A cloud warehouse steel coil data processing method, system, device and medium

Abstract

The application provides a cloud warehouse steel coil data processing method, system, device and medium, and belongs to the technical field of warehouse management in the steel industry. A double-dimension tree structure is constructed to realize steel coil management. A production dimension tree and a storage dimension tree are constructed respectively based on the steel coil production process and the warehouse hierarchical relationship. A cross-dimension association is established for the steel coil to be allocated with a unique number to support real-time query, and the query weight is dynamically adjusted according to the business scene. The position of the target steel coil in the double tree is located, four-level state marks are executed on the target steel coil and related nodes, visual properties are set for the marks, and finally dynamic visual display is realized to support interaction such as dragging and zooming and historical backtracking. The production and storage information of the steel coil is completely recorded to support quality tracing and inventory management. The cross-dimension association realizes seamless connection of the production and storage data to improve the query efficiency. The four-level state marks and the visual display intuitively present the state of the steel coil to enhance the interactive experience.

Description

Technical Field

[0001] This invention belongs to the field of warehouse management technology in the steel industry, specifically relating to a cloud warehouse steel coil data processing method, system, equipment, and medium. Background Technology

[0002] Currently, the steel industry is undergoing a critical transformation from traditional manufacturing to intelligent and green manufacturing. The full lifecycle management of steel rails and steel products has become a key aspect of the industry's digital transformation, encompassing multiple stages including production, laying, operation, maintenance, and disposal. As an important form of steel product, the digitalization and intelligentization of the full lifecycle management of steel coils has become an industry consensus.

[0003] In related technologies, the production system and the warehousing system are deployed independently and their data is not interconnected. As a result, the production system only records production information such as the processing batch and quality inspection results of steel coils, while the warehousing system only records location information such as the location and inventory quantity. When querying a steel coil, it is necessary to first log in to the production system to check the production status and then log in to the warehousing system to check the storage location. The operation is cumbersome and prone to information matching errors due to inconsistent steel coil numbers.

[0004] Location tracking is based on the steel coil processing flow recorded chronologically in an Excel spreadsheet at the production end, which cannot directly link to all raw material coils corresponding to a specific semi-finished coil. At the warehousing end, locations are recorded using a warehouse-location lookup table; however, location tracking requires filtering data row by row, and production and location information cannot be obtained synchronously. Summary of the Invention

[0005] This invention provides a cloud-based method for processing steel coil data, which achieves deep integration and unified management of steel coil data throughout its entire lifecycle. The method significantly improves location and query efficiency through a two-dimensional relational query and a dynamic weighting mechanism.

[0006] The methods include: S101: Construct a production dimension tree with the steel coil production process as the framework, where nodes store information on raw material coils, semi-finished coils, and finished coils, and are associated with production batch relationships through parent node IDs; S102: Construct a storage dimension tree according to the hierarchical relationship of warehouse, shelf, and storage location, where nodes store the physical storage location information of steel coils and associate the hierarchical location relationship through the parent node ID; S103: Assign a unique number to each steel coil, and establish a cross-dimensional association between the production dimension tree described in S101 and the warehousing dimension tree described in S102 based on the unique number to form a two-dimensional tree structure, so as to realize real-time association query between the production dimension tree and the warehousing dimension tree. S104: Adjust the priority weight of the production dimension tree and the warehouse dimension tree during querying according to the business scenario; S105: Based on a two-dimensional tree structure, locate the target steel coil and determine its specific position in the production dimension tree and the storage dimension tree; S106: Based on the target steel coil location obtained in S105, perform a fourth-level status marking on the target steel coil and its related nodes; S107: Set the corresponding visual attributes for the four levels of states marked by S106; S108: Based on the set visualization attributes, dynamically visualize the marked two-dimensional tree structure.

[0007] Preferably, step S101 specifically includes the following methods: The entire process of steel coil production was analyzed to determine the node levels and node types of the production dimension tree. The node types correspond to the various production stages of raw material coil processing, semi-finished coil processing, and finished coil forming. Define the information storage structure for each node; Set the association rules for the parent node ID, stipulating that the child node ID contains the feature segment of the parent node ID, and that the steel coil production process corresponding to the child node is later than the production process corresponding to the parent node. Collect steel coil information from each production stage, and enter the corresponding nodes according to the defined storage structure. The raw material coil node is used as the initial parent node, and its parent node ID field is set to a preset null value. When entering the semi-finished coil node, it is associated with the parent node ID of the corresponding raw material coil node. When entering the finished coil node, it is associated with the parent node ID of the corresponding semi-finished coil node. Perform relationship verification on nodes with entered information, check whether the parent node ID of each child node has a corresponding parent node, and whether the logical order of the production process corresponding to the parent node and child node conforms to the preset rules.

[0008] Preferably, step S102 specifically includes the following: defining the node structure of the production dimension tree, specifically including the unique identifier of the steel coil, the steel coil type, the production batch number, the parent node ID, the production timestamp, and the process stage status field; Basic data of steel coils is obtained through the production management system interface and stored according to the type of steel coil. Raw material coils do not have parent nodes, while semi-finished coils and finished coils have corresponding parent node identifiers. Establish the association between semi-finished product rolls and raw material rolls, and between finished product rolls and semi-finished product rolls based on the production batch number, and form a multi-level tree structure by setting the parent node ID; Store complete information about the nodes, index the parent node ID field, and update the documents using batch write mode; Verify the validity of the parent node ID when inserting a node, perform tree structure integrity checks periodically, and record the verification results.

[0009] Preferably, step S103 specifically includes the following methods: Design a unique coding structure for steel coils, specifically including a production dimension identifier segment, a timestamp segment, a storage dimension identifier segment, and a serial number segment. Each segment is connected by a separator and the total length is fixed. When a steel coil enters the warehousing stage after completing the production stage, the steel coil data management system generates a unique number for the steel coil, and fills the number field with the production stage code and the warehouse area code; A unique number field for steel coils is added to the node information of the production dimension tree, and the unique number is bound to the production node through the production batch ID to ensure that each production node corresponds to a unique number. Add a unique number field for steel coils to the node information of the storage dimension tree, and bind the unique number to the storage node through the storage location occupancy record ID to ensure that each storage node corresponds to a unique number. The unique serial numbers of steel coils in the production dimension tree and the storage dimension tree are checked for consistency. The matching of serial numbers is checked by bidirectional traversal, and nodes with no matching are marked.

