Warehousing process data management method and system for cross-border e-commerce

Abstract

The present invention relates to a warehouse process data management method and system for cross-border e-commerce. The method: real-time data acquisition and integration of cross-border e-commerce platforms and logistics service providers to obtain a unified cross-platform data set; barcode scanning and information matching of incoming goods to obtain digital goods record data; inputting the digital goods record data into a deep learning algorithm for intelligent warehouse allocation to obtain an optimal cargo location plan; batch merging and heuristic path planning of orders to obtain intelligent picking instructions; intelligent packaging material recommendations and machine learning logistics channel matching for the goods that have been picked to obtain a target package delivery plan; full-process tracking and abnormal warnings are performed through a predictive analysis model to obtain logistics full-link data records. The implementation of the present invention can integrate multi-platform data, realize intelligent warehouse management, and improve logistics efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse process data management, and in particular to a warehouse process data management method and system for cross-border e-commerce. Background Art

[0002] The warehousing and logistics management of cross-border e-commerce faces problems such as inconsistent data on multiple platforms, difficulty in tracking cargo information, and low utilization of storage space. These factors seriously restrict the operational efficiency and customer satisfaction of cross-border e-commerce.

[0003] Traditional warehouse management methods are unable to cope with the complexity and variability of cross-border e-commerce. The data island problem between multiple e-commerce platforms and logistics service providers leads to poor information flow, affecting the timeliness and accuracy of decision-making. At the same time, manual operations are prone to errors in the warehousing, shelving, and picking of goods, which not only reduces work efficiency but also increases additional labor costs. In addition, due to the lack of intelligent warehouse allocation and path planning methods, warehouse space utilization is low and picking efficiency is low, making it difficult to meet the requirements of cross-border e-commerce for rapid response. Summary of the invention

[0004] The main purpose of the present invention is to provide a warehouse process data management method and system for cross-border e-commerce, so as to integrate multi-platform data, realize intelligent warehouse management, and improve logistics efficiency.

[0005] To achieve the above object, the present invention provides a method for managing warehousing process data of a cross-border e-commerce, comprising the following steps:

[0006] Real-time acquisition and integration of order, inventory and logistics information of cross-border e-commerce platforms and logistics service providers to obtain a unified cross-platform data set;

[0007] Based on the cross-platform unified data set, barcode scanning and information matching are performed on the incoming goods to obtain digital goods record data with an internal unique identification code;

[0008] Input the digital cargo record data into a deep learning algorithm pre-trained based on historical order data for intelligent warehouse allocation to obtain an optimal cargo location solution;

[0009] The orders are batched and routed heuristically according to the optimal cargo location plan and real-time order data to obtain intelligent picking instructions;

[0010] Based on the intelligent picking instructions, intelligent packaging material recommendations and machine learning logistics channel matching are performed on the picked goods to obtain a target package delivery plan;

[0011] The target package delivery plan is input into the predictive analysis model for full-process tracking and abnormal warning to obtain full-link logistics data records.

[0012] The present invention also provides a warehousing process data management system for cross-border e-commerce, including:

[0013] The acquisition module is used to acquire and integrate the order, inventory and logistics information of cross-border e-commerce platforms and logistics service providers in real time to obtain a unified cross-platform data set;

[0014] A scanning module, used to perform barcode scanning and information matching on incoming goods based on the cross-platform unified data set to obtain digital goods record data with an internal unique identification code;

[0015] An allocation module, used for inputting the digital cargo record data into a deep learning algorithm pre-trained based on historical order data to perform intelligent warehouse allocation and obtain an optimal cargo location solution;

[0016] A planning module, used to batch merge and heuristically plan routes for orders based on the optimal cargo location plan and real-time order data, and obtain intelligent picking instructions;

[0017] A recommendation module, used to perform intelligent packaging material recommendation and machine learning logistics channel matching for the picked goods based on the intelligent picking instructions to obtain a target package delivery plan;

[0018] The tracking module is used to input the target package delivery plan into the predictive analysis model for full-process tracking and abnormal warning, and obtain the data record of the entire logistics chain.

[0019] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0020] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0021] In summary, the technical solution provided by the present invention realizes the real-time integration and unified management of cross-platform data, solves the problem of data islands, and improves the availability of information and the accuracy of decision-making. Through intelligent barcode scanning and information matching technology, rapid identification and accurate tracking of incoming goods are achieved, human errors are reduced, and the efficiency of warehousing is improved. The deep learning algorithm is used for intelligent warehouse allocation, which optimizes the utilization of warehouse space, while taking into account the relevance of goods and improving the subsequent picking efficiency. The heuristic path planning algorithm is used to realize order batch merging and intelligent picking instruction generation, which greatly improves the picking efficiency and accuracy. Through intelligent packaging material recommendation and machine learning logistics channel matching, the packaging efficiency and logistics cost are optimized, and the economy and environmental protection of distribution are improved. The introduction of predictive analysis models for full-process tracking and abnormal warning improves the visualization of the logistics process and the processing speed of abnormal events, and enhances customer satisfaction. The present invention integrates multi-platform data, realizes intelligent warehousing management, and improves logistics efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the steps of a warehousing process data management method for cross-border e-commerce in one embodiment of the present invention;

[0023] Figure 2 It is a structural block diagram of a warehousing process data management system for cross-border e-commerce in one embodiment of the present invention;

[0024] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0025] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] Reference Figure 1 This embodiment provides a method for managing warehousing process data of a cross-border e-commerce, comprising the following steps:

[0028] S1, real-time acquisition and integration of order, inventory and logistics information of cross-border e-commerce platforms and logistics service providers to obtain a unified cross-platform data set;

[0029] Among them, the APIs of multiple cross-border e-commerce platforms are processed for parallel data requests, and the original order and inventory data streams are obtained from multiple platforms at the same time to ensure the efficiency and timeliness of data acquisition. The original order and inventory data streams are cleaned and format standardized to eliminate data inconsistencies between different platforms and obtain platform data in a unified format. The platform data in a unified format is input into the distributed storage system for shard storage processing. The distributed storage system can handle high-concurrency read and write requests, and its structural design can support fast data access and maintain data reliability. In this process, the sensitive information in the storage structure is encrypted at multiple levels to ensure compliance with cross-border data security requirements and form a secure and efficient encrypted data set. A real-time data synchronization mechanism is built based on the encrypted data set. Real-time logistics tracking data is obtained by regular polling and push processing of the logistics service provider system. The real-time logistics tracking data is associated and matched with the corresponding order information to generate the full life cycle data of the order. This association can track the current status of each order and provide historical logistics information to form a complete order tracking record. Perform multi-dimensional aggregation analysis on the order life cycle data to obtain a real-time snapshot of the inventory and logistics status across platforms, reflecting the inventory status, logistics efficiency, and potential delays of each platform. Build a distributed cache layer based on real-time snapshot data. Through the distributed cache layer, data distribution and processing are effectively performed to ensure that each system and user can quickly obtain the required information in different scenarios, forming a unified cross-platform data set.

[0030] S2, based on the cross-platform unified data set, performs barcode scanning and information matching on incoming goods to obtain digital goods record data with internal unique identification codes;

[0031] Specifically, the barcode or QR code of the outer packaging of the incoming goods is scanned at multiple angles to obtain the original goods identification information. The original goods identification information is fuzzily matched with the order data in the cross-platform unified data set to obtain a preliminary matching result. Based on the preliminary matching result, the SKU (stock keeping unit), quantity and specification information of the goods are extracted to generate the goods attribute data. The goods attribute data is input into the hash algorithm for processing to ensure that the generated unique identification code is highly unique and unpredictable, and an internal unique identification code is obtained. The internal unique identification code is associated and mapped with the original goods identification information to form a corresponding relationship of the goods identification to ensure that each goods can be accurately identified and tracked. A QR code image containing detailed information of the goods is generated based on the corresponding relationship of the goods identification. In order to enhance security, an anti-counterfeiting watermark is embedded in the QR code image to ensure the security of the label during transportation and storage and prevent forgery and tampering. The security-enhanced goods label data is transmitted to the intelligent printing device for real-time printing to obtain the target goods label. After the label is printed, the pasting position of the target goods label is image recognized and verified. Through image recognition technology, the correctness of the label pasting position is ensured, and it is verified whether the label is successfully applied to the corresponding goods. Through the above steps, digital cargo record data with an internal unique identification code is obtained.

