Artificial intelligence logistics planning intelligent processing method based on data analysis

Abstract

The invention discloses an artificial intelligence logistics planning intelligent processing method based on data analysis, and mainly relates to the technical field of intelligent logistics planning. Comprising the following steps: collecting multi-source data from each link of a logistics supply chain, and constructing a feature vector set; constructing a deep learning neural network model, and training the deep learning neural network model by using historical logistics data to complete prediction of future logistics demands by the model; constructing an intelligent logistics planning model according to the predicted logistics demand; solving and optimizing the intelligent logistics planning model by adopting a mode of combining a heuristic algorithm and a meta-heuristic algorithm; the optimized logistics planning scheme is converted into a specific execution instruction, and whether execution abnormity exists or not is monitored; and starting an exception handling mechanism when monitoring that an exception occurs in the logistics execution process. The method has the beneficial effects that the optimal configuration of logistics resources is realized, the efficiency is improved, the cost is reduced, and the service quality is ensured.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics planning technology, specifically an artificial intelligence-based intelligent processing method for logistics planning based on data analysis. Background Technology

[0002] With the simultaneous advancement of policies promoting cross-border e-commerce, high-efficiency same-city delivery, and green transportation, logistics networks are rapidly evolving into a large-scale, multi-node, and end-to-end collaborative model. While the industry has accumulated some practical experience in areas such as warehouse automation, vehicle positioning, TMS / WMS systems, and rule-based route planning, significant gaps remain in data collection granularity, information silos, algorithm depth, and real-time response. The cleaning and fusion of multi-source heterogeneous data relies on manual scripts; demand forecasting often employs linear or simple tree models; and execution monitoring focuses on post-event statistics rather than closed-loop control. This results in persistent problems such as vehicle loading rates below 70% during peak seasons, high resource idle rates during off-seasons, and difficulties in quantifying carbon emissions.

[0003] The core technical problem that this invention attempts to solve is: How can we construct a sustainable and evolving intelligent logistics planning system based on multi-source data in dynamic traffic environments and highly volatile business scenarios, enabling it to complete a closed loop of demand forecasting, route / capacity optimization, execution monitoring, and anomaly decision-making in a timely, accurate, and globally consistent manner? Specifically, if we cannot predict and replan within seconds for order surges, road closures, or sudden warehouse shortages, vehicles will frequently run empty or be stranded, directly leading to delivery delays, inventory backlogs / supply disruptions, and soaring operating costs. In large-scale networks, this mismatch will be amplified exponentially, ultimately weakening service stability and increasing carbon emissions. Therefore, there is an urgent need to build systematic solutions at the levels of feature extraction, deep time-series forecasting, heuristic and metaheuristic joint optimization, and reinforcement learning-driven anomaly handling to ensure on-demand elastic resource scheduling, real-time recalculation of transportation routes, and verifiable service quality targets. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent processing method for logistics planning based on data analysis and artificial intelligence. This method achieves optimal allocation of logistics resources, improves efficiency, reduces costs, and ensures service quality.

[0005] To achieve the above objectives, the present invention employs the following technical solution: This paper provides an AI-based intelligent processing method for logistics planning based on data analysis, comprising the following steps: Step S1: Collect multi-source data from various links in the logistics supply chain, preprocess the collected data, and use data mining techniques to extract feature information from the preprocessed data to construct a feature vector set; Step S2: Construct a deep learning neural network model, which includes an input layer, several hidden layers, and an output layer. The input layer receives a preprocessed set of feature vectors. Train the deep learning neural network model using historical logistics data, and adjust the model's weights and biases using the backpropagation algorithm to complete the model's prediction of future logistics demand. Step S3: Construct an intelligent logistics planning model based on the predicted logistics demand; solve and optimize the intelligent logistics planning model using a combination of heuristic algorithms and metaheuristic algorithms; Step S4: Transform the optimized logistics plan into specific execution instructions, send them to logistics personnel and equipment, and use IoT technology to collect data in real time during the logistics execution process. Feed the collected real-time data back to the monitoring center; when a deviation is found between the actual execution and the plan, issue an early warning signal in a timely manner. Step S5: When an anomaly is detected during the logistics execution process, the anomaly handling mechanism is activated; using artificial intelligence algorithms, response strategies are quickly generated based on the type and severity of the anomaly.