[0010] Preferably, step S104 specifically includes the following methods: Classify the business scenario types for steel coil queries, and clarify production traceability scenarios, inventory optimization scenarios, and general query scenarios; Define the adjustment dimensions for query priority weights, and determine the weights to be set for the two dimensions, the production dimension tree in step S101 and the warehousing dimension tree in step S102, with the sum of the weight percentages of the two dimensions fixed at 100%. Configure corresponding weight values ​​for different business scenarios: in the production traceability scenario, set the weight of the production dimension tree to 70% and the weight of the warehousing dimension tree to 30%; in the inventory optimization scenario, set the weight of the production dimension tree to 30% and the weight of the warehousing dimension tree to 70%; in the general query scenario, set the weight of both dimensions to 50%. Configure the triggering method for weight adjustments, including manual triggering and automatic triggering; The adjusted weight values ​​are validated to check whether the weights of the production dimension tree and the warehouse dimension tree are both within the range of 0-1, and whether their sum is equal to 1. If not, the weights are not allowed to take effect and an adjustment error is indicated.

[0011] Preferably, step S105 specifically includes the following methods: Obtain the query conditions for the target steel coil, including the steel coil's unique number, production batch ID, or storage location number, which can be obtained through the query input interface or transmitted and received by the system. Based on the query conditions, locate the corresponding node in the production dimension tree, and record the node information by matching the query conditions with the node's unique coil number or production batch ID field. Extract the unique number of the steel coil from the production dimension tree node, locate the corresponding node in the storage dimension tree, and record the node location information by matching the unique number field of the steel coil. Verify the association between production dimension tree nodes and warehousing dimension tree nodes by comparing whether their unique steel coil numbers are consistent to determine the validity of the association or whether the marking is abnormal. Based on the verification results, integrate production process information and physical location information to generate a location result sheet, or generate and display an anomaly report.

[0012] Preferably, step S106 specifically includes the following methods: Define node classification identifiers that include four types: current node, upstream node, peer node, and downstream node. These identifiers are stored in the node attributes as enumeration values. Each node also has a status type field to distinguish its level. By tracing all ancestor nodes of the target steel coil upwards from the parent node ID chain of the production dimension tree, a production path is formed. Nodes on the tracing path are marked as upstream nodes, and the set of sibling nodes of each upstream node is recorded. For the target steel coil node, find all sibling nodes with the same ID as its parent node in the production dimension tree, mark the sibling nodes as sibling nodes, and include the direct child nodes of the sibling nodes as extended sibling scope. Starting from the target steel coil node, traverse down through the child node ID chain of the production dimension tree to form a subtree structure, and mark all traversed nodes as downstream nodes, including direct child nodes and nested child nodes at all levels. Create a mapping table containing node ID, state type, and set of associated node IDs. The current node maps to its own ID, the upstream node maps to the set of ancestor node IDs, the sibling node maps to the set of sibling node IDs, and the downstream node maps to the set of all descendant node IDs. Store the mapping table data, create an index on the node ID field, set the index type to a single-field index and the direction to ascending order, and configure batch write mode to optimize storage.

[0013] This application also provides a cloud warehouse steel coil data processing system, the system comprising: The production dimension tree construction module is used to construct a production dimension tree with the steel coil production process as the framework. The nodes store information about raw material coils, semi-finished coils, and finished coils, and are associated with production batch relationships through the parent node ID. The warehouse dimension tree construction module is used to construct a warehouse dimension tree according to the hierarchical relationship of warehouse, shelf, and storage location. The nodes store the physical storage location information of steel coils and associate the hierarchical location relationship with the parent node ID. The cross-dimensional association module is used to assign a unique number to each steel coil and establish a cross-dimensional association between the production dimension tree and the storage dimension tree based on the unique number, forming a two-dimensional tree structure to realize real-time association query between the production dimension tree and the storage dimension tree. The weight adjustment module is used to adjust the priority weight of the production dimension tree and the warehouse dimension tree during queries based on the business scenario. The dual-dimensional positioning module, based on a dual-dimensional tree structure, locates the target steel coil and determines its specific position in the production dimension tree and the storage dimension tree. The status marking module is used to perform four-level status marking on the target steel coil and its related nodes based on the obtained target steel coil positioning. The visualization mapping module is used to set corresponding visualization attributes for the four levels of marked states; The visualization module dynamically displays the marked two-dimensional tree structure based on the set visualization attributes.

[0014] According to another embodiment of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the cloud warehouse steel coil data processing method.

[0015] According to another embodiment of this application, a storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the cloud warehouse steel coil data processing method.

[0016] As can be seen from the above technical solutions, the present invention has the following advantages: The cloud-based steel coil data processing method provided by this invention constructs a production dimension tree, clearly linking the derivative relationships of steel coils from raw material coils, semi-finished coils to finished coils through parent node IDs, enabling rapid tracing of the upstream raw material source for any finished coil. The storage dimension tree, combined with the three-dimensional coordinates of the storage location and load-bearing data, allows for the physical location of the steel coil. A unique steel coil number establishes the association between the production and storage trees, and combined with indexing and caching, improves cross-dimensional query response time. Dynamic weight adjustment further optimizes resource allocation; in production traceability scenarios, production data is loaded first, and in inventory optimization scenarios, storage data is loaded first, improving query efficiency. Four-level status markers clearly distinguish the associated nodes of the target steel coil, enabling rapid identification of all finished coils to be processed from the target semi-finished coil. Visual attribute settings and drag / zoom / filter interactions facilitate system operation, allowing for visual identification of node roles. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 Flowchart of cloud warehouse steel coil data processing method; Figure 2 This is a schematic diagram of the cloud warehouse steel coil data processing system; Figure 3 This is a schematic diagram of an electronic device. Detailed Implementation

[0019] The cloud warehouse steel coil data processing method provided by this invention achieves intelligent management of the entire lifecycle of steel coils by constructing a two-dimensional tree structure, optimizing the memory-based depth-first search algorithm, and using dynamic visualization technology. This invention constructs a tree structure integrating production and warehousing dimensions, utilizes MongoDB aggregation pipelines to establish cross-dimensional composite indexes, and dynamically adjusts query priorities through adaptive weight factors. Based on the positioning results, it performs four-level status marking and maps it to visual attributes. Combining D3.js and WebGL technologies, it achieves dynamic topology display and minute-level historical backtracking, improving the intelligence level and operational efficiency of cloud warehouse management.

[0020] The following will describe in detail the cloud warehouse steel coil data processing method involved in this application. Specific details such as particular system structures and technologies are presented for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0021] It should be understood that, when used in this application specification, terms include indicating the presence of a described feature, integral, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and or collections thereof. The terms include, encompass, have, and their variations are intended to be inclusive, unless otherwise specifically emphasized.

[0022] The statements describing one or more embodiments in this application are intended to include specific features, structures, or characteristics described in that embodiment in one or more embodiments of this application. Therefore, the statements appearing in one embodiment, some embodiments, some other embodiments, and some still other embodiments in this application do not necessarily refer to the same embodiment, but are intended for one or more, but not all, embodiments, unless otherwise specifically emphasized.

