An intelligent warehouse dynamic optimization system based on digital twin technology

Abstract

This invention discloses an intelligent warehouse dynamic optimization system based on digital twin technology, comprising the following steps: S1: Creating a digital twin model: acquiring basic data of the physical warehouse and constructing an accurate digital model; S2: Data collection and integration: deploying IoT devices and achieving real-time data transmission and processing; S3: Constructing analysis algorithms: developing advanced analysis algorithms and implementing machine learning models; S4: Dynamic optimization and adjustment: automatically adjusting storage layout and optimizing inbound and outbound processes; S5: Prediction and early warning: anticipating potential problems and setting alarm mechanisms based on historical data analysis. This invention significantly improves data real-time performance and accuracy, enhances environmental monitoring capabilities, ensures cargo safety, and greatly improves space utilization.

Description

Technical Field

[0001] This invention relates to the field of warehousing technology, and in particular to an intelligent warehousing dynamic optimization system based on digital twin technology. Background Technology

[0002] In modern warehouse management, traditional warehouse management systems primarily rely on manual operations and basic database technology to track inventory status, goods location, and environmental conditions. However, with the development of e-commerce and the growth of logistics demands, these traditional methods have gradually revealed their limitations and shortcomings.

[0003] Data update lag: Existing systems typically require manual input or periodic scanning to update inventory information, which results in poor data real-time performance. Due to untimely data updates, discrepancies may arise between inventory records and actual inventory, affecting the accuracy and efficiency of the supply chain.

[0004] Limited Environmental Monitoring Capabilities: Traditional warehouse management systems have weak monitoring capabilities for environmental factors (such as temperature and humidity). In most cases, this monitoring relies on fixed-point sensors or periodic manual inspections, failing to provide comprehensive and continuous monitoring data. This limitation may lead to insufficient control over the storage conditions of sensitive goods (such as food and medicine), increasing the risk of goods damage. Lack of Predictive Functionality: Most current warehouse management systems lack effective predictive mechanisms, making it difficult to anticipate potential problems such as expired goods or insufficient inventory. If these problems are not detected and addressed in a timely manner, it will lead to decreased customer satisfaction and compromised service quality. Low Space Utilization: Traditional warehouse management systems... Warehouse layouts are typically static, lacking flexibility and dynamic adjustment capabilities. This means low space utilization, resource waste, and increased operating costs. Cumbersome inbound and outbound processes: In many existing warehouse management systems, inbound and outbound processes are often complex and time-consuming, involving multiple manual confirmations and paper document processing. These cumbersome processes not only reduce work efficiency but also increase the likelihood of errors. Insufficient decision support: Most existing systems fail to fully utilize data analytics to support decision-making. Even if some systems can collect large amounts of data, they lack sufficient analytical tools to transform this data into valuable insights, thus affecting the quality and speed of decision-making.

[0005] In summary, current warehouse management systems suffer from numerous problems, including but not limited to lagging data updates, limited environmental monitoring capabilities, lack of predictive functionality, low space utilization, cumbersome inbound and outbound processes, and insufficient decision support. These issues directly impact the overall efficiency and service quality of warehouse management, highlighting the need for a more intelligent and automated solution. This invention addresses the shortcomings of the aforementioned technologies by proposing an intelligent dynamic optimization system for warehouses based on digital twin technology. Summary of the Invention

[0006] The purpose of this invention is to address the problems of lagging data updates, limited environmental monitoring capabilities, lack of predictive functions, low space utilization, cumbersome inbound and outbound processes, and insufficient decision support in existing technologies. The invention proposes an intelligent warehousing dynamic optimization system based on digital twin technology. By creating an accurate digital model of the physical warehouse in a virtual environment and combining it with data collected by Internet of Things (IoT) devices, the system achieves comprehensive monitoring and dynamic optimization of the warehousing environment.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A smart warehouse dynamic optimization system based on digital twin technology includes the following specific steps:

[0009] S1: Create a digital twin model: Obtain basic data from the physical warehouse and build an accurate digital model;

[0010] S2: Data Collection and Integration: Deploying IoT devices and enabling real-time data transmission and processing;

[0011] S3: Building Analytics Algorithms: Developing advanced analytics algorithms and implementing machine learning models;

[0012] S4: Dynamic Optimization and Adjustment: Automatically adjusts storage layout and optimizes inbound and outbound processes;

[0013] S5: Prediction and Early Warning: Anticipate potential problems and set up alarm mechanisms based on historical data analysis.

[0014] As a further technical solution of the present invention, S1 specifically includes:

[0015] S11: Obtain basic data of the physical warehouse: Use a laser scanner or drone to conduct a comprehensive scan of the warehouse to obtain high-precision 3D point cloud data, including length, width and height, shelf location and aisle planning, etc. Use CAD software (such as AutoCAD, SolidWorks) to convert this point cloud data into an accurate warehouse layout map, and then collect basic information about the goods in the warehouse, such as type, specifications, weight and special storage requirements. This information can be directly imported through the ERP system or obtained through manual entry. The above data will serve as the basis for building a digital twin model.

[0016] S12: Constructing an accurate digital model: Using 3D modeling software (such as Unity3D, Unreal Engine, etc.) in a virtual environment, construct a digital twin model of the warehouse based on the above data. The modeling software provides rich plugins and API interfaces, facilitating the integration of various sensor data and algorithms. The digital twin model should reflect the actual warehouse situation as accurately as possible, including all physical details and logical relationships. The digital twin model should not only show the physical layout but also have the ability to simulate the operation under different scenarios. For example, it can simulate the movement path of goods and the driving route of forklifts, helping managers to understand the internal operation of the warehouse more intuitively. In addition, animation effects can be added to show the warehouse activities at different time periods.