[0032] S3, input the digital cargo record data into the deep learning algorithm pre-trained based on historical order data for intelligent warehouse allocation to obtain the optimal cargo location solution;

[0033] It should be noted that the digital cargo record data is input into the deep learning algorithm pre-trained based on historical order data. The digital cargo record data is feature extracted to generate a multidimensional feature vector containing commodity attributes, dimensions, weight and order correlation. Through feature extraction, important information is extracted from the original data. The multidimensional feature vector is input into the commodity correlation analysis module for similarity calculation to obtain the correlation strength matrix between commodities, which reflects the correlation between different commodities. Using the correlation strength matrix, the existing warehouse locations are clustered and analyzed to obtain the target location group, thereby improving the efficiency of data processing. The target location group information is integrated with the current warehouse occupancy status data to form an available location resource pool, ensuring that the existing storage space can be fully utilized when allocating locations. A multi-factor score calculation is performed for each location in the available location resource pool, taking into account various factors, including the distance from the order sorting area, the load-bearing conditions of the shelves, and the correlation of surrounding commodities, to obtain a location fit score, and objectively evaluate the suitability of each location. The location fit score is input into the reinforcement learning model for action selection to obtain a preliminary location allocation plan. The reinforcement learning model can continuously learn and optimize in a dynamic environment, and select the optimal action path based on real-time data and historical performance. At the same time, the preliminary cargo allocation plan is checked for hazardous material isolation, temperature control requirements, and batch management to ensure that all cargo allocation plans are feasible under the premise of safety and compliance. Through inspection, an effective set of cargo allocation plans is screened out. Based on the feasible plan set, the objective function of minimizing the expected picking time and maximizing space utilization is optimized and solved to obtain the optimal cargo location plan. In the optimization process, mathematical models and algorithm tools are used to effectively balance the relationship between picking efficiency and storage space utilization, making the entire warehouse management process more efficient and adaptable.

[0034] First, the multidimensional feature vector is input into the word embedding model in the commodity association analysis module for dimensionality reduction processing, and the high-dimensional commodity feature vector is converted into a low-dimensional representation vector, which effectively reduces the computational complexity and retains the main feature information between commodities. The cosine similarity is calculated for the low-dimensional representation vector of the commodity to obtain the preliminary similarity matrix between commodities, which reflects the similarity between commodities. Based on the preliminary similarity matrix, a commodity relationship graph is constructed. The commodities are used as nodes, and the similarity between commodities is used as the weight of the edge. The commodity relationship graph is analyzed to determine the importance score of each commodity. The importance score of the commodity is calculated based on its connectivity, frequency and other factors in the graph, reflecting the importance of each commodity in the overall system. The commodity importance score is weighted and fused with the preliminary similarity matrix to obtain a more accurate commodity association strength matrix. The commodity association strength matrix is ​​subjected to spectral clustering analysis to obtain the preliminary commodity category division results. Spectral clustering is a clustering method based on graph theory that can identify natural clusters in complex data structures. The preliminary commodity category division results are cross-validated with the commodity co-occurrence information in historical orders to ensure the rationality and accuracy of the category division. Through cross-validation, the results of commodity classification are optimized so that each commodity category can more accurately reflect the similarity and functional characteristics of the commodities. Based on the optimized commodity classification results, a hierarchical clustering analysis is performed on the existing cargo locations in the warehouse to obtain the preliminary cargo location group division results. Hierarchical clustering can group commodities with similar functions together according to the functions and characteristics of the commodities to form reasonable cargo location groups. The preliminary cargo location group division results are constrained and optimized with the physical layout information of the warehouse to ensure that the division of cargo location groups is not only based on the functional characteristics of the commodities, but also fully considers the actual layout and utilization efficiency of the warehouse space to obtain the final target cargo location group.

[0035] S4, batch merge and heuristic path planning are performed on orders according to the optimal cargo location plan and real-time order data to obtain intelligent picking instructions;

[0036] Specifically, the real-time order data is divided into time windows to obtain a set of orders within a fixed time period. By dividing the time windows, it is ensured that related orders are processed within a certain time period, thereby improving the processing efficiency. Based on the order set of a fixed time period, the order similarity is calculated to obtain the correlation matrix between orders, which reflects the correlation between different orders. The order is grouped using the correlation matrix between orders to obtain a preliminary order batch. Similar orders are grouped together for subsequent processing and picking. The capacity constraint check is performed on the preliminary order batch to ensure that each order batch is feasible in the actual picking process. Through the capacity constraint check, inappropriate order batches are eliminated to obtain a valid order batch. A picking task graph is constructed based on the valid order batch and the optimal cargo location solution, and the cargo location information corresponding to each order is identified. The minimum spanning tree is calculated for the picking task graph to find a picking path with the lowest cost, thereby effectively reducing the walking distance and time in the picking process and obtaining the target picking path. The target picking path is segmented and tasks are assigned based on the distribution of warehouse personnel to ensure that the picking tasks are reasonably distributed among personnel and form a multi-person collaborative picking subtask set. Through reasonable task allocation, picking efficiency and accuracy can be improved. At the same time, the execution order of the multi-person collaborative picking subtask set is optimized through dynamic planning to ensure that the tasks can be completed in the optimal order and improve the overall efficiency of the picking operation. After the optimization is completed, structured picking instruction data is generated based on the optimal picking task execution sequence, including cargo location positioning, commodity confirmation and quantity verification information to ensure the accuracy and efficiency of the picking process. The structured picking instruction data is visualized to generate an interactive picking navigation interface that is suitable for mobile terminal display, and output intelligent picking instructions.

[0037] S5, based on the intelligent picking instructions, intelligent packaging material recommendations and machine learning logistics channel matching are performed on the picked goods to obtain the target package delivery plan;

[0038] Among them, based on the intelligent picking instructions, the goods that have been picked are subjected to three-dimensional scanning and processing to obtain the precise volume and weight data of the goods. The precise volume and weight data are matched and calculated with the preset packaging material specification library to obtain a preliminary packaging solution set. By combining the physical characteristics of the goods with the specifications of different packaging materials, the most suitable packaging solution for the goods is found. Packing optimization is performed based on the preliminary packaging solution set to minimize the occupation of packaging space and the amount of materials used, thereby reducing the overall logistics cost. Through the algorithm model, different packaging methods are simulated, and the most efficient solution in terms of space and materials is selected. The optimized packaging solution is compared and analyzed with the historical packaging feedback data to obtain a packaging reliability score, which helps to identify which packaging solutions perform well in actual operations and which may have room for improvement. Based on the packaging reliability score, the packaging solution is adjusted to generate a more intelligent packaging recommendation solution to ensure that the selected solution can effectively protect the goods during transportation. Cost accounting is performed on the intelligent packaging recommendation solution. The price of each packaging material is evaluated to obtain packaging cost data. The packaging cost data is input into the total logistics cost calculation model. Based on the packaging cost data and destination information, a preliminary screening of available logistics channels is performed to obtain a set of candidate logistics channels. The screening process comprehensively considers the service characteristics and cost information of each logistics channel. The timeliness and reliability of candidate logistics channels are predicted to generate a performance scoring matrix to provide a quantitative comparison basis for different logistics channels. The performance scoring matrix can reflect the performance of each logistics channel in multiple dimensions. The performance scoring matrix is ​​optimized for cost, timeliness and reliability to obtain the optimal logistics channel selection result. Through multi-objective optimization, the best balance between cost, speed and reliability can be found when selecting a logistics channel. The selected optimal logistics channel is integrated with the intelligent packaging recommendation solution to generate a target package delivery solution that includes packaging guidance and logistics information.