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figure 1 The diagram shows a flowchart of a cloud warehouse steel coil data processing method in a specific embodiment. The method includes: S101: Construct a production dimension tree with the steel coil production process as the framework. The nodes store information about raw material coils, semi-finished coils, and finished coils, and are associated with production batch relationships through the parent node ID.

[0025] In some embodiments, the production dimension tree takes the process flow sequence of steel coil raw materials, semi-finished products, and finished products as its core framework, and the production information stored in the nodes includes specific fields: steel coil type identifier, production batch ID, processing equipment number, quality inspection result, production completion time, steel coil material, and specifications.

[0026] This embodiment involves a tree hierarchy structure divided into three levels: the root node represents the overall steel coil production process, the first-level child nodes represent the raw material processing stage, the semi-finished product rolling stage, and the finished product finishing stage, and each first-level child node corresponds to a specific steel coil node.

[0027] In this embodiment, the parent node ID adopts the node type-number format. For example, if the raw material coil node ID is P-RM-001, the parent node ID of the semi-finished coil node processed from it is directly set to P-RM-001, forming a relationship chain of raw material coils, semi-finished coils, and finished coils. The tree structure clearly distinguishes steel coils at different production stages, making it easier for the production department to count the capacity of each stage.

[0028] S102: Construct a storage dimension tree based on the hierarchical relationship of warehouse, shelf, and storage location. The nodes store the physical storage location information of steel coils and associate the hierarchical location relationship with the parent node ID.

[0029] In some embodiments, the warehousing dimension tree is constructed according to the physical space hierarchy of warehouses, shelves, and storage locations. Each level node stores specific location and attribute information: the warehouse node fields include warehouse number, factory area, floor area, number of shelf groups that can be accommodated, and warehouse type.

[0030] This embodiment involves shelf node fields including shelf number, row and column position, number of shelf layers, number of storage locations per layer, and shelf material. Storage location node fields include storage location number, three-dimensional coordinates, and maximum load capacity.

[0031] In this embodiment, the parent node ID rule is that the parent node ID of a shelf node is the corresponding warehouse number, and the parent node ID of a storage location node is the corresponding shelf number. The warehouse, as the top-level node, contains multiple shelf child nodes, and each shelf child node contains multiple storage location child nodes. The parent node ID clarifies the hierarchical affiliation of each node, making the tree structure match the actual warehouse space layout, transforming the three-dimensional warehouse space into structured two-dimensional tree data, and realizing the digital mapping of physical locations.

[0032] S103: Assign a unique number to each steel coil, and establish a cross-dimensional association between the production dimension tree described in S101 and the warehousing dimension tree described in S102 based on the unique number, forming a two-dimensional tree structure to realize real-time association query between the production dimension tree and the warehousing dimension tree.

[0033] In some embodiments, each steel coil is assigned a globally unique number in the format COIL-YYYYMMDD-6-digit serial number. This number is automatically generated by the production management system when the steel coil enters the production process and is unique for life.

[0034] A new BCBJW_ID field is added to both the production and warehouse dimension tree nodes to store this unique identifier, enabling a single identifier to be bound to nodes in both trees. The specific technical implementation of cross-dimensional association is based on a MongoDB database: a single-field index is created for the BCBJW_ID field in each of the two collections. When executing a join query through the aggregation pipeline (e.g., querying for steel coils), the corresponding production information is first matched from the collection, and then a lookup is used to join the warehouse information with the same BCBJW_ID in the warehouse_tree collection, ultimately returning integrated production and warehouse data.

[0035] The lifetime binding of the unique number in this embodiment ensures the consistency of information throughout the entire life cycle of the steel coil. Even if the steel coil is transferred between warehouses, the BCBJW_ID remains unchanged, and historical production and storage records can still be traced.

[0036] S104: Adjust the priority weight of the production dimension tree and the warehouse dimension tree during querying based on the business scenario.

[0037] In some embodiments, three core business scenarios and corresponding weighting rules are defined. Specifically, in the production traceability scenario, the production dimension has a weight of 0.7 and the warehousing dimension has a weight of 0.3. When querying, 70% of the computing resources are allocated first to load the production dimension tree data, while the warehousing data is loaded later.

[0038] The inventory optimization scenario involved in this embodiment is that the production dimension has a weight of 0.3 and the warehousing dimension has a weight of 0.7. The warehousing dimension tree data is loaded first, and the production data is loaded on demand.

[0039] The general query scenario involved in this embodiment is as follows: both dimensions have a weight of 0.5, data is loaded synchronously, and there is no priority difference; weight adjustment supports two triggering methods: manual triggering and automatic triggering. The weight activation mechanism is query resource allocation, that is, the higher-weighted dimension occupies CPU query resources and memory first. For example, in the production traceability scenario, the loading of the production dimension tree data is started only after the production dimension tree data is loaded.

[0040] In some specific embodiments, step S104 specifically includes the following: Step S1041: Divide the business scenario types of steel coil queries, and clarify the production traceability scenario, inventory optimization scenario and general query scenario. The production traceability scenario is used to obtain steel coil production process information, the inventory optimization scenario is used to obtain steel coil physical storage location information, and the general query scenario has no clear bias.

[0041] In some embodiments, the criteria for determining whether a production traceability scenario is valid is that the query request contains production-related keywords such as the quality inspection results of the processing equipment for the production batch. The criteria for determining whether an inventory optimization scenario is valid is that the query request contains warehousing-related keywords such as the inventory quantity of warehouse shelf locations. The criteria for determining whether a general query scenario is valid is that the query request only contains the unique number of the steel coil and has no additional attribute filtering conditions.

[0042] Step S1042: Define the adjustment dimension of query priority weight, determine the weight to be set for the two dimensions of production dimension tree in step S101 and warehouse dimension tree in step S102, and the sum of the weight percentages of the two dimensions is fixed at 100%.

[0043] In some embodiments, by limiting the relationship between adjustment dimensions and weights, a two-dimensional weight complementary model is constructed to ensure that weight adjustments always revolve around the core dual-tree of cross-dimensional association, avoiding adjustment chaos caused by too many dimensions.

[0044] Step S1043: Configure corresponding weight values ​​for different business scenarios. In the production traceability scenario, the weight of the production dimension tree is set to 70% and the weight of the warehousing dimension tree is set to 30%; in the inventory optimization scenario, the weight of the production dimension tree is set to 30% and the weight of the warehousing dimension tree is set to 70%; in the general query scenario, the weight of both dimensions is set to 50%.

