An intelligent warehousing system based on supply chain management

Abstract

The present invention discloses an intelligent warehousing system based on supply chain management, which relates to the technical field of warehousing management, and includes a collaborative working system composed of a data acquisition module, a data processing module, an intelligent prediction module, a decision optimization module, an execution control module and a data sharing module. The data acquisition module obtains warehousing environment, inventory status and logistics status data in real time, the data processing module cleans and analyzes the data to generate dynamic parameters, the intelligent prediction module uses big data algorithms to predict inventory demand changes and supply chain anomalies, the decision optimization module generates the optimal strategy for inventory adjustment and resource allocation, the execution control module accurately controls the execution operation of warehousing equipment, and the data sharing module realizes real-time data synchronization between upstream and downstream, which ultimately significantly improves the dynamic response capability of the warehousing system and the supply chain collaboration efficiency, effectively reduces the risk of inventory surplus and shortage, and ensures the service capability and profitability of the enterprise.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing management, and in particular to an intelligent warehousing system based on supply chain management. Background Art

[0002] With the continuous development of supply chain management, modern warehousing systems have become a critical link in the supply chain. Traditional warehousing management methods often rely on manual operations and independent equipment control, making them difficult to adapt to the diverse, high-frequency, and dynamic storage needs of multiple categories. Furthermore, data silos are prevalent, making it difficult to achieve real-time sharing and collaboration between inventory information and other supply chain links, such as orders and logistics. In recent years, intelligent warehousing systems based on artificial intelligence, big data, and the Internet of Things (IoT) have gradually become a research hotspot. They use automation, digitization, and intelligent means to improve warehousing efficiency, reduce costs, and optimize inventory management.

[0003] The existing technology has the following shortcomings:

[0004] Current intelligent warehousing systems based on supply chain management lack the real-time decision-making and adaptive capabilities to handle dynamic demand fluctuations and supply chain anomalies. When unexpected supply chain anomalies occur (such as logistics delays, supplier supply shortages, or sudden changes in order demand), warehousing systems are often unable to effectively predict and quickly adjust inventory scheduling strategies. This deficiency can lead to severe inventory overstocking or shortages, and even cause overall supply chain operational imbalances, impacting a company's service capabilities and profitability. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent warehousing system based on supply chain management to solve the shortcomings of the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent warehousing system based on supply chain management, comprising a data acquisition module, a data processing module, an intelligent prediction module, a decision optimization module, an execution control module and a data sharing module;

[0007] Data collection module, used to collect real-time data on warehouse environment, inventory status and logistics status through IoT devices;

[0008] A data processing module, connected to the data acquisition module, for classifying, cleaning and analyzing the collected real-time data to generate dynamic parameters related to inventory management;

[0009] An intelligent prediction module, connected to the data processing module, is used to predict inventory demand changes and supply chain anomalies based on big data algorithms and machine learning models;

[0010] A decision optimization module, connected to the intelligent forecasting module, for generating an optimal strategy for inventory adjustment and resource allocation based on forecast results;

[0011] An execution control module, connected to the decision optimization module, is used to control the storage equipment to perform corresponding inventory operations according to the generated strategy;

[0012] The data sharing module is used to achieve real-time synchronization of inventory data between upstream and downstream of the supply chain.

[0013] Preferably, in the intelligent forecasting module, after analyzing the inventory demand change trend in the future period by a time series analysis algorithm, the inventory demand fluctuation value is calculated. The calculation method of the inventory demand fluctuation value is:

[0014] Collect historical inventory demand data to form a time series , perform differential processing on the data: Where, is the time series after difference;

[0015] Fitting an ARIMA model to capture the trend and volatility of the time series is expressed as: ; In the formula, c is a constant term, is the autoregressive coefficient, is the moving average coefficient, 、 is white noise; the residual of the ARIMA model Used to measure the difference between actual demand and forecast demand, and calculate the forecast value through the fitted ARIMA model , the expression is: ; Calculate residuals , the expression is: ; Calculate the inventory demand fluctuation value, the expression is: ; Where HLK is the inventory demand fluctuation value, is the residual mean, and n is the length of the time series.