[0039] S6, input the target package delivery plan into the predictive analysis model for full-process tracking and abnormal warning, and obtain the data record of the entire logistics chain.

[0040] Among them, the target package delivery plan is input into the predictive analysis model, the estimated arrival time of the package at each logistics node is predicted, and the package full-time prediction sequence is generated to provide a data-based time frame. A logistics state transition diagram is constructed based on the package full-time prediction sequence to reflect the state changes of the package between different logistics nodes. Markov chain analysis is performed on the logistics state transition diagram to help identify the state transition probability matrix of each node and reveal the probability of the package transferring between each node. The state transition probability matrix is ​​input into the Bayesian network model to perform probabilistic reasoning on potential logistics abnormal events and obtain the probability distribution of abnormal events. Through the analysis of historical data, potential risk points are identified to facilitate early action. Threshold screening is performed on the probability distribution of abnormal events to identify a set of high-risk abnormal events. Based on this set, early warning trigger rules are generated. These rules guide the subsequent abnormal handling process to ensure that potential problems can be responded to quickly. At the same time, the real-time logistics tracking data is compared and analyzed with the package full-time prediction sequence to obtain the logistics progress deviation value. Through deviation analysis, the gap between the actual logistics progress and the expected progress is revealed. The logistics progress deviation value is input into the anomaly detection algorithm to obtain the abnormal status mark, which indicates which packages have problems during transportation. Based on the abnormal status mark and the warning trigger rule, intelligent warning information is generated, and this information is pushed to the corresponding processing module in time to ensure that relevant personnel can respond quickly. Through the early warning mechanism, the losses caused by logistics anomalies can be effectively reduced. The abnormal processing response data is structured and associated with the original logistics tracking data to form an enhanced logistics event record. At the same time, the enhanced logistics event record is encrypted and distributedly stored to ensure data security and integrity, and obtain the logistics full-link data record.

[0041] Specifically, feature extraction is performed on the target package delivery plan to obtain a target feature vector containing package weight, volume, destination and logistics channel information. The feature vector is used to systematically describe the basic attributes of the package. The target feature vector is input into the time series encoder in the predictive analysis model to perform time series embedding of the package features, convert the package feature information into a representation in the time dimension, and obtain the time series representation of the package. Based on the time series representation of the package, an attention weight matrix is ​​constructed, and similarity calculations are performed on similar packages in the historical logistics data to identify the most similar K historical package samples. By calculating the similarity between packages, historical cases with similar features can be effectively found. The K most similar historical package samples are input into the long short-term memory network (LSTM) for time series prediction to obtain the preliminary arrival time estimate of each logistics node. The LSTM model can effectively capture long-term dependencies and improve the accuracy of prediction. The preliminary arrival time estimate is processed by conditional random field (CRF) to model the time series dependencies between nodes. Through the CRF model, the node arrival time series is optimized to ensure that the mutual influence between nodes is considered in the prediction process. The optimized node arrival time series is integrated with real-time traffic information and weather data to obtain the adjusted time series. The adjusted time series is adaptively smoothed to reduce the impact of data fluctuations and obtain smoothed time prediction results. Smoothing can eliminate noise and improve the stability and accuracy of the prediction results. The smoothed time prediction results are compared and analyzed with the service level agreement of the logistics service provider to ensure that the prediction results are within the acceptable service range. For the prediction results that exceed the service level agreement, necessary corrections are made to ensure that the final package full-time prediction series has high reliability and practicality.

[0042] In one example, the order, inventory and logistics information of cross-border e-commerce platforms and logistics service providers are acquired and integrated in real time to obtain a unified cross-platform data set, including:

[0043] Process data requests in parallel on the APIs of multiple cross-border e-commerce platforms to obtain the original order and inventory data streams, clean the original order and inventory data streams and perform format standardization to obtain platform data in a unified format;

[0044] The platform data in a unified format is input into the distributed storage system for shard storage processing to obtain a data storage structure with high concurrent read and write, and the sensitive information in the data storage structure is encrypted at multiple levels to obtain an encrypted data set that meets cross-border data security requirements;

[0045] Build a real-time data synchronization mechanism based on encrypted data sets, conduct regular polling and push processing on the logistics service provider system, obtain real-time logistics tracking data, and associate and match the real-time logistics tracking data with the corresponding order information to obtain the order life cycle data;

[0046] Perform multi-dimensional aggregation analysis on the order life cycle data to obtain a real-time snapshot of the inventory and logistics status across platforms, and build a distributed cache layer based on the real-time snapshot data to perform data distribution processing to obtain a unified cross-platform data set.

[0047] In this example, a data request framework is designed that can efficiently interact with the APIs of different platforms. Through the parallel request mechanism, the original order and inventory data streams of multiple cross-border e-commerce platforms are obtained simultaneously. The original order and inventory data streams are cleaned and formatted. By writing a data cleaning algorithm, the data returned by each platform is deduplicated, missing values ​​are filled, and converted into a unified format to obtain platform data in a unified format. The platform data in a unified format is input into the distributed storage system for shard storage processing to establish a data storage structure with high concurrent read and write. By sharding the data, better load balancing and higher system availability are achieved, so that the system can still respond quickly when facing a large number of requests. For sensitive information in the data storage structure, multi-layer encryption processing is performed to ensure data security. For example, the user's personal information is encrypted using the AES encryption algorithm to ensure that even if the data is leaked, sensitive information will not be illegally accessed. The encryption mechanism meets the cross-border data security requirements and ensures the security of data during transmission and storage. Based on the encrypted data set, a real-time data synchronization mechanism is built. Through this mechanism, the logistics service provider system is regularly polled and pushed to obtain real-time logistics tracking data, including key information such as the current location and transportation status of the package. The real-time logistics tracking data is associated and matched with the corresponding order information to form a complete order life cycle data, which can be tracked and recorded in all links from order placement, shipment to final delivery. Multi-dimensional aggregation analysis is performed on the order life cycle data to obtain a real-time snapshot of the inventory and logistics status across platforms. Including the inventory level of each platform, as well as the efficiency and cost of different logistics channels. Based on the real-time snapshot data, a distributed cache layer is built to achieve fast access and distribution of data. Through the cache mechanism, users and systems can access the required data more quickly, improving the response speed of the overall system. For example, when a user queries the inventory of a certain product, the data is read directly from the cache without having to access the database again, saving query time. A unified cross-platform data set is obtained. The data set contains order and inventory information of each platform and has real-time logistics status.

[0048] In one example, barcode scanning and information matching are performed on incoming goods based on a cross-platform unified data set to obtain digital goods record data with an internal unique identification code, including:

[0049] Scan the barcode or QR code of the outer packaging of the incoming goods at multiple angles to obtain the original goods identification information, and then fuzzy match the original goods identification information with the order data in the cross-platform unified data set to obtain a preliminary matching result;

[0050] Based on the preliminary matching results, the SKU, quantity and specification information of the goods are extracted to obtain the goods attribute data, and the goods attribute data is input into the hash algorithm to generate a unique identification code to obtain an internal unique identification code;

[0051] The internal unique identification code is associated and mapped with the original cargo identification information to obtain the cargo identification correspondence, and a QR code image containing detailed cargo information is generated based on the cargo identification correspondence, and an anti-counterfeiting watermark is embedded in the QR code image to obtain a security-enhanced cargo label;

[0052] The security-enhanced cargo label data is transmitted to the intelligent printing device for real-time printing to obtain the target cargo label, and the pasting position of the target cargo label is image-recognized and verified to obtain digital cargo record data with an internal unique identification code.

