Hierarchical progressive multi-condition purchase order automatic splitting method based on multi-source data fusion

Abstract

The application discloses a layered progressive multi-condition procurement order automatic splitting method based on multi-source data fusion, relates to the technical field of enterprise resource planning and supply chain management, and discloses a layered progressive multi-condition procurement order automatic splitting method based on multi-source data fusion. Standardized spare part order data sets are generated through multi-source data fusion preprocessing, layered progressive splitting logic is implemented according to multi-dimensional splitting condition configuration information, and the automatic and accurate splitting of procurement orders is realized in combination with full-dimensional compliance verification and intelligent merging mechanism. The layered progressive multi-condition procurement order automatic splitting method can improve the splitting efficiency of procurement orders, reduce the manual error rate, realize dynamic business data fusion and accurate splitting, reduce the number of fragmented suborders, and enhance the traceability and compliance of the procurement process.

Description

Technical Field

[0001] This application relates to the fields of enterprise resource planning and supply chain management, and in particular to a hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion. Background Technology

[0002] In the spare parts procurement management practices of industrial enterprises, the original demand orders submitted by maintenance departments, project sites, or end customers typically cover dozens to hundreds of heterogeneous materials. Each material has different delivery time requirements, supplier constraints, budget control rules, and inventory status attributes. Such complex orders cannot be directly entered into the procurement execution process and must be broken down into a standardized set of sub-orders that conform to business specifications to adapt to the order placement, approval process, supplier fulfillment, and financial reconciliation stages. Currently, the industry generally relies on manual verification to handle the breakdown tasks. Operators need to compare material attributes with back-end business rules item by item. Processing a single order often takes more than several hours, which not only causes significant waste of human resources but also easily leads to operational risks such as material classification errors, supplier qualification mismatches, or budget omissions due to subjective oversight. Such errors directly lead to frequent incidents of incorrect or missed procurement, and even trigger production interruptions such as equipment downtime. The manual order allocation process relies solely on static data at the moment of order submission, failing to dynamically correlate with real-time inventory consumption data, the progress of goods in transit, supplier capacity fluctuations, and remaining budget limits in the financial module. This results in a severe disconnect between the allocation results and the actual business environment. For example, redundant purchase orders may be generated even when inventory already meets demand, or excess orders may be allocated when supplier fulfillment capabilities decline. Furthermore, manual operations lack standardized logging mechanisms, making it difficult to trace the root cause of subsequent fulfillment anomalies or budget overruns.

[0003] Some enterprises have introduced simple automated order splitting tools based on fixed rules to replace manual operations, but such systems have structural flaws. The systems only read surface-level order information, such as material codes or category classifications, and cannot connect to the multi-dimensional dynamic data sources in the spare parts management backend, including key business parameters such as real-time inventory levels, supplier historical performance ratings, monthly budget remaining amounts, and material control levels. This results in a disconnect between the splitting logic and the enterprise's actual management needs. The splitting dimension design is too simplistic, only supporting simple order splitting by material attributes or suppliers, failing to achieve multi-condition collaborative judgment based on delivery timeliness, budget limits, inventory thresholds, supplier capabilities, and material attributes. When faced with mixed-attribute orders, the system cannot distinguish the processing priority between urgent and regular materials, often causing top-priority urgent needs to be squeezed out by regular orders. Furthermore, due to the lack of hierarchical and progressive logic, the splitting results in a large number of fragmented sub-orders, significantly reducing procurement efficiency. Existing solutions have not established a full-dimensional compliance verification mechanism, failing to automatically identify issues such as missing material information, abnormal purchase quantities, unqualified suppliers, or budget overruns in sub-orders. They also lack intelligent merging capabilities for fragmented orders, further exacerbating the proliferation of invalid orders.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion, which aims to improve the efficiency of purchase order splitting, reduce human error rate, and enhance the traceability and compliance of the procurement process.

[0006] The hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion proposed in this application includes: Acquire first-type data and second-type data, and perform fusion preprocessing on the first-type data and second-type data to generate a standardized spare parts order dataset; wherein, the first-type data is spare parts demand order data submitted by customers, and the second-type data is multi-dimensional dynamic business data from the backend of the spare parts management system; Obtain multi-dimensional splitting condition configuration information, which includes multiple splitting dimensions and the judgment threshold, weight ratio and priority ranking of each splitting dimension; Based on the standardized spare parts order dataset and the multi-dimensional splitting condition configuration information, the spare parts orders are split layer by layer according to the priority of each splitting dimension from high to low, generating an initial set of sub-orders. Perform full-dimensional compliance verification on each sub-order in the initial sub-order set, remove invalid empty sub-orders, and merge fragmented sub-orders that meet the same merging rule to generate the final sub-order set; Based on the final set of sub-orders, a standardized purchase sub-order carrying a unique splitting rule tag is generated, and the standardized purchase sub-order is synchronized to each business module of the spare parts management system.

[0007] In one embodiment, the multiple splitting dimensions include delivery timeliness, supplier capability, budget limit, inventory control, and material attribute. The default priority of each splitting dimension, from high to low, is as follows: delivery timeliness, supplier capability, budget limit, inventory control, and material attribute. The weighting, priority order, and judgment threshold of each splitting dimension can all be manually customized. Obtaining the multi-dimensional splitting condition configuration information includes: Obtain delivery timeliness configuration information, which includes timeliness thresholds for dividing materials into multiple timeliness levels according to the required delivery time, and rules for forcibly separating materials of different timeliness levels into independent orders; Obtain supplier capability configuration information, which includes the maximum supply quantity threshold and performance rating qualification standards for each qualified supplier. Obtain budget limit configuration information, which includes the upper limit of the single-item purchase budget and the upper limit of the single order approval amount; Obtain inventory management configuration information, which includes safety stock threshold and overstocking judgment criteria; Obtain material attribute configuration information, which includes material control level classification standards, material category classification rules, and warehouse area division rules.

[0008] In one embodiment, the step of performing a layer-by-layer splitting determination on the spare parts orders based on the standardized spare parts order dataset and the multi-dimensional splitting condition configuration information, according to the priority of each splitting dimension from high to low, to generate an initial set of sub-orders includes: Based on the standardized spare parts order dataset and the delivery timeliness configuration information, all materials in the order are divided into multiple timeliness material subsets according to the delivery timeliness threshold, so as to achieve the initial isolation of materials with different timeliness levels. For each time-sensitive material subset, based on the standardized spare parts order dataset and the supplier capability configuration information, the first sub-order set is generated by splitting the qualified suppliers bound to the material according to the supplier dimension. For each sub-order in the first sub-order set, based on the standardized spare parts order dataset and the budget limit configuration information, a second sub-order set is generated by splitting it a second time according to the budget limit rules. For each sub-order in the second sub-order set, based on the inventory control configuration information, the material attribute configuration information, and the real-time inventory status and material control attributes of each material in the standardized spare parts order dataset, a fine-tuning and split is performed to generate the initial sub-order set.

[0009] In one embodiment, based on the standardized spare parts order dataset and the delivery timeliness configuration information, the steps of dividing all materials in the order into multiple timeliness material subsets according to the delivery timeliness threshold to achieve preliminary isolation of materials with different timeliness levels include: Extract the required delivery time for each material from the standardized spare parts order dataset; The delivery time of each material is compared with the timeliness threshold in the delivery timeliness configuration information to determine the timeliness level of each material; wherein, the timeliness level includes super urgent, urgent, regular and long term. All materials belonging to the same time-sensitive grade are grouped into the same time-sensitive material subset, forming multiple time-sensitive material subsets, and the different time-sensitive material subsets are independent of each other.

[0010] In one embodiment, the step of generating a first sub-order set by splitting the standardized spare parts order dataset and the supplier capability configuration information according to the qualified suppliers bound to the materials for each time-sensitive material subset includes: For each material in the current time-sensitive material subset, query the list of qualified suppliers bound to each material from the standardized spare parts order dataset, and gather all materials bound to the same qualified supplier to form a preliminary sub-order, generating a preliminary sub-order set; Iterate through each preliminary sub-order in the preliminary sub-order set. If the purchase quantity of a single material in any preliminary sub-order exceeds the maximum supply threshold of the corresponding supplier, split the single material into a fulfillable sub-order and an excess sub-order. The purchase quantity of the fulfillable sub-order does not exceed the maximum supply threshold, and the purchase quantity of the excess sub-order is the excess portion. If a supplier's performance rating is lower than the qualified rating standard in any preliminary sub-order, the material corresponding to that supplier will be removed from the preliminary sub-order, and an independent sub-order will be generated after matching the material with an alternative supplier. All the preliminary sub-orders after the above splitting process are used as the first sub-order set.

[0011] In one embodiment, the step of generating a second sub-order set by further splitting each sub-order in the first sub-order set according to the budget limit rules based on the standardized spare parts order dataset and the budget limit configuration information includes: Iterate through each sub-order in the first sub-order set, obtain the purchase quantity and standard purchase price of each material from the standardized spare parts order dataset, and calculate the purchase amount of each material in each sub-order and the total amount of the sub-order one by one; Materials whose purchase amount exceeds the upper limit of the single item purchase budget are separated from their original sub-orders and generated as independent sub-orders for single item budget control; After completing the budget control breakdown for each item, calculate the total amount of each sub-order. Sub-orders whose total amount exceeds the single order approval amount limit are then split into multiple compliant sub-orders according to the single order approval amount limit. The set of all sub-orders after the individual item budget control split and the total order amount control split is taken as the second set of sub-orders.

