A warehouse position data dynamic regulation and management system and method based on big data analysis

Abstract

The application discloses a warehouse position data dynamic regulation and management system and method based on big data analysis, and belongs to the technical field of warehouse position regulation and management. The application collects a cargo information table, constructs a directed graph topology model, adopts a Dijkstra algorithm, combines the cargo information table, plans an initial delivery path with the shortest path time for each cargo, calculates congestion indexes of various delivery paths, searches for a region with the highest cargo density in a congestion cargo set when the congestion indexes exceed a threshold value, determines the region as a congestion source region, transfers the cargo in the congestion source region to a target region, evaluates the influence of order changes on existing delivery paths, locally adjusts and regionally transfers the affected cargo, monitors the cargo position, combines a path planning model, judges whether the cargo moving track intersects with other cargo paths, and reallocates the passing right of the conflict region according to the cargo delivery time urgency and the cargo specification when the intersection occurs.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse control and management, and in particular to a warehouse data dynamic control and management system and method based on big data analysis. Background Art

[0002] With the rapid development of the e-commerce industry and the increasing sophistication of supply chain management, the demand for efficient and intelligent management in warehousing and logistics is becoming increasingly urgent. Traditional warehouse management models, which rely primarily on manual experience and simple information systems, are no longer able to meet the needs of rapidly warehousing and warehousing massive volumes of goods and dynamically scheduling complex orders. Although some companies have introduced technologies such as warehouse management systems (WMS) and automated guided vehicles (AGVs) in recent years, they still face drawbacks such as slow response and inefficient decision-making when faced with order fluctuations, route congestion, and resource misallocation.

[0003] Traditional route planning is mostly based on fixed rules or simple algorithms, lacking consideration for real-time traffic conditions and dynamic changes in cargo. When route congestion or order changes occur, the route cannot be adjusted in a timely manner, resulting in reduced shipping efficiency and even cargo backlogs and delivery delays. Existing warehouses typically rely on manual inspections or post-event remediation when faced with route congestion, failing to predict congestion risks in advance. For dynamic events such as changes in cargo order shipping times and warehouse layout adjustments, existing systems are unable to quickly assess the scope and extent of their impact and often adopt a "one-size-fits-all" approach, leading to global scheduling imbalances. During cargo transfers and route adjustments, there is a lack of effective conflict detection and avoidance mechanisms, which can easily lead to secondary congestion. Summary of the Invention

[0004] The purpose of the present invention is to provide a warehouse data dynamic control management system and method based on big data analysis to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present application provides a method for dynamically regulating and managing warehouse data based on big data analysis, comprising the following steps:

[0007] Collect cargo data and corresponding order data, perform data preprocessing, and generate a cargo information table. Obtain the warehouse layout and construct a directed graph topology model, with shelf and aisle intersections as nodes and aisles as edges. Use the Dijkstra algorithm, combined with the cargo information table, to plan an initial shipping route with the shortest path time for each item based on the directed graph topology model.

[0008] Based on the initial shipping route, the traffic status of each shipping route is obtained and the congestion index of each shipping route is calculated. When the congestion index of a shipping route exceeds a threshold, a congested cargo set is obtained and the area with the highest cargo density in the congested cargo set is searched to determine the congestion root area. Target areas with cargo density below the set threshold are selected, and cargo in the congestion root area is transferred to the target area, updating the initial shipping route to the existing shipping route.

[0009] When a change in the shipment time of a goods order is detected, the impact of the order change on the existing shipment route is evaluated; for the affected goods, local adjustments are made based on their existing shipment routes; if local adjustments cannot resolve the impact, the affected goods are transferred to other regions;

[0010] During regional transfer or local adjustment, the cargo location is monitored and, combined with the path planning model, it is determined whether the cargo movement trajectory intersects with other cargo paths. If an intersection occurs, the movement of cargo in the conflicting area is suspended, and a priority scheduling algorithm is used to reallocate the right of way to the conflicting area based on the urgency of the cargo's departure time and the cargo specifications.

[0011] In combination with the first aspect, in a first implementation of the first aspect of the present application, obtaining the warehouse layout, constructing a directed graph topology model, setting the intersection points of shelves and aisles as nodes, and setting the aisles as edges, includes:

[0012] Obtain the warehouse layout, mark the endpoints of all shelves, the intersections of aisles, and the connection points between aisles and shelves as nodes, assign a unique number to each node, record its position in the warehouse coordinate system, and mark the node type; connect adjacent nodes to form edges based on the direction of the aisles and the connection relationship between shelves; when there is a physical channel or a feasible cargo movement path between two nodes, establish an edge between the two nodes, and the direction of each edge is determined by the normal movement direction of the cargo; add attribute information to each edge, including the length of the edge, maximum load capacity, travel time, and restrictions.

[0013] In conjunction with the first aspect, in a second implementation of the first aspect of the present application, it is characterized in that the Dijkstra algorithm is used in combination with the cargo information table to plan an initial shipping path with the shortest path time for each cargo based on a directed graph topology model, including:

[0014] For each cargo in the cargo information table, the node in the directed graph topology model corresponding to the current location of the cargo is used as the starting point, and the node corresponding to the shipping area is used as the target destination; the current shortest distance from the starting point to the node and the current shortest path from the starting point to the node are set for each node in the directed graph topology model; from all nodes whose shortest paths have not been determined, the node with the shortest distance to the starting point is selected as the current node; all neighbor nodes of the current node are traversed, and the distance from the starting point through the current node to the neighbor node is calculated; when the distance is less than the shortest distance currently recorded by the neighbor node, the shortest distance of the neighbor node is updated to the calculated value, and the shortest path of the neighbor node is updated to the path passing through the current node; the current node is marked as a node with a determined shortest path, and no distance update calculation is performed on it subsequently; the current node is repeatedly selected, the neighbor node is updated, and the determined node is marked until the target destination node is marked as a node with a determined shortest path. At this time, the path from the starting point to the target destination obtained is the initial shipping path of the cargo.