[0053] In this example, the barcode or QR code on the outer packaging of the incoming goods is scanned at multiple angles to obtain the original goods identification information. Multi-angle scanning can not only improve the accuracy of recognition, but also ensure that the identification information can be obtained even in the case of uneven lighting or reflection on the packaging surface. The original goods identification information is fuzzy matched with the order data in the cross-platform unified data set, and the fuzzy matching algorithm is used to deal with the problem of inaccurate matching caused by irregular data input or damaged labels. Through fuzzy matching, a preliminary matching result is obtained. Based on the preliminary matching result, the SKU (stock keeping unit), quantity and specification information of the goods are extracted to obtain the goods attribute data. Through natural language processing technology and data parsing algorithms, the required information is extracted from the matching results. The goods attribute data is input into the hash algorithm for processing to generate a unique identification code. The generated internal unique identification code can ensure the uniqueness of each piece of goods in the warehouse management system and avoid confusion caused by similar products. The internal unique identification code is associated and mapped with the original goods identification information to establish a corresponding relationship between the goods identification. Through the data mapping algorithm, it is ensured that the original identification of each piece of goods can be accurately associated with its internal unique identification. A QR code image containing detailed information of the goods is generated based on the corresponding relationship of the goods identification. This QR code contains the basic information of the goods and is embedded with an anti-counterfeiting watermark to enhance its security. The anti-counterfeiting watermark uses invisible watermark technology to embed the watermark information into the QR code to ensure that the security of the goods label is improved without affecting readability. The data of the security-enhanced goods label is transmitted to the smart printing device for real-time printing. Smart printing devices usually have high-resolution printing capabilities to ensure the clarity and accuracy of the QR code image. After printing, the target goods label is affixed to the corresponding goods, and the image recognition and verification of the affixed position of the target goods label are performed to ensure that the position and direction of the label meet the standards. This is achieved through computer vision technology, and the image processing algorithm is used to analyze the label's fit to ensure that each piece of goods can be accurately marked. Digital goods record data with an internal unique identification code is obtained. These data can be queried and tracked in the warehouse management system, and integrated with other systems (such as the order management system and logistics management system) to achieve full life cycle management of goods.

[0054] In one example, digital cargo record data is input into a deep learning algorithm pre-trained based on historical order data for intelligent warehouse allocation, and the optimal cargo location solution is obtained, including:

[0055] The digitized cargo record data is input into a deep learning algorithm pre-trained based on historical order data, and features are extracted from the digitized cargo record data to obtain a multi-dimensional feature vector containing commodity attributes, dimensions, weight, and order relevance;

[0056] The multi-dimensional feature vector is input into the commodity association analysis module to calculate the similarity, and the association strength matrix between commodities is obtained. Based on the association strength matrix between commodities, the existing warehouse storage locations are clustered and analyzed to obtain the target storage location group.

[0057] The target cargo location group information is integrated with the current warehouse occupancy status data to obtain an available cargo location resource pool;

[0058] A multi-factor score is calculated for each cargo location in the available cargo location resource pool, including the distance from the order sorting area, the load-bearing capacity of the shelf, and the correlation with the surrounding products, to obtain a cargo location suitability score;

[0059] The cargo space suitability score is input into the reinforcement learning model for action selection to obtain a preliminary cargo space allocation plan. The preliminary cargo space allocation plan is then checked for dangerous goods isolation, temperature control requirements, and batch management to obtain a feasible solution set.

[0060] Based on the feasible solution set, the optimal storage location solution is obtained by optimizing the objective function of minimizing the expected picking time and maximizing the space utilization.

[0061] In this example, the digitized cargo record data is preprocessed to clean the noise and inconsistency in the data to ensure that it is suitable for feature extraction. The key elements in the digitized cargo record are extracted, such as product attributes, size, weight, and order relevance. The raw data is converted into a multidimensional feature vector by applying a deep learning model, such as a convolutional neural network or a recurrent neural network. The multidimensional feature vector is input into the product relevance analysis module for similarity calculation. This module uses methods such as cosine similarity or Euclidean distance to calculate the similarity between products and obtain a correlation strength matrix between products. By analyzing this matrix, it is understood which products are similar in characteristics and may be related in demand. Based on the correlation strength matrix between products, cluster analysis is performed on the existing warehouse locations to identify the target location groups. For example, if some locations store products with the same weight and high sales frequency, these locations are grouped together to improve picking efficiency. The target location group information is fused with the current warehouse occupancy status data to obtain an available location resource pool. Consider the frequency of use and storage efficiency of the locations to ensure the optimal allocation of resources. In the available cargo space resource pool, the weights for calculating the multi-factor score for each cargo space are obtained through historical data analysis, reflecting the needs of actual operations. The cargo space fitness score is input into the reinforcement learning model for action selection. Using the feedback mechanism of reinforcement learning, the allocation plan is optimized by simulating different strategies for cargo space allocation. After multiple iterations, the model selects a preliminary cargo space allocation plan. The preliminary plan is further reviewed, including dangerous goods isolation, temperature control requirements, and batch management checks to ensure that the selected cargo space meets the various standards of warehouse management. The results of these checks generate a set of feasible solutions, providing multiple options for the final cargo space allocation. Based on the feasible solution set, the optimization solution is solved by minimizing the expected picking time and maximizing the space utilization objective function. Taking into account the urgency of the order and the actual use of the cargo space, the algorithm model is used to find the optimal cargo space solution. By evaluating different solutions, the best cargo space allocation solution that can respond to orders quickly and use storage space efficiently is found.

[0062] In one example, the multidimensional feature vector is input into the commodity association analysis module for similarity calculation to obtain the association strength matrix between commodities, and cluster analysis is performed on the existing cargo locations in the warehouse based on the association strength matrix between commodities to obtain the target cargo location group, including:

[0063] Input the multi-dimensional feature vector into the word embedding model in the product association analysis module for dimensionality reduction processing to obtain the low-dimensional representation vector of the product, and calculate the cosine similarity of the low-dimensional representation vector of the product to obtain the preliminary similarity matrix between the products;

[0064] Based on the preliminary similarity matrix between commodities, a commodity relationship graph is constructed, and the relationship analysis is performed on the commodity relationship graph to obtain commodity importance scores, and the commodity importance scores are weightedly fused with the preliminary similarity matrix between commodities to obtain the correlation strength matrix between commodities;

[0065] Perform spectral clustering on the correlation strength matrix between commodities to obtain preliminary commodity classification results, and cross-validate the preliminary commodity classification results with the commodity co-occurrence information in historical orders to obtain optimized commodity classification results;

[0066] Based on the optimized commodity category classification results, hierarchical clustering is performed on the existing storage locations in the warehouse to obtain the preliminary storage location group classification results. The preliminary storage location group classification results and the warehouse physical layout information are then constrained and optimized to obtain the target storage location group.