[0012] In one embodiment, the step of fine-tuning and splitting each sub-order in the second sub-order set based on the inventory control configuration information, the material attribute configuration information, and the real-time inventory status and material control attributes of each material in the standardized spare parts order dataset to generate the initial sub-order set includes: For each sub-order in the second sub-order set, query the real-time inventory quantity and in-transit material information of each material from the standardized spare parts order dataset. Materials with real-time inventory quantities lower than the safety stock threshold are identified as insufficient inventory materials and split separately. Materials with purchase quantities exceeding the safety stock threshold are identified as excess stock materials and split separately. For each sub-order in the second sub-order set, query the material control level of each material from the standardized spare parts order dataset, separate the materials belonging to the preset special control level from the original sub-order, and generate an independent sub-order for the controlled material; For each sub-order in the second sub-order set, query the storage area to which each material belongs from the standardized spare parts order dataset, and split the materials belonging to different storage areas into independent sub-orders; The initial sub-order set is composed of all sub-orders after being split by inventory dimension and material attribute dimension.

[0013] In one embodiment, a full-dimensional compliance check is performed on each sub-order in the initial sub-order set, including: Verify the completeness of material information for each sub-order, and identify abnormal sub-orders with missing material codes, specifications, and requirement fields; Verify the accuracy of the purchase quantity for each sub-order, and identify any abnormal sub-orders with purchase quantities of zero or negative, or purchase quantities that do not match the original order. Verify the supplier matching consistency of each sub-order one by one, and investigate abnormal sub-orders where the supplier is not in the preset qualified supplier list for the material; Verify the compliance of the budget amount for each sub-order, and identify abnormal sub-orders where the purchase amount of a single item exceeds the upper limit of the single item purchase budget, or where the total amount of the sub-order exceeds the upper limit of the single order approval amount. Verify the delivery time matching of each sub-order one by one, and identify abnormal sub-orders where the material requirement delivery time within the sub-order is inconsistent with the sub-order's timeliness level; Verify the compliance of inventory control for each sub-order, and identify abnormal sub-orders whose purchase quantity exceeds the inventory control threshold and has not been independently split; Each abnormal sub-order is marked and pushed to the backend for manual review.

[0014] In one embodiment, merging fragmented sub-orders that satisfy the same merging rule includes: Iterate through the initial set of sub-orders and remove invalid empty sub-orders with zero material quantity; Among the remaining sub-orders, multiple sub-orders that simultaneously meet the following conditions will be merged into a single sub-order: belonging to the same supplier, belonging to the same delivery timeliness level, belonging to the same budget level, and belonging to the same warehouse area; For the splitting process of each material, the splitting trigger conditions, matching rules, data comparison before and after splitting, and splitting execution time are recorded to generate a splitting traceability log, and the splitting traceability log is bound and stored with the corresponding sub-order.

[0015] In one embodiment, the steps of generating standardized purchase sub-orders carrying unique splitting rule tags based on the final sub-order set, and synchronizing the standardized purchase sub-orders to each business module of the spare parts management system include: Assign a unique splitting rule label and a unique traceability number to each sub-order in the final sub-order set to generate standardized procurement sub-orders; The standardized procurement sub-orders are synchronized to the procurement management module, approval workflow module, supplier docking module, financial budget module, and inventory control module of the spare parts management system; In response to user actions, at least one of the following operations is performed based on the standardized purchase sub-order: order export operation, purchase approval operation, and push operation to supplier.

[0016] The proposed method for automatic splitting of purchase orders based on multi-source data fusion and hierarchical progressive multi-condition is to generate a standardized spare parts order dataset through multi-source data fusion preprocessing, and implement hierarchical progressive splitting logic according to multi-dimensional splitting condition configuration information. Combined with full-dimensional compliance verification and intelligent merging mechanism, it can realize the automated and accurate splitting of purchase orders, improve the efficiency of purchase order splitting, reduce the human error rate, realize dynamic business data fusion and accurate splitting, reduce the number of fragmented sub-orders, and enhance the traceability and compliance of the procurement process. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an embodiment of the hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion, as provided in this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] In the spare parts procurement management scenario of industrial enterprises, spare parts demand orders submitted by enterprise operation and maintenance departments, project sites, or end customers typically contain a variety of spare parts materials with different attributes and control requirements. A single original order cannot be directly used for procurement ordering, approval processes, supplier fulfillment, and financial reconciliation; it must be split into multiple standardized sub-orders that conform to procurement specifications. In current technology, order splitting is still mainly done manually, which is time-consuming, inefficient, and labor-intensive when processing a single complex order. Moreover, manual operation is prone to problems such as incorrect material classification, supplier mismatch, and budget omissions. Splitting errors can lead to a chain of losses such as incorrect procurement, missed procurement, delayed delivery, and even equipment downtime. At the same time, manual splitting can only be judged based on static instantaneous data and cannot be linked to dynamic business data such as inventory consumption, arrival of materials in transit, changes in supplier supply capacity, and remaining monthly procurement budget. The splitting results are prone to being out of touch with real-time business rules, and the manual splitting process lacks standardized operation logs, making it impossible to trace the source of problems when subsequent fulfillment anomalies or budget overruns occur. Some companies have tried to use simple automated splitting systems based on fixed rules to replace manual operations, but such systems have technical defects such as data isolation, single splitting dimension, lack of hierarchical priority logic, and easy generation of fragmented invalid orders.

[0024] Based on this, the embodiments of this application provide a hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion, referring to... Figure 1 The hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion includes steps S100 to S500, wherein: Step S100: Obtain the first type of data and the second type of data, and perform fusion preprocessing on the first type of data and the second type of data to generate a standardized spare parts order dataset; wherein, the first type of data is spare parts demand order data submitted by customers, and the second type of data is multi-dimensional dynamic business data from the backend of the spare parts management system. Step S200: Obtain multi-dimensional splitting condition configuration information, which includes multiple splitting dimensions and the judgment threshold, weight ratio and priority ranking of each splitting dimension. Step S300: Based on the standardized spare parts order dataset and the multi-dimensional splitting condition configuration information, splitting judgment is performed layer by layer on the spare parts orders according to the priority of each splitting dimension from high to low, generating an initial sub-order set; Step S400: Perform full-dimensional compliance verification on each sub-order in the initial sub-order set, remove invalid empty sub-orders, and merge fragmented sub-orders that meet the same merging rule to generate the final sub-order set; Step S500: Based on the final sub-order set, generate standardized purchase sub-orders carrying unique splitting rule tags, and synchronize the standardized purchase sub-orders to each business module of the spare parts management system.

[0025] In this embodiment, multi-source data fusion refers to the process of integrating, cleaning, and unifying data from different business systems or data sources. This aims to eliminate data silos, form a comprehensive and consistent data view, and provide data support for subsequent business decisions. Layered progressive splitting refers to applying complex splitting conditions layer by layer to order data according to a preset priority order. Each layer of splitting is refined based on the results of the previous layer, until all dimensions of splitting are determined, thereby achieving refined and structured order splitting. The standardized spare parts order dataset refers to the collection of all spare parts order data after multi-source data fusion preprocessing, where the data has been uniformly formatted and standardized. This dataset contains all the necessary information for order splitting, such as material codes, required quantities, required delivery times, and supplier information.

[0026] In this embodiment, the multi-dimensional splitting condition configuration information refers to a set of rules and parameters used to guide the order splitting process. This information includes multiple different splitting dimensions, such as delivery timeliness, supplier capabilities, budget limits, etc., as well as the specific judgment threshold, weight ratio, and execution priority order for each dimension. The initial sub-order set refers to the set of preliminary splitting results after hierarchical progressive splitting judgment, but before compliance verification and optimization merging. The final sub-order set refers to the set of sub-orders that conforms to procurement specifications and can be directly used in subsequent business processes, formed on the basis of the initial sub-order set after full-dimensional compliance verification, invalid sub-order removal, and fragmented sub-order merging optimization. The standardized procurement sub-order refers to the procurement order generated from the final sub-order set and assigned a unique splitting rule label and traceability number. This order has a uniform format and can be directly synchronized to various business modules of the spare parts management system for circulation.

[0027] In this embodiment, the hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion first acquires a first type of data and a second type of data, and then performs fusion preprocessing on the first and second types of data to generate a standardized spare parts order dataset. The first type of data consists of spare parts demand order data submitted by customers, while the second type of data consists of multi-dimensional dynamic business data from the spare parts management system backend. Specifically, the first type of data can be obtained from spare parts demand information submitted by customers in spreadsheets, emails, or entered through a web interface. The second type of data can be directly read from the database of the spare parts management system, such as inventory information, supplier qualifications, and historical purchase records. As one implementation method, data can be manually exported from different systems or files. Subsequently, this data can be manually cleaned, format-converted, and field-mapped using data processing tools to eliminate data redundancy and inconsistencies, ultimately forming a standardized spare parts order dataset with a unified format. This dataset can be a structured file, such as a CSV file, or a database table containing material details for all orders to be split.

[0028] Furthermore, this method obtains multi-dimensional splitting condition configuration information, which includes multiple splitting dimensions and corresponding judgment thresholds, weight percentages, and priority rankings for each dimension. For example, splitting dimensions may include material category, supplier, urgency level, etc. Judgment thresholds can be set as the maximum purchase quantity for each material category or the maximum supply capacity for each supplier. Weight percentages and priority rankings are used to guide the decision-making order during the splitting process. As one implementation method, this configuration information can be manually entered and maintained by the user in the system's backend configuration interface. Alternatively, this configuration information can be stored in a simple text file or XML file and read by the system at runtime.

[0029] Therefore, based on the standardized spare parts order dataset and the multi-dimensional splitting condition configuration information, the system performs splitting judgments layer by layer on the spare parts orders according to the priority of each splitting dimension from high to low, generating an initial set of sub-orders. Specifically, the system first performs a preliminary division of the materials in the standardized spare parts order dataset according to the highest priority splitting dimension (e.g., urgency). For example, all urgent materials are separated from regular materials. Then, for each preliminary material set, further splitting is performed according to the second highest priority dimension (e.g., supplier), separating materials from different suppliers again. This process can be manually performed by reviewing each material in the standardized spare parts order dataset one by one according to the configuration information and splitting it manually according to the set priority and threshold, thereby forming multiple preliminary sub-order lists.