[0015] In combination with the first aspect, in a third implementation of the first aspect of the present application, obtaining the traffic status of each shipping path based on the initial shipping path and calculating the congestion index of each shipping path include:

[0016] Factors influencing route congestion include cargo speed, cargo intervals, equipment operating status, and route occupancy. Based on the initial shipping route, the actual cargo speed along the route is estimated based on the time difference between two adjacent monitoring points and the distance between the two points. This is then compared with the standard speed for that route. The time intervals between consecutive passages of the same type of cargo or cargo from the same order are counted. When the time interval is less than a set threshold, it indicates that cargo is concentrated during that period, potentially causing congestion. The operating status of transport equipment along the route is analyzed to determine whether it is causing obstruction, as well as the scope and extent of the obstruction. Furthermore, the space occupied by cargo and transport equipment along the route is used to determine whether the route is saturated or overloaded.

[0017] Set a weight for each factor and quantify the score of each factor; multiply the score of each factor by the corresponding weight and sum them up to obtain the congestion index of each shipping route.

[0018] In conjunction with the first aspect, in a fourth implementation of the first aspect of the present application, when the congestion index of a shipping route exceeds a threshold, obtaining a congested cargo set, searching for an area with the highest cargo density in the congested cargo set, and determining it as the congestion root area includes:

[0019] When the congestion index of a shipping route exceeds a threshold, all cargoes that are planned to be shipped along the congested route and have not yet started shipping or are in transit are screened based on the cargo information table and route planning records. The corresponding cargo data is then aggregated into a congested cargo set. The current location of each cargo in the congested cargo set is converted into spatial coordinates. The spatial coordinates of each cargo are used as a data point to form a data set for cluster analysis.

[0020] According to the actual situation of the warehouse, the parameters of the DBSCAN algorithm are set, the neighborhood radius and the minimum number of points are determined, and a data point that has not been visited is selected from the data set as the starting point. The number of data points within the neighborhood radius of this point is checked. When the number of data points in the neighborhood is greater than or equal to the minimum number of points, the point is identified as a core point, and all points in its neighborhood are grouped into a preliminary cluster with the core point as the center. For each core point in the cluster, the data points in its neighborhood that have not been visited are checked, and points that meet the conditions are added to the cluster, and the range of the cluster is continuously expanded until no new data points can be included. After traversing all data points, isolated data points that are not included in any cluster are marked as outliers. For each formed cluster, its cargo density is calculated. The density calculation is based on the ratio of the number of cargo in the cluster to the spatial range covered by the cluster. The higher the ratio, the denser the cargo in the area. The density of all clusters is compared, and the cargo area corresponding to the cluster with the highest density is selected and identified as the source area of ​​congestion.

[0021] In combination with the first aspect, in a fifth implementation of the first aspect of the present application, screening a target area where the cargo density is lower than a set threshold, transferring cargo from the congestion source area to the target area, and updating the initial shipping route to the existing shipping route include:

[0022] Based on the warehouse's actual storage capacity and historical operational data, a cargo density threshold is set to measure the congestion level of an area. All areas of the warehouse are traversed, and areas with cargo density below the set threshold are screened to form a target area set. During the screening process, areas where cargo handling operations are in progress, equipment failures, or other areas unsuitable for receiving cargo are excluded.

[0023] Based on the specifications, shipping time and order priority of the goods in the congestion root area, give priority to transferring goods with later shipping time, smaller volume and lighter weight to avoid affecting the shipment of urgent orders; plan a transfer path from the congestion root area to the target area for each batch of goods to be transferred. The transfer path planning must avoid the shipping path in use to ensure that there is no conflict with other cargo transportation during the transfer process; according to the planned path and sequence, use the selected handling equipment to transfer the goods in the congestion root area to the target area one by one; after the goods are transferred, update the location information of the relevant goods in the goods information table, modify its current location to the specific location of the target area, and simultaneously update the inventory data in the warehouse management system; update the initial shipping path to the existing shipping path.

[0024] In conjunction with the first aspect, in a sixth implementation of the first aspect of the present application, when a change in the delivery time of a goods order is detected, evaluating the impact of the order change on the existing delivery path includes:

[0025] In the goods information table, filter out other goods in the same order as the changed order goods, determine the current location, existing shipping route and shipping time of other goods in the same order, and evaluate the impact of the order change on the shipping plan of other goods in the same order; based on the existing shipping route of the changed order goods, find other goods that are being transported or planned to be transported on the route to form a set of goods associated with the same route; analyze the shipping time, priority and specifications of different goods in the set of goods associated with the same route to determine whether the time adjustment of the changed order goods will cause transportation conflicts for goods on the same route; when there are process dependencies in warehouse operations, check the goods that have upstream and downstream relationships with the changed order goods.

[0026] In conjunction with the first aspect, in a seventh implementation of the first aspect of the present application, during the regional transfer or local adjustment process, monitoring the cargo location and, in combination with the path planning model, determining whether the cargo movement trajectory intersects with other cargo paths includes:

[0027] The path planning model is trained using historical cargo transportation data, warehouse operation logs, and equipment operation records. The path planning model uses a deep learning algorithm. The model's learning objectives include minimizing the probability of path conflicts, shortening transportation time, and balancing equipment loads. The path planning strategy is optimized through simulation scenarios. The model parameters are dynamically adjusted according to the real-time operation status of the warehouse. For cargo that is being transferred or partially adjusted, its starting location, target location, estimated departure time, and cargo specifications are input into the path planning model to predict the movement trajectory of the cargo from the starting point to the end point, including the nodes passed, the estimated arrival time at each node, and the speed change. The predicted movement trajectory is compared with the existing shipping routes of other cargo to determine whether the cargo movement trajectory intersects with other cargo routes.

[0028] In conjunction with the first aspect, in an eighth implementation of the first aspect of the present application, when an intersection occurs, suspending the movement of goods in the conflicting area, and using a priority scheduling algorithm to reallocate the right of way in the conflicting area based on the urgency of the goods' departure time and the specifications of the goods, including:

[0029] When an intersection occurs, cargo movement in the conflicting area is suspended, and a pause instruction is sent to all transport equipment and operators involved in operations in the area. The departure times of cargo in the conflicting area are compared, and cargo with earlier departure times is given higher priority. When there are cargo with the same departure time, the order priorities are further compared. The difficulty of passage is assessed based on cargo specifications. In the case of equal priority, smaller and more mobile cargo is given priority. A comprehensive priority score is calculated for each cargo in the conflicting area, combining the urgency of the departure time and cargo specifications, to form a prioritized list.