[0067] In this example, the multi-dimensional feature vector is input into the word embedding model in the product association analysis module for dimensionality reduction. The word embedding technology is used to convert the high-dimensional product feature vector into a low-dimensional representation vector, reducing the computational complexity and retaining the important semantic relationship between products. Word embedding models, such as Word2Vec or GloVe, map each product to a smaller vector space so that semantically similar products are close to each other in the space. Suppose the original feature vector of the product is , after being processed by the word embedding model, the low-dimensional representation vector obtained is ,in , indicating that the number of product features is reduced after dimensionality reduction. The cosine similarity of the low-dimensional representation vector of the product is calculated to evaluate the similarity between the products. The formula for calculating cosine similarity is:

[0068] ;

[0069] in, and Represent the low-dimensional feature vectors of two commodities, · represents the vector inner product, Represents the norm of the vector. Through this formula, a preliminary similarity matrix is ​​generated, and each element in the matrix sim Display products and products The similarity between them. Based on the preliminary similarity matrix between products, a product relationship graph is constructed. In this graph, nodes represent products, and the weights of edges are determined by the similarity values. By analyzing the relationship graph, the importance score of each product is calculated. The importance score is evaluated using centrality indicators, such as degree centrality, and the calculation formula is:

[0070] ;

[0071] in, Representation Node The degree centrality of For Node The neighbor set of For Node With Node The edge weights (similarity values) between them are calculated. The importance scores of the products are weighted and fused with the preliminary similarity matrix to obtain the association strength matrix. Spectral clustering is performed on the association strength matrix between products. Spectral clustering is a graph-based clustering method that usually involves constructing a Laplace matrix. Laplace matrix is defined as:

[0072] ;

[0073] in, is the degree matrix, is a similarity matrix. By performing eigendecomposition on the Laplace matrix, its eigenvalues ​​and eigenvectors are obtained. The preliminary commodity classification results are cross-validated with the commodity co-occurrence information in the historical orders to obtain the optimized commodity classification results. Based on the optimized commodity classification results, the existing warehouse locations are hierarchically clustered. Hierarchical clustering shows the relationship between commodities by constructing a tree diagram, and clustering is performed by calculating the distance between commodities. Assume that the distance calculation formula between commodities is:

[0074] ;

[0075] in, Display products and products The Euclidean distance between and Commodities and products In the Through the above steps, the target storage location group is obtained.

[0076] In one example, orders are batched and heuristically routed according to the optimal cargo location solution and real-time order data to obtain intelligent picking instructions, including:

[0077] Divide the real-time order data into time windows to obtain the order set within a fixed time period, and calculate the order similarity based on the order set within the fixed time period to obtain the correlation matrix between orders;

[0078] The order correlation matrix between orders is grouped to obtain preliminary order batches, and the capacity constraint check is performed on the preliminary order batches to obtain valid order batches;

[0079] Construct a picking task graph based on valid order batches and optimal storage location solutions, calculate the minimum spanning tree for the picking task graph, and obtain the target picking path;

[0080] The target picking path is segmented and tasks are assigned based on the distribution of warehouse personnel to obtain a set of picking subtasks for multi-person collaboration. The execution sequence of the multi-person picking subtasks is optimized by dynamic planning to obtain the optimal picking task execution sequence.

[0081] Generate structured picking instruction data based on the optimal picking task execution sequence, the structured picking instruction data includes cargo location positioning, commodity confirmation and quantity verification information;

[0082] The structured picking instruction data is visualized to generate an interactive picking navigation interface suitable for mobile terminal display and output intelligent picking instructions.

[0083] In this example, the real-time order data is divided into time windows, a suitable time period is determined, and the order data is effectively organized into sets to obtain the order set within the fixed time period. Based on the order set within the fixed time period, the order similarity calculation is performed to identify similar orders. The attributes of the order, such as the order amount, product category, and quantity, are represented by feature vectors to form a correlation matrix between orders. Each element in the matrix represents the similarity between different orders. For example, suppose the feature vectors of two orders are and , using the cosine similarity calculation formula:

[0084] ;

[0085] where · represents the vector inner product, and Represents the norm of the vector. The order similarity matrix obtained by calculation is used to group the orders and form preliminary order batches. In this process, the capacity of each batch is checked to ensure that all orders will not exceed the storage limit of the warehouse during the allocation and processing of the storage location, and the valid order batches are obtained. Based on the valid order batches and the optimal storage location plan, a picking task graph is constructed. In this graph, the nodes represent each order, and the edges represent the path required to pick goods from one order to the next order. By calculating the minimum spanning tree of the picking task graph, an efficient target picking path is found to ensure the minimum cost and time in the picking process. The target picking path is segmented. Tasks are reasonably allocated according to the distribution of personnel in the warehouse to ensure that the burden of each staff member is appropriate and the work efficiency is maximized. A set of picking subtasks for multi-person collaboration is generated, and the execution order of the subtask set is optimized by dynamic programming to obtain the optimal picking task execution sequence, ensuring the shortest time and the most efficient use of resources during the execution process. Based on the optimal picking task execution sequence, structured picking instruction data is generated, including detailed cargo location positioning, commodity confirmation and quantity verification information, to ensure that each picking task has clear instructions and reduce errors in manual operations. In order to improve picking efficiency, the structured picking instruction data is visualized to generate an interactive picking navigation interface suitable for mobile terminal display. In this way, warehouse staff can more intuitively follow the system instructions to perform picking operations, and finally output intelligent picking instructions to achieve optimization and intelligence of the entire picking process. For example, within a certain period of time, five similar orders are identified from the order data. After calculating the similarity matrix of their feature vectors, it is found that the similarity between them is high, so these orders are batched and processed. After confirming that the goods of these orders can be reasonably stored in the warehouse through capacity constraint checks, the corresponding picking task graph is generated, the optimal picking path is found, and the segmented tasks are dynamically allocated to ensure maximum work efficiency and resource utilization in the warehouse.

[0086] In one example, based on the intelligent picking instructions, intelligent packaging material recommendations and machine learning logistics channel matching are performed on the picked goods to obtain the target package delivery plan, including:

[0087] Based on the intelligent picking instructions, the picked goods are 3D scanned to obtain the accurate volume and weight data of the goods, and the accurate volume and weight data are matched and calculated with the preset packaging material specification library to obtain a preliminary packaging solution set;

[0088] Cartoning optimization is performed based on the preliminary packaging solution set to obtain an optimized packaging solution that minimizes packaging space and material usage. The optimized packaging solution is compared and analyzed with historical packaging feedback data to obtain a packaging reliability score. The packaging solution is adjusted based on the packaging reliability score to obtain an intelligent packaging recommendation solution.

[0089] Perform cost accounting on the intelligent packaging recommendation plan to obtain packaging cost data, and input the packaging cost data into the total logistics cost calculation model;

[0090] Based on the packaging cost data and destination information, the available logistics channels are preliminarily screened to obtain a set of candidate logistics channels, and the timeliness and reliability of the candidate logistics channels are predicted to obtain a performance scoring matrix;

[0091] The performance scoring matrix is ​​optimized for multiple objectives including cost, timeliness and reliability to obtain the optimal logistics channel selection result. The optimal logistics channel selection result is then integrated with the intelligent packaging recommendation plan to generate a target package delivery plan that includes packaging guidance and logistics information.

[0092] In this example, based on the intelligent picking instructions, the products that have been picked are scanned in three dimensions to obtain the accurate volume and weight data of the products. The scanning system uses high-precision sensors and imaging technology to capture the three-dimensional shape and size of the products, and combines with weighing equipment to obtain weight information to generate a volume and weight data. and weight The eigenvector of The precise volume and weight data are matched and calculated with the preset packaging material specification library to determine the preliminary packaging solution set. The packaging material specification library stores the sizes and load-bearing capacities of various packaging materials. Through comparative analysis, the packaging solution that meets the volume and weight requirements of the product is selected. and maximum load-bearing If the solution can meet the demand of the goods, it will be included in the preliminary packaging solution set. Packing optimization is performed based on the preliminary packaging solution set. Through algorithm calculation, the performance of each solution in minimizing packaging space and material usage is analyzed to obtain an optimized packaging solution. After the optimization is completed, the optimized packaging solution is compared and analyzed with the historical packaging feedback data to obtain the packaging reliability score. Based on the score, the packaging solution is adjusted to obtain the intelligent packaging recommendation solution. The intelligent packaging recommendation solution is costed to generate specific packaging cost data. The packaging cost data is input into the total logistics cost calculation model to ensure that the cost of the entire logistics process is reasonably evaluated. Based on the packaging cost data and destination information, the available logistics channels are preliminarily screened to generate a set of candidate logistics channels. Timeliness and reliability predictions are performed on each candidate channel to obtain a performance score matrix. Multi-objective optimization of cost, timeliness and reliability is performed on the performance score matrix to determine the optimal logistics channel selection result. The multi-objective optimization algorithm is used to ensure the selection of an economical and efficient logistics solution. After integrating the optimal logistics channel selection result with the intelligent packaging recommendation solution, a target package delivery solution containing packaging guidance and logistics information is generated. This solution ensures the safety of packages, effective cost control, and efficient delivery time during transportation.