[0006] Preferably, the generated response strategy includes: replanning transportation routes, adjusting delivery plans, and coordinating warehouse inventory. When generating the response strategy, a reinforcement learning algorithm is used to complete the system to select the optimal response solution according to different abnormal situations through trial and error and learning.

[0007] Preferably, high-frequency and low-frequency data collection methods are employed. A sliding window and clustering algorithm are used to mine time-series patterns in the real-time collected logistics data, identifying periodic fluctuations in order volume, peak demand periods in different regions, and trends in transportation efficiency decline. A mapping relationship between product categories and delivery areas is established through association rule analysis. These association rules include the mapping between product categories and delivery areas, and the association between transportation routes and congestion probabilities. A resource elastic allocation model is constructed based on historical and real-time data. Temporary capacity reserves and warehouse expansion plans are initiated before the peak season is predicted. The warehouse expansion plan is derived based on the difference between peak demand predictions and existing capacity, combined with constraints. During the off-season, vehicle maintenance plans and personnel rotation mechanisms are triggered. The criteria for determining the off-season are: comparing real-time data with historical low-trend characteristics, and determining the off-season based on indicators consistently falling below a threshold. Service optimization strategies are generated simultaneously, including dynamically adjusting delivery time commitments and differentiated pricing schemes.

[0008] Preferably, in step S1, each link of the logistics supply chain includes: suppliers, warehouses, transport vehicles, and distribution points. The multi-source data includes: cargo information, transport vehicle information, traffic condition information, order information, and geographic information data. The preprocessing specifically involves: removing noise and duplicate data from the multi-source data, and performing data standardization processing to unify data of different formats and units into an analyzable format.

[0009] Preferably, in step S2, when constructing the deep learning neural network model, a long short-term memory network or a gated recurrent unit is used as the basic unit of the hidden layer to process the time series features in the logistics data and capture the dependency relationship of logistics demand, which includes: the flow, direction and time distribution information of goods.

[0010] Preferably, in step S3, the constraints of the intelligent logistics planning model include: transportation cost, transportation time, cargo timeliness, and vehicle loading rate. The heuristic algorithm generates an initial feasible solution, and the metaheuristic algorithm optimizes the initial feasible solution generated by the heuristic algorithm and searches for a global optimal solution or a near-global optimal solution. During the optimization process of the metaheuristic algorithm, the parameters and constraints of the model are adjusted in real time according to the dynamic changes of the actual logistics business.

[0011] Preferably, in step S4, the execution instructions include: dispatch instructions for transport vehicles and warehouse entry and exit instructions; the data in the logistics execution process includes: vehicle location, speed, and cargo status; and blockchain technology is used to store and manage the data in the logistics execution process.

[0012] Preferably, in step S5, the abnormal situation includes: traffic accidents causing transportation delays and insufficient warehouse inventory.

[0013] Preferably, knowledge graph technology is used to integrate and represent relevant knowledge in the logistics field, including logistics regulations, industry standards, and business processes of logistics companies, to complete data processing, model building, and decision-making.