[0030] In this embodiment, the method performs a full-dimensional compliance check on each sub-order in the initial sub-order set, removes invalid or empty sub-orders, and merges fragmented sub-orders that meet the same merging rule to generate a final sub-order set. The full-dimensional compliance check may include checking the completeness of material information, the accuracy of purchase quantities, the consistency of supplier matching, and the compliance of budget amounts for each sub-order. For example, the system can check for missing material codes, zero or negative purchase quantities, or supplier-material mismatches. Invalid or empty sub-orders, such as sub-orders with zero material quantities, will be directly removed. For fragmented sub-orders, i.e., multiple similar sub-orders with small quantities or generated by splitting rules, they can be integrated according to preset merging rules. For example, sub-orders from the same supplier, with the same delivery time, the same budget level, and belonging to the same warehouse area can be merged. As one implementation method, this verification and merging process can be performed manually, checking each sub-order in the initial sub-order set one by one, and performing the merging operation based on experience.

[0031] Finally, based on this final set of sub-orders, standardized procurement sub-orders carrying unique splitting rule tags are generated and synchronized to various business modules of the spare parts management system. Specifically, the system assigns a unique identifier, such as an order number or a splitting rule tag, to each sub-order in the final set to ensure traceability in subsequent processes. The format of these standardized procurement sub-orders is standardized to facilitate seamless integration with other modules in the spare parts management system. These standardized procurement sub-orders can be exported to a system-recognizable file format, such as XML or JSON, and then automatically synchronized to the procurement management module, approval workflow module, supplier integration module, financial budget module, and inventory control module of the spare parts management system via API interface or file transfer. Alternatively, this generation and synchronization process can be implemented manually by adding an identifier to each final sub-order, adjusting its format to a system-acceptable format, and then submitting it to relevant departments via manual import or email.

[0032] In this embodiment, a standardized spare parts order dataset is constructed by integrating multi-source dynamic business data. Based on multi-dimensional splitting condition configuration information, a hierarchical and progressive automatic splitting of spare parts procurement orders is achieved. This effectively solves the problems of low efficiency, error-proneness, and high cost associated with traditional manual splitting, and overcomes the limitations of existing automated systems such as isolated data, single splitting dimensions, and lack of priority logic. This method can generate standardized sub-orders that conform to procurement specifications, reduce fragmented orders, improve the accuracy and efficiency of order processing, and provide traceable evidence for subsequent procurement processes, thereby optimizing the spare parts procurement management process.

[0033] In some of the embodiments described above in this application, a hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion is proposed. This method obtains multi-dimensional splitting condition configuration information and splits spare parts orders layer by layer based on this information. However, in practical applications, if these splitting dimensions are not clearly defined and refined, and a flexible configuration mechanism is not provided, it is difficult to ensure the comprehensiveness, accuracy, and business adaptability of the splitting process, which may lead to splitting results that do not meet actual business needs or are inefficient.

[0034] To address this, this application further proposes obtaining multi-dimensional splitting condition configuration information, including defining multiple splitting dimensions, such as delivery timeliness, supplier capability, budget limit, inventory control, and material attribute. The default priority of each splitting dimension, from highest to lowest, is: delivery timeliness, supplier capability, budget limit, inventory control, and material attribute. The weighting, priority order, and judgment threshold of each splitting dimension can all be manually customized. Furthermore, obtaining the multi-dimensional splitting condition configuration information specifically includes obtaining configuration information for delivery timeliness, supplier capability, budget limit, inventory control, and material attribute.

[0035] In this embodiment, the delivery timeliness dimension refers to the consideration of splitting orders based on the required delivery time of materials. Its purpose is to ensure that urgent materials are prioritized and differentiated from regular materials to meet different delivery timeliness requirements. The supplier capability dimension refers to the consideration of splitting orders based on the supplier's supply capacity and performance. Its purpose is to ensure that orders are allocated to qualified suppliers capable of fulfilling their obligations and to avoid overloading or failing to meet specific requirements of any single supplier. The budget limit dimension refers to the consideration of splitting orders based on purchase amount restrictions. Its purpose is to ensure that purchasing activities comply with financial budget regulations and approval processes, and to avoid over-budget purchasing. The inventory control dimension refers to the consideration of splitting orders based on the inventory status and inventory strategy of materials. Its purpose is to optimize inventory levels and avoid over-purchasing or production disruptions due to insufficient inventory. The material attribute dimension refers to the consideration of splitting orders based on the characteristics of the materials themselves, such as importance, value, and scarcity. Its purpose is to ensure that special materials are handled specially and meet their management and storage requirements.

[0036] In this embodiment, the default priority settings for each of the above-mentioned subdivision dimensions are based on the general importance of procurement operations. Delivery time is often the primary concern, followed by supplier fulfillment capabilities, then financial compliance, and finally inventory optimization and material characteristics. To adapt to the company's actual business strategies, changes in the market environment, or the needs of specific procurement projects, the weighting, priority ranking, and judgment thresholds of each subdivision dimension can be manually customized. This configurability is achieved through a user interface or configuration file, allowing system administrators or business personnel to flexibly input or select corresponding parameter values. For example, during certain special periods, budget limits may require higher priority, or the inventory control thresholds for specific materials may need to be set more strictly.

[0037] In this embodiment, when obtaining delivery timeliness configuration information, it includes timeliness thresholds for dividing materials into multiple timeliness levels based on required delivery time. For example, "within 3 days" can be set as top-priority urgent, "within 7 days" as urgent, "within 30 days" as regular, and "more than 30 days" as long-term. These thresholds can be flexibly configured according to business needs. In addition, it also includes rules for mandating independent order processing for materials of different timeliness levels. For example, all "top-priority urgent" materials must be processed in separate orders to ensure they receive the highest priority processing.

[0038] In this embodiment, when acquiring supplier capability configuration information, it includes the maximum supply quantity threshold and performance rating qualification standards for each qualified supplier. The maximum supply quantity threshold refers to the maximum quantity limit that a single qualified supplier can provide within a certain period or for a specific material. This helps avoid allocating orders exceeding a supplier's capacity to a single supplier, thereby reducing performance risk. This threshold can be set based on the supplier's historical performance, capacity reports, or contractual agreements. The performance rating qualification standards refer to the minimum standards for measuring whether a supplier's performance meets the requirements. For example, minimum scores or levels can be set for indicators such as on-time delivery rate, quality pass rate, and service response speed.

[0039] In this embodiment, when obtaining budget limit configuration information, it includes the single-item purchase budget limit and the single-order approval amount limit. The single-item purchase budget limit refers to the maximum limit that the purchase amount of a single material cannot exceed. This helps control the purchase risk of high-value single items and may trigger additional approval processes. The single-order approval amount limit refers to the maximum limit that the total amount of a single sub-order cannot exceed. Sub-orders exceeding this limit may require higher-level approval or further splitting to comply with approval authority.

[0040] In this embodiment, the inventory control configuration information includes a safety stock threshold and an overstocking determination criterion. The safety stock threshold refers to the minimum inventory level set to ensure uninterrupted material supply. When the real-time inventory falls below this threshold, the system will trigger a procurement request or perform special processing on related orders. The overstocking determination criterion refers to the rules for judging whether the procurement quantity exceeds a reasonable inventory level. For example, it can be set that if the procurement quantity exceeds a certain multiple of the safety stock level or exceeds a certain percentage of the historical average consumption, it is considered overstocking, which may require separate splitting or additional approval.

[0041] In this embodiment, when obtaining material attribute configuration information, it includes material control level classification standards, material category categorization rules, and warehouse allocation rules. Material control level classification standards refer to rules that divide materials into different control levels based on attributes such as importance, value, and scarcity. For example, materials can be divided into categories A, B, and C, where category A materials may require stricter procurement and inventory management. Material category categorization rules refer to rules that group materials with similar characteristics or uses into the same category. This helps to group materials of the same category together as much as possible during splitting, facilitating centralized procurement and management. Warehouse allocation rules refer to rules that allocate materials to specific warehouse areas based on their storage requirements or physical location. During splitting, it is necessary to ensure that materials in the same sub-order can be stored in the same or compatible warehouse areas.

[0042] In this embodiment, through the above technical solution, this application clearly defines several key dimensions on which purchase order splitting is based, including delivery timeliness, supplier capability, budget limit, inventory control, and material attribute dimensions. The introduction of these dimensions makes order splitting no longer a vague, singular consideration, but rather a comprehensive and refined evaluation from multiple business perspectives. Simultaneously, by setting default priorities for each dimension and allowing manual customization of weight percentages, priority rankings, and judgment thresholds, the flexibility and business adaptability of the splitting strategy are greatly enhanced. Specifically, delivery timeliness configuration information ensures priority processing of urgent materials, supplier capability configuration information guarantees order fulfillment, budget limit configuration information maintains financial compliance, inventory control configuration information optimizes inventory management, and material attribute configuration information ensures the proper handling of special materials. This multi-dimensional and configurable splitting condition enables the system to generate more reasonable, efficient and business-compliant purchase sub-orders based on the enterprise's actual complex business rules and dynamically changing market environment. This improves the automation level and decision-making quality of purchase order splitting, and effectively avoids problems such as procurement delays, cost overruns or inventory backlogs caused by improper splitting.

[0043] In some of the embodiments described above in this application, a method is proposed to perform splitting judgments on spare parts orders layer by layer based on a standardized spare parts order dataset and multi-dimensional splitting condition configuration information, according to the priority of each splitting dimension from high to low, to generate an initial set of sub-orders. However, in actual operation, if there is a lack of clear and progressive splitting logic, the splitting process may be chaotic and inefficient, making it difficult to effectively handle the complex relationships between different dimensional conditions, thereby affecting the accuracy and compliance of the final sub-orders.

