for its memory area and stored in a secure storage area as a baseline fingerprint. The real-time hash value of the enclave memory is recalculated every 30 seconds and compared with the baseline fingerprint. In addition to monitoring the memory content, the enclave's code execution flow is monitored to detect abnormal instruction jumps or unauthorized API calls.

[0180] Anomaly determination is based on the following multi-dimensional indicators:

[0181] Hash value mismatch: The difference between the real-time hash and the reference value exceeds the threshold (e.g., three consecutive inconsistencies);

[0182] Abnormal memory access pattern: Unusual memory read and write operations are detected (such as unauthorized processes accessing enclave memory);

[0183] Abnormal resource usage: Enclave CPU or memory usage suddenly increases (for example, exceeding 200% of the preset threshold).

[0184] Meeting any of the conditions triggers an exception alarm.

[0185] When an anomaly is detected, the circuit breaker mechanism is activated immediately:

[0186] Immediately suspend Enclave operation: terminate all currently processing sensitive operations and freeze the Enclave process;

[0187] Memory data erasure: Use secure erase algorithms (such as NIST standard overwrite) to clear sensitive data in the enclave memory;

[0188] Network isolation: Cut off the connection between the Enclave and the external network to prevent data leakage;

[0189] Log forensics: Encrypt and store the timestamps, memory snapshots, and operation logs of abnormal events for subsequent audit analysis;

[0190] Alarm notification: Send real-time alarms (such as text messages and emails) to administrators, prompting manual intervention and investigation.

[0191] System recovery and subsequent processing:

[0192] Automatic restart: After ensuring the safety of the environment, the system automatically restarts the enclave and reinitializes the baseline hash;

[0193] Vulnerability repair: If the exception is caused by a software vulnerability, the patch automatic update process is triggered;

[0194] Manual review: The security team analyzes log data to identify the source of the attack and strengthen defense strategies.

[0195] Establish an audit mechanism driven by smart contracts to automatically verify the achievement of SLAs for warehousing and distribution services, and trigger automatic compensation processes in the event of violations.

[0196] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A digital park supply chain warehousing and distribution integrated service system, characterized by: include: A dynamic warehouse resource collaborative scheduling module collects data in real time through 5G-IoT, builds a three-dimensional digital twin model, and uses quantum genetic algorithms to dynamically allocate storage locations and equipment, optimize the storage ratio between hot and cold areas, and improve warehouse efficiency and inventory turnover. A multimodal intelligent route planning module integrates GIS, traffic data, and drone imagery, using an improved ant colony algorithm and a dynamic cost function to plan routes in real time. It supports collaboration between unmanned vehicles and drones, shortening delivery times and reducing energy consumption. The blockchain federated learning security hub module uses layered blockchain and federated learning technologies to ensure encrypted data storage and secure cross-enterprise sharing. It also automatically audits warehousing and distribution services through smart contracts to protect data privacy and compliance.

2. The digital park supply chain warehousing and distribution integrated service system according to claim 1 is characterized in that: The specific execution process of the dynamic warehouse resource collaborative scheduling module is as follows: S101, Real-time collection and fusion of multi-source heterogeneous data: 5G-IoT devices deployed in various areas of the warehouse collect data of different dimensions in real time at millisecond frequency; The Kalman filter algorithm is used in data preprocessing to eliminate noise interference. The formula is expressed as: Among them, F k is the state transfer matrix, H k is the observation matrix, w k 、v k are process noise and observation noise respectively; is the estimated value of the state at the kth moment; B k is the control input matrix; u k is the control input at the kth moment; z k is the observed value; S102, 3D digital twin modeling and dynamic profiling: Build a 3D digital twin warehouse model based on collected data, and use a spatiotemporal convolutional network to dynamically profile warehouse resources; The network structure includes spatiotemporal convolutional layers and gated recurrent units. The spatiotemporal feature extraction formula is: g(X) = σ(W s (W t ×X+b t )+b s ), where g(X) is the spatiotemporal convolution output, b t 、b s is the bias term; W s 、W t are spatial and temporal convolution kernels respectively, X is the input tensor, and σ is the activation function; S103, Quantum Genetic Algorithm-driven Flexible Resource Allocation: When an order arrives, a multi-objective matching of storage location, equipment, and personnel is achieved based on an improved quantum genetic algorithm; Algorithm definition fitness function: f = α × T 效率 +β×C 成本 +γ×S 安全 , where the dynamic weight coefficient satisfies α+β+γ=1, T 效率 、C 成本 、S 安全 They are order processing efficiency index, resource usage cost standardization value and equipment load safety factor respectively; quantum chromosome encoding adopts quantum revolving door update strategy, the formula is: The algorithm outputs the Pareto optimal solution within 100ms; A dynamic priority weight adjustment mechanism is introduced to adaptively update α, β, and γ according to the real-time order urgency.