[0017] As a further technical solution of the present invention, in S11, after the data collection is completed, the collected data is cleaned and organized to remove redundant information and ensure the consistency and accuracy of the data. In particular, for the storage requirements of goods, it is necessary to clarify the required temperature and humidity range, as well as whether special conditions such as moisture protection and sun protection are required.

[0018] As a further technical solution of the present invention, in S12, constructing an accurate digital model specifically includes:

[0019] S121: Preprocess the acquired 3D point cloud data by using a point cloud filtering algorithm (such as the moving least squares filter) to remove noise points. The expression is as follows: ,in: These are the filtered point coordinates. Let j be the coordinates of the j-th point in the original point cloud data. For point Point The weight is defined as , For point The bandwidth parameter at the location is used to control the decay rate of the weights; the filtered point cloud data reflects the actual shape and structure of the warehouse more smoothly and accurately.

[0020] S122: The preprocessed point cloud data is meshed, and a 3D mesh model of the warehouse is constructed using the Poisson reconstruction algorithm. Its energy function expression is: ,in: Represents the constructed three-dimensional surface. It is a balance parameter. It is the estimated normal vector field of the point cloud. Let x represent the distance from point x to surface S. A three-dimensional mesh model is obtained by minimizing this energy function. This model accurately represents the warehouse's boundaries and internal structure, including the spatial relationships of shelves, aisles, and other components. Simultaneously, topology optimization is performed on the mesh model based on Euler's formula. (V is the number of vertices, E is the number of edges, and F is the number of faces). Adjust the topology of the mesh to make it more reasonable and compact, reduce unnecessary mesh faces and vertices, and improve the computational efficiency and rendering speed of the model.

[0021] S123: Based on the constructed 3D mesh model, interpolation algorithms (such as bicubic interpolation) are used to spatially interpolate the storage information of goods in the warehouse to determine the specific location and distribution of goods in 3D space. The interpolation formula is as follows:

[0022] ,

[0023] in: For interpolated cargo storage information (such as temperature, humidity, etc.) in location The value at that location, The interpolation coefficients are determined by solving a system of equations from surrounding points with known stored information. Given the coordinates of the known storage information points; then, the storage requirements parameters for the goods (such as temperature requirements) are... Humidity requirements Is moisture protection required? Sunscreen (etc.) are associated with the interpolated storage location information to construct a digital twin model database containing physical layout and storage logic, including moisture-proof parameters. and sun protection parameters These can be represented by binary variables (1 indicates needed, 0 indicates not needed); when simulating warehouse operations, a path planning algorithm (such as...) is used. The algorithm calculates and optimizes the movement path of goods and the travel route of forklifts, and its heuristic function... The cost function is defined as the straight-line distance from the current node n to the target node. The total estimated cost function represents the actual movement cost (including distance, time, etc.) from the starting node to the current node n. By searching The shortest path is used to determine the optimal goods movement and forklift routes, thereby enabling accurate simulation and dynamic optimization of warehouse operations.

[0024] As a further technical solution of the present invention, S2 specifically includes:

[0025] S21: Deploy IoT devices: Select a variety of IoT devices with high precision and low power consumption, such as temperature and humidity sensors (e.g., DHT22), RFID readers (e.g., Impinj R420), and high-definition cameras (e.g., Hikvision DS2CD2T86G24I). These devices not only provide high-quality data but also ensure stable operation over long periods. Based on the actual layout and needs of the warehouse, rationally arrange the installation locations of the IoT devices before installation. For example, install multiple temperature and humidity sensors in the cold storage area to ensure monitoring of every corner, and install cameras on main aisles to track the flow of goods in real time, thereby monitoring the environmental conditions and cargo status within the warehouse. These devices automatically collect data, reducing errors caused by human intervention. The selection of IoT devices should consider factors such as coverage, accuracy, and power consumption to meet the needs of specific application scenarios.

[0026] S22: Real-time data transmission and processing: Use wireless communication protocols (such as Zigbee, Wi-Fi, MQTT, CoAP) to transmit data collected by IoT devices to the central control system in real time, ensuring efficient and secure data transmission. These protocols support bidirectional communication, facilitating remote management and control. To ensure data security, encrypted transmission is recommended. The central control system performs preliminary processing and classification on the received data to ensure its accuracy and timeliness. First, format conversion and standardization are performed, for example, filtering out outliers through algorithms to improve data quality. Then, the data is classified and stored according to business needs. For example, temperature and humidity data are stored in a dedicated database for easy subsequent analysis and querying, and cargo location information is updated in real time to the digital twin model to ensure the model's accuracy.

[0027] As a further technical solution of the present invention, S3 specifically includes:

[0028] S31: Develop advanced analytics algorithms: Design algorithms to optimize storage strategies, considering factors such as product type, demand forecasting, inventory levels, storage density, and inbound / outbound frequency. These algorithms aim to maximize warehouse space utilization while reducing operating costs. For example, for frequently inbound / outbound goods, deep learning-based predictive algorithms can be used to pre-arrange optimal storage locations. For perishable goods, a comprehensive evaluation combining environmental parameters (such as temperature and humidity) is necessary to ensure that storage conditions meet requirements. Apply machine learning models to analyze historical data, identify patterns, and predict future trends. For example, by learning from sales data from the past few years, predict the demand for goods in the next year. Write algorithm code using Python or R.