[0044] To address this, this application further proposes a step-by-step approach to generate an initial sub-order set by performing splitting judgments on spare parts orders layer by layer, based on a standardized spare parts order dataset and multi-dimensional splitting condition configuration information, according to the priority of each splitting dimension from high to low. This includes: dividing all materials in the order into multiple time-sensitive material subsets according to delivery time-sensitive thresholds based on the standardized spare parts order dataset and delivery time-sensitive configuration information, achieving initial isolation of materials with different time-sensitive levels; for each time-sensitive material subset, splitting according to the qualified suppliers bound to the materials based on the standardized spare parts order dataset and supplier capability configuration information, generating a first sub-order set; for each sub-order in the first sub-order set, performing a second split according to budget limit rules based on the standardized spare parts order dataset and budget limit configuration information, generating a second sub-order set; and for each sub-order in the second sub-order set, performing fine-tuning splitting based on inventory control configuration information, material attribute configuration information, and the real-time inventory status and material control attributes of each material in the standardized spare parts order dataset, generating the initial sub-order set.

[0045] In this embodiment, when dividing all materials in an order into multiple time-sensitive material subsets, the system compares and judges the required delivery time of each material in the standardized spare parts order dataset based on the preset time-sensitive thresholds in the delivery time-sensitive configuration information. For example, materials with required delivery times within 3 days can be classified as "Extremely Urgent," those within 3-7 days as "Urgent," those within 7-30 days as "Regular," and those more than 30 days as "Long-Term." This method allows for the initial classification and isolation of materials with different delivery urgency, ensuring that subsequent splitting processes prioritize materials with high time-sensitive requirements and avoid order processing delays due to time-sensitive differences.

[0046] In this embodiment, when splitting each time-sensitive material subset by supplier dimension, the system queries the list of qualified suppliers bound to each material in the standardized spare parts order dataset. For materials within the same time-sensitive material subset, if they are bound to the same qualified supplier, these materials will be grouped into the same preliminary sub-order. This step aims to initially integrate materials with the same delivery timeframe and that can be supplied by the same supplier, laying the foundation for subsequent supplier capability verification and splitting.

[0047] In this embodiment, when performing secondary splitting on each sub-order within the first sub-order set, the system adheres to the single-item procurement budget cap and single-order approval amount cap rules defined in the budget limit configuration information. For each sub-order in the first sub-order set, the system calculates the procurement amount for each material and the total amount of the entire sub-order. If the procurement amount for a material exceeds the single-item procurement budget cap, or the total amount of the entire sub-order exceeds the single-order approval amount cap, the sub-order will be further split. For example, materials exceeding the budget will be separated into independent sub-orders, or excess sub-orders will be tiered according to the approval amount cap to ensure that each sub-order complies with financial budget and approval requirements.

[0048] In this embodiment, when fine-tuning and splitting each sub-order in the second sub-order set, the system comprehensively considers inventory control configuration information, material attribute configuration information, and the real-time inventory status and material control attributes of each material in the standardized spare parts order dataset. For example, the system queries the real-time inventory quantity and in-transit information of materials. If the real-time inventory quantity is lower than the safety stock threshold, the material may need to be urgently procured and split separately; if the procurement quantity far exceeds the safety stock threshold, it may be identified as overstocking and also needs to be handled independently. At the same time, the system will also separate materials belonging to the special control level from their original sub-orders for special management according to the material control level classification standard. In addition, if the materials in a sub-order belong to different warehouse areas, they may also need to be split into independent sub-orders to facilitate subsequent warehouse management and logistics distribution.

[0049] In this embodiment, through the above technical solution, this application provides a layered and progressive order splitting strategy. First, initial isolation is performed based on the most macro-level and highest-priority delivery time dimension to ensure that urgent materials are processed first. Then, based on time-sensitivity isolation, supplier capabilities are further considered to avoid over-purchasing or the risk of unqualified suppliers fulfilling their obligations. Next, a budget limit is introduced for secondary splitting to strictly control procurement costs and approval processes. Finally, through refined adjustments to inventory management and material attributes, compliance of each sub-order in terms of inventory, material characteristics, and warehousing is ensured. This layered and progressive splitting mechanism effectively resolves potential conflicts and complexities between multiple dimensions, making the splitting process logically clear, efficient, and accurate. It improves the automation and compliance level of purchase order splitting, and reduces the error rate and workload of manual intervention.

[0050] In the process of stratifying spare parts orders, the first and crucial step is to initially isolate materials based on their required delivery time. However, without a clear and standardized classification mechanism, it will be difficult to efficiently and accurately identify and distinguish materials of different urgency levels. This may result in urgent materials not being processed in a timely manner, or non-urgent materials occupying urgent resources, thereby affecting overall procurement efficiency and delivery timeliness.

[0051] To address this, this application further proposes a step based on a standardized spare parts order dataset and delivery timeliness configuration information, dividing all materials in an order into multiple timeliness material subsets according to delivery timeliness thresholds, thereby achieving preliminary isolation of materials with different timeliness levels. This step specifically includes: extracting the required delivery time of each material from the standardized spare parts order dataset; comparing the required delivery time of each material with the timeliness threshold in the delivery timeliness configuration information to determine the timeliness level of each material; wherein the timeliness levels include extremely urgent, urgent, regular, and long-term; and grouping all materials belonging to the same timeliness level into the same timeliness material subset, forming multiple timeliness material subsets, with each subset being independent of the others.

[0052] In this embodiment, the required delivery time of each material is extracted one by one from the standardized spare parts order dataset. This step aims to obtain key timeliness information for each material from the standardized spare parts order dataset. The standardized spare parts order dataset typically contains detailed fields such as material code, quantity, unit price, and required delivery time. Extracting the required delivery time of each material one by one ensures that subsequent timeliness level determination is based on accurate and complete original data. This can be achieved through database query languages ​​(such as SQL), API calls, or data parsing tools to obtain the corresponding delivery time field in each material record.

[0053] In this embodiment, the required delivery time of each material is compared with the timeliness thresholds in the delivery timeliness configuration information to determine the timeliness level of each material. This step is the core of material timeliness classification. The delivery timeliness configuration information predefines a series of timeliness thresholds. For example, a required delivery time within the next 3 days is "Urgent," 3-7 days is "Urgent," 7-30 days is "Regular," and more than 30 days is "Long-term." The system logically compares the required delivery time of each material with these preset thresholds to accurately determine the timeliness level to which the material should belong. This comparison mechanism based on configuration information makes the division of timeliness levels highly flexible and configurable, and can adapt to the timeliness requirements under different business scenarios.

[0054] In this embodiment, all materials belonging to the same timeliness level are grouped into a single timeliness material subset, forming multiple timeliness material subsets that are independent of each other. After determining the timeliness level of each material, this step aggregates all materials with the same timeliness level into an independent timeliness material subset. For example, all materials classified as "Ultimate Urgent" will form a "Ultimate Urgent" timeliness material subset. The independence of these subsets means that materials with different timeliness levels will not be confused. This independence forms the basis for subsequent tiered splitting, ensuring that different procurement strategies, supplier matching rules, or approval processes can be applied to materials with different timeliness levels, avoiding resource misallocation or inefficiency caused by timeliness confusion.

[0055] In this embodiment, through the above technical solution, this application provides a clear, automated, and standardized method for initial isolation based on the required delivery time of materials. By extracting the required delivery time of each material from the standardized spare parts order dataset and accurately comparing it with the timeliness threshold in the delivery timeliness configuration information, the timeliness level of each material can be accurately determined, such as extremely urgent, urgent, regular, and long-term. Subsequently, materials belonging to the same timeliness level are grouped into independent timeliness material subsets, thereby achieving effective isolation of materials with different timeliness levels. This not only solves the problem of vague material timeliness level classification and low efficiency in the initial stage of hierarchical progressive splitting, but also lays a solid foundation for subsequent refined splitting based on supplier capabilities, budget limits, inventory control, and other dimensions. This initial isolation ensures that urgent materials can be prioritized, and non-urgent materials will not occupy urgent resources, improving the accuracy, efficiency, and overall delivery timeliness of purchase order splitting, and avoiding procurement process chaos and resource waste caused by timeliness confusion.

[0056] In some of the embodiments described above in this application, a method is proposed to perform layer-by-layer splitting judgment on spare parts orders based on a standardized spare parts order dataset and multi-dimensional splitting condition configuration information, according to the priority of each splitting dimension from high to low, to generate an initial set of sub-orders. Specifically, after dividing all materials in an order into multiple time-sensitive material subsets according to delivery timeliness thresholds to achieve initial isolation of materials with different timeliness levels, it is necessary to split each time-sensitive material subset according to the supplier dimension based on the standardized spare parts order dataset and supplier capability configuration information, according to the qualified suppliers bound to the materials, to generate the first set of sub-orders. However, when initially aggregating only based on qualified suppliers bound to materials, the actual supply capacity and historical performance of suppliers may not be fully considered, leading to the allocation of purchase quantities to a single supplier exceeding its maximum supply quantity, or the allocation of orders to suppliers with poor performance ratings, thereby causing risks such as procurement delays, supply interruptions, or quality problems.

[0057] To address this, this application further proposes a step for generating a first sub-order set by splitting each time-sensitive material subset according to the supplier dimension based on a standardized spare parts order dataset and supplier capability configuration information, according to the qualified suppliers bound to the materials. This step includes: for each material in the currently processed time-sensitive material subset, querying the list of qualified suppliers bound to each material from the standardized spare parts order dataset, aggregating all materials bound to the same qualified supplier to form preliminary sub-orders, and generating a preliminary sub-order set; traversing each preliminary sub-order in the preliminary sub-order set, and for cases where the purchase quantity of a single material in any preliminary sub-order exceeds the maximum supply quantity threshold of the corresponding supplier, splitting the single material into a fulfillable sub-order and an excess sub-order, wherein the purchase quantity of the fulfillable sub-order does not exceed the maximum supply quantity threshold, and the purchase quantity of the excess sub-order is the excess portion; for cases where a supplier's fulfillment rating in any preliminary sub-order is lower than the qualified rating standard, removing the material corresponding to that supplier from the preliminary sub-order, and generating an independent sub-order after matching a candidate supplier for that material; and using all preliminary sub-orders after the above splitting process as the first sub-order set.