[0030] According to the priority list, the order of goods passing through the conflict area is determined, and the goods with the highest priority are given priority to obtain the right of passage; according to the passage order, the corresponding transportation equipment is dispatched and the route of the transportation equipment to the conflict area is planned.

[0031] In a second aspect, the present application provides a warehouse data dynamic control and management system based on big data analysis, including:

[0032] The initial shipping route planning module includes a cargo information table generation unit, a directed graph topology model construction unit, and an initial shipping route planning unit. The cargo information table generation unit collects cargo data and corresponding order data, performs data preprocessing, and generates a cargo information table. The directed graph topology model construction unit obtains the warehouse layout and constructs a directed graph topology model, setting the intersection points of shelves and aisles as nodes and the aisles as edges. The initial shipping route planning unit uses the Dijkstra algorithm, combined with the cargo information table, and based on the directed graph topology model to plan an initial shipping route with the shortest path time for each cargo.

[0033] Shipping route update module: includes: a congestion index calculation unit, a congestion root area determination unit, and a shipping route update unit; wherein the congestion index calculation unit obtains the traffic status of each shipping route based on the initial shipping route and calculates the congestion index of each shipping route; when the congestion index of a shipping route exceeds a threshold, the congestion root area determination unit obtains a congested cargo set, searches for the area with the highest cargo density in the congested cargo set, and determines it as the congestion root area; the shipping route update unit selects a target area with a cargo density lower than the set threshold, transfers the cargo in the congestion root area to the target area, and updates the initial shipping route to the existing shipping route;

[0034] The affected goods decision module includes an order change impact assessment unit, a local adjustment unit, and a regional transfer unit. The order change impact assessment unit assesses the impact of order changes on existing shipping routes when a change in the shipment time of a goods order is detected. The local adjustment unit makes local adjustments to the existing shipping routes of the affected goods. The regional transfer unit transfers the affected goods to different regions when local adjustments cannot resolve the impact.

[0035] Right-of-way allocation module: includes: an intersection judgment unit and a right-of-way allocation unit; among them, the intersection judgment unit monitors the location of the goods during the area transfer or local adjustment process, and combines the path planning model to determine whether the cargo movement trajectory intersects with other cargo paths; when an intersection occurs, the right-of-way allocation unit suspends the movement of goods in the conflicting area and adopts a priority scheduling algorithm to reallocate the right of way in the conflicting area based on the urgency of the cargo outbound time and the cargo specifications.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. This invention uses the Dijkstra algorithm combined with big data analysis to plan an initial shipping route with the shortest path time for each cargo, and calculates the congestion index based on real-time traffic status. When congestion occurs, the intelligent algorithm quickly locates the congestion root area, selects the optimal target area for cargo transfer, and updates the shipping route in real time, achieving dynamic route optimization and effectively improving shipping efficiency.

[0038] 2. When the present invention detects a change in the delivery time of a goods order, it can quickly evaluate its impact on the existing delivery route and flexibly adopt local adjustment or regional transfer strategies based on the degree of impact. During the adjustment process, the path planning model and real-time location monitoring are combined to achieve accurate prediction of the goods movement trajectory, timely discover and resolve path conflicts, and ensure the stability and efficiency of global scheduling.

[0039] 3. The present invention realizes the full process automated management from data collection, route planning, congestion handling to dynamic adjustment, which reduces manual intervention and reduces the risk of human error; at the same time, through the priority scheduling algorithm and intelligent conflict avoidance mechanism, it realizes the rational allocation and efficient utilization of resources, significantly improving the space utilization rate and overall operational efficiency of the warehouse. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of the steps of a method for dynamically controlling and managing warehouse data based on big data analysis of the present invention;

[0041] Figure 2 This is a system structure diagram of a warehouse data dynamic control and management system based on big data analysis of the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution.

[0044] like Figure 1 As shown in the schematic diagram of the steps of a method for dynamic regulation and management of warehouse data based on big data analysis, the present application provides a method for dynamic regulation and management of warehouse data based on big data analysis, including the following steps:

[0045] Step S100: Collect cargo data and corresponding order data, perform data preprocessing, and generate a cargo information table. Obtain the warehouse layout and construct a directed graph topology model, with shelf and aisle intersections set as nodes and aisles as edges. Use the Dijkstra algorithm, combined with the cargo information table, to plan an initial shipping route with the shortest path time for each cargo based on the directed graph topology model.

[0046] Specifically, the warehouse layout is obtained, and the endpoints of all shelves, the intersections of aisles, and the connection points between aisles and shelves are marked as nodes. Each node is given a unique number, its position in the warehouse coordinate system is recorded, and the node type is marked; according to the direction of the aisle and the connection relationship between the shelves, adjacent nodes are connected to form edges. When there is a physical channel or a feasible cargo movement path between two nodes, an edge is established between the two nodes. The direction of each edge is determined according to the normal movement direction of the cargo; attribute information is added to each edge, including the length of the edge, maximum load capacity, travel time, and restriction conditions.

[0047] Furthermore, for each cargo in the cargo information table, the node in the directed graph topology model corresponding to the current location of the cargo is used as the starting point, and the node corresponding to the shipping area is used as the target end point; the current shortest distance from the starting point to the node and the current shortest path from the starting point to the node are set for each node in the directed graph topology model; from all nodes whose shortest paths have not been determined, the node with the shortest distance to the starting point is selected as the current node; all neighbor nodes of the current node are traversed, and the distance from the starting point through the current node to the neighbor node is calculated; when the distance is less than the shortest distance currently recorded by the neighbor node, the shortest distance of the neighbor node is updated to the calculated value, and the shortest path of the neighbor node is updated to the path passing through the current node; the current node is marked as a node with a determined shortest path, and no distance update calculation is performed on it subsequently; the current node is repeatedly selected, the neighbor node is updated, and the determined node is marked until the target end point node is marked as a node with a determined shortest path. At this time, the path from the starting point to the target end point obtained is the initial shipping path of the cargo.