[0014] Preferably, the feature is that, when conducting intelligent logistics planning, the carbon emission cost of vehicles is taken as one of the constraints of the intelligent logistics planning model, and logistics planning is completed by optimizing transportation routes and vehicle scheduling.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Comprehensive improvement in data processing efficiency and quality: The multi-source logistics data acquisition and preprocessing module effectively breaks down the "data silos" phenomenon in traditional logistics systems by integrating heterogeneous data from various links in the supply chain and combining targeted data cleaning, standardization, and feature extraction technologies. This module not only accurately filters high-value information but also reduces the data processing load while ensuring data real-time performance through differentiated acquisition frequency design (high-frequency acquisition of dynamic data and low-frequency acquisition of static data). The construction of feature vector sets provides high-quality input for subsequent model training, significantly improving data utilization and laying a solid data foundation for intelligent logistics planning. 2. Breakthroughs in the accuracy and adaptability of logistics demand forecasting: Deep learning-based prediction models employ time series processing units such as LSTM / GRU, combined with regularization and adaptive learning rate strategies, to address the problem that traditional statistical methods struggle to capture the dynamic patterns of logistics demand. In practical applications, order volume prediction errors are significantly reduced, especially in demand-fluctuation scenarios such as e-commerce promotions. Prediction response speed is greatly improved, enabling accurate prediction of changes in cargo flow and direction, providing a scientific basis for warehousing and capacity allocation, and effectively avoiding inventory backlogs or shortages. 3. Overall optimization and cost control of logistics resource allocation: The intelligent planning model achieves global optimality in resource allocation under multiple constraints by integrating heuristic and metaheuristic algorithms. In transportation route planning, vehicle loading rates are improved while empty-running rates are significantly reduced; simultaneously, the introduction of environmental factors reduces carbon emissions in logistics, meeting green development requirements while helping enterprises obtain policy dividends and social recognition. 4. Intelligent upgrade of execution monitoring and anomaly handling: The combination of IoT real-time monitoring and blockchain technology enables transparent management of the entire logistics process, improving data credibility and cargo traceability efficiency. The anomaly handling module employs reinforcement learning algorithms, significantly reducing the time required to generate response strategies for unexpected situations such as traffic accidents and inventory shortages, thus substantially improving service stability. 5. Three-dimensional construction of decision support system: Knowledge graph technology integrates knowledge from logistics regulations, industry standards, and other fields, significantly improving the accuracy of compliance assessments for planning solutions. Deep data mining capabilities can predict peak and off-peak seasons in advance, supporting enterprises in strategic resource reserves. The collaborative operation of these modules forms a closed-loop system of "data collection - intelligent prediction - dynamic planning - real-time monitoring - optimization iteration," improving the overall operational efficiency of logistics companies, significantly reducing overall costs, and building technological barriers and core competitiveness in the fierce market competition. Attached Figure Description

[0016] Figure 1This is a flowchart of the AI-based intelligent processing method for logistics planning based on data analysis, as described in this invention. Detailed Implementation

[0017] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined in this application.

[0018] like Figure 1 As shown, this embodiment provides an AI-based intelligent processing method for logistics planning based on data analysis, including the following steps: Step S1: Collect multi-source data from various links in the logistics supply chain, preprocess the collected data, and use data mining techniques to extract feature information from the preprocessed data to construct a feature vector set; Step S2: Construct a deep learning neural network model, which includes an input layer, several hidden layers, and an output layer. The input layer receives a preprocessed set of feature vectors. Train the deep learning neural network model using historical logistics data, and adjust the model's weights and biases using the backpropagation algorithm to complete the model's prediction of future logistics demand. Step S3: Construct an intelligent logistics planning model based on the predicted logistics demand; solve and optimize the intelligent logistics planning model using a combination of heuristic algorithms and metaheuristic algorithms; Step S4: Transform the optimized logistics plan into specific execution instructions, send them to logistics personnel and equipment, and use IoT technology to collect data in real time during the logistics execution process. Feed the collected real-time data back to the monitoring center; when a deviation is found between the actual execution and the plan, issue an early warning signal in a timely manner. Step S5: When an anomaly is detected during the logistics execution process, the anomaly handling mechanism is activated; using artificial intelligence algorithms, response strategies are quickly generated based on the type and severity of the anomaly.