[0058] In this embodiment, when performing supplier-level segmentation, the system first retrieves material information from the currently processed time-sensitive material subset from the standardized spare parts order dataset and queries the list of qualified suppliers associated with these materials. The list of qualified suppliers typically includes a list of suppliers that have passed qualification verification and possess supply capabilities. The system then aggregates all materials associated with the same qualified supplier to form a "preliminary sub-order." For example, if both material A and material B specify supplier X as a qualified supplier, they will be grouped into a preliminary sub-order handled by supplier X. By traversing all materials and performing this aggregation operation, a set containing multiple preliminary sub-orders is ultimately generated. This step aims to perform preliminary supplier allocation for orders based on the direct association between materials and suppliers.

[0059] Building upon this foundation, to ensure that the actual supply capacity of suppliers is fully considered, the system further iterates through each preliminary sub-order in the preliminary sub-order set. For any single material included in any preliminary sub-order, the system checks whether its purchase quantity exceeds the maximum supply threshold of the supplier bound to that material. This maximum supply threshold is part of the supplier's capacity configuration information, reflecting the supplier's maximum supply capacity within a specific time period or for a specific material. If the purchase quantity of a single material is found to exceed this threshold, the system will intelligently split the purchase demand for that material. Specifically, a portion of the purchase quantity will form a "fulfillable sub-order," the quantity of which is limited to within the maximum supply threshold, ensuring that the supplier can actually fulfill this part of the order. The portion of the purchase quantity exceeding the threshold will form an independent "excess sub-order," and this excess demand needs to be handled subsequently through other suppliers or procurement strategies. This splitting mechanism effectively avoids the fulfillment risk caused by a single supplier being overloaded.

[0060] Meanwhile, to mitigate the risks associated with collaborating with low-performing suppliers, the system also performs a performance rating check on suppliers involved in the initial sub-orders. The system retrieves the preset performance rating criteria from the supplier capability configuration information and compares them with the current performance rating of the suppliers bound to the initial sub-order. If a supplier's performance rating is found to be below the qualifying standard, the system considers that supplier unsuitable for the current order. In this case, the materials corresponding to that supplier are removed from the original initial sub-order. Subsequently, the system re-matches one or more qualified alternative suppliers for these removed materials based on preset alternative supplier matching rules (e.g., based on material attributes, geographical location, historical cooperation, urgency, etc.) and generates new "independent sub-orders" for these materials. This ensures that only suppliers with a good performance record ultimately accept the order, thereby guaranteeing the quality and timeliness of procurement. Finally, after the initial collection based on the qualified supplier list, the splitting of over-purchases, and the removal and redistribution of materials from suppliers with unqualified performance ratings, all the remaining preliminary sub-orders, as well as the newly generated fulfillable sub-orders, over-purchase sub-orders, and independent sub-orders, together constitute the "first sub-order set".

[0061] In this embodiment, the above-described technical solution effectively addresses potential issues of insufficient supply capacity and fulfillment risks at the supplier level by progressively splitting spare parts orders in a hierarchical manner. First, by querying and initially aggregating the list of qualified suppliers associated with the materials, initial supplier allocation for the orders is achieved. Second, for situations where the purchase quantity of a single material exceeds the supplier's maximum supply threshold, the system intelligently splits the order into fulfillable sub-orders and excess sub-orders, avoiding fulfillment risks caused by suppliers overloading their supply and ensuring a reasonable allocation of procurement needs. Simultaneously, for suppliers with fulfillment ratings below the qualified standard, the system promptly identifies and removes their corresponding materials from the order, automatically matching alternative suppliers to generate independent sub-orders. This effectively avoids procurement delays and quality issues that may arise from collaborating with low-performing suppliers, ensuring a smooth procurement process and reliable material supply. These refined supplier-level splitting strategies ensure that the generated "first sub-order set" fully considers the supplier's actual capabilities and historical performance before entering the subsequent budget limit splitting stage, improving the accuracy, compliance, and execution efficiency of procurement order splitting and reducing procurement risks.

[0062] In some of the embodiments described above in this application, the purchase order has been initially split into a first set of sub-orders based on delivery timeliness and supplier capability. However, even after these splitting dimensions, sub-orders within the first set of sub-orders may still have budget compliance issues. For example, the purchase amount of a single material in a sub-order may far exceed the preset single-item purchase budget limit, or the total amount of the entire sub-order may exceed the single-order approval limit. This can hinder subsequent purchase approval processes and may even require complex manual secondary splitting and adjustment, thereby reducing the efficiency and accuracy of automated processing.

[0063] In response, this application further proposes a step for generating a second set of sub-orders by performing secondary splitting on each sub-order in the first sub-order set, based on the standardized spare parts order dataset and the budget limit configuration information, according to the budget limit rules. Iterate through each sub-order in the first sub-order set, obtain the purchase quantity and standard purchase price of each material from the standardized spare parts order dataset, and calculate the purchase amount of each material in each sub-order and the total amount of the sub-order one by one; Materials whose purchase amount exceeds the upper limit of the single item purchase budget are separated from their original sub-orders and generated as independent sub-orders for single item budget control; After completing the budget control breakdown for each item, calculate the total amount of each sub-order. Sub-orders whose total amount exceeds the single order approval amount limit are then split into multiple compliant sub-orders according to the single order approval amount limit. The set of all sub-orders after the individual item budget control split and the total order amount control split is taken as the second set of sub-orders.

[0064] In this embodiment, the method first iterates through each sub-order in the first sub-order set, obtaining the purchase quantity and standard unit price of each material from the standardized spare parts order dataset, and calculating the unit purchase amount of each material and the total amount of the sub-order. This step aims to provide an accurate financial data foundation for subsequent budget limit splitting. The system automatically iterates through each sub-order in the first sub-order set. For each sub-order, the system accesses the pre-generated standardized spare parts order dataset, queries and extracts the purchase quantity and corresponding standard unit price of the material based on the material identifier (such as material code) contained in the sub-order. Subsequently, the system calculates the unit purchase amount of each material (i.e., purchase quantity multiplied by standard unit price) and sums the unit purchase amounts of all materials in the same sub-order to obtain the total amount of the sub-order. This process ensures that all budget-related financial data is accurately and completely calculated, providing a reliable basis for subsequent budget compliance judgment and splitting operations.

[0065] Based on this, materials whose single-item purchase amount exceeds the single-item purchase budget limit are separated from their original sub-orders, generating independent sub-orders for single-item budget control. This step is used to resolve budget overrun issues that may be caused by a single high-value material. After completing the above calculation, the system compares the single-item purchase amount of each material with the preset single-item purchase budget limit. If it finds that the single-item purchase amount of a certain material exceeds the limit, the system will immediately logically separate the material from its original sub-order. Subsequently, the system will create a new, independent sub-order for the separated material and mark it as an "independent sub-order for single-item budget control." After the original sub-order removes the material, its material list and total amount will be updated accordingly. This approach ensures that even if a single material has a high value, it will not affect the procurement process of other materials in the original sub-order, while allowing over-budget items to be managed and approved independently.

[0066] Furthermore, after completing the individual item budget control split, the total amount of each sub-order is calculated. Sub-orders whose total amount exceeds the single-order approval amount limit are then split into multiple compliant sub-orders according to the single-order approval amount limit. This step aims to ensure that the total amount of each sub-order meets the preset approval authority, avoiding complex approval processes or failures due to excessively high order amounts. After all individual item budget control split operations are completed, the system will recalculate the total amount of all current sub-orders (including the original sub-orders after individual item splits and newly generated independent individual item budget control sub-orders). For sub-orders whose total amount still exceeds the single-order approval amount limit, the system will further process them according to preset tiered splitting rules. For example, the system can split an excessive sub-order into two or more compliant sub-orders whose total amount does not exceed the approval limit based on factors such as the value, quantity, importance, or priority of the materials. Splitting strategies may include: prioritizing high-value materials to form independent orders, or splitting a large quantity of similar materials in batches to ensure that each newly generated sub-order meets the total amount limit requirement. This process effectively breaks down large orders into multiple smaller, approvable orders, thus improving approval efficiency.

[0067] In this embodiment, all sub-orders after the individual item budget control split and the total order amount control split are ultimately used as the second sub-order set. This step involves the aggregated management of all sub-orders after budget-dimensional splitting. The system will summarize all sub-orders generated during the aforementioned individual item budget control splitting and total order amount control splitting processes (including unsplit sub-orders, newly generated independent sub-orders under individual item budget control, and multiple compliant sub-orders generated after gradient splitting). These sub-orders, after refined budget-dimensional processing, collectively constitute the second sub-order set. This set will serve as input for subsequent refined splitting based on inventory control and material attribute dimensions, ensuring that subsequent processing is conducted on a basis that already meets budget compliance requirements.

[0068] In this embodiment, through the above technical solution, after initially completing the breakdown of delivery timeliness and supplier capability dimensions, this application further introduces a secondary breakdown mechanism for budget limits. First, by accurately calculating the material purchase quantity, standard unit price, purchase amount, and total sub-order amount for each sub-order in the first sub-order set, a solid data foundation is provided for subsequent budget compliance judgment. Second, for materials whose purchase amount exceeds the single-item purchase budget limit, they are separated from their original sub-orders and generated as independent sub-orders with budget control, effectively preventing a single high-value material from hindering the approval process of the entire sub-order due to budget overruns. Furthermore, after completing the single-item budget control breakdown, the system recalculates the total amount of each sub-order and performs a tiered breakdown of sub-orders whose total amount exceeds the single-order approval amount limit, generating multiple compliant sub-orders, thereby ensuring that the total amount of each sub-order complies with the preset approval permissions. This layered and progressive budget control strategy improves the financial compliance of purchase orders, reduces manual intervention and approval delays caused by budget overruns, and greatly improves the automation level and efficiency of the procurement process. By splitting over-budget materials or orders independently or in stages, subsequent inventory control and fine-tuning of material attributes can be carried out on a more compliant and controllable basis, thus ensuring the smoothness and efficiency of the entire procurement splitting process.