[0093] In one example, the target package delivery plan is input into the predictive analysis model for full-process tracking and abnormal warning, and the full-chain logistics data record is obtained, including:

[0094] Input the target package delivery plan into the predictive analysis model, predict the estimated arrival time of the package at each logistics node, and obtain the package's full-process time prediction sequence;

[0095] Based on the package full-process time prediction sequence, a logistics state transition diagram is constructed, and Markov chain analysis is performed on the logistics state transition diagram to obtain the state transition probability matrix of each node;

[0096] The state transition probability matrix is ​​input into the Bayesian network model to perform probability reasoning on potential logistics abnormal events and obtain the probability distribution of abnormal events.

[0097] Perform threshold screening on the probability distribution of abnormal events to obtain a set of high-risk abnormal events, and generate warning trigger rules based on the set of high-risk abnormal events;

[0098] Compare and analyze the real-time logistics tracking data with the package full-time prediction sequence to obtain the logistics progress deviation value, and input the logistics progress deviation value into the anomaly detection algorithm to obtain the abnormal state mark;

[0099] Generate intelligent warning information based on abnormal status mark and warning trigger rules, and push the intelligent warning information to the corresponding processing module to obtain abnormal processing response data;

[0100] The exception handling response data is structured and associated with the original logistics tracking data to obtain an enhanced logistics event record, which is then encrypted and distributedly stored to obtain a full-link logistics data record.

[0101] In this example, the target package delivery plan is input into the predictive analysis model to predict the estimated arrival time of the package at each logistics node. By using historical logistics data and related features, the prediction model can generate a package full-time prediction sequence, which represents the estimated arrival time of the package at each key node. Based on the package full-time prediction sequence, a logistics state transition diagram is constructed to structure the flow of packages between nodes. Using the Markov chain analysis method, the logistics state transition diagram is analyzed to obtain the state transition probability matrix of each node. This matrix can reflect the probability of a package arriving from one node to the next node. The elements in the matrix represent the transition probability between different nodes, which is specifically expressed as:

[0102] ;

[0103] in, For slave nodes To Node The state transition probability, Represents a slave node Arrival Node The number of times, For Node The total number of departures. By establishing a state transition probability matrix, the dynamic behavior of logistics can be analyzed more clearly. The state transition probability matrix is ​​input into the Bayesian network model for probabilistic reasoning to identify potential logistics abnormal events. Through this model, the probability distribution of abnormal events is obtained, which is expressed as:

[0104] ;

[0105] in, It is known evidence Case Events The posterior probability of For the event Observe evidence when it occurs The probability of For events The prior probability of The marginal probability of the evidence. The obtained probability distribution is used for threshold screening to identify a high-risk abnormal event set, and based on this, an early warning trigger rule is generated so as to respond in time in actual operation. In the comparative analysis of real-time logistics tracking data and the package full-time prediction sequence, the deviation value of the logistics progress is calculated. By inputting the deviation value into the anomaly detection algorithm, the abnormal state can be judged and marked. Based on the abnormal state marking and early warning trigger rules, intelligent early warning information is generated and pushed to the corresponding processing module to obtain abnormal processing response data. The response data is structured and associated with the original logistics tracking data to form an enhanced logistics event record, including all information related to logistics anomalies. In order to ensure the security of data, the enhanced logistics event record is encrypted and distributedly stored to ensure the security of data during transmission and storage, and to ensure the integrity and security of the data of the entire logistics link, so that logistics companies can respond to abnormal situations in time and improve operational efficiency. For example, in a certain transportation, if the prediction model shows that the package is expected to arrive at a certain node in 2 hours, but the actual arrival time is 3 hours, the deviation value is calculated as -1 hour, which is then marked as an abnormality, and corresponding warning information is generated, and measures are taken to deal with it, thereby avoiding potential customer complaints and losses.

[0106] In one example, the target package delivery plan is input into the predictive analysis model to predict the estimated arrival time of the package at each logistics node, and the package full-process time prediction sequence is obtained, including:

[0107] Perform feature extraction on the target package delivery plan to obtain a target feature vector containing package weight, volume, destination, and logistics channel information;

[0108] The target feature vector is input into the time series encoder in the predictive analysis model, and the package features are subjected to time series embedding processing to obtain a time series representation of the package;

[0109] Based on the time series representation of the package, an attention weight matrix is ​​constructed, and similarity calculations are performed on similar packages in the historical logistics data to obtain the most similar K historical package samples;

[0110] The K most similar historical package samples are input into the long short-term memory network for time series prediction to obtain the preliminary arrival time estimate of each logistics node. The preliminary arrival time estimate is processed by conditional random field to model the temporal dependency relationship between nodes and obtain the optimized node arrival time series.

[0111] The optimized node arrival time series is fused with real-time traffic information and weather data to obtain an adjusted time series, and the adjusted time series is adaptively smoothed to obtain a smoothed time prediction result;

[0112] The smoothed time prediction results are compared and analyzed with the service level agreement of the logistics service provider, and the prediction results that exceed the service level agreement are corrected to obtain the full-process time prediction sequence of the package.

[0113] In this example, the target package delivery solution is feature extracted to obtain a target feature vector containing package weight, volume, destination, and logistics channel information. Each dimension of the vector reflects the key attributes of the package, for example, the package weight ( ),volume( )、Destination Code( ) and selected logistics channels ( ). It is expressed as:

[0114] ;

[0115] The target feature vector is input into the time series encoder in the predictive analysis model, which uses a deep learning algorithm to perform time series embedding processing on the package features and generate a time series representation of the package. The time series encoding process is implemented using a recurrent neural network or a long short-term memory network to effectively capture the dynamic changes of package features over time. Assume that the time series embedding process is expressed as:

[0116] ;

[0117] in, is the time step The hidden state of represents the computational function of a recurrent neural network or a long short-term memory network, is the input feature vector of the current time step, is the hidden state of the previous time step. Based on the obtained package time series representation, an attention weight matrix is ​​constructed to quantify the relevance of similar packages in historical logistics data. By calculating the similarity of the feature vectors and using metrics such as cosine similarity, the similarity matrix is ​​obtained:

[0118] ;

[0119] in, Indicates and The similarity between the feature vectors of packages, and are the eigenvectors of the two packages, Represents the norm of the vector. Through the similarity matrix, find the closest A sample of historical packages. The closest historical package samples are input into the long short-term memory network for time series prediction to obtain the preliminary arrival time estimate of each logistics node. The long short-term memory network can capture the long-term dependencies in the sequence. In order to better model the temporal dependencies between nodes, the preliminary arrival time is processed by conditional random field (CRF) to optimize the temporal relationship between nodes and obtain the optimized node arrival time series. The optimized node arrival time series is fused with real-time traffic information and weather data to adjust the time series to more accurately reflect the actual situation. Adaptive smoothing is performed on the adjusted time series to eliminate short-term fluctuations and obtain smoothed time prediction results. The smoothed time prediction results are compared and analyzed with the service level agreement of the logistics service provider, and the prediction results that exceed the service level agreement are corrected to ensure service quality and customer satisfaction. The final generated package full-time prediction sequence is the accurate arrival time estimate.