[0019] Specifically, including: I. Implementation process of multi-source logistics data acquisition and preprocessing module: This step aims to build a comprehensive and high-quality logistics data foundation to support subsequent intelligent planning. The specific implementation steps are as follows: 1. Multi-source data acquisition solution: The data collection scope covers the entire logistics supply chain, specifically including: Cargo information: The warehouse management system collects information such as weight, volume, quantity, category, packaging type, shelf life, and destination of the goods every 6 hours.

[0020] Transportation vehicle information: The vehicle terminal is used to collect information on the vehicle's model, load, volume, current location, speed, fuel consumption, and vehicle status (such as whether it is operating normally or needs maintenance). The data is collected every 30 minutes, and real-time data is collected if any abnormal situation occurs during the journey.

[0021] Traffic information: Real-time traffic conditions, road congestion, traffic accident information, and traffic control information are collected by connecting with the information platform of traffic management departments, using the API interface of navigation software, and vehicle GPS, etc. The collection frequency is once every 5 minutes.

[0022] Order information: The system retrieves order number, order time, customer information, shipping address, order amount, expected delivery time, etc., from the order management system in real time.

[0023] Geographic information data includes the geographical coordinates of warehouses, distribution points, and suppliers, as well as surrounding road networks and topography. It is acquired through a geographic information system (GIS) and updated monthly.

[0024] 2. Data cleaning and standardization: Data cleaning: The collected data was cleaned using the following methods: Noisy Data Processing: The density-based Local Outlier Factor (LOF) algorithm is used to identify noisy data. Given a dataset D, for each data point p, its LOF(p) is calculated. When LOF(p) > a threshold, the data is considered noisy and is removed. The calculation formula is as follows: ; in, Representing data points of Nearest neighbor set For data points Locally achievable density, , for arrive The reachable distance, , Representing data points and The distance between them. In this embodiment, The value is set to 5, and the threshold is set to 1.5.

[0025] Duplicate data processing: Identify duplicate data and retain the latest record by comparing key attributes of the data (such as order number, unique identifier of goods, etc.).

[0026] Data standardization processing: Data of different formats and units are converted into a unified analyzable format. For numerical data, the min-max normalization method is used, as shown in the following formula: ; in, The original data, This is the minimum value of the feature. The maximum value of this feature. The data is standardized, with values ​​ranging from [0,1]. For categorical data, one-hot encoding is used.

[0027] 3. Feature extraction and feature vector set construction: Principal Component Analysis (PCA) is used to extract features from the preprocessed data. Let the standardized data matrix be... Each row represents a sample, and each column represents a feature. , For the sample size, The number of features. The steps of the PCA algorithm are as follows: Calculate the data matrix covariance matrix ,in for The mean vector; Solving the covariance matrix eigenvalues and the corresponding feature vector ; Select the eigenvectors corresponding to the k largest eigenvalues ​​to form the feature matrix. ; Data matrix Projection onto the feature matrix The above yields the dimensionality-reduced feature vector set. ; In this embodiment, the k value is determined based on the cumulative contribution rate of the feature values. When the cumulative contribution rate reaches 90%, the selection stops. The final feature vector set can effectively describe the key attributes and status of logistics operations.

[0028] II. Implementation process of building and training module for logistics demand forecasting model based on deep learning: 1. Deep learning neural network model construction: The constructed deep learning neural network model adopts an LSTM (Long Short-Term Memory) structure, which can effectively process time-series data and capture the long-term dependencies in logistics demand. The model structure is as follows: Input layer: Receives the preprocessed feature vector set The input dimension is ; Hidden layers: Contain 3 LSTM layers and 2 fully connected layers. The first LSTM layer has 128 neurons, the second LSTM layer has 64 neurons, and the third LSTM layer has 32 neurons; the first fully connected layer has 64 neurons, and the second fully connected layer has 32 neurons. A dropout layer is added after each LSTM layer with a dropout rate of 0.2 to prevent overfitting. Output layer: A fully connected layer is used, with 3 output dimensions, corresponding to the predicted logistics demand values ​​for the next 7 days, 15 days, and 30 days, respectively.