[0069] In some of the embodiments described above in this application, spare parts orders are broken down layer by layer based on dimensions such as delivery timeliness, supplier capabilities, and budget limits, generating a second set of sub-orders. However, in actual procurement, if more refined factors such as the real-time inventory status, control level, and warehousing affiliation of materials are not further considered, it may lead to a disconnect between purchase orders and actual inventory management. For example, purchasing a large amount of materials that are already in stock may cause stockpiling, or critical materials may affect production due to insufficient inventory in a timely manner, or materials from different warehouses may be mixed up in a single order, increasing the complexity of subsequent warehousing and logistics.

[0070] In response, this application further proposes a step to generate an initial sub-order set by finely adjusting and splitting each sub-order in the aforementioned second sub-order set based on inventory control configuration information, material attribute configuration information, and the real-time inventory status and material control attributes of each material in the standardized spare parts order dataset.

[0071] In this embodiment, for each sub-order in the aforementioned second sub-order set, the system queries the real-time inventory quantity and in-transit information of each material from the standardized spare parts order dataset. This step aims to refine the sub-orders using real-time inventory data, avoiding procurement waste or supply interruptions due to improper inventory management. The system compares the current inventory quantity and the quantity of materials in transit but not yet received into the warehouse for each queried material with a preset safety stock threshold. If the real-time inventory quantity of a material is lower than the safety stock threshold, it indicates a risk of insufficient inventory and requires urgent procurement. Therefore, it is separated from the atomic order to form an independent "sub-order for materials with insufficient inventory" for subsequent priority processing. Conversely, if the procurement quantity of a material significantly exceeds the safety stock threshold, it may lead to inventory backlog. Therefore, it is identified as "excess stock" and separated independently for more prudent procurement decisions, such as adjusting the procurement quantity or postponing procurement. This separation ensures a high degree of consistency between procurement behavior and inventory strategy.

[0072] Simultaneously, for each sub-order in the aforementioned second sub-order set, the system queries the material control level of each material from the standardized spare parts order dataset. Materials belonging to the preset "Special Control Level" are separated from their original sub-orders, generating independent sub-orders for controlled materials. This step ensures independent and strict procurement control for critical, high-value, or specially managed materials. Material control levels are categorized based on factors such as the material's importance, value, scarcity, and risk level, for example, into Special, Level 1, and Level 2. Materials identified as "Special Control Level" typically require higher-level approval processes, stricter supplier selection, or more specialized logistics arrangements. Separating them from ordinary material sub-orders to form "independent sub-orders for controlled materials" ensures that these critical materials receive due attention and special handling in subsequent procurement processes, reducing procurement risks.

[0073] Furthermore, for each sub-order in the aforementioned second sub-order set, the system queries the standardized spare parts order dataset to determine the warehouse area to which each material belongs, splitting materials belonging to different warehouse areas into independent sub-orders. This step aims to optimize the efficiency of subsequent warehousing and logistics operations. In large enterprises or multi-warehouse scenarios, different materials may be assigned to different warehouse areas for management, such as ambient temperature warehouses, cold storage warehouses, and hazardous materials warehouses. If a sub-order contains materials that need to be stored in different warehouse areas, it increases the complexity and error rate of operations during receiving, warehousing, and storage. By splitting materials belonging to different warehouse areas into independent sub-orders, it ensures that the materials in each sub-order can be uniformly sent to or stored in the designated warehouse area, thereby simplifying the logistics process, improving warehouse management efficiency, and reducing potential confusion and errors.

[0074] Finally, all sub-orders after being split along the inventory and material attribute dimensions are used as the initial sub-order set. This step is the final result of the aforementioned fine-tuning and splitting process. After completing the multi-dimensional splitting based on inventory status, material control level, and warehouse location, all adjusted and newly generated sub-orders are aggregated to form the final initial sub-order set. Each sub-order in this set has undergone multiple rounds of rigorous screening and splitting across multiple dimensions to ensure its compliance and optimization in terms of delivery timeliness, suppliers, budget, inventory, material attributes, and warehousing, laying a solid foundation for subsequent compliance verification and procurement execution.

[0075] In this embodiment, through the aforementioned technical solution, after the initial breakdown of spare parts orders based on delivery time, supplier capabilities, and budget limits, this application further introduces refined adjustments in inventory control and material attribute dimensions. Specifically, by querying the inventory quantity and in-transit information of materials in real time and comparing it with the safety stock threshold, it can promptly identify materials with insufficient inventory and make emergency purchases to avoid production interruptions. Simultaneously, excess spare parts exceeding the safety stock threshold are independently separated, effectively avoiding unnecessary inventory backlog and capital occupation. Furthermore, by identifying and separating materials with special control levels, the procurement process of critical materials is strictly controlled, reducing procurement risks. Moreover, by splitting materials according to their warehousing location, materials within each sub-order can be uniformly stored, greatly simplifying subsequent warehousing and logistics operations and improving the overall supply chain's operational efficiency and accuracy. This multi-dimensional and refined splitting strategy makes the generated initial sub-order set more in line with actual business needs and management standards. It effectively solves the problem of insufficient consideration of refined factors such as inventory, material attributes and warehousing in traditional purchase order splitting, thereby improving the scientific nature of procurement decisions and the smoothness of execution.

[0076] In some of the embodiments described above in this application, a hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion is proposed. This method generates an initial set of sub-orders by splitting spare parts orders layer by layer. However, under the complex splitting logic and the interaction of multi-dimensional conditions, the generated initial set of sub-orders may still have potential problems such as incomplete data, inaccurate quantities, improper supplier matching, budget overruns, mismatched delivery times, or non-compliant inventory management. If these problems are not effectively identified and addressed, they will directly affect the smooth execution of subsequent procurement processes and the compliance of procurement results.

[0077] To address this, this application further proposes a method for performing comprehensive compliance verification on each sub-order in the initial sub-order set to ensure the validity and compliance of each sub-order. Specifically, this verification process includes detailed checks across multiple dimensions. First, a material information integrity verification is performed. This step aims to check the completeness of material information in each sub-order, such as whether material codes, specifications, and other key requirement fields are missing. Through this verification, abnormal sub-orders with incomplete material information due to incomplete data entry or system processing anomalies can be effectively identified, ensuring the accuracy of purchased material information and avoiding procurement errors or delays caused by missing information.

[0078] Secondly, the accuracy of the purchase quantity is verified. This verification step is responsible for checking the accuracy of the purchase quantity of each sub-order. It checks for cases where the purchase quantity is zero or negative, as well as abnormal sub-orders where the purchase quantity is inconsistent with the original order requirements. This measure can prevent inventory backlog, shortages, or financial accounting problems caused by quantity errors, ensuring that the purchase quantity matches the actual demand.

[0079] Next, a supplier matching consistency check is performed. This check aims to ensure that the suppliers matched to the materials in the sub-order are compliant. It identifies abnormal sub-orders where the supplier is not in the preset list of qualified suppliers for that material. Through this check, procurement from unqualified or unauthorized suppliers can be avoided, thereby ensuring procurement quality and supply chain stability.

[0080] Furthermore, a budget compliance check is performed. This check verifies whether the budget amount for each sub-order complies with pre-defined financial regulations. It identifies abnormal sub-orders where the purchase amount for a single item exceeds the budget limit for that item, or where the total amount of the sub-order exceeds the approved limit for a single order. This check is crucial for controlling procurement costs, adhering to financial regulations, and preventing over-budget procurement.

[0081] In addition, a delivery time matching check is performed. This check aims to ensure that the required delivery time of materials within a sub-order is consistent with the timeliness level to which the sub-order belongs. It identifies abnormal sub-orders where the required delivery time of materials within a sub-order does not match the timeliness level of the sub-order. Through this check, it is possible to effectively avoid warehousing pressure caused by delays in urgent materials or early arrival of non-urgent materials due to delivery time mismatch, ensuring the accurate execution of the procurement plan.

[0082] Finally, inventory control compliance verification is performed. This verification step is used to identify abnormal sub-orders where the purchase quantity exceeds the inventory control threshold and has not been independently split. For example, if the purchase quantity of a certain material significantly exceeds the safety stock threshold but was not processed independently in the previous splitting stage, it will be identified as abnormal. This verification helps optimize inventory management, avoid over-purchasing or inventory backlog, and ensure reasonable inventory levels.

[0083] In this embodiment, through the above-described technical solution, after automatically splitting and generating an initial set of sub-orders, the system can perform comprehensive and detailed compliance checks on these sub-orders. This multi-dimensional verification mechanism can promptly detect and mark various abnormal sub-orders, such as incomplete material information, inaccurate purchase quantities, supplier mismatches, budget overruns, inconsistent delivery times, and non-compliant inventory management. By pushing these abnormal sub-orders to the backend for manual review, this application, while achieving automated splitting, also provides the necessary interface for human intervention and correction, effectively avoiding potential risks and errors that may arise from automated processes. This not only improves the accuracy and compliance of purchase sub-orders, reducing the error rate and rework costs in subsequent purchase execution stages, but also ensures the smoothness and efficiency of the purchase process through refined verification, providing more reliable data support for enterprise procurement decisions.

[0084] In some of the above implementation methods, splitting spare parts orders layer by layer using multi-dimensional splitting conditions may generate a large number of fragmented sub-orders, even including some invalid sub-orders with zero material quantity. These fragmented and invalid sub-orders not only increase the management complexity and operational costs of subsequent procurement processes, but may also lead to data redundancy and low processing efficiency, which is not conducive to the automation and intelligent management of the procurement process.