[0016] Preferably, in the intelligent prediction module, the logistics delay situation is analyzed and then the logistics delay abnormal value is calculated. The calculation method of the logistics delay abnormal value is:

[0017] Collecting logistics delay data , sort the delay data from small to large to facilitate quantile calculation, calculate the first quartile Q1, Q1 is the 25% position point in the data set, calculate the third quartile Q3, Q3 is the 75% position point in the data set, calculate the interquartile range IQR, the interquartile range is the difference between Q3 and Q1: ; Calculate the outlier range, where the lower bound for: Upper bound for: , collect the delay data that is smaller than the next term and larger than the previous term, establish a data set, and calculate the standard deviation of the data set as the logistics delay outlier.

[0018] Preferably, in the intelligent prediction module, the inventory demand fluctuation value and the logistics delay anomaly value are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the comprehensive risk value label of inventory demand and logistics delay as the prediction target, and takes minimizing the sum of the prediction errors of the comprehensive risk value labels of all inventory demand and logistics delays as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the model training is stopped. The comprehensive risk value of inventory demand and logistics delay is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0019] Preferably, the obtained comprehensive risk value of inventory demand and logistics delay is compared with a gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the security analysis value of the encryption system is compared with the first standard threshold and the second standard threshold respectively;

[0020] If the combined risk of inventory demand and logistics delay is greater than the second standard threshold, it indicates that the combined risk of inventory demand and logistics delay is high. In this case, a first-level warning signal is generated, marking it as a high-risk level.

[0021] If the security analysis value of the encryption system is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the combined risk of inventory demand and logistics delay is medium. At this time, a second-level warning signal is generated, marking it as a medium risk level.

[0022] If the security analysis value of the encryption system is less than the first standard threshold, it means that the comprehensive risk of inventory demand and logistics delay is low. At this time, a third-level warning signal is generated and it is marked as a low risk level.

[0023] Preferably, in the decision optimization module, the optimal strategy for inventory adjustment and resource allocation is generated based on the forecast results, specifically:

[0024] Cost minimization includes transportation costs, inventory holding costs, and out-of-stock costs. The objective function is expressed as: Where, is the unit transportation cost, from point i to point j, is the amount of cargo transported from point i to point j, is the inventory holding cost, is the inventory, is the out-of-stock cost, the unit loss caused by the failure of product k to meet demand, The out-of-stock quantity;

[0025] Maximizing efficiency means maximizing the total amount of goods that meet demand or minimizing the transportation time: ; is the total quantity of goods that meet the demand point j, and the inventory constraint is: The total amount of goods shipped out of each storage point shall not exceed its inventory , demand constraints: ; The demand of each demand point j Should be satisfied; Path constraint: Replenishment paths should not be repeated and must comply with path planning rules.

[0026] Preferably, based on the genetic algorithm solution, an initial solution is randomly generated to form an initial population; each individual represents an inventory adjustment plan; the fitness of each individual is calculated according to the objective functions C and E: ; is the weight of the efficiency target; according to the fitness value, excellent individuals are selected to enter the next generation, some of the selected individuals are exchanged to generate new individuals, and the individual characteristics are randomly adjusted; when the fitness value converges or the maximum number of iterations is reached, it stops; the genetic algorithm finally outputs the individual with the highest fitness value as the optimal inventory adjustment plan, including: cargo distribution plan: the amount of cargo distributed from each inventory point to the demand point; replenishment path: the shortest transportation path or the lowest cost path; priority: generating a priority processing order based on the urgency of the demand.

[0027] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0028] 1. The present invention solves the problem of the existing technology's lack of real-time decision-making and adaptive capabilities in dealing with dynamic demand fluctuations and supply chain anomalies by constructing an intelligent warehousing system based on supply chain management. The system uses the data acquisition module to obtain real-time data on the warehousing environment, inventory status, and logistics status. After cleaning, classification, and analysis by the data processing module, dynamic parameters are generated. The intelligent prediction module uses big data algorithms and machine learning models to accurately predict changes in inventory demand and logistics anomalies. The decision optimization module generates inventory adjustment and resource allocation strategies that minimize costs and maximize efficiency. Finally, the execution control module directs the warehousing equipment to complete specific operations. At the same time, the data sharing module realizes real-time synchronization of inventory data between upstream and downstream of the supply chain, effectively improving the system's collaborative capabilities and response efficiency.