[0048] In one specific example, the experiment was conducted in a medium-sized warehouse measuring 80 meters long and 60 meters wide. Ten rows of shelves were neatly arranged, each 50 meters long and 2 meters wide. The aisles between the shelves were 4 meters wide, and the shipping area was located on the right side of the warehouse. RFID readers, visual sensors, and other equipment were deployed throughout the warehouse to collect real-time data on goods and orders.

[0049] Ten items and their corresponding orders were randomly selected for research. Item G001, with specifications of 50cm long, 40cm wide, and 30cm high, was stored in the 5th slot on the 2nd shelf of R03. It was associated with order D001, with a delivery requirement of 2 hours. Item G002, with specifications of 60cm long, 50cm wide, and 40cm high, was located in the 10th slot on the 1st shelf of R06, corresponding to order D002, with a delivery requirement of 3 hours. Item G003, with specifications of 40cm long, 30cm wide, and 20cm high, was located in the 8th slot on the 3rd shelf of R02, with a delivery requirement of 1.5 hours for order D003. The remaining seven items also had their own specifications, storage locations, and delivery time requirements. After data cleaning and integration, a complete item information table was formed.

[0050] A detailed count revealed a total of 65 shelf endpoints, aisle intersections, and aisle-to-shelf connections within the warehouse. The upper-left corner of shelf R03 was labeled node N012, with its position in the warehouse coordinate system at 12 meters horizontally and 20 meters vertically. The node type was labeled shelf node. Similarly, each node was assigned a unique number, its coordinates recorded, and its type labeled.

[0051] Based on the aisle orientation and shelf connections, a total of 120 edges were established. A physical passage exists between node N012 and adjacent aisle node N013. Following the normal direction of goods moving from the shelves to the shipping area, an edge E035 is established from N012 to N013. Edge E035 is 4 meters long and can carry a maximum of two pieces of cargo simultaneously. Based on historical cargo movement speed data, the transit time is estimated to be approximately 0.5 minutes, with no special restrictions. Edge E067, connecting nodes N025 and N026, is 6 meters long and can carry a maximum of one piece. The transit time is 0.8 minutes, with regulations requiring slowing down for large cargo.

[0052] Taking cargo G001 as an example, its location corresponds to node N032, and its shipping area corresponds to node N065. When planning a route, the current shortest distance of node N032 is first set to 0, the remaining nodes are set to infinity, and the current shortest paths of all nodes are set to null. N032 is first selected as the current node, and its neighboring nodes N033 and N031 are traversed. The distance from N032 through each neighboring node is calculated. If the distance is less than the currently recorded shortest distance of the neighboring node, the shortest distance and shortest path of the neighboring node are updated. This process of selecting the current node, updating neighboring nodes, and marking confirmed nodes is repeated until node N065 is marked as the node with the confirmed shortest path. The final initial shipping route for cargo G001 is determined to be N032 → N033 → N037 → N045 → N052 → N065, with an estimated travel time of 3.2 minutes.

[0053] The same method was applied to the remaining nine shipments. Shipment G002 departed from node N047, passing through nodes N048, N053, and N059, ultimately reaching the shipping area node N065, with an estimated travel time of 4.1 minutes. Shipment G003's route began at N018, passed through several nodes, and ultimately reached N065, with an estimated travel time of 5.0 minutes. In this way, an initial shipping route with the shortest path time was planned for each shipment.

[0054] Step S200: Based on the initial shipping path, the traffic status of each shipping path is obtained and the congestion index of each shipping path is calculated. When the congestion index of a shipping path exceeds a threshold, a congested cargo set is obtained, and the area with the highest cargo density in the congested cargo set is searched and determined as the congestion source area. Target areas with cargo density below the set threshold are selected, and cargo in the congestion source area is transferred to the target area, and the initial shipping path is updated to the current shipping path.

[0055] Specifically, factors affecting route congestion include cargo speed, cargo intervals, equipment operating status, and route occupancy. Based on the initial shipping route, the actual cargo speed along the route is estimated based on the time difference between two adjacent monitoring points and the distance between the two points, and compared with the standard speed for that route. The time intervals between consecutive passages of the same type of cargo or cargo from the same order are counted. When the time interval is less than a set threshold, it indicates that cargo is concentrated during that period, potentially causing congestion. The operating status of transportation equipment along the route is analyzed to determine whether it is causing obstruction to the route, as well as the scope and extent of the obstruction. Furthermore, the space occupied by cargo and transportation equipment along the route is used to determine whether the route is saturated or overloaded.

[0056] Set a weight for each factor and quantify the score of each factor; multiply the score of each factor by the corresponding weight and sum them up to obtain the congestion index of each shipping route.

[0057] Furthermore, when the congestion index of a shipping route exceeds a threshold, all cargoes that are planned to be shipped along the congested route and have not yet started shipping or are in transit are screened based on the cargo information table and route planning records. The corresponding cargo data is then aggregated into a congested cargo set. The current location of each cargo in the congested cargo set is converted into spatial coordinates. The spatial coordinates of each cargo are used as a data point to form a data set for cluster analysis.

[0058] According to the actual situation of the warehouse, the parameters of the DBSCAN algorithm are set, the neighborhood radius and the minimum number of points are determined, and a data point that has not been visited is selected from the data set as the starting point. The number of data points within the neighborhood radius of this point is checked. When the number of data points in the neighborhood is greater than or equal to the minimum number of points, the point is identified as a core point, and all points in its neighborhood are grouped into a preliminary cluster with the core point as the center. For each core point in the cluster, the data points in its neighborhood that have not been visited are checked, and points that meet the conditions are added to the cluster, and the range of the cluster is continuously expanded until no new data points can be included. After traversing all data points, isolated data points that are not included in any cluster are marked as outliers. For each formed cluster, its cargo density is calculated. The density calculation is based on the ratio of the number of cargo in the cluster to the spatial range covered by the cluster. The higher the ratio, the denser the cargo in the area. The density of all clusters is compared, and the cargo area corresponding to the cluster with the highest density is selected and identified as the source area of ​​congestion.