[0029] S32: Implementing Machine Learning Models: Collect sufficient historical data as training samples, including goods entry and exit records, environmental parameter changes, order demands, etc. Use data analysis libraries such as Pandas and NumPy for data preprocessing (e.g., missing value imputation, data standardization) and feature extraction (e.g., extracting seasonal and trend features from time series). Select a suitable machine learning framework (e.g., TensorFlow, PyTorch), and use methods such as cross-validation to input the processed data into the model for training to improve its generalization ability and prediction accuracy. The model training process requires a large amount of labeled data as input, so the preliminary data preparation work is crucial. Continuously adjust the model parameters and optimize the model performance to better adapt to different warehousing scenarios. Regularly evaluate the model's performance and adjust it according to the actual situation. For example, with seasonal changes, the demand for certain commodities may fluctuate significantly, at which point the model needs to be retrained to adapt to new market demands.

[0030] As a further technical solution of the present invention, S31 specifically includes:

[0031] S311: Optimization algorithm for storage location of frequently entering and leaving goods: An optimization strategy based on a genetic algorithm is adopted, and its fitness function is defined as Fitness. ,in: This indicates the distance between the product storage location and the inlet / outlet of the warehouse; the shorter the distance, the better. The larger the value; This refers to the frequency of goods entering and leaving the warehouse; the higher the frequency, the better. The larger the value; and These are weighting coefficients, used to balance the effects of distance and frequency on fitness, and satisfying... Through iterative operations (selection, crossover, mutation) of genetic algorithms, the optimal storage location arrangement scheme for goods that maximizes fitness is found, thereby realizing the optimal storage location planning for goods with high frequency of inbound and outbound operations, reducing the time for goods to enter and exit the warehouse, and improving warehouse operation efficiency.

[0032] S312: Comprehensive evaluation algorithm for storage conditions of perishable goods: Construct a comprehensive evaluation function: Evaluation = γ·T_similarity + δ·H_similarity + θ·S_meet, where: T_similarity represents the similarity between the actual storage temperature and the required temperature of the perishable goods, calculated as follows: T_actual is the actual storage temperature, T_required is the required temperature for perishable goods, and T_range is the allowable temperature fluctuation range. H_similarity is the similarity between the actual storage humidity and the required humidity, calculated similarly to T_similarity. H_actual, H_required, and H_range represent the actual storage humidity, required humidity, and allowable humidity fluctuation range, respectively. S_meet is a binary variable; if the actual storage environment meets the special storage requirements of perishable goods (such as moisture-proof and sun-proof), then S_meet = 1; otherwise, it is 0. γ, δ, and θ are weighting coefficients used to measure the importance of each factor in the comprehensive evaluation, and satisfy γ + δ + θ = 1. Through this evaluation function, the various potential storage locations of perishable goods are comprehensively evaluated, and the location with the highest Evaluation value is selected as its optimal storage location, ensuring that the storage conditions of perishable goods meet the requirements and extending their shelf life.

[0033] S313: Demand Forecasting Algorithm Based on Deep Learning (Taking LSTM Network as an Example): Using historical sales data as input sequence, an LSTM model is built to predict the demand for goods in the next year. The update formula for the hidden state of the LSTM unit is: ,in: It is the first The hidden state of time It is the activation value of the output gate, calculated as It is a weight matrix. It is a bias term. It is the feature vector corresponding to the preprocessed data at each time step. This represents the sigmoid activation function; It is the first The cell state at time t is updated using the following formula: It is the activation value of the forget gate, calculated in the same way. Similarly, using Parameters; It is the activation value of the input gate, calculated in a similar way using the corresponding parameters.

[0034] As a further technical solution of the present invention, S32 specifically includes: employing Cross-validation can be used to improve the generalization ability and prediction accuracy of a model.

[0035] S321: Divide the collected historical data (including goods entry and exit records, changes in environmental parameters, order demands, etc.) into categories. There are n subsets, each of roughly the same size, and the nth subset is... During the second cross-validation, the first... One subset is used as the validation set, and the rest... A subset is used as the training set;

[0036] S322: The model is trained on the training set and then validated on the validation set, and the validation error is calculated.

[0037] S323: Calculate the average validation error after K rounds of cross-validation. ,in It is the first The model's performance is evaluated based on the average validation error. If the average validation error is large, the model parameters are adjusted (such as adjusting the number of hidden layer neurons and the learning rate), and retraining and cross-validation are performed until the model parameters that minimize the average validation error are found. This optimizes the model's performance and enables it to better adapt to different warehousing scenarios. At the same time, when commodity demand fluctuates due to factors such as seasonal changes, new historical data is collected periodically, and the model is retrained according to the above training process to update the model's parameters and structure, enabling it to adapt to new market demands and maintain the ability to accurately analyze and optimize warehouse operations.

[0038] As a further technical solution of the present invention, S4 specifically includes:

[0039] S41: Automatically adjust storage layout: Based on the results of the analysis algorithm, the system automatically adjusts the shelf layout to maximize space utilization. For example, for frequently accessed goods, they can be placed near the exit to reduce handling time. The system can also automatically adjust the storage order according to the shelf life of the goods to avoid waste caused by expiration.

[0040] S42: Optimize inbound and outbound processes: Based on real-time data analysis, dynamically adjust the inbound and outbound order to reduce waiting time and operational complexity. For example, during peak hours, prioritize urgent orders to ensure timely delivery. Introduce automated equipment (such as AGVs, Automated Guided Vehicles) to assist in completing inbound and outbound operations, further improving efficiency. AGVs can navigate autonomously according to system instructions to complete the handling of goods, reducing labor costs.