[0120] Reference Figure 2 This embodiment provides a warehousing process data management system for cross-border e-commerce, including:

[0121] Acquisition module 1 is used to acquire and integrate the order, inventory and logistics information of cross-border e-commerce platforms and logistics service providers in real time to obtain a unified cross-platform data set;

[0122] Scanning module 2, used to scan barcodes and match information of incoming goods based on a cross-platform unified data set to obtain digital goods record data with an internal unique identification code;

[0123] Allocation module 3, used to input the digital cargo record data into the deep learning algorithm pre-trained based on historical order data for intelligent warehouse allocation, and obtain the optimal cargo location plan;

[0124] Planning module 4, used to batch merge and heuristically plan the order paths according to the optimal cargo location plan and real-time order data, and obtain intelligent picking instructions;

[0125] Recommendation module 5, used to perform intelligent packaging material recommendation and machine learning logistics channel matching for the picked goods based on the intelligent picking instructions, and obtain the target package delivery plan;

[0126] Tracking module 6 is used to input the target package delivery plan into the predictive analysis model for full-process tracking and abnormal warning, and obtain the data record of the entire logistics chain.

[0127] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0128] Reference Figure 3In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0129] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0130] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0131] In summary, a warehousing process data management method for cross-border e-commerce is proposed in the embodiment of the present invention, which realizes the real-time integration and unified management of cross-platform data, solves the problem of data islands, and improves the availability of information and the accuracy of decision-making. Through intelligent barcode scanning and information matching technology, the rapid identification and accurate tracking of incoming goods are realized, human errors are reduced, and the efficiency of warehousing is improved. The deep learning algorithm is used for intelligent warehouse allocation, which optimizes the utilization rate of warehouse space, takes into account the relevance of goods, and improves the efficiency of subsequent picking. The heuristic path planning algorithm is used to realize order batch merging and intelligent picking instruction generation, which greatly improves the picking efficiency and accuracy. Through intelligent packaging material recommendation and machine learning logistics channel matching, the packaging efficiency and logistics cost are optimized, and the economy and environmental protection of distribution are improved. The introduction of predictive analysis model for full-process tracking and abnormal warning improves the visualization of the logistics process and the processing speed of abnormal events, and enhances customer satisfaction. The present invention integrates multi-platform data, realizes intelligent warehousing management, and improves logistics efficiency.

[0132] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0133] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, system, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, system, article or method. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, system, article or method including the element.

[0134] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for managing warehousing process data for cross-border e-commerce, characterized in that: The following steps are involved: Real-time acquisition and integration of order, inventory and logistics information of cross-border e-commerce platforms and logistics service providers to obtain a unified cross-platform data set; Based on the cross-platform unified data set, barcode scanning and information matching are performed on the incoming goods to obtain digital goods record data with an internal unique identification code; The digitized cargo record data is input into a deep learning algorithm pre-trained based on historical order data for intelligent warehouse allocation to obtain an optimal cargo location plan; specifically comprising: inputting the digitized cargo record data into a deep learning algorithm pre-trained based on historical order data, and performing feature extraction on the digitized cargo record data to obtain a multidimensional feature vector including commodity attributes, size, weight and order correlation; inputting the multidimensional feature vector into a word embedding model in the commodity correlation analysis module for dimensionality reduction processing to obtain a low-dimensional representation vector of the commodity, and performing cosine similarity calculation on the low-dimensional representation vector of the commodity to obtain a preliminary similarity matrix between commodities; constructing a commodity relationship graph based on the preliminary similarity matrix between commodities, performing relationship analysis on the commodity relationship graph to obtain commodity importance scores, and performing weighted fusion on the commodity importance scores and the preliminary similarity matrix between commodities to obtain a correlation strength matrix between commodities; performing spectral clustering on the correlation strength matrix between commodities to obtain a preliminary commodity category division result, and The product category division results are cross-validated with the product co-occurrence information in the historical orders to obtain the optimized product category division results; based on the optimized product category division results, the existing cargo locations in the warehouse are hierarchically clustered to obtain the preliminary cargo location group division results, and the preliminary cargo location group division results are constrained and optimized with the warehouse physical layout information to obtain the target cargo location group; the target cargo location group information is integrated with the current warehouse occupancy status data to obtain the available cargo location resource pool; a multi-factor score calculation is performed on each cargo location in the available cargo location resource pool, including the distance from the order sorting area, the shelf load-bearing condition and the correlation of the surrounding products to obtain the cargo location fitness score; the cargo location fitness score is input into the reinforcement learning model for action selection to obtain a preliminary cargo location allocation plan, and the preliminary cargo location allocation plan is checked for dangerous goods isolation, temperature control requirements and batch management to obtain a feasible solution set; based on the feasible solution set, the optimal cargo location plan is obtained by optimizing the objective function of minimizing the expected picking time and maximizing the space utilization rate; The orders are batched and routed heuristically according to the optimal cargo location plan and real-time order data to obtain intelligent picking instructions; Based on the intelligent picking instructions, intelligent packaging material recommendations and machine learning logistics channel matching are performed on the picked goods to obtain a target package delivery plan; The target package delivery plan is input into the predictive analysis model for full-process tracking and abnormal warning to obtain full-link logistics data records.

2. The cross-border e-commerce warehousing process data management method according to claim 1 is characterized in that: The real-time acquisition and integration of order, inventory and logistics information of cross-border e-commerce platforms and logistics service providers to obtain a cross-platform unified data set includes: Perform parallel data request processing on the APIs of multiple cross-border e-commerce platforms to obtain original order and inventory data streams, and perform data cleaning and format standardization on the original order and inventory data streams to obtain platform data in a unified format; Input the platform data in the unified format into the distributed storage system for shard storage processing to obtain a high-concurrency read-write data storage structure, and perform multi-layer encryption processing on the sensitive information in the data storage structure to obtain an encrypted data set that meets cross-border data security requirements; A real-time data synchronization mechanism is built based on the encrypted data set to periodically poll and push the logistics service provider system to obtain real-time logistics tracking data, and the real-time logistics tracking data is associated and matched with the corresponding order information to obtain the order life cycle data; A multi-dimensional aggregation analysis is performed on the order life cycle data to obtain a real-time snapshot of the cross-platform inventory and logistics status, and a distributed cache layer is built based on the real-time snapshot data to perform data distribution processing to obtain a cross-platform unified data set.

3. The cross-border e-commerce warehousing process data management method according to claim 1 is characterized in that: The barcode scanning and information matching of the incoming goods based on the cross-platform unified data set to obtain digital goods record data with an internal unique identification code includes: Scan the barcode or QR code of the outer packaging of the incoming goods at multiple angles to obtain original goods identification information, and perform fuzzy matching between the original goods identification information and the order data in the cross-platform unified data set to obtain a preliminary matching result; Extracting the SKU, quantity and specification information of the goods based on the preliminary matching result to obtain the goods attribute data, and inputting the goods attribute data into a hash algorithm to generate a unique identification code to obtain an internal unique identification code; The internal unique identification code is associated and mapped with the original cargo identification information to obtain a cargo identification correspondence, and a two-dimensional code image containing detailed cargo information is generated based on the cargo identification correspondence, and an anti-counterfeiting watermark is embedded in the two-dimensional code image to obtain a security-enhanced cargo label; The security-enhanced cargo label data is transmitted to an intelligent printing device for real-time printing to obtain a target cargo label, and image recognition and verification are performed on the pasting position of the target cargo label to obtain digital cargo record data with an internal unique identification code.