3. The digital park supply chain warehousing and distribution integrated service system according to claim 2 is characterized in that: The execution process of the dynamic storage resource collaborative scheduling module also includes: S104, elastic resource scaling engine and dynamic control of hot and cold zones: The elastic resource scaling engine dynamically adjusts the storage ratio of hot and cold zones based on the LSTM prediction model; The hot and cold zones are divided based on the product access frequency λ, and the hot zone threshold is defined as: λ 热 =μ 访问 +2σ 访问 , automatically expand the temporary storage area by 30% during the promotion period, and optimize the cold storage compressor power through the energy consumption model: Where V 冷藏 、V 常温 are the volume of the refrigerated area and the volume of the room temperature area, μ 访问 is the average frequency of product visits, 2σ 访问 is the standard deviation of the frequency of commodity visits; κ is the heat transfer coefficient, ΔT is the temperature difference between the cold storage and the outside world; S105, Digital Twin Simulation and Reinforcement Learning Optimization: A dynamic simulation is launched every 5 minutes to optimize inventory turnover based on deep reinforcement learning. Construct a Markov decision process, where the state space S includes the storage location status, order queue, etc., and the action space A is the resource adjustment strategy; The Q-learning update rule is: The optimal warehousing strategy is generated through thousands of simulations; where η is the learning rate and ξ is the discount factor.

4. The digital park supply chain warehousing and distribution integrated service system according to claim 2 is characterized in that: The dynamic warehouse resource collaborative scheduling module introduces a dynamic priority weight adjustment mechanism. The specific operation steps are as follows: The initial weights are set as efficiency α0 = 0.5, cost β0 = 0.3, and safety γ0 = 0.2; According to the proportion of real-time urgent orders R 紧急 , increase the efficiency weight α in proportion, the formula is: α=α0+η×R 紧急 (η=0.3,α≤0.8); The remaining weights are dynamically distributed according to the initial proportion to ensure normalization: In order to avoid algorithm oscillation caused by weight mutation, exponential smoothing filter is introduced: α(t)=λ×α(t-1)+(1-λ)α 新 (λ=0.7); Fitness function reconstruction: Inject dynamic weights into the fitness function of the quantum genetic algorithm and define the multi-objective optimization problem: f = α(t) × T 效率 +β(t)×C 成本 +γ(t)×S 安全 , where T 效率 、C 成本 、S 安全 They are the inverse of order processing time, the normalized value of resource usage cost, and the equipment load safety factor; Using the quantum bit encoding storage allocation scheme, the quantum revolving door update strategy is adjusted to: The optimization direction of urgent orders is amplified by the dynamic weight α(t).

5. The digital park supply chain warehousing and distribution integrated service system according to claim 1 is characterized in that: The specific operation steps of the multimodal intelligent path planning module are as follows: S201. Multi-source data fusion and 3D path network construction: Integrate park GIS geographic information, real-time traffic flow data, and drone aerial images to construct a 3D path network; The Bayesian data fusion algorithm is used to eliminate multi-source data conflicts. The formula is: Where Path is the path feasibility event; D1 and D2 are GIS data and UAV image data respectively; P(·) is the probability distribution function; The network nodes include ground roads, air routes and indoor passages, and the node connection weights are initialized as travel time; S202, Dynamic cost function modeling and weight adaptation: Establish a dynamic cost function C total , comprehensive time cost C time , energy consumption cost C energy , safety cost C safety :C total =w1×C time +w2×C energy +w3×C safety ; Time weight w1: Based on the order timeliness requirement, if the remaining time T 剩余 <T 阈值 , then w1←w1+0.1; Safety weight w3: Through the road condition monitoring unit, if the risk level R≥2, then w3←w3+0.15; Constraints: w1+w2+w3=1, initial values ​​w1=0.45, w2=0.3, w3=0.