[0029] 2. The present invention calculates inventory demand fluctuation values ​​and logistics delay anomalies through an intelligent prediction module, and predicts comprehensive risk values ​​based on comprehensive feature vectors. Combined with multi-level early warning signals, the system can identify and respond to high-risk situations in advance, reducing the risk of excess or shortage inventory; it uses genetic algorithms to optimize inventory allocation plans, replenishment paths and priorities, significantly improving resource allocation efficiency and transportation efficiency, and reducing operating costs; through modular design and data sharing mechanisms, the system achieves deep collaboration and transparent management of upstream and downstream supply chains, providing an efficient solution for the intelligent transformation of supply chain management. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0031] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0033] For examples, see Figure 1 As shown, the intelligent warehousing system based on supply chain management described in this embodiment includes a data acquisition module, a data processing module, an intelligent prediction module, a decision optimization module, an execution control module and a data sharing module;

[0034] Data collection module, used to collect real-time data on warehouse environment, inventory status and logistics status through IoT devices;

[0035] A data processing module, connected to the data acquisition module, for classifying, cleaning and analyzing the collected real-time data to generate dynamic parameters related to inventory management;

[0036] An intelligent prediction module, connected to the data processing module, is used to predict inventory demand changes and supply chain anomalies based on big data algorithms and machine learning models;

[0037] A decision optimization module, connected to the intelligent forecasting module, for generating an optimal strategy for inventory adjustment and resource allocation based on forecast results;

[0038] An execution control module, connected to the decision optimization module, is used to control the storage equipment to perform corresponding inventory operations according to the generated strategy;

[0039] The data sharing module is used to achieve real-time synchronization of inventory data between upstream and downstream of the supply chain.

[0040] In the data acquisition module, temperature and humidity sensors installed in the warehouse environment collect real-time temperature and humidity information to ensure that storage conditions meet specific cargo requirements, such as the low temperature environment required for cold chain storage of goods. Radio frequency identification (RFID) tags and barcode readers attached to the goods collect real-time information such as the unique identification, location, and quantity of the goods. RFID readers periodically scan the goods' tags using wireless signals to obtain storage location and status data. Logistics trackers (such as GPS devices) record the location, route, and arrival time of goods in transit, ensuring that logistics process data is synchronized with the warehouse system.

[0041] Various sensors and monitoring devices are connected to the data acquisition module via Wi-Fi or 5G networks. After the devices go online, they automatically register their identities and join the system's IoT network.

[0042] The data collection module acquires data from various sensors and devices at a preset frequency (e.g., every minute). Environmental monitoring equipment periodically reads temperature and humidity values, RFID readers periodically scan cargo tags, and logistics trackers continuously upload cargo location and status data.

[0043] The data acquisition module's built-in data caching mechanism initially stores the collected raw data and uses a data filtering algorithm to remove noise and redundant information. For example, when an RFID reader repeatedly scans the same tag's signal, only the most recent record is retained.

[0044] The data acquisition module transmits the cleaned data to the data processing module through a secure communication protocol (such as MQTT or HTTPs) for subsequent classification analysis and optimization processing.

[0045] When temperature and humidity sensors detect that the storage environment is outside of a preset range (e.g., above 25°C), the system issues a real-time alert to management and triggers refrigeration equipment to adjust the environment. When RFID readers detect the movement of goods or changes in inventory, inventory information is immediately updated in the system. For example, when goods are moved to the shipping area, the inventory quantity is automatically reduced, eliminating delays or errors caused by manual recording. When the logistics tracker detects that goods have arrived at the warehouse, the system automatically marks the batch of goods as "arrived" and triggers the warehousing process.

[0046] The data processing module is connected to the data acquisition module and is used to classify, clean and analyze the collected real-time data to generate dynamic parameters related to inventory management.

[0047] The core components of the data processing module include: Data classification unit: This unit categorizes data into warehouse environment data, inventory status data, and logistics status data based on data source and content type. Data cleaning unit: This unit formats the classified data and removes abnormal or redundant information. Data analysis unit: This unit generates dynamic parameters such as inventory turnover rate, probability of abnormal environmental indicators, and inventory fluctuation trends through real-time analysis. Data storage unit: This unit stores the cleaned and analyzed data and dynamic parameters in a database for use by subsequent modules.

[0048] The data classification unit receives the real-time data stream from the data acquisition module and classifies it according to its source and attributes: Warehouse environment data: such as the ambient temperature and humidity values ​​provided by temperature and humidity sensors. Inventory status data: such as the unique identification, location, and quantity of goods captured by RFID readers. Logistics status data: such as the real-time location and arrival time of goods provided by GPS devices. Classification results are labeled with tags, such as "ENV" (environmental data), "STOCK" (inventory data), and "LOG" (logistics data).