[0085] In response, this application further proposes a method for merging fragmented sub-orders that meet the same merging rules, specifically including: traversing the initial sub-order set and removing invalid empty sub-orders with zero material quantity; merging multiple sub-orders that simultaneously meet the following conditions to form a sub-order among the remaining sub-orders: belonging to the same supplier, belonging to the same delivery timeliness level, belonging to the same budget level, and belonging to the same warehouse area; for the splitting process of each material, recording the splitting trigger conditions, matching rules, data comparison before and after splitting, and splitting execution time, generating a splitting traceability log, and binding and storing the splitting traceability log with the corresponding sub-order.

[0086] In this embodiment, when processing the initial set of sub-orders, the system first iterates through each sub-order in the set and checks the quantity of materials it contains. If a sub-order is found to have a material quantity of zero, it is determined to be an invalid empty sub-order and removed from the set. Such sub-orders are usually generated due to the splitting logic under specific edge conditions and have no actual procurement significance. Removing them can effectively reduce the amount of data processed subsequently, avoid unnecessary processing of invalid data, and thus improve system operating efficiency and data quality. For example, the system can set up a filter or validation module to perform this check immediately after the sub-order is generated and remove sub-orders that meet the conditions from the set.

[0087] Based on this, for the remaining sub-orders after initial clearing, the system will perform an intelligent merging operation to resolve the order fragmentation problem. During the multi-dimensional splitting process, due to the different splitting rules of different dimensions, materials that could originally be merged may be split into different sub-orders. This application identifies fragmented sub-orders with high consistency by setting strict merging conditions: "belonging to the same supplier, belonging to the same delivery timeliness level, being in the same budget level, and belonging to the same warehouse area." When multiple sub-orders simultaneously meet these four conditions, they will be logically or physically merged into a new sub-order. For example, the system can use hash mapping or grouping algorithms to group sub-orders using these four conditions as keys, merging sub-orders within the same group. This merging mechanism can effectively integrate resources, reduce the number of purchase orders, simplify subsequent supplier communication, approval processes, and logistics arrangements, and improve overall procurement efficiency.

[0088] In this embodiment, to ensure the transparency and traceability of the entire order splitting process, this application also records in detail the splitting trigger conditions, matching rules, data comparison before and after splitting, and splitting execution time for each material, and generates a splitting traceability log. These logs are then bound and stored with the corresponding sub-orders. For example, when a material is split because it exceeds the supplier's maximum supply threshold, the log records the original quantity of the material, the quantity after splitting, the suppliers involved, the maximum supply threshold, and the splitting time. This traceability mechanism is of great significance for subsequent procurement audits, anomaly investigations, process optimization, and responsibility definition. It provides transparent data support and enhances the reliability and credibility of the system.

[0089] In this embodiment, through the above technical solution, the system can automatically identify and eliminate invalid sub-orders with zero material quantity, effectively purifying the initial sub-order set and avoiding the processing of meaningless data, thereby improving the efficiency and accuracy of data processing. Secondly, by setting clear merging rules, fragmented sub-orders belonging to the same supplier, the same delivery timeliness level, the same budget level, and the same warehouse area are intelligently merged, greatly reducing the total number of sub-orders. This not only simplifies the procurement management process and reduces the frequency and workload of manual intervention, but also makes subsequent procurement approvals, supplier coordination, and logistics arrangements more centralized and efficient, effectively solving the problem of excessive order fragmentation that may result from multi-dimensional splitting. Furthermore, by recording detailed splitting trigger conditions, matching rules, data comparisons before and after splitting, and execution times, and generating splitting traceability logs bound to sub-orders, a transparent and traceable record is provided for the entire procurement order splitting process. This allows managers to clearly understand the reasons and processes behind the formation of each sub-order, facilitating procurement audits, problem investigation, and process optimization, thereby improving the scientific nature of procurement decisions and the level of management refinement, ensuring the compliance and controllability of the procurement process.

[0090] In some of the embodiments described above in this application, multi-source data fusion and hierarchical progressive splitting can generate a final set of sub-orders that meet various complex conditions. However, in the actual procurement process, these finely split sub-orders may face problems such as insufficient standardization, difficulty in information traceability, and low integration efficiency with various business modules of the existing spare parts management system, thereby affecting the smooth progress of subsequent procurement approval, supplier docking, and inventory management.

[0091] To address this, this application further proposes the following steps: generating standardized procurement sub-orders carrying unique splitting rule tags based on the final sub-order set, and synchronizing the standardized procurement sub-orders to each business module of the spare parts management system. Specifically, this involves assigning a unique splitting rule tag and a unique traceability number to each sub-order in the final sub-order set to generate standardized procurement sub-orders; synchronizing the standardized procurement sub-orders to the procurement management module, approval workflow module, supplier docking module, financial budget module, and inventory control module of the spare parts management system; and responding to user operations by performing at least one of the following operations based on the standardized procurement sub-orders: order export operation, procurement approval operation, and supplier push operation.

[0092] Specifically, assigning a unique splitting rule label and a unique traceability number to each sub-order in the final sub-order set to generate standardized procurement sub-orders refers to: A unique splitting rule tag serves as metadata for a sub-order, concisely summarizing the specific splitting conditions and rules that led to its creation. For example, a sub-order might be tagged "Urgent Expense - Supplier A - Budget Exceedance Split," clearly indicating its special characteristics in delivery timeliness, supplier selection, and budget control. This tag can be a structured string, an encoding, or a predefined enumeration value, facilitating system identification and subsequent business processing. Its purpose is to provide subsequent business modules and personnel with a basis for quickly understanding the characteristics of sub-orders; for example, sub-orders tagged "Urgent Expense" may be automatically identified and prioritized by the system.

[0093] A unique traceability number is a globally unique identifier throughout the entire procurement lifecycle, used to ensure the traceability of each sub-order. This number can be automatically generated by the system, for example, using a GUID (Globally Unique Identifier) ​​format, or a composite code combining the original order number, the split batch, and the sub-order sequence number. Through this traceability number, it is possible to trace back from the final procurement sub-order to the original spare parts requirement order and view the conditions and changes it underwent at each split stage, thereby ensuring data integrity, transparency, and auditability.

[0094] Standardized procurement sub-orders mean that the generated sub-order data structure conforms to predefined specifications and formats, containing all necessary procurement information fields such as material code, quantity, unit price, supplier information, delivery date, splitting rule tags, and traceability number. This standardization ensures that sub-orders can be seamlessly parsed, processed, and stored by different modules within the spare parts management system, avoiding integration barriers caused by inconsistent data formats and improving the efficiency and accuracy of data exchange.

[0095] In this embodiment, synchronizing the standardized purchase sub-orders to the purchase management module, approval workflow module, supplier docking module, financial budget module, and inventory control module of the spare parts management system specifically means: Synchronization refers to the real-time or near-real-time transmission and updating of standardized procurement sub-order data to multiple relevant business modules within the spare parts management system. This synchronization can be achieved through various technologies such as message queues, API calls, shared database views, or event-driven mechanisms, ensuring that each module obtains the latest and most accurate sub-order information and avoiding information lag or inconsistency.

[0096] The procurement management module, upon receiving standardized procurement sub-orders, is responsible for incorporating them into the overall procurement plan and managing subsequent procurement execution, such as generating procurement contracts, tracking order status, and managing goods receipt.

[0097] The approval workflow module automatically triggers the corresponding approval process based on preset rules such as the amount, material type, and supplier rating of standardized procurement sub-orders, pushing the sub-orders to users with approval authority for review, thus ensuring the compliance of procurement activities.

[0098] The supplier integration module transforms standardized procurement sub-order information into a procurement order format that can be sent to suppliers. It then automatically or manually sends the order to the corresponding suppliers via Electronic Data Interchange (EDI), supplier portal, email, or API interface, enabling seamless communication with suppliers.

[0099] The financial budget module updates the expenditure of relevant budget items in real time based on the purchase amount of standardized purchase sub-orders, and performs budget control, cost accounting and funding planning to ensure that procurement activities are carried out within the budget.

[0100] The inventory management module updates future inventory forecasts based on material information and quantities in standardized purchase sub-orders, providing data support for warehousing planning, inventory turnover analysis, and safety stock management, thereby optimizing inventory levels.

[0101] In this embodiment, responding to a user operation, performing at least one of the following operations based on the standardized purchase sub-order: order export operation, initiating purchase approval operation, and pushing operation to supplier, specifically refers to: User operations refer to instructions actively triggered by purchasing personnel, managers, or other authorized users on the spare parts management system interface, such as clicking buttons or selecting menu items.

[0102] The order export function allows users to export data from one or more standardized purchase sub-orders in a specific format (such as Excel, PDF, CSV) for offline analysis, report generation, auditing, or data exchange with external systems, improving data availability and flexibility.

[0103] Initiating a procurement approval process allows users to manually select one or a batch of sub-orders and trigger them into the approval process. This is especially suitable for scenarios requiring manual confirmation, special approval, or urgent approval, providing a flexible way to initiate the approval process.

[0104] By pushing orders to suppliers, users can directly select standardized purchase sub-orders from the system interface and instruct the system to send them as formal purchase orders to the corresponding suppliers, simplifying the communication process with suppliers and improving procurement efficiency.

[0105] In this embodiment, through the above-mentioned technical solution, this application effectively solves the problems of insufficient standardization, difficulty in information traceability, and low integration efficiency with existing spare parts management systems in the actual procurement process after finely splitting sub-orders. Specifically, each sub-order is assigned a unique splitting rule label and a unique traceability number, and standardized procurement sub-orders are generated, so that each sub-order has a clear identity and traceability, greatly improving the transparency and accuracy of order management. At the same time, these standardized procurement sub-orders are synchronized to the procurement management module, approval workflow module, supplier docking module, financial budget module, and inventory control module of the spare parts management system, realizing seamless data flow and sharing in various business links, avoiding information silos and duplicate entry, and improving the automation level and operational efficiency of the overall procurement process. In addition, responding to user operations to execute operations such as order export, initiating procurement approval, and pushing to suppliers, the system can directly convert complex splitting results into executable business instructions, further enhancing the system's practicality and user experience, and ensuring that the value of the multi-condition splitting strategy can be fully reflected in subsequent procurement execution.