[0041] As a further technical solution of the present invention, S5 specifically includes:

[0042] S51: Anticipate potential problems based on historical data analysis: Utilize machine learning models to analyze past data, identify factors that may lead to problems (such as expired goods, insufficient inventory), and formulate countermeasures in advance (such as increasing inventory, adjusting storage conditions, etc.) to avoid problems from occurring. For example, by analyzing sales data, predict goods that may face inventory shortages in the next few months and arrange procurement plans in advance; for possible emergencies, such as weather changes affecting logistics and distribution, the system can also provide corresponding suggestions to help enterprises prepare.

[0043] S52: Set up an alarm mechanism: When an abnormal situation is detected, the system automatically generates an alarm to notify relevant personnel so that they can respond and handle the situation quickly. For example, when the temperature in a certain area exceeds the set range, the system will immediately send an alarm to the management personnel. The alarm mechanism also includes automatically executing preset operations, such as automatically adjusting the air conditioning temperature to restore the normal working environment. Establish a complete emergency response process to ensure that relevant personnel can take action quickly in the event of an emergency to minimize losses.

[0044] The beneficial effects of this invention are as follows:

[0045] 1. Significantly improves data real-time performance and accuracy: By integrating IoT devices and real-time data transmission technology, the time interval for inventory information updates is shortened to the second level, ensuring data real-time performance. Compared with the traditional method of relying on manual data entry (which usually takes several hours or even days), this system reduces data update delay by more than 90%, greatly improving data accuracy and management efficiency.

[0046] 2. Enhanced environmental monitoring capabilities and improved cargo safety: By deploying IoT devices such as temperature and humidity sensors and RFID tags, environmental parameters within the warehouse can be monitored 24 / 7, with a coverage rate exceeding 95%. This is significantly superior to traditional methods that rely solely on fixed-point sensors or periodic manual inspections, the latter often resulting in coverage rates below 50%.

[0047] 3. Significantly improved space utilization: Advanced analytics algorithms automatically adjust storage layout, resulting in an average increase of 20%-30% in warehouse space utilization. In contrast, traditional static layout designs typically have lower space utilization and struggle to flexibly adapt to changing demands. Attached Figure Description

[0048] Figure 1 This is a flowchart of an intelligent warehouse dynamic optimization system based on digital twin technology proposed in this invention. Detailed Implementation

[0049] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0050] Please see the appendix Figure 1 A smart warehouse dynamic optimization system based on digital twin technology includes the following specific steps:

[0051] S1: Create a digital twin model: Obtain basic data from the physical warehouse and build an accurate digital model;

[0052] S11: Obtain basic data of the physical warehouse: Use a laser scanner or drone to conduct a comprehensive scan of the warehouse to obtain high-precision 3D point cloud data, including length, width and height, shelf location and aisle planning, etc. Use CAD software (such as AutoCAD, SolidWorks) to convert this point cloud data into an accurate warehouse layout map, and then collect basic information about the goods in the warehouse, such as type, specifications, weight and special storage requirements. This information can be directly imported through the ERP system or obtained through manual entry. The above data will serve as the basis for building a digital twin model.

[0053] After data collection is completed, the collected data is cleaned and organized to remove redundant information and ensure data consistency and accuracy. In particular, for the storage requirements of goods, it is necessary to clarify the required temperature and humidity range, as well as whether special conditions such as moisture protection and sun protection are required.

[0054] S12: Constructing an accurate digital model: Using 3D modeling software (such as Unity3D, Unreal Engine, etc.) in a virtual environment, construct a digital twin model of the warehouse based on the above data. The modeling software provides rich plugins and API interfaces, which facilitates the integration of various sensor data and algorithms. The digital twin model should reflect the actual warehouse situation as much as possible, including all physical details and logical relationships. The digital twin model should not only show the physical layout, but also have the ability to simulate the operation under different scenarios. For example, it can simulate the movement path of goods, the driving route of forklifts, etc., to help managers understand the internal operation of the warehouse more intuitively. In addition, animation effects can be added to show the warehouse activities at different time periods.

[0055] S121: Preprocess the acquired 3D point cloud data by using a point cloud filtering algorithm (such as the moving least squares filter) to remove noise points. The expression is as follows: ,in: These are the filtered point coordinates. Let j be the coordinates of the j-th point in the original point cloud data. For point Point The weight is defined as For point The bandwidth parameter at the location is used to control the decay rate of the weights; the filtered point cloud data reflects the actual shape and structure of the warehouse more smoothly and accurately.

[0056] S122: The preprocessed point cloud data is meshed, and a 3D mesh model of the warehouse is constructed using the Poisson reconstruction algorithm. Its energy function expression is: ,in: Represents the constructed three-dimensional surface. It is a balance parameter. It is the estimated normal vector field of the point cloud. Let x represent the distance from point x to surface S. A three-dimensional mesh model is obtained by minimizing this energy function. This model accurately represents the warehouse's boundaries and internal structure, including the spatial relationships of shelves, aisles, and other components. Simultaneously, topology optimization is performed on the mesh model based on Euler's formula. (V is the number of vertices, E is the number of edges, and F is the number of faces). Adjust the topology of the mesh to make it more reasonable and compact, reduce unnecessary mesh faces and vertices, and improve the computational efficiency and rendering speed of the model.