25.

6. The digital park supply chain warehousing and distribution integrated service system according to claim 5 is characterized in that: The multimodal intelligent path planning module further comprises: S203, staged path optimization engine: In the processing stage, R-tree index is used to compress the path search space to reduce computational complexity; The coverage area of ​​R-tree node N is defined as: MBB(N)=[min(x i ),max(x i )]×min[(y i ),max(y i )], where (x i ,y i ) are child node coordinates, excluding invalid path branches; The improved ant colony algorithm is applied in the core planning stage, and the pheromone update rule is: Q is the pheromone intensity constant, ρ is the volatility coefficient, θ ij is the pheromone concentration of path (i, j), is the pheromone increment released by the kth ant on the path (i, j); Introducing the elite ant strategy to retain the top 10% of paths to accelerate convergence; S204, real-time dynamic adjustment mechanism: The vehicle OBU data is synchronized and uploaded to the vehicle terminal every 10 seconds, with the position (x, y) and speed v, to update the real-time traffic matrix M. traffic ; Sudden obstacle handling: When a roadblock is detected, the incremental Dijkstra algorithm is triggered to replan the path, reducing the time complexity to O((E+N)logN); Multi-objective conflict resolution: If there is a conflict in paths, the Nash equilibrium game model is used to assign priorities: where s i is the path strategy of vehicle i, s -i is the path strategy set of other vehicles, u i The utility function of vehicle i ensures that all parties maximize their benefits; Introducing a dynamic time window adjustment strategy to dynamically shorten the route replanning interval based on traffic flow forecasts; S204, Multi-vehicle Collaborative Channel Management: Differentiated access strategies include: Unmanned vehicles: limited to ground roads, maximum speed v max =30km / h; UAV: air route height h ≥ 50m, obstacle avoidance distance d safe =10m; Manual forklift: Indoor aisles have the highest priority, and right of way is dynamically allocated; Conflict detection model: Detect potential collisions based on the space-time cube and define conflict conditions: where p i (t) is the coordinate of vehicle i at time t, d min For a safe distance.

7. The digital park supply chain warehousing and distribution integrated service system according to claim 6 is characterized in that: The specific steps of introducing the dynamic time window adjustment strategy in S204 are as follows: Use long short-term memory network to predict traffic flow in the park: The model inputs include historical traffic data, real-time sensor data, and weather information. Through training, the LSTM predicts traffic flow trends for the next 5-15 minutes and identifies upcoming peak periods. Combine LSTM prediction results with real-time data to comprehensively determine peak hours: Traffic threshold: When the predicted traffic exceeds 80% of the road capacity, peak determination is triggered; Congestion index: Real-time monitoring of road congestion index, a continuous increase is considered a peak; Event-driven: sudden activity is directly marked as a temporary peak; Dynamically adjust the path replanning interval: During off-peak hours: By default, path replanning is performed every 10 seconds; Moderate peak: interval shortened to 7 seconds; During severe peak hours: the interval is shortened again to 5 seconds, and the routes are updated in real time across the entire area; Path replanning execution process: Data synchronization: collect the latest traffic data; Path calculation: Uses an improved ant colony algorithm to quickly generate new paths, prioritizing bypassing congested areas; Conflict detection: Analyze potential conflicts between the new path and drones and forklifts using a space-time cube model; Path distribution: Push the optimized path to the vehicle terminal in real time to ensure execution consistency; Exception handling: If an extreme event is detected, immediate replanning is triggered, skipping the fixed interval limit.