[0049] The data cleaning unit processes the classified data to ensure accuracy and consistency. It uses preset thresholds to eliminate abnormal data. For example, when a temperature sensor's value deviates from the normal range (e.g., below -50°C or above 80°C), it is marked as abnormal and discarded. For continuously collected duplicate data (e.g., multiple RFID tag reads of the same item within a short period of time), the most recent valid record is retained. Data formats from different data sources are standardized to the system standard. For example, timestamps are uniformly converted to ISO 8601 format.

[0050] The data analysis unit performs real-time calculations and dynamic parameter generation on the cleaned data: based on inventory inbound and outbound data, it calculates the cargo turnover rate over a period of time using the formula: turnover rate = total outbound volume / average inventory; it dynamically displays cargo circulation efficiency, providing a basis for optimizing inventory decisions.

[0051] By analyzing the changing trends of historical environmental data, we can analyze the probability of future environmental anomalies. For example, we can predict whether the safe storage range is likely to be exceeded based on the rate of change of temperature and humidity. Inventory fluctuation trend analysis: We use time series analysis algorithms (such as the ARIMA model) to predict short-term fluctuations in inventory levels, providing a reference for replenishment strategies.

[0052] The data processing module stores the cleaned raw data and the dynamic parameters generated through analysis in a relational database (such as MySQL) and a distributed database (such as MongoDB). The cleaned data is then used for subsequent query and verification. The dynamic parameters are transmitted in real time to the decision optimization module via an API interface to support optimized calculations for inventory scheduling and resource allocation.

[0053] An intelligent prediction module, connected to the data processing module, is used to predict inventory demand changes and supply chain anomalies based on big data algorithms and machine learning models;

[0054] After analyzing the inventory demand change trend over the next period of time using the time series analysis algorithm, the inventory demand fluctuation value is calculated. The calculation method for the inventory demand fluctuation value is:

[0055] Collect historical inventory demand data to form a time series , ensure that the data are recorded at uniform time intervals (such as daily, weekly, or monthly). Check the stationarity of the data (such as whether the mean and variance remain stable over time). Use unit root tests (such as the ADF test) to determine whether differencing is necessary. If the data are non-stationary, make them stationary using first-order or multiple-order differencing: Where, is the time series after difference.

[0056] The ARIMA model consists of three core parameters: p is the order of the autoregressive (AR) term, which represents the relationship between the current value and the previous p values. d is the number of differencing steps required to achieve data stationarity. q is the order of the moving average (MA) term, which represents the relationship between the current value and the previous q error terms. p and q are determined using ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) plots. d is determined by the number of differencing steps.

[0057] Fitting an ARIMA model to capture the trend and volatility of the time series is expressed as: ; Where c is the constant term (intercept), is the autoregressive coefficient, is the moving average coefficient, 、 is white noise (error term). Use maximum likelihood estimation to fit the model and determine the parameters.

[0058] Residuals from the ARIMA model Used to measure the difference between actual demand and forecast demand, and calculate the forecast value through the fitted ARIMA model , the expression is: ; Calculate residuals , the expression is: ; Calculate the inventory demand fluctuation value, the expression is: ; Where HLK is the inventory demand fluctuation value, is the residual mean, and n is the length of the time series.

[0059] Large inventory demand fluctuations indicate dramatic changes in demand, with significant fluctuations. This often indicates that inventory demand is frequently impacted by external factors, such as seasonal sales peaks, sudden surges in market demand, supply chain instability, or emergencies. These anomalies place high demands on inventory management, requiring the system to respond quickly to avoid stockouts caused by surges in demand or inventory backlogs caused by sudden decreases in demand. Furthermore, large fluctuations may indicate that the demand forecasting model needs to be optimized to further improve its adaptability to dynamic demand changes.

[0060] Conversely, when inventory demand fluctuations are low, it indicates that inventory demand is stable, with minimal fluctuations. This typically indicates relatively stable market demand or a high degree of coordination between warehousing and supply chain management. In this case, companies can better optimize their inventory structure and reduce operating costs caused by excessive fluctuations. However, excessively low demand fluctuations can also mask underlying market shifts or anomalies, such as uncapturing hidden demand. Therefore, even with low fluctuations, companies must continuously monitor demand trends to ensure they remain sensitive to market changes and respond promptly to potential anomalies.