[0059] Furthermore, based on the warehouse's actual storage capacity and historical operational data, a cargo density threshold is set to measure the congestion level of an area. All areas of the warehouse are traversed, and areas with cargo density below the set threshold are screened to form a target area set. During the screening process, areas where cargo handling operations are in progress, equipment failures, or other areas unsuitable for receiving cargo are excluded.

[0060] Based on the specifications, shipping time and order priority of the goods in the congestion root area, give priority to transferring goods with later shipping time, smaller volume and lighter weight to avoid affecting the shipment of urgent orders; plan a transfer path from the congestion root area to the target area for each batch of goods to be transferred. The transfer path planning must avoid the shipping path in use to ensure that there is no conflict with other cargo transportation during the transfer process; according to the planned path and sequence, use the selected handling equipment to transfer the goods in the congestion root area to the target area one by one; after the goods are transferred, update the location information of the relevant goods in the goods information table, modify its current location to the specific location of the target area, and simultaneously update the inventory data in the warehouse management system; update the initial shipping path to the existing shipping path.

[0061] In one specific embodiment, after completing the initial shipping route planning for 10 items, the system begins real-time monitoring of the route's traffic status. For example, for item G001, the route N032 → N033 → N037 → N045 → N052 → N065 is set at a standard speed of 8 meters per minute. It takes 0.7 minutes for item G001 to pass through adjacent monitoring points N032 to N033 (a distance of 4 meters). The actual speed is approximately 5.7 meters per minute, resulting in a score of 60 points (out of a maximum of 100) compared to the standard speed.

[0062] During the same time period, the intervals between the three other items of cargo belonging to order D001 and G001 passing through node N032 were all less than the set threshold (1 minute), indicating significant cargo concentration. This factor received a score of 30 points. At this time, a forklift malfunctioned and stalled at node N037, causing a backlog of cargo behind it. This factor received a score of 20 points for the Equipment Operation Status factor. Statistics show that the cargo and equipment occupied 90% of the total capacity of the route, indicating an overload. This factor received a score of 10 points for the Path Occupancy Status factor.

[0063] Weights were assigned to each factor: cargo speed (35%), cargo interval (25%), equipment operating status (20%), and path occupancy (20%). The sum of the scores for each factor multiplied by the corresponding weight yielded a congestion index of 33.5 for this path. Similarly, the delivery routes for the remaining cargo were evaluated. The delivery route for cargo G003, due to the concentrated transport of large cargo within a short period of time and a collision between two AGVs within the aisle, had a congestion index of 78, exceeding the set threshold of 60 points and was therefore classified as a congested path.

[0064] For the congested G003 shipping route, the system screens out five items that are planned to be shipped along this route and have not yet been transported or are in transit, forming a congested cargo set. The current locations of these items are converted into spatial coordinates. For example, item G003 is located at coordinates (10, 18, 2) and item G011 is located at coordinates (12, 19, 2). The DBSCAN algorithm parameters are set with a neighborhood radius of 3 meters and a minimum number of points of 2. The coordinate point corresponding to item G003 is selected from the dataset as the starting point. The coordinate point of item G011 is checked within its 3-meter neighborhood. If the number of data points reaches the minimum number of points, item G003 is identified as the core point, and the two constitute a preliminary cluster. The cluster is further expanded to include other items that meet the conditions in the neighborhood, ultimately forming a high-density cluster containing three items. The cargo density of each cluster is calculated. The cluster covers a spatial range of 15 cubic meters and contains 3 pieces of cargo, with a density of 0.2 pieces / cubic meter, which is higher than other clusters. The area where the cargo corresponding to this cluster is located is determined to be the source of congestion.

[0065] Based on historical warehouse operational data, a cargo density threshold of 0.15 pieces / cubic meter was set. All warehouse areas were traversed, and three areas with cargo densities below this threshold and no operational conflicts were selected as target areas. Target area A (coordinate ranges: 20-25 meters and 30-35 meters) had a cargo density of 0.12 pieces / cubic meter, ample space, and proximity to the shipping channel. Of the three items in the congestion source area, item G012 was shipped the latest and was smaller (30 × 20 × 15 cm), making it a priority for transfer. A transfer path was planned from the congestion source area to target area A: N018 → N019 → N024 → N020 → target area A. This path avoids active shipping routes. An idle AGV was used to transfer item G012 along the planned path to target area A. Upon transfer, the location of item G012 in the cargo information table was immediately updated with the specific coordinates of target area A, and the warehouse management system inventory data was also updated simultaneously. At the same time, the system re-plans the shipping route of cargo G012 to target area A→N020→N024→N030→N036→N044→N051→N065, updates the initial shipping route to the existing shipping route, and completes the entire congestion handling process.

[0066] Step S300: When a change in the shipment time of a goods order is detected, the impact of the order change on the existing shipment route is evaluated; for the affected goods, local adjustments are made based on their existing shipment routes; if the local adjustments cannot resolve the impact, the affected goods are transferred to other regions;

[0067] Specifically, in the goods information table, filter out other goods in the same order as the changed order goods, determine the current location, existing shipping route and shipping time of other goods in the same order, and evaluate the impact of the order change on the shipping plan of other goods in the same order; based on the existing shipping route of the changed order goods, find other goods that are being transported or planned to be transported on the route to form a set of goods associated with the same route; analyze the shipping time, priority and specifications of different goods in the set of goods associated with the same route to determine whether the time adjustment of the changed order goods will cause transportation conflicts for the goods on the same route; when there is process dependency in warehouse operations, check the goods that have upstream and downstream relationships with the changed order goods.

[0068] Step S400: During the regional transfer or local adjustment process, the cargo location is monitored and, in combination with the path planning model, it is determined whether the cargo movement trajectory intersects with other cargo paths. If an intersection occurs, the movement of cargo in the conflicting area is suspended, and a priority scheduling algorithm is used to reallocate the right of way to the conflicting area based on the urgency of the cargo's departure time and the cargo specifications.