8. The digital park supply chain warehousing and distribution integrated service system according to claim 1 is characterized in that: The specific operation steps of the blockchain federated learning security hub module are as follows: S301. Layered blockchain architecture and data sharding storage: Design a layered blockchain architecture, divided into application layer and transport layer: Application layer: Based on Hyperledger Fabric to record warehouse and distribution operation logs, the PBFT consensus algorithm is used to ensure transaction consistency; Block generation time T block satisfy: Where f is the number of fault nodes, N node is the total number of nodes, t prop , t verify They are proposal propagation time and verification time respectively; Transport layer: Use IPFS to store the time series data generated by IoT devices, and the data shard hash value H shard Recorded on the chain to ensure that it cannot be tampered with: H shard =SHA-3(D shard ||Nonce); S302, Dynamic Data Encryption and Quantum Security Enhancement: Implementing differentiated encryption strategies includes: Warehousing data: Using the national secret SM9 algorithm, identity-based public key encryption, the encryption process is: C SM9 =E pk (M,ID warehouse ), where ID warehouse It is the unique identifier of the database, and the private key is distributed and generated by the key generation center; Data delivery: Using quantum-resistant NTRU algorithm, polynomial ring encryption: Where c(x) is the ciphertext polynomial, m(x) is the plaintext polynomial, h(x) is the public key polynomial, and e(x) is the small coefficient noise. is the modulus; S303, Cross-enterprise federated learning and security aggregation: Building a federated learning platform, participants P i Local training LSTM demand forecasting model, model parameter θ i Aggregation via secure multi-party computation: Local training: Where L is the loss function, D i For local datasets; Secure aggregation: Adopting additive secret sharing, each participant splits the parameters Aggregate global model: where Θ global is the global model parameter, w i，j is the jth parameter shard of participant i, where p is a large prime number to ensure privacy; Develop a Trusted Execution Environment (TEE) to process sensitive order information in the Intel SGX enclave, ensuring data availability but not visibility. Utilize dynamic trust metrics to periodically monitor the enclave memory status, triggering a fuse mechanism when anomalies occur to prevent data leaks. S304. Establish an audit mechanism driven by smart contracts to automatically verify the achievement of SLAs for warehousing and distribution services, and trigger an automatic compensation process in the event of a violation.

9. The digital park supply chain warehousing and distribution integrated service system according to claim 8 is characterized in that: In S303, dynamic trust metrics are used to periodically detect the enclave memory status and trigger the fuse mechanism when an abnormality occurs. The specific operation steps are as follows: During the Enclave initialization phase, the encrypted hash value of its memory area is calculated and stored in the secure storage area as a baseline fingerprint; Recalculate the real-time hash value of the enclave memory every 30 seconds and compare it with the baseline fingerprint; In addition to memory contents, it also monitors the code execution flow of the enclave to detect abnormal instruction jumps or unauthorized API calls; Anomaly determination is based on the following multi-dimensional indicators: Hash value mismatch: The difference between the real-time hash value and the benchmark value exceeds the threshold; Abnormal memory access pattern: Unusual memory read and write operations are detected; Abnormal resource usage: Enclave CPU or memory usage suddenly increases; Meeting any of the conditions triggers an abnormal alarm; When an anomaly is detected, the circuit breaker mechanism is activated immediately: Immediately suspend Enclave operation: terminate all currently processing sensitive operations and freeze the Enclave process; Memory data erasure: Use secure erasure algorithms to clear sensitive data in the Enclave memory; Network isolation: Cut off the connection between the Enclave and the external network to prevent data leakage; Log forensics: Encrypt and store the timestamps, memory snapshots, and operation logs of abnormal events for subsequent audit analysis; Alarm notification: Send real-time alarms to administrators, prompting manual intervention and investigation; System recovery and subsequent processing include: Automatic restart: After ensuring the safety of the environment, the enclave is automatically restarted and the baseline hash is reinitialized; Vulnerability repair: If the anomaly is caused by a software vulnerability, the patch automatic update process is triggered; manual review: The security team analyzes log data, determines the source of the attack and strengthens the defense strategy.

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