[0045] In some embodiments, in production traceability scenarios, a production dimension tree weight of 0.7 is used to prioritize the retrieval of production dimension tree data during queries, while warehouse dimension tree data is loaded only as auxiliary information. In inventory optimization scenarios, a warehouse dimension tree weight of 0.7 is used to prioritize the retrieval of warehouse dimension tree data, while production dimension tree data is loaded only on demand. In general query scenarios, an equal weight of 0.5 ensures that both dimension tree data are loaded synchronously without priority differences. All weight values ​​are pre-stored in the system's scenario-weight mapping table, associating scenario identifiers with corresponding weight combinations. Based on the core information requirements of different scenarios, query weights are tilted towards key dimensions, causing the query system to prioritize allocating computing resources to load high-weight dimension data before supplementing low-weight dimension data.

[0046] Step S1044: Set the triggering method for weight adjustment, including manual triggering and automatic triggering. Manual triggering is performed by the administrator selecting the business scenario and confirming the weight through the weight configuration interface of the steel coil data management system. Automatic triggering is performed by the system automatically calling the preset weight after identifying the business scenario based on the keywords in the query request.

[0047] In some embodiments, when manually triggered, the administrator logs into the weight management module of the steel coil data management system, selects the target scenario in the scenario selection drop-down box, and the system displays the preset weight corresponding to the scenario. After the administrator confirms, they click the "Activate" button to complete the adjustment. When automatically triggered, the system parses the keywords in the query request, matches the corresponding weight combination in the scenario-weight mapping table through the scenario identifier, and automatically sets the weight as the priority weight for the current query.

[0048] Step S1045: Perform validity verification on the adjusted weight values. Check whether the values ​​of the production dimension tree weight and the warehouse dimension tree weight are both within the range of 0-1, and whether the sum of the two is equal to 1. If not, prohibit the weight from taking effect and prompt an adjustment error.

[0049] In some embodiments, validity verification is performed by the system's weight verification subroutine. During verification, the adjusted production dimension tree weight value P and the warehouse dimension tree weight value W are first extracted. It then checks whether P and W both satisfy 0≤P≤1 and 0≤W≤1, and then checks whether P+W equals 1. If P=1.2 or P+W=1.1, the verification subroutine generates a weight value anomaly message, displays the reason for the anomaly, and prohibits the weight from being applied to queries. The weight must be readjusted and verified again. Through preset numerical range rules and logical relationship rules, the adjusted weight values ​​undergo compliance checks, eliminating invalid or incorrect weight configurations. This ensures that the query logic runs normally after weight adjustment, guaranteeing the accuracy and reliability of the query results.

[0050] S105: Based on a two-dimensional tree structure, the target steel coil is located to determine its specific position in the production dimension tree and the storage dimension tree.

[0051] In some embodiments, the input query conditions for location support three types: unique steel coil number BCBJW_ID, production batch ID, and storage location number.

[0052] This embodiment employs a cache-first and search-first approach for location services. The cache check first queries the cache. In the forward cache, if a match is found, the location result is returned directly. In the reverse cache, if the query condition is a storage location number, the corresponding shelf and warehouse can be quickly located using the reverse cache.

[0053] When a cache miss occurs, the MDFS algorithm is executed: In the production dimension tree, starting from the query conditions, nodes are traversed in a depth-first manner, traversing the target node using the parent node ID and child node ID. In the warehouse dimension tree, a multi-threaded parallel search is used, evenly dividing the tree into four subtrees. Each subtree independently searches for the target node, and the results are merged through atomic operations. The production dimension returns the node hierarchy path, and the warehouse dimension returns the physical location path. The results are then stored in the cache.

[0054] S106: Based on the target steel coil location obtained in S105, perform four-level status marking on the target steel coil and its related nodes; the four-level status marking includes the current node (target steel coil node), upstream node (parent node on the target steel coil path and its sibling and lower level nodes), sibling node (nodes at the same level as the target steel coil) and downstream node (all subtree nodes of the target steel coil).

[0055] In some embodiments, based on the positioning result of S105, the node ID of the target steel coil in the production and storage dimension tree is determined. Four-level status nodes are marked, with a new `status` field added to the node attributes during marking. The current node is only the dual-tree node corresponding to the target steel coil, and a target text identifier is added next to the node after marking. Upstream nodes in the production tree include the target node's parent node, the parent node's sibling nodes, and the parent node's child nodes.

[0056] In the storage tree, the target storage location is defined by its parent shelf node, its sibling shelves, and its subordinate storage locations.

[0057] Sibling nodes are all nodes in the production tree that share the same parent node as the target node. Other storage locations in the storage tree that share the same shelf as the target location are also considered, with a status of SAME - LEVEL. Downstream nodes are all nodes in the production tree that have the target node as their parent node. All nodes in the storage tree that have the target node as their parent node are also considered downstream nodes. Finally, the marking results—node ID, tree type, status, and marking time—are stored in a MongoDB `status_mark` collection, supporting subsequent queries of the marking history. By clearly defining the node range for the four status levels and then completing the marking through field assignment, the essence is to categorize the related nodes of the target steel coil according to their closeness, facilitating quick differentiation of node roles.

[0058] In some specific embodiments, step S106 specifically includes the following: Step S1061: Define node classification identifiers that include four types: current node, upstream node, peer node, and downstream node. Store these identifiers in the node attributes using enumeration values. Add a status type field to each node to distinguish its level.

[0059] In some embodiments, node state types are defined by enumeration values, transforming abstract hierarchical relationships into storable identifier data.

[0060] Step S1062: Trace all ancestor nodes of the target steel coil upwards through the parent node ID chain of the production dimension tree to form a production path, mark the nodes on the traced path as upstream nodes, and record the set of sibling nodes of each upstream node.

[0061] In some embodiments, the parent node ID chain of the production dimension tree is used to trace upwards, obtaining all ancestor nodes recursively or iteratively. This accurately identifies upstream nodes and provides foundational data for state analysis.

[0062] Step S1063: For the target steel coil node, find all sibling nodes with the same ID as its parent node in the production dimension tree, mark the sibling nodes as sibling nodes, and include the direct child nodes of the sibling nodes as extended sibling scope.

[0063] In some embodiments, sibling nodes are found by matching parent node IDs in the tree structure, and the scope is expanded to include direct child nodes to broaden the analysis scope at the same level. This comprehensively covers related nodes at the same level, supports horizontal comparative analysis, and enhances data integrity by expanding the scope.

[0064] Step S1064: Starting from the target steel coil node, traverse all descendant nodes downwards through the child node ID chain of the production dimension tree to form a subtree structure. Mark all traversed nodes as downstream nodes, including direct child nodes and nested child nodes at all levels.

[0065] In some embodiments, downward traversal is achieved through a child node ID chain, recursively obtaining all descendant nodes to form a subtree structure.

[0066] Step S1065: Create a mapping table containing node ID, state type, and set of associated node IDs. The current node maps to its own ID, the upstream node maps to the set of ancestor node IDs, the sibling node maps to the set of sibling node IDs, and the downstream node maps to the set of all descendant node IDs.