[0069] Specifically, historical cargo transportation data, warehouse operation logs and equipment operation records are used to train a path planning model. The path planning model uses a deep learning algorithm. The model's learning objectives include minimizing the probability of path conflicts, shortening transportation time and balancing equipment loads, and optimizing path planning strategies through simulation scenarios. The model parameters are dynamically adjusted according to the real-time operation status of the warehouse. For cargo that is being transferred or partially adjusted within a region, its starting position, target position, expected departure time and cargo specifications are input into the path planning model to predict the movement trajectory of the cargo from the starting point to the end point, including the nodes passed, the expected arrival time at each node and the speed change. The predicted movement trajectory is compared with the existing shipping routes of other cargo to determine whether the cargo movement trajectory intersects with the routes of other cargo.

[0070] Furthermore, when an intersection occurs, the movement of goods in the conflicting area is suspended, and a suspension instruction is sent to all transportation equipment and operators involved in the area. The departure times of goods in the conflicting area are compared, and goods with earlier departure times are given higher priority. When there are goods with the same departure time, the order priorities are further compared. The difficulty of passage is assessed based on the specifications of the goods. In the case of the same priority, smaller and more flexible goods are given priority. A comprehensive priority score is calculated for each piece of goods in the conflicting area, combining the urgency of the departure time and the specifications of the goods, to form a priority list.

[0071] According to the priority list, the order of goods passing through the conflict area is determined, and the goods with the highest priority are given priority to obtain the right of passage; according to the passage order, the corresponding transportation equipment is dispatched and the route of the transportation equipment to the conflict area is planned.

[0072] In one specific embodiment, a path planning model was trained using a deep learning algorithm based on three months of warehouse operation data, including 100,000 cargo transport records, 5,000 hours of equipment operation logs, and 2,000 warehouse operation scheduling records. The model's learning objectives were set as: minimizing the probability of path conflicts (weighted 40%), shortening transport times (weighted 35%), and balancing equipment loads (weighted 25%). The path planning strategy was optimized by simulating 10,000 different warehouse operation scenarios, such as peak order periods and equipment failure periods. During a routine operation, the warehouse's real-time operation status indicated a surge in order volume and three AGVs were under maintenance. Based on this information, the system dynamically adjusted the path planning model parameters, increasing the weight of path conflict avoidance parameters to 50% and reducing the weight of equipment load balancing parameters to 15%, prioritizing the smooth transportation of goods.

[0073] When transferring cargo G012 from the congestion source area to target area A, the path planning model was fed with its starting location (coordinates 10, 18, 2), target location (target area A, coordinate ranges 20-25 meters and 30-35 meters), estimated departure time (10:00:00), and cargo dimensions (30 × 20 × 15 cm). The model predicted the following trajectory for cargo G012: N018 → N019 → N024 → N020 → target area A, with an estimated arrival time of 10:02:30 at node N020. The speed would decrease from 6 meters per minute to 4 meters per minute on the road section from N019 to N024 to avoid other vehicles.

[0074] Meanwhile, cargo G009 is being transported along the initial shipping route N028→N027→N022→N021→N026→N031→N037→N045→N052→N065, and is expected to arrive at node N024 at 10:02:25. Comparing G012's predicted trajectory with G009's existing shipping route reveals that the two will intersect at node N024, resulting in a path conflict.

[0075] The system immediately issued a pause command to the AGVs and operators involved in the conflicting area (near node N024), halting the movement of items G012 and G009. Comparing the two shipments' departure times, G009 arrived at 10:30 AM, while G012 arrived at 11:00 AM. G009's departure time was earlier, so it was initially assigned a higher priority.

[0076] Further checking order priorities revealed that order D009, to which G009 belongs, was a standard order. While G012's order had no special markings, it retained priority due to its earlier shipment time. Furthermore, considering the cargo specifications, G009 measures 45×35×25cm, while G012 measures 30×20×15cm. Given the same priority, G012 was more easily passed, but due to G009's earlier shipment time, G009 ultimately received a higher overall priority score.

[0077] Based on the comprehensive priority ranking, the order for cargo to pass through the conflicting area is: G009 is released first, followed by G012. The system dispatches the idle AGV closest to G009 and plans a quick route from its current location to node N024. After G009 successfully passes, the system dispatches the device to move G012 along the original planned route to target area A, successfully resolving the route conflict.

[0078] like Figure 2 As shown in the system structure diagram of a warehouse data dynamic control and management system based on big data analysis, this application provides a warehouse data dynamic control and management system based on big data analysis, including:

[0079] The initial shipping route planning module includes a cargo information table generation unit, a directed graph topology model construction unit, and an initial shipping route planning unit. The cargo information table generation unit collects cargo data and corresponding order data, performs data preprocessing, and generates a cargo information table. The directed graph topology model construction unit obtains the warehouse layout and constructs a directed graph topology model, setting the intersection points of shelves and aisles as nodes and the aisles as edges. The initial shipping route planning unit uses the Dijkstra algorithm, combined with the cargo information table, and based on the directed graph topology model to plan an initial shipping route with the shortest path time for each cargo.

[0080] Shipping route update module: includes: a congestion index calculation unit, a congestion root area determination unit, and a shipping route update unit; wherein the congestion index calculation unit obtains the traffic status of each shipping route based on the initial shipping route and calculates the congestion index of each shipping route; when the congestion index of a shipping route exceeds a threshold, the congestion root area determination unit obtains a congested cargo set, searches for the area with the highest cargo density in the congested cargo set, and determines it as the congestion root area; the shipping route update unit selects a target area with a cargo density lower than the set threshold, transfers the cargo in the congestion root area to the target area, and updates the initial shipping route to the existing shipping route;

[0081] The affected goods decision module includes an order change impact assessment unit, a local adjustment unit, and a regional transfer unit. The order change impact assessment unit assesses the impact of order changes on existing shipping routes when a change in the shipment time of a goods order is detected. The local adjustment unit makes local adjustments to the existing shipping routes of the affected goods. The regional transfer unit transfers the affected goods to different regions when local adjustments cannot resolve the impact.

[0082] Right-of-way allocation module: includes: an intersection judgment unit and a right-of-way allocation unit; among them, the intersection judgment unit monitors the location of the goods during the area transfer or local adjustment process, and combines the path planning model to determine whether the cargo movement trajectory intersects with other cargo paths; when an intersection occurs, the right-of-way allocation unit suspends the movement of goods in the conflicting area and adopts a priority scheduling algorithm to reallocate the right of way in the conflicting area based on the urgency of the cargo outbound time and the cargo specifications.