[0057] S123: Based on the constructed 3D mesh model, interpolation algorithms (such as bicubic interpolation) are used to spatially interpolate the storage information of goods in the warehouse to determine the specific location and distribution of goods in 3D space. The interpolation formula is as follows: ,

[0058] in: For interpolated cargo storage information (such as temperature, humidity, etc.) in location The value at that location, The interpolation coefficients are determined by solving a system of equations from surrounding points with known stored information. Given the coordinates of the known storage information points; then, the storage requirements parameters for the goods (such as temperature requirements) are... Humidity requirements Is moisture protection required? Sunscreen (etc.) are associated with the interpolated storage location information to construct a digital twin model database containing physical layout and storage logic, including moisture-proof parameters. and sun protection parameters These can be represented by binary variables (1 indicates needed, 0 indicates not needed); when simulating warehouse operations, a path planning algorithm (such as...) is used. The algorithm calculates and optimizes the movement path of goods and the travel route of forklifts, and its heuristic function... The cost function is defined as the straight-line distance from the current node n to the target node. The total estimated cost function represents the actual movement cost (including distance, time, etc.) from the starting node to the current node n. By searching The shortest path is used to determine the optimal goods movement and forklift routes, thereby enabling accurate simulation and dynamic optimization of warehouse operations.

[0059] S2: Data Collection and Integration: Deploying IoT devices and enabling real-time data transmission and processing;

[0060] S21: Deploy IoT devices: Select a variety of IoT devices with high precision and low power consumption, such as temperature and humidity sensors (e.g., DHT22), RFID readers (e.g., Impinj R420), and high-definition cameras (e.g., Hikvision DS2CD2T86G24I). These devices not only provide high-quality data but also ensure stable operation over long periods. Based on the actual layout and needs of the warehouse, rationally arrange the installation locations of the IoT devices before installation. For example, install multiple temperature and humidity sensors in the cold storage area to ensure monitoring of every corner, and install cameras on main aisles to track the flow of goods in real time, thereby monitoring the environmental conditions and cargo status within the warehouse. These devices automatically collect data, reducing errors caused by human intervention. The selection of IoT devices should consider factors such as coverage, accuracy, and power consumption to meet the needs of specific application scenarios.

[0061] S22: Real-time data transmission and processing: Use wireless communication protocols (such as Zigbee, Wi-Fi, MQTT, CoAP) to transmit data collected by IoT devices to the central control system in real time, ensuring efficient and secure data transmission. These protocols support bidirectional communication, facilitating remote management and control. To ensure data security, encrypted transmission is recommended. The central control system performs preliminary processing and classification on the received data to ensure its accuracy and timeliness. First, format conversion and standardization are performed, for example, filtering out outliers through algorithms to improve data quality. Then, the data is classified and stored according to business needs. For example, temperature and humidity data are stored in a dedicated database for easy subsequent analysis and querying, and cargo location information is updated in real time to the digital twin model to ensure the model's accuracy.

[0062] S3: Building Analytics Algorithms: Developing advanced analytics algorithms and implementing machine learning models;

[0063] S31: Develop advanced analytics algorithms: Design algorithms to optimize storage strategies, considering factors such as product type, demand forecasting, inventory levels, storage density, and inbound / outbound frequency. These algorithms aim to maximize warehouse space utilization while reducing operating costs. For example, for frequently inbound / outbound goods, deep learning-based predictive algorithms can be used to pre-arrange optimal storage locations. For perishable goods, a comprehensive evaluation combining environmental parameters (such as temperature and humidity) is necessary to ensure that storage conditions meet requirements. Apply machine learning models to analyze historical data, identify patterns, and predict future trends. For example, by learning from sales data from the past few years, predict the demand for goods in the next year. Write algorithm code using Python or R.

[0064] S311: Optimization algorithm for storage location of frequently entering and leaving goods: An optimization strategy based on a genetic algorithm is adopted, and its fitness function is defined as Fitness. ,in: This indicates the distance between the product storage location and the inlet / outlet of the warehouse; the shorter the distance, the better. The larger the value; This refers to the frequency of goods entering and leaving the warehouse; the higher the frequency, the better. The larger the value; and These are weighting coefficients, used to balance the effects of distance and frequency on fitness, and satisfying... Through iterative operations (selection, crossover, mutation) of genetic algorithms, the optimal storage location arrangement scheme for goods that maximizes fitness is found, thereby realizing the optimal storage location planning for goods with high frequency of inbound and outbound operations, reducing the time for goods to enter and exit the warehouse, and improving warehouse operation efficiency.

[0065] S312: Comprehensive evaluation algorithm for storage conditions of perishable goods: Construct a comprehensive evaluation function: Evaluation = γ·T_similarity + δ·H_similarity + θ·S_meet, where: T_similarity represents the similarity between the actual storage temperature and the required temperature of the perishable goods, calculated as follows: T_actual is the actual storage temperature, T_required is the required temperature for perishable goods, and T_range is the allowable temperature fluctuation range. H_similarity is the similarity between the actual storage humidity and the required humidity, calculated similarly to T_similarity. H_actual, H_required, and H_range represent the actual storage humidity, required humidity, and allowable humidity fluctuation range, respectively. S_meet is a binary variable; if the actual storage environment meets the special storage requirements of perishable goods (such as moisture-proof and sun-proof), then S_meet = 1; otherwise, it is 0. γ, δ, and θ are weighting coefficients used to measure the importance of each factor in the comprehensive evaluation, and satisfy γ + δ + θ = 1. Through this evaluation function, the various potential storage locations of perishable goods are comprehensively evaluated, and the location with the highest Evaluation value is selected as its optimal storage location, ensuring that the storage conditions of perishable goods meet the requirements and extending their shelf life.