[0067] In some embodiments, a mapping table is established to enable fast querying of node status and associated nodes, and indexes are used to optimize the performance of association queries. This improves the efficiency of status analysis, and the mapping structure facilitates data maintenance and expansion.

[0068] Step S1066: Store the mapping table data, create an index on the node ID field, set the index type to a single-field index and the direction to ascending order, and configure batch write mode to optimize storage.

[0069] Optionally, document-oriented database features can be used to store mapping data, indexing mechanisms can accelerate node queries, batch writes can optimize storage performance, and periodic verification can ensure data consistency. This ensures data reliability and improves query response speed.

[0070] Step S107: Set corresponding visual attributes for the four levels of states marked in S106; the visual attributes include background color, border style, icon and node size.

[0071] In some embodiments, the visualization attributes of the fourth-level status nodes adopt a unified standard, and the attribute parameters are stored in the front-end configuration file.

[0072] Optionally, by using multi-dimensional visual differences in color, shape, size, and symbols, the abstract status field can be transformed into intuitive visual features. Users can identify the node status visually without needing to view node attribute details. This allows for quick location of target nodes and related nodes, avoids visual confusion, and improves information retrieval efficiency.

[0073] Step S108: Based on the set visualization attributes, dynamically visualize the marked two-dimensional tree structure. The dynamic visualization supports drag-and-drop, zoom, and filtering interactive operations, and can realize historical backtracking of the evolution of the steel coil's state.

[0074] In some embodiments, the visualization is implemented based on D3.js and WebGL technologies. D3.js's tree layout algorithm constructs a two-dimensional tree visualization framework, enabling basic interactions such as node rendering, dragging, and scaling. WebGL's high-performance graphics processing capabilities support rapid frame switching on the timeline. Historical backtracking, through time points, querying historical data, and re-rendering the two-tree, restores the state and position of the steel coil at different points in time. Essentially, it combines real-time visualization with historical data playback, achieving visualized traceability of the entire lifecycle of the steel coil.

[0075] For example, if a steel coil is transferred from the semi-finished goods warehouse to the finished goods warehouse at 17:00 on March 3, 2021, dragging the timeline will clearly show the changes in the warehouse location before and after the transfer, making it easier to identify abnormalities in warehouse scheduling. The export function can also meet the reporting needs. Managers can export visual screenshots or data tables for production and warehouse meeting reports.

[0076] In one embodiment of the present invention, based on step S101, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner. Step S101 specifically includes the following methods: Step S1011: Analyze the entire process of steel coil production, determine the node level and node type of the production dimension tree, and the node types correspond to each production stage of raw material coil processing, semi-finished coil processing, and finished coil forming.

[0077] In some embodiments, the streamlined steel coil production process includes raw material receiving and inspection, hot rolling, cold rolling, heat treatment, and finished product inspection. Node types are labeled as raw material processing node, primary semi-finished product processing node, semi-finished product processing node, and finished product forming node. Thus, based on the actual production sequence and process dependencies of the steel coils, the continuous production process is discretized into independently definable nodes, ensuring that the node hierarchy corresponds to the upstream and downstream relationships of the production process.

[0078] Step S1012: Define the information storage structure for each node, which specifically includes the basic attribute field of the steel coil, the attribute field of the production process, and the association identifier field, wherein the association identifier field is used for subsequent node association.

[0079] In some embodiments, the scope of information to be stored for each node is defined by structured fields, ensuring that steel coil information from different production stages has a dedicated storage location, and the association identifier field provides a standardized data interface for the association between subsequent nodes.

[0080] Step S1013: Set the parent node ID association rule, which stipulates that the child node ID contains the feature segment of the parent node ID, and the steel coil production process corresponding to the child node is later than the production process corresponding to the parent node.

[0081] In some embodiments, the parent node ID adopts the format of production process code, equipment number, processing date, and daily sequence code, and the child node ID is based on the parent node ID with the child process suffix added. The logical order of production processes is raw material processing node, primary semi-finished product processing node, refined semi-finished product processing node, and finished product forming node. The production processes corresponding to the child nodes must not be associated in reverse.

[0082] Step S1014: Collect steel coil information from each production stage, and enter the corresponding nodes according to the storage structure defined in step S1012. The raw material coil node is used as the initial parent node, and its parent node ID field is set to a preset null value. When entering the semi-finished coil node, it is associated with the parent node ID of the corresponding raw material coil node. When entering the finished coil node, it is associated with the parent node ID of the corresponding semi-finished coil node.

[0083] In some embodiments, information collection involves obtaining the processing equipment number and processing time through the PLC system of the production equipment, manually entering the quality inspection results through the quality inspection terminal, and automatically collecting the actual weight of the steel coil through a weighing sensor and comparing it with the theoretical weight before recording it. By combining automatic collection with manual selection, accurate entry of steel coil information is achieved, and the dependency relationship between child nodes and parent nodes is directly established through the directional association of parent node IDs.

[0084] Step S1015: Perform relationship verification on the nodes with entered information, check whether the parent node ID of each child node has a corresponding parent node, and whether the logical order of the production process corresponding to the parent node and child node conforms to the preset rules.

[0085] In some embodiments, during verification, the parent node ID field of all nodes is traversed to query the database for a node corresponding to that ID. If the node does not exist, it is marked as an invalid association. The parent node code is compared with the child node code using the node type code. A verification report is generated for the marked abnormal nodes, including the abnormal node ID, abnormal type, and error reason, and then fed back to the production management terminal.

[0086] In this way, association errors can be detected and corrected in a timely manner, preventing erroneous associations from affecting subsequent cross-dimensional association queries between the production dimension tree and the warehouse dimension tree, and ensuring the structural integrity and data accuracy of the production dimension tree.

[0087] In one embodiment of the present invention, based on step S102, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner. Step S102 specifically includes the following methods: Step S1021: Design a node structure that includes a unique identifier for the steel coil, the steel coil type, the production batch number, the parent node ID, the production timestamp, and the process stage status field. The node data structure is stored in key-value pair format.

[0088] In some embodiments, the basic building blocks of the production dimension tree are defined by a standardized data structure, key-value pair storage ensures field scalability, and the parent node ID field serves as the core of association to explicitly express the production process.

[0089] Step S1022: Obtain basic steel coil data through the production management system interface, classify and store it in a MongoDB collection according to steel coil type. Each document contains the complete fields defined in step S1021. The CBJW_ID field of the raw material coil document is set to null, and the CBJW_ID field of the semi-finished coil and finished coil documents points to the corresponding parent node.

[0090] In some embodiments, after obtaining raw data from the production system, the data is classified and stored according to the type of steel coil to form an initial structure, with the raw material coil serving as the root node and no parent node set.