[0061] After analyzing the logistics delay situation, the logistics delay abnormal value is calculated. The calculation method of the logistics delay abnormal value is:

[0062] Collecting logistics delay data The unit can be minutes, hours, or days, depending on the timeliness of logistics. The data must be non-negative, such as: {2,3,4,10,12,50,60} (unit: hour).

[0063] Sort the delay data from smallest to largest to facilitate quantile calculation: Lsorted = [2,3,4,10,12,50,60]. Calculate the first quartile Q1, where Q1 is the 25% point in the data set, the third quartile Q3, where Q3 is the 75% point in the data set, and the interquartile range IQR, where the interquartile range is the difference between Q3 and Q1: ; Calculate the outlier range, where the lower bound for: Upper bound for: ,The delay data that is smaller than the next term and larger than the previous term are collected, and a data set is established. The standard deviation of the data set is calculated as the logistics delay outlier.

[0064] Larger outlier values ​​indicate a greater deviation from the normal range, and a higher degree of abnormality. This may indicate significant problems within the logistics chain, such as major transport disruptions (such as traffic accidents or inclement weather), supply chain bottlenecks (such as warehouse mismanagement or misallocation of resources), or even systemic issues (such as an overloaded or disrupted logistics network). Large outlier values ​​often significantly impact a company's operational efficiency and customer satisfaction, potentially leading to chain reactions such as delivery delays, order cancellations, and damaged brand reputation.

[0065] Conversely, smaller outliers indicate that logistics delays are closer to normal and less severe. Small outliers may be caused by sporadic, non-systemic factors, such as temporary traffic jams or minor scheduling errors. These anomalies typically have a limited impact on overall logistics operations and can be easily corrected through local optimization or rapid intervention. However, frequent small outliers may indicate potential process management or resource allocation issues, requiring in-depth investigation combined with trend analysis to prevent small issues from accumulating into larger problems.

[0066] The inventory demand fluctuation values ​​and logistics delay anomalies are converted into comprehensive feature vectors, which are used as inputs of the machine learning model. The machine learning model uses each set of comprehensive feature vectors to predict the comprehensive risk value label of inventory demand and logistics delay as the prediction target, and takes minimizing the sum of prediction errors of all comprehensive risk value labels of inventory demand and logistics delay as the training target. The machine learning model is trained until the sum of prediction errors reaches convergence, and the model training is stopped. The comprehensive risk value of inventory demand and logistics delay is determined based on the model output results. The machine learning model is a polynomial regression model.

[0067] The method for obtaining the comprehensive risk value of inventory demand and logistics delay is to obtain the corresponding function expression from the comprehensive feature vector training data of the trained machine learning model: Where, is the output function of the model, HLK is the inventory demand fluctuation value, GLH is the logistics delay abnormal value, It is the comprehensive risk value of inventory demand and logistics delay.

[0068] Compare the obtained comprehensive risk value of inventory demand and logistics delay with the gradient standard threshold, which includes a first standard threshold and a second standard threshold, and the first standard threshold is smaller than the second standard threshold. Compare the security analysis value of the encryption system with the first standard threshold and the second standard threshold respectively;

[0069] If the combined risk value of inventory demand and logistics delays is greater than the second standard threshold, it indicates that the combined risk of inventory demand and logistics delays is high. At this time, a first-level warning signal is generated and it is marked as a high-risk level. Relevant personnel must intervene immediately and take emergency measures, such as replenishing stock, accelerating transportation, or adjusting supply chain nodes.

[0070] If the security analysis value of the encryption system is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the comprehensive risk of inventory demand and logistics delay is medium. At this time, a second-level warning signal is generated, and it is marked as a medium risk level, and adjustments are made according to the specific situation.

[0071] If the security analysis value of the encryption system is less than the first standard threshold, it means that the combined risk of inventory demand and logistics delay is low. At this time, a third-level warning signal is generated, marking it as a low-risk level. Normal daily monitoring is carried out to keep the system running.

[0072] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the second-level warning signal is greater than that of the third-level warning signal. Relevant personnel can take corresponding treatment measures according to different warning signal levels.

[0073] The decision optimization module is connected to the intelligent prediction module and is used to generate the optimal strategy for inventory adjustment and resource allocation based on the prediction results.

[0074] Build an inventory scheduling optimization model with the goal of minimizing costs and maximizing efficiency. Based on genetic algorithms, calculate inventory adjustment plans, including cargo allocation, replenishment paths and priorities, and output the optimal decision plan for execution by the execution control module.