[0066] S313: Demand Forecasting Algorithm Based on Deep Learning (Taking LSTM Network as an Example): Using historical sales data as input sequence, an LSTM model is built to predict the demand for goods in the next year. The update formula for the hidden state of the LSTM unit is: ,in: It is the first The hidden state of time It is the activation value of the output gate, calculated as It is a weight matrix. It is a bias term. It is the feature vector corresponding to the preprocessed data at each time step. This represents the sigmoid activation function; It is the first The cell state at time t is updated using the following formula: It is the activation value of the forget gate, calculated in the same way. Similarly, using Parameters; It is the activation value of the input gate, calculated in a similar way using the corresponding parameters;

[0067] S32: Implementing Machine Learning Models: Collect sufficient historical data as training samples, including goods entry and exit records, environmental parameter changes, order demands, etc. Use data analysis libraries such as Pandas and NumPy for data preprocessing (e.g., missing value imputation, data standardization) and feature extraction (e.g., extracting seasonal and trend features from time series). Select a suitable machine learning framework (e.g., TensorFlow, PyTorch), and use methods such as cross-validation to input the processed data into the model for training to improve its generalization ability and prediction accuracy. The model training process requires a large amount of labeled data as input, so the preliminary data preparation work is crucial. Continuously adjust the model parameters and optimize the model performance to better adapt to different warehousing scenarios. Regularly evaluate the model's performance and adjust it according to the actual situation. For example, with seasonal changes, the demand for certain commodities may fluctuate significantly, at which point the model needs to be retrained to adapt to new market demands.

[0068] use Cross-validation can be used to improve the generalization ability and prediction accuracy of a model.

[0069] S321: Divide the collected historical data (including goods entry and exit records, changes in environmental parameters, order demands, etc.) into categories. There are n subsets, each of roughly the same size, and the nth subset is... During the second cross-validation, the first... One subset is used as the validation set, and the rest... A subset is used as the training set;

[0070] S322: The model is trained on the training set and then validated on the validation set, and the validation error is calculated.

[0071] S323: Calculate the average validation error after K rounds of cross-validation. ,in It is the first The model's performance is evaluated based on the average validation error. If the average validation error is large, the model parameters are adjusted (such as adjusting the number of hidden layer neurons and the learning rate), and retraining and cross-validation are performed until the model parameters that minimize the average validation error are found. This optimizes the model's performance and enables it to better adapt to different warehousing scenarios. At the same time, when commodity demand fluctuates due to factors such as seasonal changes, new historical data is collected periodically, and the model is retrained according to the above training process to update the model's parameters and structure, enabling it to adapt to new market demands and maintain the ability to accurately analyze and optimize warehouse operations.

[0072] S4: Dynamic Optimization and Adjustment: Automatically adjusts storage layout and optimizes inbound and outbound processes;

[0073] S41: Automatically adjust storage layout: Based on the results of the analysis algorithm, the system automatically adjusts the shelf layout to maximize space utilization. For example, for frequently accessed goods, they can be placed near the exit to reduce handling time. The system can also automatically adjust the storage order according to the shelf life of the goods to avoid waste caused by expiration.

[0074] S42: Optimize inbound and outbound processes: Based on real-time data analysis, dynamically adjust the inbound and outbound order to reduce waiting time and operational complexity. For example, during peak hours, prioritize urgent orders to ensure timely delivery. Introduce automated equipment (such as AGVs, Automated Guided Vehicles) to assist in completing inbound and outbound operations, further improving efficiency. AGVs can navigate autonomously according to system instructions to complete the handling of goods, reducing labor costs.

[0075] S5: Prediction and Early Warning: Anticipate potential problems and set up alarm mechanisms based on historical data analysis;

[0076] S51: Anticipate potential problems based on historical data analysis: Utilize machine learning models to analyze past data, identify factors that may lead to problems (such as expired goods, insufficient inventory), and formulate countermeasures in advance (such as increasing inventory, adjusting storage conditions, etc.) to avoid problems from occurring. For example, by analyzing sales data, predict goods that may face inventory shortages in the next few months and arrange procurement plans in advance; for possible emergencies, such as weather changes affecting logistics and distribution, the system can also provide corresponding suggestions to help enterprises prepare.

[0077] S52: Set up an alarm mechanism: When an abnormal situation is detected, the system automatically generates an alarm to notify relevant personnel so that they can respond and handle the situation quickly. For example, when the temperature in a certain area exceeds the set range, the system will immediately send an alarm to the management personnel. The alarm mechanism also includes automatically executing preset operations, such as automatically adjusting the air conditioning temperature to restore the normal working environment. Establish a complete emergency response process to ensure that relevant personnel can take action quickly in the event of an emergency to minimize losses.

[0078] Digital twin: A digital twin is a virtual representation of a physical product or system, achieved by integrating data such as physical models, sensor updates, and operational history to map the physical world to the virtual world. In this invention, it refers to creating a precise digital model of a warehouse and its internal environment, the location of items, and other information.

[0079] Internet of Things (IoT) devices: The Internet of Things refers to a network that connects any object to the Internet through various information sensing devices, such as radio frequency identification (RFID), infrared sensors, global positioning system (GPS), laser scanners, etc., according to agreed protocols, to exchange information and communicate, thereby achieving intelligent identification, positioning, tracking, monitoring, and management. In this invention, these devices are used to collect data such as temperature, humidity, inventory levels, and cargo location within the warehouse in real time.