[0083] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A method for dynamic control and management of warehouse data based on big data analysis, characterized in that: The following steps are involved: Collect cargo data and corresponding order data, perform data preprocessing, and form a cargo information table; Obtain the warehouse layout and construct a directed graph topology model, with shelf and aisle intersections as nodes and aisles as edges. Using the Dijkstra algorithm and the cargo information table, the directed graph topology model is used to plan an initial shipping route with the shortest path time for each cargo. The Dijkstra algorithm is used in combination with the cargo information table to plan an initial shipping route with the shortest path time for each cargo based on a directed graph topology model, including: For each cargo in the cargo information table, take the node in the directed graph topology model corresponding to the current location of the cargo as the starting point, and the node corresponding to the shipping area as the target destination; set the current shortest distance from the starting point to the node and the current shortest path from the starting point to the node for each node in the directed graph topology model; from all nodes whose shortest paths have not been determined, select the node with the shortest distance to the starting point as the current node; traverse all neighboring nodes of the current node and calculate the distance from the starting point through the current node to the neighboring node; when the distance is less than the shortest distance currently recorded by the neighboring node, update the shortest distance of the neighboring node to the calculated value, and update the shortest path of the neighboring node to the path passing through the current node; mark the current node as a node with a determined shortest path, and no further distance update calculation will be performed on it subsequently; repeat selecting the current node, updating the neighboring nodes, and marking the determined nodes until the target destination node is marked as a node with a determined shortest path. At this time, the path from the starting point to the target destination obtained is the initial shipping path of the cargo; Based on the initial shipping route, the traffic status of each shipping route is obtained and the congestion index of each shipping route is calculated. When the congestion index of a shipping route exceeds a threshold, a congested cargo set is obtained and the area with the highest cargo density in the congested cargo set is searched to determine the congestion root area. Target areas with cargo density below the set threshold are selected, and cargo in the congestion root area is transferred to the target area, updating the initial shipping route to the existing shipping route. When a change in the shipment time of a goods order is detected, the impact of the order change on the existing shipment route is evaluated; for the affected goods, local adjustments are made based on their existing shipment routes; if local adjustments cannot resolve the impact, the affected goods are transferred to other regions; During regional transfer or local adjustment, the cargo location is monitored and, combined with the path planning model, it is determined whether the cargo movement trajectory intersects with other cargo paths. If an intersection occurs, the movement of cargo in the conflicting area is suspended, and a priority scheduling algorithm is used to reallocate the right of way to the conflicting area based on the urgency of the cargo's departure time and the cargo specifications.

2. A method for dynamic control and management of warehouse data based on big data analysis according to claim 1, characterized in that: The method of obtaining the warehouse layout and constructing a directed graph topology model, setting the intersection points of shelves and aisles as nodes and the aisles as edges, includes: Obtain the warehouse layout, mark the endpoints of all shelves, the intersections of aisles, and the connection points between aisles and shelves as nodes, assign a unique number to each node, record its position in the warehouse coordinate system, and mark the node type; connect adjacent nodes to form edges based on the direction of the aisles and the connection relationship between shelves; when there is a physical channel or a feasible cargo movement path between two nodes, establish an edge between the two nodes, and the direction of each edge is determined by the normal movement direction of the cargo; add attribute information to each edge, including the length of the edge, maximum load capacity, travel time, and restrictions.

3. The method for dynamic control and management of warehouse data based on big data analysis according to claim 1 is characterized in that: The method of obtaining the traffic status of each delivery path based on the initial delivery path and calculating the congestion index of each delivery path includes: Factors influencing route congestion include cargo speed, cargo intervals, equipment operating status, and route occupancy. Based on the initial shipping route, the actual cargo speed along the route is estimated based on the time difference between two adjacent monitoring points and the distance between the two points. This is then compared with the standard speed for that route. The time intervals between consecutive passages of the same type of cargo or cargo from the same order are counted. When the time interval is less than a set threshold, it indicates that cargo is concentrated during that period, potentially causing congestion. The operating status of transport equipment along the route is analyzed to determine whether it is causing obstruction, as well as the scope and extent of the obstruction. Furthermore, the space occupied by cargo and transport equipment along the route is used to determine whether the route is saturated or overloaded. Set a weight for each factor and quantify the score of each factor; multiply the score of each factor by the corresponding weight and sum them up to obtain the congestion index of each shipping route.

4. The method for dynamic control and management of warehouse data based on big data analysis according to claim 1 is characterized in that: When the congestion index of a shipping route exceeds a threshold, a congested cargo set is obtained, and an area with the highest cargo density in the congested cargo set is searched to determine the congestion root area, including: When the congestion index of a shipping route exceeds a threshold, all cargoes that are planned to be shipped along the congested route and have not yet started shipping or are in transit are screened based on the cargo information table and route planning records. The corresponding cargo data is then aggregated into a congested cargo set. The current location of each cargo in the congested cargo set is converted into spatial coordinates. The spatial coordinates of each cargo are used as a data point to form a data set for cluster analysis. According to the actual situation of the warehouse, the parameters of the DBSCAN algorithm are set, the neighborhood radius and the minimum number of points are determined, and a data point that has not been visited is selected from the data set as the starting point. The number of data points within the neighborhood radius of this point is checked. When the number of data points in the neighborhood is greater than or equal to the minimum number of points, the point is identified as a core point, and all points in its neighborhood are grouped into a preliminary cluster with the core point as the center. For each core point in the cluster, the data points in its neighborhood that have not been visited are checked, and points that meet the conditions are added to the cluster, and the range of the cluster is continuously expanded until no new data points can be included. After traversing all data points, isolated data points that are not included in any cluster are marked as outliers. For each formed cluster, its cargo density is calculated. The density calculation is based on the ratio of the number of cargo in the cluster to the spatial range covered by the cluster. The higher the ratio, the denser the cargo in the area. The density of all clusters is compared, and the cargo area corresponding to the cluster with the highest density is selected and identified as the source area of ​​congestion.