[0075] Cost minimization includes transportation costs, inventory holding costs, and stock-out costs.

[0076] The objective function is expressed as: Where, is the unit transportation cost, from point i to point j, is the amount of cargo transported from point i to point j, is the inventory holding cost (holding cost per unit of inventory), is the inventory, is the out-of-stock cost, the unit loss caused by the failure of product k to meet demand, Out of stock quantity.

[0077] Maximizing efficiency means maximizing the total amount of goods that meet demand or minimizing the transportation time: ; is the total quantity of goods that meet the demand point j, and the inventory constraint is: The total amount of goods shipped out of each storage point shall not exceed its inventory . Demand constraints: ; The demand of each demand point j It should be satisfied as much as possible. Path constraint: The replenishment path should not be repeated and must comply with the path planning rules.

[0078] The solution is based on genetic algorithm, which is an optimization method based on biological evolution, which continuously improves the solution through selection, crossover and mutation.

[0079] Randomly generate initial solutions (cargo allocation plans, replenishment paths, etc.) to form the initial population.

[0080] Each individual represents a possible inventory adjustment scenario.

[0081] Calculate the fitness of each individual according to the objective functions C and E: ; is the weight of the efficiency target.

[0082] Excellent individuals are selected to enter the next generation based on their fitness values, and roulette or tournament selection strategies are commonly used.

[0083] Perform partial exchanges (such as route exchange or cargo distribution adjustment) on the selected individuals to generate new individuals.

[0084] Randomly adjust certain characteristics of individuals (such as changing replenishment routes or redistributing goods).

[0085] Stop when the fitness value converges or the maximum number of iterations is reached.

[0086] The genetic algorithm ultimately outputs the individual with the highest fitness value as the optimal inventory adjustment plan, which includes: Goods allocation plan: The amount of goods to be allocated from each inventory location to the demand location; Replenishment path: The shortest transportation path or the lowest cost path; Priority: Generates a priority order based on the urgency of the demand.

[0087] The execution control module is connected to the decision-making optimization module and is used to control the storage equipment to perform corresponding inventory operations according to the generated strategy. It receives the output data of the decision-making optimization module, including: the cargo allocation plan: the quantity and destination of cargo to be allocated to each inventory point; the replenishment route: the optimal route for transporting cargo; the priority plan: the urgency of each task and the execution order. The data is parsed and converted into an executable instruction format, for example: sorting instructions: allocating cargo to a specific loading area; transport instructions: controlling the automated guided vehicle (AGV) to transport cargo to the loading area; loading and unloading instructions: loading cargo onto the transport vehicle using a robotic arm.

[0088] Based on the parsed task information, specific tasks are assigned to various types of storage equipment: Automatic sorters: sort goods according to demand and assign them to the corresponding transport loading areas. Example command: Sorting Task = {Goods ID: G001, Destination: Loading Area 1} Destination: Loading Area 1}; Automated Guided Vehicles (AGVs): perform cargo handling tasks, such as moving sorted goods from the storage area to the loading area. Example command: Handling Task = {Goods ID: G001, Start Point: Storage Area A, End Point: Loading Area 1}.

[0089] Move goods from the loading area to the transport vehicle. Example command: Loading Task = {Cargo ID: G001, Origin: Loading Area 1, Destination: Transport Vehicle 1}. Environment and Equipment: If the storage environment (such as temperature and humidity) needs to be adjusted, send commands to control the operation of related equipment.

[0090] IoT sensors monitor the operating status of each device (such as sorting progress, AGV position, and robotic arm movements). Receive real-time feedback from the device on task execution and update the status of task completion. For example: Success feedback: After completing a command, the device returns a success status. Failure feedback: If a failure occurs, the module reschedules the task or sends a maintenance request.

[0091] Based on real-time feedback, we dynamically adjust unfinished tasks or abnormal situations. If an AGV fails a task (e.g., a blocked path), we reallocate the task to a backup AGV. We adjust route planning to avoid blocked areas. If a loading area experiences a backlog of cargo, we increase AGV frequency to move the cargo to the shipping area promptly.

[0092] The data sharing module is used to achieve real-time synchronization of inventory data between upstream and downstream of the supply chain.