[0029] 2. Model training process: Dataset partitioning: The historical logistics data is divided into training set, validation set and test set in a ratio of 7:2:1; Loss function: The mean squared error (MSE) is used as the loss function, and the formula is as follows: ; in, For the sample size, This represents the actual logistics demand. Forecasted logistics demand; Optimization Algorithm: The Adam optimizer is used, and its update formula is as follows: ; ; ; ; ; in, and These are the first-order moment estimate and the second-order moment estimate, respectively. This is the current gradient. and These are momentum parameters, set to 0.9 and 0.999 respectively. This is the learning rate, initially set to 0.001. It is an extremely small constant, let it be... , These are model parameters; Training process: Batch gradient descent is used with a batch size of 32 and 100 training epochs. During training, the model's loss on the validation set is calculated after each epoch. Training stops when the validation set loss stops decreasing for 10 consecutive epochs to prevent overfitting. Simultaneously, a learning rate decay strategy is employed: when the validation set loss decreases further, the learning rate is multiplied by 0.5 to improve training efficiency.

[0030] 3. Model Evaluation: After training, the model is evaluated on the test set using mean absolute error (MAE) and root mean square error (RMSE) as evaluation metrics, with the following formulas: ; ; In this embodiment, the model on the test set It is 5.2. The value of 7.8 indicates that the model has high prediction accuracy.

[0031] III. Implementation Process of Intelligent Logistics Planning Model Construction and Optimization Module: 1. Construction of intelligent logistics planning model: The constructed intelligent logistics planning model aims to minimize the total logistics cost, while considering constraints such as transportation time, cargo timeliness, vehicle loading rate, and carbon emissions. The model's objective function and constraints are as follows: Objective function: ; in, The total logistics cost For transportation costs, For warehousing costs, For the cost of damaged goods, Cost of carbon emissions; Transportation costs The calculation formula is: ; in, For the number of transport vehicles. For the number of delivery points, For vehicles From the point of origin to the delivery point distance, 0-1 variables (1 represents a vehicle) For delivery points Delivery (0 indicates otherwise). For vehicles Unit distance transportation cost For vehicles Fixed costs (such as driver wages, vehicle depreciation, etc.). Variables are 0-1 (1 indicates the use of a vehicle). (0 indicates otherwise). Storage costs The calculation formula is: ; in, For the number of warehouses, For warehouse Unit warehousing cost For warehouse Inventory levels; Cost of damaged goods The calculation formula is: ; in, For the types and quantities of goods, For the first The value of the goods For the first Damage rate of the goods For the first The transportation time for this type of goods; carbon emission costs The calculation formula is: ; in, For vehicles Carbon emissions per unit distance, Cost per unit of carbon emissions; Constraints: Vehicle loading rate constraint: For all ; in, For delivery points The demand for goods, For vehicles Maximum load capacity; Transportation time constraints: For all ; in, For vehicles From the point of origin to the delivery point The delivery time For vehicles Maximum permissible transportation time; Time constraints on goods: For all ; in, For the first The maximum permissible transport time for this type of goods; Carbon emission constraints: ; in, The maximum permissible carbon emissions; Variable constraints: For all ; ; For all ; For all ; 2. Model optimization solution: An initial feasible solution is generated using a heuristic algorithm (greedy algorithm), and then optimized using a genetic algorithm. The specific steps are as follows: Greedy algorithm to generate initial feasible solution: Clustering is performed based on the geographical location of the delivery points, grouping delivery points that are close to each other into the same group; For each cluster group, select the warehouse closest to that group as the shipping point; Based on the demand for goods at the distribution points and the vehicle's loading capacity, allocate appropriate transport vehicles to each cluster group to ensure that the vehicle loading rate is not less than 80%. Genetic Algorithm Optimization: Encoding: Integer encoding is used. Each chromosome represents a vehicle dispatching scheme. The length of the chromosome is the number of delivery points, and the value of each gene represents the vehicle number delivering goods to that delivery point. Fitness function: The reciprocal of the objective function Z is used as the fitness function, i.e. A higher fitness value indicates a better solution; Selection operation: The roulette wheel selection method is used, and the probability of an individual being selected is determined based on its fitness value. The higher the fitness value, the higher the probability of being selected. Crossover operation: A single-point crossover method is used. A crossover point is randomly selected, and the genes of the two parent chromosomes are exchanged at the crossover point to generate offspring chromosomes. The crossover probability is set to 0.8. Mutation operation: Randomly select a gene and replace its value with another vehicle number, with the mutation probability set to 0.05; Termination condition: When the algorithm has iterated for 100 generations or the fitness value has stopped changing for 20 consecutive generations, the iteration stops and the optimal solution is output.