5. The method for dynamic control and management of warehouse data based on big data analysis according to claim 1 is characterized in that: The method of screening target areas where the cargo density is lower than a set threshold, transferring cargo from the congestion source area to the target area, and updating the initial shipping route to the existing shipping route includes: Based on the warehouse's actual storage capacity and historical operational data, a cargo density threshold is set to measure the congestion level of an area. All areas of the warehouse are traversed, and areas with cargo density below the set threshold are screened to form a target area set. During the screening process, areas where cargo handling operations are in progress, equipment failures, or other areas unsuitable for receiving cargo are excluded. Based on the specifications, shipping time and order priority of the goods in the congestion root area, give priority to transferring goods with later shipping time, smaller volume and lighter weight to avoid affecting the shipment of urgent orders; plan a transfer path from the congestion root area to the target area for each batch of goods to be transferred. The transfer path planning must avoid the shipping path in use to ensure that there is no conflict with other cargo transportation during the transfer process; according to the planned path and sequence, use the selected handling equipment to transfer the goods in the congestion root area to the target area one by one; after the goods are transferred, update the location information of the relevant goods in the goods information table, modify its current location to the specific location of the target area, and simultaneously update the inventory data in the warehouse management system; update the initial shipping path to the existing shipping path.

6. The method for dynamic control and management of warehouse data based on big data analysis according to claim 1 is characterized in that: When a change in the shipment time of a goods order is detected, evaluating the impact of the order change on the existing shipment path includes: In the goods information table, filter out other goods in the same order as the changed order goods, determine the current location, existing shipping route and shipping time of other goods in the same order, and evaluate the impact of the order change on the shipping plan of other goods in the same order; based on the existing shipping route of the changed order goods, find other goods that are being transported or planned to be transported on the route to form a set of goods associated with the same route; analyze the shipping time, priority and specifications of different goods in the set of goods associated with the same route to determine whether the time adjustment of the changed order goods will cause transportation conflicts for goods on the same route; when there are process dependencies in warehouse operations, check the goods that have upstream and downstream relationships with the changed order goods.

7. The method for dynamic control and management of warehouse data based on big data analysis according to claim 1 is characterized in that: During the regional transfer or local adjustment process, monitoring the cargo location and combining the path planning model to determine whether the cargo movement trajectory intersects with other cargo paths include: The path planning model is trained using historical cargo transportation data, warehouse operation logs, and equipment operation records. The path planning model uses a deep learning algorithm. The model's learning objectives include minimizing the probability of path conflicts, shortening transportation time, and balancing equipment loads. The path planning strategy is optimized through simulation scenarios. The model parameters are dynamically adjusted according to the real-time operation status of the warehouse. For cargo that is being transferred or partially adjusted, its starting location, target location, estimated departure time, and cargo specifications are input into the path planning model to predict the movement trajectory of the cargo from the starting point to the end point, including the nodes passed, the estimated arrival time at each node, and the speed change. The predicted movement trajectory is compared with the existing shipping routes of other cargo to determine whether the cargo movement trajectory intersects with other cargo routes.

8. The method for dynamic control and management of warehouse data based on big data analysis according to claim 1 is characterized in that: When an intersection occurs, the movement of goods in the conflicting area is suspended, and a priority scheduling algorithm is used to reallocate the right of way in the conflicting area based on the urgency of the goods' departure time and the specifications of the goods, including: When an intersection occurs, cargo movement in the conflicting area is suspended, and a pause instruction is sent to all transport equipment and operators involved in operations in the area. The departure times of cargo in the conflicting area are compared, and cargo with earlier departure times is given higher priority. When there are cargo with the same departure time, the order priorities are further compared. The difficulty of passage is assessed based on cargo specifications. In the case of equal priority, smaller and more mobile cargo is given priority. A comprehensive priority score is calculated for each cargo in the conflicting area, combining the urgency of the departure time and cargo specifications, to form a prioritized list. According to the priority list, the order of goods passing through the conflict area is determined, and the goods with the highest priority are given priority to obtain the right of passage; according to the passage order, the corresponding transportation equipment is dispatched and the route of the transportation equipment to the conflict area is planned.

9. A warehouse data dynamic control and management system based on big data analysis, using the warehouse data dynamic control and management method based on big data analysis according to any one of claims 1 to 8, comprising: The initial shipping route planning module includes a cargo information table generation unit, a directed graph topology model construction unit, and an initial shipping route planning unit. The cargo information table generation unit collects cargo data and corresponding order data, performs data preprocessing, and generates a cargo information table. The directed graph topology model construction unit obtains the warehouse layout and constructs a directed graph topology model, setting the intersection points of shelves and aisles as nodes and the aisles as edges. The initial shipping route planning unit uses the Dijkstra algorithm, combined with the cargo information table, and based on the directed graph topology model to plan an initial shipping route with the shortest path time for each cargo. Shipping route update module: includes: a congestion index calculation unit, a congestion root area determination unit, and a shipping route update unit; wherein the congestion index calculation unit obtains the traffic status of each shipping route based on the initial shipping route and calculates the congestion index of each shipping route; when the congestion index of a shipping route exceeds a threshold, the congestion root area determination unit obtains a congested cargo set, searches for the area with the highest cargo density in the congested cargo set, and determines it as the congestion root area; the shipping route update unit selects a target area with a cargo density lower than the set threshold, transfers the cargo in the congestion root area to the target area, and updates the initial shipping route to the existing shipping route; The affected goods decision module includes an order change impact assessment unit, a local adjustment unit, and a regional transfer unit. The order change impact assessment unit assesses the impact of order changes on existing shipping routes when a change in the shipment time of a goods order is detected. The local adjustment unit makes local adjustments to the existing shipping routes of the affected goods. The regional transfer unit transfers the affected goods to different regions when local adjustments cannot resolve the impact. Right-of-way allocation module: includes: an intersection judgment unit and a right-of-way allocation unit; among them, the intersection judgment unit monitors the location of the goods during the area transfer or local adjustment process, and combines the path planning model to determine whether the cargo movement trajectory intersects with other cargo paths; when an intersection occurs, the right-of-way allocation unit suspends the movement of goods in the conflicting area and adopts a priority scheduling algorithm to reallocate the right of way in the conflicting area based on the urgency of the cargo outbound time and the cargo specifications.

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