[0093] Inventory data collection: Data sources include: inventory status recorded in real time by the warehouse management system (WMS). Dynamic inventory parameters (such as inventory turnover rate and inventory fluctuation value) provided by the data processing module. Goods location information and environmental status provided by IoT devices. Data standardization processing: To ensure data compatibility, the collected inventory data is uniformly converted into a standardized format (such as JSON or XML). Data storage: A distributed database (such as MongoDB or Cassandra) is used to store inventory data to ensure data reading efficiency under high concurrency. Real-time distribution of inventory data is achieved based on message queue technology (such as Kafka and RabbitMQ): The data collection module pushes inventory update events to the message queue. The data sharing module subscribes to update events in the message queue and sends them to upstream and downstream nodes in the supply chain in real time.

[0094] Synchronization method: Push mode: The data sharing module actively pushes updated data to subscribers (supply chain nodes). Pull mode: Nodes actively request the latest inventory data based on demand. Data encryption: The TLS protocol is used to ensure the security of data transmission. Sensitive data (such as product batches and shipping prices) is encrypted for storage and transmission. Permission management: A role-based access control (RBAC) mechanism is used to ensure that different nodes can only access data within the authorized scope. For example: Suppliers can only view raw material inventory, and retailers can only view end product inventory. Data verification: During data transmission, hash checksums (such as MD5) are used to verify data integrity to prevent data loss or tampering.

[0095] When inventory changes (such as inbound, outbound, and transfers), the data sharing module pushes the updated data to all subscribed nodes in real time. Historical changes in inventory data are recorded using timestamps, supporting query and audit of historical data by upstream and downstream nodes.

[0096] Specific application scenarios for data sharing include: Supplier side: Shared data: Raw material inventory status. Application scenario: When inventory falls below the safety stock threshold, suppliers can receive real-time replenishment notifications and prepare stock in advance.

[0097] Manufacturers: Shared data: Work-in-progress inventory and production progress. Application scenario: Manufacturers dynamically adjust production plans based on inventory changes to avoid excessive backlogs or production interruptions.

[0098] Distributor side: Shared data: end product inventory and delivery status. Application scenario: Distributors optimize delivery plans based on inventory distribution to reduce transportation costs.

[0099] On the retailer side, shared data includes product inventory and sales figures. Application scenario: Retailers can view inventory status in real time and adjust promotional activities to avoid out-of-stock or slow-selling products.

[0100] In this embodiment, the system includes a data acquisition module, a data processing module, an intelligent prediction module, a decision optimization module, an execution control module, and a data sharing module. These modules operate in a collaborative manner to achieve closed-loop optimization of intelligent warehousing and supply chain management. The data acquisition module acquires real-time data on the warehouse environment, inventory status, and logistics status through IoT devices. The data processing module classifies, cleans, and analyzes the collected data to generate dynamic parameters. The intelligent prediction module predicts changes in inventory demand and supply chain anomalies based on big data algorithms and machine learning models. The decision optimization module combines the prediction results to generate the optimal strategy for inventory adjustment and resource allocation. The execution control module directs warehousing equipment to complete specific operations based on the optimization strategy. The data sharing module ensures real-time synchronization of inventory data across the upstream and downstream supply chains, improving supply chain collaboration efficiency and responsiveness.

[0101] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0102] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0103] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. An intelligent warehousing system based on supply chain management, characterized by: It includes data acquisition module, data processing module, intelligent prediction module, decision optimization module, execution control module and data sharing module; Data collection module, used to collect real-time data on warehouse environment, inventory status and logistics status through IoT devices; A data processing module, connected to the data acquisition module, is used to classify, clean and analyze the collected real-time data and generate dynamic parameters related to inventory management; An intelligent prediction module, connected to the data processing module, is used to predict inventory demand changes and supply chain anomalies based on big data algorithms and machine learning models; Specifically, the method includes: analyzing the inventory demand change trend over a period of time in the future through a time series analysis algorithm, and calculating the inventory demand fluctuation value. The calculation method of the inventory demand fluctuation value is: Collect historical inventory demand data to form a time series , perform differential processing on the data: Where, is the time series after difference; Fitting an ARIMA model to capture the trend and volatility of the time series is expressed as: ; In the formula, c is a constant term, is the autoregressive coefficient, is the moving average coefficient, 、 is white noise; the residual of the ARIMA model Used to measure the difference between actual demand and forecast demand, and calculate the forecast value through the fitted ARIMA model , the expression is: ; Calculate residuals , the expression is: ; Calculate the inventory demand fluctuation value, the expression is: ; Where HLK is the inventory demand fluctuation value, is the residual mean, n is the length of the time series; A decision optimization module, connected to the intelligent forecasting module, for generating an optimal strategy for inventory adjustment and resource allocation based on forecast results; An execution control module, connected to the decision optimization module, is used to control the storage equipment to perform corresponding inventory operations according to the generated strategy; The data sharing module is used to achieve real-time synchronization of inventory data between upstream and downstream of the supply chain.