[0032] IV. Implementation Process of Logistics Execution and Real-time Monitoring Module 1. Execution instruction generation and sending The optimized logistics plan is translated into specific execution instructions, including vehicle dispatch instructions and warehouse inbound / outbound instructions. For example, vehicle dispatch instructions include information such as vehicle number, departure time, route, delivery point sequence, and unloading quantity. These instructions are then transmitted to vehicle terminals and the warehouse management system via IoT technology, ensuring that relevant personnel and equipment can receive and execute them in a timely manner. 2. Real-time data acquisition and monitoring: By utilizing vehicle-mounted GPS, warehouse sensors, and delivery point cameras, data on the logistics process is collected in real time, such as vehicle location, speed, remaining fuel, cargo temperature and humidity, warehouse inventory, and inbound / outbound records. This data is transmitted to a data center and displayed in real time through a visual monitoring platform, allowing managers to monitor the progress of logistics operations at any time. Simultaneously, blockchain technology is used to store and manage the collected data. Each block in the blockchain contains the hash value of the previous block, the data information of the current block, and a timestamp, ensuring the immutability and traceability of the data. The data storage structure in the blockchain is as follows: Each block's data includes transaction ID, data sender, data receiver, data content, and timestamp. Through blockchain technology, the credibility of logistics data is greatly enhanced, enabling rapid tracing of the goods' flow and identification of responsible parties in the event of a cargo dispute.

[0033] V. Implementation process of the exception handling and dynamic adjustment module: 1. Abnormal situation identification: By analyzing real-time collected logistics data, potential anomalies can be identified. For example, a sudden and sustained decrease in vehicle speed may indicate traffic congestion; a warehouse inventory level below the safety stock threshold may indicate insufficient inventory. Anomaly detection thresholds are set, and the system issues warning signals when data exceeds or falls below these thresholds. 2. Application of reinforcement learning algorithms in anomaly handling: This module employs reinforcement learning algorithms to generate optimal strategies for handling anomalies. The basic principle of reinforcement learning is that through the interaction between an agent and its environment, the agent learns which actions, under different conditions, will yield the greatest reward. In this module, the agent represents the anomaly handling system, the environment represents the various states in the logistics execution process, the actions represent strategies for dealing with anomalies, and the reward represents the degree to which the logistics operation returns to normal after taking action. The state space S of the reinforcement learning algorithm includes the type and severity of the anomaly, the current state of the logistics operation, etc.; the action space A includes replanning transportation routes, adjusting delivery times, and urgently allocating goods, etc.; the reward function R is calculated using the following formula: ; in, For transportation delays caused by abnormal circumstances, The maximum allowable delay time, Additional costs incurred due to exceptional circumstances. This represents the logistics cost under normal circumstances. and Weighting coefficients ( ); Through training with reinforcement learning algorithms, the system can automatically select the optimal response strategy based on different abnormal situations. For example, when encountering traffic congestion, the system can replan the transportation route and choose a route with less congestion; when there is a shortage of inventory, the system can urgently transfer goods from other warehouses. 3. Dynamically adjust logistics planning schemes: Based on the generated response strategies, logistics planning schemes are dynamically adjusted. For example, when transportation routes change, transportation time and costs are recalculated, and vehicle dispatch instructions are updated; when goods are reassigned, warehouse inventory levels and inbound / outbound plans are readjusted. This dynamic adjustment mechanism ensures that logistics operations can return to normal as quickly as possible, minimizing losses caused by abnormal situations.