[0091] Step S1023: Based on the production batch number and process flow matching rules, establish the association between semi-finished product rolls and raw material rolls, and between finished product rolls and semi-finished product rolls. Set the CBJW_ID of the semi-finished product roll to the BCBJW_ID of the corresponding raw material roll, and set the CBJW_ID of the finished product roll to the BCBJW_ID of the corresponding semi-finished product roll, forming a multi-level tree structure.

[0092] In some embodiments, relationships between nodes are established based on batch number matching rules. Semi-finished rolls inherit batch characteristics from raw material rolls, and finished rolls inherit characteristics from semi-finished rolls, forming a production evolution chain. This accurately reflects the actual production relationship, and batch number matching ensures the correctness of the association.

[0093] Step S1024: Store complete information for each node, create an index on the CBJW_ID field, set the index type to single-field index, the index direction to ascending order, and configure the document update strategy to batch write mode.

[0094] In some embodiments, complete node information can be stored based on embedded documents, and an indexing mechanism can accelerate CBJW_ID queries, while ascending order sorting optimizes range query performance. The benefits include a single document containing complete information reducing the number of queries, indexing improving the efficiency of join queries, and ordered storage enhancing the speed of range retrieval.

[0095] Step S1025: When inserting a node, verify whether CBJW_ID exists. If it does not exist, trigger the exception handling process, periodically perform tree structure integrity checks, verify the validity of the association relationship by traversing the CBJW_ID of all nodes, and establish a data repair log to record the verification results.

[0096] In some embodiments, insertion-time validation ensures the validity of associations, periodic integrity checks identify and repair broken associations, and traversal validation covers all nodes. The benefits include maintaining the integrity of the tree structure, timely correction of data errors through anomaly handling, periodic checks to prevent data corruption, and ensuring the traceability of production relationships.

[0097] Step S1026: Add a version number field to each node to record the change history of node information, set the version number generation rule to a combination of timestamp and sequence number, configure the version rollback mechanism, and support querying specific version node information.

[0098] In some embodiments, the version number field records the node change history, the combination of timestamp and sequence number ensures version uniqueness, and the version rollback mechanism supports historical status query.

[0099] In one embodiment of the present invention, based on step S103, the following will provide a possible embodiment and its specific implementation will be described in a non-limiting manner. Step S103 specifically includes the following methods: Step S1031: Design the coding structure for the unique number of the steel coil, which specifically includes a production dimension identifier segment, a timestamp segment, a storage dimension identifier segment, and a serial number segment. The production dimension identifier segment corresponds to the production link code in the production dimension tree, and the storage dimension identifier segment corresponds to the warehouse area code in the storage dimension tree. The segments are connected by separators, and the total length is fixed at 32 characters.

[0100] In some embodiments, the core identifiers of the production and warehousing dimensions are integrated into a unique number through segmented coding, so that the number has both global uniqueness and can be directly associated with the key information of the two-dimensional tree.

[0101] Step S1032: Determine the triggering condition and generating entity for the unique number of the steel coil. Set the triggering condition to be when the steel coil enters the warehousing stage after completing the production stage. The generating entity is the steel coil data management system. Read the production stage code and target warehouse area code of the current steel coil and fill in the fields of the number.

[0102] In some embodiments, the timing of number generation is based on the flow node of the steel coil from production to storage, ensuring that the number is generated before the steel coil is recorded in both the two-dimensional tree; the number field information is automatically obtained through multi-system data interaction.

[0103] Step S1033: Add a unique number field for steel coils to the information storage structure of each node in the production dimension tree, and bind the unique number generated in step S1032 to the corresponding steel coil production dimension tree node. The binding method is to associate the unique number with the steel coil production batch ID of the production dimension tree node, so as to ensure that each production dimension tree node corresponds to only one unique number.

[0104] In some embodiments, the existing production batch ID in the production dimension tree is used as an intermediate association to establish a one-to-one mapping between production nodes and unique numbers, ensuring that each node in the production dimension tree is bound to only one coil number. This reuses existing fields in the production dimension tree, eliminating the need to restructure the node storage structure and reducing the difficulty of system modification. The one-to-one binding rule avoids query confusion caused by a single production node being associated with multiple numbers, ensuring the accuracy of the association between the production dimension tree and the numbers.

[0105] Step S1034: Add a unique number field for steel coils to the information storage structure of each node in the storage dimension tree. Bind the unique number generated in step S1032 to the corresponding storage dimension tree node of the steel coil. The binding method is to associate the unique number with the location occupancy record ID of the storage dimension tree node to ensure that each storage dimension tree node corresponds to only one unique number.

[0106] In some embodiments, the existing location occupancy record ID in the storage dimension tree is used as an intermediate association to establish a one-to-one mapping between storage nodes and unique numbers, ensuring that each node in the storage dimension tree is bound to only one steel coil number.

[0107] Step S1035: Perform consistency verification on the unique coil numbers bound in the production dimension tree and the storage dimension tree. The verification method is to traverse the unique coil numbers of all nodes in the production dimension tree, query whether there is a matching number in the storage dimension tree, traverse the unique coil numbers of all nodes in the storage dimension tree, query whether there is a matching number in the production dimension tree, mark the nodes with no matching numbers, and feed back to the data management terminal.

[0108] In some embodiments, bidirectional traversal and exact matching are used to identify nodes in the two-dimensional tree whose numbers exist but have no corresponding numbers in the other tree, ensuring the consistency of numbers in both trees. This promptly detects omissions or errors in number binding, ensuring the accuracy and completeness of cross-dimensional relational queries.

[0109] In one embodiment of the present invention, based on step S105, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner. Step S105 specifically includes the following methods: Step S1051: Obtain the query conditions for the target steel coil. The query conditions include the unique number of the steel coil, the production batch ID or the storage location number. Receive the query conditions manually entered by the user through the query input interface of the steel coil data management system, or receive the query condition data transmitted by the production management system and the warehouse management system.

[0110] Step S1052: Based on the query conditions, locate the node in the production dimension tree constructed in step S101. Through the production tree positioning module of the steel coil data management system, match the query conditions with the steel coil unique number production batch ID field of the production dimension tree node, find the production dimension tree node whose field value is completely consistent with the query conditions, and record the production process information and node ID of the node.

[0111] In some embodiments, the production tree positioning module has built-in field matching rules, prioritizing the unique steel coil number as the matching field. If the query condition is the production batch ID, only the production batch ID field of the production dimension tree node is matched. During the matching process, the system traverses the node index table of the production dimension tree to quickly locate the corresponding node without traversing all nodes. After successful positioning, the recorded production process information includes the processing equipment number, quality inspection result, and production completion time. The index table is used to achieve rapid matching between query conditions and production dimension tree nodes, prioritizing the more unique steel coil number as the matching field to ensure that a unique node is located.