2. The intelligent warehousing system based on supply chain management according to claim 1, characterized in that: In the intelligent prediction module, the logistics delay situation is analyzed and the logistics delay abnormal value is calculated. The calculation method of the logistics delay abnormal value is: Collecting logistics delay data , sort the delay data from small to large to facilitate quantile calculation, calculate the first quartile Q1, Q1 is the 25% position point in the data set, calculate the third quartile Q3, Q3 is the 75% position point in the data set, calculate the interquartile range IQR, the interquartile range is the difference between Q3 and Q1: ; Calculate the outlier range, where the lower bound for: Upper bound for: , collect the delay data that is smaller than the next term and larger than the previous term, establish a data set, and calculate the standard deviation of the data set as the logistics delay outlier.

3. The intelligent warehousing system based on supply chain management according to claim 2, characterized in that: In the intelligent prediction module, the inventory demand fluctuation value and the logistics delay anomaly value are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of the machine learning model. The machine learning model uses each set of comprehensive feature vectors to predict the comprehensive risk value label of inventory demand and logistics delay as the prediction target, and minimizes the sum of the prediction errors of the comprehensive risk value labels of all inventory demand and logistics delays as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the model training is stopped. The comprehensive risk value of inventory demand and logistics delay is determined based on the model output results. Among them, the machine learning model is a polynomial regression model.

4. The intelligent warehousing system based on supply chain management according to claim 3, characterized in that: Compare the obtained comprehensive risk value of inventory demand and logistics delay with the gradient standard threshold, which includes a first standard threshold and a second standard threshold, and the first standard threshold is smaller than the second standard threshold. Compare the security analysis value of the encryption system with the first standard threshold and the second standard threshold respectively; If the combined risk of inventory demand and logistics delay is greater than the second standard threshold, it indicates that the combined risk of inventory demand and logistics delay is high. In this case, a first-level warning signal is generated, marking it as a high-risk level. If the security analysis value of the encryption system is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the combined risk of inventory demand and logistics delay is medium. At this time, a second-level warning signal is generated, marking it as a medium risk level. If the security analysis value of the encryption system is less than the first standard threshold, it means that the comprehensive risk of inventory demand and logistics delay is low. At this time, a third-level warning signal is generated and it is marked as a low risk level.

5. The intelligent warehousing system based on supply chain management according to claim 1, characterized in that: In the decision optimization module, the optimal strategy for inventory adjustment and resource allocation is generated based on the forecast results. Specifically: Cost minimization includes transportation costs, inventory holding costs, and out-of-stock costs. The objective function is expressed as: Where, is the unit transportation cost, from point i to point j, is the amount of cargo transported from point i to point j, is the inventory holding cost, is the inventory, is the out-of-stock cost, the unit loss caused by the failure of product k to meet demand, The out-of-stock quantity; Maximizing efficiency means maximizing the total amount of goods that meet demand or minimizing the transportation time: ; is the total quantity of goods that meet the demand point j, and the inventory constraint is: The total amount of goods shipped out of each storage point shall not exceed its inventory , demand constraints: ; The demand of each demand point j Should be satisfied; Path constraint: Replenishment paths should not be repeated and must comply with path planning rules.

6. The intelligent warehousing system based on supply chain management according to claim 5, characterized in that: Based on the genetic algorithm solution, the initial solution is randomly generated to form the initial population; each individual represents an inventory adjustment plan; the fitness of each individual is calculated based on the objective functions C and E: ; is the weight of the efficiency goal; according to the fitness value, excellent individuals are selected to enter the next generation, some of the selected individuals are exchanged to generate new individuals, and the individual characteristics are randomly adjusted; Stop when the fitness value converges or reaches the maximum number of iterations; The genetic algorithm ultimately outputs the individual with the highest fitness value as the optimal inventory adjustment plan, including: cargo allocation plan: the amount of cargo allocated from each inventory point to the demand point; replenishment path: the shortest transportation path or the lowest cost path; priority: generating a priority processing order based on the urgency of demand.

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