[0034] VI. Implementation Process of Knowledge Graph and Big Data Analytics in Decision Support: Knowledge graph construction: This involves integrating and representing relevant knowledge in the logistics field, such as logistics regulations, industry standards, business processes of logistics companies, common problems and solutions, to construct a logistics knowledge graph. Entities in the knowledge graph include logistics companies, transport vehicles, warehouses, distribution points, goods, and regulatory provisions; relationships between entities include hierarchical relationships, cooperative relationships, transportation relationships, and storage relationships. Entity extraction algorithm: A deep learning model based on BERT-BiLSTM-CRF is used for entity recognition. First, textual data from the logistics field (such as regulatory documents and business manuals) is preprocessed, including word segmentation, stop word removal, and part-of-speech tagging. The processed data is then input into the BET model to obtain contextual semantic vectors. Next, a BiLSTM layer captures sequence features, and finally, a CRF layer outputs entity labels (such as "transport vehicle" and "delivery point"). Model training employs cross-validation, using entity recognition accuracy (P), recall (R), and F1 score as evaluation metrics. Training stops when the F1 score stabilizes above 0.92. Relation extraction algorithm: Employs an attention-based bidirectional gated recurrent unit (Att-BiGRU) model. Sentences containing entity pairs are encoded, semantic features are extracted through a BiGRU layer, and the attention mechanism is used to strengthen the weights of key information between entities. Finally, a softmax layer is used to classify and output the relation type (e.g., "affiliation" or "transportation"). Negative sampling is used during training to balance the sample distribution. Knowledge integration and completion: Integration phase: The entity linking algorithm is used to align the newly extracted entities with the existing knowledge base. Cross-data source entity matching is achieved by calculating entity name similarity (edit distance) and attribute similarity (Jaccard coefficient), with the matching threshold set to 0.85. The completion phase involves using a knowledge reasoning algorithm based on a translation model (TransE) to predict missing entity relations. Entities and relations are mapped to a low-dimensional vector space, and the loss function is optimized. ; in express , Let be the marginal parameter, so that the correct triplet is... The distance is less than the faulty triplet .

[0035] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An AI-based intelligent processing method for logistics planning based on data analysis, characterized in that: Includes the following steps: Step S1: Collect multi-source data from various links in the logistics supply chain, preprocess the collected data, and use data mining techniques to extract feature information from the preprocessed data to construct a feature vector set; Step S2: Construct a deep learning neural network model, which includes an input layer, several hidden layers, and an output layer. The input layer receives a preprocessed set of feature vectors. Train the deep learning neural network model using historical logistics data, and adjust the model's weights and biases using the backpropagation algorithm to complete the model's prediction of future logistics demand. Step S3: Construct an intelligent logistics planning model based on the predicted logistics demand; solve and optimize the intelligent logistics planning model using a combination of heuristic algorithms and metaheuristic algorithms; Step S4: Transform the optimized logistics planning scheme into specific execution instructions, send them to logistics personnel and equipment, and use IoT technology to collect data in real time during the logistics execution process, and feed the collected real-time data back to the monitoring center; When a deviation is found between the actual implementation and the planned scheme, an early warning signal should be issued in a timely manner; Step S5: When an anomaly is detected during the logistics execution process, the anomaly handling mechanism is activated; using artificial intelligence algorithms, response strategies are quickly generated based on the type and severity of the anomaly.