[0112] Step S1053: Extract the unique steel coil number from the production dimension tree node located in step S1052. Use the unique steel coil number as a query condition to locate the node in the warehouse dimension tree constructed in step S102. Through the warehouse tree positioning module of the steel coil data management system, match the unique steel coil number with the unique steel coil number field of the warehouse dimension tree node to find the corresponding warehouse dimension tree node, and record the warehouse number, shelf number, and storage location number of the warehouse dimension tree node.

[0113] In some embodiments, the unique number of the steel coil established based on S103 is cross-dimensionally associated, and the unique number obtained from the production dimension tree positioning is used as the sole basis for the warehouse dimension tree positioning, thereby realizing a strong association of dual-dimensional tree positioning.

[0114] Step S1054: Verify the correlation between the production dimension tree node obtained in step S1052 and the storage dimension tree node obtained in step S1053. Check whether the unique coil numbers of the two nodes are exactly the same. If they are the same, the location association is determined to be valid. If they are different, the location association is marked as abnormal, and the ID and unique number of the abnormal node are recorded.

[0115] In some embodiments, the correlation verification uses a full field value comparison method, extracting the unique coil identification string from the production dimension tree node and comparing it character by character to see if they are completely consistent. If they are inconsistent, the system automatically queries the unique coil identification string-node ID mapping table to confirm whether there is an error in the identification string entry or node binding, and writes the error type into the exception record. This allows for the timely detection of data errors in the two-dimensional tree correlation.

[0116] Step S1055: If the verification in step S1054 is valid, integrate the production process information of the production dimension tree node with the physical location information of the warehousing dimension tree node to generate a target steel coil positioning result sheet; if the verification is abnormal, generate a positioning abnormality report and display the positioning result or abnormal information through the result output module of the steel coil data management system.

[0117] In some embodiments, the located two-dimensional information is structured and integrated to form a clear output result, while providing troubleshooting guidance for anomalies, ensuring that the location results can be directly used for subsequent business operations. The structured results facilitate users to quickly obtain key information, and multiple output methods meet the needs of different business scenarios.

[0118] It should be understood that the sequence number of each step in the above embodiments is not for the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0119] The following are embodiments of the cloud warehouse steel coil data processing system provided in this disclosure. This system and the cloud warehouse steel coil data processing methods in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the cloud warehouse steel coil data processing system, please refer to the embodiments of the above cloud warehouse steel coil data processing methods.

[0120] like Figure 2 As shown, the system includes: The production dimension tree construction module 201 is used to construct a production dimension tree with the steel coil production process as the framework. The nodes store information about raw material coils, semi-finished coils, and finished coils, and are associated with production batch relationships through the parent node ID. The warehouse dimension tree construction module 202 is used to construct a warehouse dimension tree according to the hierarchical relationship of warehouse, shelf, and storage location. The nodes store the physical storage location information of steel coils and associate the upper and lower level location relationships through the parent node ID. The cross-dimensional association module 203 is used to assign a unique number to each steel coil and establish a cross-dimensional association between the production dimension tree and the storage dimension tree based on the unique number, forming a two-dimensional tree structure to realize real-time association query between the production dimension tree and the storage dimension tree. The weight adjustment module 204 is used to adjust the priority weight of the production dimension tree and the warehouse dimension tree during querying according to the business scenario. The dual-dimensional positioning module 205, based on a dual-dimensional tree structure, locates the target steel coil and determines its specific position in the production dimension tree and the storage dimension tree. The status marking module 206 is used to perform four-level status marking on the target steel coil and its related nodes based on the obtained target steel coil positioning. The visualization mapping module 207 is used to set corresponding visualization attributes for the four levels of the marked state; The visualization module 208 dynamically visualizes the marked two-dimensional tree structure based on the set visualization attributes.

[0121] like Figure 3 As shown, this application also provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored in the memory and executable on the processor 101. When the processor 101 executes the program, it implements the steps of the cloud warehouse steel coil data processing method.

[0122] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments described and / or claimed herein.

[0123] In this embodiment, processor 101 may be implemented using at least one of an application-specific integrated circuit, a programmable logic device, a field-programmable gate array, a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such an implementation may be implemented within a controller. For software implementation, implementations such as processes or functions may be implemented with separate software modules that allow the performance of at least one function or operation. Software code may be implemented by a software application (or program) written in any suitable programming language, and the software code may be stored in memory and executed by the controller.

[0124] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like.

[0125] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0126] The present invention also provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the cloud warehouse steel coil data processing method.

[0127] The storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0128] The storage medium stores a program product capable of implementing the methods described above in this specification. In some possible implementations, various aspects of this disclosure can also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the exemplary methods section of this specification according to various exemplary embodiments of this disclosure.

[0129] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing steel coil data in a cloud warehouse, characterized in that the method... include: S101: Construct a production dimension tree with the steel coil production process as the framework, where nodes store information on raw material coils, semi-finished coils, and finished coils, and are associated with production batch relationships through parent node IDs; S102: Construct a storage dimension tree according to the hierarchical relationship of warehouse, shelf, and storage location, where nodes store the physical storage location information of steel coils and associate the hierarchical location relationship through the parent node ID; S103: Assign a unique number to each steel coil, and establish a cross-dimensional association between the production dimension tree described in S101 and the warehousing dimension tree described in S102 based on the unique number to form a two-dimensional tree structure, so as to realize real-time association query between the production dimension tree and the warehousing dimension tree. S104: Adjust the priority weight of the production dimension tree and the warehouse dimension tree during querying according to the business scenario; S105: Based on a two-dimensional tree structure, locate the target steel coil and determine its specific position in the production dimension tree and the storage dimension tree; S106: Based on the target steel coil location obtained in S105, perform a fourth-level status marking on the target steel coil and its related nodes; S107: Set the corresponding visual attributes for the four levels of states marked by S106; S108: Based on the set visualization attributes, dynamically visualize the marked two-dimensional tree structure.

2. The cloud warehouse steel coil data processing method according to claim 1, characterized in that, Step S101 specifically includes the following methods: The entire process of steel coil production was analyzed to determine the node levels and node types of the production dimension tree. The node types correspond to the various production stages of raw material coil processing, semi-finished coil processing, and finished coil forming. Define the information storage structure for each node; Set the association rules for the parent node ID, stipulating that the child node ID contains the feature segment of the parent node ID, and the steel coil production process corresponding to the child node is later than the production process corresponding to the parent node. Collect steel coil information from each production stage, and enter the corresponding nodes according to the defined storage structure. The raw material coil node is used as the initial parent node, and its parent node ID field is set to a preset null value. When entering the semi-finished coil node, it is associated with the parent node ID of the corresponding raw material coil node. When entering the finished coil node, it is associated with the parent node ID of the corresponding semi-finished coil node. Perform relationship verification on nodes with entered information, check whether the parent node ID of each child node has a corresponding parent node, and whether the logical order of the production process corresponding to the parent node and child node conforms to the preset rules.