[0080] Advanced analytics algorithms: Advanced analytics algorithms refer to techniques that utilize methods such as statistics, machine learning, and deep learning to process and analyze large amounts of data to extract valuable information or patterns. In this invention, these algorithms are used to automatically adjust storage strategies, optimize space utilization, and improve inbound and outbound processes.

[0081] Machine Learning: Machine learning is an artificial intelligence (AI) technique that enables computers to learn from data and make decisions without being directly programmed. In this invention, machine learning is used to continuously optimize the predictive capabilities and decision-making processes of a system, such as anticipating potential problems (expired goods or insufficient inventory), and to improve accuracy through continuous learning.

[0082] Dynamic optimization: Dynamic optimization refers to a method or system that can self-adjust based on real-time changes in data or conditions. In this invention, dynamic optimization is manifested in adjusting the storage strategy and inbound / outbound processes of items in the warehouse in real time based on the latest data provided by IoT devices and the results of advanced analytics algorithms, in order to maximize efficiency and service quality.

[0083] Smart warehousing: Smart warehousing upgrades traditional warehousing using automation and information technology to improve work efficiency, reduce labor costs, and enhance inventory management accuracy. The system proposed in this invention is part of smart warehousing, which achieves comprehensive monitoring and dynamic optimization of the warehouse environment through digital twin technology and IoT devices.

[0084] Experimental verification:

[0085] To verify the effectiveness of this invention, various experimental tests were conducted. The following is a summary of the basic principles and results of the experimental verification:

[0086] A simple experiment verifies the principle: By simulating warehouse environments of different sizes, the work efficiency, space utilization, and error rate before and after using this system are compared. The experiment sets up a control group and an experimental group, each running for a period of time, and the changes in various indicators are recorded.

[0087] Analysis results show that the overall operational efficiency of the warehouse has been significantly improved after adopting this system, while errors caused by human negligence have been reduced. Especially during peak periods, the system's intelligent scheduling function has greatly shortened the time for goods to enter and leave the warehouse.

[0088] As can be seen from the above description, the embodiments of the present invention achieve the following technical effects: significantly improving data real-time performance and accuracy:

[0089] Quantitative indicators: By integrating IoT devices and real-time data transmission technology, the time interval for inventory information updates is shortened to the second level, ensuring the real-time nature of the data; compared with the traditional method of relying on manual data entry (which usually takes several hours or even days), this system reduces data update delay by more than 90%, greatly improving data accuracy and management efficiency.

[0090] Theoretical explanation: The central control system performs preliminary processing and classification on the received data, and filters out outliers through algorithms, ensuring data quality. This mechanism not only reduces human error, but also improves the transparency and traceability of the entire warehouse management process.

[0091] Enhancing environmental monitoring capabilities and ensuring cargo safety:

[0092] Quantitative indicators: After deploying IoT devices such as temperature and humidity sensors and RFID tags, the environmental parameters in the warehouse can be monitored 24 / 7 with a coverage rate of over 95%. This is significantly better than traditional methods that rely solely on fixed-point sensors or periodic manual inspections, the latter of which often have a coverage rate of less than 50%.

[0093] Theoretical explanation: By monitoring key environmental parameters such as temperature and humidity in real time, the system can detect and handle abnormal situations immediately, such as the problem that excessively high temperatures may cause food spoilage. In addition, by using machine learning models to analyze historical data, the system can also provide early warnings of potential risks, thereby effectively ensuring the safety and quality of goods.

[0094] Significantly improve space utilization:

[0095] Quantitative indicators: The use of advanced analysis algorithms to automatically adjust the storage layout has increased the warehouse space utilization rate by an average of 20% to 30%. In contrast, the space utilization rate under the traditional static layout design is usually lower and it is difficult to flexibly respond to changes in demand.

[0096] Structural Features Analysis: The system automatically calculates the optimal storage location based on factors such as the frequency of goods entering and leaving the warehouse and their size, and updates the shelf layout in real time. For example, for goods that frequently enter and leave the warehouse, they are placed as close as possible to the warehouse entrance to reduce handling distance and maximize the use of every inch of space. This dynamic adjustment mechanism not only improves space utilization but also optimizes logistics routes and further enhances operational efficiency.