2. The intelligent processing method for logistics planning based on data analysis according to claim 1, characterized in that, The generated response strategies include: replanning transportation routes, adjusting delivery plans, and coordinating warehouse inventory. When generating response strategies, a reinforcement learning algorithm is used. Through trial and error and learning, the system selects the optimal response plan according to different abnormal situations.

3. The intelligent processing method for artificial intelligence logistics planning based on data analysis according to claim 1, characterized in that, By employing high-frequency and low-frequency data collection, and using sliding window and clustering algorithms to mine time-series patterns in real-time logistics data, the system identifies periodic fluctuations in order volume, peak periods of regional demand, and trends in transportation efficiency decline. A mapping relationship between goods categories and delivery areas is established through association rule analysis. The association rules include: the mapping between goods categories and delivery areas, and the association between transportation routes and congestion probabilities. A resource elastic allocation model is constructed based on historical and real-time data. Temporary capacity reserves and warehouse expansion plans are initiated before the peak season is predicted. The warehouse expansion plan is obtained by combining the difference between the peak demand forecast and the existing capacity with constraints. During the off-season, vehicle maintenance plans and personnel rotation mechanisms are triggered. The criteria for determining the off-season are: comparing real-time data with historical low-trend characteristics, and determining the off-season based on indicators that are consistently below a threshold. Service optimization strategies are generated simultaneously, including: dynamically adjusting delivery time commitments and differentiated pricing schemes.

4. The intelligent processing method for logistics planning based on data analysis according to claim 1, characterized in that, In step S1, the various links in the logistics supply chain include: suppliers, warehouses, transport vehicles, and distribution points. The multi-source data includes: cargo information, transport vehicle information, traffic condition information, order information, and geographic information data. The preprocessing specifically involves: removing noise and duplicate data from the multi-source data, and performing data standardization processing to unify data of different formats and units into an analyzable format.

5. The intelligent processing method for logistics planning based on data analysis according to claim 1, characterized in that, In step S2, when constructing the deep learning neural network model, a long short-term memory network or a gated recurrent unit is used as the basic unit of the hidden layer to process the time series features in the logistics data and capture the dependency relationship of logistics demand. The logistics demand includes: the flow, direction and time distribution information of goods.

6. The intelligent processing method for artificial intelligence logistics planning based on data analysis according to claim 1, characterized in that, In step S3, the constraints of the intelligent logistics planning model include: transportation cost, transportation time, cargo timeliness and vehicle loading rate. The heuristic algorithm generates an initial feasible solution, and the metaheuristic algorithm optimizes the initial feasible solution generated by the heuristic algorithm and searches for a global optimal solution or a near-global optimal solution. During the optimization process of the metaheuristic algorithm, the parameters and constraints of the model are adjusted in real time according to the dynamic changes of the actual logistics business.

7. The intelligent processing method for logistics planning based on data analysis according to claim 1, characterized in that, In step S4, the execution instructions include: dispatch instructions for transport vehicles and warehouse entry and exit instructions. The data in the logistics execution process includes: vehicle location, speed, and cargo status. Blockchain technology is used to store and manage the data in the logistics execution process.

8. The intelligent processing method for logistics planning based on data analysis according to claim 1, characterized in that, In step S5, the abnormal situations include: traffic accidents causing transportation delays and insufficient warehouse inventory.

9. The intelligent processing method for artificial intelligence logistics planning based on data analysis according to claim 8, characterized in that, Knowledge graph technology is used to integrate and represent relevant knowledge in the logistics field, including logistics regulations, industry standards, and business processes of logistics companies, to complete data processing, model building, and decision-making.

10. The intelligent processing method for artificial intelligence logistics planning based on data analysis according to claim 9, characterized in that, When conducting intelligent logistics planning, the carbon emission cost of vehicles is taken as one of the constraints of the intelligent logistics planning model, and logistics planning is completed by optimizing transportation routes and vehicle scheduling.

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