[0106] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion, characterized in that, The method includes: Acquire first-type data and second-type data, and perform fusion preprocessing on the first-type data and second-type data to generate a standardized spare parts order dataset; wherein, the first-type data is spare parts demand order data submitted by customers, and the second-type data is multi-dimensional dynamic business data from the backend of the spare parts management system, the multi-dimensional dynamic business data including real-time inventory level status data, in-transit material arrival progress data, supplier historical performance rating data, monthly budget remaining amount data, and material control level data; Obtain multi-dimensional splitting condition configuration information, which includes multiple splitting dimensions and the corresponding judgment threshold, weight ratio, and priority ranking for each splitting dimension; wherein, the multiple splitting dimensions include delivery timeliness dimension, supplier capability dimension, budget limit dimension, inventory control dimension, and material attribute dimension, and the default priority of each splitting dimension from high to low is as follows: delivery timeliness dimension, supplier capability dimension, budget limit dimension, inventory control dimension, and material attribute dimension, and the weight ratio, priority ranking, and judgment threshold of each splitting dimension can all be manually customized; Based on the standardized spare parts order dataset and the multi-dimensional splitting condition configuration information, the spare parts orders are split layer by layer according to the priority of each splitting dimension from high to low, generating an initial set of sub-orders. A full-dimensional compliance check is performed on each sub-order in the initial sub-order set to remove invalid or empty sub-orders, and fragmented sub-orders that meet the same merging rule are merged to generate a final sub-order set; wherein, the fragmented sub-orders are multiple sub-orders that simultaneously meet the following criteria: belonging to the same supplier, belonging to the same delivery timeliness level, being in the same budget level, and belonging to the same warehouse area; Based on the final set of sub-orders, a standardized purchase sub-order carrying a unique splitting rule tag is generated, and the standardized purchase sub-order is synchronized to each business module of the spare parts management system.

2. The hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion as described in claim 1, characterized in that, The process of obtaining multi-dimensional splitting condition configuration information includes: Obtain delivery timeliness configuration information, which includes timeliness thresholds for dividing materials into multiple timeliness levels according to the required delivery time, and rules for forcibly separating materials of different timeliness levels into independent orders; Obtain supplier capability configuration information, which includes the maximum supply quantity threshold and performance rating qualification standards for each qualified supplier; Obtain budget limit configuration information, which includes the upper limit of the single-item purchase budget and the upper limit of the single order approval amount; Obtain inventory management configuration information, which includes safety stock threshold and overstocking judgment criteria; Obtain material attribute configuration information, which includes material control level classification standards, material category classification rules, and warehouse area division rules.

3. The hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion as described in claim 2, characterized in that, Based on the standardized spare parts order dataset and the multi-dimensional splitting condition configuration information, the steps of performing splitting judgments layer by layer on the spare parts orders according to the priority of each splitting dimension from high to low, and generating an initial set of sub-orders, include: Based on the standardized spare parts order dataset and the delivery timeliness configuration information, all materials in the order are divided into multiple timeliness material subsets according to the delivery timeliness threshold, so as to achieve the initial isolation of materials with different timeliness levels. For each time-sensitive material subset, based on the standardized spare parts order dataset and the supplier capability configuration information, the first sub-order set is generated by splitting the qualified suppliers bound to the material according to the supplier dimension. For each sub-order in the first sub-order set, based on the standardized spare parts order dataset and the budget limit configuration information, a second sub-order set is generated by splitting it a second time according to the budget limit rules. For each sub-order in the second sub-order set, based on the inventory control configuration information, the material attribute configuration information, and the real-time inventory status and material control attributes of each material in the standardized spare parts order dataset, a fine-tuning and split is performed to generate the initial sub-order set; For each sub-order in the second sub-order set, based on the inventory control configuration information, the material attribute configuration information, and the real-time inventory status and material control attributes of each material in the standardized spare parts order dataset, the steps to perform fine-tuning and splitting to generate the initial sub-order set include: For each sub-order in the second sub-order set, query the real-time inventory quantity and in-transit material information of each material from the standardized spare parts order dataset. Materials with real-time inventory quantities lower than the safety stock threshold are identified as insufficient inventory materials and split separately. Materials with purchase quantities exceeding the safety stock threshold are identified as excess stock materials and split separately. For each sub-order in the second sub-order set, query the material control level of each material from the standardized spare parts order dataset, separate the materials belonging to the preset special control level from the original sub-order, and generate an independent sub-order for the controlled material; For each sub-order in the second sub-order set, query the storage area to which each material belongs from the standardized spare parts order dataset, and split the materials belonging to different storage areas into independent sub-orders; The initial sub-order set is composed of all sub-orders after being split by inventory dimension and material attribute dimension.

4. The hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion as described in claim 3, characterized in that, Based on the standardized spare parts order dataset and the delivery timeliness configuration information, the steps of dividing all materials in the order into multiple timeliness material subsets according to the delivery timeliness threshold to achieve preliminary isolation of materials with different timeliness levels include: Extract the required delivery time for each material from the standardized spare parts order dataset; The delivery time of each material is compared with the timeliness threshold in the delivery timeliness configuration information to determine the timeliness level of each material; wherein, the timeliness level includes super urgent, urgent, regular and long term. All materials belonging to the same time-sensitive grade are grouped into the same time-sensitive material subset, forming multiple time-sensitive material subsets, and the different time-sensitive material subsets are independent of each other.

5. The hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion as described in claim 3, characterized in that, For each time-sensitive material subset, based on the standardized spare parts order dataset and the supplier capability configuration information, the steps for splitting the materials according to the qualified suppliers bound to the materials and generating the first sub-order set include: For each material in the current time-sensitive material subset, query the list of qualified suppliers bound to each material from the standardized spare parts order dataset, and gather all materials bound to the same qualified supplier to form a preliminary sub-order, generating a preliminary sub-order set; Iterate through each preliminary sub-order in the preliminary sub-order set. If the purchase quantity of a single material in any preliminary sub-order exceeds the maximum supply threshold of the corresponding supplier, split the single material into a fulfillable sub-order and an excess sub-order. The purchase quantity of the fulfillable sub-order does not exceed the maximum supply threshold, and the purchase quantity of the excess sub-order is the excess portion. If a supplier's performance rating is lower than the qualified rating standard in any preliminary sub-order, the material corresponding to that supplier will be removed from the preliminary sub-order, and an independent sub-order will be generated after matching the material with an alternative supplier. All the preliminary sub-orders after the above splitting process are used as the first sub-order set.

6. The hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion as described in claim 3, characterized in that, For each sub-order in the first sub-order set, based on the standardized spare parts order dataset and the budget limit configuration information, the steps of performing a second split according to the budget limit rules to generate the second sub-order set include: Iterate through each sub-order in the first sub-order set, obtain the purchase quantity and standard purchase price of each material from the standardized spare parts order dataset, and calculate the purchase amount of each material in each sub-order and the total amount of the sub-order one by one; Materials whose purchase amount exceeds the upper limit of the single item purchase budget are separated from their original sub-orders and generated as independent sub-orders for single item budget control; After completing the budget control breakdown for each item, calculate the total amount of each sub-order. Sub-orders whose total amount exceeds the single order approval amount limit are then split into multiple compliant sub-orders according to the single order approval amount limit. The set of all sub-orders after the individual item budget control split and the total order amount control split is taken as the second set of sub-orders.

7. The hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion as described in claim 1, characterized in that, Perform full-dimensional compliance verification on each sub-order in the initial sub-order set, including: Verify the completeness of material information for each sub-order, and identify abnormal sub-orders with missing material codes, specifications, and requirement fields; Verify the accuracy of the purchase quantity for each sub-order, and identify any abnormal sub-orders with purchase quantities of zero or negative, or purchase quantities that do not match the original order. Verify the supplier matching consistency of each sub-order one by one, and investigate abnormal sub-orders where the supplier is not in the preset qualified supplier list for the material; Verify the compliance of the budget amount for each sub-order, and identify abnormal sub-orders where the purchase amount of a single item exceeds the upper limit of the single item purchase budget, or where the total amount of the sub-order exceeds the upper limit of the single order approval amount. Verify the delivery time matching of each sub-order one by one, and identify abnormal sub-orders where the material requirement delivery time within the sub-order is inconsistent with the sub-order's timeliness level; Verify the compliance of inventory control for each sub-order, and identify abnormal sub-orders whose purchase quantity exceeds the inventory control threshold and has not been independently split; Each abnormal sub-order is marked and pushed to the backend for manual review.

8. The hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion as described in claim 1, characterized in that, The step of performing full-dimensional compliance verification on each sub-order in the initial sub-order set, removing invalid or empty sub-orders, and merging fragmented sub-orders that meet the same merging rule also includes: Iterate through the initial set of sub-orders and remove invalid empty sub-orders with zero material quantity; For the splitting process of each material, the splitting trigger conditions, matching rules, data comparison before and after splitting, and splitting execution time are recorded to generate a splitting traceability log, and the splitting traceability log is bound and stored with the corresponding sub-order.

9. The hierarchical, progressive, multi-condition automatic splitting method for purchase orders based on multi-source data fusion as described in claim 1, characterized in that, Based on the final set of sub-orders, the steps of generating standardized purchase sub-orders carrying unique splitting rule tags and synchronizing the standardized purchase sub-orders to each business module of the spare parts management system include: Assign a unique splitting rule label and a unique traceability number to each sub-order in the final sub-order set to generate standardized procurement sub-orders; The standardized procurement sub-orders are synchronized to the procurement management module, approval workflow module, supplier docking module, financial budget module, and inventory control module of the spare parts management system; In response to user actions, at least one of the following operations is performed based on the standardized purchase sub-order: order export operation, purchase approval operation, and push operation to supplier.

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