3. The cloud warehouse steel coil data processing method according to claim 1, characterized in that, Step S102 specifically includes the following: defining the node structure of the production dimension tree, which specifically includes the unique identifier of the steel coil, the steel coil type, the production batch number, the parent node ID, the production timestamp, and the process stage status field; Basic data of steel coils is obtained through the production management system interface and stored according to the type of steel coil. Raw material coils do not have parent nodes, while semi-finished coils and finished coils have corresponding parent node identifiers. Establish the association between semi-finished product rolls and raw material rolls, and between finished product rolls and semi-finished product rolls based on the production batch number, and form a multi-level tree structure by setting the parent node ID; Store complete information about the nodes, index the parent node ID field, and update the documents using batch write mode; Verify the validity of the parent node ID when inserting a node, perform tree structure integrity checks periodically, and record the verification results.

4. The cloud warehouse steel coil data processing method according to claim 1, characterized in that, Step S103 specifically includes the following methods: Design a unique coding structure for steel coils, specifically including a production dimension identifier segment, a timestamp segment, a storage dimension identifier segment, and a serial number segment. Each segment is connected by a separator and the total length is fixed. When a steel coil enters the warehousing stage after completing the production stage, the steel coil data management system generates a unique number for the steel coil, and fills the number field with the production stage code and the warehouse area code; A unique number field for steel coils is added to the node information of the production dimension tree, and the unique number is bound to the production node through the production batch ID to ensure that each production node corresponds to a unique number. Add a unique number field for steel coils to the node information of the storage dimension tree, and bind the unique number to the storage node through the storage location occupancy record ID to ensure that each storage node corresponds to a unique number. The unique serial numbers of steel coils in the production dimension tree and the storage dimension tree are checked for consistency. The matching of serial numbers is checked by bidirectional traversal, and nodes with no matching are marked.

5. The cloud warehouse steel coil data processing method according to claim 1, characterized in that, Step S104 specifically includes the following methods: Classify the business scenario types for steel coil queries, and clarify production traceability scenarios, inventory optimization scenarios, and general query scenarios; Define the adjustment dimensions for query priority weights, and determine the weights to be set for the two dimensions, the production dimension tree in step S101 and the warehousing dimension tree in step S102, with the sum of the weight percentages of the two dimensions fixed at 100%. Configure corresponding weight values ​​for different business scenarios: in the production traceability scenario, set the weight of the production dimension tree to 70% and the weight of the warehousing dimension tree to 30%; in the inventory optimization scenario, set the weight of the production dimension tree to 30% and the weight of the warehousing dimension tree to 70%; in the general query scenario, set the weight of both dimensions to 50%. Configure the triggering method for weight adjustments, including manual triggering and automatic triggering; The adjusted weight values ​​are validated to check whether the weights of the production dimension tree and the warehouse dimension tree are both within the range of 0-1, and whether their sum is equal to 1. If not, the weights are not allowed to take effect and an adjustment error is indicated.

6. The cloud warehouse steel coil data processing method according to claim 1, characterized in that, Step S105 specifically includes the following methods: Obtain the query conditions for the target steel coil, including the steel coil's unique number, production batch ID, or storage location number, which can be obtained through the query input interface or transmitted and received by the system. Based on the query conditions, locate the corresponding node in the production dimension tree, and record the node information by matching the query conditions with the node's unique coil number or production batch ID field. Extract the unique number of the steel coil from the production dimension tree node, locate the corresponding node in the storage dimension tree, and record the node location information by matching the unique number field of the steel coil. Verify the association between production dimension tree nodes and warehousing dimension tree nodes by comparing whether their unique steel coil numbers are consistent to determine the validity of the association or whether the marking is abnormal. Based on the verification results, integrate production process information and physical location information to generate a location result sheet, or generate and display an anomaly report.

7. The cloud warehouse steel coil data processing method according to claim 1, characterized in that, Step S106 specifically includes the following methods: Define node classification identifiers that include four types: current node, upstream node, peer node, and downstream node. These identifiers are stored in the node attributes as enumeration values. Each node also has a status type field to distinguish its level. By tracing all ancestor nodes of the target steel coil upwards from the parent node ID chain of the production dimension tree, a production path is formed. Nodes on the tracing path are marked as upstream nodes, and the set of sibling nodes of each upstream node is recorded. For the target steel coil node, find all sibling nodes with the same ID as its parent node in the production dimension tree, mark the sibling nodes as sibling nodes, and include the direct child nodes of the sibling nodes as extended sibling scope. Starting from the target steel coil node, traverse down through the child node ID chain of the production dimension tree to form a subtree structure, and mark all traversed nodes as downstream nodes, including direct child nodes and nested child nodes at all levels. Create a mapping table containing node ID, state type, and set of associated node IDs. The current node maps to its own ID, the upstream node maps to the set of ancestor node IDs, the sibling node maps to the set of sibling node IDs, and the downstream node maps to the set of all descendant node IDs. Store the mapping table data, create an index on the node ID field, set the index type to a single-field index and the direction to ascending order, and configure batch write mode to optimize storage.

8. A cloud-based steel coil data processing system, characterized in that, The system is used to implement the cloud warehouse steel coil data processing method as described in any one of claims 1 to 7; The system includes: The production dimension tree construction module is used to construct a production dimension tree with the steel coil production process as the framework. The nodes store information about raw material coils, semi-finished coils, and finished coils, and are associated with production batch relationships through the parent node ID. The warehouse dimension tree construction module is used to construct a warehouse dimension tree according to the hierarchical relationship of warehouse, shelf, and storage location. The nodes store the physical storage location information of steel coils and associate the hierarchical location relationship with the parent node ID. The cross-dimensional association module is used to assign a unique number to each steel coil and establish a cross-dimensional association between the production dimension tree and the storage dimension tree based on the unique number, forming a two-dimensional tree structure to realize real-time association query between the production dimension tree and the storage dimension tree. The weight adjustment module is used to adjust the priority weight of the production dimension tree and the warehouse dimension tree during queries based on the business scenario. The dual-dimensional positioning module, based on a dual-dimensional tree structure, locates the target steel coil and determines its specific position in the production dimension tree and the storage dimension tree. The status marking module is used to perform four-level status marking on the target steel coil and its related nodes based on the obtained target steel coil positioning. The visualization mapping module is used to set corresponding visualization attributes for the four levels of marked states; The visualization module dynamically displays the marked two-dimensional tree structure based on the set visualization attributes.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the cloud warehouse steel coil data processing method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the cloud warehouse steel coil data processing method as described in any one of claims 1 to 7.

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