[0097] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

[0098] This invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this specification. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An intelligent warehouse dynamic optimization system based on digital twin technology, characterized in that, The specific steps include the following: S1: Creating a Digital Twin Model: Acquiring basic data from the physical warehouse and building an accurate digital model; specifically including: S11: Obtain basic data of the physical warehouse: Use a laser scanner or drone to conduct a comprehensive scan of the warehouse to obtain high-precision 3D point cloud data, use CAD software to convert this point cloud data into an accurate warehouse layout map, and then collect basic information about the goods in the warehouse. S12: Construct an accurate digital model: Using 3D modeling software in a virtual environment, construct a digital twin model of the warehouse based on the above data. The digital twin model should reflect the actual warehouse situation as closely as possible and should also be able to simulate operations under different scenarios; specifically: S121: The acquired 3D point cloud data is preprocessed, and noise points are removed using a point cloud filtering algorithm. The filtered point cloud data reflects the actual shape and structure of the warehouse more smoothly and accurately. S122: The preprocessed point cloud data is meshed, and a 3D mesh model of the warehouse is constructed using the Poisson reconstruction algorithm. Its energy function expression is: ,in: Represents the constructed three-dimensional surface. It is a balance parameter. It is the estimated normal vector field of the point cloud. Let x represent the distance from point x to surface S. The three-dimensional mesh model is obtained by minimizing this energy function. At the same time, the mesh model is topologically optimized by adjusting the mesh topology according to Euler's formula. S123: Based on the constructed 3D mesh model, interpolation algorithms are used to spatially interpolate the storage information of goods within the warehouse, determining the specific location and distribution of goods in 3D space. Then, the storage requirements parameters of the goods are correlated with the interpolated storage location information to construct a digital twin model database containing physical layout and storage logic. When simulating warehouse operations, path planning algorithms are used to calculate and optimize the movement paths of goods and the travel routes of forklifts. Its heuristic function... The cost function is defined as the straight-line distance from the current node n to the target node. The total estimated cost function represents the actual movement cost from the starting node to the current node n. By searching The shortest path is used to determine the optimal cargo movement and forklift travel route; S2: Data Collection and Integration: Deploying IoT devices and enabling real-time data transmission and processing; S3: Building Analytics Algorithms: Developing advanced analytics algorithms and implementing machine learning models; specifically including: S31: Develop advanced analytics algorithms: Design algorithms to optimize storage strategies, apply machine learning models to analyze historical data, identify patterns and predict future trends, and write algorithm code using Python or R. S32: Implementing a machine learning model: Collect sufficient historical data as training samples, perform data preprocessing and feature extraction using data analysis libraries, select a suitable machine learning framework, input the processed data into the model for training to improve its generalization ability and prediction accuracy, continuously adjust model parameters, optimize model performance to better adapt to different warehousing scenarios, and regularly evaluate model performance and adjust it according to actual conditions; specifically: S321: Divide the collected historical data into There are n subsets, each subset being equal in size or differing by no more than one sample. During the second cross-validation, the first... One subset is used as the validation set, and the rest... A subset is used as the training set; S322: The model is trained on the training set and then validated on the validation set, and the validation error is calculated. S323: Calculate the average validation error after K rounds of cross-validation. ,in It is the first The model's performance is evaluated based on the average validation error. If the average validation error is not minimized, the model parameters are adjusted, and the model is retrained and cross-validated until the model parameters that minimize the average validation error are found. At the same time, when the demand for goods fluctuates due to seasonal changes, new historical data is collected periodically, and the model is retrained according to the training process of steps S321 to S323 to update the model's parameters and structure. S4: Dynamic Optimization and Adjustment: Automatically adjusts storage layout and optimizes inbound and outbound processes; S5: Prediction and Early Warning: Anticipate potential problems and set up alarm mechanisms based on historical data analysis.

2. The intelligent warehouse dynamic optimization system based on digital twin technology according to claim 1, characterized in that, In step S11, after the data collection is completed, the collected data is cleaned and organized to remove redundant information and ensure the consistency and accuracy of the data.

3. The intelligent warehouse dynamic optimization system based on digital twin technology according to claim 1, characterized in that, S2 specifically includes: S21: Deploy IoT devices: Select a variety of IoT devices with high precision and low power consumption, such as temperature and humidity sensors, RFID readers, and high-definition cameras. Based on the actual layout and needs of the warehouse, arrange the installation locations of the IoT devices reasonably and then install them to monitor the environmental conditions and cargo status in the warehouse in real time. These devices automatically collect data. S22: Real-time data transmission and processing: Using wireless communication protocols, data collected by IoT devices is transmitted to the central control system in real time. The central control system performs preliminary processing and classification on the received data to ensure the accuracy and timeliness of the data.

4. The intelligent warehouse dynamic optimization system based on digital twin technology according to claim 1, characterized in that, S31 specifically includes: S311: Optimization algorithm for storage location of frequently entering and leaving goods: An optimization strategy based on genetic algorithm is adopted, and its fitness function is defined as Fitness; through the iterative operation of genetic algorithm, the storage location arrangement scheme of goods that maximizes Fitness is found. S312: Comprehensive evaluation algorithm for storage conditions of perishable goods: Construct a comprehensive evaluation function: Evaluation similarity similarity meet, where: Similarity indicates the degree of similarity between the actual storage temperature and the required temperature for perishable goods; Similarity refers to the degree of similarity between the actual stored humidity and the required humidity. meet is a binary variable , , It is a weighting coefficient; through this evaluation function, the various potential storage locations of perishable goods are comprehensively evaluated, and the location with the highest Evaluation value is selected as its optimal storage location. S313: Demand forecasting algorithm based on deep learning: Using historical sales data as input sequence, an LSTM model is built to predict the demand for goods in the next year.

5. The intelligent warehouse dynamic optimization system based on digital twin technology according to claim 1, characterized in that, S4 specifically includes: S41: Automatically adjust storage layout: Based on the results of the analysis algorithm, the system automatically adjusts the shelf layout to maximize space utilization. The system can also automatically adjust the storage order according to the shelf life of the goods. S42: Optimize the inbound and outbound process: Based on real-time data analysis, dynamically adjust the inbound and outbound order to reduce waiting time and operational complexity, and introduce automated equipment to assist in completing inbound and outbound operations.

6. The intelligent warehouse dynamic optimization system based on digital twin technology according to claim 1, characterized in that, S5 specifically includes: S51: Anticipate potential problems based on historical data analysis: Utilize machine learning models to analyze past data, identify factors that may cause problems, formulate countermeasures in advance, and provide corresponding suggestions for possible emergencies; S52: Set up an alarm mechanism: When an abnormal situation is detected, the system automatically generates an alarm to notify relevant personnel so that they can respond and handle the situation quickly. The alarm mechanism also includes automatically executing preset operations and establishing a complete emergency response process to ensure that relevant personnel can take action quickly in the event of an emergency.

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