4. The cross-border e-commerce warehousing process data management method according to claim 1 is characterized in that: The step of batch merging and heuristic path planning the orders according to the optimal cargo location plan and the real-time order data to obtain intelligent picking instructions includes: Divide the real-time order data into time windows to obtain an order set within a fixed time period, and calculate order similarity based on the order set within the fixed time period to obtain a correlation matrix between orders; Grouping the correlation matrix between the orders to obtain preliminary order batches, and performing capacity constraint check on the preliminary order batches to obtain valid order batches; Constructing a picking task graph based on the valid order batches and the optimal cargo location plan, performing a minimum spanning tree calculation on the picking task graph, and obtaining a target picking path; The target picking path is segmented, and tasks are assigned based on the distribution of warehouse personnel to obtain a set of picking subtasks for multi-person collaboration, and the execution sequence of the picking subtasks for multi-person collaboration is optimized by dynamic programming to obtain an optimal picking task execution sequence; Generating structured picking instruction data based on the optimal picking task execution sequence, wherein the structured picking instruction data includes cargo location positioning, commodity confirmation and quantity verification information; The structured picking instruction data is visualized to generate an interactive picking navigation interface adapted to the display of a mobile terminal and output intelligent picking instructions.

5. The cross-border e-commerce warehousing process data management method according to claim 1 is characterized in that: The intelligent packaging material recommendation and machine learning logistics channel matching are performed on the picked goods based on the intelligent picking instructions to obtain a target package delivery plan, including: Based on the intelligent picking instructions, three-dimensional scanning is performed on the picked goods to obtain accurate volume and weight data of the goods, and the accurate volume and weight data are matched and calculated with a preset packaging material specification library to obtain a preliminary packaging solution set; Cartoning optimization is performed based on the preliminary packaging solution set to obtain an optimized packaging solution that minimizes packaging space and material usage, and the optimized packaging solution is compared and analyzed with historical packaging feedback data to obtain a packaging reliability score, and the packaging solution is adjusted based on the packaging reliability score to obtain an intelligent packaging recommendation solution; Performing cost accounting on the intelligent packaging recommendation plan to obtain packaging cost data, and inputting the packaging cost data into a total logistics cost calculation model; Based on the packaging cost data and the destination information, preliminarily screening the available logistics channels to obtain a set of candidate logistics channels, and predicting the timeliness and reliability of the set of candidate logistics channels to obtain a performance scoring matrix; The performance scoring matrix is ​​subjected to multi-objective optimization of cost, timeliness and reliability to obtain an optimal logistics channel selection result, and the optimal logistics channel selection result is integrated with the intelligent packaging recommendation plan to generate a target package delivery plan including packaging guidance and logistics information.

6. The cross-border e-commerce warehousing process data management method according to claim 1 is characterized in that: The target package delivery plan is input into the predictive analysis model for full-process tracking and abnormal warning, and the full-link logistics data record is obtained, including: Input the target package delivery plan into the predictive analysis model, predict the estimated arrival time of the package at each logistics node, and obtain the package full-process time prediction sequence; Constructing a logistics state transition diagram based on the package full-process time prediction sequence, performing Markov chain analysis on the logistics state transition diagram, and obtaining a state transition probability matrix of each node; The state transition probability matrix is ​​input into the Bayesian network model to perform probability reasoning on potential logistics abnormal events to obtain the probability distribution of abnormal events; Performing threshold screening on the probability distribution of the abnormal event occurrence to obtain a set of high-risk abnormal events, and generating an early warning trigger rule based on the set of high-risk abnormal events; Comparing and analyzing the real-time logistics tracking data with the full-time prediction sequence of the package to obtain a logistics progress deviation value, and inputting the logistics progress deviation value into an anomaly detection algorithm to obtain an abnormal state mark; Generate intelligent warning information based on the abnormal state mark and warning triggering rules, and push the intelligent warning information to the corresponding processing module to obtain abnormal processing response data; The exception handling response data is structured and associated with the original logistics tracking data to obtain an enhanced logistics event record, and the enhanced logistics event record is encrypted and distributedly stored to obtain a full-link logistics data record.

7. The cross-border e-commerce warehousing process data management method according to claim 6 is characterized in that: The target package delivery plan is input into the predictive analysis model to predict the estimated arrival time of the package at each logistics node to obtain the package full-process time prediction sequence, including: Performing feature extraction processing on the target package delivery plan to obtain a target feature vector including package weight, volume, destination and logistics channel information; Inputting the target feature vector into the time series encoder in the predictive analysis model, performing time series embedding processing on the package features, and obtaining a time series representation of the package; An attention weight matrix is ​​constructed based on the temporal representation of the package, and similarity is calculated for similar packages in the historical logistics data to obtain the most similar K historical package samples; Input the K most similar historical package samples into the long short-term memory network for time series prediction to obtain preliminary arrival time estimates for each logistics node, and perform conditional random field processing on the preliminary arrival time estimates to model the temporal dependency relationship between nodes and obtain an optimized node arrival time series; The optimized node arrival time series is integrated with real-time traffic information and weather data to obtain an adjusted time series, and the adjusted time series is adaptively smoothed to obtain a smoothed time prediction result; The smoothed time prediction results are compared and analyzed with the service level agreement of the logistics service provider, and the prediction results that exceed the service level agreement are corrected to obtain the full-process time prediction sequence of the package.

8. A warehouse process data management system for cross-border e-commerce, characterized in that: For implementing the steps of the method according to any one of claims 1 to 7, the system comprises: The acquisition module is used to acquire and integrate the order, inventory and logistics information of cross-border e-commerce platforms and logistics service providers in real time to obtain a unified cross-platform data set; A scanning module, used to perform barcode scanning and information matching on incoming goods based on the cross-platform unified data set to obtain digital goods record data with an internal unique identification code; The allocation module is used to input the digitized cargo record data into a deep learning algorithm pre-trained based on historical order data for intelligent warehouse allocation to obtain an optimal cargo location plan; specifically comprising: inputting the digitized cargo record data into a deep learning algorithm pre-trained based on historical order data, and performing feature extraction on the digitized cargo record data to obtain a multidimensional feature vector including commodity attributes, size, weight and order correlation; inputting the multidimensional feature vector into the word embedding model in the commodity correlation analysis module for dimensionality reduction processing to obtain a low-dimensional representation vector of the commodity, and performing cosine similarity calculation on the low-dimensional representation vector of the commodity to obtain a preliminary similarity matrix between commodities; constructing a commodity relationship graph based on the preliminary similarity matrix between commodities, performing relationship analysis on the commodity relationship graph to obtain commodity importance scores, and performing weighted fusion on the commodity importance scores and the preliminary similarity matrix between commodities to obtain a correlation strength matrix between commodities; performing spectral clustering on the correlation strength matrix between commodities to obtain a preliminary commodity category division result, and The preliminary commodity category division results are cross-validated with the commodity co-occurrence information in the historical orders to obtain the optimized commodity category division results; based on the optimized commodity category division results, the existing cargo locations in the warehouse are hierarchically clustered to obtain the preliminary cargo location group division results, and the preliminary cargo location group division results are constrained and optimized with the warehouse physical layout information to obtain the target cargo location group; the target cargo location group information is integrated with the current warehouse occupancy status data to obtain the available cargo location resource pool; a multi-factor score calculation is performed on each cargo location in the available cargo location resource pool, including the distance from the order sorting area, the shelf load-bearing condition and the correlation of the surrounding commodities to obtain the cargo location fitness score; the cargo location fitness score is input into the reinforcement learning model for action selection to obtain a preliminary cargo location allocation plan, and the preliminary cargo location allocation plan is checked for dangerous goods isolation, temperature control requirements and batch management to obtain a feasible solution set; based on the feasible solution set, the optimal cargo location plan is obtained by optimizing the objective function of minimizing the expected picking time and maximizing the space utilization rate; A planning module, used to batch merge and heuristically plan routes for orders based on the optimal cargo location plan and real-time order data, and obtain intelligent picking instructions; A recommendation module, used to perform intelligent packaging material recommendation and machine learning logistics channel matching for the picked goods based on the intelligent picking instructions to obtain a target package delivery plan; The tracking module is used to input the target package delivery plan into the predictive analysis model for full-process tracking and abnormal warning, and obtain the data record of the entire logistics chain.

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